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28,812 Article Results

Electric load forecasting using ARIMA model for time series data

10.11591/ijict.v14i3.pp830-836
Balasubramanian Belshanth , Haran Prasad , Thirumalaivasal Devanathan Sudhakar
Any country's economic progress is heavily reliant on its power infrastructure, network, and availability, as energy has become an essential component of daily living in today's globe. Electricity's distinctive quality is that it cannot be stored in huge quantities, which explains why global demand for home and commercial electricity has grown at an astonishing rate. On the other hand, electricity costs have varied in recent years, and there is insufficient electricity output to meet global and local demand. The solution is a series of case studies designed to forecast future residential and commercial electricity demand so that power producers, transformers, distributors, and suppliers may efficiently plan and encourage energy savings for consumers. However, load prognosticasting has been one of the most difficult issues confronting the energy business since the inception of electricity. This study covers a new one–dimensional approach algorithm that is essential for the creation of a short–term load prognosticasting module for distribution system design and operation. It has numerous operations, including energy purchase, generation, and infrastructure construction. We have numerous time series forecasting methods of which autoregressive integrated moving average (ARIMA) outperforms the others. The auto–regressive integrated moving average model, or ARIMA, outperforms all other techniques for load forecasting.
Volume: 14
Issue: 3
Page: 830-836
Publish at: 2025-12-01

Modeling chemical kinetics of geopolymers using physics informed neural network

10.11591/ijict.v14i3.pp822-829
Blesso Abraham , Thirumalaivasal Devanathan Sudhakar
Using a physics informed neural network for the analysis of geopolymers as an alternate material for cement can be a viable approach, as neural networks are capable of modeling complex, nonlinear relationships in data, which can be beneficial for representing the dynamics of chemical properties. If you have a substantial amount of theoretical data, a neural network can learn patterns and relationships in the data, even when the underlying system dynamics are not well-defined or are difficult to model analytically. A welltrained neural network can generalize from the training data to make predictions for unseen scenarios, which can be useful for real-time analysis of the material.
Volume: 14
Issue: 3
Page: 822-829
Publish at: 2025-12-01

The bootstrap procedure for selecting the number of principal components in PCA

10.11591/ijict.v14i3.pp1136-1145
Borislava Toleva
The initial step in determining the number of principal components for both classification and regression involves evaluating how much each component contributes to the total variance in the data. Based on this analysis, a subset of components that explains the highest percentage of variance is typically selected. However, multiple valid combinations may exist, and the final choice is often made manually by the researcher. This study introduces a novel yet straightforward algorithm for the automatic selection of the number of principal components. By integrating ANOVA and bootstrapping with principal component analysis (PCA), the proposed method enables automatic component selection in classification tasks. The algorithm is evaluated using three publicly available datasets and applied with both decision tree and support vector machine (SVM) classifiers. Results indicate that this automated procedure not only eliminates researcher bias in selecting components but also improves classification accuracy. Unlike traditional methods, it selects a single optimal combination of principal components without manual intervention, offering a new and efficient approach to PCAbased model development.
Volume: 14
Issue: 3
Page: 1136-1145
Publish at: 2025-12-01

An artificial intelligent system for cotton leaf disease detection

10.11591/ijict.v14i3.pp950-959
Priyanka Nilesh Jadhav , Pragati Prashant Patil , Nitesh Sureja , Nandini Chaudhari , Heli Sureja
This study aims to develop a deep learning-based system for the detection and classification of diseases in cotton leaves, with the goal of aiding in early diagnosis and disease management, thereby enhancing agricultural productivity in India. The study utilizes a dataset of cotton leaf images, classified into four categories: Fusarium wilt, Curl virus, Bacterial blight, and Healthy leaves. The dataset is used to train and evaluate various CNN models such as basic CNN, VGG19, Xception, InceptionV3, and ResNet50. These models were evaluated on their accuracy in identifying the presence of diseases and classifying cotton leaf images into the respective categories. The models were trained using standard deep learning frameworks and optimized for high performance. The results indicated that ResNet50 achieved the highest accuracy of 100%, followed by InceptionV3 with 98.75%, and VGG19 and Xception both with 97.50%. The basic CNN model showed an accuracy of 96.25%. These models demonstrated strong potential for accurate multi-class classification of cotton leaf diseases. This study emphasizes the potential of deep learning in agricultural diagnostics. Future research can focus on improving model robustness, incorporating larger datasets, and deploying the system for real-time field use to assist farmers in disease management and improving cotton production.
Volume: 14
Issue: 3
Page: 950-959
Publish at: 2025-12-01

Comparative analysis of u-net architectures and variants for hand gesture segmentation in parkinson’s patients

10.11591/ijict.v14i3.pp972-982
Avadhoot Ramgonda Telepatil , Jayashree Sathyanarayana Vaddin
U-Net is a well-known method for image segmentation, and has proven effective for a variety of segmentation challenges. A deep learning architecture for segmenting hand gestures in parkinson’s disease is explored in this paper. We prepared and compared four custom models: a simple U-Net, a three-layer U-Net, an auto encoder-decoder architecture, and a U-Net with dense skip pathways, using a custom dataset of 1,000 hand gesture images and their corresponding masks. Our primary goal was to achieve accurate segmentation of parkinsonian hand gestures, which is crucial for automated diagnosis and monitoring in healthcare. Using metrics including accuracy, precision, recall, intersection over union (IoU), and dice score, we demonstrated that our architectures were effective in delineating hand gestures under different conditions. We also compared the performance of our custom models against pretrained deep learning architectures such as ResNet and VGGNet. Our findings indicate that the custom models effectively address the segmentation task, showcasing promising potential for practical applications in medical diagnostics and healthcare. This work highlights the versatility of our architectures in tackling the unique segmentation challenges associated with parkinson’s disease research and clinical practice.
Volume: 14
Issue: 3
Page: 972-982
Publish at: 2025-12-01

Adaptive tilt acceleration derivative filter control based artificial pancreas for robust glucose regulation in type-I diabetes mellitus patient

10.11591/ijece.v15i6.pp5297-5313
Smitta Ranjan Dutta , Akshaya Kumar Patra , Alok Kumar Mishra , Ramachandra Agrawal , Dillip Kumar Subudhi , Lalit Mohan Satapathy , Sanjeeb Kumar Kar
This study proposes an Aquila optimization–based tilt acceleration derivative filter (AO-TADF) controller for robust regulation of blood glucose (BG) levels in patients with type-I diabetes mellitus (TIDM) using an artificial pancreas (AP). The primary objective is to develop a controller that ensures normo-glycemia (70–120 mg/dl) while enhancing stability, accuracy, and robustness under physiological uncertainties and external disturbances. The AO algorithm tunes the control gains of the TADF controller to minimize the integral time absolute error (ITAE), ensuring optimal insulin infusion in real time. The AO-TADF controller introduces a filtered structure to improve the dynamic response and noise rejection capability, effectively handling the nonlinear nature of glucose-insulin dynamics. Simulation results demonstrate that the proposed approach achieves a faster settling time (230 minutes), lower peak overshoot (3.9 mg/dl), and reduced noise (1%) compared to conventional proportional integral derivative (PID), fuzzy, sliding mode (SM), linear quadratic gaussian (LQG), and H∞ controllers. The closed-loop system achieves a stable glucose level of 81 mg/dl under varying meal and exercise disturbances, validating the superior performance and robustness of the AO-TADF approach.
Volume: 15
Issue: 6
Page: 5297-5313
Publish at: 2025-12-01

Machine learning model for accurate prediction of coronary artery disease by incorporating error reduction methodologies

10.11591/ijece.v15i6.pp5655-5666
Santhosh Gupta Dogiparthi , Jayanthi K. , Ajith Ananthakrishna Pillai , K. Nakkeeran
Coronary artery disease (CAD) remains a leading cause of mortality worldwide, with an especially high burden in developing countries such as India. In light of increasing patient loads and limited medical resources, there is an urgent need for accurate and reliable diagnostic support systems. This study introduces a machine learning (ML) framework that aims to enhance CAD prediction accuracy by specifically addressing the reduction of false negatives (FN), which are critical in medical diagnostics. Utilizing a stacked ensemble model comprising five base classifiers and a meta-classifier, the framework integrates cost-sensitive learning, classification threshold tuning, engineered features, and manual weighting strategies. The model was developed using a clinically acquired dataset from the Jawaharlal Institute of postgraduate medical education and research (JIPMER), consisting of 428 patient records with 36 original features. Evaluation metrics show that the proposed model achieved an accuracy of 92.19%, sensitivity of 98%, and an F1-score of 95.15%. These improvements are significant in a clinical context, potentially reducing missed diagnoses and improving patient outcomes. The model is intended for deployment in cardiology outpatient settings and demonstrates a scalable, adaptable approach to medical diagnostics.
Volume: 15
Issue: 6
Page: 5655-5666
Publish at: 2025-12-01

Platforma: a modular and agile framework for simplified platformer game development

10.11591/ijece.v15i6.pp5535-5542
Rickman Roedavan , Abdullah Pirus Leman , Bambang Pudjoatmodjo
Research on game development frameworks has been extensively conducted; however, most frameworks are still too general. Conventional game frameworks are challenging for students who are new to game development, especially with their limited information and skills. Beginner game developers should ideally be guided by a practical and specific framework to help them better understand the structure of game development in a more directed manner. This paper proposes platformer modular and agile framework (Platforma) that specifically designed for platformer game development. The framework is built based on the atomic design model, breaking down each minor feature of a platformer game element and grouping these features into more specific modules. The framework was tested on three teams of students. Each team was tasked with developing a platformer game with a minimum of 15 levels of the reach game goals typology. Testing results involving 100 respondents using the game experience questionnaire (GEQ) indicated that the games developed had a positive aspect score of 3.48 and a negative aspect score of 2.65. Overall, these results suggest that the Platforma can serve as an effective guide for beginners in developing platformer games.
Volume: 15
Issue: 6
Page: 5535-5542
Publish at: 2025-12-01

Evaluating clustering algorithms with integrated electric vehicle chargers for demand-side management

10.11591/ijece.v15i6.pp5837-5846
Ayoub Abida , Redouane Majdoul , Mourad Zegrari
The integration of electric vehicles (EVs) and their effects on power grids pose several challenges for distribution operators. These challenges are due to uncertain and difficult-to-predict loads. Every electric vehicle charger (EVC) has its specific pattern. This challenge can be addressed by clustering methods to determine EVC energy consumption clusters. Demand side management (DSM) is an effective solution to manage the incoming load of EVs and the large number of EVCs. Considering the challenges of peak consumptions and valleys, the adoption of vehicle-to-grid (V2G) technology requires mastering load clusters to develop energy management systems for distributors. This work used clustering algorithms (K-means, DBSCAN, C-means, BIRCH, Mean-Shift, OPTICS) to identify load curve patterns, and for performance evaluation of algorithms, it worked on metrics like the Silhouette coefficient, Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI) to evaluate results. C-means achieves the best overall clustering performance, evidenced by the highest Silhouette coefficient (0.30) and a strong Calinski-Harabasz score (543). Mean-Shift excels in the Davies-Bouldin Index (1.13) but underperforms on other metrics. BIRCH provides a balanced approach, delivering moderate results across evaluated metrics.
Volume: 15
Issue: 6
Page: 5837-5846
Publish at: 2025-12-01

Enhancing system integrity with Merkle tree: efficient hybrid cryptography using RSA and AES in hash chain systems

10.11591/ijece.v15i6.pp5679-5689
Irza Nur Fauzi , Farikhin Farikhin , Ferry Jie
An analysis is conducted to address the growing threats of data theft and unauthorized manipulation in digital transactions by integrating \structures within hash chain systems using hybrid cryptography techniques, specifically Rivest-Shamir-Adleman (RSA) and advanced encryption standard (AES) algorithms. This approach leverages AES for efficient symmetric data encryption and RSA for secure key exchanges, while the hash chain framework ensures that each data block is cryptographically linked to its predecessor, reinforcing system integrity. The Merkle tree structure plays a crucial role by allowing precise and rapid detection of unauthorized data changes. Empirical analyses demonstrate notable improvements in both the efficiency of cryptographic processes and the robustness of data validation, underscoring the method’s applicability in high data throughput environments such as educational institutions. This research makes a substantive contribution to information security by offering a sophisticated solution that strengthens data protection practices, ensuring greater resilience against increasingly sophisticated data threats.
Volume: 15
Issue: 6
Page: 5679-5689
Publish at: 2025-12-01

Fractional fuzzy based static var compensator control for damping enhancement of inter-area oscillations

10.11591/ijece.v15i6.pp5130-5143
Tarik Zabaiou , Khadidja Benayad
Over time, the insertion of flexible alternating current transmission system (FACTS) components in the power grid became primordial to maintain the overall system stability. This paper proposed an innovative approach called hybrid auxiliary damping control based wide-area measurements for the static var compensator (SVC). The presented controller is a fractional-order fuzzy proportional integral derivative (FOFPID). Its principal task is to damp inter-area low frequency oscillations (LFOs) and to improve the power system stability over the transient dynamics. Then, a metaheuristic grey wolf optimization (GWO) method is applied to adjust the controller’s gains. The SVC-based FOFPID control scheme is implemented in a two-area four- machine test system employing the rotor speed deviations of generators as input signal. A comparative analysis of the elaborated controller with the integer PID and the fractional-order PID (FOPID) is performed to emphasize its effectiveness under a three-phase perturbation. Furthermore, a load variation effect test is completed to attest the control strategy robustness. Based on dynamic simulation results and performance indices, the suggested controller shows its robustness and provides increased efficiency for inter- area oscillations damping.
Volume: 15
Issue: 6
Page: 5130-5143
Publish at: 2025-12-01

Comparative study of traditional edge detection methods and phase congruency based method

10.11591/ijict.v14i3.pp868-880
Rajendra Vasantrao Patil , Vinodpuri Rampuri Gosavi , Govind Mohanlal Poddar , Suman Kumar Swarnkar
Finding relevant and crucial details from images and effectively interpreting what they represent are two of image processing's main goals. An edge is the line that separates an object from its backdrop and shows where two things meet. Mining the picture's borders for extracting useful data remains one of the trickiest steps in understanding of an image. The borders of the objects may be used to build the image's edges, which are its basic characteristics. There are different types of traditional edge retrieval techniques that are conventionally categorized as first order and second gradient based methods such as Roberts, Prwitt, Kirsch, Robinson, canny, Laplacian and Laplacian of gaussian. The majority of research and review work on edge detection algorithms focuses on conventional algorithms and soft computing based methods, neglecting illumination invariant phase congruency based edge detector. This study aims to compare traditional derivative based edge detection algorithms with log Gabor wavelet based edge detector phase congruency. This work does a thorough examination of various edgedetecting approaches, including traditional boundary detection methods and log Gabor wavelet based method. To test effectiveness of edge detection algorithms, experimental results are obtained on images from DRIVE, STARE, and BSDS500 dataset.
Volume: 14
Issue: 3
Page: 868-880
Publish at: 2025-12-01

A recommendation system for teaching strategies according to learning styles

10.11591/ijict.v14i3.pp983-992
Juan Francisco Figueroa-Pérez , Manuel Rodríguez-Guerrero , Alan Ramírez-Noriega , Yobani Martínez-Ramírez
Teaching strategies (TS) are resources, procedures, techniques, and/or methods that teachers use as instruments to promote meaningful learning in students and that have proven to be efficient as support in classroom teaching. This paper describes a recommendation system (RS) for teaching strategies according to learning styles (RSTSLS) that helps to determine the most appropriate TS to use according to the learning style (LS) of the students based on Felder and Silverman’s learning styles model (FSLSM). A working example of the system is provided, as well as the results of its functional and non-functional tests, which were satisfactory. It is concluded that the system can be useful as a support tool for teachers, allowing them to adapt their TS according to the LS of their students.
Volume: 14
Issue: 3
Page: 983-992
Publish at: 2025-12-01

Legal challenges of artificial intelligence in the European Union’s digital economy

10.11591/ijict.v14i3.pp960-971
Volodymyr I. Kudin , Tamara Kortukova , Maryna Dei , Andrii Onyshchenko , Petro Kravchuk
This article critically examines the legal and regulatory challenges posed by artificial intelligence (AI) within the European Union’s (EU) digital economy, focusing on the recently adopted EU Ai Act (Regulation 2024/1689). While previous studies have addressed AI's ethical and theoretical dimensions, this research fills a gap by analyzing the Act’s practical application across its temporal, personal, material, and territorial scopes. The study adopts a qualitative legal methodology, integrating doctrinal interpretation, comparative analysis, and systemic review of EU and international legal instruments. Key findings reveal that the EU AI Act establishes a pioneering risk-based regulatory framework, distinguishing itself through strong safeguards for fundamental rights, transparency, and human oversight. However, critical limitations remain, including ambiguous high-risk classifications, reliance on provider self-assessment, and exemptions for national security that may undermine human rights protections. The article compares the EU approach with those of the United States and China, illustrating divergent models of AI regulation and their global implications. It argues that while the EU AI Act sets an ambitious precedent, its success depends on effective enforcement, stakeholder compliance, and international regulatory convergence. By addressing these challenges, the EU can shape a globally influential framework for ethical and responsible AI deployment. This study contributes to the evolving academic and policy debate on AI governance by offering practical insights and recommendations for future research and legal development.
Volume: 14
Issue: 3
Page: 960-971
Publish at: 2025-12-01

Does empathy and awareness of bullying affect the performance of Moroccan students in PISA?

10.11591/ijict.v14i3.pp860-867
Ilyas Tammouch , Abdelamine Elouafi , Soumaya Nouna
Socioemotional skills, such as empathy and bullying awareness, play a pivotal role in shaping students' personal and academic development. These skills are increasingly recognized as critical factors influencing educational outcomes, particularly in addressing challenges like bullying that can hinder learning. This study examines the impact of empathy and bullying awareness on the academic performance of Moroccan students, using data from the 2018 Programme for International Student Assessment (PISA). To ensure robust causal inference in high-dimensional data, the double/debiased machine learning (DML) technique is employed. The findings reveal that higher levels of empathy and awareness of bullying significantly enhance performance across reading, mathematics, and science, with the most notable improvements observed in reading. These results remain consistent across various demographic and socioeconomic groups, highlighting their robustness. The study underscores the importance of integrating socioemotional learning into educational practices to foster academic success and create supportive school environments. By contributing to the growing evidence on non-cognitive skills in education, this research offers valuable insights for educators and policymakers seeking to improve student outcomes.
Volume: 14
Issue: 3
Page: 860-867
Publish at: 2025-12-01
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