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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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Real-Time Sign Language Recognition System for Physically Challenged Community Using Deep Learning Technique 8th

Real-Time Sign Language Recognition System for Physically Ch...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Devi, V. Anjana Bhuvaneswari, E. Tummala, Rama Krishna Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Associate Professor Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai India Accenture Solutions Pvt. Ltd Chennai India

ISBN: (纸本)9789819784219

Language disparities have always existed in human civilization. In their community, deaf individuals use sign languages to communicate with one another, but it can be difficult to do so when interacting with the wider population. The project comprises a real-time sign language recognition system that enables hearing-impaired individuals and persons who do not know sign language to effortlessly communicate. The most used sign language in South Asian nations is Indian sign language. Convolution neural networks, specifically Tensor Flow object detection, are utilized to assess and train the model on the images that are used. The transfer training approach is used to train the deep learning model with various hand movements. Loss is anticipated to be decreased. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Elicitation of Intracerebral Hemorrhage Using Deep Learning 2

Elicitation of Intracerebral Hemorrhage Using Deep Learning

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2nd International Conference on Signal and Information Processing, IConSIP 2022

作者： Patil, Saraswati Jaybhaye, Sangita M. Mamulwar, Ashwini Rambhad, Ayush Prajapati, Mihir Mante, Suresh Vishwakarma Institute of Technology Department of Computer Engineering Assistant Professor Pune411037 India

ISBN: (纸本)9781728168852

The paper presents modeling and prediction for Intracerebral hemorrhage. The elicitation of intracerebral bleeding from veins and arteries that damages the internal stability and mental control of an individual could help in life saving situations, thus reducing the risk of intracerebral hemorrhage to a great extent. Vascular bleeding involves a lot of fragilities, and therefore if not treated and diagnosed soon, this can cause life-threatening situations. Deep learning algorithms shows indubitable results for medical image recognition and classification in the healthcare sector. Neuro physicians face difficulties in diagnosing hemorrhage in the beginning stages of intracranial bleeding, thereby adding to possibilities of misdiagnosis. This challenge faced by Neuro physicians worldwide is considered for the research work in the present paper. It provides a solution model for elicitation of ICH within a CT scan with the support of CNN. An AI-based Web platform is developed to classify CT scans and predict ICH type using the model. © 2022 IEEE.

关键词： computerized tomography

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DoS Defense Using Modified Naive Bayes 4th

DoS Defense Using Modified Naive Bayes

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Shrivastava, Rajesh Kumar Singh, Simar Preet Pujahari, Abinash Bennett University UP Greater Noida India Assistant Professor Computer Science and Engineering SRM University AP Andhra Pradesh Amaravati522502 India

ISBN: (纸本)9789819900848

IoT devices depend on a network for communication and convenient information sharing. However, networks suffer from security threats such as DoS attack, RPL attack. We propose a honeypot-based method to detect such attacks. In proposed solution, Cowrie honeypot is used to capture adversaries activities. Captured unlabeled data is analyses by a modified Naive Bayes algorithm in combination with K-means clustering approach. Proposed solution effectively identifies DoS attacks and helps to strengthen the firewall and intrusion detection system functionalities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Denial-of-service attack

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A Novel Adaptive Fault Tolerance Algorithm Towards Robust and Reliable Distributed Applications to Reuse System Components 10th

A Novel Adaptive Fault Tolerance Algorithm Towards Robust an...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Banothu, Lalu Chandra Mohan, M. Sunil Kumar, Charupalli Professor Department of Computer Science and Engineering JNTUH College of Engineering Hyderabad India Professor & Dean in CSE Apollo University Andhra Pradesh Chittoor India

ISBN: (纸本)9789811974540

Distributed component-based systems relay on components that run in different servers located geographically. In large distributed applications experiencing faults is common due to complexity, heterogeneity and a host of other reasons. Therefore, realizing a reliable and fault-tolerant distributed system is challenging. Existing techniques such as N-version programming and modular redundancy have certain drawbacks such as increased complexity and error prone. In this paper, we proposed an algorithm known as Adaptive Fault Tolerance (AFT) algorithm which evaluates different functions or components in the system and assigns weights to them besides following a novel redundancy strategy. The algorithm exploits function calls and frequency of calls to arrive at finding importance of functions. This know-how helps in identifying functions that are vulnerable and the functions that are reliable. The AFT helps in achieving fault-tolerant distributed component-based applications comprising of untrusted components. Experimental results showed that AFT outperforms many existing fault-tolerant approaches. Thus, the proposed algorithm helps in realizing robust and reliable distributed applications that reuse components and drive home businesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fault tolerance

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Packet Scheduling in the Underwater Network Using Active Priority and QLS-Based Energy-Efficient Backpressure Technique 6th

Packet Scheduling in the Underwater Network Using Active Pri...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Caroline Mary, A. Senthil Kumar, A.V. Saleh, Omar S. Research Scholar Department of Computer Science Hindusthan College of Arts and Science Coimbatore India Assistant Professor Department of Applied Mathematics and Computational Sciences PSGCollege of Technology Coimbatore India Professor and Director Department of MCA Hindusthan College of Arts and Science Coimbatore India Ministry of Higher Education and Scientific Research Baghdad Iraq

ISBN: (纸本)9789819940707

Underwater communications are playing a major role in recent times as underwater happenings have gained interest. Especially in scenarios based on natural calamities like tsunamis, pollution detection, and surveillance, underwater communications are indispensable. Parameters like battery life span and capabilities of sensor nodes play a vital role in the underwater environment. QoS has to be taken care of in underwater communication. Congestion avoidance helps in using energy efficiently in underwater communication. Simulation results prove that this algorithm overcomes the drawback of a normal backpressure-based, static priority algorithm where packet loss, delay, the energy of nodes, and routing overhead are reduced overall, and packet delivery ratio and throughput are increased in special cases of real-time important packets. As underwater nodes are battery-operated, it is essential to have energy-efficient scheduling algorithm. The proposed algorithm solves the drawback of the backpressure algorithm by having energy-efficient queue length stabilizer (QLS) technique, where the backlog is adjusted such that no queues are affected and the energy consumption is less. And real-time packets are treated effectively such that real-time applications can be supported using this scheduling algorithm. The novelty of the proposed method is congestion control methodology with active priority and energy efficiency for an underwater network for effective scheduling of packets where queue backlog size is also adjusted virtually based on energy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Scheduling algorithms

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Automatic Creation of Quality Images from Text using Multiple Generative Adversial Network

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Procedia computer science 2023年 230卷 955-963页

作者： M.S. Geetha Devasana V. Krishna Kumar R. Selvaraj T. Satheesh Assistant Professor Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore-641022 India Assistant Professor Department of Computer Science and Engineering Dr.N.G.P Institute of Technology Coimbatore-641048 India Assistant Professor Department of Artificial Intelligence and Data Science Dr.N.G.P Institute of Technology Coimbatore-641048 India

Automatic creation of realistic images is a tedious process even though the state-of-the-art AI/ML algorithms are employed. There is a lot of demand for such automatic image generators that could create high quality images. Many have this problem of visualizing things from the explanations they hear about. Thus, the text to image generation problem is necessary because it has significant applications in CAD, art generation and many more. This is a challenging task since the image should be realistic and consistent with the text. One of the most common uses of modern conditional generative models is the generation of visuals from natural language. Viewing an image makes us easily understand what that image is, rather than hearing someone describing the image. To bridge the semantic gap between text and image, Generative Adversarial Network (GAN) systems are used to achieve high accuracy. The proposed system helps in generation of superior quality images that are meaningfully consistent with the text.

关键词： Machine Learning Generative Adversarial Network (GAN) Text to Image and Natural Language Processing

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Design and Simulation of Microstrip Patch Antennas with Textile Dielectrics for Body Sensor Networks (BSNs) and Wearable IoT Applications

Design and Simulation of Microstrip Patch Antennas with Text...

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International Conference on Advances in Data science and Computing Technologies, ASDC 2022

作者： Mahamuni, Chaitanya Vijaykumar Assistant Professor Department of Computer Engineering Fr. Conceicao Rodrigues Institute of Technology University of Mumbai Navi Mumbai Maharashtra Vashi India

ISBN: (纸本)9789819936557

Wireless Body Area Networks (WBANs) connect sensors, i.e., independent nodes or computing devices located in the clothes, on the human body, or under a person’s skin, expanding over the whole body and connected through wireless networks. WBANs find a wide range of applications in the medical sector, like implanting Body Sensor Networks (BSNs) for cancer screening, cardiovascular (CVD) monitoring, remote health monitoring systems, and telemedicine. They are also used for non-medical applications like sports and fitness monitoring, physical state monitoring, biometrics, etc. A textile antenna uses wearable, durable, and flexible textiles as either dielectric substrates or conductive materials. Microstrip patch antennas are widely used in WBAN and IoT applications due to their numerous advantages, like high gain, directivity, and ease of construction. In addition, these antennas have other safety considerations, e.g., they well isolate the radiating element from the body to meet the rated Specific Absorption Rate (SAR). In this paper, microstrip patch antennas with different types of textile dielectrics and conducting materials have been designed and simulated in MATLAB r2020a. According to the simulation results, textile antennas are suited for use with GSM, GPS, WLAN, and other technologies operating in the Industry Scientific Medical (ISM) band of frequencies. Furthermore, a comparative analysis of the results shows that the microstrip antenna with the flectron patch and the polyurethane dielectric gives the highest gain of 26.96 and an efficiency of 99%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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A novel random finite element model for holistically modeling of the frost effects on soils and cold region pavements

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Journal of Infrastructure Preservation and Resilience 2022年第1期3卷 1-14页

作者： Dong, Shaoyang Jiang, Yusheng Yu, Xiong Graduate Research Assistant Department of Civil and Environmental Engineering Case Western Reserve University ClevelandOH United States Opal J. and Richard A. Vanderhoof Professor and Chair Department of Civil and Environmental Engineering Professor of Electrical Engineering and Computer Science Case Western Reserve University ClevelandOH United States

This paper describes the development of a random finite element model (RFEM) that allows holistic simulation of frozen soil behaviors, including the effects of phase transition and the consequent internal stress and volume changes. The performance of the model is firstly validated with laboratory experiments. The model is implemented to simulate the effects of frost action on pavement. The coupled thermal-mechanical actions including the mechanical responses of subgrade soils subjected to freezing temperature and its effects on the pavement structure are analyzed. The results show that the frost action and expansion of ice lenses change the interaction modes between pavement layers. This leads to increase of stress and deformation in the pavement layer. Methods to mitigate the effects of frost heave are analyzed with this model. The simulation results indicate that the detrimental effects of frost heave on the pavement structure can be mitigated by increasing the thickness of base layer, use of thermal insulation layer or improve drainage in the subgrade layer. The RFEM combines the advantages of discrete element model (DEM) in holistically describing the microstructure effects and in the finite element model (FEM) in terms of computational efficiency. This allows to focus research on understanding the behaviors of individual soils phase and their interfacial interactions. © The Author(s) 2022.

关键词： Pavements

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Convolutional Neural Networks for MRI-Based Brain Tumor Classification 3

Convolutional Neural Networks for MRI-Based Brain Tumor Clas...

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3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Soewu, Taiwo Singh, Dalwinder Rakhra, Manik Chakraborty, Gouri Shankar Singh, Arun Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University Department Computer Science and Engineering Phagwara India Lovely Professional University Assistant Professor Department of Computer Science and Engineering Phagwara India Lovely Professional University Phagwara Student IEEE Member Department of Computer Science and Engineering Punjab India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

Recently, the rate at which people die from cancer disease is very alarming. The major reason is because the cancer cells were not detected at the early stages. It may be difficult for medical professionals to evaluate the results of medical imaging techniques such as magnetic resonance imaging (MRI) because cancer cells may not be adequately represented for detection. This is why deep learning techniques are needed for earlier detection of cancer cells so as to reduce the death rate from *** this paper, we have applied the convolutional neural network on some MRI image dataset downloaded from Kaggle. The model was trained to be able to detect tumors in brain MRI images. The performance of the model was evaluated and it achieved 97.8% score for the accuracy, 98.5% specificity, 96.2% recall, 98.5% F1-score and 97.3% precision. A graph was drawn to compare the training loss to the validation loss, as well as the training accuracy to the validation accuracy. © 2022 IEEE.

关键词： Magnetic resonance imaging

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