Energy-Efficient Multi-Hop Clustering in WSN Using Intelligent Swarm-Based Algorithms

Chandra Sekar; K. R. N.

doi:https://doi.org/10.54216/IJBES.090205

International Journal of Neutrosophic Science | 2025

Investigating Inclusion, Neighborhood, and Partial Sums Properties for a General Subclass of Analytic Functions

The study of geometric properties within the subclass of analytic functions has garnered significant attention in recent years due to its complex and intricate interplay between geometric function theory and complex analysis. This area of study provides deep insights into both mathematical theory and its practical applications. The exploration of these properties is not only of theoretical interest but also offers valuable implications for various applications in mathematical and engineering disciplines. In particular, this paper focuses on a detailed examination of the inclusion, neighborhood, and partial sums properties within a broad and general subclass of analytic functions. This class of functions is defined through a generalized multiplier transformation operator, which adds a layer of complexity to their analysis. By investigating these specific properties, this study aims to validate and build upon many existing findings documented in the literature, offering new perspectives and contributing to a deeper understanding of the field.

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Mohamed Illafe mail -

Maisarah Haji Mohd mail -

Feras Yousef mail -

Shamani Supramaniam mail

link https://doi.org/10.54216/IJNS.250341

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

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International Journal of Neutrosophic Science | 2025

Neutrosophic sets in IUP-algebras: a new exploration

The notions of neutrosophic IUP-subalgebras, neutrosophic IUP-ideals, neutrosophic IUP-filters, and neutrosophic strong IUP-ideals of IUP-algebras are introduced, and their basic properties are investigated. Conditions for neutrosophic sets to be neutrosophic IUP-subalgebras, neutrosophic IUP-ideals, neutrosophic IUPfilters, and neutrosophic strong IUP-ideals of IUP-algebras are provided. Relations between neutrosophic IUP-subalgebras (resp., neutrosophic IUP-ideals, neutrosophic IUP-filters, neutrosophic strong IUP-ideals) and their level subsets are considered.

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Kannirun Suayngam mail -

Pongpun Julatha mail -

Rukchart Prasertpong mail -

Aiyared Iampan mail

link https://doi.org/10.54216/IJNS.250343

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

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Journal of Cybersecurity and Information Management | 2025

A Novel Design of a Quadratic Koch Fractal Nano Antenna for THz Application

The study, called "A Novel Design of a Quadratic Koch Fractal Nanoantenna," aims to create and study a brand-new microstrip nanoantenna that works in the THz range, specifically between 100 and 130 THz, and can handle a wide range of optical communication frequencies. We examine two unique geometries, specifically the quadratic Koch fractal patch (QKF) and the complementary quadratic Koch fractal patch (CQKF), utilizing two different dielectric materials as substrates. We employ silicon (Si) dielectric material because of its high dielectric constant (11.9), while we use the silicon dioxide (SiO2) dielectric material because of its dielectric constant (4). The feeding method employed to stimulate these nanoantennas has been waveguide feed at a frequency of 50 Ω.We have employed a software simulator, available for purchase as CST STUDIO SUITE, to achieve the established objectives for assessing the performance of each proposed nanoantenna.

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Ammar Nadal Shareef mail -

Amer Basim Shaalan mail -

Hayder Salah Naeem mail -

Mustafa Albdairi mail

link https://doi.org/10.54216/JCIM.150207

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Deep Learning for Handwritten Digit Recognition System: A Convolution Neural Network Approach

Artificial intelligence techniques including deep learning play a major role in all fields and in line with the advancement in technology. Handwritten digit recognition is an important issue in the field of computer vision, which is used in wide applications such as optical character recognition and handwritten digits. In the current research, we describe a unique deep learning technique that uses a Convolutional Neural Network (CNN) framework with better normalization algorithms and adjusted hyperparameters for improved efficiency as well as generalize. Contrasting conventional techniques, our methodology concentrates on minimizing overfitting through the use of adjustable rate of abandonment and innovative pooling procedures, resulting in greater accuracy in handwriting number classification. Following considerable research, the recommended approach obtains an outstanding classification accuracy of 99.03%, proving its ability to recognize intricate structures in handwritten numbers. The approach's usefulness is reinforced by a complete review of measures including recall, accuracy, F1 score, as well as confuse matrix assessment, which show improvements throughout all digit categories. . The results of the investigation highlight the innovative conceptual layout and optimization methodologies used, representing a substantial leap in the realm of number identification.

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Maha A. Al-Bayati mail

link https://doi.org/10.54216/FPA.170226

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Classification of Tomato Diseases Using Deep Learning Method

With an average annual intake of almost 20 kilograms per person, tomatoes are the most consumed vegetable worldwide. Diseases brought on by dangerous organisms are among the most important factors adversely affecting tomato production's output and quality. Depending on the climate and environmental conditions, tomatoes can be afflicted by a variety of illnesses throughout the planting and growing phases. It is essential for tomato growers to identify possible infections and take the appropriate preventative measures. Applications of artificial intelligence have grown in popularity recently. AI is being used in agriculture to identify plant illnesses. This research uses deep learning, a branch of artificial intelligence, to categories common tomato diseases. In the beginning, samples of frequently seen tomato illnesses were gathered from tomato growers in Kirkuk. Once there were enough data, the system developed with image processing algorithms produced meaningful images. Using a CNN-based GoogleNet deep learning system, the resulting dataset was trained and diseases were classified. The results show that the deep learning system that was constructed has a high degree of success and dependability when it comes to tomato disease classification.

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Adnan M. A. Shakarji mail -

Adem Gölcük mail

link https://doi.org/10.54216/JISIoT.140217

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2024

Deep Learning-Based Image Super-Resolution for Enhanced Medical Diagnostics

Medical imaging has become a critical tool in diagnostics, but low-resolution images often limit the precision of diagnosis and treatment. This study presents a deep learning-based image super-resolution framework designed to enhance the quality and clarity of medical images, specifically tailored for radiology, dermatology, and histopathology. The proposed framework uses a Convolutional Neural Network (CNN) architecture with a Residual Dense Network (RDN) backbone, improving visual details and retaining clinically relevant features. Training on a diverse dataset of MRI, CT, and X-ray images, the model achieved a 35% improvement in Peak Signal-to-Noise Ratio (PSNR) and a 42% improvement in Structural Similarity Index Measure (SSIM) compared to conventional interpolation techniques. Our method also demonstrated an increase of 48% in diagnostic accuracy when integrated into radiological workflows, enhancing radiologists' ability to identify pathologies with subtle visual indicators. Experimental results show that our super-resolution framework provides a fourfold increase in resolution while minimizing computational cost by 30% using optimized GPU-based processing. This innovative approach to super-resolution has the potential to significantly impact the diagnostic field by enabling clearer and more detailed medical imaging for improved patient outcomes.

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K. R. N. Aswini mail -

A. Babiyola mail -

K. Dhineshkumar mail

link https://doi.org/10.54216/IJBES.090201

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2024

Explainable AI for Automated Feature Extraction in Medical Image Segmentation

Automated feature extraction and segmentation of medical images are essential for accurate diagnostics, enabling the identification of relevant structures with minimal human intervention. This study introduces an Explainable AI (XAI) framework for automated feature extraction in medical image segmentation, aiming to enhance transparency in deep learning models used in medical imaging. The proposed framework uses a Convolutional Neural Network (CNN) with integrated attention mechanisms and layer-wise relevance propagation (LRP) to identify critical features while segmenting regions of interest. Testing on datasets of MRI brain scans and CT liver scans, the model achieved an accuracy of 94%, a Dice similarity coefficient (DSC) of 0.88, and an Intersection over Union (IoU) score of 0.83. These results outperform conventional CNN-based segmentation techniques by 10% on average, highlighting the framework's precision in identifying and segmenting intricate structures, including lesions and abnormalities. Additionally, the XAI components provide visual explanations of the segmentation process, enabling clinicians to understand which features influenced the model's decisions. This enhanced transparency is crucial for building trust in AI-driven medical solutions, ultimately facilitating their integration into clinical workflows.

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N. Gobi mail -

M. Balakrishnan mail -

S. R. Indurekaa mail -

A. B. Arockia Christopher mail

link https://doi.org/10.54216/IJBES.090202

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2024

Bio-Inspired Image Enhancement Algorithms for Underwater Surveillance

Underwater surveillance relies heavily on image quality, yet underwater environments present unique challenges, including low visibility, color distortion, and light scattering. This study proposes a bio-inspired image enhancement algorithm designed to address these challenges by mimicking adaptive mechanisms found in marine organisms. The algorithm integrates a multi-scale Retinex model with a bio-inspired filter based on visual properties of aquatic species, optimizing contrast and color balance for improved image clarity. Tested on various underwater image datasets, the proposed method achieved a 45% improvement in contrast enhancement and a 38% reduction in color distortion compared to traditional enhancement techniques. Furthermore, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) improved by 42% and 35%, respectively. The results demonstrate the algorithm’s effectiveness in enhancing visibility and detail, enabling more accurate object detection and classification in underwater surveillance. The bio-inspired approach offers a practical solution for underwater monitoring, particularly valuable for applications in marine research, environmental monitoring, and security.

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A. Babiyola mail -

Chandra Sekar P. mail -

K. R. N. Aswini mail

link https://doi.org/10.54216/IJBES.090203

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2024

An Adaptive Optimization Algorithm for Personalized Learning Pathways in E-Learning

This paper presents an adaptive optimization algorithm for personalized learning pathways in e-learning environments. The proposed algorithm dynamically adjusts the learning path for each student based on their performance, preferences, and learning behavior. By integrating machine learning techniques with a rule-based system, the algorithm optimizes content delivery and ensures a tailored learning experience that aligns with individual needs. The system continuously monitors learners’ progress, adapts to their evolving knowledge levels, and suggests the most relevant resources and activities to enhance engagement and comprehension. Experimental results demonstrate significant improvements in learning outcomes, reduced time to completion, and enhanced user satisfaction, making the approach a promising solution for personalized e-learning systems.

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Senthil Kumar R. mail -

T. Ramesh mail -

K. R. N. Aswini mail

link https://doi.org/10.54216/IJBES.090204

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2024

Energy-Efficient Multi-Hop Clustering in WSN Using Intelligent Swarm-Based Algorithms

Efficient energy management in Wireless Sensor Networks (WSNs) is vital for extending network lifetime, particularly in applications requiring continuous monitoring in remote or challenging environments. This study proposes an energy-efficient multi-hop clustering approach for WSNs, utilizing intelligent swarm-based algorithms to optimize cluster formation and data routing. By applying Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) techniques, the proposed method dynamically selects optimal cluster heads and minimizes energy consumption during multi-hop data transmission. The algorithm was evaluated on simulated WSN scenarios with varying node densities, achieving an average energy savings of 28% compared to traditional clustering methods and a 35% increase in network lifetime. Additionally, the proposed approach improved packet delivery ratio and reduced latency by 20% and 15%, respectively. This swarm-based, energy-efficient clustering framework is well-suited for applications in environmental monitoring, smart agriculture, and industrial automation, where prolonged network operation is essential.

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Chandra Sekar P. mail -

K. R. N. Aswini mail

link https://doi.org/10.54216/IJBES.090205

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new