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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 25 Documents
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Search results for , issue "Vol 13, No 3: December 2024" : 25 Documents clear
A novel Hj-index based model to assess the researchers using scopus database Lakshmi Devi, Voora V. V. Eswari; Amiripalli, Shanmuk Srinivas
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.pp380-387

Abstract

There are many factors that can influence the impact and influence of research, including the quality and originality of the research, relevance and importance of the research, clarity and effectiveness of the research communication, placement of the research in high-impact journals, collaboration and networking, and timing of the research. Identifying active genuine researcher is a sub problem of raising stars in a research area. This problem was addressed by enhancing H-index in Scopus database. Researchers should consider these factors when conducting and communicating their research to maximize its impact and influence. Additionally, there are several metrics used to evaluate the impact and influence of journals and researchers such as H-index, SNIP, CiteScore, and SJR. These metrics take into account different aspects of productivity and impact, and can provide a more comprehensive view of a journal or researcher's influence within their field. In addition to the above metrics, Hj-index was proposed and compared with the H-index to find active genuine researcher in a group.
An ORSAC method for data cleaning inspired by RANSAC Jenkins, Thomas; Goodwin, Autumn; Talafha, Sameerah
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.pp484-498

Abstract

In classification problems, mislabeled data can have a dramatic effect on the capability of a trained model. The traditional method of dealing with mislabeled data is through expert review. However, this is not always ideal, due to the large volume of data in many classification datasets, such as image datasets supporting deep learning models, and the limited availability of human experts for reviewing the data. Herein, we propose an ordered sample consensus (ORSAC) method to support data cleaning by flagging mislabeled data. This method is inspired by the random sample consensus (RANSAC) method for outlier detection. In short, the method involves iteratively training and testing a model on different splits of the dataset, recording misclassifications, and flagging data that is frequently misclassified as probably mislabeled. We evaluate the method by purposefully mislabeling subsets of data and assessing the method’s capability to find such data. We demonstrate with three datasets, a mosquito image dataset, CIFAR-10, and CIFAR-100, that this method is reliable in finding mislabeled data with a high degree of accuracy. Our experimental results indicate a high proficiency of our methodology in identifying mislabeled data across these diverse datasets, with performance assessed using different mislabeling frequencies.
Revolutionizing agricultural efficiency with advanced coconut harvesting automation Davincy R., Yona; Veemaraj, Ebenezer; Edwin, E. Bijolin; Kirubakaran S., Stewart; Thanka, M. Roshni; Neola J., Dafny
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.pp537-546

Abstract

The precision coconut harvesting system aims to develop an efficient system for accurately detecting coconuts in agricultural landscapes using advanced image processing techniques. Coconut cultivation is vital to many tropical economies and precise monitoring is essential for optimizing yield and resource utilization. Traditional methods of coconut detection are labor-intensive and time-consuming. The proposed computer vision-based approach automates and enhances coconut detection by analyzing high-resolution images of coconut plantations. Pre-processing techniques improve image quality and object detection algorithms such as convolutional neural networks (CNNs) identify coconut clusters. Challenges like lighting variations and background clutter are addressed using feature extraction and pattern recognition. A user-friendly interface visualizes detection results, aiding farmers in timely decision-making. Extensive testing on diverse datasets evaluates system effectiveness. This model aims to advance precision agriculture, enhancing productivity and informing coconut farmers' decision-making processes. Using a CNN model, the accuracy of coconut detection based on its ripeness was 98.8%.
Application of monarch butterfly optimization algorithm for solving optimal power flow Jung, Chan-Mook; Pagidipala, Sravanthi; Salkuti, Surender Reddy
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.pp519-526

Abstract

This paper proposes a highly flexible, robust, and efficient constraint-handling approach for the solution of the optimal power flow (OPF) problem and this solution lies in the ability to solve the power system problem and avoid the mathematical traps. Centralized control of the power system has become inevitable, in the interest of secure, reliable, and economic operation of the system. In this work, OPF is solved by considering the three distinct objectives, generation cost minimization, power loss minimization, and enhancement of voltage stability index. These three objectives are solved separately by considering the evolutionary-based monarch butterfly optimization (MBO) algorithm. This MBO algorithm is validated on the IEEE 30 bus network and the obtained results are compared with differential evolution, particle swarm optimization, genetic algorithm, and Jaya algorithm. The obtained results reveal that among the various optimization algorithms considered in this work, the MBO evolves as the best algorithm for all three case studies.
Extraction of association rules in a diabetic dataset using parallel FP-growth algorithm under apache spark Fakir, Youssef; Khalil, Salim; Fakir, Mohamed
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.pp445-452

Abstract

This research paper focuses on enhancing the frequent pattern growth (FP-growth) algorithm, an advanced version of the Apriori algorithm, by employing a parallelization approach using the Apache Spark framework. Association rule mining, particularly in healthcare data for predicting and diagnosing diabetes, necessitates the handling of large datasets which traditional methods may not process efficiently. Our method improves the FP-growth algorithm’s scalability and processing efficiency by leveraging the distributed computing capabilities of apache spark. We conducted a comprehensive analysis of diabetes data, focusing on extracting frequent itemsets and association rules to predict diabetes onset. The results demonstrate that our parallelized FP-growth (PFP-growth) algorithm significantly enhances prediction accuracy and processing speed, offering substantial improvements over traditional methods. These findings provide valuable insights into disease progression and management, suggesting a scalable solution for large-scale data environments in healthcare analytics. 

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