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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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Ensemble approach to rumor detection with BERT, GPT, and POS features Pattanaik, Barsha; Mandal, Sourav; Tripathy, Rudra Mohan; Sekh, Arif Ahmed
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.pp276-286

Abstract

As vast amounts of rumor content are transmitted on social media, it is very challenging to detect them. This study explores an ensemble approach to rumor detection in social media messages, leveraging the strengths of advanced natural language processing (NLP) models. Specifically, we implemented three distinct models: (i) generative pre-trained transformer (GPT) combined with a bidirectional long short-term memory (BiLSTM) network; (ii) a model integrating part-of-speech (POS) tagging with bidirectional encoder representations from transformers (BERT) and BiLSTM, and (iii) a model that merges POS tagging with GPT and BiLSTM. We included additional features from the dataset in all these models. Each model captures different linguistic, syntactical, and contextual features within the text, contributing uniquely to the classification task. To enhance the robustness and accuracy of our predictions, we employed an ensemble method using hard voting. This technique aggregates the predictions from each model, determining the final classification based on the majority vote. Our experimental results demonstrate that the ensemble approach significantly outperforms individual models, achieving superior accuracy in identifying rumors. To determine the performance of our model, we used PHEME and Weibo datasets available publicly. We found our model gave 97.6% and 98.4% accuracy, respectively, on the datasets and has outperformed the state-of-the-art models.
Optimizing warehouse management system with blockchain and machine learning predictive data analytics Hande, Kapil N.; Chandak, Manoj B.
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp362-369

Abstract

Blockchain technology is proving to be a disruptive technology in many areas of supply chain, manufacturing, medical, agriculture, and so on. Warehouses are an inevitable part of the supply chain. Issues like space optimization, route optimization, quick item pick-up, demand forecasting, and transaction management are of importance to address in warehouse management systems (WMS). Traditional database systems have limitations of interoperability among different entities involved in warehouses. This paper presents an innovative application of blockchain technology and machine learning (ML) to build a smart warehouse management system in Web3 (SWMW3). We developed a decentralized application (DApp) using Web3.0 principles, integrating ReactJS for the frontend, express for the backend, and blockchain through smart contracts. This integration enhances security and transparency by storing WMS operational data in the blockchain and automating payments and verifications through smart contracts. Additionally, we implemented a ML model for predicting the total time from order receipt to delivery, leveraging historical data to optimize workflow, reduce delays, and improve overall efficiency. This combination of blockchain for secure transactions and ML for predictive analytics generates a robust, efficient, and optimized management system for the warehouse.
Automatic vehicle accident detection and alerting notification using internet of things Savani, Vijay; Pandya, Viranchi; Senghani, Dhairya; Nahta, Shreya; Agrawal, Risheen
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.pp315-324

Abstract

Immigrants in developing countries have indirectly encouraged increased automobile use, leading to a strong association between automobile accidents and their victims. However, recent technological developments, especially artificial intelligence and electronics, seem promising in overcoming these risks. This research paper focuses on complex systems developed using internet of things (IoT) technology. The system integrates various components such as micro controller, radio frequency identification (RFID) card reader for license validation, liquid crystal display (LCD), Ultrasonic sensor for interference, measuring device and global positioning system (GPS) unit. Additionally, the system has a simple mail transfer protocol (SMTP) server that can send timely email alerts to emergency responds and log email addresses for real-time emergency detection. This facilitates rapid response and emergency rescue, thereby reduces the risk of accidents and increases overall safety.
Transformer-based abstractive indonesian text summarization Aurelia, Miracle; Monica, Sheila; Girsang, Abba Suganda
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp388-399

Abstract

The volume of data created, captured, copied, and consumed worldwide has increased from 2 zettabytes in 2010 to over 97 zettabytes in 2020, with an estimation of 181 zettabytes in 2025. Automatic text summarization (ATS) will ease giving points of information and will increase efficiency at the time consumed to understand the information. Therefore, improving ATS performance in summarizing news articles is the goal of this paper. This work will fine-tune the BART model using IndoSum, Liputan6, and Liputan6 augmented dataset for abstractive summarization. Data augmentation for Liputan6 will be augmented with the ChatGPT method. This work will also use r ecall-oriented understudy of gisting evaluation (ROUGE) as an evaluation metric. The data augmentation with ChatGPT used 10% of the clean news article from the Liputan6 training dataset and ChatGPT generated the abstractive summary based on that input, culminating in over 36 thousand data for the model’s fine-tuning. BART model that was finetuned using Indosum, Liputan6, and augmented Liputan6 dataset has the best ROUGE-2 score, outperforming ORACLE’s model although ORACLE still has the best ROUGE-1 and ROUGE-L score. This concludes that fine-tuning the BART model with multiple datasets will increase the performance of the model to do abstractive summarization tasks.
Enhancing credit card security using RSA encryption and tokenization: a multi-module approach Saha, Mainak; Basu, M. Trinath; Gupta, Arpita; Ashrith, K.; Vardhan Reddy, Chevella Vamshi; Reddy, Shashanth; Reddy, Rohith
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.pp132-140

Abstract

The security of credit card information remains a critical challenge, with existing methods often falling short in safeguarding data integrity, confidentiality, and privacy. Traditional approaches frequently transmit sensitive information in unencrypted formats, exposing it to significant risks of unauthorized access and breaches. This study introduces a robust security framework that leverages Rivest-Shamir-Adleman (RSA) encryption and tokenization to protect credit card information during transactions. The proposed solution is structured into three key modules: merchant, tokenization, and token vault. The merchant module works in tandem with the tokenization module to generate transaction validation tokens and securely transmit credit card data. The token vault, maintained on a secure cloud storage platform, acts as a restricted-access database, ensuring that sensitive information is encrypted and inaccessible to unauthorized entities. Through this multi-layered approach, the study demonstrates a significant enhancement in the security of credit card transactions, effectively mitigating the risks of data breaches and unauthorized disclosures. The findings indicate that the proposed method not only addresses existing security vulnerabilities but also offers a scalable and efficient solution for protecting financial transactions.
Advanced optimization load frequency control for multi - islanded micro grid system with tie-line loading by using PSO Pavan, Gollapudi; Babu, A. Ramesh; Prabhakar, Bollu; Venkata Sai, T. Datta; Rajeshwari, M.; Reddy, N. Raj; Kishore, P. Venkata
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.pp298-306

Abstract

This manuscript presents the design of a microgrid featuring solar and wind as uncontrollable energy sources, alongside controllable sources like batteries and a diesel generator, aiming to address power supply variations resulting from load fluctuations. Controllers are imperative to mitigate these challenges, and the manuscript emphasizes the need for precise tuning of gain values for optimal electrical energy utilization. In lieu of the trial-and-error approach, particle swarm optimization (PSO) is employed for enhanced steady-state response in the Microgrid. The study also introduces the application of proportional-integral (PI), proportional-integral-derivative (PID), and PID with feed forward (PIDF) controllers to effectively address and resolve identified issues ensuring improved system performance and consistent power supply stability in the microgrid system.
Implementing gamification in campus canteen using MDA framework: an overview Wibowo, Florentia Novena; Wang, Gunawan
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp422-427

Abstract

This study describes the creation of a mobile-based gamification design for an online canteen system. Long lunch queues make students feel uncomfortable ordering food in the canteen, although student comfort is very important. The increasing number of students causes long queues, resulting in significantly shorter lunch hours and causing discomfort for students. To address this issue, the campus might develop an online canteen using gamification. Gamification is a method that applies gaming knowledge to create experiences that encourage and engage people in non-game environment. Compared to traditional canteen systems, an online canteen that uses gamification can provide students with new experiences by offering attractive rewards, increasing motivation to order food and beverages online, and minimizing the perception of long lineups. Although this proposed design has not yet been tested, researchers believe it has practical applications.
Strategic Deployment of EV Charging Infrastructure: An In-Depth Exploration of Optimal Location Selection and CC-CV Charging Strategies Mishra, Debani Prasad; Nayak, Pranav Swaroop; Kumar, Aman; 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.pp259-267

Abstract

The continued expansion of the electric vehicle (EV) market necessitates strategic planning for the placement of charging stations to ensure efficient access and utilization of electric infrastructure. This paper presents a comprehensive review of the critical factors in optimizing the selection of EV charging station locations, along with the implementation of Constant Current-Constant Voltage (CC-CV) charging models. The study addresses the challenges and opportunities in identifying the most effective locations for charging stations to accommodate the growing demand for sustainable transportation. Furthermore, it examines the benefits of adopting CC-CV charging models to improve the charging process, achieving a balance between charging speed and battery longevity. Through this analysis, the review aims to provide valuable insights to stakeholders involved in the development and expansion of EV charging infrastructure, thereby supporting the transition to a more sustainable and extensive electric mobility ecosystem.
Personalized learning model based on machine learning algorithms Jin, Zhang; Kamsin, Amirrudin
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp470-475

Abstract

Machine learning algorithms have been widely applied in the field of personalized learning within educational information technology. By leveraging big data analysis and data mining techniques, machine learning can help identify patterns and trends in students' learning behaviors, preferences, and performance. This information can then be used to tailor educational resources and experiences to meet the individual needs and unique characteristics of each learner. Machine learning has made great progress and achievements in the teaching process of universities, but there are also some shortcomings. Such as data dependence, over-fitting and under-fitting, explanatory problems, need a lot of computing resources, data bias, sensitive to outliers, cannot solve all problems, and the challenge of data privacy, through the analysis of machine learning algorithm model, efforts to find ways to expand the dimension of personalized learning classroom, meet the students in learning objectives, learning content, learning methods of the special characteristics and unique needs, to guide students to actively explore and research, obtain innovation and appropriate learning results.
Enhanced pulse shaping filters for minimizing interference in GFDM signals for 5G cellular networks Tadikamalla, Sairam Vamsi; Mohanta, Harish Chandra; Terlapu, Sudheer Kumar; Mandhapati, Vamshi Krishna
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.pp153-163

Abstract

The imminent rise of 5th generation (5G) wireless standards heralds a pivotal era in cellular communication. Among the challenges faced, selecting an optimal multiple access technique stands out as crucial for achieving the desired blend of low latency, high data rates, and throughput. Generalized frequency division multiplexing (GFDM) emerges as a promising can-didate meeting 5G requirements. This study introduces two innovative pulse shaping filters (PSFs) the better than raised cosine filter (BRCF) and modified bartlett hanning filter (MBHF) paired with various modulation schemes such as binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and quadrature amplitude modulation (QAM) to assess GFDM signal performance. Considering its spectrum efficiency, QAM modulation emerges as the preferred choice. Performance evaluation of the PSFs entails analyzing symbol error rate (SER) against signal to noise ratio (SNR) across different modulation schemes.

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