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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 44 Documents
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Search results for , issue "Vol 15, No 1: March 2026" : 44 Documents clear
Self-adaptive firefly algorithm-based capacitor banks and distributed generation allocation in hybrid networks Kim, Seong-Cheol; Pagidipala, Sravanthi; Reddy Salkuti, Surender
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp374-383

Abstract

Power system deregulation has made significant changes to the power grid through various technologies, privatization of entities, and improved efficiency and reliability. This work mainly focuses on different combinations of distributed generation (DG) and capacitor banks (CBs) integration to cater to multiple technical, economic, environmental, and reliable concerns. A new optimal planning framework is proposed for optimally allocating the DG units and CBs to achieve multiple objectives. In this work, an augmented objective function is formulated by considering active power losses, voltage deviation, and voltage stability index objectives. This objective function is solved considering various equality and inequality constraints. This work proposes a novel approach for allocation of DGs and CBs in the radial distribution systems (RDSs) using an evolutionary-based self-adaptive firefly algorithm (SAFA). The effectiveness of the developed planning approach is demonstrated on IEEE 33 bus RDS in MATLAB software. The obtained results indicate that proposed planning approach resulted in reduced power losses, voltage deviations, and improved voltage stability.
Analysis of congestion management using generation rescheduling with augmented Mountain Gazelle optimizer Natarajan, Chidambararaj; Karunanithy, Aravindhan; Jothika, S.; Linda Joice, R. P.
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp57-65

Abstract

This study presents an original blockage of the executive’s approach utilizing age rescheduling with the augmented mountain gazelle optimizer (AMGO). Enlivened by the versatility of mountain gazelles, AMGO is applied to enhance age plans for a reasonable power framework situation. The strategy successfully mitigates clogs, taking into account functional imperatives, market elements, and vulnerabilities. Recreation results show AMGO’s heartiness, seriousness, and proficiency in contrast with existing strategies. Notwithstanding its heartiness in blockage the board, the AMGO presents a state-of-the-art versatile element, enlivened by the spryness of mountain gazelles, empowering constant changes in accordance with developing power framework conditions and contrasted and genetic algorithms and PSO. The review adds to propelling streamlining methods for clogging the executives, offering a promising device for improving power framework, unwavering quality and productivity.
High gain multi-layered microstrip patch antenna for x- band applications Nageswara Rao, Jada; Ramana Reddy, Ragipindi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp343-355

Abstract

This research investigates the development of a multi-stacked microstrip antenna featuring two patch elements positioned in a layered configuration. The antenna design incorporates three substrates with different dielectric constants, separated by an air gap, to evaluate their impact on improving bandwidth and gain. The primary objective of this research is to enhance the efficiency of a microstrip patch antenna by utilising a multilayer substrate structure. Simulation results indicate that stacking substrates with varying dielectric properties significantly enhances antenna performance. The bandwidth increases considerably, from 1.38 GHz to 2.37 GHz, while the peak gain improves from 6.6 dBi to 7.9 dBi. These advancements highlight the antenna's effectiveness in operating within the X-band frequency range, making it suitable for wireless and satellite communication systems. The design and its performance were analysed using high-frequency structure simulator (HFSS) simulation software, which validated its practical feasibility. This innovative configuration addresses the bandwidth limitations typically associated with conventional microstrip antennas, ensuring improved operational efficiency for modern communication technologies. The findings highlight the benefits of utilising a multi-stacked structure to achieve superior antenna performance, particularly in advanced communication applications.
Video-based physical violence detection model for efficient public space surveillance Erick, Erick; Soewito, Benfano
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp161-170

Abstract

This study aims to develop an effective real-time model for detecting violence in public spaces, focusing on achieving a balance between accuracy and computational efficiency. We evaluate various model architectures, with the main comparison between the ConvLSTM2D and Conv3D models commonly used in video analysis to capture spatial and temporal features. The ConvLSTM2D model, combined with preprocessing layers such as change detection and motion blur, showed optimal performance, achieving 86% accuracy after Bayesian optimization. With a low parameter count of 25,137, this model enables fast inference in just 0.010 seconds, making it suitable for real-time applications that require efficient computation. In contrast, the Conv3D model, which is also combined with preprocessing layers such as change detection and motion blur and has more than nine million parameters, shows a lower accuracy of 77.5% as well as a slower inference time of 0.025 seconds, making it unsuitable for real-time applications. The results of this study show that the ConvLSTM2D model is promising for real-time violence detection systems in public spaces, where a fast and accurate response is essential to prevent further acts of violence.
Neurophysiological impact of Vedic chanting on human brainwaves: a spectral electroencephalogram analysis using Gabor transform Nalluri, Veera Raghava; K. Kishor Sonti, V. J.
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp276-286

Abstract

Electroencephalogram (EEG) analysis explores brainwave changes resulting from Vedic chanting (VC) in this experimental study. In this study participants received Vedic recitations from the Rig Veda (RV), Yajur Veda (YV), Sama Veda (SV), and Atharva Veda (AV) which were evaluated through alpha wave (8-12 Hz) measurement to evaluate relaxation response effects known to cause cognitive relaxation and mindfulness. The research captured EEG signals from twenty participants who belonged to four age categories between twenty and fifty years using a fourteen-channel EEG recording system. The signals underwent wavelet-based denoising procedures and Gabor transform (GT) enabled their spectral analysis. Scientists calculated the relaxation factor (RF) for understanding Vedic chant effects on human beings. Vedic Sama provided maximum relaxation effects leading to a 25% RF enhancement whereas YV produced a 20% increase and RV generated 15% enhancement and AV yielded 10% relaxation. The participants between 30 and 45 years old experienced the largest relaxation effects yet their left-brain hemisphere enhanced alpha waves stronger than their right brain region. The statistical methods supported that these results showed meaningful variations. Neural relaxation results from VC practice according to research evidence which shows SV provides the most powerful relaxation effects.
Adaptive intrusion detection system with DBSCAN to enhance banking cybersecurity Periyasamy, Sathiyaseelan; Kumar, Anubhav; Muthulakshmi, Karupusamy; Elumalai, Thenmozhi; Kaliyaperumal, Prabu; Perumal, Rajakumar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp247-256

Abstract

The accelerating pace of digital transformation in the banking sector has highlighted the critical need for comprehensive cybersecurity strategies capable of countering evolving cyber threats. This study introduces an innovative intrusion detection framework tailored for banking environments, leveraging the CICIDS2017 and CSECICIDS2018 datasets for evaluation and validation. The proposed framework integrates data preprocessing, feature reduction, and advanced attack detection methods to enhance detection accuracy. A basic autoencoder is utilized for dimensionality reduction, streamlining input data while preserving essential attributes. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is then applied for attack detection, enabling the detection of intricate attack patterns and their classification into specific attack groups. The proposed adaptive intrusion detection system (IDS) framework demonstrates outstanding performance, achieving precision, recall, F1-score, and accuracy rates exceeding 98%. Comparative evaluations against conventional techniques, such as support vector machines (SVM), long short-term memory (LSTM), and K-means, highlight its superiority in terms accuracy and computational efficiency. This research address key challenges, including high-dimensional datasets, class imbalance, and dynamic threat landscapes, offering a scalable and efficient solution to enhance the security of banking operations and enable proactive threat mitigation in the sector.
Integrating smart technologies for sustainable crop management in hydroponics N., Jeyaprakash; M., Jayachandran; T., Poornavikash
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp39-45

Abstract

Hydroponics has become a game-changing technique in agriculture's constantly changing terrain, upending traditional soil-based farming. The smart hydroponics management system, a cutting-edge method intended to maximize plant development and resource use, is presented in this study. The approach aims to push the limits of conventional farming, drawing inspiration from sustainable horticultural concepts as well as the principles described in Howard M. Resh's book on hydroponic production. This abstract integrates cuttingedge sensor technology and automation methodologies to capture the core of the smart hydroponics management system. It presents the system as a complete answer to the problems facing modern agriculture, rather than just a technique of cultivation. By drawing comparisons with seminal works in computer vision, the unique character of the system is highlighted, demonstrating a dedication to advanced and flexible agricultural techniques.
An integration clustering and multi-target classification approach to explore employability and career linearity Intan Ghayatrie, Nadzla Andrita; Fitrianah, Devi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp189-197

Abstract

This study analyzes job placement waiting times and job linearity among female science, technology, engineering, and mathematics (STEM) graduates using clustering and multi-target classification (MTC) models. The K-means least trimmed square (LTS) algorithm, known for its robustness against outliers, was employed for clustering. With k = 2 and a trimming percentage of 30%, the model achieved a silhouette score of 77%, resulting in two distinct clusters: ideal and non-ideal. To enhance the dataset for classification, synthetic data was generated using the adaptive synthetic (ADASYN)-gaussian method. Principal component analysis (PCA) was used for visualization purposes, along with overlapping histograms, to illustrate that the synthetic data distribution closely resembled the original. For classification, a random forest (RF) model was used to predict both jobs waiting time and job linearity. Hyperparameter tuning produced an optimal model with a classification accuracy of 92%. Cross-validation (CV) confirmed the model’s robustness, with F1-micro and F1-macro scores of 94% and 93%, respectively. Results show that although women in STEM are underrepresented, 73% of the female alumni analyzed belonged to the short job waiting group. Furthermore, a strong negative correlation between GPA and job waiting time suggests that higher-GPA graduates tend to secure employment more quickly.
DeepRetina: a multimodal framework for early diabetic retinopathy detection and progression prediction Ramasamy, Sunder; Mohanraj, Brindha; Pushpanathan, Sridhar; Elumalai, Thenmozhi; Kaliyaperumal, Prabu; Perumal, Rajakumar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp152-160

Abstract

Diabetic retinopathy (DR) remains one of the top causes of vision loss globally, and early detection and accurate progression prediction are critical in its management. This paper introduces DeepRetina, a deep learning framework that integrates state-of-the-art multimodal retinal imaging techniques with patient-specific clinical data for the improved diagnosis and prognosis of DR. DeepRetina harnesses cutting-edge convolutional neural networks (CNNs) and attention mechanisms to jointly analyze optical coherence tomography (OCT) scans and fundus photographs. The architecture further includes a temporal module that investigates the longitudinal changes in the retina. DeepRetina fuses these heterogeneous data sources with patient clinical information in pursuit of early detection of DR and provides personalized predictions for the progression of the disease. We use a specially designed CNN architecture to process high-resolution retinal images, coupled with a self-attention mechanism that focuses on the most relevant features. This recurrent neural network (RNN) module empowers it to integrate time-series data that captures the evolution of retinal abnormalities. Another neural network branch considering patientspecific clinical data, such as demographic information, medical history, and laboratory test results, was taken into account and concatenated with the imaging features for a holistic analysis. DeepRetina achieved 95% sensitivity, 98% specificity for early DR detection, and a 0.92 area under the curve (AUC) for 5-year progression prediction, outperforming existing methods.
Optimizing solar energy forecasting and site adjustment with machine learning techniques Prasad Mishra, Debani; Kumar Sahu, Jayanta; Ranjan Nayak, Soubhagya; Panda, Anurag; Paramjit Dash, Priyanshu; Reddy Salkuti, Surender
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp384-392

Abstract

Estimation of solar radiation is a key task in optimizing the operation of power systems incorporating high levels of photovoltaic (PV) generation. This paper discusses the application of machine learning techniques, namely extreme gradient boosting (XGBT) and random forest (RF), to improve accuracy in the forecasting of solar radiation while adapting for different sites. Utilizing datasets such as meteorological and solar radiation data, the suggested models demonstrate the enhancement of forecasting accuracy by 39% from traditionally applied statistical practices. Along with this, this study also encompasses how endogenous and exogenous factors could be involved in better predictions of solar energy availability. From our findings, XGBT, as well as other machine learning techniques, do enjoy superior performance levels when it comes to the forecasting of solar radiation, which in turn promotes efficient management and potential adaptation of solar energy systems. This study demonstrates how this last generation of algorithms could be applied to noticeably improve the efficiency of solar power forecasting and thereby contribute to more sustainable and reliable energy systems as a byproduct of that.

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