Bulletin of Electrical Engineering and Informatics
The Bulletin of Electrical Engineering and Informatics (BEEI), ISSN: 2089-3191, e-ISSN: 2302-9285, is open to submissions from scholars and experts in the wide areas of electrical engineering and informatics from around the world. The BEEI is an open-access peer-reviewed journal published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) that continuously presents the results of original research, reviews, surveys, or new data/concepts across all electrical engineering, electronics, instrumentation, control, robotics, telecommunication, computer engineering, information systems, information technology, and informatics (computer science) fields of interest. Articles must be understandable and written in standard English. This journal is indexed by Scopus (Elsevier)/ScimagoJR and has CiteScore: 3.9, SNIP: 0.685, and SJR: 0.306. The Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia has given this journal its SINTA 1 (S1) seal of approval. This journal is published bimonthly (February, April, June, August, October & December) in both print and online versions. All publications are available in full text and are free to download. Bulletin of Electrical Engineering and Informatics Open Access Publisher:Institute of Advanced Engineering and Science (IAES) ISSN:2089-3191E-ISSN:2302-9285 Subject area: Mathematics: Control and Optimization Computer Science: Computer Science (miscellaneous) Engineering: Control and Systems Engineering Computer Science: Computer Networks and Communications Computer Science: Information Systems Engineering: Electrical and Electronic Engineering Physics and Astronomy: Instrumentation Computer Science: Hardware and Architecture
Institute of Advanced Engineering and Science
COPYRIGHT AND LICENSING AGREEMENT This is to certify that the submitted manuscript is an original work, has not been published previously, and is not being considered for publication elsewhere. Upon acceptance, the article will be published by Institute of Advanced Engineering and Science (IAES) under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). The authors retain the copyright to the work. We declare that: This manuscript has not been published previously in the same or substantially similar form. It is not currently under review or consideration for publication by any other journal. All necessary permissions have been obtained for the use of any third-party material included in this manuscript. We agree that upon publication, the article will be distributed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). License Grant We, the authors, retain all copyrights to the submitted article, and grant to IAES the non-exclusive right to reproduce, publish, translate, and distribute the article in any form or medium, including print and electronic formats. The article may be used, shared, or adapted by others, provided that: Proper attribution is given to the original author(s) and source, A link to the license is included, Any changes are indicated, and Any derivative works are licensed under the same terms (ShareAlike). Full license details: https://creativecommons.org/licenses/by-sa/4.0 Author Responsibility The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who has obtained the consent of the co-author(s), if any. After submission of this agreement signed by the corresponding author, changes in authorship or order of authors will not be accepted. Retained Rights Authors retain all proprietary rights in any process, method, or material described in the work. Authors may distribute or reuse the work for personal or institutional use, or include it in other works, with appropriate credit and under the same license. Authors may deposit the published version of the article in institutional or public repositories.
Comparative analysis of modular outer rotor and modular inner rotor permanent magnet flux switching machine
A modular rotor is an advanced design in electric motor (EM) technology, offering enhanced performance and efficiency for various applications. Its segmented structure allows for optimized magnetic flux paths, reducing iron losses and improving energy efficiency. The modular rotors can be classified into modular inner rotors and modular outer rotors (ORs). While both have their applications, modular inner rotor permanent magnet flux switching machines (PMFSMs) face limitations, including lower torque density, inefficient magnetic flux operation, and reduced cooling capability, making them less suitable for high-torque applications. This paper presents an analysis of the comparison between a new modular OR and a modular inner rotor PMFSM. The outer OR-PMFSM offers higher torque density due to more efficient magnetic flux positioning, resulting in a better torque-to weight ratio. The design and analysis are conducted using JMAG designer version 18 under no-load and load conditions to test the proposed design's effectiveness. Hence, the suggested design obtained an increment of torque and power of 6.06% and 5.4%, respectively. In conclusion, the modular OR produces higher torque and power than the modular OR. The proposed motor can be very useful in electric bike applications for practical high performance and low cost.
Copyright (c) 2026 Roshada Ismail, Erwan Sulaiman, Syed Muhammad Naufal Syed Othman, Nur Afiqah Mostaman, Irfan Ali Soomro https://creativecommons.org/licenses/by-sa/4.0
Optimized FOI-TD controller for automatic generation control of restructured power systems with sodium-ion batteries
This article examines a novel control technique known as the fractional order integral-tilt derivative (FOI-TD) controller, which was used for automatic generation control (AGC) with a two-area restructured power system. The FOI-TD controller enhances performance by integrating tilt- derivative control with fractional-order control. Fractional-order control offers greater flexibility and allows for more precise tuning of the controller's response. The tilt component modifying the proportional term minimizes overshoot and improves settling times. The control parameters of the FOI-TD controller are optimized using the moth flame optimization (MFO) method and the efficacy of the given algorithm is associated with recent studies using meta-heuristic techniques. Analysis indicates that the MFO-optimized FOI-TD controller substantially enhances the dynamic response of the AGC loop and compared with traditional controllers. This enhancement includes reduced peak deviations, shorter settling times, and better suppression of area frequencies and tie-line power oscillations across different transaction scenarios within restructured power system. To further enhance AGC performance, fast-acting energy storage systems, like sodium- ion batteries (SIB), are integrated into the control area. The coordinated operation of the SIB units improves system dynamics and stability by mitigating initial frequency drops and tie-line power variations caused by sudden load disturbances.
Copyright (c) 2026 Nanthini B S, Ilanji Akilandam Chidambaram, Rajeswaran Sivasangari https://creativecommons.org/licenses/by-sa/4.0
Three-phase voltage dips in load nodes of the electrical network of the Santiago de Cuba province measure for its correction
The growing need to supply quality electrical energy to end-users has given rise to various studies and research on the technical parameters that this energy should possess. In addition, many users perceive that many of the problems in the operation of their household appliances are related to the quality of the electrical energy that reaches their homes. One of the fundamental problems affecting the quality of electrical power is the so called voltage dips, which are nothing more than disturbances of great relevance due to their direct consequences, secondary effects, and frequency of occurrence. In this research, a study is conducted to evaluate the occurrence of three-phase voltage dips in a simplified electrical network in Santiago de Cuba, Cuba. As a result, the Santiago Norte substation presents the most detrimental behavior. In addition, 50% of the simulations performed on the behavior of the Santiago East Substation, which handles a voltage of 13.8 kV, showed that it is the substation where the deepest voltage dips occur. Based on the results obtained, it has been decided to change the transformer tapping of the affected substations to reduce the occurrence of voltage dips and ensure stable operation in the event of three phase faults in the power system under analysis.
Copyright (c) 2026 José Ricardo Nuñez-Alvarez, Janette Cervantes-Oliva, Ramon Antonio Zambrano-Mero, Hernan Hernández-Herrera, Yolanda Llosas-Albuerne https://creativecommons.org/licenses/by-sa/4.0
Using LabVIEW software for remote voltage, current, and THD measuring and improving in the electrical grid
The increasing penetration of photovoltaic (PV) systems has raised concerns about power quality due to the harmonic distortion injected during DC–AC conversion by inverters. These harmonics can interfere with nearby electrical devices and compromise grid stability. This paper presents a LabVIEW-based virtual instrument designed specifically for real-time monitoring, analysis, and mitigation of voltage and current harmonics in grid-connected PV systems. The core contribution of this work lies in the development of an interactive, software-based platform that not only measures total harmonic distortion (THD) in real time but also allows users to design and simulate harmonic filters dynamically. The system achieved a reduction of current THD by 52% and voltage THD by 73% under tested conditions, demonstrating its effectiveness in improving power quality. Unlike conventional hardware-dependent solutions, the proposed tool offers a low-cost, easily customizable, and scalable approach suitable for educational laboratories, field diagnostics, and smart grid applications. By enabling remote control and filter tuning through LabVIEW, this solution supports the growing demand for intelligent power quality management and facilitates the seamless integration of renewable energy into modern electrical grids.
Copyright (c) 2026 Gentian Dume, Alfred Pjetri, Andi Hida https://creativecommons.org/licenses/by-sa/4.0
Effect of the retro-filling process on kraft paper and gasket materials in transformers
Mineral oil (MO) is the most widely used insulating liquid in oil-filled transformers. Numerous studies have investigated the use of alternative synthetic and ester-based insulating oils as replacements for MO. However, the effects of the retro-filling process on transformer materials remain insufficiently explored. This study examines the impact of retro-filling and thermal ageing on transformer kraft paper and gasket materials. Kraft paper strips, fluoroelastomer (FE) and nitrile butadiene rubber (NBR) gaskets, MO, synthetic oil (SO), and ester oil (EO) are used in the investigation. Initially, three sample bottles containing MO undergo thermal ageing for 200 hours at 130 °C. Subsequently, the retro-filling process is performed using MO, SO, and EO respectively. The samples are then subjected to an additional 200 hours of ageing at the same temperature. Results indicate that EO demonstrates superior performance in terms of shore hardness (+1.06%), while SO better preserves the tensile strength of kraft paper strips. These findings suggest that the retro-filling process can contribute to extending the service life of transformers.
Copyright (c) 2026 Nor Hidayah Rahim, Imran Sutan Chairul, Sharin Ab Ghani, Mohd Shahri, Ahmad Khiar, Arfah Syahida Mohd Nor, Farrah Salwani Abdullah https://creativecommons.org/licenses/by-sa/4.0
Evaluating solar photovoltaic panel orientations for an open-field internet of things framework
This paper presents a comparative experimental analysis of horizontal, vertical, and 45° tilt photovoltaic (PV) panel orientations, evaluated with and without load conditions in a tropical environment. Simultaneous measurements of open-circuit voltage (Voc), short-circuit current (Isc), and average power were conducted over three consecutive days to facilitate orientation-specific performance comparisons. Results show that the horizontal orientation demonstrated robust midday performance, achieving 3.65 W with load on one of the test day, but declined sharply in post-noon periods. The vertical orientation consistently produced lower average power outputs, approximately 3.1 W with load. As a reference, the 45° tilt consistently produced the highest output, with average load powers of 3.77 W, 3.41 W, and 3.64 W over the three days. This performance exceeded horizontal orientations by 2–5% and vertical orientations by 15–20%. Both horizontal and tilt orientations consistently surpassed internet of things (IoT) operational thresholds of 3.3–5.0 V and 100–200 mA required for low power sensor nodes, ensuring excess energy for storage. In contrast, the vertical orientation posed a risk of inadequate current in late afternoon periods. Thus, the results indicate that the orientation selection should be environment-driven. Horizontal or tilted orientations are suitable for rural and open-field IoT settings, while vertical orientations are advantageous for space-constrained or dust-prone environments.
Copyright (c) 2026 Khairul Anuar Mohamad, Mohamad Syahmi Nordin, Hairul Hafizi Hasnan, Ahmad Fateh Mohamad Nor, Rohaiza Hamdan, Nazirah Mohamad Abdullah, Nor Anija Jalaludin https://creativecommons.org/licenses/by-sa/4.0
Design and analysis of hybrid filters for harmonic reduction in three-phase nonlinear rectifier loads
Rectifiers are included in the non-linear load category and can create resonance in a distribution network, which can be called harmonics. In a distribution network, rectifiers (non-linear loads) distort voltage and current harmonics, resulting in power losses and reduced power quality in the network. Harmonics in the distribution power grid cannot be eliminated, but can be reduced to values that can still be tolerated and evaluated until they are by the IEEE 519-2014 Standard. This research discusses reducing harmonic values in distribution networks designed in a simulation to implement a combination (hybrid) of series active and passive filters of the high pass and high pass type-C types. The results of research using the installation of high pass filters, type C filters, series active filters, hybrid-1 filters, and hybrid-2 filters, respectively, for each current total harmonic distortion (THD) value are 15.93%, 16.15%, 8.51%, 0.27%, and 0.25% where the current THD value when using the two hybrid filters is below the specified standard limit, namely 5%. By installing a hybrid filter, the harmonic value in each feeder is much better than installing a single series active filter, a high pass filter, and a type-C filter.
Copyright (c) 2026 Yulianta Siregar, Daniel Julian Sinaga, Nur Nabila Mohamed, Dedet Candra Riawan, Muldi Yuhendri https://creativecommons.org/licenses/by-sa/4.0
Challenges and opportunities in integrating electric vehicles with distributed renewable generation
Electric vehicles (EVs) and distributed generation (DG) based on renewable energy sources (RES), mainly solar photovoltaics (PV) and wind, are the two main pillars of the current smart grid. EVs are growing in popularity. They promise improved resilience and sustainability through their synergistic combination. This review essay offers a thorough analysis of the opportunities and difficulties present in this pairing. We examine that managing the dual intermittency of renewable energy and EV mobility puts pressure on grid stability and power quality. This study carefully compares the performance, economic feasibility, and grid implications of several integration architectures, ranging from hybrid systems and vehicle-to-grid (V2G) ecosystems to solar and wind-powered charging stations. This work also analyzes the important trade-offs between grid hosting capacity (HC), cost vs reliability, and infrastructure centralization. According to the study, the main socioeconomic and technical obstacles to scalability are antiquated regulations, battery degradation issues, and a lack of standardized interoperability. To turn EVs from a grid burden into a versatile asset for a decarbonized energy future, the study concludes by outlining critical future research topics, highlighting the necessity of AI-driven energy management systems (EMSs), sophisticated vehicle-to-everything (V2X) services, and dynamic HC analysis.
Copyright (c) 2026 Mahammad Anwar, Abdul Khadar Asundi, Shabana Sultan Sarmas https://creativecommons.org/licenses/by-sa/4.0
Performance evaluation of enhanced pulse width modulation techniques for cascaded multilevel inverter
The nine-level multi-level inverter (MLI) uses a DC voltage supply, seven switches, four capacitors, and twelve diodes to generate an AC output with nine distinct voltage levels. Control strategies like pulse width modulation (PWM), fuzzy logic control (FLC), and time ratio control (TRC) regulate the inverter’s performance. Multi-carrier PWM techniques such as APOD, POD, PD, and multi-reference PWM are applied for precise control. The system is simulated in MATLAB, and performance is evaluated based on output voltage RMS and total harmonic distortion (THD), ensuring compliance with IEEE standards. FLC and TRC have been proposed as control approaches for the mighty nine-level MLI. The proposed inverter uses 60% fewer components than traditional designs while giving better power quality. All methods performed better than existing field-programmable gate array (FPGA)-based systems that had 19.86% THD. The performance evaluation includes output voltage rms and THD indices, requirements that must meet IEEE standards. The proposed strategies are compared with existing techniques in the literature.
Copyright (c) 2026 Jayaprakasam Vinothini, Ravindran Ramkumar https://creativecommons.org/licenses/by-sa/4.0
Optimized control approach for bidirectional wireless power transfer systems with vehicle-to-grid integration
The transition to electric vehicles (EVs) has intensified the need for efficient vehicle-to-grid (V2G) and grid-to-vehicle (G2V) systems. Bidirectional wireless power transfer (BWPT) presents a seamless and intelligent approach to energy exchange, particularly under dynamic tariff and grid demand conditions. This study aims to model and simulate a Python-based rule-driven BWPT system to evaluate energy efficiency and economic performance in V2G/G2V applications. A synthetic dataset representing grid demand and time-of-use (TOU) pricing over seven days was used to simulate real-world operating conditions. The model incorporates state-of-charge (SoC) dynamics, bidirectional power control logic, and profit calculation using a 15-minute resolution over 672 time steps. The simulation achieved a total energy exchange of 122.8 kWh and a cumulative net profit of ?536.67, with daily profits averaging ?76.6. SoC levels were effectively maintained between 20% and 90%, and power flows adapted accurately to tariff variations. The study confirms the feasibility of a lightweight, reproducible BWPT model capable of delivering optimized energy management and economic returns. The simulation approach offers strong potential for academic, research, and pre-deployment evaluation of intelligent charging systems.
Copyright (c) 2026 Mareedu Hari Venkatesh, Malligunta Kiran Kumar, Chandra Babu Guttikonda, Thalanki Venkata Sai Kalyani, Kambhampati Venkata Govardhan Rao https://creativecommons.org/licenses/by-sa/4.0
Advanced artificial intelligence-based multi-sensor fusion for environmental perception in autonomous electric vehicles
As autonomous electric vehicles (AEVs) continue to evolve, the demand for robust obstacle detection systems becomes increasingly critical to ensure safety, efficiency, and adaptability in real-world environments. This review presents a comprehensive synthesis of recent advancements in sensor fusion technologies, emphasizing the integration of light detection and ranging(LiDAR), radar, and camera-based vision systems. It highlights the role of deep learning architectures—such as you only look once (YOLO), convolutional neural networks (CNNs), and related neural models—in enhancing object detection, classification, and segmentation. The review categorizes key research themes, including fusion methodologies, real-time processing, edge computing, performance in adverse weather conditions, pedestrian detection, and sensor calibration. Special attention is paid to techniques that merge spatial, velocity, and semantic data to mitigate individual sensor limitations. The paper also discusses hardware-accelerated solutions for low-latency inference and the use of lightweight models for deployment on edge devices. Benchmark datasets, of vehicle-to-everything (V2X) and internet of thing (IoT)-based infrastructure, and calibration challenges are examined for their roles in ensuring accuracy and reliability. Drawing from over 100 referenced studies, this work serves as a foundational resource for researchers and developers aiming to advance artificial intelligence (AI)-based sensor fusion systems in next-generation AEVs.
Copyright (c) 2026 Billu Naveen, Malligunta Kiran Kumar, Thalanki Venkata Sai Kalyani, Thulasi Bikku, Kambhampati Venkata Govardhan Rao https://creativecommons.org/licenses/by-sa/4.0
Optimized fuzzy sliding mode control for energy-efficient wastewater treatment
Wastewater treatment is essential for environmental sustainability and public health. However, existing control strategies struggle with system nonlinearity, disturbances, and high energy consumption (EC). This study proposes a robust self-organizing fuzzy sliding mode controller (SOFSMC) to enhance effluent quality, energy efficiency, and system adaptability in wastewater treatment plants (WWTPs). By integrating sliding mode control(SMC) with a self-organizing fuzzy logic system (SOFLS), the controller improves adaptability and reduces the chattering effect. The newly developed JAYA optimization algorithm is used to fine-tune control parameters, optimizing both energy use and pollutant removal. Simulation results show SOFSMC outperforms proportional integral derivative (PID), standard SMC, and fuzzy logic controllers (FLCs). EQI is reduced by 48.3% and 28.4% compared to PID and FLC, respectively. EC is significantly optimized, and settling time and chattering amplitude (CA) are reduced by 28% and 75%, respectively. SOFSMC offers a scalable, energy-efficient, and robust solution for advanced wastewater treatment.
Copyright (c) 2026 Manjunatha Badiger, Varuna Kumara, Raghunatha, Savidhan Shetty C S https://creativecommons.org/licenses/by-sa/4.0
A smart parcel locker system with parcel status and image notifications via LINE application
During the COVID-19 pandemic, online shopping grew rapidly, leading to more parcel deliveries. However, recipients are not always home to receive them. Some existing systems send alerts via SMS or email, but these methods often incur additional costs. To address this issue, we developed a smart parcel box. It uses IoT and solar power to work efficiently and sustainably, and it sends free delivery alerts through the LINE notify app, which is popular in Thailand. The prototype was tested using four parcel sizes: CD-sized (15×15×15 cm), 0+4 (17×11×10 cm), 2A (14×20×12 cm), and M-sized (27×43×20 cm). Results showed that the 0+4 parcel size had the fastest average detection and notification time of 8.42 s, followed by CD sized (9.81 s), 2A (9.9 s), and M-sized parcels (10.1 s). The M-sized parcel had the lowest standard deviation (SD) (0.42 s), while 0+4 was the least consistent (1.718 s). Differences in detection times were due to parcel size and placement but did not affect notifications. The system achieved 100% detection across all sizes, and the images allowed users to verify parcel details and sender information. These results demonstrate that the prototype is reliable, energy-efficient, and suitable for further development and adaptation in smart delivery systems.
Copyright (c) 2026 Chaiyong Soemphol, Chonlatee Photong, Piyapat Panmuang https://creativecommons.org/licenses/by-sa/4.0
Dual axis solar tracker and monitoring system based on internet of things
In this paper, the application of internet of things (IoT) technology in development of a dual-axis solar tracking system is presented. Sting capacity of piezoelectric material is applied a footstep energy generation system. Using Arduino MEGA as the main controller for the system, light-dependent resistors (LDRs) have been used for sunlight detection and maximum light intensity. Two servo motors have been employed to rotate the solar panel towards the position of the sun as detected by the LDR. Ethernet Shield is used as an intermediary between the hardware device and the IoT monitoring system through the Cayenne platform. Alert notifications are included to inform a remote user through phone or mail (or both) when a sensor has reached a certain predefined event. There is a 21.97 increased energy output buy the proposed system as compared to the single-axis solar tracker. Further test results of the manufactured prototype indicate that solar tracker data can be transmitted simply and monitored directly online, and the solar tracker is capable of receiving commands from the IoT monitoring application.
Copyright (c) 2026 Yonis M. Yonis Buswig, Shanti Faridah Salleh, Al-Khalid Othman, Norhuzaimin Julai, Azuka Affam, Lennevey Kinidi, Hazrul Mohamed Basri, Kasumawati Lias https://creativecommons.org/licenses/by-sa/4.0
Fault-tolerant trajectory tracking of mobile robots via model-free intelligent PID and fault observer
This paper addresses the problem of unexpected errors, such as model uncertainties and actuator faults, that degrade the performance of wheeled mobile robots (WMR). To overcome these challenges, a fault-tolerant control (FTC) approach is developed in which model-free control (MFC) is merged with an intelligent-proportional integral derivative (i-PID) controller and complemented by a fault observer (FO). Unlike existing approaches, the proposed controller does not rely on accurate system modeling; instead, MFC ensures robustness to time-varying parameters, and while i-PID enhances trajectory tracking through adaptive gain adjustment. The FO estimates actuator faults in real time and compensates for their effects, ensuring reliable operation even under severe conditions. The closed-loop stability is rigorously analyzed via Lyapunov theory. MATLAB/Simulink results show reduced tracking errors, improved stability, and strong robustness under both model uncertainties and actuator faults, including time-varying mass and inertia, validating the effectiveness and practical potential of the proposed FTC.
Copyright (c) 2026 Sy Phuong Ho, Manh Tien Ngo, Duy Tan Ngo, Dinh Tu Duong, Van Du Phan, Kim Duyen Ha Thi, Thanh Tri Nguyen, Minh Dang Dau, Thu Uyen Le Thi https://creativecommons.org/licenses/by-sa/4.0
Instant-incidental-close-loop scheme to improve battery life in wireless sensor applications
This work presents a simple new idea for implementing algorithms in microcontrollers that govern duty cycle transition settings in wireless sensors in closed-loop circuits that run on batteries. The switching time controlled to get end point and average duty-cycle (STEADY) method is used. Duty cycle regulation algorithms are used by instantaneous incidental closed loop (IICL) schemes to regulate current flow in circuits that serve as power consuming loads, facilitating quick and seamless transitions from open to closed loop. By using the STEADY algorithm on IICL, the issue of conserving battery energy to increase battery life can be resolved. Additional advantages of the STEADY algorithm include its low memory utilization of 4 Kbytes, or 12.5% of the 32 Kbytes of available memory slot, simplicity, and ease of implementation on microcontroller devices. Final testing using the IICL STEADY algorithm on the VD-2023 wireless DC voltage sensor prototype series shows that 627 mAh is more energy efficient than battery life in the IICL scheme without the STEADY algorithm applied, which is 26 minutes 37 seconds. The wake time is set to one millisecond, and the sleep time is set to one minute, and the battery power is provided by a 280 mAh battery. This results in a battery life of 8 hours 16 minutes 57 seconds.
Copyright (c) 2026 Yopi Sopian, Faizal Arya Samman, Muhammad Niswar https://creativecommons.org/licenses/by-sa/4.0
Design and fabrication of a novel millimeter-wave bandstop filter using transversal signal interference technique
This paper presents the design, simulation, and fabrication of a miniaturized millimeter-wave band-stop filter (BSF) on microstrip technology. The proposed arrangement is based on the concept of transversal interference of signals, and is realized as a pair of parallel transmission lines with the appropriate characteristic impedance and electrical length. Such scheme provides sharpening signal discrimination without resonator structures additional to the filter. The filter is fabricated on a ROGERS RT/Duroid 5880 substrate, selected for its favorable properties at high frequencies, including a thickness of 0.13 mm, a relative dielectric constant of 2.2, and a loss tangent of 0.0009. The optimized design was validated through electromagnetic simulations by two types of electromagnetic solvers, and then fabricated and measured by coplanar waveguide (CPW) probe to confirm its practical performance.
Copyright (c) 2026 Salaheddine Barou, Jamal Zbitou, Stéphane Ginestar, Mohammed El Gibari https://creativecommons.org/licenses/by-sa/4.0
Enhancement of methane detection using graphene-doped zinc oxide with Blynk internet of things for real-time monitoring
Methane is a potent greenhouse gas, significantly contributing to global warming gases when released into the atmosphere. Methane, a naturally occurring gas, has no harmful impacts on human life at low concentrations. As concentrations rise, symptoms like fatigue, headaches, nausea, irritability, and speech difficulties increase due to asphyxiation. Zinc oxide (ZnO) and graphene-doped ZnO gas sensors were fabricated using a screen-printing technique onto a Kapton film to compare their performance to methane gas at room temperature. A silver paste was used as the interdigitated electrode, deposited on the Kapton film using a screen-printing technique, and fired at 150 °C for 15 minutes. Next, ZnO and graphene-doped ZnO pastes were deposited onto the interdigitated electrode using a screen-printing technique and became the second layer of the gas sensor. The sensing layer was annealed at 200 °C for 60 minutes. All gas sensors responded well to methane gas at room temperature. As a comparison, the graphene-doped ZnO gas sensor responded better to 6,700 ppm of methane gas than the ZnO gas sensor at room temperature. The highest response of graphene-doped ZnO to methane gas was produced by 5 wt. % of graphene doped into ZnO with a response value of 11.5.
Copyright (c) 2026 Siti Amaniah Mohd Chachuli, Fatin Liyana Syakinah Mohd Yatim, Nur Hazahsha Shamsudin, Omer Coban https://creativecommons.org/licenses/by-sa/4.0
Non-prioritized channel assignment improvement based on call traffic intensity and artificial neural network
The non-prioritized (NP) channel assignment model is characterized by a high call dropping probability (CDP) of handover calls and an increasing mobile call traffic volume due to the proliferation of mobile devices. In this study, the one-dimensional Markovian NP model has been improved upon using an artificial neural network (ANN) as a prediction mechanism of CDP using predicted traffic intensity and channel parameters to assign calls of different types to channels. A simulation comparison of the CDP of existing NP channel assignment with the NP with traffic intensity (CDPT) and with the ANN traffic intensity prediction model (CDPANN) was carried out and the study shows that the CDP was reduced drastically when the NP channel assignment with ANN assisted trained model was used putting signal quality into consideration. The CDPT has reduced CDP by 3%, 15%, and 40%, while the CDPANN has reduced CDP by 6%, 20%, and 50% for signal quality factors of 0.2 (poor), 0.5 (good), and 0.8 (very good) respectively. This study has shown that under varying radio frequency signal quality conditions, the ANN assisted channel assignment approach will help minimise the problem of high CDP associated with NP channel assignment and thereby improve ubiquitous mobile communication.
Copyright (c) 2026 Adeyinka Ajao Adewale, Oritsematosan Laura Whyte, Omolola Faith Ademola, Gabriel Oluwatobi Sobola https://creativecommons.org/licenses/by-sa/4.0
Design and evaluation of a Python-based network automation system for internet of things devices
The increasing demand for the internet of things (IoT) and massive machine-type communications has significantly expanded network size and complexity. Recent research indicates that 95% of network tasks are monitored manually, leading to configuration complexity, human errors, faults, downtime risks, and time consumption. Network automation emerges as a practical solution by reducing administrative overhead and enabling reliable, scalable, and self-managing networks through scripting and standardized programming languages. This paper proposes a model for automated networks using Python-based methods, specifically Paramiko, Netmiko, and the network automation and programmability abstraction layer with multivendor support (NAPALM), to configure the enhanced interior gateway routing protocol (EIGRP) within the graphical network simulator-3(GNS3) environment. The performance of the automated network was evaluated using two scenarios: with threading and without threading. Key metrics included execution time, configuration accuracy, error rates, and resource utilization. Simulation results demonstrate that the automated approach significantly outperforms manual configuration. In addition, the automated model with threading outperformed the automated model without threading, achieving execution time reductions up to 67% and 100% configuration accuracy with zero errors. These findings underscore the effectiveness of the proposed system for automating complex network tasks in large-scale IoT deployments.
Copyright (c) 2026 Eslam Samy El-Mokadem, Bilal Bataineh, Samy El-Mokadem, Abdelmoty M. Ahmed, Mohamed A. Torad https://creativecommons.org/licenses/by-sa/4.0
Design and performance analysis of slot-based DGS-MIMO for sub-band allocation in 5G/6G networks
Wireless communication systems are quickly evolving to fulfil the increasing need for higher spectral efficiency, lower latency, and seamless connectivity. Although the conventional multiple-input multiple-output (MIMO) architecture improves reliability and throughput, it still faces high hardware complexity, spectral inefficiency, and interference issues in dense environments. Existing solutions, such as double-sided microstrip patch antennas or 8×8 MIMO arrays, yield moderate improvements but are not flexible enough for next-generation networks. To overcome these limitations, this paper puts forward a slot-based distributed generalized spatial modulation (DGS-MIMO) framework, which integrates dynamic antenna subset activation and adaptive sub-band allocation. In this way, the number of radio frequency (RF) chains is reduced, power consumption is lower, and spectral utilization is improved. Experimental validation shows excellent impedance matching (return loss up to -36.29 dB), high gain (6.98 dB), high radiation efficiency, and low signal reflection (voltage standing wave ratio (VSWR) as low as approximately 1.03). These results prove the robustness and efficiency of the proposed system in comparison with conventional designs. Besides the performance enhancement, the framework has great potential to be applied in real-world 5G/6G applications, especially in internet of things (IoT) deployments and vehicular communication scenarios where scalability, energy efficiency, and reliability are important.
Copyright (c) 2026 Priethamje Vithya K, Varalakshmi L M https://creativecommons.org/licenses/by-sa/4.0
Time-response modelling of an ice cream cone-shaped UWB antenna in the 5G spectrum for IoT applications
The development of wireless communication technology, especially fifth-generation (5G) networks, requires ultra-wideband (UWB) antennas capable of supporting high-speed data transmission, low latency, and massive connectivity for internet of things (IoT) applications. This research proposes an ice cream cone shaped UWB antenna design with a semicircular patch, a conical transition structure, and a feed path, designed to produce a nearly omnidirectional radiation pattern with high efficiency. The design is modeled using CST Studio Suite for frequency domain analysis and MATLAB for time response analysis with a modulated Gaussian pulse signal input. Simulation results show that the antenna is able to maintain the input waveform with little amplitude attenuation and phase shift and maintain two dominant spectrum peaks at ±6 GHz without significant distortion. Return loss (S11) measurements show a bandwidth of 4.40 GHz(3.80–8.20 GHz) with a minimum value of –28 dB, while the voltage standing wave ratio (VSWR) is close to 1, indicating optimal impedance matching and low power reflection. The group delay is stable, and the 2D radiation pattern shows nearly omnidirectional characteristics. This antenna is deemed suitable for portable 5G IoT devices, with recommendations for physical fabrication and field testing.
Copyright (c) 2026 Yoga Tri Nugraha, Adam Pangestu, Mawardi, Muhammad Irwanto, Muzammil Jusoh https://creativecommons.org/licenses/by-sa/4.0
Performance analysis of rate-splitting multiple access in multi-RIS-assisted wireless communication
This paper presents a novel rate-splitting multiple access (RSMA) framework with partial reconfigurable intelligent surface (PRIS) selection over Nakagami-m fading channels, a scenario that has not been comprehensively analyzed in prior works. To this end, we conduct a detailed performance analysis of a wireless communication system that incorporates multiple reconfigurable intelligent surfaces (RISs) as cooperative relays. The proposed framework integrates RSMA with a PRIS selection strategy, enabling efficient data transmission from the base station (BS) to two users with distinct channel conditions. Closed-form expressions for the outage probability and achievable throughput are derived over independent and non-identically distributed (i.n.i.d.) Nakagami-m fading channels. The analysis highlights the role of PRIS in enhancing system efficiency and ensuring fair resource allocation between users. In addition, asymptotic evaluations offer deeper insights into the system’s behavior under varying channel dynamics. Simulation results are provided to validate the theoretical findings, demonstrating a close match with the analytical expressions and confirming the robustness of the proposed approach.
Copyright (c) 2026 Hong-Nhu Nguyen, Xuan-Tien Nguyen, Sang-Quang Nguyen https://creativecommons.org/licenses/by-sa/4.0
GNN data association based multi-target localization and vital signs monitoring using SIMO-UWB sensor network
This paper presents a method for multi-target localization and vital signs monitoring using impulse ultra-wideband (UWB) technology. A single-input multiple-output (SIMO) sensor network is employed to enable simultaneous signal reception, which introduces challenges in signal separation and precise target positioning. The primary difficulty arises from the data association problem, where measurements must be correctly assigned to their respective targets, particularly in cases of overlapping signals or closely spaced targets. To address this issue, a time of arrival (TOA) algorithm is applied to estimate target ranges, while all possible measurement–target associations are evaluated. Subsequently, true target positions are obtained through trilateration combined with the global nearest neighbor (GNN) method. For vital signs monitoring, the continuous-time Fourier transform(CTFT) is utilized to estimate respiratory and cardiac rates. Experimental results demonstrate high accuracy, with relative errors of 1.42% in distance estimation, 2.34% in breathing rate, and 0.73% in heart rate estimation.
Copyright (c) 2026 Meraouli Hadjer Rania, Slimane Zohra, Abdelmalek Abdelhafid https://creativecommons.org/licenses/by-sa/4.0
Optimizing feature selection in multilayer ensemble models for improved HAR accuracy
Human activity recognition (HAR), is an increasingly significant research area as it can be used in many fields of application such as; health care, elderly monitoring, sports training, and smart homes. In this research we developed a novel multi-layer ensemble model based on a combination of a genetic algorithm (GA) to optimize feature selection and hierarchical learning to solve the issues of high dimensional data, feature redundancy and over fitting in HAR. Our model systematically reduces the number of features required to recognize activities while maintaining the most important features; thus, allowing the base learner to learn patterns across multiple layers. We demonstrated through experiments using three standard benchmark datasets-UCI HAR, WISDM, and PAMAP2, that our method significantly outperformed standard methods achieving 96.8% accuracy, and reduced the amount of feature sets by more than 70%. Evaluation metrics including; precision, recall, F1-score, and ROC-AUC, further validated the robustness of our model; while statistical tests confirmed the improvement in performance. Additionally, our framework improved the efficiency and interpretability of our model, which will enable it to be practically implemented in real time environments. These results demonstrate the potential of combining feature selection optimized by a GA and hierarchical ensembles in HAR, and provide avenues for future work in cross domain adaptability and multimodal HAR systems.
Copyright (c) 2026 Dhiraj Prasad Jaiswal, Ashok Kumar Shrivastava https://creativecommons.org/licenses/by-sa/4.0
Pavement damage image classification using deep learning with inspection system: a case study in Morocco
Road and highway authorities rely on pavement management systems(PMS), in particular regular pavement condition inspections, to manage and preserve this infrastructural heritage. To this end, visual surveys are regularly conducted to detect and classify pavement damage, assess pavement condition, and derive performance indicators. However, manual pavement inspection can be a subjective and time-consuming process that requires a high level of skill from those responsible for inspection and monitoring. This study proposes a machine learning (ML) technique to automatically classifying digital images of national road surfaces captured by a camera mounted on a smart vehicle equipped with a multifunctional road inspection system (SMAC). The image dataset, captured on different roads in Morocco, includes five classes of pavement damage and one class of no damage. The experimental results indicate that the ResNet50 model achieves superior classification accuracy of approximately 94%. This research contributes to the automation of road monitoring processes and provides road managers with an effective tool for planning and executing maintenance operations with enhanced reliability and efficiency.
Copyright (c) 2026 Youssef Aouni, Souad El Moudni El Alami, Mohammed Berrahal, Mohammed Boukabous, Mohammed Qachar https://creativecommons.org/licenses/by-sa/4.0
Improved feature-based hybrid deep learning for multiclassification of ultrasound thyroid nodules
Ultrasonography is frequently used to identify thyroid nodules. Because of their internal features, variable appearances, and ill-defined borders, it might be difficult for a hospitalist to distinguish amongst benign and malignant forms of the nodule based solely on visual inspection. Although deep learning, a subset of artificial intelligence, has significantly advanced medical image recognition, challenges remain in achieving accurate and efficient diagnosis of thyroid nodules. To identify and classify thyroid nodules, this study uses an innovative hybrid DL-assisted multi-classification technique. A median blur eliminates salt-and-pepper noise, and this is followed by segmentation using a method based on enhanced pooling integrated U-Net (EPIU-Net). To produce a single histogram series, features are recovered from the segmented image, including multi-texton, and local ternary pattern (LTP) based patterns. Following feature extraction, the data is expanded and input into a fusion classification model utilizing Deep Maxout and convolutional neural network (CNN) to categorize nodules. This work uses 2 types of datasets and for both datasets, we achieved great results with our hybrid technique across all performance criteria. 0.976, 0.008, 0.992, and 0.017 are the corresponding values for accuracy, false discovery rate (FDR), sensitivity, false negative rate (FNR). Moreover proposed work is verified by k-fold method.
Copyright (c) 2026 Mayuresh Gulame, Deepthi D. Kulkarni, Priya Khune, Nilesh N. Thorat, Ashwini G. Shahapurkar, Vijaya S. Patil, Sumit Arun Hirve, Aarti Pimpalkar https://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence and machine learning in healthcare: a comprehensive review
Artificial intelligence (AI) and machine learning (ML) are reshaping healthcare by supporting faster diagnosis, predictive modeling, and efficient clinical workflows. This review examines 52 recent studies to assess how these technologies are applied across diagnostics, predictive analytics, patient monitoring, operations, treatment, and ethical considerations. Results show substantial progress in imaging, genomics, drug discovery, and hospital management, where systems often match or surpass human performance. At the same time, challenges such as limited generalizability, data bias, privacy concerns, and lack of interpretability remain significant barriers to adoption. This review identifies common strengths and gaps by grouping existing work into six themes, offering a structured view of current developments. The findings suggest that the future of AI in medical care lies in transparent, fair, and clinically validated systems that can scale across diverse populations and settings.
Copyright (c) India Rosepreet Kaur Bhogal, Ajmer Singh https://creativecommons.org/licenses/by-sa/4.0
Data-driven modelling of Aquilaria essential oils via dual GC profiling and multicollinearity diagnostics
Aquilaria-derived essential oils are chemically diverse and hold significant value in pharmaceuticals, fragrances, and traditional medicine. However, the complexity of their chemical composition presents challenges in statistical modelling, particularly due to multicollinearity among biosynthetically related compounds. This study investigates the extent of multicollinearity in Aquilaria essential oil data using multiple linear regression (MLR) and variance inflation factor (VIF) analysis. A regression model was constructed using three compounds, ? -guaiene, 10-epi ? -eudesmol, and ? -eudesmol, across 360 samples, with VIF and collinearity diagnostics applied to assess model validity. The model explained 93% of the variance in species classification, which is substantially higher than values typically reported in earlier chemometric studies of Aquilaria oils. This demonstrates that even a limited number of carefully selected compounds, when supported by diagnostic safeguards, can achieve strong classification accuracy. These findings emphasize the importance of applying multicollinearity diagnostics to improve the interpretability and reliability of chemometric analyses. The study contributes a robust analytical framework for future research and practical applications in species authentication, essential oil quality control, and conservation of Aquilaria resources.
Copyright (c) 2026 Nur Athirah Syafiqah Noramli, Noor Aida Syakira Ahmad Sabri, Muhammad Ikhsan Roslan, Nurlaila Ismail, Zakiah Mohd Yusoff, Mohd Nasir Taib https://creativecommons.org/licenses/by-sa/4.0
Entropy augmented energy detection for cognitive radio: robust spectrum sensing under fading channels
Cognitive radio systems provide an intelligent solution to spectrum scarcity by dynamically accessing underutilized frequency bands licensed to primary users. Among various sensing techniques, energy detection (ED) is extensively adopted due to its inherent simplicity and minimal computational requirements, but suffers from poor performance at low signal-to-noise ratio (SNR) environments under channel impairments such as additive white gaussian noise (AWGN) and multipath fading. This paper proposes an enhanced spectrum sensing approach by integrating ED with entropy-based techniques, specifically Kapur and Renyi entropy measures. The proposed methods are evaluated under AWGN and various fading environments with binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulations. Simulation results demonstrate substantial improvements in detection performance. The results show that entropy-based enhancements significantly improve the reliability of spectrum sensing in cognitive radio (CR) systems operating under challenging channel conditions. Among the fading models, the Nakagami channel causes the greatest degradation in detection probability, followed by the Rayleigh fading channel. ED with Renyi entropy improves Pd by 15-fold and 8-fold, compared to ED under Nakagami and Rayleigh channels respectively.
Copyright (c) 2026 Lingeswari Ponnusamy, Marichamy Perumalsamy https://creativecommons.org/licenses/by-sa/4.0
PULMO-NET: blockchain-integrated lung cancer classification using Golden Jackal Optimization and GoogleNet
Lung cancer (LC) is a malignant disease caused by uncontrolled cell growth in the lungs, often associated with smoking and environmental factors. However, accurate LC classification is particularly challenging due to poor image quality, variability in imaging conditions, and noise artifacts in medical scans. In this work, a novel PULMO-NET is proposed for classifying LC using dual-modality imaging (CXR and CT). The dual-modality images are preprocessed using an adaptive trilateral (ADT) filter and segmented using the Golden Jackal Optimization (GJO). The segmented lung regions are refined using the dragonfly algorithm (DA) which enables accurate extraction of diamond-shaped tumor patterns. Additionally, a blockchain-based system with local nodes is integrated to collect real-time patient data. GoogleNet uses inception modules to capture multi-scale features, enabling accurate classification of lung images into normal, non-small cell lung cancer (NSCLC), and small cell lung cancers (SCLC). The proposed PULMO-NET achieves the classification accuracy (AC) of 98.91% and F1 score of 96.51%. The PULMO-NET model improves the overall AC by 1.91%, 7.78%, and 4.33% better than Inception-v3, TPOT_SVM, and LeNet–DenseNet respectively.
Copyright (c) 2026 Angel Mary Azhakesan, Thanammal Kakkumperumal Krishnammal https://creativecommons.org/licenses/by-sa/4.0
Facial expression recognition for emotional state identification using deep convolutional neural network
Facial expressions represent one of the most significant forms of non-verbal communication, with psychologists identifying six universal expressions: happiness, sadness, surprise, anger, fear, and disgust. Recognizing these expressions presents considerable challenges due to the subtlety of facial movements and variations across individuals. This paper presents a deep learning-based system for facial expression recognition (FER) that employs convolutional neural networks (CNNs) to classify emotional states. We investigate both a novel CNN architecture developed from scratch and established transfer learning approaches, evaluating their performance on the FER-2013 dataset. Our experimental results demonstrate that the proposed custom CNN architecture achieves 72.93% accuracy when combined with comprehensive data augmentation techniques, outperforming several baseline models. The system shows particular strength in recognizing fundamental emotions while maintaining computational efficiency suitable for real-time applications.
Copyright (c) 2026 Abdelhakim Gharbi, Abdeljalil Gattal, Issam Bendib https://creativecommons.org/licenses/by-sa/4.0
Deep learning–based real time speed limit sign detection with YOLOv12 on edge AI platforms for embedded ADAS
This research examined a real-time speed-limit sign detection framework based upon deep learning using the YOLOv12 neural network, optimized for the use of small edge devices that are embedded advanced driver assistance systems (ADAS). You only look once version 12 (YOLOv12) achieved a remarkable detection performance, while maintaining efficient computation, utilizing significantly optimized lightweight attention modules with an R-ELAN backbone capable of small and partially occluded detection. A custom dataset comprising 23,000 annotated images was prepared and augmented to ensure robustness under varying conditions. Model training utilized quantization-aware techniques and optimization via TensorRT and ONNX Runtime. Deployment and performance were rigorously evaluated on resource-constrained edge platforms, specifically NVIDIA Jetson Nano and Raspberry Pi 5. Experimental results demonstrated exceptional detection performance, achieving a precision of 99.0%, recall of 99.1%, and mean average precision (mAP@50) of 99.2%, confirming YOLOv12’s suitability for reliable, real-time ADAS implementation in intelligent transportation and autonomous vehicles.
Copyright (c) 2026 Mohammed Chaman, Anas El Maliki, Youssef Natij, Hamad Dahou, Abdelkader Hadjoudja https://creativecommons.org/licenses/by-sa/4.0
Analysis of machine learning methods for detection of cataracts
Cataracts remain the leading cause of visual impairment worldwide. We focus on improving the you only look once (YOLO) architecture through targeted optimization to enhance feature extraction. We trained the optimized YOLOv8 detector using 11,274 annotated fundus and anterior segment images. During training, five-fold cross-validation, color magnification, and stochastic weight averaging (SWA) were applied to ensure convergence. In the external test set, the model achieved an F1-score of 98.9% and an mAP50 of 0.995. On an NVIDIA RTX A2000 GPU, the inference speed reached 520 frames per second. Our network enables real-time cataract diagnosis on low-cost GPUs, surpassing previous ResNet- and MobileNet-based benchmarks by ?4% in F1-score and reducing output latency by 68%.
Copyright (c) 2026 Anastassiya Tyunina, Sabina Rakhmetulayeva, Eduard Schiller https://creativecommons.org/licenses/by-sa/4.0
Deep learning architectures for medical image segmentation: an organized analysis of CNN-based models and uses
Numerous techniques, especially those based on deep learning (DL), have been developed and applied to a wide range of tasks, including image recognition, classification, object detection, and image segmentation, as a result of extensive research in the field of image processing. Image processing has become crucial in the medical field, with segmentation emerging as a crucial method for organ identification, disease detection, and abnormality analysis in medical images. Convolutional neural networks (CNNs), one of the many approaches, have recently demonstrated great promise in resolving intricate problems associated with medical image analysis because of their capacity to automatically learn hierarchical features. In this review, we discuss recent developments in deep CNNs for medical image segmentation. The architectures and features of the most popular CNN-based models are examined, along with the different publicly accessible medical imaging datasets that are used in studies and the evaluation metrics that are frequently used to gauge segmentation performance and accuracy, also the advantages and disadvantages of each one. In addition, we look at comparative research and the shortcomings of existing approaches, offering suggestions for future lines of clinical relevance.
Copyright (c) 2026 Cherifa Abdellaoui, Samia Belkacem, Noureddine Messaoudi https://creativecommons.org/licenses/by-sa/4.0
Comparative evaluation of AlexNet, SqueezeNet, VGG16, and ResNet50 for gender and hijab detection
This study aims to detect gender based on facial images with and without hijab features, with the expected outcome of distinguishing gender from these facial features. The method involves comparing the performance of four convolutional neural network (CNN) architectures: AlexNet, SqueezeNet, VGG16, and ResNet50. A total of 170 facial images were directly collected using smartphone cameras. The dataset consists of two classes: 68 male faces and 102 female faces, among which 78 images of females feature hijabs, while 24 do not. The validation stage with 40 images (15 males and 25 females) showed that the AlexNet architecture achieved the highest validation accuracy at 100%, followed by ResNet50 with 97.50%, VGG16 with 95%, and SqueezeNet with 92.50%. The testing stage with 40 images (20 males and 20 females, including 10 females with hijabs and 10 without) showed that ResNet50 classified 38 images correctly, achieving 95% accuracy. AlexNet classified 37 images correctly with 92.50% accuracy, SqueezeNet classified 36 images correctly with 90% accuracy, and VGG16 classified 34 images correctly with 85% accuracy. The contribution of this research shows that AlexNet achieves the highest validation accuracy, while ResNet50 provides the best accuracy in facial image detection for determining gender and hijab features.
Copyright (c) 2026 Aji Supriyanto, Theresia Dwiati Wismarini, Herny Februariyanti, Arief Jananto, Fitri Damaryanti, Hilmy Nurakmal Satria https://creativecommons.org/licenses/by-sa/4.0
Advancements in UAV-based traffic monitoring: a systematic review of deep learning and edge computing
Rapid urbanization necessitates innovative traffic monitoring solutions. Traditional methods (fixed sensors/CCTV) face limitations in coverage, adaptability, and real-time processing. This review examines advancements (2015–2024) in vision-based unmanned aerial vehicle (UAV) traffic monitoring systems, evaluating their effectiveness in vehicle detection, traffic analysis, and congestion management. A systematic preferred reporting items for systematic reviews and meta-analyses (PRISMA)-guided analysis of 2,895 articles from IEEE Xplore, Scopus, Web of Science, and ACM Digital Library identified 49 eligible studies. Quantitative performance metrics (detection accuracy and latency) were standardized for cross-study comparison. Modern systems achieve >94% detection accuracy and <40 ms latency through edge computing and deep learning (e.g., you look only once (YOLO) and Faster region-based convolutional neural network (Faster R-CNN)). Multi-sensor fusion improves robustness by 35% in challenging conditions. However, battery life (reduced by 40% under processing load) and regulatory barriers remain critical constraints. Artificial intelligence (AI)-driven UAV systems enable real-time, high-accuracy traffic monitoring but require solutions for power efficiency and scalability. Future integration of 5G/6G and swarm intelligence holds promise for next-generation smart traffic management.
Copyright (c) 2026 Mohamed S. Sawah, Mohammed Tawfik, Issa Alsmadi https://creativecommons.org/licenses/by-sa/4.0
A proactive approach to software security using DCodeBERT for vulnerability management
The complexity of modern software has increased security risks, emphasizing the need for automated detection and correction. DCodeBERT, a CodeBERT-based vulnerability detection and remediation framework, is introduced in this study. DCodeBERT uses a multi-task learning framework with shared-private layers, gradient normalization, and uncertainty weighting to stand out. This architecture lets the model capture general representations while preserving task-specific details. From open-source repositories and vetted vulnerability databases, 85,000 code snippets—vulnerable, clean, and repaired—were collected. C, C++, Java, and Python programming languages (PLs) make this dataset highly usable. DCodeBERT surpasses CodeGPT, VulDeePecker, CodeT5 Small, GraphCodeBERT, and Devign in accuracy, precision, recall, and F1-score. Statistics show that the improvements are significant, and qualitative inspection shows that the resulting patches fix buffer overflows and injection problems within semantic validity. This novel approach combines multi-task optimization with natural and PL semantic integration for high cross-language performance. The findings show that DCodeBERT improves vulnerability management in software development settings.
Copyright (c) 2026 Indurthi Ravindra Kumar, Shaik Abdul Hameed, Polasi Sushma, Jose Pitchaiya, Veeramreddy Surya Narayana Reddy, Maganti Syamala https://creativecommons.org/licenses/by-sa/4.0
Resource cost management in cloud service environment
Cloud computing has revolutionized information technology (IT) infrastructure by enabling on-demand access to scalable resources. However, the elasticity and complexity of cloud billing models introduce significant challenges for effective resource cost management. This paper proposes a hybrid framework integrating statistical models auto regressive integrated moving average (ARIMA), machine learning techniques long short-term memory (LSTM), and optimization methods deep deterministic policy gradient (DDPG) to forecast and manage cloud costs with enhanced accuracy and adaptability. The framework is empirically validated using synthetic billing datasets and real-world cloud provider data, with performance evaluated via root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics. Results demonstrate 15-25% improvement in cost prediction accuracy over baseline models and up to 20% cost savings through dynamic resource allocation. The framework extends beyond traditional VM-based workloads to support serverless computing (amazon web services (AWS) Lambda and Azure Functions) and container-based applications (Docker and Kubernetes), addressing the growing adoption of microservices architectures. Comparative analysis with existing tools (AWS Cost Explorer and Azure Advisor) reveals superior adaptability in multi-cloud environments. The paper concludes with discussions of emerging paradigms including FinOps practices, AIOps automation, and sustainability-aware resource allocation, outlining future research directions toward explainable AI-driven cost governance.
Copyright (c) 2026 Kong FanYong, Fang-Fang Chua, Amy Hui-Lan Lim https://creativecommons.org/licenses/by-sa/4.0
Evaluation of the possibility of using an antenna switch with a wideband matching device
This paper investigates the feasibility of integrating a broadband matching device (BMD) with a high-frequency switch into the antenna system of the MIC RL-400M radio relay station, part of the "ROSA" radar complex. The aim is to compensate for antenna impedance changes caused by adverse weather conditions such as snowfall, icing, and wet snow, which reduce power transmission efficiency. Various types of high-frequency switches, including relays, PIN diodes, and transistors, were analyzed. A transistor-based switch (HMC349AMS8G) was selected due to its low insertion loss, high reliability, and wide operating frequency range. The BMD structure was synthesized to minimize impedance variation, and its performance was evaluated through simulation in AWR Microwave Office and experimental measurements in the 394–450 MHz range. Results showed an average power loss reduction of 0.7 dB and a 6.9% increase in radio link range compared to operation without a switch. The proposed solution enhances the stability and efficiency of radar and radio relay systems, ensuring reliable operation in challenging environmental conditions.
Copyright (c) 2026 Zhanat Manbetova, Assel Yerzhan, Zharkyn Altynov, Zukhra Rakhimzhanova, Gulzada Mussapirova, Pavel Boykachev, Assanali Iskakov, Maria Poleshchuk https://creativecommons.org/licenses/by-sa/4.0
Gift recommendation with multilabel clustering
In the rapidly evolving e-commerce landscape, personalized gift recommendation systems play a crucial role in enhancing customer satisfaction and driving sales. This study introduces a gift recommendation system using a multilabel clustering approach with the using four algorithms, aiming to provide personalized and accurate product suggestions. The proposed system compares the performance of four algorithms: K-nearest neighbors (K-NN), decision trees, random forest, and eXtreme gradient boosting (XGBoost). Through extensive model training and hyperparameter tuning, XGBoost demonstrated superior performance with a label ranking average precision score of 95% and minimal overfitting, outperforming other algorithms in accuracy and runtime. The results highlight the effectiveness of XGBoost in managing complex data and delivering precise recommendations, making it a valuable tool for improving user experience and increasing revenue in e-commerce platforms.
Copyright (c) 2026 Violitta Yesmaya, Rini Wongso https://creativecommons.org/licenses/by-sa/4.0
A machine learning framework for dynamic and balanced computing resource allocation in 5G networks
The swift expansion of fifth-generation (5G) networks has heightened the difficulty of distributing computing and transmission resources amidst the demands for extensive connectivity, ultra-low latency, and high throughput. This paper presents an innovative hybrid framework that combines deep learning (DL) with bird swarm optimization (BSO) to achieve dynamic and balanced resource allocation in mobile edge–cloud environments. A DL model based on long short-term memory (LSTM) forecasts user demand and channel conditions, while BSO enhances offloading and power distribution to reduce latency, energy usage, and expenses. In a setup utilizing non-orthogonal multiple access (NOMA) and mobile edge computing (MEC), the proposed DL–BSO approach demonstrates an impressive improvement of up to 54% compared to heuristic methods in simulations that reflect realistic traffic and channel conditions. The framework demonstrates a strong ability to adjust to different loads, rendering it ideal for applications that require low latency, including autonomous driving and augmented reality. The constraints involve dependence on precise forecasts and scalability issues in extensive implementations, which will be tackled in forthcoming research focused on 6G advancements.
Copyright (c) 2026 Shaik Abdul Hameed, Indurthi Ravindra Kumar, Chavali Amaresh, Kanchana Rajendran, Zarapala Sunitha Bai, Maganti Syamala https://creativecommons.org/licenses/by-sa/4.0
Automated detection of rice plant diseases using dual stage thresholding and twin support vector machine
Rice plants are susceptible to various diseases such as brown spot, BLB, and blast, caused by viral, bacterial, or fungal infections, which significantly affect both the quantity and quality of rice production. This study introduces an automated method for detecting these diseases using dual thresholding (DT) in segmentation combined with twin support vector machine (TW-SVM) classification. Early detection and accurate identification of rice leaf diseases are crucial for effective management and optimization of production. The proposed method leverages the strengths of TW-SVM, including its ability to handle high-dimensional data efficiently. The approach is compared with three SVM-based techniques: basic SVM, least-square SVM, and proximal SVM. Simulations are performed using images from both a public dataset and a real-time drone image dataset. Thirteen features, including color, texture, and shape, are extracted for classification. Results show that the proposed dual stage thresholding (DST) TW-SVM achieves superior performance in terms of time complexity and accuracy, with 95% accuracy on the public dataset and 99.3% accuracy on the drone image dataset.
Copyright (c) 2026 Snehaprava Acharya, Prasant Kumar Patra, Umesh Chandra Samal, Prabodh Kumar Sahoo, Ankur Goyal, Prince Jain https://creativecommons.org/licenses/by-sa/4.0
Improving delivery mode forecasting with deep neural network: a time-based convolutional network strategy
The caesarean section is one of the most frequently performed surgical procedures worldwide, with profound implications for maternal and neonatal health. Accurate prediction of delivery mode is essential for guiding clinical decisions, minimizing unnecessary surgical interventions, and improving patient outcomes. This study introduces a deep neural learning technique based on a temporal convolutional neural network (DNLTC) to classify delivery type—caesarean section versus normal vaginal delivery using maternal and obstetric data. The proposed model was evaluated against traditional machine learning (ML) approaches, including artificial neural networks (ANN), support vector machines (SVM), and decision trees (DT). Experimental results show that the DNLTC achieved the highest overall accuracy (85%), surpassing ANN (80%), SVM (68.8%), and DT (65%). TCNN also demonstrated strong clinical reliability, with a sensitivity of 94%, specificity of 91%, and a perfect F1-score of 100%. These findings highlight the advantages of incorporating temporal feature learning into delivery mode prediction, enabling the detection of subtle, sequential patterns that conventional models may overlook. By providing more accurate and robust predictions, the proposed framework can support obstetricians in making timely, evidence-based decisions, ultimately enhancing maternal and newborn health outcomes.
Copyright (c) 2026 Saeed Hamouda, Ayman Mohamed, Hany A. Elsalamony https://creativecommons.org/licenses/by-sa/4.0
A systematic study on advanced intelligent techniques in automated guided vehicles control for Industry 5.0 perspective
Automated guided vehicles (AGVs) play a crucial role in streamlining operations within manufacturing plants, warehouses, and distribution centers. As the industrial landscape transitions from Industry 4.0 to Industry 5.0, there is an increasing demand for more advanced, intelligent control systems to support the evolving complexity of these environments. This paper presents a systematic study of the advanced intelligent techniques driving the autonomous behavior of AGVs, with a focus on their application in Industry 5.0. The review categorizes intelligent techniques—such as machine learning, soft computing, game theory, and other intelligent algorithms—used for enhancing AGV functionalities including path planning, task scheduling, and energy-efficient operation. Emphasis is placed on how these approaches enable adaptability and smarter decision-making in dynamic industrial settings. The study concludes with key insights and outlines future research directions to further enhance AGV performance in the context of Industry 5.0.
Copyright (c) 2026 Prema C. Thalaivar, Madhusudhan K. N., Bhagya R. https://creativecommons.org/licenses/by-sa/4.0
An intelligence framework for threat detection and response in cloud–IoT-assisted enterprise environments
Cloud-internet of things (IoT)-enabled enterprise environments have become an integral part of modern infrastructures, but their increasing interconnectedness makes them vulnerable to sophisticated and rapidly evolving cyber threats. Existing methods for intrusion detection and threat intelligence often suffer from limitations such as high false alarms, low adaptability to new attacks, and computational overhead. To address these challenges, this paper presents an intelligent hybrid framework for threat detection and response in cloud-IoT-enabled enterprises. The proposed system adopts a two-stage architecture: an autoencoder (AE)-based anomaly detector serves as the first security layer to identify deviations from normal traffic behavior, while a convolutional neural network-long short-term memory (CNN-LSTM) model with an attention mechanism serves as the second layer to classify known attack categories with high accuracy. A response mechanism is further integrated to log events, assign severity scores, apply automated protections, and generate real-time alerts, transforming detection into proactive prevention. The system has been evaluated on the benchmark CSE-CIC-IDS2018 dataset, where the anomaly detector achieved an accuracy of 98.4% with a false positive rate of 2%, while the CNN-LSTM-Attention intrusion classifier achieved an accuracy of 99.42%.
Copyright (c) 2026 Amith Shekhar Chandrashekhar, Sarala D V, Ambuja K, Rajani Kallhalli Channarayappa, Karanam Sunil Kumar https://creativecommons.org/licenses/by-sa/4.0
Efficient transformer architecture for sarcasm detection: a study on compression and performance
This sarcasm detection is a crucial subtask in natural language processing (NLP) particularly for sentiment analysis and conversational AI. Its complexity lies in interpreting context, tone, and intent beyond literal meanings. Traditional models often struggle to capture such nuances, especially in informal and diverse language settings. Moreover, existing approaches lack computational efficiency and fail to adapt well across different domains. This study evaluates three benchmark datasets—News Headlines, Mustard, and Reddit (SARC)—representing structured, scripted, and conversational sarcasm, respectively. Each dataset poses unique linguistic and contextual challenges. The proposed methodology integrates transformer-based models (RoBERTa and DistilBERT) with context summarization using BART and metadata embedding. A comparative analysis is conducted on both linguistic accuracy and computational efficiency. The novelty lies in aligning sarcasm detection performance with architectural optimization for real-time deployment. Evaluation is conducted using accuracy, F1-score, Jaccard coefficient, precision, and recall. Results show that RoBERTa delivers peak performance, while DistilBERT achieves a 1.74× speedup with competitive results, making it suitable for scalable and efficient sarcasm detection.
Copyright (c) 2026 Parul Dubey, Amit Mishra, Aruna Singh, Murtuza, Akshita Chanchlani, Pushkar Dubey https://creativecommons.org/licenses/by-sa/4.0
Improving genomic classification via Pearson-based SNP selection: a comparison of k-NN, SVM, and random forest
Accurate genomic classification is vital for precision health and population studies, yet high-dimensional single-nucleotide polymorphism (SNP) data (p>>n) amplify noise, redundancy, and overfitting. This study evaluates a simple, model-independent Pearson-based selection that ranks SNPs by feature–label correlation, and assesses k-nearest neighbors (k-NN), linear support vector machine (SVM), and random forest (RF) under leakage-free stratified Monte Carlo cross-validation (MCCV). Performance increases monotonically with |r|: the strongest tiers reach ?99–100% accuracy; SVM leads in mid tiers (RF second), while k-NN is competitive mainly at the extremes. A matched-dimensionality PCA-120 baseline (TRAIN-only) attains parity for SVM/RF and trails slightly for k-NN at the 10% test size. With 120-SNP panels, prediction medians are ?0.30 ms (SVM), 1.81–1.83 ms (k-NN), and 34–35 ms (RF), supporting CPU-only deployment. A consensus panel combining correlation evidence with principal component analysis (PCA) selection frequency yields interpretable Top-20/Top-120 subsets and |r|-based operating thresholds. Overall, Pearson-based selection provides a transparent, reproducible baseline for small-sample SNP classification, offering accuracy competitive with PCA at lower computational complexity and straightforward extensions to broader cohorts and multi-omics integration.
Copyright (c) 2026 Prihanto Ngesti Basuki, Sri Yulianto Joko Prasetyo, Adi Setiawan https://creativecommons.org/licenses/by-sa/4.0
Dynamic weight adaptation in soft voting for emotion detection using neural networks
Confirming elevated accuracy and speed in multi-label automatic emotion classification endures to pose extensive challenges. Old-style machine learning (ML) models are broadly used for this. However, large-scale fast embryonic textual information often obstructs their performance. Deep learning (DL) models resolve the former problem efficiently, but fine-tuning the hyperparameter entails a lot of work and experience. Ensemble learning practices offer enhanced accuracy, but classical soft voting classifiers with static weights fall short to adapt effectively to diverse data traits. To tackle this limitation, this study proposes a novel ensemble framework that employs a neural network (NN) based dynamic weight adaptation within a soft voting classifier. The model dynamically adjusts the weights of core ML classifiers based on their real-time predictive likelihood and performance statistics. This adaptive weighting suggestively enhances the model’s ability in detecting nuanced emotional expressions in text, improving responsiveness and generalization. Comprehensive experiments conducted on yardstick emotion dataset demonstrate that proposed integration of NN driven adaptive weighting within an ensemble framework outpaces traditional approaches, capturing an overall classification accuracy of approximately 98% thus offering a scalable and robust solution for real-world sentiment analysis applications.
Copyright (c) 2026 Nisha, Rakesh Kumar https://creativecommons.org/licenses/by-sa/4.0
Security challenges in the internet of things for higher education: a study of vulnerabilities and emerging solutions
The growing use of internet of things (IoT) technologies in higher education is transforming how institutions manage infrastructure, deliver teaching, and engage with students. While these advancements offer considerable benefits, they also introduce significant security risks. Common threats include weak access controls, insufficient data protection, outdated software, exposure to denial-of-service (DoS) attacks, and lack of physical safeguards for connected devices. This study provides a comprehensive review of these vulnerabilities within academic environments and proposes a security framework adapted to the specific operational and technical realities of universities. Unlike generic approaches, this research focuses on the unique challenges of higher education, such as decentralized information technology (IT) structures, limited resources, and diverse user groups. The main contribution lies in identifying and evaluating security measures that are both effective and applicable in academic contexts. These include encryption methods, identity verification techniques, secure update mechanisms,and intelligent systems for detecting abnormal behavior. The analysis is supported by case examples from real institutions, illustrating both successes and limitations of current practices. This work aims to guide educational institutions in improving the resilience of their IoT systems. It also outlines areas for future research, particularly in the development of lightweight and scalable security solutions suited to the evolving needs of smart learning environments.
Copyright (c) 2026 Kamal Elhattab, Driss Naji, Abdelouahed Ait Ider, Abdelali Joumad, Abdelkbir Ouisaadane, Karim Abouelmehdi https://creativecommons.org/licenses/by-sa/4.0
A mathematical model to cluster reviewers for online review system
Online business models accept reviews or feedback from customers which are processed and analyzed for important business decisions. Online reviews are helpful to understand the usefulness or popularity of a product. However, it has been observed that sometimes fake reviews are frequently used to boost the popularity of one's own product or to damage reputation of competitors' products. Henceforth it is an interesting research problem to validate reviews or trustworthiness of reviewers. In this paper, a mathematical model is introduced to rate and cluster reviewers based on relevant parameters. It has been observed from business intelligence perspective, that grouping reviewers into different clusters, rather than ranking them individually based on their authenticity, would be more beneficial for potential buyers to understand the quality of reviewers. In the proposed model, clustering is performed using two weighted scores based on average opinion variance and product price. The mean shift clustering algorithm is used to dynamically slab the product price attribute while Jenks Natural Breaks Optimization (JNBO) method and K-means algorithm are applied for the reviewer clustering. Further this research work analyses the impact of product price on reviewer rating and validates the result using t-test statistical method. The proposed methodology is experimented on Amazon datasets to show efficacy of the model.
Copyright (c) 2026 Runa Ganguli, Akash Mehta, Takaaki Goto, Soumya Sen https://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence-based solar radiation forecasting for energy optimization in sustainable buildings
The work presented in this article aligns with our university's commitment to advancing renewable energy sources. We can better plan and optimize energy use if we are aware of the factors that affect solar energy generation. This study examines the application of artificial neural networks (ANNs) in forecasting global horizontal irradiance (GHI) within the context of sustainable energy. The primary objective is to enhance the accuracy and reliability of solar irradiance forecasts, thereby improving the performance of renewable energy systems, such as concentrated solar power (CSP). This article provides an overview of solar radiation, the physical factors that influence its distribution, and the impact of panel tilt angle on energy production. It presents a case study in Morocco, which uses a hybrid approach to predict solar radiation. The results demonstrate that ANN, employing advanced machine learning (ML) methods, provides more accurate and reliable forecasts than traditional models. This advance could improve energy planning, reduce uncertainty, and enable better management of solar energy production and storage systems. Our results suggest that this approach has increased forecast accuracy.
Copyright (c) 2026 Imad Laabab, Said Ziani, Abdellah Benami https://creativecommons.org/licenses/by-sa/4.0
Papulosquamous SkinSense: a hybrid artificial intelligence model with visual explanations and chatbot
Accurate diagnosis of papulosquamous skin diseases such as Psoriasis (PS), Lichen Planus (LP), Pityriasis Rosea (PR), and Pityriasis Rubra Pilaris(PRP) is challenging due to their visually similar features, particularly during healing stages. This study presents an optimized deep learning framework to improve diagnostic accuracy and interpretability. A dataset of 3,120 DermsNetZ images was used, with preprocessing through contrast limited adaptive histogram equalization(CLAHE) to enhance lesion visibility. Pretrained convolutional neural networks (CNNs) including MobileNetV2, InceptionV3, NASNet, and hybrid models were evaluated using accuracy, precision, recall, and F1-score. Among these, MobileNetV2 combined with gradient-class activation mapping (Grad-CAM) achieved the best results, delivering 94% accuracy, 95% precision, and strong F1-scores, while offering explainable artificial intelligence (AI) through lesion localization. To translate these results into practice, the SkinSense Detection App was developed, integrating with transfer learning, class balancing, augmentation, and Grad-CAM visualization within a user-friendly interface. The app also incorporates a large language model (LLM-powered) chatbot for real-time, personalized feedback. With an overall success rate of 98.08% and user ratings between 4.6–4.8/5, the system demonstrates high reliability and accessibility. This study highlights the value of interpretable deep learning in dermatology, bridging technical accuracy with clinical usability and offering scope for expansion to larger datasets and diverse skin conditions.
Copyright (c) 2026 Krupali Rupesh Dhawale, Arvind R. Bhagat Patil https://creativecommons.org/licenses/by-sa/4.0
Power-LSTM for smart greenhouse: a novel deep learning approach to temperature prediction in a Mexican case study
This paper addresses the challenge of predicting internal temperature in green-house environments, a critical aspect of optimizing crop growth and ensuring resource efficiency. While machine learning (ML) techniques have been widely applied to predict greenhouse climates, deep learning (DL) methods offer the po-tential to capture more complex relationships within the data. In this study, we present a comprehensive evaluation of ML and DL models, along with our pro-posed power-long short-term memory (PLSTM) model, to predict the internal temperature of a greenhouse using a database from Mexico. We compared tradi-tional ML models such as linear regression (LR) and extreme gradient boosting (XGBoost) with DL architectures like gated recurrent unit (GRU), artificial neu-ral networks (ANN), hybrid LSTM-ANN and LSTM-RNN architectures. Our proposed PLSTM model outperformed both ML and DL models, achieving the R2 score of 0.9710, and root mean square error (RMSE) equal to 0.1710, high-lighting its superior ability to predict complex time-series data.
Copyright (c) 2026 Salma Ait Oussous, Dauris Lail Madama, Rachid El Bouayadi, Aouatif Amine https://creativecommons.org/licenses/by-sa/4.0
Implementation of meta-heuristic and deep learning algorithms for power system cybersecurity
Power system cyber security is crucial due to their criticality. Cybersecurity is essential to protect vital infrastructure as power systems digitize. Meta-heuristic and deep learning techniques are used to improve power system cyber security in this paper. To evaluate their performance, the suggested approach is compared to traditional supervised machine learning algorithms including artificial neural networks (ANNs), convolutional neural networks (CNNs), and support vector machines (SVMs). The technique optimizes deep learning model hyper parameters and architectures to detect cyber risks. Cyberattacks on power systems can cause service outages and cascading failures with extensive social implications. Meta-heuristic and deep learning algorithms are integrated to improve power system cyber security in this study. Deep learning is good at pattern recognition and anomaly detection, while meta-heuristic algorithms optimize efficiently. A complete threat detection and mitigation strategy is proposed by merging these methodologies. The proposed methodology tests classic supervised machine learning algorithms such ANNs, CNNs, and SVMs. Simulations showed the algorithm worked better. It beat competition in accuracy, precision, recall, and F1-score.
Copyright (c) 2026 Baddu Naik Bhukya, Samanthaka Mani Kuchibhatla, Naresh Kumar Bhagavatham, Tirumalasetti Lakshmi Narayana, Madhava Rao Chunduru, Balakrishnan Koustubha Madhavi https://creativecommons.org/licenses/by-sa/4.0
Deep learning techniques business performance optimization in micro, small, and medium-sized enterprises: systematic review
The application of deep learning is transforming how micro, small, and medium-sized enterprises (MSMEs) operate. By using data-driven insights, these firms overcome traditional analytical limitations and improve decision-making. This study explores factors influencing deep learning adoption in MSMEs, identifies effective strategies, and compares performance between companies that implement these methods and those that do not. The objective is to analyze the impact of deep learning on optimizing the performance of MSMEs. The methodology consisted of a scientific review following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) system and a bibliometric analysis to map international contributions. The results show that techniques such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, transformers, and deep reinforcement learning (DRL) are crucial for marketing strategy prediction, customer experience personalization, and inventory management, leading to better return on investment (ROI), loyalty, and efficiency. Despite the potential benefits, there's still no enough research on how small businesses with limited resources use these methods and deal with issues like poor infrastructure and data access. Deep learning is essential for MSMEs' sustainability and competitiveness, even if there are challenges.
Copyright (c) 2026 Carlos Roberto Sampedro Guaman, Miguel Angel Cano Lengua, Ciro Rodriguez Rodriguez, Igor Aguilar-Alonso https://creativecommons.org/licenses/by-sa/4.0
Hybrid AI-driven anomaly detection and sequential attack classification for securing IoT networks
Internet of things (IoT) systems are often inherently heterogeneous and the constantly evolving cyber threat presents a variety of attack vectors that can expose sensitive data across multiple mission-critical applications. The existing intrusion detection methods are often prone to zero-day attacks and specific to limited known intrusions. This paper designs a hybrid and multi level cyber-threat detection framework based on the robust data preprocessing scheme, correlation-based optimal feature selection and integrated anomaly and intrusion detection using a supervised learning approach. In the first stage, a random forest (RF)-based binary anomaly detector is designed as a fast primary threat filter against zero-day threats by detecting traffic anomalies without any prior attack signal. In the second stage, an adaptive, time-aware long short-term memory (LSTM) model performs multi-class intrusion classification using time-lag analysis in traffic flows to accurately identify and classify known attack types with high precision. The proposed framework is evaluated on the network flow telemetry of network–internet of things–version 2 (NF-ToN-IoT-V2) dataset and achieved 99% accuracy in both binary and multiclass settings, with a lower response time of 7.8 ms.
Copyright (c) 2026 Gauri Sameer Rapate, Ambuja Krishnappa, Sarala Duggonahalli Veeresh, Karanam Sunil Kumar, Bellary Kursheed https://creativecommons.org/licenses/by-sa/4.0
Harmonic path planning using Quarter-Sweep Boosted AOR iterative method
This paper presents the findings of a study that examined the effectiveness of the application of Quarter-Sweep Boosted AOR with the 9-Point Laplacian operator using the families of relaxation methods for computing the solutions of Laplace's equation to obtain the harmonic potentials+ This work is a continuation from the past study that applied the standard 5-Point Laplacian to solve path planning issue that a mobile robot faces when working in indoor environment. The robot can navigate from a given starting position to a goal position by following the safest path, ensuring it avoids any obstacles and minimizes the risk of collisions. By utilizing Laplace's equation and computing the distribution of potential values in the simulated environments, the robot can determine the safest path that avoids obstacles present in the environment. This method ensures that the robot moves along a path where the potential for collisions is minimized. The findings confirm that Quarter-Sweep Boosted AOR (QSBAOR) outperforms Half-Sweep Boosted AOR (HSBAOR) and Full-Sweep Boosted AOR (FSBAOR). QSBAOR and HSBAOR show 75% and 50% reduction respectively, compared to FSBAOR in terms of computational complexity.
Copyright (c) 2026 Sumiati Suparmin, Azali Saudi https://creativecommons.org/licenses/by-sa/4.0
Data mining approach for stunting clusters in Jumput Rejo
The target of reducing the stunting prevalence rate by 14% in 2024 which has been set by the government needs to be of concern to be implemented by the local health office. The purpose of the research is to cluster toddler anthropometry data with data mining algorithm. Optimize K-Means (KM) algorithm with elbow method use to cluster toddler anthropometry data (sex, height, weight, age, and health care center). A set of 580 children's anthropometric measurements were analyzed and categorized based on their similarity. Cluster 1 comprises 150 members and exhibits a narrower range of age and height values compared to the other clusters. Cluster 2, with 124 members, displays a broader range of age and height values compared to both Cluster 1 and Cluster 3. Cluster 3, consisting of 150 members, demonstrates age and height values that are higher than Cluster 1 but lower than Cluster 2 and Cluster 4. Finally, Cluster 4, encompassing 156 members, exhibits age and height values that are higher than those in the other clusters that many children are stunted based on standard anthropometric table for assessing children's nutritional status. The cluster optimization yielded four distinct clusters, which will serve as the input for identifying clusters during the data grouping process using the KM algorithm.
Copyright (c) 2026 Amir Ali, Purwanto, Mundakir https://creativecommons.org/licenses/by-sa/4.0
An integrated hybrid U-Net and EfficientNetV2-S approach for brain tumor segmentation and classification
Brain tumors involve the uncontrolled proliferation of cells either inside or adjacent to brain tissue, which frequently results in poor patient outcomes. Accurate diagnosis and early identification are vital for effective treatment planning. In this study, we develop a sequential deep learning pipeline for automatic brain tumor segmentation and classification using the publicly accessible Figshare dataset, comprising 3,064 images from 233 patients. We present a double hybrid encoder model based on the U-Net architecture, which combines complementary feature extractors to improve segmentation performance. The model achieves a loss of 0.047, an intersection over union (IoU) of 86.89%, and a Dice score of 95.27%, surpassing the performance of conventional U-Net and U-Net++ architectures. For classification, we utilize a modified EfficientNetV2-S, which is lightweight and achieves a 99% F1-score and 99% accuracy, while being less computationally intensive and faster to train than deeper frameworks such as ResNet50V2. Model performance was evaluated using cross-validation, which included fault detection to improve reliability. We propose that our framework can reliably and efficiently analyze brain tumors and serve as an important component in clinical decision-making in neuro-oncology.
Copyright (c) 2026 Marwa Abbas, Ashraf A. M. Khalaf, Hussein Mogahed, Aziza I. Hussein, Lamya Gaber, Mohamed Mourad Mabrook https://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of unidirectional and bidirectional RNNs for ECG arrhythmia detection using augmented MIT-BIH data
Accurate classification of electrocardiogram (ECG) signals is essential for early arrhythmia detection. This study compares the performance of unidirectional and bidirectional recurrent neural networks (RNN), specifically gated recurrent unit (GRU)-based architectures, for classifying ECG beats as normal or arrhythmic. ECG data were sourced from the MIT-BIH Arrhythmia Database using the WFDB toolkit. Each beat was segmented into a 128-sample window centered on the R-peak and labeled into two classes. To address severe class imbalance (6,279 normal vs. 43 arrhythmic beats), data augmentation techniques—jittering and scaling—were applied, resulting in a balanced dataset. Both models were trained under identical conditions, with evaluation based on accuracy, precision, recall, F1-score, and other statistical metrics. The unidirectional RNN achieved poor recall (9.0%) despite high precision, yielding an overall accuracy of 54.0%. In contrast, the bidirectional RNN significantly outperformed, achieving 98.17% accuracy, 98.39% precision, 97.92% recall, and a 98.16% F1-score. The results demonstrate that bidirectional temporal modeling provides substantial improvements in ECG classification, especially for detecting minority class arrhythmias. This study highlights the importance of both data augmentation and model architecture in developing effective deep learning solutions for real-time ECG analysis and clinical diagnostics.
Copyright (c) 2026 Sabura Banu Urundai Meeran, Nafeena Abdul Munaf https://creativecommons.org/licenses/by-sa/4.0
Python-based stock price prediction using backpropagation neural networks: a case study on ANTM
Accurate stock price prediction is critical for informed investment decisions. Today, stock trading has become a popular option as a source of income among people, due to its potential for rapid gains in a short time, but, due to fluctuating stock prices, it can cause great losses in exchange. This study aims to forecast the closing price using the backpropagation neural network algorithm so that it can be used as a decision support for potential investors and traders in this research, the system was built using the Python programming language, and the stock price data used were shares of the company Aneka Tambang Tbk (ANTM). The results of this research are root mean squared error (RMSE) values, additional labels for prediction results, and graphs for comparison of the original data with the predicted data. Based on the testing result, the best value of RMSE is 3.786, the mean absolute percentage error (MAPE) value is 0.001 which indicates that the prediction results are very close to the actual value.
Copyright (c) 2026 Prind Triajeng Pungkasanti, Febrian Wahyu Christanto, Fadhilatut Tasyriqul Hajjas Sabat, Christine Dewi, Eryan Ahmad Firdaus https://creativecommons.org/licenses/by-sa/4.0
Deep spatiotemporal signal learning with transformers for multi-day wildfire forecasting
Wildfire forecasting is a critical challenge in environmental signal processing and disaster response planning. The ability to interpret multimodal spatiotemporal signals is essential for early warning systems and resource deployment. This study addresses these limitations by proposing a unified prediction-to-action framework. We utilized four open-access datasets—wildland fire emissions database (WFED), fire information for resource management system (FIRMS), Sentinel Hub, and a custom moderate resolution imaging spectroradiometer+shuttle radar topography mission (ERA5+MODIS+SRTM) fusion—covering fire occurrences, vegetation indices, meteorological parameters, and topographic features. These heterogeneous signals were preprocessed, aligned, and transformed into structured tensors for model training and evaluation. We use a transformer-based system to understand long-term patterns in space and time, enhanced by a belief–desire–intention (BDI) reasoning module that connects our predictions to flexible wildfire response plans. The novelty lies in the integration of signal-aware attention mechanisms with symbolic decision modeling. Model performance was evaluated using F1-score, intersection over union (IoU), mean absolute error (MAE), and directional accuracy. The suggested framework did better than the basic convolutional neural network (CNN) models, reaching an F1-score of 0.74, a directional accuracy of 84.3%, and lowering the MAE to 7.6 km², while also providing clear and relevant action suggestions.
Copyright (c) 2026 Parul Dubey, Gaurav Vishnu Londhe, Vinay Keswani, Akshita Chanchlani, Murtuza, Pushkar Dubey https://creativecommons.org/licenses/by-sa/4.0
Robust Arabic tweet NER via label-aware data augmentation and AraBERTv2
Named entity recognition (NER) is vital for turning unstructured social media text into structured information. However, Arabic tweets pose distinct challenges; informality, brevity, dialectal variation, and inconsistent orthography. This study targets those challenges by coupling targeted data augmentation with a transformer model, bert-base-arabertv2. We design a lightweight augmentation pipeline—synonym replacement, name and location replacement, and deletion of third-person Arabic names—to expand linguistic variety and reduce overfitting under limited annotation. The approach is simple, but deliberate: preserve labels when substituting entities with type-consistent alternatives; remove corresponding tags when deleting names; and keep tweet semantics intact where possible. We then fine-tune bert-base-arabertv2 on the combined original and augmented data and evaluate on a held-out set of tweets. The result is a substantial gain in overall performance: F1=0.93 with augmentation versus 0.72 without. These findings indicate that controlled, label-aware augmentation can improve robustness and generalization for Arabic tweet NER, where data scarcity and linguistic variability otherwise degrade accuracy. Beyond empirical gains, our work offers a practical recipe—clear augmentation heuristics and a standard transformer backbone—that can be replicated and adapted to similar low-resource, noisy domains. This contributes to more reliable Arabic social media analysis and downstream information extraction.
Copyright (c) 2026 Brahim Ghazoui, Ismail El Bazi, Ibtissam Essadik, Brahim Ait Benali, Hicham Moussa https://creativecommons.org/licenses/by-sa/4.0
A multimodal framework for secure digital document fortification using Morse code and biometric watermarking
In an era of escalating cybersecurity threats, traditional authentication methods become more susceptible to attacks like phishing, brute force, and identity theft. With the aim to counter these difficulties, this paper introduces a multi-layered authentication that merges facial recognition, eye-tracking based Morse code verification, biometric verification using convolutional neural network (CNN) and cryptographic watermark with Rubik's encryption. The document fortification system proposed here improves security by integrating biometric authentication, behavioral verification, and encryption-based watermarking to provide both user authentication and document integrity. The authentication process begins with facial recognition, where multi-task cascaded convolutional neural network (MTCNN) detects facial features and FaceNet generates unique embeddings for identity verification. Upon successful authentication, users input a Morse code password via an eye-blinking mechanism, which is decoded and validated. Additionally, fingerprint and iris recognition using CNN models further enhance security. The Rubik’s encryption algorithm secures biometric watermarks within digital documents, preventing tampering. An one-time password (OTP)-based re-authentication mechanism ensures only authorized users can access encrypted files. Experimental results demonstrate the system’s high accuracy and resilience against security threats, making it a robust and scalable authentication framework. This research highlights the potential of multi-factor authentication (MFA) in modern cyber-security, offering a future-ready solution for securing sensitive digital documents such as images and pdf files.
Copyright (c) 2026 Tresa Maria Josylin, Ganeshayya Shidaganti, Vishwachetan Dasegowda, Anasuya Jadagerimath, Prakash Sheelvanthmath https://creativecommons.org/licenses/by-sa/4.0
Gated recurrent unit model for assessment of food quality based on E-nose sensors supported with one-way analysis of variance
Ensuring the quality of global food supplies has emerged as a significant challenge in recent times. Overseeing perishable items' excellence, freshness, and longevity poses considerable intricacy. A special kind of system established on electronic scent detection systems has been engaged for quality assessment. Recent advancements have concentrated on integrating electronic scent detection systems with machine learning (ML) and deep learning (DL), which comprise encouraging remedies to meet these hurdles. Mainly, this investigation aims to present a pioneering strategy for addressing this issue by binding DL with electronic olfaction technology. Gated recurrent units (GRU) were used for classification actions. The research entails examining from the literature a benchmark dataset acquired from electronic noses (E-noses) across beef cuts. These cuts are allocated into four classes: i) outstanding, ii) satisfactory, iii) passable, and iv) spoiled, depending on their quality. The proposed model, exploiting a GRU for classification tasks, was developed with active dataset attributes identified over the analysis of variance (ANOVA) feature selection method. As a consequence, three key features were selected and employed for the classification process, such as MQ5, MQ137, and total volatile content (TVC). Experimental outcomes demonstrate an impressive classification accuracy of 99.77%, accomplished by the proposed model across further literature models.
Copyright (c) 2026 Mohammad A. Alsharaiah, Yousef K. Sanjalawe, Sharif Naser Makhadmeh, Rizik M. Al-Sayyed, Bashar Awad Al-Shboul https://creativecommons.org/licenses/by-sa/4.0
Video classification of Indonesian traditional dance using a hybrid CNN-LSTM model with pose estimation
The preservation and recognition of traditional Indonesian dances face challenges due to limited digital documentation and declining intergenerational transmission. Manual annotation of dance videos is time-consuming and prone to subjectivity, creating urgency for automated solutions. This study proposes a deep learning-based approach combining convolutional neural networks (CNN) for spatial feature extraction and long short-term memory (LSTM) for temporal modeling to recognize traditional dance movements from video sequences. The system leverages OpenPose for keypoint detection and gesture estimation, enabling frame-wise pose representation prior to classification. A hyperparameter tuning process was applied to optimize the CNN-LSTM architecture using 80% of the dataset for training and 20% for testing. Experimental results show the proposed model achieved a macro accuracy of 98.4%, with perfect precision, recall, and F1-score. This research contributes to cultural heritage digitization and intelligent video analysis by enabling accurate, real-time classification of traditional dances, providing a foundation for future systems in education, archiving, and motion-driven applications.
Copyright (c) 2026 Candra Irawan, Heru Pramono Hadi, Cahaya Jatmoko, Mohamed Doheir https://creativecommons.org/licenses/by-sa/4.0
Household automatic clothes dryer with temperature control
Problems arising from the impact of irregular weather changes on the process of drying clothes, especially jeans, are an obstacle for housewives. Commercial clothes dryers are expensive and can cause fabric damage. Cleanliness and dryness of clothes play a vital role in everyday life. This article offers a solution in the form of an automatic clothes dryer with a temperature controller using the Arduino Uno microcontroller. This tool has a humidity sensor, LCD screen, indicator light, heater, fan, and DC motor. When the humidity is high, the tool automatically activates the heater, fan, and red indicator light. After the drying process is complete, the green indicator light turns on, indicating that the clothes are dry. This study aims to develop other similar research by designing a clothes dryer control system based on Arduino Uno. Experiments show that this dryer is more efficient, taking 80 minutes compared to 240 minutes of manual drying in the sun. The humidity setting at 20 HR is the set point to ensure an optimal drying process. The contribution of this research lies in the development of efficient clothes dryer technology that can be implemented practically.
Copyright (c) 2026 Misbahul Jannah, Kartika, Mukhlis, Muthmainnah, Arnawan Hasibuan, Widyana Verawaty Siregar, Rizky Almunadiansyah, Armen Abta, Asran https://creativecommons.org/licenses/by-sa/4.0
Accurate hybrid prediction model for poverty line, number, and percentage of impoverished individuals
Poverty remains a major social issue in many developing countries, including Indonesia, as seen in the Central Java region. Over the last five years, the number of impoverished people in Central Java has shown fluctuations, with data from the Central Statistics Agency indicating figures of 3,897.20 thousand (2018), 3,743.23 thousand (2019), 3,980.90 thousand (2020), 4,109.7 thousand (2021), and 3,831.44 thousand (2022). Analyzing these trends is crucial for future poverty reduction efforts. This study aims to develop a web-based predictive system capable of forecasting the poverty line, as well as the number and percentage of poor residents in Central Java. The research utilizes a hybrid forecasting model that integrates the Holt-Winters triple exponential smoothing (HWTES) method with fuzzy time series (FTS), alongside algorithmic approaches such as rate of change (RoC) and frequency-based segmentation. The model's accuracy, evaluated using the average absolute percentage error (MAPE), shows low error rates: 0.9% for the number of impoverished people, 1.6% for the percentage, and 0.7% for the poverty threshold. Compared to the standard HWTES model, this hybrid model demonstrates greater precision. As a result, it can serve as an effective tool to support strategic planning and enhance poverty alleviation programs in Central Java.
Copyright (c) 2026 Toni Wijanarko Adi Putra, Yohanes Suhari, Achmad Solechan, Solikhin, M. Zakki Abdillah https://creativecommons.org/licenses/by-sa/4.0
CT-based lung cancer detection using spatially localized integral transforms with U-NET classification
Lung cancer remains the leading cause of cancer-related mortality worldwide, emphasizing the need for early, accurate, and scalable detection methods. Low-dose computed tomography (LDCT) has improved early diagnosis, yet challenges like image noise, low contrast, and subtle nodule features often limit reliable interpretation in large-scale screening. This paper proposes a computationally efficient computer-aided detection (CADe) framework that integrates a rotation-invariant, spatially localized integral transform feature extraction with a U-Net-based classifier to enhance lung nodule detection and segmentation. The approach strengthens spatial feature representation while maintaining low computational and memory demands, enabling real-time use in resource-limited clinical settings. Implemented in MATLAB and evaluated on the Cancer Imaging Archive (TCIA) dataset, the system achieved 99.32% classification accuracy, 88.88% specificity, 84.21% precision, 87.3% intersection over union (IoU), and 92.9% dice similarity coefficient (DSC). These results show clear improvements over conventional methods, particularly in rotational robustness and efficiency—key requirements for scalable screening. Although precision and IoU could be further optimized, the framework demonstrates strong potential for clinical adoption. By providing accurate, fast, and robust nodule analysis, this work advances practical high-performance tools for early lung cancer detection, especially in resource-constrained environments, ultimately contributing to better patient survival rates.
Copyright (c) 2026 Abel Belay Wossene, Dawit Haile Assefa, Ayodeji Olalekan Salau, Sepiribo Lucky Braide, Aitizaz Ali, Ting Tin Tin https://creativecommons.org/licenses/by-sa/4.0
Delineating 12-lead ECG for automated ST-elevation and ST depression detection using deep learning
ST-elevation or ST-depression are markers of an abnormal heart condition detected through an electrocardiogram (ECG) where the tracing in the ST-segment is unusually elevated above the TP-segment (baseline). Identifying the localization of the ST-segment on an ECG is difficult because even a minor change in the ST-segment can be obscured by filtering processes. The 12-lead ECG signal is a non-invasive tool in the early detection of ST-elevation based on ST- and TP-segment, with quick and accurate interpretation. This study proposes a standard 12-lead ECG delineation model using deep learning (DL). The ECG signal has been segmented to Pstart–Pend, Pend–QRSstart, QRSstart–Rpeak, Rpeak–QRSend, QRSend-Tstart, Tstart–Tend, and Tend–Pstart. The study interpreted ST-elevation or -depression using an ECG delineation approach guided by medical rules. The findings revealed that the DL model achieved an average accuracy of 99.18%, sensitivity of 92.55%,specificity of 99.55%, precision of 92.61%, and F1-score of 92.52% in limb leads. Similarly, in chest leads, the DL model attained an accuracy of 99.16%, sensitivity of 93.10%, specificity of 99.53%, precision of 93.32%, and F1-score of 93.11%. This study also validated the DL-predicted results by a cardiologist from Mohammad Hoesin Hospital, Indonesia.
Copyright (c) 2026 Bambang Tutuko, Annisa Darmawahyuni, Alexander Edo Tondas, Muhammad Naufal Rachmatullah, Firdaus, Ade Iriani Sapitri, Anggun Islami, Sukemi, Muhammad Fachrurrozi, Siti Nurmaini, Rendy Isdwanta, Jordan Marcelino https://creativecommons.org/licenses/by-sa/4.0
Optimizing the implementation of the Saber post-quantum cryptography scheme with a hybrid architecture
This paper presents a hardware–software hybrid implementation of the Saber key encapsulation mechanism (KEM) on a Terasic DE10-Nano board, which combines an ARM Cortex-A9 processor and an Intel Cyclone V field programmable gate array (FPGA). By offloading computationally intensive polynomial multiplication to a dedicated FPGA module, the hybrid design significantly reduces execution time. Experimental results show that compared to a software-only approach, the hybrid design decreases execution time by 40% for key generation, 35% for encapsulation, and 50% for decapsulation. The consistent performance gains were confirmed across the LightSaber, Saber, and FireSaber parameter sets, demonstrating that CPU-FPGA co-design offers significant efficiency improvements for post quantum cryptography (PQC), especially on platforms with limited resources.
Copyright (c) 2026 Sabyrzhan Atanov, Khuralay Moldamurat, Luigi La Spada, Makhabbat Bakyt, Adil Maidanov https://creativecommons.org/licenses/by-sa/4.0
PID controller tuning performance evaluation based on integral time square error for coupled tank system
This study presents a conclusive performance evaluation based on integral time between the conventional and optimization based proportional integral derivatives (PID) tuning method. The tuning method of interest are including trial and error, auto-tuning, Ziegler-Nichols (ZN), Cohen-Coon (CC), and particle swarm optimization (PSO). The coupled tank system (CTS) is used for the system under consideration as it’s one of the popular technologies in industrial control application. Previous study had compared the tuning performance in terms of its transient response. The transient consists of several parameters such as rise time (Tr), settling time (Ts), peak time (Tp), steady state error, overshoot, and steady state error. Due to that, a conclusive performance comparison could not be achieved. Hence this study proposed integral time squared error evolution which is based on only one parameter which also reflects good overall transient response performance. The results show that of all tuning methods, PSO provide the smallest integral time square error (ITSE) value, while trial and error provide the highest with a value of 12.84 and 203.10, respectively. The ITSE also reflects the transient response performances.
Copyright (c) 2026 Nur Asmiza Selamat, Atikah Razi, Syahar Azalia Ab Shukor, Nur Hazahsha Shamsudin, Fardila M. Zaihidee https://creativecommons.org/licenses/by-sa/4.0
Wind energy and energy potential assessment on Ambon Island
Ambon Island has a high dependence on fossil fuels, which causes environmental and energy security problems, even though the potential for renewable energy, especially wind, is enormous according to national energy plan data. This study aims to assess the technical and economic feasibility of wind energy development in Latuhalat Village as a case study. Wind speed and direction data for the period January-December 2024 from the Ambon Maritime Meteorological Station were analyzed, showing that the prevailing winds are from the east and southeast. Assuming an air density of 1.225 kg/m³, a turbine efficiency of 35%, and a rotor sweep area of 78.54 m², the estimated annual energy reaches 24,528 kWh. A capacity factor of 30% results in a realistic output of 7,358 kWh/year. A horizontal-axis wind turbine with inverse taper blades is recommended to suit local wind characteristics, producing 0.7-46.8 kW of power with a system efficiency of 67%. This study concludes that Latuhalat Village has viable wind energy potential for further development. Its implementation requires a holistic approach encompassing technical, economic, social, and policy aspects to support sustainable energy transition on Ambon Island.
Copyright (c) 2026 Lory Marcus Parera, Abhi Rizky Heluth, Mey Chyntia Yesaya, Johanis Leuwol https://creativecommons.org/licenses/by-sa/4.0
Advanced data balancing techniques with machine learning models for acute liver failure prediction
Amongst various diseases, one of the severe diseases is acute liver failure (ALF) and it is a quick decline in liver health that normally lasts a few days to a few weeks. Machine learning (ML) techniques can play a valuable role in the diagnosis and management of ALF. The proposed study made an effort to remedy the issue of the Kaggle Dataset's class imbalance by carrying out an exhaustive experimental assessment making use of two distinct approaches, namely synthetic minority oversampling technique (SMOTE) and synthetic minority oversampling technique and edited nearest neighbours (SMOTE-ENN). Both SMOTE-balanced and SMOTE-ENN balanced datasets are used to train the support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), and stacking models. Compared to the SMOTE method, the results demonstrated that the SMOTE-ENN balanced dataset achieved a considerable increase in the accuracy of its predictions. The results showed that the KNN algorithm has attained 99.52\% accuracy, along with a precision of 99.07\%, recall of 99.35\%, and F1 measure of 99.04\%. As a result, we discovered that a data balancing method that is not overly complicated and a supervised ML algorithm could be used to forecast ALF with very high accuracy and excellent potential for utility.
Copyright (c) 2026 Pradnya Borkar, Snehal Bankatrao Shinde, Mayank Jichkar, Mahek Humne, Sagarkumar Badhiye, Tausif Diwan, Nileshchandra Pikle https://creativecommons.org/licenses/by-sa/4.0
Spatio-temporal pedestrian detection in video: comparative evaluation of VGG16 with recurrent neural networks
Pedestrian detection is a crucial application in video surveillance, autonomous driving, and traffic monitoring. Thus, reliable surveillance is required for individual decision making and safety. The study aims to compare two models, one based on VGG16 for feature extraction, coupled with a long short-term memory (LSTM), and the other simply a dense model, for pedestrian detection in video. The integration of an attention mechanism to improve feature discrimination across frames along with a lightweight structure for real-time processing that enables cross-domain generalization to diverse datasets is novelty of this work. We exploit the pre-trained VGG16 model on ImageNet, extracting spatial features from all the frames of the videos. We then feed these spatial features through an LSTM to capture temporal dependencies. The dense model uses just the spatial features and throws into the bin of information the time holds for them. We apply accuracy, precision, recall, and specificity as metrics in evaluation models on a labeled dataset of pedestrian video clips. Experimental results show that the VGG+LSTM model performs better than the dense model by giving a higher accuracy and performing better on temporal variations of frames. The LSTM-based approach achieves 0.96 accuracy over multivariate datasets.
Copyright (c) 2026 Tanya Gupta, Neera Batra https://creativecommons.org/licenses/by-sa/4.0
Intelligent innovation across disciplines: key trends from recent research in AI, IoT, and automation
The fast growth of artificial intelligence (AI), the Internet of Things (IoT), and automation is changing research and business in many fields. This issue has 76 new pieces of research that show new ways to use technology and new ways to accomplish things in healthcare, communication networks, smart energy, farming, and industrial automation. Deep learning and hybrid AI models improve disease identification, medical picture segmentation, and patient monitoring in healthcare, focusing on both accuracy and ease of understanding. In communication and the IoT, contributions include smart resource allocation for 5G and 6G networks, spectrum sensing, and cybersecurity frameworks that deal with efficiency, latency, and resilience in connected environments. AI and the IoT are important for sustainability and city planning. For example, smart energy management, predicting the weather in greenhouses, and using drones to monitor traffic are all examples of this. Also, the development of autonomous guided cars and Industry 5.0 automation shows how intelligent systems can be used in industrial operations. These works show how AI, IoT, and automation are coming together and how they could change the way people operate, make decisions, and solve problems in many fields. This issue shows how important it is to use ideas from many fields to drive the next generation of smart innovation
Copyright (c) 2026 Tole Sutikno https://creativecommons.org/licenses/by-sa/4.0