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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Chigambayeva, Diana Goryakin, Makar Batova, Polina Department of Computational and Data Science Astana IT University Astana Kazakhstan Depatment of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

Functional and mathematical models for the distribution of academic workload at the stage of preparing the educational process at a university are considered, which make it possible to largely determine the uniformity of teachers’ workload with various types of academic work when fulfilling the criteria and restrictions adopted for the distribution. ©2024 IEEE.

关键词： functional model mathematical model study load educational process educational work types of educational work

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Machine Learning Strategy for Detecting and Analyzing Fake Online Content

Machine Learning Strategy for Detecting and Analyzing Fake O...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Gopalakrishnan, Ishita Maharajan, Harene Sankar, Vedha Bharanikumar, Varsha Oliver A, Sheryl Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798350308167

Sensational and fake news are created and spread through social media to achieve the intended end. It may also involve narration of a true fact however being deliberately *** idea of this research is to identify the authenticity of the news/information present on the web in the form of text, links and images. The main aim is to feed in some dynamic information and determine whether it's real/misleading(fake). In this paper, we propose a novel model for our real-time application that detects fake news by examining the accuracy of some content, link or image given by the user, and predicting its authenticity based on a score. © 2023 IEEE.

关键词： Authentication factor Automated google search Fake news detection Information credibility Machine learning RSS feed Web authenticity

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SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

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computers, Materials & Continua 2023年第5期75卷 2383-2396页

作者： Abida Sharif Muhammad Imran Sharif Muhammad Attique Khan Nisar Ali Abdullah Alqahtani Majed Alhaisoni Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Department of Computer Science HITEC University TaxilaPakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based ***,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network *** IoVbased content distribution is an emerging concept in which all the vehicles are connected via the *** to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new *** this,a novel strategy mediator framework is presented in this paper for managing the network resources ***-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the *** to the limited memory of edge devices,the delectable bloom filters are used for caching and ***,the proposed scheme is compared with the existing variants for validating its effectiveness.

关键词： Internet of vehicles(IOV) content dissemination multi mediator data traffic management

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Identification of DNA N4-methylcytosine Sites via Multiview Kernel Sparse Representation Model

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1236-1245页

作者： Ai, Chengwei Tiwari, Prayag Yang, Hongpeng Ding, Yijie Tang, Jijun Guo, Fei Tianjin University School of Computer Science and Technology College of Intelligence and Computing Tianjin300072 China Aalto University Department of Computer Science Espoo02150 Finland Quzhou610056 China University of South Carolina Department of Computational Science and Engineering CO29208 United States Central South University School of Computer Science and Engineering Changsha410017 China

Identifying DNA N4-methylcytosine (4mC) sites is of great significance in biological research, such as chromatin structure, DNA stability, DNA-protein interaction, and controlling gene expression. However, the traditional sequencing technology to identify 4mC sites is very time-consuming. In order to detect 4mC sites, we develop a multiview learning method for achieving more effectively via merging multiple feature spaces. Furthermore, we think about whether the multiview learning method can improve the across species classification ability by fusing data of multiple species. In our study, we propose a multiview Laplacian kernel sparse representation-based classifier, called MvLapKSRC-HSIC. First, we make use of three feature extraction methods [position-specific trinucleotide propensity, nucleotide chemical property, and DNA physicochemical properties) to extract the DNA sequence features. MvLapKSRC-HSIC uses a kernel sparse representation-based classifier with graph regularization. In order to maintain the independence between various views, we add a multiview regularization term constructed by Hilbert-Schmidt independence criterion (HSIC). In the experiments, MvLapKSRC-HSIC is applied on six datasets, so as to compare with other popular methods in single-species and cross-species experiments. All experimental results show that MvLapKSRC-HSIC is superior to other outstanding methods on both single species and cross species. Importantly, MvLapKSRC-HSIC can identify a series of potential DNA 4mC sites, which have not yet been experimentally evaluate on multiple species and merit further research. © 2020 IEEE.

关键词： Feature extraction

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SecureChainAI: Integrating Blockchain and Artificial Intelligence for Enhanced Security in IoT Environments 5

SecureChainAI: Integrating Blockchain and Artificial Intelli...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Thamaraiselvi, K. Pushpalatha, A. Chidambarathanu, K. Wankhede, Jaishri P. Alagumuthukrishnan, S. Sarveshwaran, Velliangiri New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Karpagam College of Engineering Department of Computer Science and Design Coimbatore India R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India Cmr Institute of Technology Department of Computer Science and Engineering Hyderabad India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798331504403

Internet of Things (IoT) systems develop with paramount importance for security in view of the increased deployment of IoT devices over critical sectors like healthcare, industry automation, and smart cities. The risk exposure of such systems is extremely high to potent cyber threats, including those that may result in data integrity and privacy issues and operational unreliability. Security in IoT systems has thus emerged through heterogeneous integration with blockchain and artificial intelligence models. This blockchain technology provides a decentralized, immutable ledger for secure data transactions. AI models, especially those designed by anomaly detection and prediction algorithms, can identify and mitigate cyber threats. In this paper, the authors propose the SecureChainAI model that synergistically integrates artificial intelligence algorithms with blockchain to safeguard IoT environments. The two core building blocks that will make this proposed model real are CaSNet and the EchoChain algorithm. The CaSNet model integrates LSTM, RNN, and GRU techniques with novelty detection for proficient attack prediction and detection using their peculiar capabilities in sequential data handling and the identification of novel attack patterns. The EchoChain algorithm puts blockchain technology into the IoT infrastructure to provide security in communication and integrity of the values. This makes the architecture lightweight, shifting computational loads to the collector node, and hence suitable for IoT devices with low computational resources. Evaluations of the performance necessary to detect different classes of cyberattacks using SecureChainAI have improved on the detection accuracy, precision, recall, f1-score, and reduced error rates. © 2024 IEEE.

关键词： Network intrusion

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Agricultural repercussions caused by the pandemic period

Agricultural repercussions caused by the pandemic period

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Seema Dev Aksatha, D. Michael, G. Vennira Selvi, G. Manikandan, T. Gururama Senthilvel, P. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India Department of Computer Science and Engineering SIMATS School of Engineering SIMATS University Chennai India

ISBN: (纸本)9781032669663

COVID-19 has affected India’s agriculture system. However, the latest quarterly GDP predictions post-COVID scenario show the resilience of the Indian agricultural sector, which is expected to be the sole sector to expand 4% for fiscal years. In this context, the major purpose is to assemble a summary of early evidence on COVID-19‘s influence on India’s agriculture industry, including production, marketing, and consumption, and then devise pragmatic measures to recover and thrive after the pandemic. The epidemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity costs, changing consumption patterns, the poll found. India’s agriculture business was devastated by the outbreak. The crisis has allowed the state to launch many programmes and make important changes. After COVID-19, coordinated effort is needed to break the global stalemate. This review study shows how the COVID-19 epidemic has damaged global. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems 9

Graph Neural Networks for Optimizing Reliability in Vehicle-...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Neelamegam, G. Revathy, J. Kannukkiniyal, M. Devendran, M. Akshya, J. Choudhry, Mani Deepak Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Artificial Intelligence and Data Science Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Hindustan institute of technology Department of Computer Science and Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350377972

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models;it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data. © 2024 IEEE.

关键词： Graph neural networks

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Analysis of Various Motion Detection and Facial Recognition Based Home Security Systems 2nd

Analysis of Various Motion Detection and Facial Recognition ...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Karthikeyan, P. Nidhi, Shashank Vishnu Kumar, S.R. Karunakaran, V. Velliangiri, S. Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Department of Computer Science and Engineering-AI and ML Jain Deemed to be University Bangalore India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9783031689079

A combination of AI and IoT makes a great home security system. IoT collects data from the surroundings, while AI takes the data and makes predictions accordingly. In this case, these predictions help in creating alerts. CCTV cameras were traditionally used for home security systems, with attendees required to monitor the footage. Because of technological advancements, this process can be completed with the least amount of human intervention. Other work has been done in the field of home security systems that use facial recognition and motion detection. Those systems don’t exhibit good accuracy in detecting multiple human faces. This work focuses on a detailed survey of different facial recognition techniques along with motion detection techniques as per the requirements of a home security system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motion tracking

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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

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Melady Classification Enhancing Accuracy with Supervised Learning Methods

Melady Classification Enhancing Accuracy with Supervised Lea...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Roobini, M.S. Sohan Chidvilas, Bodapati Manohari, Gowri Babu, K. Praveen, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering India SRM Institute of Science and Technology Department of Computational Intelligence India

ISBN: (纸本)9798350319200

About one person dies every minute from heart disease, consequently, it has surpassed war as the largest cause of death in the twenty-first century. This includes both the male and female demographics, as well as the ratio may fluctuate depending on age bracket & sex. Nothing here suggests that persons of various ages are immune to cardiovascular disease. Classifying the underlying causes and diseases associated with this condition has become more difficult in recent years. In this study, we explore the different classification methods and the Flask Framework used for cardiac illness. As a result, it is drawing attention as a "fatal disease"that may kill a person in the absence of any outward signs of illness. Particularly important in cardiology, early and accurate diagnosis of heart illness is a cornerstone of effective healthcare. Classifying cardiac illness might be difficult at times because of a general lack of funds in the healthcare profession. The medical community and their patients stand to gain a great deal by making use of appropriate technological help in this area. This problem may be handled by using Data science methodologies. There are now established tendencies in Data science, and a wide range of research is being conducted in this area. This research seeks to apply the RF, KNN, Naive Bayes algorithm techniques to create a classifier model for the of Cardiovascular disease. We opted for the Flask framework in bringing to life our ingenious plan. This makes it easier to label needed values straight on the page thanks to HTML and CSS, marrying our pre-created models accurately to selected variables. What's great is the website's inviting user-friendly structure. Its well-structured layout simplifies deciphering categorized values. Plus, one major charm of our optimized site is that it helps users promptly get an insight into the status of an illness even before it's fully blown. © 2023 IEEE.

关键词： Flask Health care Heart disease KNN Naive Bayes RF

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