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A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain

Neural Computing and Applications 2024年第34期36卷 21545-21560页

作者： Alam, Tanweer Gupta, Ruchi Ahamed, N. Nasurudeen Ullah, Arif Department of Computer Science Faculty of Computer and Information Systems Islamic University of Madinah P.O.Box. 170 Madinah42351 Saudi Arabia Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad Abdul Kalam Technical University Lucknow India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Department of Computer Science Faculty of Computing and Artificial Intelligence Air University Islamabad Pakistan

Decision-making is crucial in fully autonomous vehicle operations and is expected to greatly influence future transportation systems. Observing the current driving status of autonomous vehicles is vital for its decision-making process. The autonomous connected vehicles on the road send significant data about their movements to the server to maintain continuous training. With the Proof of Authority (PoA) consensus process, blockchain technology provides a valid, decentralised and secure option to improve transactions throughput and minimise delay. The limited computational capacity of vehicles poses a challenge in achieving high accuracy and low latency while training self-driving algorithms. GPT-4V surpassed challenging autonomous systems in scene interpretation and causal thinking. GPT-4V has ability to navigate circumstances without access to database, interpret intentions, and make sound decisions in real-world driving scenarios. The reward function and different driving conditions are organised to allow an optimal search to find the most efficient driving style while ensuring safety. The consequences of the Blockchain-enabled decision-making model (DMM) for Self-Driving Vehicles (SDV) primarily based on GPT-4V and Federated Reinforcement Learning (FRL) would, likely, upgrades in decision-making accuracy, operational performance, statistics integrity, and potentially enhanced learning skills in SDV. Integrating blockchain technology, superior language modelling GPT-4V and FRL may lead to multiplied safety, reliability, and decision-making ability in SDV. This study utilised the Simulation of Urban MObility (SUMO) simulator to assess the ability of SDV to maintain its desired speed consistently and securely in a highway setting using proposed DMM. This study indicates that the suggested DMM, utilising the driving state evaluation approach for SDV, can help these vehicles operate safely and effectively. The performance of the proposed model, such as CPU utilisation

关键词： Reinforcement learning

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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

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Multimedia Tools and Applications 2025年第18期84卷 19223-19253页

作者： Anusha, N. Tapas Bapu, B.R. S, Selvakumaran Vijayaraj, A. Ramesh Kumar, C. P, Raji Department of IoT School of Computer science and Engineering VIT Tamil Nadu Vellore India Department of Electronics and Communication Engineering S.A.Engineering College Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Information Technology R.M.K Engineering College R.S.M Nagar Tamil Nadu Kavaraipettai India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India

Wireless sensor network (WSN) is one of the essential components of a multi-hop cyber-physical system comprising many fixed or moving sensors. There are many common attacks in WSN, which can quickly harm a WSN system. In this manuscript, Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) with war strategy optimization (WSO) algorithm is introduced for detecting intrusion in WSN. Initially, the input data is attained from the NSL-KDD and CICIDS2017 datasets. The data is then passed onto Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) based pre-processing. After that, the filtered data is sent to the DIWGAN, which detects the intrusion and classifies the fraud data in the network. Additionally, the WSO algorithm is introduced to enhance the classification parameters of DIWGAN. By then the performance of the proposed DIWGAN-WSO-IDS methodology is evaluated using Python Platform. Also, the performance of the proposed methodology is calculated using various metrics like accuracy, precision, recall, f-measure, mean square error, and attack detection rate. Thus, the proposed technique has attained 29.87%, 31.7%, and 26.8% higher accuracy, 23.54%, 20.09%, and 26.98% higher recall, 12.76%, 10.96%, and 6.7% higher precision. And 6.85%, 4.56%, and 8.56% lower MSE than the existing techniques like BFSGSRF, MHICA-SSA, and CNN-LSTM methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wireless sensor networks

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RANSACs for 3D Rigid Registration:A Comparative Evaluation

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1861-1878页

作者： Jiaqi Yang Zhiqiang Huang Siwen Quan Zhiguo Cao Yanning Zhang the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710072China the School of Electronic and Control Engineering Chang’an UniversityXi’an 710064China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China IEEE

Estimating an accurate six-degree-of-freedom(6-Do F)pose from correspondences with outliers remains a critical issue to 3D rigid *** sample consensus(RANSAC)and its variants are popular solutions to this *** there have been a number of RANSAC-fashion estimators,two issues remain ***,it is unclear which estimator is more appropriate to a particular ***,the impacts of different sampling strategies,hypothesis generation methods,hypothesis evaluation metrics,and stop criteria on the overall estimators remain *** work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive *** objective is to thoroughly compare estimators in the RANSAC family,and evaluate the effects of each key stage on the eventual 6-Do F pose estimation *** have been carried out on four standard datasets with different application scenarios,data modalities,and *** provide us with input correspondence sets with a variety of inlier ratios,spatial distributions,and *** on the experimental results,we summarize remarkable outcomes and valuable findings,so as to give practical instructions to real-world applications,and highlight current bottlenecks and potential solutions in this research realm.

关键词： 3D rigid registration performance evaluation point cloud pose estimation

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A Comprehensive Analysis of the Internet of Things (IOT) in the Context of 5G Wireless Systems 4

A Comprehensive Analysis of the Internet of Things (IOT) in ...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Mohseni, Milad Kanwer, Budesh Singh, Navdeep Krishna, M Hari Rajalakshmi, B. Alkhafaji, Mohammed Ayad Islamic Azad University Department of Computer and Engineering Tabriz Iran Poornima Institute of Engineering & Technology Department of Artificial Intelligence & Data Science India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Computer Science Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

The fast development of 5G wireless technologies (IoT) has been a major contributor to the rapid growth and expansion of the Internet of Things! This in-depth study investigates the mutually advantageous relationship between 5G and the Internet of Things (IoT), focusing on how the combination assists in the development of massive device networks, enhances connectivity, and enables the development of real-time applications. There are now new opportunities for innovative applications in smart cities, industrial automation, healthcare, and other industries owing to 5G, which enables Internet of Things devices to communicate with one another at a speed and efficiency that has never been seen before. In spite of the benefits, however, there are challenges to be faced, such as the construction of infrastructure to support the expansion of the Internet of Things and the assurance of robust security. This article provides an analysis of these components and a blueprint for leveraging the advantages of Internet of Things (IoT) with 5G while minimizing any potential downsides. © 2024 IEEE.

关键词： Internet of things

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Disaster Management Ecosystem Using Crowdsourced data, Deep Learning and NLP

Disaster Management Ecosystem Using Crowdsourced Data, Deep ...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Vaishali Savale Siddhi Patil Atharva Raut Mrunmayee Phadke Rahul Sundkar Rajkumar Dongre Department of Artificial Intelligence and Data Science Engineering Vishwakarma Institute of Technology Pune Maharashtra India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

In recent years, the frequency and intensity of natural disasters have increased, necessitating more efficient and responsive disaster management systems. This paper presents a novel disaster management solution leveraging crowdsourced data to enhance disaster response and coordination. The system bridges the gap between citizens and emergency management officials by composing a mobile application for citizens to send real-time disaster alerts and a web application for administrators to monitor and manage these alerts. A customized DBSCAN algorithm, adapted to use haversine distance, is deployed to detect clusters of disaster alerts in real time, indicating potential disaster events. Upon cluster detection, administrators can delineate affected areas on a map, triggering notifications to users within these regions and enabling them to send help requests with images and messages. These help requests are processed using the BLIP model for image captioning and the ChatGPT API for categorization, assigning relevant tags to each request. The system provides administrators with real-time data analysis features, including map filtering and citizen help request category visualization, enabling more informed decision-making. Utilizing a cloud-based architecture ensures scalability, reliability, and efficient processing of large volumes of data. This research demonstrates the system's capability to provide timely alerts and comprehensive situational awareness, ultimately contributing to more efficient disaster management.

关键词： Cloud computing Disasters Scalability Decision making Disaster management Computer architecture Chatbots Real-time systems Reliability Monitoring

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BookChain: A Secure Library Book Storing and Sharing in Academic Institutions using Blockchain technology

BookChain: A Secure Library Book Storing and Sharing in Acad...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Chinnasamy, P. Elumalai, Anupriya Ayyasamy, Ramesh Kumar Kavya, S.P. Dhanasekaran, S. Kiran, Ajmeera MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India VIT - AP University School of Computer Science and Engineering India Faculty of Information and Communication Technology Kampar Malaysia KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Deemed to Be University Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu Srivilliputtur India

ISBN: (纸本)9798350348217

By exploiting wireless operators, contemporary bookcrossing allows people to share books conveniently and hastens the spread of knowledge. Nevertheless, the widespread use of smartphone bookcrossing has been seriously hampered by the lack of tracking, resulting in missing and disappearance of textbooks, additionally, it leads to organizational inefficiency. Hence, we suggest using the emerging blockchain technology to exploit the advantages of its immutability and encrypted smart contract in order to overcome these difficulties. In this research, we propose a BookChain, a blockchain-based intra-campus book-sharing system that is verifiable and effective. BookChain securely records all of the book's interacting entities on blockchain because every consumer could retrace the book's renting activity, hence lowering the risk of a book being misplaced. Additionally, BookChain promotes the use of smart contracts to streamline the distribution of books with very little human interference, maximizing productivity. The experimental findings demonstrate the efficiency and affordability of the suggested system even with a large number of concurrent participants. © 2023 IEEE.

关键词： Smart contract

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An In-Depth Review of AI-Based Techniques for Early Diagnosis of Breast Cancer: Evaluation of CAD System Design and Classification Methodologies

An In-Depth Review of AI-Based Techniques for Early Diagnosi...

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2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Shaban, Warda M. Abdullah, Ahmed A. Ashraf, Eman Nile Higher Institute for Engineering and Technology Artificial Intelligence Lab Dept. of Communications and Electronics Engineering Mansoura Egypt Delta University for Science and Technology Faculty of Engineering Dept. of Electronics and Communications Engineering Gamasa Egypt

ISBN: (纸本)9798350326062

One of the most prevalent forms of cancer among women worldwide is breast cancer, the leading cause of mortality. The vital procedure of early breast cancer detection can help with the right medical care, prevent the spread of cancer cells, as well as lower the risk of death and disability. The two most prevalent techniques for correctly detecting and classifying breast cancer through the use of Computer-Aided Diagnosis (CAD) technology are artificial intelligence (AI) and machine learning (ML). This work intends to highlight the significance of applying AI for breast cancer early detection to reduce its risks. The various techniques for recognizing breast cancer are also addressed. Also, the design process of the CAD system, including pre-processing stage, segmentation, extraction of features, and feature selection, is explained. The classification methodologies for detecting breast cancer will then be evaluated. © 2023 IEEE.

关键词： Computer aided diagnosis

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Enhancing Histopathological Image Classification through InceptionResNetV2: A Deep Learning Approach 2

Enhancing Histopathological Image Classification through Inc...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Mahesh, R.T. Shenbagavalli, P. Mohanraj, T. Amala, P. Anitha, K. Thakur, Arastu Department of Computer Science and Engineering Bangalore India Rathinam Technical Campus Department Of Artificial Intelligence And Data Science Coimbatore India Karpagam Academy of Higher Education Coimbatore India Erode Sengunthar Engineering College Erode India CSI College of Engineering Department of Information Technology Nilgiris India

ISBN: (纸本)9798331534950

Histopathological image analysis is a critical task in medical diagnostics, aiding in the accurate classification and staging of diseases. Traditional image analysis methods, while effective, often lack the precision and scalability provided by modern deep learning techniques. This study presents a sophisticated use of the InceptionResNetV2 architecture that has been optimized for the categorization of histological pictures from the colon and lungs. The InceptionResNetV2 model was trained on a comprehensive dataset of histopathological images of lung and colon cancer, which were pre-processed and augmented to enhance their quality and diversity. The model was adjusted to operate without the top layer and included additional dense layers with dropout for regularization to prevent overfitting. A custom learning rate adjustment callback was implemented during training to optimize key performance metrics. The final model achieved remarkable classification accuracy, with precision and recall rates exceeding 99%. This performance underscores the capability of InceptionResNetV2 in handling complex image features associated with histopathological data. The research showed that deep learning might significantly increase medical image analysis's prediction accuracy and efficiency, providing useful assistance for clinical decision-making. The results imply that these models may be expanded to include more medical imaging and diagnostic processes, offering a scalable solution for global healthcare systems. © 2024 IEEE.

关键词： Lung cancer

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Tomato Leaf Disease Detection through Machine Learning based Parallel Convolutional Neural Networks 7

Tomato Leaf Disease Detection through Machine Learning based...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Shetty, Sahana Selvanayaki, S. Sumathi, T. Nageswari, C.Shobana Nisha Devi, K. Suganyadevi, S. FET-Jain Department of CSE-AIML Cyber Security School of CSE Jayanagar Bangalore India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India Dr.Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India R.M.D. Engineering College Department of ECE Kavaraipettai601206 India Bannari Amman Institute of Technology Department: Artificial Intelligence and Data Science Sathyamangalam India KPR Institute of Engineering and Technology Department of ECE Coimbatore India

ISBN: (纸本)9798350397253

Agriculture is the primary economic activity in a significant number of nations. Yet, plant-related illnesses are the most difficult obstacle for farmers to overcome. The leaf is the portion of the plant that is noticed the most, but the process of segmenting an image of a sick lesion on a leaf suffers from challenges related to uneven lighting and a crowded, complicated natural environment. The combination of colour balancing and superpixel is the foundation of the approach that has been suggested as a solution to these issues. To begin, the picture that is being read in will be converted into a color-balanced image so that the effects of uneven lighting may be removed. Second, using the superpixel operation, compact areas are produced from the modified picture. An experimentally calculated threshold is then applied to the Histogram of Gradients (HOG) and colour channels of the superpixel to separate the undesirable backdrop from the image of the leaf. In order to identify a sick or contaminated picture, K-means clustering is employed. Pyramid of HOG (PHOG), an expanded version of HOG, together with Grey Level Co-occurrence Matrix (GLCM) characteristics are used to depict a sick infected portion of the body. In the end, many disease classifiers are evaluated against one another, and Random Forest (RF) is chosen as the best option. An experiment is carried out to verify the effectiveness of the proposed approach's accuracy using natural photographs obtained from a variety of farms, images downloaded from the internet, and the Plant-Village colour dataset. This is done so that the resilience of the method can be evaluated. © 2023 IEEE.

关键词： Color

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Fault optimisation on IEEE 14-bus system with machine learning-based SPLIT TCSC

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International Journal of Power and Energy Conversion 2022年第3-4期13卷 319-347页

作者： Agrawal, Niharika Khan, Faheem Ahmed Gowda, Mamatha Department of Electrical and Electronics Engineering Ghousia College of Engineering Karnataka Ramanagaram District 562 159 India Department of Artificial Intelligence and Data Science BGS College of Engineering and Technology Mahalakshmi Puram Karnataka Bengaluru560 086 India

All the operations of the power system should be smooth, continuous, reliable, and efficient. An electric fault interrupts the normal flow of power, damages the electrical equipment, creates abnormal voltages and currents, and deteriorates the system’s power quality. In this manuscript, a novel device, the SPLIT TCSC, is implemented with the traditional proportional-integral (PI) controller and with controllers based on machine learning algorithms such as artificial neural network, and random forest for fault optimisation on IEEE 14-bus system. These controllers showed better results than the PI controller with respect to voltage during fault time and THD reduction. The voltage drop is around 30 kV, and the THD is around 1.95% with the system based on random forest. The results showed that power is utilised more effectively with the application of these algorithms in the system, along with improvements in the power quality, reliability, and efficiency of the system. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Efficiency

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