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31,456 Article Results

Optimizing radial basis function networks for harmful algal bloom prediction: a hybrid machine learning approach

10.11591/ijece.v15i6.pp5647-5654
Nik Nor Muhammad Saifudin Nik Mohd Kamal , Ahmad Anwar Zainuddin , Amir ‘Aatieff Amir Hussin , Ammar Haziq Annas , Normawaty Mohammad-Noor , Roziawati Mohd Razali
The deployment of artificial intelligence in environmental monitoring demands models balancing efficiency, interpretability, and computational cost. This study proposes a hybrid radial basis function network (RBFN) framework integrated with fuzzy c-means (FCM) clustering for predicting harmful algal blooms (HABs) using water quality parameters. Unlike conventional approaches, our model leverages localized activation functions to capture non-linear relationships while maintaining computational efficiency. Experimental results demonstrate that the RBFN-FCM hybrid achieved high accuracy (F1-score: 1.00) on test data and identified Chlorophyll-a as the strongest predictor (r = 0.94). However, real-world validation revealed critical limitations: the model failed to generalize datasets with incomplete features or distribution shifts, predicting zero HAB outbreaks in an unlabeled 11,701-record dataset. Comparative analysis with Random Forests confirmed the RBFN-FCM's advantages in training speed and interpretability but highlighted its sensitivity to input completeness. This work underscores the potential of RBFNs as lightweight, explainable tools for environmental forecasting while emphasizing the need for robustness against data variability. The framework offers a foundation for real-time decision support in ecological conservation, pending further refinement for field deployment.
Volume: 15
Issue: 6
Page: 5647-5654
Publish at: 2025-12-01

A comparative study of large language models with chain-of thought prompting for automated program repair

10.11591/ijai.v14.i6.pp4579-4589
Eko Darwiyanto , Rizky Akbar Gusnaen , Rio Nurtantyana
Automatic code repair is an important task in software development to reduce bugs efficiently. This research focuses on developing and evaluating a chain-of-thought (CoT) prompting approach to improve the ability of large language models (LLMs) in automated program repair (APR) tasks. CoT prompting is a technique that guides LLM to generate step-by-step explanations before providing the final answer, so it is expected to improve the accuracy and quality of code repair. This research uses the QuixBugs dataset to evaluate the performance of several LLM models, including DeepSeek-V3 and GPT-4o, with two prompting methods, namely standard and CoT prompting. The evaluation is based on the average number of plausible patches generated as well as the estimated token usage cost. The results show that CoT prompting improves performance in most models compared with the standard. DeepSeek-V3 recorded the highest performance with an average of 36.6 plausible patches and the lowest cost of $0.006. GPT-4o also showed competitive results with an average of 35.8 plausible patches and a cost of $0.226. These results confirm that CoT prompting is an effective technique to improve LLM reasoning ability in APR tasks.
Volume: 14
Issue: 6
Page: 4579-4589
Publish at: 2025-12-01

Metaheuristic optimization for sarcasm detection in social media with embedding and padding techniques

10.11591/ijai.v14.i6.pp5027-5037
Geeta Sahu , Manoj Hudnunkar
Sarcasm is a sophisticated mode of expression that allows speakers to express their opinions subtly. Stakeholders provide unstructured messages with extended phrases, making it difficult for computers and people to understand. This research aims to develop a sarcasm detection method to identify words in phrases as sarcastic or non-sarcastic from text, utilizing natural language processing appliances. The first step is pre-processing, when the padding and embedding are performed. Zero padding and end padding are used for the padding. At the same time, different embedding techniques, such as word2vec, Glove, and BERT, are used. Following pre-processing, the features are extracted from the pre-processed data, including "information gain, chi-square, mutual information, and symmetrical uncertainty-based features." Then, a hybrid optimization technique known as clan-updated grey wolf optimization (CU-GWO) is used for optimized features and weight selection. An ensemble technique was applied to extract optimal features. The classifiers in the proposed suggested ensemble technique with deep convolution neural network (DCNN). DCNN offers fine weight tuning and detection results.The performance analysis and its impact on the proposed model for sarcasm detection are classified with good accuracy into sarcastic and non-sarcastic categories. The results are also compared with against those of the GloVe and BERT techniques.
Volume: 14
Issue: 6
Page: 5027-5037
Publish at: 2025-12-01

Image-based assessment of cattle manure-induced soil erosion in grazing systems

10.11591/ijece.v15i6.pp5360-5370
Cristian Gómez-Guzmán , Yeison Alberto Garcés-Gómez
Extensive livestock farming significantly impacts soil erosion, necessitating accurate monitoring and assessment to mitigate environmental damage and enhance sustainable pasture management. This study employs unsupervised classification of high-resolution drone imagery to detect and quantify soil erosion associated with cattle manure in pastures, focusing on evaluating classification algorithms, identifying relevant spectral and textural features, and quantifying the extent and severity of erosion. The results demonstrate the effectiveness of unsupervised classification in identifying erosion zones and their impact on soil health and water quality. Field validation confirms the accuracy of the analysis, emphasizing the need for sustainable management practices such as controlled manure redistribution and soil conservation to mitigate erosion and protect natural resources. This approach offers practical tools for mitigating the environmental impacts of semi-extensive livestock farming and promoting more sustainable management. The findings provide practical recommendations for sustainable pasture management, contributing to environmental conservation and the long-term health of live-stock systems.
Volume: 15
Issue: 6
Page: 5360-5370
Publish at: 2025-12-01

Exploring feature selection method for microarray classification

10.11591/ijece.v15i6.pp5584-5593
Muhammad Zaky Hakim Akmal , Devi Fitrianah
Effectively selecting features from high-dimensional microarray data is essential for accurate cancer detection. This study explores the pivotal role of feature selection in improving the accuracy of classifying microarray data for ovarian cancer detection. Utilizing machine learning techniques and microarray technology, the research aims to identify subtle gene expression patterns that indicate ovarian cancer. The research explores the utilization of principal component analysis (PCA) for dimensionality reduction and compares the effectiveness of feature selection techniques such as artificial bee colony (ABC) and sequential forward floating selection (SFFS). The dataset used in this study comprises of 15154 genes, 253 instances, and 2 classes related to ovarian cancer. Through a comprehensive analysis, the study aims to optimize the classification process and improve the early detection of ovarian cancer. Moreover, the study presents the classification accuracy results obtained by PCA, ABC, and SFFS. While PCA achieved an accuracy of 96% and SFFS yielded a classification accuracy of 98%, ABC demonstrated the highest classification accuracy of 100%. These findings underscore the effectiveness of ABC as the preferred choice for feature selection in improving the classification accuracy of ovarian cancer detection using microarray data.
Volume: 15
Issue: 6
Page: 5584-5593
Publish at: 2025-12-01

Automated legal content management system for multi-country integration

10.11591/ijai.v14.i6.pp4511-4519
Hardik Pawar , Nidhi Prakash , Smriti Srivastava , Sneha M. , Shaik Mohideen Syedabdulkader , Pratiba D. , Sandhya S.
This paper presents an automated legal content management system (CMS) designed for multi-country integration, addressing the complex challenges of legal document migration across more than 180 countries while ensuring regulatory compliance and accessibility standards. The system implements a hierarchical four-level architecture, migrating more than 2,740 legal documents with zero data loss incidents through fault-tolerant processing pipelines. The automated portable document format (PDF) migration component demonstrates exceptional efficiency, processing documents 36 times faster compared to manual approaches, while article migration achieves 230 times faster processing speeds. The integrated artificial intelligence (AI)-powered accessibility enhancement system generates contextually appropriate alt text descriptions, allowing organizations to process 10,000 images annually with savings of $14,990. The complete country migration process, covering both PDF and article processing, executes in 30 seconds compared to 56 minutes for manual processing, representing a 112-fold improvement in performance. System scalability demonstrates linear performance characteristics up to more than 5,000 documents with consistent processing metrics while maintaining compliance across diverse regulatory frameworks. These quantitative improvements establish a new paradigm for automated legal content management, providing a scalable foundation for global enterprises managing multi jurisdictional legal documentation requirements.
Volume: 14
Issue: 6
Page: 4511-4519
Publish at: 2025-12-01

Optimizing parameter selection in bidirectional encoder portrayal for transformers algorithm using particle swarm optimization for artificial intelligence generate essay detection

10.11591/ijece.v15i6.pp5543-5554
Tegar Arifin Prasetyo , Rudy Chandra , Wesly Mailander Siagian , Horas Marolop Amsal Siregar , Samuel Jefri Saputra Siahaan
This research proposes a novel method for detecting artificial intelligence (AI)-generated essays by integrating the bidirectional encoder representations from transformers (BERT) model with particle swarm optimization (PSO). Unlike traditional approaches that rely on manual hyperparameter tuning, this study introduces a systematic optimization technique using PSO to improve BERT’s performance in identifying AI-generated content. The key problem addressed is the lack of effective, real-time detection systems that preserve academic integrity amidst rapid AI advancements. This optimization enhances the model’s detection accuracy and operational efficiency. The research dataset consisted of 46,246 essays, which, after data cleaning, were refined to 44,868. The model was then tested on 9,250 essays. Initial evaluations showed BERT's accuracy ranging from 83% to 94%. After being optimized with PSO, the model achieved an accuracy of 98%, an F1-score of 98.31%, precision of 97.75%, and recall of 98.87%. The model was deployed using a FastAPI-based web interface, enabling real-time detection and providing users with an efficient way to quickly verify text authenticity. This research contributes a scalable, automated solution for AI-generated text detection and offers promising implications for its application in various academic and digital content verification contexts.
Volume: 15
Issue: 6
Page: 5543-5554
Publish at: 2025-12-01

Robotic product-based manipulation in simulated environment

10.11591/ijece.v15i6.pp5894-5903
Juan Camilo Guacheta-Alba , Anny Astrid Espitia-Cubillos , Robinson Jimenez-Moreno
Before deploying algorithms in industrial settings, it is essential to validate them in virtual environments to anticipate real-world performance, identify potential limitations, and guide necessary optimizations. This study presents the development and integration of artificial intelligence algorithms for detecting labels and container formats of cleaning products using computer vision, enabling robotic manipulation via a UR5 arm. Label identification is performed using the speeded-up robust features (SURF) algorithm, ensuring robustness to scale and orientation changes. For container recognition, multiple methods were explored: edge detection using Sobel and Canny filters, Hopfield networks trained on filtered images, 2D cross-correlation, and finally, a you only look once (YOLO) deep learning model. Among these, the custom-trained YOLO detector provided the highest accuracy. For robotic control, smooth joint trajectories were computed using polynomial interpolation, allowing the UR5 robot to execute pick-and-place operations. The entire process was validated in the CoppeliaSim simulation environment, where the robot successfully identified, classified, and manipulated products, demonstrating the feasibility of the proposed pipeline for future applications in semi-structured industrial contexts.
Volume: 15
Issue: 6
Page: 5894-5903
Publish at: 2025-12-01

6G internet of things networks for remote location surgery also a review on resource optimization strategies, challenges, and future directions

10.11591/ijece.v15i6.pp5968-5977
Md Asif , Tan Kaun Tak , Pravin R. Kshirsagar
Remote location surgery presents stringent requirements for wireless communication, particularly in terms of reliability, speed, and low latency. The emergence of sixth-generation (6G) wireless networks is expected to address these challenges effectively. With the rapid expansion of internet of things (IoT) applications in healthcare, maintaining real-time connectivity has become essential. Ensuring such performance in 6G-enabled IoT networks relies heavily on the implementation of advanced resource optimization techniques. Recent studies have focused on improving key performance metrics, including latency, reliability, energy efficiency, spectral efficiency, data rate, and bandwidth usage. Comprehensive reviews of these techniques reveal a growing emphasis on multi-objective optimization strategies to balance conflicting requirements. Research has also highlighted limitations in existing approaches, suggesting the need for further innovation, particularly for mission-critical applications like remote surgery. Within this context, 6G IoT systems have demonstrated the potential to maintain high data rates and stable throughput, both of which are essential for safe and responsive surgical operations conducted over long distances. These findings underscore the importance of continued development in resource management to fully enable remote healthcare delivery through advanced wireless technologies.
Volume: 15
Issue: 6
Page: 5968-5977
Publish at: 2025-12-01

Power loss reduction and stability enhancement of power system through transmission network reconfiguration

10.11591/ijece.v15i6.pp6012-6026
Titus Terwase Akor , Theophilu Chukwudolue Madueme , Chibuike Peter Ohanu , Tole Sutikno
The power network faces several challenges as electricity usage rises and the frequency of partial and total grid disruptions is of great concern. This paper addresses the problem of voltage instability and high-power losses in transmission network, which threatens the stability of the power grid. The MATLAB R2023a/MATPOWER 5.0 is used to develop a model and analyze using the Newton-Raphson load flow method. The analysis reveals a marginal voltage violation at Bus 13 (below 0.95 p.u.). To enhance stability and efficiency, the network was reconfigured using a hybrid whale algorithm and particle swarm optimization (WAPSO) approach, incorporating new transmission lines (5-8 and 13-14) to improve connectivity and reduce congestion. The reconfiguration reduced active power losses by 29.5% (from 36.013 to 25.371 MW) and reactive power losses by 29.8% (from 301.30 to 211.59 MVAr). The system demonstrated first swing stability, with rotor angles remaining below π/2 (1.5669 rad maximum deviation) and fault clearance within the critical clearing time (0.2 s). Optimized exciter gains and a damping coefficient of 1.5 p.u. ensured effective oscillation suppression and stable generator voltages at 1.05 p.u. The hybrid WAPSO approach proved effective in optimizing voltage and rotor angle stability, enabling the network to meet a 24.086 p.u. load demand while enhancing overall grid reliability.
Volume: 15
Issue: 6
Page: 6012-6026
Publish at: 2025-12-01

Securing post-quantum cryptography: side-channel resilience in CRYSTALS-Kyber key encapsulation mechanism

10.11591/ijai.v14.i6.pp5251-5267
Shreyas Kasture , Sudhanshu Maurya , Alakshendra Pratap Singh , Amit Shukla , Arnav Kotiyal , Kashish Mirza
This study evaluates side-channel vulnerabilities in hardware implementations of the cryptographic suite for Algebraic lattices (CRYSTALS)-Kyber key encapsulation mechanism (KEM) using correlation and differential power analysis (DPA) techniques. Unprotected field-programmable gate array (FPGA) implementations across all Kyber parameter sets were successfully compromised, revealing significant information leakage. Attack complexity scaled linearly with key size. Additive Boolean masking provided varying protection levels, with 4-bit masking offering a 100× security increase at notable performance cost. Performance characterization showed increased slice utilization and reduced maximum frequency for higher-order masking. A novel hybrid countermeasure combining higher-order masking with controlled time randomization enhanced protection against machine learning-based attacks. Comprehensive power trace analysis using 12-bit precision at 500 MS/s sampling rates was conducted. Statistical evaluation utilized Pearson's correlation and Welch's t-tests with a 0.8 threshold for key recovery. Real world validation in IoT, financial, and satellite scenarios highlighted practical post-quantum cryptography (PQC) deployment challenges. The study provides concrete design guidance for efficiently securing hardware Kyber implementations against side-channel attacks.
Volume: 14
Issue: 6
Page: 5251-5267
Publish at: 2025-12-01

Intuitive effectiveness degree of research methodologies for spectrum sensing in cognitive radio network

10.11591/ijece.v15i6.pp5699-5707
Pushpa Yellappa , Dr.Keshavamurthy Keshavamurthy
The phenomenon of spectrum sensing plays an essential role in cognitive radio network (CRN) that is performed in real-time for better adaptability to dynamic usage of spectrum. However, efficient decision-making is often noted to be affected by dynamic environmental condition, interference, and noise leading to declination in performance. In recent times, there are proposals for various methodologies addressing such issues targeting towards improving spectrum sensing along with machine learning and energy detection approach, which is gaining its pace for technical research implementation. Irrespective of this advancement, ambiguity shrouds regarding the contrast effectiveness associated with these methods and their appropriateness in different situation. Hence, this manuscript presents a comprehensive and yet crisp review work to offer concise assessment of latest methodologies towards spectrum sensing used in CRN ecosystem. The paper has an inclusion of existing techniques, presents their potentials and shortcomings, exhibited evolving trends of research, extracts key gaps and challenges. The prime intention of this review work is towards guiding the future researchers and scholars by facilitating deeper insight towards the recent state of technologies in spectrum sensing.
Volume: 15
Issue: 6
Page: 5699-5707
Publish at: 2025-12-01

On big data predictive analytics-trends, perspectives, and challenges

10.11591/ijece.v15i6.pp5978-5985
Yassine Benlachmi , Abdelaziz El Yazidi , Abdallah Rhattoy , Moulay Lahcen Hasnaoui
The world is experiencing explosive growth in numerous sectors such as healthcare, engineering, scientific studies, business, social networking. This growth is causing an immense surge in data generation too. And with the emergence of technologies like internet of things (IoT), Mobile, and cloud computing, the volume of data being produced is skyrocketing. However, making sense of this colossal amount of data is a daunting challenge. Enter big data computing, a new paradigm that blends large datasets with advanced analytical techniques. Big data is characterized by the three V's: Volume, velocity, and variety, and refers to massive datasets. By processing this data, we can uncover new opportunities and gain valuable insights into market trends. Traditional techniques are simply not equipped to handle the scale of Big Data. The purpose of this article is to gather reviews of various predictive analytics applications related to big data and the advantages of using big data analytics across various decision-making domains.
Volume: 15
Issue: 6
Page: 5978-5985
Publish at: 2025-12-01

Classification of regional language dialects using convolutional neural network and multilayer perceptron

10.11591/ijai.v14.i6.pp5017-5026
Fahmi B. Marasabessy , Dwiza Riana , Muji Ernawati
Regional languages are vital for communication and preserving cultural identity, safeguarding local heritage. However, globalization and modernization endanger their existence as they are increasingly replaced by national or global languages. Despite progress in dialect recognition research, particularly for certain languages, further studies are needed to improve model performance and address less-represented dialects, including those in Indonesia. This study enhances a custom-built dataset for dialect recognition through the application of data augmentation techniques, specifically adding noise, time stretching, and pitch shifting. Using Mel-frequency cepstral coefficients (MFCC) for feature extraction, it evaluates the performance of convolutional neural network (CNN) and multilayer perceptron (MLP) in classifying six Indonesian dialects. Results indicate that CNN outperformed, achieving 97.92% accuracy, 97.90% recall, 97.97% precision, 97.92% F1-score, and a kappa score of 97.49% with combined augmentation techniques, setting a foundation for further research.
Volume: 14
Issue: 6
Page: 5017-5026
Publish at: 2025-12-01

Optimizing sparse ternary compression with thresholds for communication-efficient federated learning

10.11591/ijai.v14.i6.pp4902-4912
Nithyanianjan Murthy Chittaiah , Manjula Sunkadakatte Haladappa
Federated learning (FL) enables decentralized model training while preserving client data privacy, yet suffers from significant communication overhead due to frequent parameter exchanges. This study investigates how varying sparse ternary compression (STC) thresholds impact communication efficiency and model accuracy across the CIFAR-10 and MedMNIST datasets. Experiments tested thresholds ranging from 1.0 to 1.9 and batch sizes of 10, 15, and 20. Results demonstrated that selecting thresholds between 1.2 and 1.5 reduced total communication costs by approximately 10–15%, while maintaining acceptable accuracy levels. These findings suggest that careful threshold tuning can achieve substantial communication savings with minimal compromise in model performance, offering practical guidance for improving the efficiency and scalability of FL systems.
Volume: 14
Issue: 6
Page: 4902-4912
Publish at: 2025-12-01
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