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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 35 Documents
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Search results for , issue "Vol 14, No 1: April 2025" : 35 Documents clear
Explainable zero-shot learning and transfer learning for real time Indian healthcare Saigaonkar, Swati; Narawade, Vaibhav
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp91-101

Abstract

Clinical note research is globally recognized, but work on real-time data, particularly from India, is still lagging. This study initiated by training models on medical information mart for intensive care (MIMIC) clinical notes, focusing on conditions like chronic kidney disease (CKD), myocardial infarction (MI), and asthma using the structured medical domain bidirectional encoder representations from transformers (SMDBERT) model. Subsequently, these models were applied to an Indian dataset obtained from two hospitals. The key difference between publicly available datasets and real-time data lies in the prevalence of certain diseases. For example, in a real-time setting, tuberculosis may exist, but the MIMIC dataset lacks corresponding clinical notes. Thus, an innovative approach was developed by combining a fine-tuned SMDBERT model with a customized zero-shot learning method to effectively analyze tuberculosis-related clinical notes. Another research gap is the lack of explainability because deep learning (DL) models are inherently black-box. To further strengthen the reliability of the models, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) explanations were projected along with narrative explanations which generated explanations in a natural language format. Thus, the research provides a significant contribution with ensemble technique of zero-shot learning and SMDBERT model with an accuracy of 0.92 as against the specialized models like scientific BERT (SCIBERT), biomedical BERT (BIOBERT) and clinical BioBERT.
Data analysis and visualization on titanic and student’s performance datasets-an exploratory study Kim, Seong-Cheol; Salkuti, Surender Reddy; Suresh, Alka Manvayalar; Sankaran, Madhu Sree
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp68-76

Abstract

Exploratory data analysis (EDA) is all about exploring the data in order to identify any underlying pattern before you try to use it to make a predictive model. It also plays a major role in the data discovery process as it is used to analyze data and to recapitulate their different characteristics, which is displayed efficiently with the help of data visualization methods. This paper aims to identify errors in the dataset, to understand the existing hidden structure and to identify new ones, to detect points in a dataset that deviate to a greater extent from the collected data (outliers), and also to find any relationship or intersection between the variables and constants. Two datasets are used namely ‘Titanic’ and ‘student’s performance’ to perform data analysis and ‘data visualization’ to depict ‘exploratory data analysis’ which acts as an important set of tools for recognizing a qualitative understanding. The datasets were explored and hence it assisted with identifying patterns, outliers, corrupt data, and discovering the relationship between the fields in the dataset.
An model for structured the NoSQL databases based on machine learning classifiers Benmakhlouf, Amine
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp229-239

Abstract

Today, the majority of data generated and processed in organizations is unstructured. NoSQL database management systems perform the management of this data. The problem is that these unstructured databases cannot be analyzed by traditional OLAP analytical treatments. The latter are mainly used in structured relational databases. In order to apply OLAP analyses on NoSQL data, the structuring of this data is essential. In this paper, we propose a model for structuring the data of a document-oriented NoSQL database using machine learning (ML). This method is broken down into three steps, first the vectorization of documents, then the learning via different ML algorithms and finally the classification, which guarantees that documents with the same structure will belong to the same collection. Therefore, the modeling of a data warehouse can be carried out in order to create OLAP cubes. Since the models found by learning allow the parallel computation of the classifier, our approach represents an advantage in terms of speed since we will avoid doubly iterative algorithms, which rely on textual comparisons (TC). A comparative study of the performances is carried out in this work in order to detect the most efficient methods to perform this type of classification.
Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis Khan, Niaz Ashraf; Bin Hafiz, Md. Ferdous; Pramanik, Md. Aktaruzzaman
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp11-19

Abstract

Predictive modelling plays a crucial role in healthcare, particularly in forecasting mortality due to heart failure. This study focuses on enhancing predictive modelling and interpretability in heart failure prediction through advanced boosting algorithms, ensemble methods, and SHapley Additive exPlanations (SHAP) analysis. Leveraging a dataset of patients diagnosed with cardiovascular diseases (CVD), we employed techniques such as synthetic minority over-sampling technique (SMOTE) and bootstrapping to address class imbalance. Our results demonstrated exceptional predictive performance, with the gradient boosting (GBoost) model achieving the highest accuracy of 91.39%. Ensemble techniques further enhanced performance, with the voting classifier (VC), stacking classifier (SC), and Blending achieving accuracies of 91.00%. SHAP analysis uncovered key features such as time, Serum_creatinine, and Ejection_fraction, significantly impacting mortality prediction. These findings highlight the importance of transparent and interpretable machine learning models in healthcare decision-making processes, facilitating informed interventions and personalized treatment strategies for heart failure patients.
The integration of discrete contourlet transform in OFDM framework for future wireless communication Mohamed Nerma, Mohamed Hussien; Ahmed Abdo, Adam Mohamed
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp182-194

Abstract

In the upcoming era, the forthcoming sixth-generation (6G) wireless communication network will demand highly efficient technology to support extensive capacity, ultra-high speeds, low latency, scalability, and adaptability. While the current fifth-generation (5G) wireless communication system relies on OFDM technology, the evolution towards a beyond 5G wireless communication system necessitates a new OFDM framework. This study introduces a novel OFDM system that integrates the discrete Contourlet transform. A comparative analysis has been conducted among the proposed system, conventional OFDM, and curvelet-based OFDM systems. The results indicate that the proposed system offers improvements in bit error rate (BER), reduced computational complexity, decreased peak-to-average power ratio (PAPR), and enhanced power spectrum density (PSD) when contrasted with both the traditional and curvelet-based systems.
Planar hexagonal patch multiple input multiple output 4x4 antenna for UWB applications Nasrul, Nasrul; Firdaus, Firdaus; Zahra, Nurraudya Tuz; Rachmawati, Maulidya
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp174-181

Abstract

The combination of Multiple Input Multiple Output (MIMO) antennas and Ultra-Wideband (UWB) technology offers several advantages, including reduced interference, improved isolation, and optimized dual paths. These benefits extend the range and enhance signal quality. However, designing UWB-MIMO antennas presents challenges, such as achieving low mutual coupling for high isolation and creating small-sized antennas suitable for portable devices while being effective for UWB frequencies in a MIMO configuration. The proposed antenna is a 4x4 planar MIMO antenna with a hexagon-shaped patch, a partial ground plane featuring an inverted L-stub on the left side, and a plus-shaped slot in the centre ground. It has dimensions of 32 x 32 x 1.6 mm³ and is capable of achieving a wide bandwidth of 3-12.5 GHz. The antenna's performance measurements are impressive: return loss below -10 dB at frequencies of 3-12.5 GHz, mutual coupling below -16.5 dB, Envelope Correlation Coefficient (ECC) bellow 0.005, Diversity gain of more than 9.97, Total Active Reflection Coefficient (TARC) below -10 dB. Based on these results, the proposed antenna offers excellent performance for UWB applications, featuring high efficiency, minimal interference between antenna elements, and optimal diversity performance.
Assessing the user experience of marker-based 3D WebAR applications using user experience questionnaire Tuah, Nooralisa Mohd; Wan Ahmad, Wan Nooraishya; Andrias, Ryan MacDonell; Ajor, Dg. Senandong; Sura, Suaini; Ahmad Rodzuan, Ahmad Rizal
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp31-41

Abstract

Marker-based 3D web-based augmented reality (WebAR) applications are an emerging field that merges web technologies with augmented reality. WebAR has gained popularity because of its ability to provide users with a reliable and autonomous platform. Yet, a limited investigation has verified its application and user perspective on its ability to function. This study is designed to evaluate the user experiences of marker-based 3D WebAR applications using the user experience questionnaire (UEQ). This study assesses various elements of the user experience, including attractiveness, clarity, engagement, efficiency, and innovation, utilizing the UEQ. This study aims to analyze user perceptions and interaction patterns thoroughly to get useful insights into the usability and user satisfaction aspects of marker-based 3D WebAR apps. The findings reveal that the WebAR app is both appealing and efficient, instilling confidence in its users. This underscores the pivotal role of user experience in shaping the effectiveness and reception of WebAR applications. This research has the potential to influence the creation of more user-focused and engaging marker-based 3D WebAR experiences, improving user engagement and immersion in web-based augmented reality environments.
Finite state machine for retro arcade fighting game development Firdaus, Muhammad Bambang; Waksito, Alan Zulfikar; Tejawati, Andi; Taruk, Medi; Anam, M. Khairul; Irsyad, Akhmad
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp102-110

Abstract

Traditional fighting games are a competitive genre where players engage in one-on-one combat, aiming to reduce their opponent's health points to zero. These games often utilize two-dimensional (2D) graphics, enabling players to execute various character movements such as punching, jumping, and crouching. This research investigates the effectiveness of the finite state machine (FSM) method in developing a combo system for a retro fighting game, focusing on its implementation within the Godot Engine. The FSM method, which structures game behavior through states, events, and actions, is central to the game's control system. By employing the game development life cycle (GDLC) methodology, this study ensures a systematic and structured approach to game design. Special attention is given to the regulation of the combo hit system for the game's protagonist in Brawl Tale. The research culminates in the successful development of the retro fighting game Brawl Tale, demonstrating that the FSM method significantly enhances the fluidity and responsiveness of character movements. The findings suggest that the FSM method is an effective tool for simplifying and improving gameplay mechanics in retro-style fighting games.
Teaching learning based optimization algorithm for effective analysis of power quality using dynamic voltage restorer Das, Soumya Ranjan; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp268-275

Abstract

In this study, the load voltage is dynamically restored utilising the dynamic voltage restorer (DVR) using the voltage injection approach. The injected voltage is generated using a voltage-source inverter (VSI), which is necessary to correct for the utility network's sag and swell characteristics voltage. The restoration process is dependent on the condition and quality of the utility system, i.e., it injects energy into the external system for the duration of voltage sag, and during voltage swell, energy is absorbed by the compensator from the external system, causing an rise in dc link voltage, which is connected across the VSI. In this study two different controllers are employed based on a learning based optimized algorithm. The simulation results are shown using two different controllers and the performance of the proposed controller is found to be a better one.
A hybrid approach of pattern recognition to detect marine animals Balachandran, Vijayalakshmi; Shanmugavel, Thanga Ramya; Kadarkarayandi, Ramar; Kandhasamy, Vijayalakshmi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp240-249

Abstract

Acquiring up-to-date knowledge about various animals will have a significant impact on effectively managing species within the ecosystem. Manually identifying animals and their traits continues to be a costly and time-consuming process. The development of a system using the most recent developments in computer vision machine learning was necessary to address the issues of detecting sharks and aquatic species in areas filled with surfers, rocks, and various other potential false positives. In the ocean most of the species are cold-blooded animals hence they cannot be tracked with thermal cameras. Ocean’s dynamic environment affects simple techniques like color separation, intensity histograms, and optical flow. Hence a hybrid approach using convolutional neural network - support vector machine (CNN-SVM) classifier is proposed to perform the pattern recognition. A CNN is employed for feature extraction by using the histogram of gradients value. Subsequently, a SVM classifier is employed to identify and categorise marine species in the vicinity of the seacoast. This serves to notify individuals who engage in swimming activities in the ocean. The suggested model is evaluated against alternative machine learning approaches, and it achieves a superior accuracy of 95% compared to the others.

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