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28,451 Article Results

Modelling and simulation of maximum power point tracking on partial shaded PV based-on a physical phenomenon-inspired metaheuristic algorithm

10.11591/ijeecs.v39.i3.pp1923-1937
Prisma Megantoro , Joy Sefine Dona Saya , Muhammad Akbar Syahbani , Marwan Fadhilah , Pandi Vigneshwaran
Maximum power point tracking (MPPT) is a technique to optimize the photovoltaic (PV) current generation, so it can improve the efficiency of solar energy harvesting. MPPT works by searching the voltage which generates the maximum power, called the maximum power point (MPP). MPP value changes by the fluctuance of ambient temperature and solar insolation level depicted by the I-V curve. Searching the MPP will be more complex if the partial shading is happened. The effect of partial shading will rise to more than one local MPPs. In this research, an optimization algorithm is modeled and simulated the MPPT technique in partial shading. The optimization uses the new metaheuristic algorithm which inspired from a physical phenomenon, called Archimedes optimization algorithm (AOA). The AOA uses mathematical modeling which has convergence capabilities, balanced exploration, and exploitation and is suitable for solving complex optimization technique, like MPPT. The research used varies partial insolation percentage. The implementation of MPPT-AOA compared to other metaheuristic algorithms to analysis its performance in the aspect of PV system parameters and tracking process parameters. The simulation result shows that the AOA can enrich the MPPT technique and improve the solar energy harvesting which is superior to other algorithms.
Volume: 39
Issue: 3
Page: 1923-1937
Publish at: 2025-09-01

Optimizing energy efficiency and improved security in wireless sensor networks using energy-centric MJSO and MACO for clustering and routing

10.11591/ijeecs.v39.i3.pp1964-1975
Srinivas Kalaskar , Channappa Bhyri
Wireless sensor networks (WSNs) play a pivotal role in various applications, but their energy-constrained nature poses significant challenges to their sustainable operation. In this paper, we propose a novel approach to enhance energy efficiency in WSNs by leveraging energy-centric multi-objective jaya search optimization (MJSO) and multi-objective ant colony optimization (MACO) for clustering and routing. Our method aims to address the energy consumption issues by optimizing clustering and routing strategies simultaneously. The energy-centric MJSO algorithm is employed to intelligently organize sensor nodes into clusters, considering energy consumption, network coverage, and connectivity. The multi-objective MACO algorithm optimizes routing paths by balancing energy consumption and network lifetime objectives. Through integration and simulations, the approach enhances energy efficiency in WSNs for various applications like environmental monitoring and smart cities, advancing energy-efficient clustering and routing. By integrating energy-centric MJSO and MACO into clustering and routing protocols, WSNs can achieve significant improvements in energy efficiency and security while maintaining reliable communication and data delivery.
Volume: 39
Issue: 3
Page: 1964-1975
Publish at: 2025-09-01

Prediction of Parkinson's disease using feature selection and ensemble learning techniques

10.11591/ijeecs.v39.i3.pp1736-1744
Sharan T. D. , Sujata Joshi
Parkinson's disease (PD) is a progressive neurodegenerative disorder that significantly impacts quality of life and healthcare systems. Early detection is crucial for timely interventions that can mitigate disease progression and improve patient outcomes. This study leverages advanced machine learning (ML) techniques to detect PD using speech features as non-invasive biomarkers. A dataset containing 754 features derived from sustained vowel phonations of 252 individuals (188 PD patients, 64 healthy controls) was analyzed. The dataset, originally collected by Istanbul University and publicly hosted via the UCI ML repository, was accessed through Kaggle for preprocessing and analysis. To identify the most predictive features, we employed recursive feature elimination (RFE), random forest importance, lasso regression, and the boruta algorithm—ensuring robust feature selection while reducing dimensionality. The XGBoost model, optimised using synthetic minority oversampling technique (SMOTE) for class balancing, achieved an accuracy of 96.69%, a recall of 96%, and an F1-score of 98%. Model robustness was validated through 5-fold cross-validation, yielding an average accuracy of 89.54%. These findings establish a scalable, costeffective, and non-invasive framework for early PD detection, demonstrating the potential of speech analysis and ML in neurodegenerative disease management.
Volume: 39
Issue: 3
Page: 1736-1744
Publish at: 2025-09-01

Utilizing metaheuristic optimization with transfer learning for efficient colorectal carcinoma detection in biomedical imaging

10.11591/ijeecs.v39.i3.pp1693-1703
Lova Naga Babu Ramisetti , Desidi Narsimha Reddy , Harikrishna Pathipati , Yenumula Srividya , Swetha Pesaru
Colorectal cancer (CRC) is the third most popular cancer across the world. Its morbidity and death are reduced by early screening and detection. The screening outcomes are enhanced by computer-aided detection (CAD) and artificial intelligence (AI) in screening models. Contemporary imaging technologies such as near-infrared (NIR) fluorescence and optical coherence tomography (OCT) are implemented to identify the early-phase CRC of the gastrointestinal tract (GI tract) via the identification of morphological and microvasculature changes. Most recently, deep learning (DL)-based approaches have been used directly on raw data. Nevertheless, they are hampered by biomedical data deficiency. These studies can enhance metaheuristic optimization using the transfer learning to detect colorectal cancer successfully (MHOTL-ECRCD). The MHOTL-ECRCD method concentrates on biomedical imaging of CRC categorization and detection. MHOTL-ECRCD minimizes noise through the process of adaptive bilateral filtering (ABF). In MHOTL-ECRCD methodology, Inception-ResNet-V2 is adopted to learn the inherent and complicated image preprocessing features thus used during feature extraction. To classify CRC and detect it, the gated recurrent unit (GRU) approach is applied. Lastly, parameters of the GRU model are optimized with a human evolutionary algorithm. Good classification results of MHOTL-ECRCD are demonstrated by a number of benchmark dataset trials. MHOTL-ECRCD technology superseded the recent techniques as large volumes of comparison were made.
Volume: 39
Issue: 3
Page: 1693-1703
Publish at: 2025-09-01

Predictive machine learning for smart grid demand response and efficiency optimization

10.11591/ijpeds.v16.i3.pp1628-1636
J. C. Vinitha , J. Sumithra , M. J. Suganya , P. Aileen Sonia Dhas , Balaji Ramalingam , Sivakumar Pushparaj
This paper explores the evolution of smart grids (SGs) and how they enable consumers to schedule household appliances based on demand response programs (DRs) provided by distribution system operators (DSOs). This study looks at and compares four distinct regression models: linear regression, random forest regressor, gradient boosting regressor, and support vector regressor. This is being done because more and more people are using machine learning (ML) methods to make this process better. The models are trained and tested using a dataset that includes a variety of parameters, such as humidity, temperature, and the amount of power used by appliances. Mean squared error (MSE) and R-squared values are two important performance measures that are used to judge these models and see how well they can make predictions. These results reveal that the gradient boosting regressor was the most accurate model for figuring out how much energy smart homes use. This algorithm could be a great tool for better managing energy use because it can figure out the complicated connections between the things that are input and the amount of energy that appliances use. This study makes a big difference in the creation of strong regression models by emphasizing how important it is to be accurate when making predictions. This, in turn, helps to enhance energy sustainability and economic stability in smart home environments.
Volume: 16
Issue: 3
Page: 1628-1636
Publish at: 2025-09-01

The Jordanian passage to sustainable electrical power: case study of challenges and opportunities

10.11591/ijpeds.v16.i3.pp2082-2089
Emad Awada
As the global energy sector faces significant challenges due to limited conventional resources and environmental concerns, many countries have adopted precautionary measures to secure and develop new energy resources. For instance, Jordan faces a severe shortage of natural conventional energy resources, compounded by rapid population growth driven by both locals and refugees. With over 90% of its energy imported, Jordan heavily depends on neighboring and international suppliers, leaving the country vulnerable and insecure due to political and economic fluctuations. To overcome these challenges, Jordan must establish comprehensive policies and plans to achieve energy production, conservation, and sustainability. This case study explores Jordan’s energy sources and security, highlighting strategies for long-term sustainable electrical energy development. The analysis focuses on addressing challenges, proposing alternative solutions, and advancing efficient plans for energy expansion. Key strategies include embracing renewable energy sources, enhancing conservation, and leveraging technological advancements to improve efficiency and a resilient energy sector.
Volume: 16
Issue: 3
Page: 2082-2089
Publish at: 2025-09-01

Study of parallel operation single phase H-bridge CSI and H-bridge VSI

10.11591/ijpeds.v16.i3.pp1721-1730
Suroso Suroso , Winasis Winasis , Retno Supriyanti
In some applications, parallel operation of some single-phase inverters with different characteristics is a necessity, such as in a photovoltaic power conversion system. Each power inverter with its power source works, delivering power to a common load which cannot be supplied by a single power inverter. This paper proposed a novel parallel operation of two different power inverter circuit types. H-bridge voltage source inverter (HB VSI) and H-bridge current source inverter (HB-CSI), supplying AC power to a common load. The proposed inverter system was examined and its operation characteristics were analyzed using computer simulation. Moreover, a laboratory prototype of the inverter system was made and examined to validate some principal characteristics of the inverter system experimentally. Test results showed that by combining the HB-VSI and HB CSI, a lower distortion of load current was achieved, specifically, total harmonic distortion (THD) of Iload was less than 1%. This phenomenon happens even the THD of AC currents generated by HB-VSI and HB-CSI at 6.95% and 6.18%, respectively.
Volume: 16
Issue: 3
Page: 1721-1730
Publish at: 2025-09-01

Cluster-based routing protocol in wireless sensor network

10.11591/ijpeds.v16.i3.pp1939-1948
Shireen Bashar Ghanem , Aws Zuheer Yonis
Wireless sensor networks (WSNs) play a crucial role in various domains, including military, industrial, and environmental applications, due to their capability to monitor and transmit data efficiently. However, one of the major challenges in WSNs is energy consumption, as sensor nodes rely on limited power sources for data acquisition, processing, and communication. Efficient energy management is essential to prolong network lifespan and maintain performance. To address this issue, several energy-efficient routing techniques have been developed. Among these, the low-energy adaptive clustering hierarchy (LEACH) has gained significant attention for its ability to optimize power consumption through hierarchical clustering. This study investigates the performance of the LEACH protocol under different deployment configurations. We proposed and evaluate a circular sensing field as an alternative to the traditional square and rectangular field. Simulation results show that the circular field achieves better energy efficiency and network longevity across various packet sizes and base station (BS) locations. These findings highlight the importance of deployment geometry in enhancing WSN sustainability.
Volume: 16
Issue: 3
Page: 1939-1948
Publish at: 2025-09-01

The road feeling control system on the steer by wire system uses fuzzy logic control based on swarm optimization

10.11591/ijpeds.v16.i3.pp1496-1504
Fachrudin Hunaini , Gigih Priyandoko , Gatot Subiyakto , Purbo Suwandono
This paper presents an optimal control system for enhancing road feel in a steer-by-wire (SbW) system using fuzzy logic control (FLC) optimized with modified quantum particle swarm optimization (MQPSO). The objective is to improve the driver experience by providing realistic torque feedback, thereby replicating the steering sensations typically generated by road conditions. This feedback is essential for conveying information about vehicle dynamics and road surface variations through opposing torque applied to the steering interface. An artificial intelligence-based control system utilizing FLC was developed to manage the road feel feedback within the SbW system. The inputs to the FLC include steering angle, vehicle speed, and steering ratio, as well as key physical factors such as inertia and friction, all of which influence the generation of steering torque. The FLC parameters were optimized using MQPSO to achieve a more accurate and responsive road feel torque output. A Simulink model was constructed to simulate the proposed system. The simulation results demonstrate that the optimized FLC significantly improves the performance of the steering motor torque feedback mechanism. This study contributes to the advancement of steer-by-wire technology by proposing an optimal torque control framework and highlighting the effectiveness of integrating FLC with MQPSO in enhancing road feel dynamics.
Volume: 16
Issue: 3
Page: 1496-1504
Publish at: 2025-09-01

IoT-based real-time monitoring of river water quality: a case study of the Selangor River

10.11591/ijeecs.v39.i3.pp1541-1552
Nur Aqilah Ahmad Jafri , Arni Munira Markom , Yusrina Yusof , Norhafizah Burham , Marni Azira Markom
Monitoring river water quality is crucial for preserving freshwater ecosystems, ensuring public health, and supporting resource management. Traditional methods, while accurate, lack the scalability and real-time capabilities needed for proactive intervention. This study introduces an IoT based water quality monitoring system for the Selangor River, integrating sensors for pH, temperature, turbidity, and total dissolved solids (TDS) with a NodeMCU ESP32 microcontroller. To complement the IoT system, a handheld test pen was used to measure salinity and electrical conductivity (EC), offering additional insights into water quality. Field tests at four stations along the river revealed significant spatial variations. Station 1, near the river mouth, showed high salinity, EC, and TDS, indicating saltwater intrusion, with relatively low turbidity. Stations 2 and 3 recorded the highest turbidity levels, suggesting sedimentation and upstream activities, with moderate salinity and EC. Station 4, upstream, demonstrated stable freshwater characteristics, with low salinity, EC, and turbidity levels. The IoT system reliably monitored real-time parameters, and its measurements were validated against those from the handheld test pen. Minor discrepancies in TDS and temperature readings highlighted the importance of calibration.
Volume: 39
Issue: 3
Page: 1541-1552
Publish at: 2025-09-01

Design and analysis of seven-level hybrid modified H-bridge multilevel inverter

10.11591/ijpeds.v16.i3.pp1731-1739
Arpan Dwivedi , Raman Kumar , Sailesh Sourabh , Vikash Rajak , Vikash Kumar Singh , Maruti Nandan Mishra
This paper introduces a novel boosting multilevel inverter that utilizes switched capacitors. Current multilevel inverters (MLIs) face several issues, such as complex structures, intricate switching controls, and challenges in generating gate pulses, numerous components, and high voltage stress on semiconductors. The increase in the number of levels adds to the complexity and cost of the circuit and can reduce reliability in some cases. The proposed topology creates a 7-level voltage waveform using 9 switches, 1 diode, and 2 capacitors, and it triples the voltage gain. The capacitors maintain self balanced operation without the need for additional circuits. A simple logic gate-based pulse-width modulation (PWM) technique is presented to ensure power balancing of the capacitors. The proposed 7-level switched capacitor boosting multilevel inverter features a reduced switch count, lower voltage stress, and built-in fault tolerance. The paper includes a comprehensive comparison of various related topologies. The proposed topology is simulated in PSIM, with simulation results presented for different parameters.
Volume: 16
Issue: 3
Page: 1731-1739
Publish at: 2025-09-01

Cost-effective optimization of unified power quality conditioner in wind energy conversion systems using a hybrid EnHBA-GWO algorithm

10.11591/ijpeds.v16.i3.pp2043-2054
Shaziya Sultana , Umme Salma
The rapid integration of wind energy conversion systems (WECS) into modern power networks has led to pressing power quality concerns, including voltage instability, harmonic distortion, and reactive power imbalance. To address these challenges, this study introduces a hybrid optimization strategy that combines the global search capabilities of the enhanced honey badger algorithm (EnHBA) with the local exploitation strengths of the grey wolf optimizer (GWO) for the best operational parameters of a unified power quality conditioner (UPQC). Extensive simulations in MATLAB Simulink demonstrate significant improvement in performance. The proposed method achieves 95% energy efficiency, a power factor of 0.99, and total harmonic distortion (THD) down to 5%, meeting IEEE 519-2022 standards. This outcome reflects an effective balance between cost and power quality performance, highlighting the potential of hybrid optimization to improve grid stability and efficiency in renewable energy environments.
Volume: 16
Issue: 3
Page: 2043-2054
Publish at: 2025-09-01

Enhanced performance of PV systems using a smart discrete solar tracker with fuzzy-ant colony controller

10.11591/ijpeds.v16.i3.pp2090-2102
Imam Abadi , Najela Rafia Elchoir , Ali Musyafa , Harsono Hadi , Dwi Nur Fitriyanah
A solar tracker is a combination of mechanical and electrical systems that can be used to move a solar panel to follow the sun's direction. This solar tracker system is expected to optimize the output power of photovoltaics. Based on existing research, many solar tracking systems have been developed using active tracking methods to increase the power consumption of the components of solar trackers. Therefore, a passive solar tracking system was used to reduce the solar tracker's internal energy consumption. In this study, a passive smart discrete solar tracker was designed with 3 positions and 5 tracking positions based on a fuzzy-ant colony controller (ACO). The design of a passive solar tracker based on a fuzzy-ACO has a performance index (average) of 0.45 s, a settling time of 0.701 s, a maximum overshoot of 0.5%, and a steady-state error of 0.05%. From the design, the 3-position passive solar tracker with fuzzy-ACO control can increase efficiency with a gross energy gain of 42.79% for 10 hours compared to a fixed PV. The 5-position passive solar tracker using fuzzy ACO control increased the efficiency with a gross energy gain of 43.99%.
Volume: 16
Issue: 3
Page: 2090-2102
Publish at: 2025-09-01

Dual-aware EV charging scheduling with traffic integration

10.11591/ijpeds.v16.i3.pp1446-1456
Maneesh Yadav , Satyaranjan Jena , Chinmoy Kumar Panigrahi , Ranjan Keshari Pati , Jayanta Kumar Sahu
Electric vehicle adoption is a trend in many countries, and the demand for charging station infrastructure is at a rapid pace. The placement of charging stations is the key research topic of many researchers, but charging scheduling is also a problem that is going to rise in the near future. The proper charger utilization, maintaining coordination between charging stations, and satisfying users' demands are some of the key challenges. The traffic pattern is uncertain, coordination of distances between charging stations and users is done by Euclidean distance. The traffic-aware fair charging scheduling (TAFCS) strategy is proposed, which will have a balance on charger utilization and user prioritization, and keep the fairness by equal distribution of electric vehicles among all the charging stations having a centralized charging system monitored by an aggregator. The distribution of the traffic pattern of electric vehicles is performed by Monte Carlo simulation. The proposed system is tested on the IEEE 33 bus standard system using the predefined voltage limits of each bus and limiting power loss to lessen its burden. The discharging process of 50 electric vehicles (V2G) is performed by optimal placement by obtaining the weakest buses, which makes it an intelligent distribution system. This proposed charging framework is validated on MATLAB R2020a.
Volume: 16
Issue: 3
Page: 1446-1456
Publish at: 2025-09-01

Quantum machine learning ensemble for surface crack detection

10.11591/ijpeds.v16.i3.pp2112-2121
A. Sankaran , N. Palanivel , S. Dhamotharan , K. Nivas , V. Merwin Raj , M. Shivaprakash
By identifying the aspects of manual inspection methods in the context of industrial production, which are described within the undertaken research, the development of an automated visual inspection technology is driven. This causes more time to be spent on performing the checks, thus adding to the labor cost. The efficiency of the operations is reduced, and there is a tendency for errors due to fatigue in checking 24/7. The proposed solution for a new product is designed to change the approach of the existing manufacturing process by using the automated system to self-inspect the surface and notify of its defects during manufacturing. As an enhancing advancement, this new development aims to address apprehensions pertaining to manual examination as the world transitions into the fault tolerant period. Lastly, this approach fits the universal grail of further developing industrial capacities, with the resulting thought process extending to the incorporation of technologies such as quantum computing with the current requirements of manufacturing. Other potential applications of this approach, including aerospace applications of ultrasonic testing or thermography in the detection of surface cracks, might also help improve this approach in the future.
Volume: 16
Issue: 3
Page: 2112-2121
Publish at: 2025-09-01
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