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Deep learning on medical image analysis

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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Secure Channel Creation for Task Delivery in Vehicular Cloud Computing to Avoid Denial of Service (DoS) 3

Secure Channel Creation for Task Delivery in Vehicular Cloud...

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3rd International Conference on Automation, Computing and Renewable systems, ICACRS 2024

作者： Meenambika, A. Pavalarajan, S. Lavanya, N. Anu Jerom, B. Ajith Divya, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331532420

Vehicular Cloud Computing (VCC) offers a promising platform for supporting various automotive applications and services. However, its distributed and dynamic nature makes it susceptible to Denial-of-Service (DoS) attacks, particularly during channel establishment. This research proposes a comprehensive approach to mitigate these risks by integrating a suite of security mechanisms. These include rate-limiting, resource management, traffic analysis, access control, authentication, encryption, intrusion detection, behavioral analysis, and blacklisting/whitelisting techniques. By incorporating these measures, the proposed approach enhances the security and reliability of VCC, ensuring the availability of critical automotive services and protecting vehicles from the impact of DoS attacks. © 2024 IEEE.

关键词： Cloud platforms

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Psychological structure of human trust toward autonomous vehicles, using structural equation model

Advances in Transportation Studies

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Advances in Transportation Studies 2024年 64卷 73-90页

作者： Park, H. Lee, K. Kim, S.-H. Department of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science University of Michigan-Dearborn DearbornMI48128 United States Department of Marketing Global Business and Economics College of Business and Public Management Kean University UnionNJ07083 United States

This study aimed to validate the influence of latent variables on human trust in autonomous vehicles and elucidate the underlying psychological framework. A survey with 114 queries, drawn from previous studies on trust factors, was conducted online, garnering 195 valid responses. Employing correlation and factor analysis, 51 queries were identified as significantly impacting trust levels. A structural equation model incorporating these 51 variables was constructed, revealing five higher-level psychological constructs: "Interpersonal," "System Feature," "Risk Perception," "Behavioral Intention," and "Trust," each comprising sub-factors derived from associated queries. Notably, the "Interpersonal" construct exhibited the greatest influence on "Behavioral Intention" and "Trust," while "Risk Perception" showed a negative correlation with trust, indicating higher perceived risk diminishes trust. The findings highlight the importance of understanding user interpersonal characteristics in enhancing trust in autonomous vehicles. Unlike previous studies focusing on individual variables, this research unveiled a comprehensive psychological structure by categorizing variables, factors, and constructs hierarchically. The developed model facilitates comprehension, prediction, and enhancement of user trust, guiding the development of tailored trust-fostering strategies and interventions for specific user groups and contexts. © 2024, Aracne Editrice. All rights reserved.

关键词： Risk perception

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Dataset formation for sem-based images of commercial minerals using ml algorithms: case study for zircon in the Egyptian mountains

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Multimedia Tools and Applications 2024年 1-19页

作者： khalil, kirolos N. R. El-Fishawy, Nawal Ali, Mohamed Mohamed, Mokhtar A. A. Computer Science & Engineering Department Faculty of Electronic Engineering Menofia University Shibin el Kom Egypt Nuclear Materials Authority P.O.Box:530 El–Maadi Cairo Egypt El Sadat City Egypt

Mineral identification holds paramount importance in geological and mineralogical endeavors, encompassing exploration, mining, and mineral processing. This work underscores the time-consuming and equipment-dependent nature of conventional identification methods, advocating for the integration of artificial intelligence techniques, particularly machine learning and computer vision. Commercial minerals, including zircon, are identified as linchpins of various industries, particularly ceramics and dentistry. The work elaborates on the pivotal role of SEM imaging techniques in discerning economic minerals in granitic rocks and pegmatite, emphasizing their utility in environmental science and mineral exploration. A novel computational approach is introduced, offering automation of mineral grain recognition, thereby mitigating the laborious and resource-intensive aspect of the process. The subsequent discussion pertains to the creation of a specialized SEM image dataset focusing on Egyptian commercial minerals, commencing with zircon, a dataset with foreseeable extensions. The authors anticipate that this dataset will significantly contribute to mineralogical research, facilitating precise mineral identification through AI techniques and enriching insights into Egypt’s geological wealth. © The Author(s) 2024.

关键词： Scanning probe microscopy

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Privacy-Secure and Decentralized Biometric Authentication Models Using Federated Learning Frameworks

Privacy-Secure and Decentralized Biometric Authentication Mo...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Mathivanan, P. Mahalakshmi, K. Anna University Faculty of Information and Communication Engineering Chennai India Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331510022

A biometric identifier, in contrast to other identifiers used for authentication, is a quantitative evaluation of an individual's physical attributes that is successfully used to confirm or validate the identification. Because of its remarkable and consistent texture variation, iris recognition is thought to be the most dependable biometric recognition. Concerns about security, authentication, and identification are growing in importance across all domains as technology advances daily. High-security applications use these unique patterns for iris recognition. To increase the security of biometric applications without jeopardizing individual privacy, this research suggests a novel FL technique for privacy-preserving iris detection. Traditional biometric systems rely on centralised data storage and processing, which threatens data security and misuse. Federated Learning: decentralized model training that allows multiple federated clients (like mobile devices or edge servers) to collaboratively train a global model without sharing their raw iris data. We explore how this architecture can be applied to iris detection systems, maintaining accuracy while mitigating privacy risks. Our method ensures that iris features are learned locally on individual devices, and only encrypted model updates are shared with the central server. To strengthen privacy, we incorporate differential privacy and secure aggregation techniques to prevent data leakage during model training. Experimental results demonstrate the effectiveness of the proposed framework in maintaining high detection accuracy, low communication overhead, and robust privacy guarantees. © 2024 IEEE.

关键词： Federated learning

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A Comprehensive Review of Large Language Models in Abstractive Summarization of News Articles

A Comprehensive Review of Large Language Models in Abstracti...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Bharathi Mohan, G. Akashvarma, M. Yashaswini, G. Keerthana, E. Siddharth, S. Saipooja, K. Prasanna Kumar, G. Doss, Srinath Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Botho University Faculty of Engineering and Technology Botswana

ISBN: (纸本)9798350361537

Recent years have seen significant advancements in Large Language Models (LLMs), particularly in their application to natural language processing tasks. One prominent application is the abstractive summarization of news articles, a process that distills extensive textual information into a succinct yet comprehensive representation. The significance of this task lies in its capacity to efficiently distill key information. This paper focuses on evaluating the performance of various LLMs - Llama-2-7b, T5, Bart, and Pegasus - specifically fine-tuned on the CNN Daily dataset of news articles. QLoRA and Prompt-Based Fine-Tuning Approaches are employed systematically to compare the efficacy of these models in generating summaries that capture the essence of news articles. Evaluation metrics, including ROUGE, BLEU, and cosine similarity, are utilized to assess summarization quality. The findings reveal distinctive results among the selected LLMs such that Llama-2 excels in ROUGE-1 and ROUGE-L with scoreS of 0.45 and 0.427, Pegasus demonstrates remarkable performance in ROUGE-2 and cosine similarity with scores 0.19 and 0.6 respectively. © 2024 IEEE.

关键词： Natural language processing systems

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Optimal coordination of Production, Logistics, and Maintenance 4.0: A framework for Multi-Site Stamping Plants 6

Optimal coordination of Production, Logistics, and Maintenan...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Zerrad, Raoua Diaz, Kamar Mechkouri, Meriem Hayani Sefiani, Naoufal Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaadi University Tangier Morocco Department of Industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Inventory control

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Multimodal Emotion Recognition Using computer Vision: A Comprehensive Approach 15

Multimodal Emotion Recognition Using Computer Vision: A Comp...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohan, G. Bharathi Sarrvesh, N. Hrishikeasan, N.P. Boopalamani, J. Doss, Srinath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho university Botswana

ISBN: (纸本)9798350370249

Real-time understanding and response to human emotions have become critical in today’s connected world of prevalent human-computer interaction. This study presents a novel approach to real-time emotion detection using a camera-based system that analyzes both voice and facial expressions. Our method advances the state-of-the-art by integrating deep learning techniques with parallel models for audio and visual inputs through a weighted fusion approach, a strategy not commonly employed in existing systems. Utilizing datasets such as CK+48 and the Ryerson Audio-Visual Database of Emotional Speech and Song (Ravdess), our system achieves superior performance metrics, including an accuracy of up to 92.92% and an F1 score of 92.94%. Encapsulated within a user-friendly microservice with an intuitive online interface, our solution enables seamless real-time interaction using only a webcam and microphone. Ongoing efforts focus on refining the system’s ability to recognize diverse voices and expressions, aiming to create technology that empathizes with human emotions and enhances interpersonal interactions. ©2024 IEEE.

关键词： Emotion Recognition

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Interpretable Cervical Cell Classification: A Comparative Analysis 4

Interpretable Cervical Cell Classification: A Comparative An...

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

作者： Gnanavel, Nishaanthini Inparaj, Prathushan Sritharan, Niruthikka Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (纸本)9798350384864

Cervical cancer is a significant global health issue, and traditional screening methods like Pap smears are labor-intensive and may miss some cases. Automation is needed, but it faces challenges in terms of interpretability and data availability. To address this, the paper proposes using Explainable Artificial Intelligence (XAI) techniques like GradCAM, GradCAM++, and LRP to improve the transparency and interpretability of a cervical cell classification model, making it a novel contribution to enhancing the trustworthiness of automated cervical cancer detection. Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques and develop a binary classification model, achieving a 91.94% accuracy with VGG16. The qualitative analysis of XAI methods confirmed that the model relied on nucleus and cytoplasm features, key indicators of malignancy. The least mean image entropy of 2.4849 and steep prediction confidence drop with perturbations quantitatively proved Layer-wise Relevance Propagation (LRP) to be the most effective XAI technique for cervical cell classification. © 2024 IEEE.

关键词： Artificial intelligence

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Dementia diagnosis in young adults: a machine learning and optimization approach

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Neural Computing and Applications 2024年第34期36卷 21451-21464页

作者： Talaat, Fatma M. Ibraheem, Mai Ramadan Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt Information Technology Department Faculty of Computers and Information Kafrelsheiksh University Kafrelsheikh Egypt

Individuals who are younger and have dementia often start experiencing its symptoms before they turn 65, with cases even documented in people as young as their thirties. Researchers strive for accurate dementia diagnosis to slow or halt its progression. This paper presents a novel Enhanced Dementia Detection and Classification Model (EDCM) comprised of four modules: data acquisition, preprocessing, hyperparameter optimization, and feature extraction/classification. Notably, the model uses texture information from segmented brain images for improved feature extraction, leading to significant gains in both binary and multi-class classification. This is achieved by selecting optimal features via a Gray Wolf Optimization (GWO)-driven enhancement model. Results demonstrate substantial accuracy improvements after optimization. For instance, using an Extra Tree Classifier for "normal" cases, the model achieves 85% accuracy before optimization. However, with GWO-optimized features and hyperparameters, the accuracy jumps to 97%. © The Author(s) 2024.

关键词： Adversarial machine learning

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