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Cloud Computing and Neutrosophic Logic: Navigating Uncertainty in Educational Administration

Neutrosophic Sets and Systems 2025年 78卷 102-449页

作者： Yasser, Ibrahim Abd El-Khalek, Aya A. Khalid, Huda E. Salama, A.A. Essa, Ahmed K. Engineering Faculty University of Mansoura Mansoura35516 Egypt Communications and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura35524 Egypt Telafer University The Administration Assistant for the President of the Telafer University Telafer Iraq Department of Mathematics and Computer Science Faculty of Sciences Port Said University Port Said42526 Egypt

Educational institutions face inherent uncertainties in student performance, stakeholder priorities, and data analysis. This paper explores how cloud computing, with its data storage, analytics, and collaboration tools, can address these uncertainties in educational administration. We propose a neutrosophic framework utilizing "truth," "falsity," and "indeterminacy" values to represent these complexities within educational data. Real-world case studies analyze how cloud computing can reduce uncertainties and improve decision-making. Our findings reveal that cloud computing offers significant potential for navigating the "fog" of educational administration, but its effectiveness is enhanced by considering the "indeterminacy" inherent in educational data through the neutrosophic framework. This research contributes a unique neutrosophic perspective to the use of cloud computing for improved and more informed decision-making in educational administration. © 2025, University of New Mexico. All rights reserved.

关键词： Case Studies Cloud Computing Decision-Making Educational Administration Educational Data Neutrosophic Logic Uncertainty

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Automated Crack Detection in Pipelines Using Advanced Image Processing 2

Automated Crack Detection in Pipelines Using Advanced Image ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Subramani, Nandhagopal Jithesh, M.D. Harish, R. Rithish Hari, G. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Mechanical Engineering Coimbatore India

ISBN: (纸本)9798331505745

The automated detection of pipeline cracks is vital for maintaining the safety and operational lifespan of this crucial infrastructure. Early crack detection prevents costly repairs and minimizes the risk of catastrophic failures and environmental damage. Existing methods primarily rely on manual inspections and traditional image processing techniques, which often lead to inaccuracies and inefficiencies. These methods suffer from high false-negative rates and poor adaptability to different environmental conditions. This study proposes a deep learning framework utilizing YOLOv10 to automate the detection of pipeline cracks with enhanced accuracy. Studies using deep learning report detection accuracies between 80% and 92%, although performance varies across different studies. The range reflects the influence of factors such as dataset and model specifics. © 2025 IEEE.

关键词： Deep learning

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Predicting Pest Infestation Patterns with Graph Convolutional Networks (GCN) in Precision Farming 3

Predicting Pest Infestation Patterns with Graph Convolutiona...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Sireesha, Marri Hussein, Laith Manju, M. Johri, Prashant Kuppuraj, T. Aanandha Saravanan, K. Department of Computer Science and Engineering CMR Technical Campus Hyderabad India College of technical engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Kongu Arts and Science College Department of Computer Science TamilNadu Erode India School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331542375

Due to the increases pest impacts on agricultural productivity, effective pest prediction models are required for efficient pest control in precision farming. The framework outlined in this research uses a Graph Convolutional Network (GCN) to estimate pest infestation by exploiting spatial and temporal relationships present in agri-ecosystems. The methodology combines weather, soil, pest, and crop health data into a graph-based representation of spatial units, and the directed connections between them that reflect their proximity and climatic similarity. The GCN converts these graphs to learn such complex relationship as the infestation prediction at various severity levels. The proposed model has produced a better accuracy of 94.8%, better precision of 95.2%, better recall of 94.5% and better F1-score of 94.8% as compared from Random forest, SVM, LSTM, CNN as per the comparison of table 3. Comparing them focuses on graph construction approaches and temporal aspects, mixed P/S and temporal emb has improved the model performance. This framework is practical for further pest management and employing the result without harming the environment. The results show the effectiveness of GCNs for use in pest prediction in precision farming and the opportunity to develop sustainable and scalable data-based agriculture. © 2025 IEEE.

关键词： Weed control

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Confident Pseudo-Labeled Diffusion Augmentation for Canine Cardiomegaly Detection

Confident Pseudo-Labeled Diffusion Augmentation for Canine C...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision Workshops, WACVW 2025

作者： Zhang, Shiman Polamreddy, Lakshmikar Reddy Zhang, Youshan Katz School of Science and Health Yeshiva University Graduate Computer Science and Engineering Department 205 Lexington Avenue New YorkNY10016 United States

ISBN: (纸本)9798331536626

Canine cardiomegaly, marked by an enlarged heart, poses serious health risks if undetected, requiring accurate diagnostic methods. Current detection models often rely on small, poorly annotated datasets and struggle to generalize across diverse imaging conditions, limiting their real-world applicability. To address these issues, we propose a Confident Pseudo-labeled Diffusion Augmentation (CDA) model for identifying canine cardiomegaly. Our approach addresses the challenge of limited high-quality training data by employing diffusion models to generate synthetic x-ray images and annotate Vertebral Heart Score key points, thereby expanding the dataset. We also employ a pseudo-labeling strategy with Monte Carlo Dropout to select high-confidence labels, refine the synthetic dataset, and improve accuracy. Iteratively incorporating these labels enhances the model's performance, overcoming the limitations of existing approaches. Experimental results show that the CDA model outperforms traditional methods, achieving state-of-the-art accuracy in canine cardiomegaly detection. The code implementation is available at https://***/Shira7z/CDA. © 2025 IEEE.

关键词： canine cardiomegaly detection confident pseudo-labeling diffusion augmentation

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Blockchain-Based Secure Cloud Storage for IoT Healthcare Using BiGRU and BiLSTM Models 3

Blockchain-Based Secure Cloud Storage for IoT Healthcare Usi...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chougule, Prachi Arihant Sarumathi, S. Kant, Ravi Gupta, Raj Kumar Latha, U. Pushpa Perada, Anvesh Dkte Society's Tetile and Engineering Institute Department of Electrical Engineering Ichalkaranji India Hkbk College of Engineering Department of Computer Science and Engineering Bangalore India Ysaicds Shoolini University Department of Computer and Data Sciences Solan India Sardar Vallabhbhai Patel College Bhabua Vksu Department of Physics Ara India Department of English Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States

ISBN: (纸本)9798331508456

Machine Learning (ML) algorithms have experienced a significant increase in popularity owing to the digitisation of analogue processes and other technological advancements, like the Internet of Things (IoT). Dependable datasets are crucial for the precise forecasting and resolution abilities of these algorithms, which are indispensable in healthcare, cloud computing, engineering, and finance. Conversely, training datasets are vulnerable to manipulation, thereby skewing the results. To address this issue, blockchain-based approaches have been proposed to enhance the reliability and security of cloud-stored E-Health data generated by the Internet of Things. To improve the accuracy and efficacy of healthcare prediction models, our study focusses on an advanced strategy that integrates feature selection with model training. Optimal input data is refined by feature selection employing entropy and correlation coefficient methodologies. The proposed model, employing BiGRU and BiLSTM, surpasses conventional CNNs and BiRNNs, with an average accuracy of 94.57%. When integrated with the Internet of Things (IoT), blockchain technology ensures the secure storage and transmission of electronic health records (EHRs). This strategy enhances predictive accuracy and safeguards sensitive health data in cloud infrastructures by amalgamating feature selection techniques with robust deep learning models. © 2025 IEEE.

关键词： Electronic health record

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Enhanced Interpretability in Depression Classification with the Use of Machine Learning Models with Explainable AI

Enhanced Interpretability in Depression Classification with ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Hirak Mondal Kamrul Golder Towfeka Benta Towhid Bushra Jamil Anindya Nag Anupam Tarafder Anindya Mozumder Md Asaduzzaman Computer Science and Engineering North Western University Khulna Bangladesh Computer Science and Engineering Northern University of Business and Technology Khulna Bangladesh Computer Science and Engineering Khulna University Khulna Bangladesh Software Engineering Hubei University of Technology Hubei China

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Depression, a prevalent mental health concern, demands efficient and accurate classification methods to support timely interventions. This study utilizes two mental health datasets to classify depression levels into high, medium, and low categories. Data preprocessing and visualization techniques were applied to extract meaningful patterns, followed by the implementation of six widely used machine learning algorithms: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB). Among these, the RF model demonstrated the highest accuracy, achieving 0.98 without cross-validation and 0.99 with cross-validation. Class-wise evaluation using precision, recall, and F1 score further established RF’s superior performance. To enhance interpretability and transparency, Local Interpretable Model-agnostic Explanations (LIME) was employed, providing insights into the RF model’s decision-making process. This research underscores the efficacy of the RF model for depression classification and highlights the importance of integrating explainable AI techniques in mental health diagnostics.

关键词： Radio frequency Support vector machines Accuracy Machine learning algorithms Explainable AI Decision making Mental health Nearest neighbor methods Depression Random forests

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Enhancing Measurement Precision in FBG Sensors Through Robust Temperature Compensation Mechanisms 3

Enhancing Measurement Precision in FBG Sensors Through Robus...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Satapathy, Satyapriya Mohapatra, Ambarish G. Talukdar, Jaideep Nayak, Narayan Nayak, Dipak Ranjan Khanna, Ashish Silicon University Electronics Engineering Bhubaneswar India Silicon University Basic Sciences and Humanities Bhubaneswar India MAIT Computer Science Engineering New Delhi India

ISBN: (纸本)9798331533205

With the development of Fiber Bragg Grating (FBG) sensors, applications such as Structural Health Monitoring (SHM) systems, aerospace systems, and medical diagnostics have become highly accurate and more demanding. However, the use of FBG sensors is limited when the temperature varies since it brings about lateral cross-sensitivity errors in performance due to the wavelength shifting with temperature changes. This paper suggests two simple methods of avoiding this extreme that are inexpensive to compute: linear regression and polynomial fitting. The better temperature compensation model is incorporated to check the overall performance. Linear regression is a straight-line fitting method that applies to data that does not exhibit much curvature in the 25% improvement in measurement accuracy. In cases where complex temperature dependencies are present, polynomial fits are offered which improves accuracy by up to 30% by accounting for the non-linear tail showing more complex characteristics. Investigations using laboratory experimental data show the success of these techniques and show that indeed polynomial fitting worked better than linear fitting in measurements that were time-varying. Relatively easy to apply and real-time capable are the unique attributes of the proposed methods in comparison to earlier approaches such as dual FBG and reference stripping techniques. The results appropriately indicate their contribution to improving the dependability of FBG sensors under various conditions. The work will go on to consider how adaptive models and machine learning can help future FBG sensor measurements become more robust. © 2025 IEEE.

关键词： Linear regression

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Function Scheduling with Data Security in Serverless Computing Systems

Function Scheduling with Data Security in Serverless Computi...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Srestha Saha Arvind Pandey Sourav Kanti Addya Shubha Brata Nath Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user’s QoS.

关键词： Processor scheduling Distributed ledger Data security Serverless computing Systems architecture Quality of service Simulated annealing Virtual machines Fabrics Time factors

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Securing Cloud-Based Systems: DDoS Attack Mitigation using Hypervisor-Intrusion Detection Approach

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Procedia computer science 2025年 259卷 1366-1375页

作者： Surendra Kumar Pawan Kumar Jaydip Kumar Virendra Singh Arun Singh Yadav Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Application Integral University Lucknow India Department of Computer Science University of Lucknow Lucknow Uttar Pradesh India

The principle of virtualization and cloud-based technology has facilitated various industries in establishing expansive cloud environments. The Cloud is vulnerable to emerging threats and breaches due to the nature of its networks. Creating an intrusion system that addresses security concerns in cloud networks is crucial. This paper examines specific security weaknesses associated with significant threats, such as Distributed Denial-of-Service (DDoS) assaults, which jeopardize both data security and service availability in Cloud systems. To address these concerns, a Hypervisor Controller-based detection system has been developed for the identification of DDoS attacks. The Hypervisor Controller employs Fuzzy Time Series Analysis and Expectation Maximization methods in a dual-phase process. In the preliminary phase, resource requests are categorized according to their compliance with the Service Level Agreement (SLA). Requests that contravene the Service Level Agreement (SLA) are promptly identified as potential malware and denied from system access, thereby preserving the system’s security and stability. In the second step, non-violation requests undergo rigorous examination utilizing a Fuzzy Time Series model, simulated by the Expectation Maximization method. This method proficiently manages large datasets, significantly enhancing detection accuracy. The proposed Hypervisor Controller has demonstrated effectiveness, robustness, and immediacy in detecting DDoS attacks across federated systems and cloud environments, as evidenced by experimental and performance evaluations. This study aims to enhance the security of Cloud-integrated systems by formulating comprehensive and effective solutions to address emerging threats.

关键词： Service level agreement Fuzzy time series Joint Adaptive Anomaly Hypervisor Controller Distributed Denial-of-Service

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