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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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A hybrid approach using VGG16-EffcientNetV2B3-FCNets for accurate indoor vs outdoor and animated vs natural image classification Deshmukh, Meghana; Gaikwad, Amit; Kuche, Snehal
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp903-913

Abstract

The paper introduces a hybrid approach that synergistically combines the strengths of VGG16, EfficientNetV2B3, and fully connected networks (FCNets) to achieve precise image classification. Specifically, our focus lies in discerning between basic indoor and outdoor scenes, further extended to distinguish between animated and natural images. Our proposed hybrid architecture harnesses the unique characteristics of each component to significantly enhance the model’s overall performance in fine-grained image categorization. In our methodology, we utilize VGG16 and EfficientNetV2B3 as the feature extractors. During evaluation, we examined various classification algorithms, such as VGG16, EfficientNet, Feature_Aggr_Avg, and Feature_Aggr_max, among others. Notably, our hybrid feature aggregation approach demonstrates a remarkable improvement of 0.5% in accuracy compared to existing solutions employing VGG16 and EfficientNet as feature extractors. Notably, for indoor versus outdoor image classification, feature_aggr_avgachieves an accuracy of 98.51%. Similarly, when distinguishing between animated and natural images, Feature_Aggr_Avgachieves an impressive accuracy of 99.20%. Our findings demonstrate improved accuracy with the hybrid model, proving its adaptability across diverse classification tasks. The model is promising for applications like automated surveillance, content filtering, and intelligent visual recognition, with its robustness and precision making it ideal for realworld scenarios requiring nuanced categorization.
Advanced control techniques for performance improvement of axial flux machines Anumala, Kalpana; Veligatla, Ramesh Babu
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1095-1107

Abstract

The topological advancements in twin rotor axial flux induction motors (TRAxFIMs) have spurred the interest in performance optimization and control strategies for electric vehicle (EV) applications in particular. This paper investigates for the enhanced performance of multi-level inverters (MLIs) fed TRAxFIMs with different advanced control techniques. The performance evaluation is done under variable speed conditions at constant torque and vice versa. The TRAxFIMs offer unique advantages like high power density, high efficiency and most suitable for EV applications. The performance analysis of MLIs fed TRAxFIM has been carried out with proportional-integral (PI), fuzzy controllers, and artificial neural network (ANN) controllers. The PI controller provides a conventional control approach, while the fuzzy and ANN controllers serve as advanced control strategies. The integration of MLIs and advanced control techniques with TRAxFIMs aims to enhance dynamic response, stability and efficiency. The proposed control strategies are evaluated through extensive MATLAB simulations and the potential of MLIs fed TRAxFIMs is emphasized for EV applications.

Page 47 of 47 | Total Record : 462

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