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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Marimuthu, Ramaprabha Muthulingam, Gurusigaamani Ayyanar Vinoth, N.A.S. Ganesan, Sivasathiya Ponnuchamy, Kardeepa Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Easwari Engineering College Department of Artificial Intelligence and Data Science Chennai Ramapuram India

ISBN: (纸本)9798350300857

In recent years, social media communication plays an important role in sharing messages, thoughts, comments and ideas around the world. Among them, Twitter is one of the most popular communication mediums. Twitter provides free services for sharing information's that are limited to 140 characters. Each month nearly 40 million new twitter accounts were created. Since usage of twitter increases rapidly, fake accounts were created for posting the spam messages and links for redirecting to phishing websites. Researchers proposed various machine learning methodologies for filtering the spam to handle and maintain the social network security problems. But still detecting spam tweets, malicious link in real world scenario is still a complex task. In the proposed work, a novel deep learning technique is introduced to address the above mention issues. Word Vector model is used for learning the syntax of each tweet. Later by using proceeding representation data set, binary classifier is developed. For experimental purpose 1-month twitter datasets are collected. Proposed work outperforms when compared with various existing methodologies and also to detect the non-text based as well. The ratio of spam is determined using the precision, recall, and F-measure metrics. © 2023 IEEE.

关键词： Classification (of information)

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Ciao AI: the Italian adaptation and validation of the Chatbot Usability Scale

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Personal and Ubiquitous Computing 2023年第6期27卷 2161-2170页

作者： Borsci, Simone Prati, Elisa Malizia, Alessio Schmettow, Martin Chamberlain, Alan Federici, Stefano Faculty of Behavioural Management and Social Sciences Department of Learning Data Analysis and Technology Group of Cognition Data and Education University of Twente Enschede Netherlands Department of Surgery and Cancer Faculty of Medicine NIHR London IVD Imperial College of London London United Kingdom Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia Modena Italy Computer Science Department University of Pisa Pisa Italy Molde University College Molde Norway School of Computer Science University of Nottingham Nottingham United Kingdom Department of Philosophy Social and Human Sciences and Education University of Perugia Perugia Italy

Chatbot-based tools are becoming pervasive in multiple domains from commercial websites to rehabilitation applications. Only recently, an eleven-item satisfaction inventory was developed (the ChatBot Usability Scale, BUS-11) to help designers in the assessment process of their systems. The BUS-11 has been validated in multiple contexts and languages, i.e., English, German, Dutch, and Spanish. This scale forms a solid platform enabling designers to rapidly assess chatbots both during and after the design process. The present work aims to adapt and validate the BUS-11 inventory in Italian. A total of 1360 questionnaires were collected which related to a total of 10 Italian chatbot-based systems using the BUS-11 inventory and also using the lite version of the Usability Metrics for User eXperience for convergent validity purposes. The Italian version of the BUS-11 was adapted in terms of the wording of one item, and a Multi-Group Confirmatory Factorial Analysis was performed to establish the factorial structure of the scale and compare the effects of the wording adaptation. Results indicate that the adapted Italian version of the scale matches the expected factorial structure of the original scale. The Italian BUS-11 is highly reliable (Cronbach alpha: 0.921), and it correlates to other measures of satisfaction (e.g., UMUX-Lite, τb = 0.67;p © 2023, The Author(s).

关键词： Buses

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⁠Marine Ecosystem Monitoring Based on Remote Sensing Using Underwater Image Analysis for Biodiversity Conservation Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 309-318页

作者： Rao, Chandana Narasimha Rao, A. Venkateswara Shanmugasundar, G. Hamid, Junainah Abd Haldorai, Anandakumar Naidu, G. Rama Sapthami, I. Department of Chemistry Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh Guntur 522302 India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai 600044 India Management and Science University Selangor Shah Alam Malaysia Center for Future Networks and Digital Twin Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore 641202 India Department of Electronics & amp Communication Engineering Aditya University Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The growing focus on underwater observation and use of marine resources in recent years has led to an active research area in underwater image processing and analysis. Because of its importance in marine engineering as well as aquatic robotics, underwater picture enhancement serves as a preprocessing step to increase performance of high-level vision tasks like underwater object detection as well as recognition. While a number of studies demonstrate that underwater image enhancement methods (synthetic underwater environment image) increase the detectors’ F-measure, no research has examined the connection between these two jobs. This research proposed novel technique in remote sensing–based underwater image analysis in ecosystem monitoring in biodiversity conservation model using machine learning techniques. Here, the underwater-based remote sensing image based on marine ecosystem has been collected and analysed for noise removal with normalisation. The processed image feature extracted and classified using Gaussian spatial gradient neural network (GSGNN) and multilayer pyramid Bayes network (MPBN). The output images show coral surface texture. The experimental analysis has been carried out for various underwater dataset in terms of kappa coefficient, classification accuracy, random precision, F-measure, and normalised square error. Proposed technique kappa coefficient is 94%, classification accuracy is 98%, random precision is 95%, F-measure is 93%, and Normalised square error is 63%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Biodiversity conservation model Ecosystem monitoring Machine learning Remote sensing Underwater image analysis

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Evaluation of Metaverse Tools Based on Privacy Model Using Fuzzy MCDM Approach 1

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3rd International Multi-Disciplinary Conference - Theme: Integrated sciences and technologies, IMDC-IST 2024

作者： Husin, Nor Azura Abdulsaeed, Ali A. Muhsen, Yousif Raad Zaidan, Ali Shakir Alnoor, Alhamzah Al-mawla, Zahraa Raad Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Malaysia Civil Department College of Engineering Wasit University Kut Iraq School of Management Universiti Sains Malaysia Pulau Pinang11800 Malaysia Management Technical College Southern Technical University Basrah Iraq Department of Education University of Al-Mustansiriyah Baghdad10001 Iraq

ISBN: (数字)9783031517167

ISBN: (纸本)9783031517150

The issue of uncertainty in decision modelling for control engineering tools that support industrial cyber-physical metaverse smart manufacturing systems (ICPMSMSs) can be classified as a multi-criteria decision-making challenge for three primary reasons. Firstly, there are multiple components in the industry cyber-physical system (ICPS) that need to be taken into account. Secondly, these components vary in terms of their importance. Lastly, there is a variation in the available data. Despite substantial efforts, neither of the control engineering tools developed for ICPMSMSs has been capable of completely satisfying all ICPS aspects. As a result, choosing the best possible tool to support ICPMSMSs is a difficult process. Even though studies have evaluated control engineering tools, there are still unresolved issues regarding information ambiguity and uncertainty. In order to overcome these issues, this research improves the fuzzy weighted with zero inconsistency (FWZIC) method by adding Hexagonal Fuzzy Numbers to the process of assigning weights to the ICPS’s multiple components. Subsequently, the established Hexagonal-FWZIC method will be combined with the additive ratio assessments (ARAS) method to handle the rank in modelling control engineering tools. According to the results of the Hexagonal-FWZIC analysis, cybersecurity is the most influential factor, while the digital twin component has the least. The ARAS findings indicate that, of the ten tools studied, tools_1, tool_3, and tool_4 are the best three tools. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cyber Physical System

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Generative AI: A Review on Models and Applications 2

Generative AI: A Review on Models and Applications

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Malik, Kiran Baliyan, Anupam School of Computing Science and Engineering Galgotias University UP Greater Noida India Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science AP Guntur India Matu Ram Institute of Engineering and Management Computer Science and Engineering Haryana Rohtak India Chandigarh University Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350369960

Generative Artificial Intelligence (AI) stands as a transformative paradigm in machine learning, enabling the creation of complex and realistic data from latent representations. This review paper comprehensively surveys the landscape of Generative AI, encompassing its foundational concepts, diverse models, training methodologies, applications, challenges, recent advancements, evaluation metrics, and ethical dimensions. The paper begins by introducing Generative AI's significance across various domains, presenting its pivotal role in producing synthetic data with applications spanning image synthesis, text generation, music composition, drug discovery, and more. The objectives lie in elucidating the foundational concepts, delving into model intricacies, unveiling the training procedures, exploring its application landscape, addressing challenges, envisioning future directions, and discussing ethical ramifications. The foundational section elucidates the diverse array of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, Generative Reinforcement Learning (GRL), and advanced hybrid architectures. Subsequently, evaluation metrics ranging from Inception Score to perceptual similarity metrics and human evaluations are surveyed to assess generative model performance. Finally, ethical considerations underscore the necessity for addressing biases, misuse, intellectual property concerns, and the call for responsible AI development and regulation in the Generative AI landscape. © 2023 IEEE.

关键词： Advanced Hybrid Architectures Flow-Based Models Generative Adversarial Networks (GANs) Generative Reinforcement Learning (GRL) Variational Autoencoders (VAEs)

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Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain

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Intelligent Automation & Soft Computing 2024年第2期39卷 353-379页

作者： Sohaib Latif M.Saad Bin Ilyas Azhar Imran Hamad Ali Abosaq Abdulaziz Alzubaidi Vincent Karovic Jr. Department of Computer Science The University of ChenabGujrat50700Pakistan Department of Creative Technologies Air UniversityIslamabad42000Pakistan Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran66244Saudi Arabia Department of Computer Science Umm Alqura UniversityAlQunfudah28821Saudi Arabia Department of Information Systems Faculty of ManagementComenius University in BratislavaBratislava82005Slovakia

The Internet of Things(IoT)is growing rapidly and impacting almost every aspect of our lives,fromwearables and healthcare to security,traffic management,and fleet management *** has generated massive volumes of data and security,and data privacy risks are increasing with the advancement of technology and network *** access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF ***,conventional privacy preservation methods have high latency costs and overhead for resource-constrained *** machine learning approaches were also unable to detect denial-of-service(DoS)*** study introduced a novel decentralized and secure framework for blockchain *** avoid single-point OF failure,an accredited access control scheme is incorporated,combining blockchain with local peers to record each transaction and verify the signature to ***-based attribute-based cryptography is implemented to protect data storage privacy by generating threshold parameters,managing keys,and revoking users on the *** innovative contract-based DOS attack mitigation method is also incorporated to effectively validate devices with intelligent contracts as trusted or untrusted,preventing the server from becoming *** proposed framework effectively controls access,safeguards data privacy,and reduces the risk of *** results depict that the suggested framework outperforms the results in terms of accuracy,precision,sensitivity,recall,and F-measure at 96.9%,98.43%,98.8%,98.43%,and 98.4%,respectively.

关键词： Machine learning internet of things blockchain data privacy security Industry 4.0

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Toward a Secure Industrial Wireless Body Area Network Focusing MAC Layer Protocols: An Analytical Review

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 2028-2038页

作者： Javadpour, Amir Sangaiah, Arun Kumar Ja'Fari, Forough Pinto, Pedro Memarzadeh-Tehran, Hamidreza Rezaei, Samira Saghafi, Fatemeh Harbin Institute of Technology Department of Computer Science and Technology Shenzhen150001 China Instituto Politécnico de Viana Do Castelo ADiT-Lab Electrotechnics and Telecommunications Department Porto4900-347 Portugal National Yunlin University of Science and Technology International Graduate Institute of Ai Yunlin64002 Taiwan Sharif University of Technology Department of Computer Engineering Tehran11365-8639 Iran University of Tehran Faculty of New Sciences and Technologies Tehran14155-6619 Iran University of Groningen Bernoulli Institute for Mathematics and Computer Science Groningen9712 CP Netherlands University of Tehran Faculty of Management Tehran14155-6619 Iran

Monitoring security and quality of service is essential, due to the rapid growth of the number of nodes in wireless networks. In healthcare/industrial environments, especially in wireless body area networks (WBANs), this is even more important. Because the delays and errors can directly affect patients'/scientists' health. To increase the Monitoring Quality of Services (MQoS) in WBANs, a secure medium access control (MAC) protocol needs to be developed to provide optimal services. This article provides a comprehensive review of MAC protocols in WBANs with a technical security analysis approach. Time-based, contention-based, and hybrid protocols are compared in this article, regarding MQoS and their security vulnerabilities. We have considered delay, packet loss, and energy consumption as performance evaluation criteria in WBANs, which may be degraded under a cyberattack. This work shows that there is a research gap in the literature, which is the failure of covering security and privacy issues in the MAC layer protocols. © 2005-2012 IEEE.

关键词： Time division multiple access

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Genetic Signal Processing for Categorizing Genomic Data using Convolutional Neural Networks

Genetic Signal Processing for Categorizing Genomic Data usin...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Pulicherla, Padmaja Aluvala, Srinivas Premchander, Munimanda Sai Sudha, Poranki V L R N Revanasiddappa Institute of Technology and Management Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Malla Reddy Engineering College for Women It Department Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Breakthroughs in high-throughput sequencing technology have resulted in exponential growth in acquired genetic data. Classic machine learning systems have difficulties in dealing with complex genetic data, leading to investigation of deep learning methods. A unique technique for categorizing genomic data using convolutional neural networks (CNNs) is described. The genetic signal processing approach can extract unique characteristics from genomic data. CNNs' ability to comprehend subtle patterns and correlations in cleaned-up data results in improved classification accuracy. A publicly accessible dataset is used to compare the proposed technique across various criteria. The study of genetic information using digital noise processing technology is known as genetic signal processing (GSP). GSP are used to turn the nucleotide sequence into a line graph, allowing for appropriate pattern classification. From the results, it clearly shows that proposed CNN achieves better results in terms of accuracy (0.96), precision (0.96), sensitivity (0.95), specificity (0.97) and f1-score (0.95). © 2023 IEEE.

关键词： Convolutional layers Convolutional neural networks genetic signal processing Genetic signals Genomic data

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Adaptive Federated Learning for Efficient Network Traffic management in Edge Computing 4

Adaptive Federated Learning for Efficient Network Traffic Ma...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Babu, Kuricheti Suresh Venkatasubramanian, S. Kishore, S. Sree, K. Kavya Ramya Susanna, Ch.Lavanya Rao, Goda Srinivasa Lakkshmanan, Ajanthaa Pace Institute of Technology and Sciences Department of Computer Science and Engineering AndhraPradesh Valluru India Saranathan college of engineering Department of Computer Science and Business Systems Trichy India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Aditya University Department of Ai&ml Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Kl University Department of Cse Vaddeswaram Andhra Pradesh Guntur India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603 203 India

ISBN: (纸本)9798331529635

With the increasing prevalence of edge computing environment, there is a need to establish effective network traffic management that are able to manage vast data while also protecting the privacy of users. The proposed method enhances efficiency of bandwidth, reduce the network latency and manages computational demands without managing data privacy. This is achieved by utilizing the design of edge devices. The adaptable nature of the method enables it to react in real time to different traffic situations. It optimizes the distribution of resources and improves the overall performance of the network. Additionally, it can reduce the possibility of centralized data exposure, providing robust privacy preservation through the allowance of local model training on edge devices. Finally, this method is superior. The employment of federated learning to network traffic management provide efficiency to optimize the traffic in edge computing which is both scalable and safe. This makes it suitable for a wide variety of applications that are utilized in next generation networks © 2024 IEEE.

关键词： Federated learning

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Adaptive Security management Model for Networks 4

Adaptive Security Management Model for Networks

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Maguluri, Lakshmana Phaneendra Jemi Gold, P. Sudha, S. Rahini Aishwarya, K.P. Jayanthi, L.S. Bohra, Narendra Singh Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram522502 India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tiruvallur601206 India Velammal Institute of Technology Department of Computer Science Engineering Thiruvallur601204 India Panimalar Engineering College Department of Information Technology Chennai600123 India Vidya Academy of Science and Technology Department of Mathematics Kilimanoor695024 India Graphic Era Deemed to Be University Department of Management Studies Dehradun248002 India

ISBN: (纸本)9798350300888

Adaptive security is considered as an approach in cybersecurity that analyzes events and against events and behaviors to protect a network. This study will provide details about the different algorithms being used to secure networks. These approaches are driven by a small quantity of labeled data and a massive amount of unlabeled data. In this context, contemporary semi-supervised learning strategies base their operations on the assumption that the distributions of labeled and unlabeled data are comparable. This assumption has a substantial influence on how well these strategies perform overall. If unlabeled data contain information that does not belong to a particular category, the efficiency of the system will deteriorate. © 2023 IEEE.

关键词： Convex optimization

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