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Software Reliability Assessment:An Architectural and Component Impact Analysis

Tsinghua science and technology 2025年第2期30卷 908-925页

作者： Saleh Alyahyan Mohammed Naif Alatawi Mrim M.Alnfiai Shoayee Dlaim Alotaibi Abdullah Alshammari Zaid Alzaid Hathal Salamah Alwageed Applied College in Dwadmi Shaqra UniverityShaqra 11961Saudi Arabia Department of Information Technology University of TabukTabuk 47713Saudi Arabia Department of Information Technology Taif UniversityTaif 21944Saudi Arabia Department of Artificial Intelligence and Data Science University of HailHail 55431Saudi Arabia College of Computer Science and Engineering University of Hafr AlbatinHafar Albatin 31991Saudi Arabia Department of Computer Science Islamic University of MadinahMadinah 42351Saudi Arabia College of Computer and Information Sciences Jouf UniversitySakaka 42421Saudi Arabia

In the software landscape,understanding component impacts on system reliability is pivotal,especially given the unique complexities of modern software *** paper presents a model tailored for software reliability *** approach introduces the“component influence”to measure a single component’s effect on overall system ***,we adapt a state transition model to cater to the diverse architectures of software *** a discrete-time Markov chain,we predict software *** test our model on an actual software system,finding it notably accurate and superior to existing *** work offers a promising direction for those venturing into software reliability enhancement.

关键词： software reliability architectural analysis state transition model

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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Maximizing Profit of the Service Providers through Efficient Content Placement and Retrieval in an IoT-enabled Edge-Cloud Platform 18

Maximizing Profit of the Service Providers through Efficient...

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18th IEEE International Conference on Advanced Networks and Telecommunications systems, ANTS 2024

作者： Gandhi, Preet Mahanta, Abhishriya Ghose, Manojit Sahu, Aryabartta Indian Institute of Technology Department of Computer Science and Engineering Guwahati India Indian Institute of Technology School of Computing and Electrical Engineering Mandi India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

In today's 5G era, the amount of data generated by the Internet of Things (IoT) devices is enormous. Data is processed and stored in the cloud under a traditional cloud computing architecture, and real-time processing and retrieval of these data is time-consuming. Therefore, an edge-cloud framework comes in handy where the content generated is placed on the edge nodes of the network, and it can reach the endusers in a time-bound manner. A majority of the previous research works on content placement under the edge-cloud architecture primarily aimed at improving the latency of the system. However, the approach proposed in this paper aims to maximize the profit of the service provider by effectively placing the content in a hierarchically organized edge-cloud network. We propose two strategies and compare them with random and greedy approaches for both synthetic and real data sets. The experimental results demonstrate the effectiveness of the proposed strategies, and the profit of the service providers increases by 38.81% (maximum) and 10.51% (on average) for the synthetic dataset and by 65.74% (maximum) and 22.83% (on average) for the real-world dataset when compared with the greedy approach. © 2024 IEEE.

关键词： Cloud platforms

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Detection of Parkinson’s Disease with Multiple Feature Extraction Models and Darknet CNN Classification

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computer systems science & Engineering 2022年第10期43卷 333-345页

作者： G.Prema Arokia Mary N.Suganthi Department of Information Technology Kumaraguru College of TechnologyCoimbatore641049India Department of Computer Science and Engineering Kumaraguru College of TechnologyCoimbatore641049India

Parkinson’s disease(PD)is a neurodegenerative disease in the central nervous ***,more researches have been conducted in the determination of PD prediction which is really a challenging *** to the disorders in the central nervous system,the syndromes like off sleep,speech disorders,olfactory and autonomic dysfunction,sensory disorder symptoms will *** earliest diagnosing of PD is very challenging among the doctors *** are techniques that are available in order to predict PD using symptoms and disorder *** helps to save a million lives of future by early *** this article,the early diagnosing of PD using machine learning techniques with feature selection is carried *** the first stage,the data preprocessing is used for the preparation of Parkinson’s disease *** the second stage,MFEA is used for extracting *** the third stage,the feature selection is performed using multiple feature input with a principal component analysis(PCA)***,a Darknet Convolutional Neural Network(DNetCNN)is used to classify the PD *** main advantage of using PCA-DNetCNN is that,it provides the best classification in the image dataset using *** addition to that,the results of various existing methods are compared and the proposed DNetCNN proves better accuracy,performance in detecting the PD at the initial *** achieves 97.5%of accuracy in detecting PD as ***,the other performance metrics are compared in the result evaluation and it is proved that the proposed model outperforms all the other existing models.

关键词： Parkinson’s disease multi-variant feature extraction DarkNet CNN principle component analysis feature selection machine learning deep learning

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Analysis of the Combination of Genetic Algorithm and Fuzzy Logic in Optimizing Ship Service Scheduling at Belawan Port

Analysis of the Combination of Genetic Algorithm and Fuzzy L...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Tinambunan, Kevin Pratama Suyanto Sihombing, Poltak Universitas Sumatera Utara Department of Computer Science Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331511166

Vessel service scheduling is one of the key challenges in efficient port logistics management, which requires careful handling of variables such as capacity, time, and priority. In an effort to optimize this process, this research proposes an innovative approach that combines Genetic Algorithm (AG) and Fuzzy Logic to reduce waiting time for ship services in ports, specifically in Belawan Port, Indonesia, where this research is conducted. In this research, Genetic Algorithm, as a population-based optimization method, is used to find the optimal solution by utilizing selection, crossover, and mutation mechanisms. Meanwhile, Fuzzy Logic is applied to handle uncertainty and variability in scheduling caused by factors such as the location of the ship, the length of the ship service process, and will affect the waiting time of the ship to be served. In this research, the parent selection process in Genetic Algorithm uses tournament selection method and Fuzzy Logic with Tsukamoto method. By utilizing the power of these two algorithms, the proposed system is expected to reduce the waiting time for ships to be served. The results of this research trial show that this combined approach is able to show optimization of ship services marked by a decrease in the average waiting time for ships to be served when compared to traditional scheduling methods with a decrease in the average waiting time of 38.50475%. © 2024 IEEE.

关键词： Genetic algorithms

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Analysis of Low-Power Wide-Area Network (LPWAN) Internet of Things (IoT) Telemetry system with Wireline Solution Approach Recommendation 4

Analysis of Low-Power Wide-Area Network (LPWAN) Internet of ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Igwebuike, Chidera Girma, Anteneh University of the District of Columbia Department of Computer Science and Information Technology WashingtonDC United States

ISBN: (纸本)9798350395914

The advancement of sensor networks plays a pivotal role in propelling the Internet of Things (IoT) forward. These networks, spanning from environmental monitoring to vehicle tracking, rely on battery-powered sensors ubiquitous across industries to amass extensive data sets. LPWAN (Low Power Wide Area Network) technology emerges as a cost-effective, energy-efficient solution offering extensive coverage for resilient IoT sensor networks. This study delves into Cloud telemetry-a method of gathering and transmitting data from nodes, remote sensors, or data collection systems using LPWAN technologies such as LoRa and NB-IoT. These technologies have garnered attention and present tailored solutions ideal for diverse IoT applications. © 2024 IEEE.

关键词： Telemetering systems

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Arabic Pilgrim Services Dataset: Creating and Analysis 1

Arabic Pilgrim Services Dataset: Creating and Analysis

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Al-Barhamtoshy, Hassanin M. Himdi, Hanen T. Alyahya, Mohamad King Abdulaziz University Faculty of Computer & Information Technology IT Department Jeddah Saudi Arabia University of Jeddah Faculty of Computer and Engineering Computer Science & Artificial Intelligence Department Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

With Countless Arabic news articles published daily;users have become increasingly concerned about obtaining news from credible sources. Nonetheless, to individuals, credible news sources are associated with certain countries where users have faith. Therefore, detecting the source of a news article is imperative to fake news detection and enables users a better trust in their consuming news. This paper introduces to create, filter, analyze, and evaluate a domain services-specific Arabic dataset for pilgrims. The Arabic Pilgrim Services (ArPiS) dataset is a collection of approximately 30,000 news, collected across three different Arabic countries and regions. The paper presents a creation for pilgrims' opinions measurement services dataset for text mining, text classification, clustering, and text summarization. The default basic search methods start with 124 web sites of Arabic news. Then, many of filtering features have been done to limit the dataset by pilgrim subjected services. A lot of topics are addressed, and a lot of filter with a discussion group have been made with many opinions| and extra comments. The huge of the collected data need some kind of additional effort and more analysis to produce valuable dataset. Balanced dataset is one of this extra effort, we are going to create. Therefore, the collected and annotated dataset represents real news for pilgrims' services. So, we need to build additional quantity of these data to be fake news. Accordingly, a precondition procedure invoked as a methodology to create and then annotate such dataset. © 2023 IEEE.

关键词： Classification (of information)

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Early Detection of Childhood Malnutrition Using Survey Data and Machine Learning Approaches 4

Early Detection of Childhood Malnutrition Using Survey Data ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Zumma, Thoufiq Rahaman, Anikur Islam Prova, Nuzhat Noor Haque, Tasriful Joy Bose, Julias Caeser Youki, Ravina Akter Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Washington University Science and Technology Department of Computer Science New York United States Pace University Department of Seidenberg School of Csis New York United States Victorian Institute of Technology Master of Information Technology and Systems Australia University of Wollongong Faculty of Artificial Intelligence and Science Australia University of Technology Sydney Faculty of Engineering and It Australia

ISBN: (纸本)9798331523923

Children's physical, mental and emotional development depends heavily on sleep, with age-specific sleep needs fluctuating. Malnutrition may result from eating too little, absorbing nutrients poorly, being unwell, living in poverty or not knowing enough about nutrition. Food allergies and iron insufficiency are two examples of illnesses whose onsets might differ depending on environmental exposures and genetics. By providing proper diet, getting medical attention when necessary and encouraging healthy behaviors, parents play a crucial role in supporting their children's health. For children to grow and develop properly, they need meals high in fruits, vegetables, milk, protein, calcium, iron and fiber. Key advantages of a balanced diet include aiding bone formation, improving heart health and encouraging regular bowel motions and digestion. In general, children's general health and wellness depend on caregivers being aware of their dietary requirements and giving them the proper attention. The findings of the research imply that machine learning algorithms may be useful in understanding and predicting the prevalence of malnutrition in children. Furthermore, these algorithms could provide perceptive data that might help politicians create effective policies. In this research, we make use of five different machine learning algorithms. With a decision tree, the accuracy is 0.92%, followed by svm which provides 0.91%, KNN, which provides 0.84%, GBM classifier, which provides 0.79% and random forest, which provides 0.65%. © 2025 IEEE.

关键词： Adversarial machine learning

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An Integrated Machine Learning and IoT Based Approach for Enhanced Healthcare Efficiency and Personalized Treatment 23th

An Integrated Machine Learning and IoT Based Approach for En...

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Singh, Jagendra Singh, Navneet Pratap Hasan, Dler Salih Shelke, Nitin Arvind Namdev, Arpit Girdhar, Nancy School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Information Technology University Institute of Technology RGPV Bhopal India University of La Rochelle La Rochelle France

ISBN: (纸本)9783031648120

This research suggests a comprehensive healthcare system that successfully blends machine learning (ML) and the internet of things (IoT) in order to increase healthcare efficiency and provide customized treatment in emergency ward situations. We employ a range of sensors, including those that detect pressure, temperature, oxygen levels, and heart rate, to continuously collect real-time patient data. In the outcomes of our trials, the ML models (SVM, ANN, RF, and DT) displayed varying degrees of accuracy, precision, recall, and F1 score. The most effective model, ANN, obtained an astounding accuracy rate of 92%. SVM came in second with an accuracy of 89%, followed by RF and DT in third and fourth, respectively, with 86% and 84% accuracy. Real-time data and predictive analytics are now available to healthcare professionals, which has a significant beneficial influence on efficiency and enables the customization of patient treatment plans. The number of emergencies was dramatically reduced as a result of these foresights, which improved patient outcomes. This study shows the potential for a patient-centered, technology-driven healthcare system that elevates the bar for healthcare delivery. The study results not only demonstrate the viability of our approach but also point out significant advancements in medical technology, paving the way for more effective, flexible, and patient-focused healthcare systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Quantum spin systems: Toroidal classification and geometric duality

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Physical Review B 2024年第14期110卷 L140403页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics Department of Physics and Astronomy Department of Applied Physics School of Engineering Sciences (SeRC) Wallenberg Initiative Materials Science for Sustainability (WISE) Wallenberg Initiative Materials Science WISE School of Science and Technology

We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.

关键词： Magnons Quantum correlations in quantum information Quantum information processing Quantum information theory Spintronics

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