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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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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Deep Learning-Based Approach for Arabic Visual Speech Recognition

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computers, Materials & Continua 2022年第4期71卷 85-108页

作者： Nadia H.Alsulami Amani T.Jamal Lamiaa A.Elrefaei Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt

Lip-reading technologies are rapidly progressing following the breakthrough of deep *** plays a vital role in its many applications,such as:human-machine communication practices or security *** this paper,we propose to develop an effective lip-reading recognition model for Arabic visual speech recognition by implementing deep learning *** Arabic visual datasets that have been collected contains 2400 records of Arabic digits and 960 records of Arabic phrases from 24 native *** primary purpose is to provide a high-performance model in terms of enhancing the preprocessing ***,we extract keyframes from our ***,we produce a Concatenated Frame Images(CFIs)that represent the utterance sequence in one single ***,the VGG-19 is employed for visual features extraction in our proposed *** have examined different keyframes:10,15,and 20 for comparing two types of approaches in the proposed model:(1)the VGG-19 base model and(2)VGG-19 base model with batch *** results show that the second approach achieves greater accuracy:94%for digit recognition,97%for phrase recognition,and 93%for digits and phrases recognition in the test ***,our proposed model is superior to models based on CFIs input.

关键词： Convolutional neural network deep learning lip reading transfer learning visual speech recognition

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VisTA: vision transformer-attention enhanced CNN ensemble for optimized classification of acute lymphoblastic leukemia benign and progressive malignant stages

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International Journal of Information Technology (Singapore) 2024年 1-18页

作者： Nunna, Hasmitha Krishna Altable, Ali Gundala, Pallavi Rangarajan, Prasanna Kumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

Acute lymphoblastic leukemia (ALL), a rare blood disorder marked by excessive immature lymphocyte proliferation in the bone marrow, primarily affects children but poses a more challenging prognosis in adults due to late detection. Pathologists employ bone marrow morphology and immunophenotyping to establish cell lineage based on antigens to diagnose acute lymphoblastic leukemia. These manual approaches are time-consuming and need specialized expertise. It is critical for the decision support system to train an accurate classification model to discriminate between mature and immature white blood cells along with the stratification of its phases which is a crucial component in diagnostic accuracy. Another major challenge in ALL classification arises from the morphological resemblance between leukemic B-lymphoblast cells and normal B-lymphoid progenitors. To improve the detection of leukemia, this work integrates an ensemble model of Vision transformer with attention-based pre-trained architectures that use high-resolution blood smear images, to accelerate the prompt detection of this insidious illness and its stages. The ensemble model, which combines ViT with attention-based CNNs, uses dual feature extraction methods from cell images to improve classification results. Our suggested model demonstrates promising performance with an accuracy of 99.96% on a test dataset with 1600 images. The suggested model provides a promising methodology that could be extremely useful in medical settings, allowing for the accurate classification and diagnosis of acute lymphoblastic leukemia. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Acute lymphoblastic leukemia classification Attention mechanism Benign Blood smears CLAHE Ensemble model Leukemia detection Malignant Vision transformers

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A Comprehensive Survey on GenAI-Enabled 6G: Technologies, Challenges, and Future Research Avenues

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4563-4590页

作者： Sheraz, Muhammad Chuah, Teong Chee Tareen, Wajahat Ullah khan Al-Habashna, Ala'a Saeed, Sohail Imran Ahmed, Manzoor Lee, It Ee Guizani, Mohsen Multimedia University Faculty of Artificial Intelligence and Engineering Selangor Cyberjaya63100 Malaysia University of Jeddah College of Engineering Department of Electrical and Electronic Engineering Jeddah21589 Saudi Arabia Al Hussein Technical University School of Computing and Informatics Amman Jordan Bahria University Department of Computer Engineering Karachi Pakistan School of Computer Science and Information Engineering Hubei Engineering University Xiaogan432000 China Abu Dhabi United Arab Emirates

The integration of artificial intelligence (AI) in 6G demonstrates a transformative leap in redefining network efficiency, intelligence, and adaptability. However, AI largely leverages discriminative models relying on labelled and quality data, where data accessibility remains serious concern. Generative AI (GenAI) has gained traction due to its immense potential in resolving the issue of data scarcity, complexity, and incompleteness. GenAI models excel in understanding underlying data distributions, enabling them to generate synthetic data that mirrors real-world patterns. GenAI supports adaptive learning and scenario modeling, making it indispensable for addressing the unpredictability and complexity inherent in 6G networks. Since the complexity of wireless communication systems is increasing and the demand for such systems is growing, GenAI presents new ideas for enhancing network performance, increasing system efficiency, and developing intelligent decision-making capabilities. This survey paper investigates the promising role of GenAI in the evolution of 6G networks. An in-depth discussion of notable GenAI models is presented, outlining their application in enhancing key network components. Specifically, the application of GenAI in advanced technologies including reconfigurable intelligent surfaces (RIS), unmanned aerial vehicles (UAVs), digital twins (DTs), and integrated sensing and communications (ISACs) is thoroughly investigated with respect to optimize the adaptability, flexibility, and robustness of the wireless networks. Moreover, use cases of GenAI-enabled wireless networks are presented to highlight the realization of GenAI in 6G. The paper also presents the lessons learned, existing challenges, and future research directions. This paper systematically explores GenAI and its pivotal role in the development of 6G, providing a foundation for researchers to further investigate and advance GenAI-enabled 6G. © 2020 IEEE.

关键词： Deep learning

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Deep learning on medical image analysis

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 1-35页

作者： Jiaji Wang Shuihua Wang Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Biological Sciences Xi'an Jiaotong-Liverpool UniversitySuzhouJiangsuChina Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia School of Computer Science and Engineering Southeast UniversityNanjingJiangsuChina

Medical image analysis plays an irreplaceable role in diagnosing,treating,and monitoring various *** neural networks(CNNs)have become popular as they can extract intricate features and patterns from extensive *** paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre-trained *** paper also discusses how transfer learning has been applied to different areas within medical image *** comprehensive overview aims to assist researchers,clinicians,and policymakers by providing detailed insights,helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.

关键词： CNN deep learning medical image transfer learning

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Lightweight Language Agnostic Data Sanitization Pipeline for Dealing with Homoglyphs in Code-Mixed Languages 2nd

Lightweight Language Agnostic Data Sanitization Pipeline for...

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2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Azmi, Mohammad Yusuf Jamal Aziz Navaneethakrishnan, Subalalitha Chinnaudayar Department of Computer Engineering Faculty of Engineering and Technology Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9783031584947

With the rise in hate speech on social media, numerous Natural Language Processing (NLP) techniques like text classification have been employed for detecting hate speech to make social media less toxic. However, hate speech users have started employing homoglyphs, which are characters that look identical to each other but have a different encoding or structure, to evade detection since most NLP models are trained on commonly recognized Unicode characters. In this paper we propose a novel lightweight language agnostic data sanitization pipeline which constitutes of a CNN for character level OCR followed by Symspell algorithm for candidate word generation and n-grams for word retrieval with the aim of retrieving dehomoglyphed sentences from homoglyphed sentences. We also introduce HEMNIST, an extended version of EMNIST that includes images of homoglyphs. We achieve a cosine similarity of 0.922, 0.845, 0.671, 0.508 and 0.231 between original and retrieved text at 5%, 10%, 20%, 30% and 50% masking respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Pipelines

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Helmet Detection for Motorcycle Ignition: A computer Vision and Android-Based Approach 1

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8th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2023

作者： Hutabarat, Daniel Patricko Saleh, Robby Mae, Johannes Prasad, P.W.C. Computer Engineering Department Faculty of Engineering Bina Nusantara University West Jakarta Indonesia School of Computing Mathematics and Engineering Charles Sturt University Wagga WaggaNSW2678 Australia

ISBN: (数字)9783031717734

ISBN: (纸本)9783031717727

To enhance safety and combat the motorcycle accident fatality rate, this research introduces a motorbike user authorization system that integrates computer vision technology and an Android application. The system is designed to ensure motorcycle ownership verification while also confirming correct helmet usage by riders. Built on the Raspberry Pi Zero platform, it is capable of processing camera images and communicating with the Android application via Bluetooth. Data, including the speed and accuracy of helmet detection, are analyzed, and assessed using a neural network model and an extensive validation dataset. This discovery underscores the significance of helmet usage in maintaining road safety, given the high incidence of fatal motorcycle accidents associated with rider non-compliance with helmet regulations. With an impressive 90% accuracy rate based on a dataset of 48,000 validation images, this research makes a substantial contribution to mitigating this issue and pioneers a novel approach to bolstering rider safety and compliance with regulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motorcycles

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Lip Synchronization Model For Sinhala Language Using Machine Learning 4

Lip Synchronization Model For Sinhala Language Using Machine...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Ranaweera, Dilani Weerasinghe, Ruwan Dinalankara, Randima Univeristy of Colombo School of Computing University of Colombo Colombo Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Department of Computer Engineering Colombo Sri Lanka

ISBN: (纸本)9798350384864

Realistic lip-synchronized animations can be produced by the appropriately timed voice and lip motions of the cartoon character. This process is called as 'lip synchronization'. Building a talking face for languages such as English, Korean, and Portuguese has been subject of numerous studies. Compared to other languages, Sinhala is a low-resource language due to less contribution in these researches. This research study focuses to build a frontal view of a synthetic mouth part with smooth lip movements rather than opening and closing while speaking Sinhala sentences. The most difficult challenge is to match the basic sounds, 'phonemes' with the lip movement called 'visemes'. This study has been used static viseme approach by deriving twenty three (23) Sinhala viseme classes and a deep learning model has been developed to map visemes with Sinhala letters. In system implementation, text input is provided first, after which the system produces audio and the deep learning model generates a collection of visemes based on the given text. The system interface then offers three options for playing the visemes at various speeds, including fast, normal, and slow. The user interface was created in Python, and the deep learning model is integrated into the system. This model will be very helpful to use in cartoon industry to build Sinhala speaking cartoon characters and also be used to train deaf people to read lips. © 2024 IEEE.

关键词： Python

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A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

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Materials Today: Proceedings 2023年 80卷 2701-2705页

作者： Qahatan Alsudani, Mustafa Abbdal Reflish, Salah H. Moorthy, Kohbalan Mundher Adnan, Myasar Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

Currently, effective Intrusion-detection systems (IDS) still represent one of the important security tools. However, hybrid models based on the IDS achieve better results compared with intrusion detection based on a single algorithm. But even so, the hybrid models based on traditional algorithms still face different limitations. This work is focused on providing two main goals;firstly, analysis based on the main methods and limitations of the most-recent hybrid model-based on intrusion detection, secondly, to propose a novel hybrid IDS model called TLBO-ELM based on the Firefly algorithm and Fast Learning Network. © 2021

关键词： Optimization

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