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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sumanth, N. Sai Priya, S. Vishnu Sankari, M. Kamatchi, K.S. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Sist Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

The AI-Enhanced Learning Assistant Platform is a revolutionary system designed to enhance learning, with cutting-edge features like question and answer generation, answer evaluation, identification of weak areas, recursive testing, an integrated query forum, and expert chat support. This platform makes use of artificial intelligence (AI) technology to try to satisfy the many needs that students and teachers have. Using natural language processing and machine learning, the platform's question and answer generating feature generates relevant questions on its own from the provided content. This encourages participation and in-depth subject understanding. The answer evaluation section provides quick feedback for improvement by utilizing AI algorithms to assess the accuracy and caliber of student responses. One of this platform's key advantages is its capacity to identify students' areas of weakness. Through the analysis of performance patterns and root causes, the system can generate customized recommendations and learning materials to help overcome those constraints. Through repeated practice, the program gradually pushes students to increase their understanding of the material by creating adaptive exams. Through the integrated query forum, students can collaborate and ask for assistance from others by asking questions and receiving answers from teachers and their peers. Furthermore, by enabling real-time communication between users and subject matter experts, the expert chat support tool fosters an engaging and motivating learning environment. © 2024 IEEE.

关键词： Students

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Deep learning intelligence for influencer-based topological classification for online social networks

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International Journal of Innovative Computing and Applications 2023年第4期14卷 211-219页

作者： Jain, Somya Sinha, Adwitiya Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology 62 Uttar Pradesh Noida India

Social network analysis provides quantifiable methods and topological metrics to examine the networked structure for several interdisciplinary applications. In our research, a social network of GitHub community is constructed that forms a dense network of 37,700 developers with 289,003 associations amongst them. The research involves finding the central developers in the GitHub network using graph analytics and benchmark centrality metrics, including degree, betweenness, closeness, PageRank and eigenvector;which is based upon network structural information. Our research methodology provides a breakthrough towards predicting the classification of GitHub users using artificial intelligence-based learning model trained with derived topological network centrality metrics. The proposed approach performs feature extraction for the developers by computing centrality score of each user followed by building correlation matrix using centrality parameters based on network topology. Further, the derived topological centrality scores were used as input features to train and build artificial intelligence-based models for classification. Our experimentation is better performance of artificial neural network over autoencoders, logistic regression and hyper-parameter tuned support vector machine. Certain intermediate outcomes include correlation, principal component analysis, loss monitoring, etc. The performance evaluation was performed in terms of macro and weighted F1-score, recall, precision, and accuracy. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Social networking (online)

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Disentangling Prosody Representations With Unsupervised Speech Reconstruction

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 39-54页

作者： Qu, Leyuan Li, Taihao Weber, Cornelius Pekarek-Rosin, Theresa Ren, Fuji Wermter, Stefan Institute of Artificial Intelligence Zhejiang Lab Hangzhou311121 China Department of Informatics University of Hamburg Hamburg20148 Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in speech recognition and speaker verification tasks respectively. However, it is still an open challenging question to extract prosodic information because of the intrinsic association of different attributes, such as timbre and rhythm, and because of the need for supervised training schemes to achieve robust speech recognition. The aim of this article is to address the disentanglement of emotional prosody based on unsupervised reconstruction. Specifically, we identify, design, implement and integrate three crucial components in our proposed model Prosody2Vec: (1) a unit encoder that transforms speech signals into discrete units for semantic content, (2) a pretrained speaker verification model to generate speaker identity embeddings, and (3) a trainable prosody encoder to learn prosody representations. We first pretrain Prosody2Vec on unlabelled emotional speech corpora, then fine-tune the model on specific datasets to perform Speech Emotion Recognition (SER) and Emotional Voice Conversion (EVC) tasks. Both objective and subjective evaluations on the EVC task suggest that Prosody2Vec effectively captures general prosodic features that can be smoothly transferred to other emotional speech. In addition, our SER experiments on the IEMOCAP dataset reveal that the prosody features learned by Prosody2Vec are complementary and beneficial for the performance of widely used speech pretraining models and surpass the state-of-the-art methods when combining Prosody2Vec with HuBERT representations. Audio samples can be found on our demo website. © 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

关键词： Speech recognition

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Lockstep Vs Microarchitecture: A Comparison 37

Lockstep Vs Microarchitecture: A Comparison

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37th IEEE International System-on-Chip Conference, SOCC 2024

作者： Mach, Jan Kohutka, Lukas Cicak, Pavel Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava842 16 Slovakia Slovak University of Technology Institute of Informatics Information Systems and Software Engineering Bratislava842 16 Slovakia

ISBN: (纸本)9798350377569

Safety/mission-critical applications require high dependability of the control systems. Their state-of-the-art protection approach is a system-level lockstep. This paper compares the system-level dual and triple lockstep technique to the microarchitecture-level protection of the Hardisc RISC-V core. For a fair comparison, each system is based on the same core(s) and integrates protection against bit-flips in memory and transient faults in the bus. We propose a fault injection methodology, combining pre-synthesis simulation with synthesis data to analyse the vulnerability of a system to faults. The fault injection campaigns show that the Hardisc can withstand fault rates orders of magnitude higher than the dual-core lockstep system while preserving the same area and power consumption. It comes with a 5% frequency penalty. We have shown that system failures are more frequent once the fault rate reaches an application-specific threshold (one fault in 100 clock cycles). © 2024 IEEE.

关键词： Failure rate

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Deep Learning Classifier to Recommend the Tourist Attraction in Smart Cities 2

Deep Learning Classifier to Recommend the Tourist Attraction...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Keshav Kumar, Vikas Jain, Abhishek Singh, Pranita Jain, Amit Kumar Prasad, M.S.R. Grd Institute of Management and Technology Department of Computer Science and Engineering Dehradun India School of Computer Science & Engineering Dev Bhoomi Uttrakhand University Dehradun India Uttaranchal University Department of Computer Science and Engineering Dehradun India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350371055

Provide an attraction recommendation system that uses deep learning and is powered by the Internet of Things (IoT) to develop the smart city visitor experience. Users of a smart city app or website will be able to record information about their trips, including whether they are going independently or with an accompanying companion, the sort of companion (e.g., a family with children or a lover), the purpose of their trip (business or pleasure), and personal characteristics (e.g., age, interests, etc.). Using this unique combination of input data, the suggested deep learning-based recommendations system would suggest tourist activities and sights that match the user's profile nicely. In addition, the suggested tourist attraction recommendations system uses actual data obtained from Internet of Things (IoT) devices to offer content-based information (such as previously looked-at sights) and context related data (such as weather conditions, location, time of day, etc.) to potential visitors in the smart city. Regarding the initial case [(a) planning and searching for activities earlier travelling], the suggested multi-label deep learning classifiers achieve a recall, accuracy, precision, and loss of 1.6%, 98.6%, 98.8%, and 98.7%, respectively, compared to other models such as extra tree, decision tree, k-nearest neighbour, and randomized forest. As for the second scenario, it can effectively suggest tourist spots while searching for smart city activities (b) with a recall, accuracy, precision, and loss of 2.6%, 98.4%, 98.6%, and 98.7%, respectively. © 2024 IEEE.

关键词： Deep Learning Internet of Things Personal Characteristics Recommendation System Smart City Activities

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer Science 2024年第5期5卷 1-10页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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Secure the ownership of WoT devices using secure ownership transfer framework

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International Journal of Information technology (Singapore) 2023年第4期15卷 2161-2171页

作者： Singh, Kaptan Tomar, Deepak Singh Department of Computer Science and Engineering Truba Institute of Engineering and Information Technology Bhopal 462038 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal India

The primary concern behind ownership in Web of Things (WoT) is to provide rights to own devices to operate and access. When the ownership of Internet of Things (IoT) devices changes, there is a likelihood of threats that compromise the security and privacy of sensitive data. Intrusion to the user’s private storage in a cloud is also possible. Therefore, there is a need to spread awareness regarding ownership transfer. It is essential to handle the ownership appropriately, else data and accessibility get compromised, affecting both new and previous users. This paper proposed a secure ownership transfer framework for the Web of Things. This paper intends to present ownership transfer schemes securely, and in this work, a Secured Third Party is introduced, which acts as an intermediate. They help to perform secure Ownership transfer between two owners. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Authorization Internet of things Ownership transfer Security STP Web of things WoT devices

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IoT: Communication protocols and security threats

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 1-13页

作者： Gerodimos, Apostolos Maglaras, Leandros Ferrag, Mohamed Amine Ayres, Nick Kantzavelou, Ioanna School of Computer Science and Informatics University of Thessaly Lamia Greece School of Computing at Edinburgh Napier University Edinburgh United Kingdom Technology Innovation Institute Abu Dhabi United Arab Emirates School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Engineering Dept.of Informatics and Computer Engineering University of West Attica Athens Greece

In this study, we review the fundamentals of IoT architecture and we thoroughly present the communication protocols that have been invented especially for IoT technology. Moreover, we analyze security threats, and general implementation problems, presenting several sectors that can benefit the most from IoT development. Discussion over the findings of this review reveals open issues and challenges and specifies the next steps required to expand and support IoT systems in a secure framework. © 2023 The Authors

关键词： Network architecture

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A Cybercrime Detection and Mitigation Framework for Online Credit Card Frauds

A Cybercrime Detection and Mitigation Framework for Online C...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Karnatak, Vijay Mishra, Amit Kumar Tripathi, Neha Gupta, Ashish Dumka, Ankur Sharma, Hemendra Shanker THDC Institute of Hydropower Engineering and Technology Department of Computer Science & Engineering Uttarakhand Tehri India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science & Engineering Dehradun India Tula's Institute Department of Computer Science & Engineering Dehradun India Women Intitute of Technology Dehradun India Department of Computer Science and Engineering Uttarakhand India COER University Department of Computer Science and Engineering CSA Roorkee India

ISBN: (纸本)9798350388534

The word 'cybercrime' refers to any illegal behavior that is carried out with the use of computing devices, smartphones, desktops, and laptops via the Internet. This includes any type of activity that comes under 'computer crime.' The scope of these crimes and the damage they cause can vary widely, and they frequently take advantage of flaws in computer systems. Cybercrime is a massive and rapidly growing problem that can have significant ramifications not only for individuals but also for businesses, including monetary, legal, and personal problems. Money, happiness, and ideology are some of their sinister objectives. It could devastate individuals, companies, and society. These effects range from human injury and national security threats to monetary damages and damage to reputation. Law enforcement, cybersecurity specialists, and policymakers must comprehend cybercrime's causes to develop effective prevention and prosecution strategies. Cybercrime prevention must involve individuals, companies, governments, and regulatory authorities. The fight against cybercrime is an ongoing process that calls for constant vigilance and the ability to react to new dangers quickly. In this paper, we propose a cybercrime mitigation framework. We also discuss the motivation behind online criminal activity. Further, the devastating effects of cybercrimes are discussed. Further, the conjoin of cybercrimes and social security is provided. Finally, some of the future research directions related to cybercrime mitigation framework are given. © 2024 IEEE.

关键词： Critical infrastructures

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Spatial and temporal analysis of urban heat island effect over Tiruchirappalli city using geospatial techniques

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Geodesy and Geodynamics 2023年第3期14卷 275-291页

作者： Ajay Badugu K.S.Arunab Aneesh Mathew P.Sarwesh Department of Civil Engineering National Institute of TechnologyTiruchirappalli620015Tamil NaduIndia School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014Tamil NaduIndia

Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas,known as urban heat island *** remote sensors measure the radiation emitted by ground objects,which can be used to estimate the land surface temperature and are beneficial for studying urban heat island *** present study investigates the spatial and temporal variations in the effects of urban heat island over Tiruchirappalli city in India during the summer and winter *** study also identifies hot spots and cold spots within the study *** this study,a significant land surface temperature difference was observed between the urban and rural areas,predominantly at night,indicating the presence of urban heat island at *** diurnal land surface temperature fluctuations are also detected seasonally,with a relatively higher temperature intensity during the *** trend line analysis shows that the mean land surface temperature of the study area is increasing at a rate of 0.166 K/decade with p less than *** using the spatial autocorrelation method with the urban heat island index as the key parameter,hot spots with a 99 percent confidence level and a 95 percent confidence level were found within the urban area.A hot spot with 95 and 90 percent confidence level was identified outside the urban *** spike in temperature for a particular region in the rural area is due to industry and the associated built-up *** study also identified cold spots with a 90 percent confidence level within the rural ***,cold spots with a 95 and 99 percent confidence level were not identified within the study area.

关键词： Urbanization Land surface temperature Urban heat island effect Hot spots Remote sensing

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