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29,082 Article Results

AI-MG-LEACH: investigation of MG-LEACH in wireless sensor networks energy efficiency applied the advanced algorithm

10.11591/ijece.v15i6.pp5080-5090
Hicham Ouldzira , Alami Essaadoui , Mustapha EL Hanine , Ahmed Mouhsen , Hassane Mes-Adi
Wireless sensor networks (WSNs) play a crucial role in data collection across various fields like environmental monitoring and industrial automation. The energy efficiency of these networks, powered by limited-capacity batteries, is key to their performance. Clustering protocols such as low- energy adaptive clustering hierarchy (LEACH) are widely used to optimize energy consumption. To enhance LEACH’s performance, MG-LEACH was introduced, improving cluster head selection to extend network lifespan. This study compares MG-LEACH with AI-MG-LEACH, which incorporates artificial intelligence (AI) to further improve energy efficiency by selecting cluster heads based on factors like residual energy. Simulations show AI-MG-LEACH reduces energy consumption, extends network life, and enhances data reliability, outperforming MG-LEACH.
Volume: 15
Issue: 6
Page: 5080-5090
Publish at: 2025-12-01

Citizens’ electronic satisfaction factors in electronic government services: an empirical study from Kuwait

10.11591/ijece.v15i6.pp5690-5698
Abdullah Alshehab , Ali Alfayly , Naser Alazemi
This study investigates the dimensions of service quality provided by Kuwait’s “Sahel” electronic government (e-government) application and their impact on user satisfaction among citizens and residents. Adopting a quantitative methodology based on the modified electronic government quality (e-GovQual) model, data were collected from 1,064 respondents over four weeks, assessing user experiences across usability, reliability, responsiveness, security, and efficiency dimensions. Results indicate moderate overall satisfaction, with particularly high ratings for transparency and ease of use, yet notable concerns regarding trust and data security. Satisfaction with reliability and technical support was moderate, signaling areas for improvement. The study recommends enhancing the user interface for intuitive navigation, improving real-time data synchronization between governmental entities, providing efficient technical support, and strengthening security measures to build user trust. These recommendations are crucial for advancing Kuwait’s e-government effectiveness. Future research should explore causal relationships among service quality dimensions and incorporate technical assessments by information and communication technology (ICT) experts to further enhance user satisfaction.
Volume: 15
Issue: 6
Page: 5690-5698
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

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

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

Integrity verification of medical images in internet of medical things for smart cities using data hiding scheme

10.11591/ijece.v15i6.pp5770-5781
Kilari Jyothsna Devi , Ravuri Daniel , Bode Prasad , Mohamad Khairi Ishak , Dorababu Sudarsa , Pasam Prudhvi Kiran
As technology has advanced, the internet of medical things (IoMT) has become incredibly useful. It is used to transmit a wide variety of medical images. Sensitive patient data may be altered during transmission or subject to illegal access. To overcome all of these challenges and preserve the integrity of medical images while transmission over IoMT, a blind region-based data concealing approach called medical image watermarking (MIW) is suggested. The region of interest (ROI) and region of non-interest (RONI) are the two sections that make up the medical image. The aim of the suggested MIW technique is to prevent transmission-related manipulation of medical image ROI. To provide high imperceptibility and resilience, confined integrity verification and recovery bits (CIVRB) bits are embedded in the RONI using hybrid integer wavelet transform–singular value decomposition (IWT-SVD). According to the experimental results, the suggested system is highly imperceptible (average peak signal-to-noise ratio (PSNR)=56dB), robust (average NC=0.99), and exhibits integrity verification accuracy of over 98% against a variety of image processing attacks. In terms of several watermarking properties, the proposed technique performs over state-of-the-art schemes. This method offers a dependable framework for protecting medical images in real-time IoMT applications and is suitable for smart healthcare environments.
Volume: 15
Issue: 6
Page: 5770-5781
Publish at: 2025-12-01

Combination of rough set and cosine similarity approaches in student graduation prediction

10.11591/ijece.v15i6.pp6001-6011
Ratna Yulika Go , Tinuk Andriyanti Asianto , Dewi Setiowati , Ranny Meilisa , Christine Cecylia Munthe , R. Hendra Kusumawardhana
Higher education institutions must deliver high-quality education that produces graduates who are knowledgeable, skilled, creative, and competitive. In this system, students are a vital asset, and their timely graduation rate is an important factor to consider. In the department of computer science, a challenge arises in distinguishing between students who graduate on time and those who do not. With a low on-time graduation rate of just 1.90% out of 158 graduates, this issue could negatively affect the institution's accreditation evaluation. This research employs the Case-Based Reasoning method, enhanced with an indexing process using rough sets and a prediction process utilizing cosine similarity. The testing, conducted using k-fold validation with 60%, 70%, and 80% of the data, produced average accuracy rates of 64.2%, 66.3%, and 65.6%, respectively. The test results indicate that the highest average accuracy of 66.3% was achieved with 70% of the cases.
Volume: 15
Issue: 6
Page: 6001-6011
Publish at: 2025-12-01

Enhancing semantic segmentation with a boundary-sensitive loss function: a novel approach

10.11591/ijece.v15i6.pp5327-5335
Ganesh R. Padalkar , Madhuri B. Khambete
Semantic segmentation is crucial step in autonomous driving, medical imaging, and scene understanding. Traditional approaches leveraging manually extracted pixel properties and probabilistic models, have achieved reasonable performance but suffer from limited generalization and the need for expert-driven feature selection. The rise of deep learning architectures has significantly improved segmentation accuracy by enabling automatic feature extraction and capturing intricate object details. However, these methods still face challenges, including the need for large datasets, extensive hyperparameter tuning, and careful loss function selection. This paper proposes a novel boundary-sensitive loss function, which combines region loss and boundary loss, to enhance both region consistency and edge delineation in segmentation tasks. Implemented within a modified SegNet framework, the approach proposed in the paper is evaluated with the semantic boundary dataset (SBD) dataset using standard segmentation metrics. Experimental results indicate improved segmentation accuracy, substantiating to proposed method.
Volume: 15
Issue: 6
Page: 5327-5335
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

Soil moisture prototype soil moisture sensor YL-69 for Gaharu (Aquilaria malaccensis) tree planting media

10.11591/ijict.v14i3.pp1163-1171
Rikie Kartadie , Muhammad Agung Nugroho , Adiyuda Prayitna , Adi Kusjani , Ardeana Galih Mardika
Soil moisture, defined as the amount of water present in the spaces between soil particles, plays a critical role in plant growth. Excessive soil moisture can lead to issues such as root rot, deviating from the ideal conditions required for root absorption. To address this, we developed a prototype tool using the YL-69 soil moisture sensor to monitor and control the soil moisture levels in Agarwood/Gaharu tree planting media. The prototype was designed to activate a water pump when soil moisture exceeded 80%, ensuring optimal humidity for plant growth. Once the moisture level dropped below 80%, the pump was deactivated to prevent overwatering. The YL-69 sensor demonstrated an accuracy of 88.76% under controlled conditions. This study highlights the potential of using low-cost sensors for automated soil moisture management in small-scale Gaharu cultivation.
Volume: 14
Issue: 3
Page: 1163-1171
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

Chatbot for virtual medical assistance

10.11591/ijict.v14i3.pp914-922
Aravalli Sainath Chaithanya , Sampangi Lahari Vishista , Adepu MadhuSri
A healthy population is vital for societal prosperity and happiness. Amidst busy lifestyles and the challenges posed by the COVID-19 pandemic, individuals often neglect their health needs. To address this, we introduce a novel approach utilizing a chatbot for virtual medical assistance. Tailored for individuals confined indoors or hesitant to visit hospitals for minor ailments, our chatbot offers personalized medical support by diagnosing ailments based on user-reported symptoms and engaging in interactive conversations. Leveraging a robust dataset containing 132 symptoms, 41 diseases, and corresponding medications, our chatbot employs a systematic approach for symptom refinement, enhancing diagnostic precision. Upon identifying a disease, the chatbot promptly suggests basic medications tailored to the specific ailment. Furthermore, our system integrates user demographics to evaluate medication history and current state, allowing for personalized medication recommendations based on individual needs. Through extensive testing and validation, we demonstrate the effectiveness of our chatbot in accurately predicting ailments and providing timely treatment advice. Our study introduces a novel paradigm for medicine recommendation and disease prediction, with the potential to enhance healthcare accessibility and effectiveness.
Volume: 14
Issue: 3
Page: 914-922
Publish at: 2025-12-01

Scaling of Facebook architecture and technology stack with heavy workload: past, present and future

10.11591/ijict.v14i3.pp772-782
Tole Sutikno , Laksana Talenta Ahmad
Leading social media Facebook has improved its architecture to meet user needs. Facebook has improved its systems to handle millions of users with heavy workloads and large datasets using innovative architectural solutions and adaptive strategies. The study examines Facebook’s architectural and technological advances in heavy workload and big data. To understand how Facebook scaled with a growing user base and data volume, history and system architecture will be examined. It will also examine how cloud storage and high-performance computing optimize resource utilization and maintain performance during peak user activity. Facebook is managing big data and heavy workloads with new technologies like the hybrid communication model that uses PULL and PUSH strategies for real-time messaging. Facebook switched from HBase to MyRocks for message storage to improve performance as data grew. Architectural scaling and technology stack research must prioritize data storage innovations and optimized communication protocols to handle heavy workloads and big data. The messenger Sync protocol reduces network congestion and improves synchronous communication, reducing resource consumption and maintaining performance under high load. High-performance computing (HPC) and cloud storage should be studied together to support complex compute workflows. This convergence may improve large-scale application infrastructures and encourage interdisciplinary collaboration for scalable and resilient systems.
Volume: 14
Issue: 3
Page: 772-782
Publish at: 2025-12-01

Small signal modeling of restructured boost converter in continuous conduction mode

10.11591/ijpeds.v16.i4.pp2500-2508
Anwar Muqorobin , Sulistyo Wijanarko , Muhammad Kasim , Pudji Irasari , Ketut Wirtayasa , Puji Widiyanto
This paper introduces small signal modeling of the restructured boost converter (RBC) in continuous conduction mode (CCM) by using the circuit averaging technique. The averaging technique produces linear transfer functions of the converter. The transfer functions relating the duty cycle to output voltage, duty cycle to inductor current, input voltage to output voltage, and input voltage to inductor current are obtained. To validate the converter model, power simulation (PSIM) simulations are developed, and experiments are conducted. The function of RBC is similar to a conventional boost converter, i.e., to level up the input voltage. A comparative analysis between the RBC and conventional boost converter is performed. The results highlight the advantages of RBC over a conventional boost converter.
Volume: 16
Issue: 4
Page: 2500-2508
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
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