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Effective assessment of power transformer insulation using time-varying model without temperature constraints

10.11591/ijape.v14.i2.pp373-381
Sachin Yashawant Sayais , Chandra Madhab Banerjee
This paper proposes a method for insulation diagnosis using the time-varying model. The parameters of the stated model are unique and can be recognized by the polarization current. Several methodologies have been reported for insulation diagnosis using various insulation models. However, moisture information without considering the effect of measurement temperature is bound to provide inaccurate result. Temperature significantly affects the dielectric response of materials. As the temperature rises, various important changes take place. Increased temperatures can boost molecular mobility, resulting in higher polarization, which in turn affects the material's dielectric constant and loss. Additionally, higher temperatures typically raise conductivity due to improved charge carrier mobility, further influencing the dielectric response. Hence, the effect of measurement temperature on insulation diagnosis is discussed in this paper so that responses recorded at different temperatures can be effectively compared. The proposed methodology for determining insulation state is tested using data from real-life power transformers.
Volume: 14
Issue: 2
Page: 373-381
Publish at: 2025-06-01

GradeZen: automated grading ecosystem using deep learning for educational assessments

10.11591/ijai.v14.i3.pp1809-1819
Murugavalli Elangovan , Rajeswari Kaleeswaran , Kathirvel Mohankumar , Shreya Rangachari , Ubasini Manivannan , Rishapa Pandidurai , Maria Bellarmin Joel , Preethi Meenatchi Murugesan
This study introduces a groundbreaking software solution poised to revolutionize grading procedures in higher education through advanced artificial intelligence and machine learning techniques. Leveraging cutting-edge technologies such as YOLOv8 for real-time object detection, transformer-based optical character recognition (TrOCR), and Mixtral 8x7B transformer models for complex data analysis, the software automates the grading process. By significantly reducing the time and effort required for manual grading, it aims to streamline educational practices while ensuring consistency and scalability. The study provides a comprehensive analysis of use cases, identifies key issues in current grading methods, and elucidates the rationale driving its development. This innovative approach holds immense promise for transforming educational practices, fostering student success through efficient and artificial intelligence assisted automated assessment methodologies.
Volume: 14
Issue: 3
Page: 1809-1819
Publish at: 2025-06-01

Artificial intelligence-blockchain synergy ensures Indonesia’s compliance with European Union’s Deforestation-free regulation

10.11591/ijai.v14.i3.pp1763-1771
Silfi Iriyani , Sarifah Putri Raflesia
This paper introduces a new model that incorporates blockchain and artificial intelligence (AI) in creating traceability on agricultural supply chains to meet European Union's (EU's) regulation on deforestation-free products. This model stands for the system that would be applied for monitoring origins and routes with regard to verifying the status of products being free from deforestation. Particularly, this addressed the European Union's Deforestation-free Regulation products (EUDR)-related issues in Indonesia focused on smallholders and their linkage to traceability tools. The proposed conceptual model demonstrates how blockchain technology combined with AI in agricultural supply chains enhances transparency and reliability in the line of improving environmental sustainability as well as boosting consumers' confidence. Integration of blockchain and AI increases agricultural supply chain transparency, traceability, and reliability whereby smart contracts can execute automatically such as releasing payments once certain conditions are met.
Volume: 14
Issue: 3
Page: 1763-1771
Publish at: 2025-06-01

Optimal placement of DGs in a multi-feed radial distribution system using actor-critic learning algorithm

10.11591/ijape.v14.i2.pp319-327
Neelakanteshwar Rao Battu , Surender Reddy Salkuti
Multi-feed radial distribution systems are used to reduce the losses in the system using reconfiguration techniques. Reconfiguration can reduce the losses in the system only to a certain extent. Introduction of distributed generators has vastly improved the performance of distribution systems. Distributed generators can be used for reduction of loss, the improvement of the voltage profile, and reliability enhancement. Distribution generators play a vital role in reducing the losses in the distribution system. Placement of distributed generators in a multifeed system is a complex task to be solved using classical optimization methods. Classical optimization techniques may sometimes fail to provide a converged solution. Installation of distributed generators at suitable locations in a multi-feed system is found in this paper using the actor-critic learning algorithm. Actorcritic learning approach uses temporal difference error as a signal in making judgements regarding actions to be taken for future states in accordance with rewards that have been obtained by applying the present policy. The approach is applied to a standard 16-bus distribution system for reduction of system losses, and the results are discussed.
Volume: 14
Issue: 2
Page: 319-327
Publish at: 2025-06-01

Comparative analysis of YOLOv8 techniques: OpenCV and coordinate attention weighting for distance perception in blind navigation systems

10.11591/ijece.v15i3.pp3267-3278
Ema Utami , Erwin Syahrudin , Anggit Dwi Hartanto
Blindness is a very important issue to consider in research aimed at assisting vision. This condition requires further study to provide solutions for the blind. This study evaluates and compares the effectiveness of the you only look once v8 (YOLOv8) model integrated with OpenCV and the coordinate attention weighting (CAW) technique for distance estimation in a blind navigation system. Initially, YOLOv8 integrated with OpenCV produced less than optimal results, prompting further improvement efforts to surpass the performance of CAW. The goal is to enhance the accuracy and efficiency of distance perception without the need for additional sensors. The materials used include a variety of datasets annotated with distance information to train and evaluate the model. The methods employed include integrating YOLOv8 with OpenCV for baseline comparison and applying CAW to improve distance perception through enhanced feature attention. The results show that YOLOv8+OpenCV Improved achieves the lowest mean squared error (MSE) across the entire distance range: 0-1 m (0.44), 1-2 m (0.50), 2-3 m (0.58), 3-4 m (0.64), and 4-5 m (0.71). YOLOv8+CAW also outperforms YOLOv8+OpenCV original, demonstrating a notable enhancement in accuracy. The model achieves a detection accuracy of 95.7%, showcasing the effectiveness of computer vision techniques in supporting blind navigation systems, offering precise distance estimation capabilities and reducing the reliance on external sensors. The implications include improved real-time performance and accessibility for the blind, paving the way for more efficient and reliable navigation assistance technologies.
Volume: 15
Issue: 3
Page: 3267-3278
Publish at: 2025-06-01

Trend analysis of machine learning techniques for traffic control based on bibliometrics

10.11591/ijai.v14.i3.pp2402-2411
Hilda Luthfiyah , Eko Syamsuddin Hasrito , Tri Widodo , Sofwan Hidayat , Okghi Adam Qowiy
Machine learning in traffic control for intelligent transportation systems (ML-ITSTC) aims to enhance user coordination and safety within transportation networks, ultimately improving overall traffic system performance. ML-ITSTC is achieved by leveraging data to execute machine learning algorithms in intelligent transportation management and optimizing traffic flow to prevent or reduce congestion. This paper conducts bibliometric analysis to explain the research status, development trajectory, and challenges of ML-ITSTC, drawing insights from literature in the Scopus database literature covering 2013 to November 2023. The bibliometric analysis of ML-ITSTC includes: performance analysis, science mapping analysis, and citation analysis. The evaluation of ML algorithm trends over the 10-year span indicates that traffic prediction (TP), neural networks, and deep learning are frequently used keywords. Further, an examination of keywords used over the entire period and in 2023 (up to November) shows that reinforcement learning (RL) is the latest popular approach for traffic control in transportation. The results provide a comprehensive view of the opportunities and challenges in ML-ITSTC, covering data, models, and applications, offering researchers insights into the current and future directions of ML-ITSTC research.
Volume: 14
Issue: 3
Page: 2402-2411
Publish at: 2025-06-01

Assessment of thermal characteristics in diverse lithium-ion battery enclosures and their influence on battery performance

10.11591/ijape.v14.i2.pp275-281
Mahiban Lindsay , M. Emimal
Battery technology is an emerging research domain within the automotive sector, with a focus on various battery chemistries such as Li-ion, LiFePO4, NMC, and NaCl, as well as specialized cells like LiSOCl2. These chemistries are crucial in advancing electric vehicle (EV) battery technology. Batteries are available in different packaging formats, including prismatic, cylindrical, and pouch designs, each tailored to diverse operational environments. This study investigates the impact of these various battery packages on overall battery performance. Additionally, it assesses the influence of temperature on battery efficiency, aiming to identify the optimal temperature range for maximum performance. A significant part of the research focuses on the development of efficient battery thermal management (BTM) systems, which are designed to control and maintain battery temperature within the desired range, thereby enhancing efficiency. The outcomes of this study provide valuable insights for improving the reliability and efficiency of EV batteries. These findings are crucial for ensuring optimal battery performance and safety across different field conditions. Automotive manufacturers and battery suppliers can leverage these insights to refine their product designs, ensuring the dependability and safety of EV batteries. By enhancing battery performance through improved packaging and effective thermal management, this research contributes significantly to the advancement of EV technology, making electric vehicles more reliable and efficient for consumers.
Volume: 14
Issue: 2
Page: 275-281
Publish at: 2025-06-01

An intelligent approach to design big data on e-commerce in cloud computing environment

10.11591/ijece.v15i3.pp3439-3448
salma Syed , Nadimpalli Usha Deepa Sundari , Satish Babu Dogiparti , Duggimpudi Mary Sharmila Rani , Ankireddy Yenireddy , Narayana Srinivas Kumar , Rajeev Sunkara , Buddaraju Naga Venkata Narasimha Raju
Web resources extract useful knowledge by the process of web mining. Web server maintains the log files for analyzing them from behavior of customer and improves business as the challenging task for E-commerce companies. The processing and computing of big data was increased day by day by the demand of computer system’s ability. The emphasis on data was increased gradually by the rapid development of information technology. Various businesses are exploring effective data analysis methods, and this system proposes an intelligent approach to designing big data for e-commerce in a cloud computing environment. This paper aims to develop and implement the relevancy vector (RV) algorithm, an innovative page ranking algorithm based on Hadoop distributed file system (HDFS) map reduce. The research provides customers with a robust meta search tool that makes it easy for them to understand personalized search requirements and make purchases based on their preferences. The intelligent meta search system adverse events (IMSS-AE) tool and the RV page ranking algorithm were shown to be efficient and effective by a thorough experimental evaluation in terms of reduced response time, enhanced page freshness, high personalized relevance, and high hit rates.
Volume: 15
Issue: 3
Page: 3439-3448
Publish at: 2025-06-01

ApDeC: a rule generator for alzheimer's disease prediction

10.11591/ijai.v14.i3.pp1772-1780
Sonam Vayaliparambil Maju , Gnana Prakasi Oliver Siryapushpam
Artificial intelligence (AI) paved the way and helping hand for the medical practitioners in various aspects and early disease prediction is one among many. Interdisciplinary research studies on the early prediction of diseases are often analyzed based on the accuracy of the prediction model. But how early these diseases can be predicted will not be answered in many of the research studies unless they have a time series data. This work proposes a machine learning model, ApDeC which solves the above-mentioned problem by generating association rules for the early disease prediction of Alzheimer patients. The ApDeC model calculates the probability of occurrence of eleven Alzheimer disease prediction risk factors and identifies the combination of diseases that can lead to Alzheimer disease. The association rules will be generated by considering the observed combination of risk factors. The research introduces an innovative approach that helps in the early prediction of Alzheimer disease from the risk factors/symptoms. The results show the strong correlation of diabetes and blood pressure with Alzheimer disease.
Volume: 14
Issue: 3
Page: 1772-1780
Publish at: 2025-06-01

Speed control of induction motor using fuzzy logic based on internet of things

10.11591/ijape.v14.i2.pp488-497
Charles Ronald Harahap , F. X. Arinto Setyawan , Desi Budiati
The aim of this research was to propose an innovative method of controlling the speed of an induction motor (IM) using fuzzy logic, integrated with internet of things (IoT). To achieve this aim, fuzzy logic was used to increase the performance of IM in order to obtain stable speed and high system response even in the presence of disturbances. Moreover, fuzzy logic relied on rules that used linguistic variables, and its main advantage was simple yet highly accurate, enabling the system to be efficient for determining parameters compared to the time-consuming and inefficient trial-and-error method. In this research, IoT implementation used Blynk platform to control and monitor IM speed remotely. Additionally, the components used in this research included an inverter, gate driver, Arduino Mega 2560, and NodeMCU ESP8266. Pulse width modulation (PWM) was required to obtain rotational speed of the motor through MOSFET switching process. The gate driver amplified PWM signal from Arduino Mega 2560, allowing MOSFET to operate. As a result, IM achieved a stable speed, and the system response followed the reference using fuzzy logic. In addition to this process, the system could be controlled and monitored remotely. Finally, the control system was successful, and the results were presented to show the viability of the proposed method.
Volume: 14
Issue: 2
Page: 488-497
Publish at: 2025-06-01

Review on optimal planning and operation of charging stations for electric vehicles

10.11591/ijape.v14.i2.pp359-372
M. S. Arjun , N. Mohan , K. R. Satish , Arunkumar Patil , D. P. Somashekar
Several factors need to be taken into account while planning the locations of electric vehicle charging stations. The thoughtful design and arrangement of charging stations, as a crucial component of the infrastructure supporting electric vehicles, is essential for the advancement of these kinds of vehicles. However, a number of intricate aspects, including policy economics, charging demand, user comfort when charging, and traffic circumstances, influence the design and arrangement of charging stations. With the goal to uncover competing interests and opportunities for collaboration in the operation and development of charging infrastructure, this study intends to assist researchers and technology developers in investigating cutting-edge techniques from the viewpoint of each constituent. Additionally, only a strong electric vehicle charging station (EVCS) infrastructure may provide some of the answers to the most basic EV concerns, like EV cost and range. The literature claims that several sorts of techniques, objective functions, and constraints for issue formulation have been used by the scholars. In addition, sensitivity analysis, vehicle to grid strategy, integration of distributed generation, charging kinds, objective functions, restrictions, EV load modelling, uncertainty, and optimization methodologies are examined for the most recent research publications. Discussions occur as well regarding the effects of the EV load on the distribution network, the environment, and the economy.
Volume: 14
Issue: 2
Page: 359-372
Publish at: 2025-06-01

Enhanced time series forecasting using hybrid ARIMA and machine learning models

10.11591/ijeecs.v38.i3.pp1970-1979
Vignesh Arumugam , Vijayalakshmi Natarajan
Accurate energy demand forecasting is essential for optimizing resource management and planning within the energy sector. Traditional time series models, such as ARIMA and SARIMA, have long been employed for this purpose. However, these methods often face limitations in handling nonstationary data, complexity in model tuning, and susceptibility to overfitting. To address these challenges, this study proposes a hybrid approach that integrates traditional statistical models with advanced computational methods. By combining the strengths of both approaches, the proposed models aim to enhance predictive accuracy, improve computational efficiency, and maintain robustness across varied energy datasets. Experimental results demonstrate that these hybrid models consistently outperform standalone traditional methods, providing more reliable and precise forecasts. These findings underscore the potential of hybrid methodologies in advancing energy demand forecasting and supporting more effective decision-making in energy management.
Volume: 38
Issue: 3
Page: 1970-1979
Publish at: 2025-06-01

Hardware implementation of safety smart password based GSM module controlling circuit breaker

10.11591/ijape.v14.i2.pp441-448
Rakesh G. Shriwastava , Pawan C. Tapre , Rajendra M. Rewatkar , Swapna M. Choudhary , Ramesh K. Rathod , Sham H. Mankar , Hemant R. Bhagat Patil , Salim A. Chavan
This research work highlights the hardware implementation of safety smart password-based GSM module controlling circuit breaker. Safety is the major concern in daily life for domestic activities. In current scenario, accidental death of a lineman are the major issues and to protect operators for the same. To control circuit breakers, passwords security is essential for lineman. Due to that electrical accident’s ratio is increased day to day life at the time of repairing the lines. It is also done due to lack of communication and coordination between maintenance and substation. For safety of lineman, on and off line turning operation is proposed. Secure password is for breaker operation and maintenance. In the proposed system, password is sent to the line operator's mobile phone and GSM module by automatic voltage regulator (AVR) microcontroller. Entered password and password received by the GSM receiver is match so circuit breaker will be smoothly operated. If password is incorrect, message will appear on the LCD display for security purposed and message sent to control room regarding unauthorized access to the system.
Volume: 14
Issue: 2
Page: 441-448
Publish at: 2025-06-01

Butterfly optimization-based ensemble learning strategy for advanced intrusion detection in internet of things networks

10.11591/ijece.v15i3.pp3494-3505
Mouad Choukhairi , Sara Tahiri , Ouail Choukhairi , Youssef Fakhri , Mohamed Amnai
The massive growth in internet of things (IoT) devices has led to enhanced functionalities through their interconnections with other devices, smart infrastructures, and networks. However, increased connectivity also increases the risk of cyberattacks. To protect IoT systems from these threats, intrusion detection systems (IDS) employing machine learning (ML) techniques have been developed to identify cybersecurity threats. This paper introduces a novel ensemble IDS framework called butterfly optimization-based ensemble learning (BOEL). This framework integrates the butterfly optimization algorithm (BOA) with ensemble learning techniques to improve IDS detection performance in IoT networks. BOEL is designed to accurately detect various types of attacks in IoT networks by dynamically optimizing the weights of base learners, which are the four sophisticated ML gradient-boosting algorithms (GBM, CatBoost, XGBoost, and LightGBM) for each attack category, and identifying the best weight combination for ensemble models. Experiments conducted on two public IoT security datasets, CICIDS2017 and Bot-IoT, demonstrate the robustness of the proposed BOEL in intrusion detection across diverse IoT environments, achieving 99.795% accuracy on CICIDS2017 and 99.966% accuracy on Bot-IoT. These results outline the successful application of diverse learning approaches and highlight the framework’s potential to enhance IDS in addressing IoT cyber threats.
Volume: 15
Issue: 3
Page: 3494-3505
Publish at: 2025-06-01

Robust two-stage object detection using YOLOv5 for enhancing tomato leaf disease detection

10.11591/ijai.v14.i3.pp2246-2257
Endang Suryawati , Syifa Auliyah Hasanah , Raden Sandra Yuwana , Jimmy Abdel Kadar , Hilman Ferdinandus Pardede
Deep learning facilitates human activities across various sectors, including agriculture. Early disease detection in plants, such as tomato plant that are susceptible to diseases, is critical because it helps farmers reduce losses and control the disease spread more effectively. However, the ability of machine to recognize diseased leaf objects is also influenced by the quality of data. Data collected directly from the field typically yields lower accuracy due to challenges faced in machine interpretation. To address this challenge, we propose a two-stage detection architecture for identifying infected tomato plant classes, leveraging YOLOv5 to detect objects within the images obtained from the field. We use Inception-V3 for classifying objects into known classes. Additionally, we employ a combination of two dataset: PlantDocs which represent field data, and PlantVillage dataset which serves as a cleaner dataset. Our experimental results indicate that the use of YOLOv5 in handling data under actual field conditions can enhance model performance, although the accuracy value is moderate (62.50 %).
Volume: 14
Issue: 3
Page: 2246-2257
Publish at: 2025-06-01
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