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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 506 Documents
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A high linearity low noise amplifier with modified differential inductor for bluetooth profiles Usharani, Ghattamaneni; Varadarajan, Sourirajan
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.pp323-331

Abstract

In today’s rapidly evolving communication landscape, electronic devices rely heavily on high-performance components to ensure seamless connectivity. A low-noise amplifier (LNA) is a critical front-end element in any receiver chain, where its performance significantly influences the overall system efficiency. As integrated circuits continue to shrink with advancements in technology, challenges such as linearity degradation have become increasingly prominent. This work presents a modified derivative (MD) narrowband common source low-noise amplifier (CSLNA) designed using 0.13 µm CMOS technology, offering improved linearity and frequency characteristics. The proposed design adopts a hybrid architecture, combining a folded cascode gain stage with a common-gate configuration. An optimized modified differential inductor is employed at the input for effective impedance matching and reduced noise figure (NF). The implemented LNA achieves a gain of 25.81 dB, an input return loss of –24.86 dB, and maintains a low NF of 0.3 dB at an operating frequency of 2.4 GHz. Furthermore, the linearity metrics-third-order input intercept point (IIP3) and 1 dB compression pointare significantly improved to –16.70 dBm and –21.89 dBm, respectively. These results highlight the LNA's suitability for Bluetooth and other shortrange wireless communication applications.
A meta-learning framework for leaf disease detection using vision transformer-based feature extraction, PCA, and tuned SVM classifier Venkataraman, Jayalakshmi; Devi Potluri, Bhargavi; Arumugam, Vignesh; Balasubramaniyam, Shoba; Subramani, Banumathi
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.pp267-275

Abstract

A hybrid meta-learning approach is proposed for effective leaf disease detection by integrating vision transformer (ViT), principal component analysis (PCA), and support vector classifier (SVC). The objective of this study is to accurately classify plant leaf conditions into three categories: healthy, angular leaf spot, and bean rust. The dataset consists of 1,167 labeled leaf images, divided into training (974 images), validation (133 images), and testing (60 images) sets. A pretrained ViT model is employed for feature extraction, producing a feature vector of shape (974, 64) for the training data. To mitigate the curse of dimensionality and improve classification performance, PCA is applied, reducing the features to 41 principal components while retaining 98% of the original variance and accuracy 97.85%. For the classification task, an SVC is used and fine-tuned using the Optuna hyperparameter optimization framework to enhance accuracy and generalization. A distributed deep learning strategy is incorporated to accelerate training and scale computation, while the tf.data API is utilized to construct an efficient and scalable data input pipeline. The hybrid model demonstrates strong classification performance on the test set, indicating that combining deep transformer-based feature extraction with dimensionality reduction and optimized classical machine learning classifiers is effective for plant disease identification. This approach offers a robust and computationally efficient solution for precision agriculture, enabling automated and accurate leaf disease diagnosis and supporting early intervention strategies in crop management.
Renewable energy optimization for sustainable power generation Prasad Mishra, Debani; Samal, Sarita; Kumar, Rohit; Kumar Sahoo, Arun; 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.pp365-373

Abstract

To improve sustainability in power generation, this study presents a thorough data-driven method for maximizing renewable energy sources. It employs measures like capacity utilization factor (CUF) and efficiency to evaluate the performance of solar and wind energy using historical weather and energy-generating data. The study offers practical suggestions for improving renewable energy systems, such as weather-energy correlation analysis and machine learning-based forecasting models. In addition, a comparative analysis is carried out to ascertain which energy source is better, and useful real-world data is provided, including a summary of all India’s total renewable energy generation (excluding large hydro) for June 2023 and a performance comparison year over year. A useful, data-driven approach for enhancing renewable energy is provided by this work, which advances the topic of sustainable energy.
Digital platforms and cloud computing for smart cities: a review Christopher Immanuel, William; Juliette Albert, Anitha; Jerald Jobitham, Limsa Joshi; Rebecca Selvaraj, Roselene; Sharon Ruban, Benita; Vini Robin, Bennet; Morais Allen, Andria
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.pp30-38

Abstract

The rapid urbanization of the modern world initiated the emergence of digital cities, where advanced technologies converge to optimize urban living and address the limitations of a rapidly growing population. Central to this transformation are digital platforms and cloud computing. These interconnected technologies aid in shaping the future of urban landscapes, fostering sustainability, efficiency, and improved quality of life. Digital platforms serve as the backbone of smart cities, enabling seamless integration and management of various urban services and systems. One significant application of digital platforms in smart cities is the implementation of intelligent transportation systems (ITS). By integrating real-time traffic data, public transit information, and ride-sharing services, these platforms facilitate efficient transportation management, reduce congestion, and decrease carbon emissions. Cloud computing serves as a key enabler for managing the massive data flows generated by smart city infrastructures. The scalability and flexibility offered by cloud-based solutions allow cities to manage their resources efficiently and access computing power on demand without the need for extensive physical infrastructure. Cloud computing enhances smart city development by enabling collaborative data access and interaction among diverse stakeholders, from government agencies to private firms and residents.
Lightweight deep learning approach for retinal OCT image classification: A CNN with hybrid pooling and optimized learning R. Dave, Parth; H. Domadiya, Nikunj
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.pp414-427

Abstract

Optical coherence tomography (OCT) is a non-invasive technique through which a retina specialist can see the structure behind the eye. This technol ogy offers a key role to identify various abnormalities in the retina: Drusen, diabetic macular edema (DME) and choroidal neovascularization (CNV). However, manual analysis of OCT scans can be time-consuming and prone to variability among clinicians. To address this challenge, we present a lightweight and explainable deep learning-based approach for automatic classification of retinal OCT images. The primary goal of this research is a model that delivers high diagnostic accuracy. A computer-aided suggestive method can help retinal doctors automatically classify the anomalies with more confidence and precision. In this paper, we proposed a novel approach based on deep learning: a six-layer convolutional neural network (CNN) integrated with hybrid pooling for effective feature extraction. Data augmentation and exponential learning rate is implemented to handle data imbalance between classes and for stabilized learning consecutively. Our proposed approach achieved 98.75% of accuracy while testing on the dataset. To further enhance the interpretability of the model, we also integrate explainable AI (XAI) using class activation mapping (CAM) to visualize the critical regions in the retina that contribute to the classification decisions.
A survey on fronthaul signaling of user-centric cell-free massive MIMO networks Tariq Ali, Syed; Singh, Anamika
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.pp302-312

Abstract

The mandate for high data rates in mobile communication is increasing and will continue to do so in the future. Although the latest network technologies can meet this demand, they result in more-dense networks. Networks like ultra-dense networks and massive multiple-input multiple-output provide very high data rates, but they cannot meet the future demand. The main issue with existing networks is inter-cell interference and variations in quality of service esp. at the cell edges, leading to research on new network architectures that offer intelligent coordination and collaboration capabilities are being researched, like user-centric cell-free (UC-CF) massive-multipleinput-multiple-output (mMIMO). This network combines the best of ultradense networks and mMIMO and eliminates cell edge problems. It is served by access points that cooperate and coordinate with each other. This paper reviews the challenges and opportunities in physical layer parameterfronthaul signaling for UC-CF mMIMO. We discuss the basics of the network, the importance of fronthaul signaling, and propose various approaches in the literature to address challenges and identify research gaps and provide future directions. Our aims to provide a comprehensive overview of the current state of fronthaul signaling and highlight the key issues that need to be addressed to realize its full potential.

Page 51 of 51 | Total Record : 506

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