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4th International Conference on Neural Networks, information and Communication engineering, NNICE 2024

作者： Jiang, Yiping Yu, Yi Ma, Yuelong Zhuang, Jiahao Ye, Xianze Yuan, Yifeng Su, Guanpeng Guangdong University of Science and Technology School of Computer Science Guangdong Dongguan China University of Macau Faculty of Science and Technology Fst China South China Normal University School of Information and Optoelectronic Science and Engineering Guangdong Guangzhou China Macao Polytechnic University Faculty of Applied Sciences China Zhuhai College of Science and Technology School of Computer Guangdong Zhuhai China

ISBN: (纸本)9798350394375

Typhoons are exceptionally destructive tropical cyclones that possess the capacity to inflict significant harm upon society. For institutions engaged in risk assessment and disaster mitigation, accurately forecasting typhoon paths and trajectories is of paramount importance. In this study, we propose a Typhoon Track Image Prediction model based on a Comprehensive Long Short-Term Memory (LSTM) Attention Model in the field of Deep Learning. This model significantly improves the accuracy of typhoon track prediction. To assess its performance, we utilize typhoon data from the China Typhoon Network spanning from 1949 to 2022. The Comprehensive LSTM Attention Model is compared against other popular models such as Temporal Convolutional Networks (TCN), traditional LSTM, and Seq2Seq models for forecasting the typhoon track over a 12-hour period. The results demonstrate the superiority of our proposed model, showcasing its effectiveness in terms of superior accuracy in typhoon track prediction. © 2024 IEEE.

关键词： Long short-term memory

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Leveraging AI-Driven Systems to Advance Data Science Automation 1

Leveraging AI-Driven Systems to Advance Data Science Automat...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Preetha, M. Wankhede, Disha S. Kumar, Ravindra Ezhilarasan, G. Khurana, Saniya Sahoo, Girija Shankar Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9798350359756

Records technology automation is becoming increasingly essential for groups that want to make sense of immense and complicated datasets quickly and effectively. Leveraging AI-pushed structures gives a feasible solution for facts and technological know-how automation. AI-driven systems can automate statistics processing, exploration, analysis, and validation of records units. It allows data scientists to manage massive quantities of statistics without needing to sift through them manually. AI-driven systems identify styles, discover insights, and optimize algorithms in data technological know-how tasks. Additionally, they can automate and scale up predictive and prescriptive analytics to speedy check the effect of adjustments and pick out quick wins. With advancements in AI-driven structures, it is feasible for agencies to leverage advanced analytics and a statistics-driven approach to make better selections and force their agencies forward. © 2023 IEEE.

关键词： Automation

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Text Analysis on Early Reactions to ChatGPT as a Tool for Academic Progress or Exploitation

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SN computer Science 2024年第4期5卷 366页

作者： Bukar, Umar Ali Sayeed, Md Shohel Razak, Siti Fatimah Abdul Yogarayan, Sumendra Amodu, Oluwatosin Ahmed Raja Mahmood, Raja Azlina Centre for Intelligent Cloud Computing (CICC) Faculty of Information Science & amp Technology Multimedia University Jalan Ayer Keroh Lama Bukit Beruang Melaka 75450 Malaysia Department of Electrical Electronics & amp Systems Engineering Universiti Kebangsaan Malaysia (UKM) Selangor Bangi 43600 Malaysia Information and Communication Engineering Department Elizade University Ondo State Ilara-Mokin Nigeria Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Darul Ehsan Serdang 43400 Malaysia

Large language models (LLMs) have garnered significant attention lately, and one particular implementation that has captivated users is ChatGPT, a first-of-its kind innovation that sparks intense debates among professionals regarding its ethical boundaries, especially after it was released for public use. Some of the debates are ongoing, with several people getting more used to this tool and some losing touch with several ethical concerns. Thus, it is crucial to explore the early professional’s perspectives surrounding ChatGPT to comprehend its potential impact on the educational sector and, in particular, society in general. This research aims to address this knowledge gap by employing data mining techniques on LinkedIn to analyze ChatGPT-related discussions and provide a comprehensive textual examination. The dataset for this investigation consists of 166 records written in English by LinkedIn users discussing ChatGPT in its early stage. The study utilized VOSviewer software to clean, analyze, mine, and visualize the data. Among the sample, it was observed that 49% of the users are PhD holders, predominantly with experience in scientific research and data science fields. Hence, the findings reveal that issues such as plagiarism, references, citations, manuscripts, papers, and literature reviews stand out as the major concerns. These results suggest that while ChatGPT can contribute to academic progress by fostering new knowledge, thoughts, and ideas, it can also facilitate academic misconduct, including plagiarism and inaccurate information dissemination. Therefore, this paper also provides discussions on the implications and ethical issues of ChatGPT. In particular, it is imperative to establish clear ethical boundaries and guidelines for the appropriate use of LLMs in academic settings. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Academic ChatGPT Ethics LinkedIn LLMs Text analysis Visualization VOSviewer

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Analyzing the Potential of Deep Learning for Enhancing Data Science Practices 3

Analyzing the Potential of Deep Learning for Enhancing Data ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mercilin Raajini, X. Deshmukh, Gopal B. Prabha, Savita Singh, Jagtej Agarwal, Ankita Manu, S.E. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India

ISBN: (纸本)9798350319125

Deep learning is a progressive new field of artificial intelligence that has drastically changed the landscape of technology over the last few years. It has enabled the creation of clever packages, including self-using motors, facial recognition, natural language processing, and others. It has also affected information science, with deep getting to know presenting a new set of tools to system and examine information. This paper discusses the capacity of deep gaining knowledge for enhancing records technological know-how practices, consisting of predictive analytics, visible insights, and self-studying structures. For each subject, the paper provides a short evaluation accompanied by an evaluation and discussion. Subsequently, it provides an outline of cutting-edge developments and guidelines for how records scientists can OK use their abilities and revel to capitalize on the capacity of deep learning. © 2023 IEEE.

关键词： capitalize discussion learning recommendations scientists

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Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

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computers, Materials & Continua 2022年第7期72卷 1465-1481页

作者： Ali E.Takieldeen El-Sayed M.El-kenawy Mohammed Hadwan Rokaia M.Zaki Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Intelligent Analytics Group(IAG) College of ComputerQassim UniversityBuraydahSaudi Arabia Higher Institute of Engineering and Technology KafrelsheikhEgypt Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated *** has its unique hunting technique by performing rapid bowing *** show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark ***,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization ***,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection *** strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)*** findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.

关键词： Metaheuristic optimization swarmoptimization feature selection function optimization

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Enhanced Coyote Optimization with Deep Learning Based Cloud-Intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 4319-4336页

作者： Abdullah M.Basahel Mohammad Yamin Sulafah M.Basahel E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia E-commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India

Cloud Computing(CC)is the preference of all information technology(IT)organizations as it offers pay-per-use based and flexible services to its *** the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to *** Detection System(IDS)refers to one of the commonly utilized system for detecting attacks on *** proves to be an effective and promising technique,that identifies malicious activities and known threats by observing traffic data in computers,and warnings are given when such threatswere *** current mainstream IDS are assisted with machine learning(ML)but have issues of low detection rates and demanded wide feature *** article devises an Enhanced Coyote Optimization with Deep Learning based Intrusion Detection System for Cloud Security(ECODL-IDSCS)*** ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic(ADASYN)*** detecting and classification of intrusions,long short term memory(LSTM)model is *** addition,ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud *** the presented ECODL-IDSCS model is tested on benchmark dataset,the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.

关键词： Intrusion detection system cloud security coyote optimization algorithm class imbalance data deep learning

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Defensive Auto-Updatable and Adaptable Bot Recommender System (DAABRS): A New Architecture Approach in Cloud Computing Systems 6

Defensive Auto-Updatable and Adaptable Bot Recommender Syste...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Khwaldeh, Sufian Mohit, Y. Khushwant, S. Department of Network and Information Security College of lnformation Technology University of Fujairah 1207 United Arab Emirates Department of Information Technology Faculty of Information Technology and Systems The University of Jordan Aqaba77110 Jordan Department of Mathematics University Institute of Sciences Chandigarh University Punjab Mohali140413 India Department of Computer Science & Engineering University Institute of Engineering and Technology M. D. University Rohtak124001 India

ISBN: (纸本)9798350394634

Cloud server data security makes extensive use of data centers and instantly accessible edge systems for possible transactions involving sizable and confidential data. These Edge Systems are chosen for rental based on their usefulness in situations where new privacy and security difficulties must be overcome. The use of Edge Systems is subject to dynamic mobility, which increases the danger of attacking threats. With this suggested approach, the Defensive Auto-updatable and Adaptable Bot Recommender approach (DAABRS) is used by the Cloud Center to identify the safeguarded Edge System. Cloud Data Centers with Edge Systems contain a variety of users and environments, making it simple for attackers to enter anonymously and compromise Edge System privacy. The most effective security measure against increasing risks and assaults for Edge Systems that rent more cloud storage is to use Stealth Mode Bots inside of data segments. In addition to anticipating and averting data breaches in server partitioning, tenant systems, edge systems, and containers by integrating stealth mode bots into each. Suggested use cases: Tenants, Edge Systems, or Containers for effective security-enhanced Stealth Mode Bots that are activated when a particular data segment is unplugged. Updatable and adaptive bots are used in the suggested strategy to counter Edge strategy assaults. Additionally, it uses Stealth Mode Bots to stop heavily attacked Data Containers, and machine learning will be used to update specifications to identify and prevent further assaults. © 2024 IEEE.

关键词： Containers

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Augmenting Content Retrieval Through Machine Learning 3

Augmenting Content Retrieval Through Machine Learning

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Pavan, P.S. Sripriya, T. Vikas, B. Shivank Parmar, Yuvraj Acharjya, Kalyan Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

gadget getting to know is a subset of synthetic intelligence that focuses on making predictions via reading and getting to know patterns from records. In current years, machine studying has been increasingly more carried out to numerous fields consisting of natural language processing, laptop vision, and records analytics. in the field of content material retrieval, system gaining knowledge of strategies are used to enhance the effectiveness and efficiency of search algorithms, making it less complicated for customers to discover the information they ***, content retrieval structures depend on indexing and key-word-based totally strategies to retrieve applicable information. even as these strategies have been powerful, they have barriers in dealing with complicated queries and information the context of the facts being searched for. this is where machine learning is available in, providing the functionality to research and learn from big amounts of facts to enhance the accuracy and relevance of search consequences. through the usage of machine getting to know, content retrieval systems can better recognize the purpose at the back of a person's query and generate more correct outcomes. that is accomplished through strategies such as natural language processing, which allows the system to recognize and extract which means from text and speech. moreover, gadget gaining knowledge of can be carried out to study from past person interactions and feedback to continuously enhance the search enjoy. © 2023 IEEE.

关键词： Machine learning

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Crops Leaf Diseases Recognition:A Framework of Optimum Deep Learning Features

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computers, Materials & Continua 2023年第1期74卷 1139-1159页

作者： Shafaq Abbas Muhammad Attique Khan Majed Alhaisoni Usman Tariq Ammar Armghan Fayadh Alenezi Arnab Majumdar Orawit Thinnukool Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Faculty of Engineering Imperial College LondonLondonSW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai50200Thailand

Manual diagnosis of crops diseases is not an easy process;thus,a computerized method is widely *** couple of years,advancements in the domain ofmachine learning,such as deep learning,have shown substantial ***,they still faced some challenges such as similarity in disease symptoms and irrelevant features *** this article,we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases *** proposed architecture consists of five *** the first step,data augmentation is performed to increase the numbers of training *** the second step,pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer *** features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search *** best selected features are finally classified using machine learning classifiers such as SVM,and named a few more for final classification *** proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant *** proposed architecture achieved an accuracy of 100.0%,92.9%,and 99.2%,*** with recent techniques is also performed,revealing that the proposed method achieved improved accuracy while consuming less computational time.

关键词： Crops diseases preprocessing convolutional neural network features optimization machine learning

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Threshold public key-sharing technique in block chain

Threshold public key-sharing technique in block chain

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

作者： Pande, Sagar Dhanraj Singh, Gurpreet Zekrifa, Djabeur Mohamed Seifeddine Kodgire, Shilpa Prashant Patel, Sunil A. Le, Viet-Thanh School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Faculty of Computational Science GNA University Phagwara India Centre for Quantum Computation and Communication Technology Sydney Australia Maharashtra Institute of Technology Maharashtra Aurangabad India Computer Engineering Department L.D. College of Engineering Ahmedabad India Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9781032669663

A threshold key-sharing technology-based publicly verifiable key-sharing technology is presented to explore blockchain users’ private keys’ security. After receiving key fragments, participating nodes validate them. When splitting the key, the central splitting node does evil. In the critical recovery stage, the key fragments of the nodes participating in key splicing are publicly verified to prevent them from doing evil. In the critical distribution stage, add IDs to participating nodes to track malicious nodes and update node status in real-time, and a dynamic threshold mechanism is designed. To maintain private key fragment integrity, the owner of the key fragment and the controller node redistribute it to the new participating nodes once the vital fragment node goes offline. Experimental results reveal that this scheme’s private key recovery rate is 80% and has threshold properties, traceability, unforgeability, and recoverability. © 2024 The Author(s).

关键词： Blockchain

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