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International Journal of Informatics and Communication Technology (IJ-ICT)
Published by Institute of Advanced Engineering and Science
ISSN : 22528776     EISSN : 27222616     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Communication Technology (IJ-ICT) is a common platform for publishing quality research paper as well as other intellectual outputs. This Journal is published by Institute of Advanced Engineering and Science (IAES) whose aims is to promote the dissemination of scientific knowledge and technology on the Information and Communication Technology areas, in front of international audience of scientific community, to encourage the progress and innovation of the technology for human life and also to be a best platform for proliferation of ideas and thought for all scientists, regardless of their locations or nationalities. The journal covers all areas of Informatics and Communication Technology (ICT) focuses on integrating hardware and software solutions for the storage, retrieval, sharing and manipulation management, analysis, visualization, interpretation and it applications for human services programs and practices, publishing refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in ICT.
Arjuna Subject : -
Articles 650 Documents
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FusionGait-transformer: multimodal transformer-based framework for robust gait recognition and gait-condition classification Kiran Kumar S. V. N. Madupu; Jagadeeshwar Madiraju; Naren Kodali; Soma Sekhar Goviraboyina
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1902-1920

Abstract

Gait analysis benefits from both full-body motion context and accurate temporal dynamics. Vision-only approaches degrade under occlusion and viewpoint change, while inertial-only approaches miss full body context. FusionGait transformer is a multimodal framework that fuses pose and silhouette representations with inertial time series using transformer self-attention and bidirectional cross-attention. A per-modality reliability gate weights each stream by its instantaneous signal quality, so degradation in one modality shifts the fused representation toward the other. Masked multimodal pretraining and domain adversarial training improve stability under domain shift. Evaluation uses the CMU motion capture dataset under a subject-independent protocol, with inertial signals generated analytically from the same trajectories. The framework reaches Rank-1 identification accuracy of 98.9% with equal error rate (EER) of 1.8%, and gait-condition classification F1-score of 95.7% with area under the ROC curve (AUC-ROC) of 97.6%. Reliability gating is the largest single contributor to these figures. Deployment is characterized on workstation, edge, and gateway hardware, and transmitting embeddings rather than video reduces the uplink requirement by more than four orders of magnitude, establishing feasibility for wearable healthcare settings. Reported results describe performance under controlled benchmark conditions. Clinical diagnostic claims remain conditional on validation with real wearable sensors and clinically labeled cohorts.
SSDHR classifier network for brain tumor detection using MRI images Kalpana Telkar; K. Anusudha
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1459-1468

Abstract

Brain tumors are the chaotic growth of abnormal brain cells, which is the main cause of cancer worldwide. Symptoms vary depending on tumor size and location, and these tumors can be cancerous or non-cancerous. Early detection and characterization are crucial but challenging due to the brain's complex structure. This study proposes a self-supervised classification deep hierarchical reconstruction network (SSDHR), a combination of generative and discriminative models. This network uses convolutional Kernels of different sizes for processing of input data on separate branches to extract effective discriminative features. A parallel-structured symmetric fusion attention (SFA) module is introduced, combining attention mechanisms for coordinates, time, and frequency to enhance the model's comprehension and feature selection. The SSDHR classifier network achieves the highest accuracy (91.9%) among 10 state-of-the-art classifiers, classifying tumors into 4 classes: pituitary, meningioma, glioma, and no tumor. The model's performance is evaluated using various metrics that demonstrate its effectiveness in brain tumor classification.
Approximate computing circuits for error-tolerant applications with low energy consumption Anusha Gorantla; Vamsidhar Anagani; Venkata Sridhar Gummadidala; Ramesh Babu Geddam
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1624-1634

Abstract

Energy efficiency is a crucial factor in very large-scale integrated (VLSI) circuit design, and traditional methods offer limited solutions to power intensive circuits. Recently, approximate computing techniques have emerged, enhancing key design parameters like power consumption, propagation delay, and area, albeit with a compromise in accuracy. These techniques are especially suitable for high-performance computing, as they offer reduced energy consumption while compromising some accuracy, making them ideal for use in arithmetic circuits. This paper introduces enhanced versions of approximate adders (AxAs) and subtractors (AxSCs) for fundamental operations. Building upon these designs, approximate multipliers using the Wallace tree multiplier (AxWTM) and approximate dividers (AxDs) have been developed, and their performances have been compared regarding power consumed, spatial requirements, delay time, and error frequency. Many digital image and video processing applications, along with other multimedia tools, can function effectively even with slight inaccuracies in their output results. The existing and proposed designs were applied to image squaring, image multiplication, image subtraction, and image division applications, and peak signal-to-noise ratio (PSNR) was obtained. It is found that the suggested approximate designs achieved a better PSNR for all image applications, and the results were superior to those of their counterparts.
Towards accurate diagnostics: deep-transfer learning in skin disease detection Vaibhavi Pandya; Vruti Parikh; Mohit Rathod; Sumitra Menaria; Nitesh Sureja
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1577-1588

Abstract

Automated skin disease diagnosis remains a significant challenge due to inherent variations in skin tone, texture, lighting, and occlusions such as hair. Addressing these diagnostic difficulties is vital for developing reliable clinical decision-support systems. This research presents an automated classification framework utilizing deep learning, transfer learning, and explainable artificial intelligence (XAI). The study utilizes the ISIC 2019 dataset, comprising images across eight diagnostic categories, including Melanoma, Nevi, and Keratosis. Six state-of-the-art convolutional neural network (CNN) architectures—Xception, DenseNet169, EfficientNetV2S, MobileNetV2, InceptionResNetV2, and ResNet101V2—were fine-tuned and evaluated. The workflow integrates advanced preprocessing, comprehensive model training, and XAI-based validation to ensure transparency in diagnostic predictions. Quantitative evaluation reveals that Xception and EfficientNetV2S outperformed other architectures, achieving near-optimal performance, with Xception reaching an area under the curve (AUC) of 100% and superior scores across all primary metrics. Conversely, MobileNetV2 demonstrated the lowest comparative efficacy, with an accuracy of 62.66% and an AUC of 78.10%. The integration of high performing CNNs with XAI techniques provides an accurate, interpretable, and scalable solution for dermatological diagnostics. This approach not only improves classification precision but also enhances clinical trust by providing visual explanations for model decisions.
Adaptive weighted multi-criteria decision making using modified dijkstra’s algorithm: integrating real-time traffic conditions with hybrid WSM-WPM for dynamic route optimization Basorudin Basorudin; Handaru Jati; Nurkhamid Nurkhamid; Antoni Pribadi; Satria Riki Mustafa; Puput Dani Prasetyo Adi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1929-1943

Abstract

Traffic congestion is a problem that needs to be solved. A smart route optimization system is needed to overcome this problem, not only using traditional methods but also using algorithms that have the ability to find the shortest route. This study proposes a framework system for multi-criteria decision making that adapts and improves the Dijkstra and weighted product method (WPM) algorithms. The Dijkstra algorithm is modified to be more integrated in solving the fastest travel route, anticipating traffic congestion, improving road safety, saving fuel, and providing real-time data. The weighted sum method (WSM)-WPM hybrid framework has the advantage of WSM additive normalization with the zero-dimensional properties of WPM to overcome different measurement units. Experimental results show a significant reduction in travel time and fuel consumption, as well as an increase in route safety percentage. This system maintains computational efficiency, and this research provides a practical and scalable solution for intelligent transportation systems (ITS), improving the driver experience and overall traffic network safety. The results of the congestion avoidance configuration with the highest hybrid score show a result of 0.4573, yielding a viable alternative route, namely (V1→V3→V6→V9→V12→V14).
New prediction model for school dropout based on an improvement of SVM and ABC optimization algorithm Hicham EL Yousfi Alaoui; Ziad Bousraraf; Amal Hjouji; Omar EL Ogri; Jaouad EL-Mekkaoui; Zaynab Hjouji
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1480-1492

Abstract

School dropout is a major dare for Moroccan education systems. It generates profound repercussions, both socially and economically, affecting both individuals and the entire community. To assess the risks associated with this phenomenon, it is essential to use innovative technologies, relying in particular on statistical methods and artificial intelligence models capable of providing effective results using available data. These methods offer the possibility of identifying the factors influencing the educational phenomenon and anticipating the associated risks. To understand the effect of scholars’ social, economic, and personal influences on school dropout, we propose in this work a new classifier, based on an improvement of support vector machines (SVM) and on the ABC algorithm. We also present a regression model designed to estimate academic performance based on the average grade obtained. We also propose a new regression model to predict the degree of academic achievement. The proposed model was validated on a real dataset, compiled from a questionnaire distributed to Moroccan secondary school students. Furthermore, several comparative studies were conducted with recent approaches to assess the performance of the proposed methods. All the experimental results confirm the robustness and improved performance of the developed methods, particularly with respect to accuracy and predictive capability.
Expert system for motorcycle damage diagnosis using certainty factor and case-based reasoning methods Anthony Anggrawan; Christofer Satria; Bambang Krismono Triwijoyo; Dadang Priyanto; Hasbullah Hasbullah; Victoria Cynthia Rebecca
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1829-1840

Abstract

Motorcycles are the main mode of transportation that requires maintenance due to damage. Unfortunately, damage detection on motorcycles remains traditional, despite the potential for expert systems and artificial intelligence (AI) to facilitate more straightforward diagnosis for both motorcycle users and mechanics. That is why this research aims to develop a system with an expert system approach to diagnose damage to a motorcycle. This research applies the certainty factor (CF) expert system approach and the case-based reasoning (CBR) AI method. The study found that the expert system using the CBR method outperforms the CF method in diagnosing motorcycle damage. The CF expert system method achieves an accuracy performance of 98%. In comparison, the CBR expert system method achieves an accuracy of up to 100%, or can be equated with human mechanics in diagnosing motorcycle damage. The finding implies that CBR can effectively replace human experts in detecting motorcycle damage.
From data to decisions: ensemble deep learning framework for maize seed variety classification with explainable AI Emmanuel Asante; Obed Appiah; Ezekiel Mensah Martey; Emmanuel Effah; Eric Opoku; Ernest Kwame Ampomah
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1648-1663

Abstract

Accurate and non-destructive identification of maize seed varieties is essential for maintaining seed purity, improving crop productivity, and supporting agricultural quality assurance. Although recent studies report high accuracies using advanced imaging and deep learning techniques, many rely on hyperspectral sensors or controlled acquisition environments, or offer limited interpretability, thereby restricting real-world adoption. This study investigates maize seed variety classification using low-cost RGB imaging and an ensemble deep learning framework, focusing on three widely cultivated Ghanaian varieties: Bhihilifa, Sanzal Sima, and Wang Dataa. In collaboration with Heritage Seeds Ghana, an expert-verified dataset of 17,724 maize seed images was curated under realistic acquisition conditions with an Apple iPhone 12. A baseline convolutional neural network (CNN) was developed for benchmarking, followed by transfer learning with DenseNet169, MobileNetV2, and Xception architectures. An ensemble framework integrating DenseNet169 and Xception was proposed to improve robustness and generalization. Across five-fold cross-validation, the ensemble achieved a mean accuracy of 98.89±0.17%, precision of 98.86±0.18%, recall of 98.80±0.17%, and F1-score of 98.83±0.17%. To enhance transparency, Grad-CAM and saliency maps were employed to visualize discriminative features influencing model decisions. Results demonstrate that accurate and interpretable classification of maize seed varieties is achievable using standard RGB imagery without specialized sensing equipment.
Smart poultry farming with IoT: an in-depth exploration of innovations and trends Md. Kamal Ibne Sufian; Md. Fuyad Afzal; Afifa Sultana Offa; Sajeeb Saha; Nazma Tara
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1964-1975

Abstract

The internet of things (IoT) has emerged as a key enabling technology for smart applications such as smart homes, smart transportation, and smart agriculture. In traditional poultry farming, production largely depends on manual observation and human expertise, which often leads to inefficiencies and low productivity. IoT-based smart poultry farming provides automated monitoring and control of environmental parameters such as temperature, humidity, and poultry health using sensors and wireless communication technologies. This paper presents a comprehensive literature review of IoT-based smart poultry farming systems. This study systematically reviews 20 peer-reviewed studies published between 2013 and 2025, surveys existing IoT-based poultry farming solutions with a focus on sensing components, microcontroller platforms, system implementation approaches, and reported challenges in real-world deployments.
The impact of ai recommendation systems on impulsive buying behaviour in electronic marketplaces: The mediating moderating role of fear of missing out Ninh Phu Giang; Bui Thanh Khoa
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1716-1725

Abstract

Artificial intelligence has spread rapidly across e-commerce, and in doing so it has reshaped how consumers reach purchase decisions. Scholarship to date concentrates mainly on what shoppers gain from algorithmic recommenders, whereas far fewer studies ask whether such systems also push buyers toward unplanned spending. The present work therefore tests how perceived attributes of AI recommendation engines relate to impulsive buying, treating fear of missing out (FOMO) simultaneously as an intervening variable and as a boundary condition. Survey evidence was gathered from 823 Vietnamese shoppers who regularly transact on AI-enabled marketplaces. Results show impulsive purchasing rising together with perceived AI authority, perceived task complexity, and with perceived personalization. FOMO carries a significant portion of each effect and, at the same time, amplifies its magnitude. These outcomes ground several managerial recommendations for platforms seeking to reconcile recommender deployment with responsible consumer conduct.

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