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4th International Conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Galhotra, Parth Singh, Kumar Aniruddh Hasan, Zafarul Safa, M. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM IST SRM Nagar Kattankulathur TN Chennai603203 India

ISBN: (纸本)9798331530402

This study will introduce the use of Docker for simple application packaging to investigate the impact of packaging on application deployment and management. Containerization is a technological method for packaging and consuming applications in a self-contained environment that is more efficient than virtualization. Docker is a well-known containerization platform that enables efficient creation, deployment, and management of applications by focusing and efficiently utilizing resources across multiple locations. Delivering programs and investigating their similarities in terms of consistency, efficiency, and effectiveness. It also solves security issues and multi-container issues. The approach will be to package a simple web application, deploy it via Docker, and then measure how performance and manageability are improved compared to traditional deployments. Using Docker for application deployment helps you better understand how containerization can improve and simplify deployment and management in today's software development. © 2025 IEEE.

关键词： Software design

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Enhanced Layer Extraction for Efficient Plant Disease Classification using Efficientnet B1

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SN Computer Science 2025年第4期6卷 1-15页

作者： Thakur, Arastu Thakur, Abhilasha Vivek, V. Mahesh, T.R. Krishna, K. Murali Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India Department of Agriculture Marwadi University Gujarat Rajkot India Alliance School of Advanced Computing Alliance University Chandapura - Anekal Main Road Bengaluru Anekal 562106 India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India

Agriculture is crucial for the global economy, providing sustenance and resources for various industries. However, plant diseases threaten crop quality and yield, risking severe economic impacts. Traditional plant disease diagnosis methods, like manual inspection or basic machine learning, are slow, require expertise, and struggle with scalability and adapting to new diseases. This study introduces a deep learning model using convolutional neural networks (cnns) to autonomously detect and classify plant diseases from images, enhancing speed, scalability, and accuracy. We utilize the efficientnetb1 architecture, pretrained on imagenet and fine-tuned on a dataset of bell pepper, potato, and tomato diseases, recognizing 14 diseases plus healthy plants. The model achieves 98.80% accuracy on the test dataset, with high precision, recall, and F1-scores, demonstrating its effectiveness in plant disease diagnosis. This advancement marks a significant step in automating plant disease diagnosis, offering a robust tool for effective disease management in agriculture. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agriculture Automated diagnosis Deep learning Disease management Efficientnetb1 Image classification Plant disease detection Transfer learning

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PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images

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IEEE Access 2025年 13卷 84024-84037页

作者： Munir, Kashif Usama Tanveer, Muhammad Alyamani, Hasan J. Bermak, Amine Ur Rehman, Atiq Khwaja Fareed University of Engineering and Information Technology Institute of Information Technology Rahim Yar Khan 64200 Pakistan King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah 21589 Saudi Arabia Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

Pneumonia is a critical respiratory condition characterized by inflammation in the alveoli, leading to impaired gas exchange and severe respiratory distress. It poses a significant threat to high-risk populations, including neonates, geriatric patients, and immunocompromised individuals. Early and precise detection is crucial to optimizing treatment strategies and reducing morbidity and mortality rates. To address this problem, we propose PneuX-Net, an ensemble-based feature extraction framework that integrates multiple machine learning (ML) models Random Forest (RF), Gaussian Naïve Bayes (GNB) and K-Nearest Centroid (KNC). Random Forest (RF) builds multiple decision trees to identify complex, non-linear patterns within the feature space. Gaussian Naïve Bayes (GNB) utilizes a probabilistic framework based on Bayes’ theorem to manage uncertainty in feature distributions. K-Nearest Centroid (KNC) improves class distinction by clustering feature vectors according to their proximity to centroids. The ensemble methodology harnesses the complementary strengths of these models, improving feature representation and mitigating overfitting, a prevalent issue in white-box models. To validate the effectiveness of PneuX-Net, we conduct extensive experimentation using a 10-fold cross-validation approach. Our results demonstrate that the PneuX-Net+KNC model achieves a remarkable 99.91% accuracy, highlighting its robustness and reliability in pneumonia classification tasks. This ensemble-driven methodology underscores the potential of machine learning in augmenting clinical decision-making by providing an accurate and automated pneumonia detection framework. © 2013 IEEE.

关键词： healthcare AI machine learning medical diagnosis pneumonia detection PneuX-Net

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AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models

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Multimedia Tools and Applications 2025年第4期84卷 1821-1843页

作者： Aydin, Mehmet Barua, Prabal Datta Chadalavada, Sreenivasulu Dogan, Sengul Tuncer, Turker Chakraborty, Subrata Acharya, Rajendra U. Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland Toowoomba Australia School of Engineering University of Southern Queensland Springfield LakesQLD4300 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024.

关键词： Image classification

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Optimized Convolutional Neural Networks with Multi-Scale Pyramid Feature Integration for Efficient Traffic Light Detection in Intelligent Transportation Systems

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Computers, Materials & Continua 2025年第2期82卷 3005-3018页

作者： Yahia Said Yahya Alassaf Refka Ghodhbani Taoufik Saidani Olfa Ben Rhaiem Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Civil Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Faculty of Computing and Information Technology Northern Border UniversityRafha91911Saudi Arabia College of Science Northern Border UniversityArar91431Saudi Arabia

Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.

关键词： Intelligent transportation systems(ITS) traffic light detection multi-scale pyramid feature maps advanced driver assistance systems(ADAS) real-time detection AI in transportation

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IoT-Based Real-Time Water Quality Monitoring and Sensor Calibration for Enhanced Accuracy and Reliability

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 155-170页

作者： Bakar, Azinoor Azida Abu Bakar, Zahari Abu Yusoff, Zakiah Mohd Ibrahim, Mohd Johan Mohamed Mokhtar, Nurkhairany Amyra Zaiton, Siti Nurul’Ain School of Civil Engineering College of Engineering Universiti Teknologi MARA Selangor Malaysia Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Johor Branch Johor Malaysia Civil Engineering Studies College of Engineering Universiti Teknologi MARA Johor Branch Johor Malaysia Mathematical Sciences Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA Johor Branch Johor Malaysia Faculty of Applied Sciences Universiti Teknologi MARA Johor Branch Johor Malaysia

The increasing water pollution levels and the growing demand for clean water necessitate advanced monitoring solutions. This study aims to develop an Internet of Things (IoT)-based real-time water quality monitoring system that addresses the limitations of traditional methods, which are often time-consuming and lack continuous monitoring capabilities. The system integrates pH, temperature, and turbidity sensors with an Arduino microcontroller and NodeMCU for Wi-Fi connectivity, transmitting data to a cloud platform for real-time access and analysis. The methodology involved interfacing these sensors with the microcontroller, displaying LCD data, and providing LED alerts. The findings indicate that the prototype’s readings closely align with laboratory measurements, with minor deviations in pH and temperature values and slightly more significant differences in turbidity readings. This demonstrates the prototype’s reliability and accuracy in real-time environmental monitoring. The conclusion emphasises the system’s potential to enhance water resource management through continuous and accurate tracking, enabling timely interventions. Future recommendations include improving sensor calibration, expanding the range of monitored parameters, and integrating advanced data analytics for predictive capabilities. The system’s real-time data logging capabilities significantly enhance water management by providing timely alerts and enabling proactive responses to water quality issues. The impact of this system is substantial, offering a scalable and cost-effective solution for continuous monitoring, thus contributing to improved water resource management and public health protection. © 2025 by the authors of this article.

关键词： Water well logging

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Connectivity number in fuzzy graphs with application in mobile communication networks

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Journal of Applied Mathematics and computing 2025年 1-27页

作者： Rezayi, M. Eslahchi, Ch. Borzooei, R.A. Department of Mathematics Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran Department of Data and Computer Sciences Shahid Beheshti University Tehran Iran Soft Computing Center Department of Mathematics Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran Department of Mathematics Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

This paper explores the concept of connectivity within fuzzy graphs, extending classical graph theory to model uncertainty in real-world networks. We introduce the Connectivity Number (CN) as a novel and computationally efficient metric to assess network robustness. CN quantifies the minimum strength of connectedness for any vertex in a fuzzy graph, identifying the weakest links that influence overall stability. Theoretical properties of CN are established in various fuzzy graph structures, including strong fuzzy graphs, fuzzy cycles, and complete fuzzy graphs. We further investigate the behavior of CN under six fundamental fuzzy graph operations: Cartesian product, normal product, tensor product, composition, lexicographic max product, and lexicographic min product, revealing critical insights into connectivity in composite structures. Furthermore, we demonstrate the practical relevance of CN by applying it to a mobile communication network, where it effectively identifies weak connections and informs optimization strategies. Compared to existing indices such as the Wiener index (WI) and the connectivity index (CI), CN provides a simpler and more direct measure of connectivity weaknesses, making it a valuable tool for network optimization, resilience analysis, and decision-making. Future research will explore CN in dynamic fuzzy graphs, examine its stability under network perturbations, and develop efficient algorithms for large-scale applications. © The Author(s) under exclusive licence to Korean Society for Informatics and Computational Applied Mathematics 2025.

关键词： Network theory (graphs)

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A Review on Solar Panel Adoption in Residential Malaysia: Trends, Benefits and Technologies 2nd

A Review on Solar Panel Adoption in Residential Malaysia: Tr...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Quan, Teoh Chee Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Petaling Jaya Selangor Sunway City47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This survey provides detailed perspectives on Malaysian solar energy system adoption in relation to the economic outcome and environmental and policy context. This work has been done by using comprehensive keyword lists and inclusion criteria to confirm that the findings of this survey capture as many relevant publications as possible from the research period from 2010 to 2024. Unlike numerous papers that investigate such systems with mere commercial or industrial perspective, this survey aims at assessing residential installation, and providing accurate cost benefit analysis while showing the business belief of such schemes. It also examines various opinions, especially the Malaysian government policies and strategic incentives and staking its success on public awareness and acceptance. We equally allocate a considerable portion of this survey to discussing the environmental effects and the life cycle analyses comparing solar energy with other energy types. Notably, the survey is also under sustainable development goal 7 with the goal of achieving universal access to affordable reliable, sustainable and modern energy. Such a focus also makes the identified topics relevant for Malaysia, which is useful for references in subsequent projects and activities. These would include use of web-survey, the coverage on the residential sector, the ability to incorporate policy analysis and emphasis on sustainability to the study on solar energy usage in residential Malaysia. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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Comparative Study of Classification Algorithms Using Big Data Analytics for Flight Delays Prediction 5th

Comparative Study of Classification Algorithms Using Big Dat...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Asiri, Mona Hassan AL-Malaise AL-Ghamdi, Abdullah S. Fayoumi, Ayman G. Ragab, Mahmoud Department of Information Systems Collage of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Information Systems Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah34801 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819771776

Flight delays are the most pressing cause of concern in the airline sector because they can weaken and harm airlines, passengers, and airports. Flight delay prediction has become an essential step in decision-making processes within commercial aviation. Due to the developments of recent technologies, there are many prediction models for flight delays;however, more development of accurate prediction models is still needed. Machine learning, statistical models, and other techniques can be used to create quick and accurate flight delay prediction models. Furthermore, methods for feature selection can be developed to get rid of dimensionality issues. This study aimed to compare four classification algorithms using "big data analytics" to predict flight delays, a function that can improve the whole airline sector. The selected classification algorithms were the "Gaussian Naïve Bayes," "Gradient Boosted Tree" classifier, "Logistic Regression," and "Random Forest" classifier. Many assessment techniques were applied to each one of the suggested models to show the better performance of these models. The comparison used the resulting confusion matrix to summarize prediction results for the following classification problems: "F1 Score", "Accuracy," "Recall," "Precision," "Area Under ROC," and "Area Under PR." The assessment results showed the enhanced outcomes of the "Gradient Boosted Tree" Classifier with existing techniques, and all results of "Logistic Regression" and "Naïve Bayes" are equal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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An Efficient Algorithm for Software Reliability Prediction via Harris Hawks Optimization 2nd

An Efficient Algorithm for Software Reliability Prediction v...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Kong, Li Sheng Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Software reliability has become an increasingly important characteristic as software systems continue to be integrated into all parts of our lives. While the occurrence of failure after a period of time is within the nature of all software systems, the ability to estimate and predict failure is greatly beneficial to software development and maintenance. Hence, Software Reliability Growth Models (SRGMs) were developed to fulfill this requirement. Conventional parameter estimation methods of SRGM functions were complex and left more to be desired. Consequently, metaheuristics such as Swarm Intelligence algorithms were introduced to estimate and optimize the parameters of SRGM functions. Likewise, Swarm Intelligence algorithms were found to be exceptionally effective in estimating and optimizing the parameters of SRGM functions. This paper presents a novel approach to parameter estimation on three different SRGM functions through Harris’ Hawks Optimization (HHO). The proposed HHO design is compared against numerous different variants of Swarm Intelligence algorithms and is shown to be significantly more efficient than the majority of the compared designs. Moreover, as the proposed design is a base variant of Swarm Intelligence algorithms, various avenues of future improvements have also been presented to improve its overall prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harris’ Hawks Optimization Parameter Estimation Software Reliability Growth Models Software Reliability Prediction Swarm Intelligence

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