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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 462 Documents
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Predictive insights into student online learning adaptability: elevating e-learning landscape El Ghali, Mohamed; Atouf, Issam; El Guemmat, Kamal; Talea, Mohamed
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp892-902

Abstract

In Morocco’s rapidly transforming educational landscape, this study delves into students’ adaptability to online learning environments by integrating sophisticated artificial intelligence (AI) algorithms and hyperparameter optimization techniques. This research uses the comprehensive “online learning adaptivity” dataset to identify pivotal factors influencing student flexibility and effectiveness in e-learning platforms. We applied various AI models, with a particular emphasis on the CatBoost classifier, which exhibited exceptional predictive performance, achieving an accuracy rate near 98%. This high precision in predicting student adaptiveness offers essential insights into tailoring digital education systems. The results underscore the significant potential of machine learning technologies to enhance educational methodologies by catering to the diverse needs of students. Such capabilities are instrumental for educators and policymakers dedicated to refining e-learning strategies that effectively accommodate individual learning styles, ultimately improving the broader educational outcomes in Moroccan tertiary education. These findings advocate for a more nuanced understanding of the interplay between student behavior and technological solutions, providing a roadmap for developing more responsive and effective educational platforms.
Multilingual hate speech detection using deep learning Vincent, Vincent; Zahra, Amalia
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1015-1023

Abstract

The rise of social media has enabled public expression but also fueled the spread of hate speech, contributing to social tensions and potential violence. Natural language processing (NLP), particularly text classification, has become essential for detecting hate speech. This study develops a hate speech detection model on Twitter using FastText with bidirectional long short-term memory (Bi-LSTM) and explores multilingual bidirectional encoder representations from transformers (M-BERT) for handling diverse languages. Data augmentation techniques-including easy data augmentation (EDA) methods, back translation, and generative adversarial networks (GANs)-are employed to enhance classification, especially for imbalanced datasets. Results show that data augmentation significantly boosts performance. The highest F1-scores are achieved by random insertion for Indonesian (F1-score: 0.889, Accuracy: 0.879), synonym replacement for English (F1-score: 0.872, Accuracy: 0.831), and random deletion for German (F1-score: 0.853, Accuracy: 0.830) with the FastText + Bi-LSTM model. The M-BERT model performs best with random deletion for Indonesian (F1-score: 0.898, Accuracy: 0.880), random swap for English (F1 score: 0.870, Accuracy: 0.866), and random deletion for German (F1-score: 0.662, Accuracy: 0.858). These findings underscore that data augmentation effectiveness varies by language and model. This research supports efforts to mitigate hate speech’s impact on social media by advancing multilingual detection capabilities.
A survey on ransomware detection using AI models Badrinath, Goteti; Gupta, Arpita
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1085-1094

Abstract

Data centers and cloud environments are compromised as they are at great risk from ransomware attacks, which attack data integrity and security. Through this survey, we explore how AI, especially machine learning and deep learning (DL), is being used to improve ransomware detection capabilities. It classifies ransomware types, highlights active groups such as Akira, and evaluates new DL techniques effective at real-time data analysis and encryption handling. Feature extraction, selection methods, and essential parameters for effective detection, including accuracy, precision, recall, F1-score and receiver operating characteristic (ROC) curve, are identified. The findings point to the state of the art and the state of the art in AI based ransomware detection and underscore the need for robust, real-time models and collaborative research. The statistical and graphical analyses help researchers and practitioners understand existing trends and directions for future development of efficient ransomware detection systems to strengthen cybersecurity in data centers and cloud infrastructures.
Solar-powered boost-fly back converter for efficient warehouse monitoring with flack droid Kavitha, S. Sivajothi; Usha, D.; Jamuna, V.
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp802-810

Abstract

Warehouses serve as essential infrastructure for storing a wide array of goods and are utilized by various entities. Implementing a sophisticated warehouse management system (WMS) represents a pinnacle of technological advancement. Effective warehouse maintenance is paramount, benefiting both consumers and producers alike. Typically, warehouses store items such as medicine, chemicals, food, and electronics, requiring controlled conditions of temperature and humidity. Monitoring these factors is essential to comply with regulations and maintain internal quality standards. This paper focuses on optimizing warehouse management to meet customer demands and streamline processes for packaging and production teams. Additionally, it proposes the integration of droid technology within warehouses to monitor the parameters and mitigate fire hazards, thereby enhancing the efficiency and safety of goods storage. This proactive approach not only ensures the integrity of stored products but also contributes to cost-saving measures within the warehouse. This paper introduces an innovative method to achieve a substantial increase in voltage output in a DC-DC converter while avoiding the need for excessively high duty ratios. The converter’s operation is governed by a single pulse width modulation (PWM) signal, employing a fractional-order proportional-integral-derivative controller (FOPID) for regulating the power switch. By merging boost-forward-fly back (BFF) converter topologies, the design achieves a remarkable voltage gain. Moreover, the converter efficiently recycles energy stored in the leakage inductance of the coupled inductor, thereby reducing voltage stress and minimizing power losses and thus enhancing overall converter efficiency.
Pneumonia detection system using convolutional neural network with DenseNet201 architecture Qomaruddin, Muhammad; Riansyah, Andi; Hermawan, Hildan Mulyo; Taufik, Moch
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1172-1178

Abstract

The diagnosis of pneumonia remains a significant challenge for medical practitioners worldwide, particularly in regions with limited healthcare resources. Traditional interpretation of chest X-rays is time-consuming and often subjective, especially when images are of low quality. This study presents the development of a web-based system utilizing the DenseNet201 architecture to address these challenges. A series of experiments were conducted to evaluate three optimizers Adam, Adamax, and Adadelta over fifty epochs. Among them, Adamax yielded the best performance, achieving a training accuracy of 93.67% and a validation accuracy of 94.20%. When tested on new data, the system consistently delivered high performance, with accuracy, precision, recall, and F1 score all reaching 96%. These results suggest that the proposed system has the potential to significantly enhance the accuracy and efficiency of pneumonia diagnosis based on chest X-rays.
Comparative study of traditional edge detection methods and phase congruency based method Patil, Rajendra Vasantrao; Gosavi, Vinodpuri Rampuri; Poddar, Govind Mohanlal; Swarnkar, Suman Kumar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp868-880

Abstract

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.
Navigating predictive landscapes of cloud burst prediction approaches: insights from comparative research Hingmire, Anil; Jadhav, Sunayana; Trivedi, Megha; Sankhe, Karan; Khanolkar, Omkar; Patil, Yukta
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1146-1155

Abstract

Cloud burst forecasting remains an evolving field that grapples with the complexities of atmospheric phenomena and their impact on local environments. Cloud bursts in hilly regions demand robust predictive models to mitigate risks. This study addresses the challenge of imbalanced cloud burst occurrences, emphasizing the need for accurate predictions to minimize damage. It develops and evaluates a machine learning-based forecasting approach that includes several weather factors such as temperature, humidity, wind speed, and atmospheric pressure. The study also tackles the imbalance in cloud burst data. A dual-axis chart visually merges cloud burst occurrences with weather parameters, providing insights into their relationships over time. The model’s overall accuracy is 0.68, with precision and recall for cloud burst events at 0.25 and 0.07, respectively, and an F1-score of 0.11. However, when it comes to forecasting non-cloud burst occurrences, it shows a high precision of 0.72. This study evaluates machine learning models for cloud burst prediction, highlighting random forest as the top performer with an accuracy of 85.43%, effectively balancing true positives and true negatives while minimizing misclassifications. This research contributes to cloud burst prediction, offering performance insights and suggesting avenues for future exploration.
Advancements in brain tumor classification: a survey of transfer learning techniques Jadhav, Snehal; Bharne, Smita; Narawade, Vaibhav
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1002-1014

Abstract

This survey article presents a critical review of the state-of-the-art transfer learning (TL) methodologies applied in the field of brain tumor classification, with a special emphasis on their various contributions and associated performance metrics. We will discuss various pre-processing approaches, the underlying fine-tuning strategies, whether used purely or in an end-to-end training manner, and multi-modal applications. The current study specifically highlights the application of VGG16 and residual network (ResNet) methods for feature extraction, demonstrating that leveraging highorder features in magnetic resonance imaging (MRI) images can enhance accuracy while reducing training. We further analyze fine-tuning methods in relation to their role in optimizing model layers for small, domain-specific datasets, finding them particularly effective in enhancing performance on the small brain tumor dataset. It will look into end-to-end training, which means fine-tuning models that have already been trained on large datasets to make them better. It will also present multimodal TL as a way to use both MRI and computed tomography (CT) scan data to get better classification results. Comparing different pre-trained models can provide a better understanding of the strengths and weaknesses associated with the particular brain tumor classification task. This review aims to analyze the advancements in TL for medical image analysis and explore potential avenues for future research and development in this crucial field of medical diagnostics.
Digital control of plant development through sensors and microcontrollers in Kosova Mustafa, Ragmi M.; Mustafa, Kujtim R.; Ramadani, Refik
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1072-1084

Abstract

The plant monitoring system aims to develop an automated solution for optimizing plant growth. Using the Arduino Uno ATMEGA328P microcontroller module and various sensors, this system regulates environmental conditions to promote optimal plant development. It requires adequate software to operate effectively, enabling the microcontroller to monitor and regulate climatic conditions. The primary goal of this paper is to present a comprehensive system that continuously measures parameters such as light intensity, air humidity, and soil moisture in real time within a vegetable greenhouse or a plastic-covered plant environment. This scientific paper provides an in-depth description of the hardware components used, their electronic connections, and the implementation of program code written in C++. Based on the measured physical parameters, the plant monitoring system performs specific actions, such as watering the plants and regulating the ambient temperature. In conclusion, this system effectively supports healthy plant growth and enhances the quality and yield of plant products. The paper serves as a practical example for improving plant cultivation in the agricultural sector in the Republic of Kosova.
Design of miniaturized dual-band bandpass filter with enhanced selectivity for GPS and RFID applications Mahammed Basha, Thupalli Shaik; Raaza, Arun; Bhujbal, Vishakha; Mathivanan, Meena
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp993-1001

Abstract

This article presents a miniaturized interdigital coupled dual-band bandpass filter with multiple transmission zeros/poles. Stepped impedance resonators, interdigital coupled lines, and series coupled lines make up the proposed filter design. A circuit simulator is used to analyze a proposed filter, and the magnitude and bandwidth shifts have been investigated. To confirm the proposed filter design, equations for transmission zero frequencies have been constructed and verified based on even-odd mode analysis and lossless transmission line theory. A working prototype for 2.2 GHz (RFID) and 1.38 GHz (GPS) applications is made and tested. With λg representing the guided wavelength at the first band (1.38GHz), the finished prototype is compact, measuring 0.32 λg×0.27 λg. According to the experimental findings, there is strong selectivity in the first and second passbands, with roll-off rates of 190 and 168 dB/GHz, respectively. Good isolation between the two passbands is indicated by an insertion loss of less than 20 dB.

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