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Service Recovery in NFV-Enabled Networks: Algorithm Design and Analysis

IEEE Transactions on Cloud Computing 2024年第2期12卷 800-813页

作者： Nguyen, Dung H. P. Lin, Chih-Chieh Nguyen, Tu N. Chu, Shao-I Liu, Bing-Hong Pham Van Dong University Faculty of Engineering and Technology Quang Ngai570000 Viet Nam National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung80778 Taiwan Kennesaw State University Department of Computer Science MariettaGA30060 United States

Network function virtualization (NFV), a novel network architecture, promises to offer a lot of convenience in network design, deployment, and management. This paradigm, although flexible, suffers from many risks engendering interruption of services, such as node and link failures. Thus, resiliency is one of the requirements in NFV-enabled network design for recovering network services once occurring failures. Therefore, in addition to a primary chain of virtual network functions (VNFs) for a service, one typically allocates the corresponding backup VNFs to satisfy the resiliency requirement. Nevertheless, this approach consumes network resources that can be inherently employed to deploy more services. Moreover, one can hardly recover all interrupted services due to the limitation of network backup resources. In this context, the importance of the services is one of the factors employed to judge the recovery priority. In this article, we first assign each service a weight expressing its importance, then seek to retrieve interrupted services such that the total weight of the recovered services is maximum. Hence, we also call this issue the VNF restoration for recovering weighted services (VRRWS) problem. We next demonstrate the difficulty of the VRRWS problem is NP-hard and propose an effective technique, termed online recovery algorithm (ORA), to address the problem without necessitating the backup resources. Eventually, we conduct extensive simulations to evaluate the performance of the proposed algorithm as well as the factors affecting the recovery. The experiment shows that the available VNFs should be migrated to appropriate nodes during the recovery process to achieve better results. © 2013 IEEE.

关键词： Recovery

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Intense Triad Defender for End-User Security in Cyber Physical System 2

Intense Triad Defender for End-User Security in Cyber Physic...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Sheryl Sharon, G. Maranco, M. Nidhya, R. Sivakumar, M. Coimbatore Institute of Technology Department of Computer Science and Engineering Coimbatore641014 India SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

The rapid advancement of technology has undoubtedly brought comfort to humanity, but it also necessitates robust authentication measures to ensure security in the ever-expanding e-world. This research aims to address the critical aspects of security, focusing on end-user authentication. The proposed solution incorporates multiple factors, including location-based authentication, text-based authentication with enhanced encryption standards, and facial recognition authentication. Location-based authentication serves to restrict access geographically, mitigating the risk of impersonation by enforcing strict geographic limits on facility access. Strong text passwords, utilizing a unique algorithm with a classic salt method, add an extra layer of complexity for potential attackers, making it challenging to compromise user credentials. The incorporation of facial recognition technology acts as a deterrent against non-human entities attempting unauthorized access. Leveraging deep learning and API servers ensures precision and scalability in the model, enhancing its effectiveness in real-world scenarios. These multi-factor authentication techniques collectively contribute to a secure, reliable, and forward-looking solution to address the evolving landscape of digital security threats. In adhering to the age-old adage "prevention is better than cure,"the research emphasizes the importance of proactive measures to safeguard against potential security breaches. © 2024 IEEE.

关键词： Network security

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Application of Machine Learning for Detection and Prediction of Malaria: A Review 3

Application of Machine Learning for Detection and Prediction...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Apetorgbor, Mawuli Wankhede, Vidhi Dakhare, Kajal Verma, Prateek Raut, Yudhishthir Tripathi, Chanchla Department of Science and Technology Maharashtra Wardha India Faculty of Engineering and Technology Maharashtra Wardha India Pimpri Chichwad University Department of Computer Science and Engineering Maval Sate Pune India Yeshwantrao Chavan College of Engineering Department of Computer Science and Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350381931

Mosquitoes carries a parasitic infection that affects millions of peoples globally. The initial and meticulous prediction of outbursts, patient verdict, and treatment enhancement can all considerably diminish the consequences of malaria. Machine learning is a promising method to tackle this problem with its capability to unveil and understand complex patterns from broad and multifaceted dataset. Accuracy of drug dosage and disease management in healthcare industry has proven phenomenal accuracy with use of machine learning algorithm. The current analysis explores the application of machine learning for prediction of malaria cases. This paper showcase the inclusion of numerous data source, comprising environmental factors, medical history and epidemiologic information to demonstrate how machine learning influences this to abundance of data to enhance prediction accuracy. This study also enhances the major advancements in prior malaria detection that have been made feasible and practicable by machine learning driven symptomatic technologies such prompt analytical test and image analysis of blood taints. Concerns like model comprehensibility, data secrecy, and equitable distribution of healthcare assets are also concealed in this review study. © 2024 IEEE.

关键词： Forecasting

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A Study of Deep Learning Techniques for Predicting Breast Cancer Types

A Study of Deep Learning Techniques for Predicting Breast Ca...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Ashwini, P. Suguna, N. Vadivelan, N. Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Associate Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Professor Department of Computer Science and Engineering Teegala Krishna Reddy Engineering College Hyderabad India

ISBN: (纸本)9789819947164

Breast cancer is today’s deadly health issue which causes high mortality in woman worldwide. The preliminary detection and classification may help for proper treatment of the same. Understanding the causes of this cancer using traditional machine learning techniques misread feature extraction are leading to a complication. Traditional machine learning models are best suited for feature-based raw data. The novel deep learning techniques have been introduced for diagnosis of breast abnormalities using various imaging modalities like mammogram, magnetic resonance imaging (MRI) and ultrasound with high accuracy. This survey focuses on challenges of classical machine learning models and supervene efficient prediction models using novel deep learning techniques. This review presents comparison analysis of traditional machine learning and deep learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Neutrosophic Sets and Systems: A Decade of Scientific Contribution and Growth

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Neutrosophic Sets and Systems 2025年 81卷 438-465页

作者： Saqlain, Muhammad Merigó, José M. Kumam, Poom Department of Mathematics King Mongkut’s University of Technology Thonburi Bangkok10140 Thailand School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway UltimoNSW2007 Australia

Neutrosophic Sets and Systems (NSS) has become an important Journal for neutrosophic theory and its applications in uncertainty modeling and decision sciences. In 2023, NSS celebrated its 10th anniversary, marking a decade of contributions to scientific literature. Motivated by this event, this paper provides a critical bibliometric analysis of NSS’s evolution as a leading academic journal in neutrosophy. This study explores the publication trends, citation impact, most productive authors, institutions, and global collaboration networks of NSS. The study maps bibliographic coupling, keyword co-occurrence and co-citation analysis using data from the Scopus database, secondary search using NSS webpage, and visualization tools like the VOSviewer. The findings identify Neutrosophic Sets, Decision-Making, and Fuzzy Sets as the most frequently studied keywords that highlight the increasing integration of neutrosophic approaches into multi-criteria decision-making (MCDM) and soft computing. The results indicate the most significant areas of research that are topology, soft set theory, hypersoft sets, and uncertainty modeling. The findings highlight the NSS increasing international collaboration, with contributions from America, Middle East, and Asia. Additionally, the analysis explores NSS’s alignment with sustainable development goals (SDGs), revealing notable contributions to good health and well-being (SDG 3), quality education (SDG 4), and peace, justice, and strong institutions (SDG 16). These contributions highlight how neutrosophic sets and systems is valuable across many researchers, from public health to governance to education. Overall, this paper serves as a fundamental study for researchers looking to explore emerging trends and novel applications of neutrosophic theories across diverse scientific and practical domains. © 2025, University of New Mexico. All rights reserved.

关键词： Bibliographies

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Electricity Consumption Classification using Various Machine Learning Models

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-6页

作者： Paikaray, Bijay Kumar Jena, Swarna Prabha Mondal, Jayanta Van Thuan, Nguyen Tung, Nguyen Trong Mallick, Chandrakant Bhubaneswar India Department of Electronics and Communication Engineering Centurion of University of Technology and Management Odisha India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Faculty of Engineering Technology Hung Vuong University Viet Nam Quality Assurance Dong A University Viet Nam Department of Computer Science & Information Technology GITA Autonomous College Odisha Bhubaneswar India

INTRODUCTION: As population has increased over successive generations, human dependency on electricity has increased to the point where it has become a norm and indispensable, and the idea of living without it has become unthinkable. OBJECTIVES: Machine learning is emerging as a fundamental method for performing tasks autonomously without human intervention. Forecasting electricity consumption is challenging due to the many factors that influence it;embracing modern technology with its heavy focus on machine learning and artificial intelligence is a potential solution. METHODS: This study employs various machine learning algorithms to forecast power usage and determine which method performs best in predicting the dataset based on different variables. RESULTS: Eight models were tested, including Linear Regression, DT Classifier, RF Classifier, KNN, DT Regression, SVM, Logistic Regression, and GNB Classifier. The Decision Tree model had the greatest accuracy of 98.3%. CONCLUSION: The Decision Tree model’s accuracy can facilitate efficient use of electricity, leading to both conservation of electricity and cost savings, and be a guiding light in future planning. Copyright © 2024 Paikaray, B.K. et al., licensed to EAI. This open-access article is distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transforming, and building upon the material in any medium so long as the original work is properly cited.

关键词： Forecasting

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Brain-computer Interface Artifact Removal

Brain-Computer Interface Artifact Removal

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Geetha, S. Shetty, Sahana Deemed-to-be University Faculty of Engineering and Technology Jain Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350322309

The purpose of a brain-computer interface, also known as a BCI, is to create a communication channel that converts human intention into a control signal for an output device using a brain signal, such as an electroencephalogram (EEG). In addition, artefacts are undesired potentials that helps to process the signals (EEG), with the majority of originate outside signal of the cerebral cortex. Regrettably, they have the ability to alter the form of neurological phenomena that is used to operate a BCI device. They also carry the risk of inadvertently and it can lead to the device which is being controlled in a manner that was not intended. As a result, it is necessary to steer clear of, disregard, or get rid of artefacts in the recordings of brain signals. The origin of artefacts can be traced back to both physiological and non-physiological processes. The different signals are monitored and compared for the better conclusion. © 2023 IEEE.

关键词： Electrocardiograms

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An Extensive Study of Frequent Mining Algorithms for Colossal Patterns 3rd

An Extensive Study of Frequent Mining Algorithms for Colossa...

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3rd International Conference on computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Sreenivasula Reddy, T. Sathya, R. Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India

ISBN: (纸本)9789819966899

During the last decade of research, a lot of focus has been placed on the subject of frequent pattern mining (FPM). A profitable data set with a large sum of transactions and only a few items in each transaction has been used to develop numerous FPM algorithms. Because of the rise of bioinformatics, a new sort of data set called a high-dimensional data set has emerged, with fewer transactions but a greater sum of elements in each. The execution time of classical algorithms grows with deal length. High-dimensional data sets can’t be processed by existing algorithms. But when applied to large data sets with a lot of dimensions, mining algorithms generate a huge amount of data, much of which is useless to scientists because of the little and medium-sized patterns they include. As a way to lessen the number of output patterns for mining patterns, colossal pattern mining is discussed. Since small and mid-sized patterns aren’t mined, mining algorithms for enormous patterns run faster. In this work, an extensive study of colossal patterns, existing mining algorithms with its drawback is mentioned. The definitions of FPM, high utility mining and relation of colossal patterns with others are also explained. PatternFusion is the first algorithm, which is developed for colossal patterns that is described briefly in this work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data mining

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Mathematical analysis of AMRes: unlocking enhanced recognition across audio-visual domains

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International Journal of Information technology (Singapore) 2024年 1-20页

作者： Zoughi, Toktam Deypir, Mahmood Department of Electrical and Computer Engineering Shariaty College Technical and Vocational University (TVU) Tehran Iran Faculty of Computer Engineering Shahid Sattari Aeronautical University of Science and Technology Tehran Iran

This research presents AMRes (Adaptive Windowed Convolutional Neural Network and Multiple Residual Network), a novel method that demonstrates remarkable resilience against overfitting, even in scenarios with limited training data. With meticulous design comprising up to 19 convolutional layers, it enables deep learning models to effectively extract and analyze vital information from input data while preserving it through deep layers. Through rigorous mathematical and empirical validation, the proposed method exhibits adept handling of intra- and inter-speaker variability, efficient training with limited data, and potential applicability beyond speech recognition in image-related applications, showcasing its generality. This paper establishes a solid foundation for advancements in multi-faceted recognition systems. The proposed method excels at proficiently modeling speech signals and achieving highly accurate speech recognition. Thorough evaluations utilizing well-established databases such as Switchboard, Timit, and FarsDAT for speech, and MNIST for image data underscore the method's versatility and efficacy across diverse data contexts. Comparative analysis against state-of-the-art techniques in speech recognition demonstrates the propose method's competitive and often superior performance. Notably, this method significantly reduces phoneme recognition errors by approximately 8% on notable datasets. This research provides a solid foundation for advancing the field and highlights the proposed method's potential in improving recognition systems, encouraging further exploration. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Audio-Visual Domains Deep learning Formal Verification Limited Training Data Mathematical Analysis Mathematical Foundations Speech recognition

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MRI techniques using image processing Brain Tumor Detection

MRI techniques using image processing Brain Tumor Detection

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Mahesh, T.R. Geetha, G. Deemed-to-be University Faculty of Engineering and Technology Jain Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350322309

A brain tumor develops when cells in the brain divide in an abnormal way that is not controlled by the body. This results in the growth of the tumor. Manual dissection of brain tumors identified by MRI is a process that is both necessary and time-consuming. However, it is also an extremely important step. Identifying the precise location of a brain tumor as well as its size is of the utmost importance when it comes to the efficient screening of patients and the treatment of tissue. Because of this, a proper diagnosis of a brain tumor needs to be carried out with as much promptness and accuracy as is humanly *** detection of a brain tumor using imaging technology for medical purposes is thought to be a challenging task because of the nature of the disease being investigated. The goal of this study is to provide a method that will assist in the treatment of brain tumors. This strategy will make use of the covert operations of VGG19 as well as the process of transfer learning. The approach that has been suggested will be improved throughout the course of this inquiry, with the ultimate objective being the development of a hybrid methodology. Utilizing the VGG19 and Transfer learning approaches, the purpose of this study is to develop an algorithm for the identification of tumors using magnetic resonance imaging (MRI). These methods have been applied to a wide variety of data collections that pertain to patients. They have the ability to organize their cooling effect on the final portion of the body tissues that are associated with the tissues, while at the same time keeping better the borders between the various components of the ti © 2023 IEEE.

关键词： Brain

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