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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xia, Le Sun, Yao Niyato, Dusit Zhang, Lan Zhang, Lei Imran, Muhammad Ali University of Glasgow James Watt School of Engineering Glasgow United Kingdom Nanyang Technological University College of Computing and Data Science Singapore Clemson University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350351255

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where two modes of semantic communication (SemCom) and conventional bit communication (BitCom) coexist, namely hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we comprehensively develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with several practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by employing a Lagrange primal-dual method and devising a preference list-based heuristic algorithm. Finally, numerical results validate the performance superiority of our proposed strategy compared with different benchmarks. © 2024 IEEE.

关键词： Frequency allocation

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Prediction of Delamination Size in Composite Material Using Machine Learning

Prediction of Delamination Size in Composite Material Using ...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Sikka, Dhruv Shivansh Rajeswari, D. Pushpalatha, M. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamilnadu Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9781665484251

Composite materials are famously known for providing safety and endurance to required components in the aerospace industry. Delamination of such materials is caused by rotating loads and decaying job settings which eventually pose a threat to the health of the composite layer structure and hence compromises safety of human life. data was collected using an acoustic extraction method to assess the health of the composite material by disclosing the region of the damage, type, and size on the sample material. This paper proposes a predictive method using machine learning to measure the region of delamination over the composite sheet by comparing different machine learning models. By critically assessing and comparing various machine learning methods, an optimum solution through classification and regression models is suggested. The region of the delamination may be estimated by the length of the predicted route based on the observed conditions, changes, routes and the image of the structural health of the composite material. This approach will prevent the data overfitting of the model training and thus avoid the problem of overuse in the training process. The outcomes of the experimented load cycles on the composite sample coupons show that the suggested regression model surpasses other similar approaches in predicting accurately and efficiently. © 2022 IEEE.

关键词： Adaptive boosting

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Transforming Finger Vein Template in Multi-instance Scenario 10

Transforming Finger Vein Template in Multi-instance Scenario

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10th International Conference on Information and Communication Technology, ICoICT 2022

作者： Cai, Shuxian Yau, Wei Chuen Ong, Thian Song Teng, Jackson Horlick School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia Multimedia University Faculty of Information Science and Technology Melaka Malaysia

ISBN: (数字)9781665481656

ISBN: (纸本)9781665481656

Unlike single biometric systems, multi-instance biometric systems can reduce the risk of user privacy leakage. Meanwhile, maintaining biometric security also requires template protection. However, there is still limited study focusing on template protection in multi-instance biometrics. To address such issues, this paper proposes a method for multi-instance finger vein template protection. In this work, the vein features of different finger instances are extracted at feature fusion and image fusion levels using Gabor filter method, respectively. For feature transformation, the Gabor feature blocks are transformed with Bloom filters to extract the finger vein template for secure storage and verification. When analyzing the performance, the experiments are mainly conducted with the constant key scenario to be compared with performance of the baseline Gabor feature extraction method and the genuine key scenario. It is shown that this proposed method effectively improves system performance, with equal error rate (EER) as low as 5.00% in constant key scenario, while maintaining zero error rate in genuine key scenario. Besides, the proposed method is also examined for the properties of unlinkability and irreversibility as required for a template protection method. © 2022 IEEE.

关键词： Gabor filters

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CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis

arXiv

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arXiv 2024年

作者： Wang, Fuying Wu, Feng Tang, Yihan Yu, Lequan Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong

Integrating multimodal Electronic Health Records (EHR) data-such as numerical time series and free-text clinical reports-has great potential in predicting clinical outcomes. However, prior work has primarily focused on capturing temporal interactions within individual samples and fusing multimodal information, overlooking critical temporal patterns across patients. These patterns, such as trends in vital signs like abnormal heart rate or blood pressure, can indicate deteriorating health or an impending critical event. Similarly, clinical notes often contain textual descriptions that reflect these patterns. Identifying corresponding temporal patterns across different modalities is crucial for improving the accuracy of clinical outcome predictions, yet it remains a challenging task. To address this gap, we introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data. Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings. To ensure rich cross-modal temporal semantics in the learned patterns, we introduce a contrastivebased TPNCE loss for cross-modal alignment, along with two reconstruction losses to retain core information of each modality. Evaluations on two clinically critical tasks-48-hour in-hospital mortality and 24-hour phenotype classification-using the MIMIC-III database demonstrate the superiority of our method over existing approaches. © 2024, CC BY.

关键词： Electronic health record

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Analyzing Prognosis Methods using Machine Learning Algorithms for Detecting COPD 1

Analyzing Prognosis Methods using Machine Learning Algorithm...

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1st International Conference on Applied Artificial Intelligence and computing, ICAAIC 2022

作者： Nagendra Kumar, V.V. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665497107

Chronic Obstructive Pulmonary Disorder (COPD) is a significant challenge encountered by healthcare professionals. With millions of people being affected by COPD every year, there is a need for better diagnosis conditions. Many of the earlier studies on the facets of the machine learning model's application to COPD have discussed the success in terms of accuracy, the optimal classifiers resulting in success, etc. This study, with the objective of a contemporary literature review, has focused on the earlier contributions of critical models of machine learning for COPD detection. However, based on the information collated from the models, while some classifiers are resourceful in training the system and improved accuracy is a need to address the feature selection issues. Some of the non-technical and purely medical-related studies reviewed in this study have impressed the facts about diagnosis state as false positives or false negatives (misdiagnosis) of COPD conditions. Both conditions are challenging for offering the right treatment to the patients. Thus, considering the existing patterns and the dynamics, this study has discussed three significant gaps in feature selection, the need for multiple layers of diagnosis, and the lifestyle conditions based on COPD detection models as a prospective domain for future research. © 2022 IEEE.

关键词： Computer aided diagnosis

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution 27

Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781643685489

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code is available at https://***/Tillyhere/TADT. © 2024 The Authors.

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Impact of Planar Defects on the Reversal Time of Single Magnetic Domain Nanoparticles

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Physical Review Letters 2025年第13期134卷 136702-136702页

作者： Hugo Bocquet Armin Kleibert Peter M. Derlet PSI Center for Scientific Computing Theory and Data 5232 Villigen PSI Switzerland PSI Center for Photon Science 5232 Villigen PSI Switzerland

Recent experimental investigations of individual magnetic nanoparticles reveal a diverse range of magnetic relaxation times which cannot be explained by considering their size, shape, and surface anisotropy, suggesting other factors associated with the internal microstructure of the particles are at play. In this Letter, we apply Langer’s theory of thermal activation to fcc Co nanoparticles exhibiting single domain magnetism, whose experimentally observed microstructure contain planar defects. Our analytical derivation yields an expression for the activation rate as a function of the particle size and the defect fraction, enabling a quantitative understanding of the experimental findings. These dependencies, which are exponential for both the Arrhenius exponential and its prefactor, demonstrate the critical role that structural defects can play in the magnetic stability of nanoparticles.

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A Xgboost Optimized Ensemble Model for Remaining useful Life Prediction of Aircraft Turbofan Engines

A Xgboost Optimized Ensemble Model for Remaining useful Life...

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Emerging Smart computing and Informatics (ESCI), Conference on

作者： Vaasudev Sharma Riyansh Dagar S. Sharanya Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India

Proactive maintenance for the optimized lifecycle of equipment is now effortless via condition monitoring and fault diagnosis. Recognition and avoidance of a state of failure help to achieve solidity and reliability. Artificial Intelligence and Machine Learning (ML) forecast such situations and aid in the prevention of economic and safety emergencies. This work reviews such ML algorithms and ML models that help diagnose faults in Turbofan engines via measurement of the Remaining Useful Life parameter. This proposed an ensemble machine-learning model optimized by Extreme Gradient Boosting. The approach is then evaluated via different metrics and their performance is measured to current innovative methods. The system is useful for all processes that require high reliability quality and efficiency and reduces unplanned downtime, maintenance costs and asset quality.

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A Hand Gesture-Operated System for Rehabilitation Using an End-to-End Detection Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 698-708页

作者： Dutta, H Pallab Jyoti Bhuyan, M.K. Neog, Debanga Raj Macdorman, Karl F. Laskar, R.H. Department of Electronics and Electrical Engineering Indian Institute of Technology Assam Guwahati781039 India Department of Electronics and Electrical Engineering and Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Assam Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute ofTechnology Assam Guwahati781039 India School of Informatics and Computing Indiana University IN46202 United States Department of Electronics and Communication Engineering National Institute of Technology Assam Silchar788010 India

In this article, we propose a hand gesture-operated system as an AI application to relieve discomfort and restore function in hand and arm movements caused by injuries and nerve and muscle complications. The system trains patients with hand exercises, such as performing hand gestures accurately, traversing within specified bounds, and operating a hand gesture calculator. However, the system requires accurate hand gesture detection, which is impeded by background clutter and variations in illumination and in the hand's size and angle. To address this, we developed a robust hand detection module that uses a single-stage transformer deep network. The transformer network encodes global information and uses bipartite matching to reduce the frequency of spurious detections. It drives a regression head and a classification head to localize the hand gesture in a bounding box and assign it a class label. Hand keypoints are also detected to support drawing, path traversal, and calculator use. The approach is evaluated on two benchmark datasets: 1) OUHANDS and 2) NUS. The method yields 89.6% accuracy for OUHANDS and 100% for NUS. These results indicate that precise hand detection can support a robust system for rehabilitation through hand exercises. Our experiments confirm that the users' hand function progressively improved. © 2023 IEEE.

关键词： Deep neural networks

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