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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 601 Documents
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Car parking lot availability detection using the faster R-CNN method Andi Riansyah; Alif Hakim Al Faruq; Badieah Badieah
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1038-1046

Abstract

Parking demand continues to rise as private vehicle use increases, making timely information about available spaces essential for efficient parking management. Many existing monitoring approaches still rely on fixed slot sensors or visual detectors that report accuracy without examining how confidence settings affect the final availability decision. This work investigates Faster region-based convolutional network (Faster R-CNN) with a ResNet-50 backbone for image-based parking availability detection using a public parking-lot dataset annotated in Pascal visual object classes (VOC) format. The experiment evaluates several confidence thresholds to determine how each setting changes the balance among accuracy, precision, recall, and F1-score. The most balanced setting was obtained at a threshold of 0.5, where the model achieved 95% accuracy and 97.3% for precision, recall, and F1-score. These results show that threshold configuration is an important factor in reducing missed detections and false alarms, although validation using real campus CCTV data and direct comparison with lightweight detectors remain necessary before practical deployment.
Performance evaluation of a solar-driven IoT water quality monitoring system using descriptive and ANOVA analysis Suziana Ahmad; Arfah Ahmad; Muhammad Uwais Mohammad Raffee; Amirul Syafiq Sadun; Aminurrashid Noordin; Mohd Firdaus Mohd Ab Halim
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1385-1394

Abstract

Water quality monitoring is vital for protecting aquatic ecosystems and ensuring sustainable water resource management. Traditional manual sampling methods are often costly, time-consuming, and unsuitable for real-time assessment. This study presents a newly designed solar-powered IoT-based water quality monitoring system for remote and continuous data collection. The system utilizes an ESP32 microcontroller integrated with pH, temperature, and total dissolved solids (TDS) sensors, powered by a 10W solar panel. Data is transmitted to a cloud-based platform wirelessly, enabling remote access and visualization via a mobile app. Performance evaluation included descriptive statistics and one-way ANOVA across four sampling sites. ANOVA results showed statistically significant differences (p < 0.05) in water quality parameters among locations, confirming the system’s sensitivity. Sensor accuracy was validated against standard meters, revealing mean relative errors below 5% for pH and TDS. The system reliably provides real-time, accurate data, supporting proactive water quality management. Integrating IoT with renewable energy offers a cost-effective, scalable, and energy-efficient solution for environmental monitoring in remote or resource-limited areas.
Bridging the linguistic divide: recent developments in machine translation for Indian languages Jayanand A. Kamble; Shivajirao M. Jadhav; Vinod J. Kadam
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1272-1289

Abstract

Significant advances have been achieved in machine translation (MT) in recent times, particularly state of the art (SOTA) models for languages like English and Indian having distinct grammatical structures and limited monolingual training data. This paper analyses various recent state-of-the-art variants of large language models (LLMs) and neural machine translation (NMT) for Indian languages in comparison to statistical machine translation (SMT). It tackles key questions, such as idiomatic expressions, morphologically complex grammar or the scarceness of parallel corpora. Furthermore, it studies bytewise BPE, compares translation models in terms of BLEU scores using separate and shared-vocabulary representation with copy actions between the BPE translations, and analyses how multitask learning (Caruana (1997)) and attention mechanisms can contribute to the quality of translation. In summary, it provides directions for future work by suggesting new avenues of research including better curated datasets, more efficient approaches for lowresource languages and culturally aware translations.
A pilot review of the Google cluster workload trace 2019, methodology and its alternatives: analysis of workload in large scale data centres Akash Patel; Amit Nayak; Khushi Patel; Anand Patel
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1167-1178

Abstract

Cloud data centres require shared services that are highly available, elastic capacity, managed operations, and robust recovery capabilities to facilitate the next generation of efficient, reliable, and diverse connected computing environments. Nevertheless, large-scale cloud infrastructures continue to fail regularly despite their availability, scalability, and cost efficiency, primarily due to low resource utilisation and inadequate early-stage failure management. The key to effective resource management and minimizing failures in such settings is understanding the nature of the workload and its failure modes. The current review considers the Google cluster workload trace 2019 to investigate workload and failure patterns and to generalise the results of 24 articles. The analysis is also compared with other major datasets, such as Microsoft Azure Trace, Tencent Trace, and Alibaba Trace. The paper establishes the relevance of Google cluster traces, describes the key contents of the 2019 dataset, and contrasts prior literature with respect to research objectives, trace datasets, significant results, and limitations. Moreover, it briefly describes methods for analyzing and modeling cluster traces and identifies gaps in the research that should be addressed to advance the study of cluster traces.
Machine learning models for predicting daily profitability in Mauritanian digital banking operations: a case study Mohamed Lemine Sidibba; Mohamedou Cheikh Tourad; Ahmad Outfarouin; Nema Sidi Mohamed Mawloud; Mohamedade Farouk Nanne
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp925-934

Abstract

This article presents a case study on predicting daily profitability in Mauritanian digital banking operations using machine learning models. We utilized a dataset containing detailed information on daily operations to assess predictive models and predict profitability. The study assesses various machine learning models like logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron classifier (MLPClassifier), in order to identify the most precise model for predicting daily profitability. A systematic approach guides the analysis of banking transactions by performing detailed preprocessing operations which include type conversions, feature selection and missing value management. The dataset receives systematic partitioning into three parts for training, validation and testing to establish model reliability. LR had a recall rate of 99% and an F1-score of 99%, SVM had a recall rate of 53% and an F1-score of 54%, KNN had a recall rate of 96% and an F1-score of 95%, and MLPClassifier had a recall rate of 94% and an F1-score of 97%. The findings show that the LR model performed better than the other models in terms of both recall and F1-score. Future research will investigate both deep learning approaches and hybrid models to enhance prediction accuracy.
Smart road maintenance: real-time surface damage detection and mapping with YOLOv8 Preety Singh; Bommireddipalli Likhitha; Kolla Sahithi; Donthireddy Ganesh Reddy; Dammalapati Keerthana; Kolli Prasanna Adarsh
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1331-1339

Abstract

The degradation of road surfaces presents considerable obstacles for the management of urban infrastructure. This study presents a deep learning model based on YOLOv8 that can find many types of road faults, like potholes, longitudinal cracks, transverse cracks, and alligator cracks, using pictures, video streams, and live webcam feeds. The suggested system can find things with an accuracy of 91.2%, and the confidence levels range from 60% to 95%. Streamlit has been utilized to develop a web interface that makes it easier to use in real life. It makes it easy for users to choose inputs and provides outputs with notes and boundary boxes. GPS makes it possible to find problems with great accuracy, and a graphical dashboard presents damage categories and confidence levels in real time. Road maintenance is considerably more efficient with automated detection, location mapping, and easy-to-understand visualization. It is also easier to keep a check on smart municipal infrastructure. The results demonstrate that using computer vision and geospatial analytics together could make it easier to automatically check road conditions and make better decisions about how to run a city.
Attention-enhanced VGG-16 architecture for precision weed detection in wheat fields Akanksha Bodhale; Seema Verma; Aishwary Bodhale
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1303-1312

Abstract

This study delineates five advanced convolutional neural network designs that employ deep learning for the classification of wheat weeds. The dataset consists of greyscale photos taken in agricultural fields, enhanced with RGB lighting and cropped to 256×256×3 pixels. Normalization, batch-wise augmentation, contrast stretching, or histogram equalization were all part of the preprocessing that improved picture quality and model learning efficiency. These enhancements developed feature extraction by increasing picture contrast and homogeneity. A VGG16-based model with further Conv2D layers and spatial attention is one of the five models recommended. An additional design, inspired by ResNet50 that applies residual blocks and worldwide average pooling. A hybrid RNN integrated ICNA-CNN or LSTM for spatial-temporal content learning is another. Lastly, InceptionResNetV2 is enhanced with CNN layers and a query-key attention mechanism. We used the Adam optimizer and categorical cross-entropy to estimate the loss throughout the training for the round. At the end, all the models had ReLU activation, batch normalization, MaxPooling2D, and a dropout charge of 0.5 to keep them from overfitting. Upon evaluating their performance based on accuracy, recall, reliability, and training loss, VGG16 emerged as the standout model, achieving an impressive 99.07% precision and a remarkably low training loss of just 0.0079. The study determined that VGG16 is the optimal choice for precise wheat–weed categorization because to its superior generalizability and accuracy.
Deep reinforcement learning inspired optimization framework using Optuna for brain tumor detection Aashutosh Kharb; Prachi Chaudhary
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1352-1363

Abstract

Accurate brain tumor detection is essential for effective clinical diagnosis; however, the performance of deep learning models is highly sensitive to manually selected architectures and hyperparameters. To address this challenge, this paper presents a reinforcement learning–inspired automated optimization framework for brain tumor detection that eliminates manual trial-and-error tuning of hyperparameters. The proposed approach integrates EfficientNetB0 as a fixed feature extractor (base model) with an Optuna-based reinforcement learning strategy to jointly optimize the classifier architecture and key training hyperparameters, including learning rate, batch size, dropout rate, and network depth. Unlike existing studies that rely on static or heuristically tuned models, the proposed framework dynamically adapts model configurations based on validation feedback. Experiments conducted on the BraTS 2020 MRI dataset demonstrate that the optimized model achieves an accuracy of 92%, an F1-score of 92%, and a ROC–AUC of 0.96. Additional evaluations on imbalanced and cross-dataset settings show stable minority-class performance and good generalization. The results confirm that the proposed automated optimization framework offers a robust, scalable, and clinically relevant solution for brain tumor detection, representing a significant advancement over manually tuned deep learning approaches.
An AI-powered knowledge graph-based question answering system for Charak Samhita: integrating sanskrit NLP and graph data science Sharayu Mirasdar; Mangesh Bedekar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1197-1207

Abstract

Originating in India, Ayurveda is an ancient medical system focused on holistic healing that considers the mind, body, and spirit. This study utilizes knowledge graph (KG) technology to develop a KG model for an Ayurveda question-and-answer system. The system includes modules for knowledge extraction from चरकसंहिता, कायहचहकत्सा, भैषज्यरत्नावली and द्रव्यगुण संग्रि, construction of KG from this extracted knowledge and construction of AI supported Question answer system. In the methodology, domain-specific KG is constructed in Neo4j. Entities such as diseases (Vyadhi व्याधी), symptoms (Lakshana लक्षण), doshas (दोष), herbs, and treatments are incorporated. Advanced Sanskrit natural language processing (NLP) pipelines using ByT5-Sanskrit, SanskritBERT, and fine-tuned BioBERT facilitate named entity recognition (NER) and relation extraction. Graph-based reasoning models such as graph attention networks (GAT) and graph reasoning enhanced language models (GREASELM) enhance multi-hop reasoning across Ayurvedic concepts. Evaluation was conducted using a gold-standard annotated dataset of Charak Samhita verses mapped to disease–symptom–treatment relationships. Performance metrics included precision, recall, F1-score, mean reciprocal rank (MRR), and overlap coefficient. Superior accuracy can be seen in the proposed model as compared to baseline BERT-QA and subgraph QA approaches. This research has integrated Sanskrit computational linguistics and KG science. The approach mentioned in this paper has mentioned a framework that is scalable, interpretable and culturally significant. With the focus on Ayurveda, the methodology also mentions the potential for developing cross-cultural medical questions–answering systems, thereby bridging ancient wisdom with modern technological approaches.
Evaluation of text correction using a combination of Levenshtein distance and Trie algorithm Cynthia Natalie; Abba Suganda Girsang
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1340-1351

Abstract

Nowadays, technology is advancing with various applications, especially in text processing, such as news recommendations, sentiment analysis, automatic scoring, and language translation. In some cases, spelling errors often occur when inputting text for the translation process, necessitating text correction methods to display suggestions as a result. Therefore, the problem statement raised is about how to improve the accuracy of text correction and evaluate the translation quality at the word level after correcting input text in the context of translation from Indonesian to English. This research aims to develop and evaluate the combination of Levenshtein distance algorithm and Trie to correct input text and evaluate the translation quality at the word level after correcting text. There are various text correction methods, such as Hamming distance, Levenshtein distance, Damerau-Levenshtein distance, and N-Gram. Among several text correction methods. Levenshtein distance algorithm is commonly used to calculate the distance between texts and can be enhanced by using a Trie for more efficient computation in evaluating text correction. This research method resulted in an accuracy of 82.25% with an F1 score of 84.39%, where the developed text correction model produced a good translation using BLEU score with an increase of 1.55% after text correction.

Page 58 of 61 | Total Record : 601

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