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arXiv 2022年

作者： Shiri, Fatemeh Zhuo, Terry Yue Li, Zhuang Pan, Shirui Wang, Weiqing Haffari, Reza Li, Yuan-Fang Nguyen, Van Department of Data Science AI Faculty of Information Technology Monash University Melbourne Australia Defence Science and Technology Group Adelaide Australia

There has been an increasing interest in incorporating Artificial Intelligence (AI) into Defence and military systems to complement and augment human intelligence and capabilities. However, much work still needs to be done toward achieving an effective human-machine partnership. This work is aimed at enhancing human-machine communications by developing a capability for automatically translating human natural language into a machine-understandable language (e.g., SQL queries). Techniques toward achieving this goal typically involve building a semantic parser trained on a very large amount of high-quality manually-annotated data. However, in many real-world Defense scenarios, it is not feasible to obtain such a large amount of training data. To the best of our knowledge, there are few works trying to explore the possibility of training a semantic parser with limited manually-paraphrased data, in other words, zero-shot. In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain. © 2022, CC BY-NC-ND.

关键词： Semantics

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Paraphrasing Techniques for Maritime QA system

Paraphrasing Techniques for Maritime QA system

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International Conference on information Fusion

作者： Fatemeh Shiri Terry Yue Zhuo Zhuang Li Shirui Pan Weiqing Wang Reza Haffari Yuan-Fang Li Van Nguyen Department of Data Science and AI Faculty of Information Technology Monash University Melbourne Australia Defence Science and Technology Group Adelaide Australia

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

关键词： Training Structured Query Language Human intelligence Semantics Natural languages Training data Military systems

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LHU-NET: A LIGHT HYBRID U-NET FOR COST-EFFICIENT, HIGH-PERFORMANCE VOLUMETRIC MEDICAL IMAGE SEGMENTATION

arXiv

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arXiv 2024年

作者： Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen52062 Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen28359 Germany

The rise of Transformer architectures has revolutionized medical image segmentation, leading to hybrid models that combine Convolutional Neural Networks (CNNs) and Transformers for enhanced accuracy. However, these models often suffer from increased complexity and overlook the interplay between spatial and channel features, which is vital for segmentation precision. We introduce LHU-Net, a streamlined Hybrid U-Net for volumetric medical image segmentation, designed to first analyze spatial and then channel features for effective feature extraction. Tested on five benchmark datasets (Synapse, LA, Pancreas, ACDC, BRaTS 2018), LHU-Net demonstrated superior efficiency and accuracy, notably achieving a 92.66 Dice score on ACDC with 85% fewer parameters and a quarter of the computational demand compared to leading models. This performance, achieved without pre-training, extra data, or model ensembles, sets new benchmarks for computational efficiency and accuracy in segmentation, using under 11 million parameters. This achievement highlights that balancing computational efficiency with high accuracy in medical image segmentation is feasible. Our implementation of LHU-Net is freely accessible to the research community on GitHub. © 2024, CC BY.

关键词： Convolutional neural networks

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Proprioceptive Drift Can Be Caused by Simple Sensory Prediction

Proprioceptive Drift Can Be Caused by Simple Sensory Predict...

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Conference on Artificial Life, ALIFE 2023

作者： Harada, Kohei Noguchi, Wataru Iizuka, Hiroyuki Yamamoto, Masahito Graduate School of Information Science and Technology Hokkaido University Japan Education and Research Center for Mathematical and Data Science Hokkaido University Japan Center for Human Nature Artificial Intelligence and Neuroscience Hokkaido University Japan Faculty of Information Science and Technology Hokkaido University Japan

The rubber hand illusion is a phenomenon that involves perceiving a rubber hand as part of one’s own body. The occurrence of this illusion is evaluated by the subjective report and the proprioceptive drift, in which the position of the hand is shifted in perception. The proprioceptive drift and sense of body ownership are assumed to be related;however, some research results have cast doubt on this relationship. We built a deep neural network model to simulate the rubber hand experiment to investigate the principles behind proprioceptive drift. Our deep neural network model was trained using consistent multisensory data and tested with inconsistent data, such as the rubber hand illusion. The model successfully predicted proprioceptive drift, suggesting that simple predictive learning mechanisms can account for this phenomenon. © 2023, Massachusetts Institute of technology. All rights reserved.

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Intelligent Optimization of Laser Marking Parameters Using Neural Networks and Genetic Algorithms 4

Intelligent Optimization of Laser Marking Parameters Using N...

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4th International Conference on Robotics, Automation, and Artificial Intelligence, RAAI 2024

作者： Yan, Teng Li, Wenxian Yu, Zhenxi Sun, Yuxiang Zhuang, Haoxue Zhou, Xiaohong College of Applied Technology Shenzhen University Shenzhen China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Science and Engineering The Chinese University of HongKong Shenzhen China Technical University of Applied Sciences Würzburg-Schweinfurt Faculty of Computer Science and Business Information Systems Schweinfurt Würzburg Germany

ISBN: (纸本)9798331520038

Laser marking technology is essential in precision manufacturing, but optimizing its numerous parameters is com-plex and time-consuming with traditional methods. This study developed a fully automated laser marking system using the Draco series laser from Han's Laser, incorporating automatic loading and unloading mechanisms. By utilizing neural networks and genetic algorithms, we optimized laser marking on ABS materials. The research included COMSOL simulations of laser-material interactions, analysis of parameter variations on col-oration, temperature field distribution under laser irradiation, and prediction of marking effects for practical applications. The aim is to automate processes through algorithmic integration, providing an intelligent laser marking solution. The developed methodologies and algorithms are applicable to other manufac-turing optimizations, offering valuable insights for broader in-dustrial applications. Primary research focuses include COMSOL simulation and parameter analysis, developing an AI-integrated laser production line with color prediction, and exploring au-tomated laser marking on ABS material using neural networks and genetic algorithms. © 2024 IEEE.

关键词： Genetic Algorithms Laser Marking Machine Learning Small data Neural Networks

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FMDNN: A Fuzzy-guided Multi-granular Deep Neural Network for Histopathological Image Classification

arXiv

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arXiv 2024年

作者： Ding, Weiping Zhou, Tianyi Huang, Jiashuang Jiang, Shu Hou, Tao Lin, Chin-Teng The School of Information Science and Technology Nantong University Nantong226019 China The Faculty of Data Science City University of Macau 999078 China The School of Computer Science and Technology Nantong University Nantong226019 China The Centre for Artificial Intelligence FEIT University of Technology Sydney UltimoNSW2007 Australia

Histopathological image classification constitutes a pivotal task in computer-aided diagnostics. The precise identification and categorization of histopathological images are of paramount significance for early disease detection and treatment. In the diagnostic process of pathologists, a multi-tiered approach is typically employed to assess abnormalities in cell regions at different magnifications. However, feature extraction is often performed at a single granularity, overlooking the multi-granular characteristics of cells. To address this issue, we propose the Fuzzy-guided Multi-granularity Deep Neural Network (FMDNN). Inspired by the multi-granular diagnostic approach of pathologists, we perform feature extraction on cell structures at coarse, medium, and fine granularity, enabling the model to fully harness the information in histopathological images. We incorporate the theory of fuzzy logic to address the challenge of redundant key information arising during multi-granular feature extraction. Cell features are described from different perspectives using multiple fuzzy membership functions, which are fused to create universal fuzzy features. A fuzzy-guided cross-attention module guides universal fuzzy features toward multi-granular features. We propagate these features through an encoder to all patch tokens, aiming to achieve enhanced classification accuracy and robustness. In experiments on multiple public datasets, our model exhibits a significant improvement in accuracy over commonly used classification methods for histopathological image classification and shows commendable interpretability. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Utilizing Machine Learning in Medical Diagnosis: Systematic Review and Empirical Analysis

Utilizing Machine Learning in Medical Diagnosis: Systematic ...

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information technology (ACIT)

作者： Raed Alazaidah Mohammad Hassan Lara Al-Rbabah Ghassan Samara Marina Yusof Ala'a Saeb Al-Sherideh Department of Data Science AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan College of Computer Informatics and Media Universiti Teknologi MARA (UiTM) Selanger Malaysia Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan

During the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in the domain of medical diagnosis. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model and feature selection method in mind of handling medical datasets. Hence, four different medical datasets have been used to train twenty-three classification models and four well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, False Positive ratio, Precision, and Recall. The results reveal that RandomForest, J48, and SMO classifiers are the best classifiers when it comes to handling medical datasets respectively. Furthermore, the Gain Ratio method is the best choice for handling the step of feature selection.

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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An Enhanced Residual Networks Based Framework for Early Alzheimer’s Disease Classification and Diagnosis 6th

An Enhanced Residual Networks Based Framework for Early Alzh...

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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Nath Samarendra Imoize, Agbotiname Lucky Misra, Sanjay Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Applied Data Science Institute of Energy Technology Halden Norway Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (纸本)9789819919826

Alzheimer's disease (AD) is a long-lasting, degenerative brain illness for which there is now no successful treatment. However, there are medications that can delay its growth and stop its tracks. Therefore, stopping and regulating the progression of AD depends greatly on its early diagnosis. Computer-aided diagnosis (CAD) in health care has become more and more dependent on deep learning technologies. This has help in early diagnosis and detection of various disease in health care. New techniques for the early diagnosis of neurocognitive abnormalities are now possible thanks to advancements in medical imaging and computational capacity in an effort to stop or slow down cognitive aging. Therefore, this study proposes a residual network (ResNet) type 50 for early diagnosis of AD. Convolutional neural networks (CNNs) are first used in the study for features extraction and then make the ultimate diagnosis using the ResNet-50. To create a better architecture, the problem of AD diagnosis will be addressed by utilizing and incorporating ResNet-50, which improves the current categorization problem's accuracy to its maximum. With a 98.8% accuracy rate, the proposed model using ResNet-50 produces encouraging findings for AD prediction. The study reveals that the model can effectively circumvent CNNs for the diagnostic issue with AD. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer aided diagnosis

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An Integrated Edge Computing Framework for Enhancing Cyber-Physical System Security through Network Traffic Anomaly Detection in Industrial IoT

An Integrated Edge Computing Framework for Enhancing Cyber-P...

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Emerging Technologies (GCET), Global Congress on

作者： Tamara Zhukabayeva Zulfiqar Ahmad Nurdaulet Karabayev Assel Abdildayeva Faculty of Information Technology L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Computer Science and Information Technology Hazara University Mansehra Pakistan Department of Artificial Intelligence and Big Data Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (数字)9798331542603

ISBN: (纸本)9798331542610

With the expansion of the Industrial Internet of Things (IIoT), the protection of Cyber-Physical System (CPS) is worth consideration. This research presents an integrated edge computing framework that aims at enhancing security by improving network traffic anomaly detection. It is done based on three advanced anomaly detection algorithms, namely Principal Component Analysis (PCA), isolation forest, and autoencoder. PCA is applied to the data to bring it in a compressed form where one is able to detect the outliers by having the most important features of the data extracted. The isolation forest algorithm, which was applied with the contamination factor of 0, is depicted below: 1, is used for the detection of the anomalies in the traffic, and, in fact, it was able to categorize several threats. However, the autoencoder model relies on reconstruction loss to decide if something is wrong with the data, with the 95th percentile as the standard for identifying activities that are odd. Edge computing increases the effectiveness of a framework for real-time anomaly detection and mitigation. The objective of this research is to evaluate PCA, isolation forest, and autoencoder methods and the outcome on the feasibility of the methods to enhance the security of IIoT systems.

关键词： Autoencoders Telecommunication traffic Forestry Cyber-physical systems Real-time systems Security Anomaly detection Principal component analysis Industrial Internet of Things Edge computing

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