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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Pang, Zehai Wong, Bengy Tsz Tsun Hu, Jinbing Yang, Yi Department of Physics HK Institute of Quantum Science & Technology The University of Hong Kong Pokfulam Hong Kong College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781957171395

We introduce synthetic non-Abelian gauge fields to non-Hermitian systems and study their topological consequences via a non-Abelian Hatano-Nelson model, its photonic realization via a polarization-multiplexed fiber ring resonator, and broken gauge invariance of the non-Abelian Wilson loop. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Ring gages

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Neural Networks and Deep Learning: Innovations and Applications in Soft computing 7

Neural Networks and Deep Learning: Innovations and Applicati...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Abdulkadar, Jahagirdar Imran Mohan, Chinnem Rama Bharati, P. Vijaya Palit, Shamik Rao, Gunda Srinivasa Mudholkar, Pankaj Savitribai Phule Pune University India Narayana Engineering College Department of Computer Science and Engineering Andhra Pradesh Nellore524004 India Vignans Institute of Engineering for Women Department of Cse Andhra Pradesh Visakhapatnam530049 India University of Stirling Department of Computing Science and Software Engineering RAK Campus United Arab Emirates Mallareddy University School of Management and Commerce Kompally500100 India Marwadi University Faculty of Computer Applications Gujarat Rajkot360003 India

ISBN: (纸本)9798350350067

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart Monitoring of High-Performance KY Converter in IoT-Enabled Electric Vehicle Systems

Smart Monitoring of High-Performance KY Converter in IoT-Ena...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： N. Rathika R. R. Ramya P. Ganga Sharda Patwa Balaji V S. Sivarajan Department of Electrical and Electronics Engineering 6116-Mahendra Institute of Technology Namakkal Department of Information Technology University College Of Engineering Nagercoil India Department of Electrical and Electronics Engineering Annapoorana Engineering College Tamilnadu India Department of Electrical Engineering Medi-Caps University Indore Madhyapradesh Department of Electrical and Computer Engineering MAI-NEFHI College of Engineering and Technology Eritrea Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

The smart Electric Vehicle (EV)-Improved KY Converter monitoring system, which is Internet of Things (IoT)-based, provides an advanced method for improving the dependability and efficiency of EV power systems. By utilizing IoT technology, this system keeps a close eye on the enhanced KY converter's efficiency, which is essential for controlling power conversion in electric vehicles. The Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) effectively operates the system at maximum power level with fast response and low oscillations. It effectively optimizes energy conversion from Photo Voltaic (PV) by dynamically modifying to the operating point under varying environmental conditions. The deployment of this intelligent monitoring system is expected to yield notable progress in the administration and functioning of electric vehicle power systems, hence augmenting the general sustainability of electric-powered transportation. The proposed system is implemented in MATLAB, employing its sophisticated simulation capabilities to analyse the PV system. This simulation framework enables for the testing and validation of key performance metrics of 98% efficiency exhibiting in boosting power extraction and grid stability.

关键词： Maximum power point trackers Transportation Power system stability Electric vehicles MATLAB Sustainable development Sun Monitoring Thermal stability Testing

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A comparative study on fetal head circumference measurement from ultrasound images using deep learning models 4

A comparative study on fetal head circumference measurement ...

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Wang, Yu Yao, Yudong College of Medicine and Biological Information Engineering Northeastern University China Department of Electrical and Computer Engineering Stevens Institute of Technology United States

ISBN: (纸本)9781450398343

Ultrasound imaging is the most commonly used imaging modality for the prenatal examination of pregnant women, with real-Time imaging and no radiation characteristics. Through a ultrasound image of the fetal head, doctors can measure the fetal head circumference (HC) to evaluate fetal growth and potential delivery mode. In practice, fetal HC is usually measured manually by doctors based on ultrasound images. Manual measurement of fetal HC is subjective and time-consuming, which has a negative impact on measurement accuracy and efficiency. At present, deep learning is widely investigated in the medical field. Many researchers apply deep learning to measuring fetal HC to assist doctors to accurately and quickly completing the measurement of fetal HC. In this paper, we compare the performance of eight deep learning models (U-Net, Attention U-Net, GINet, global reasoning unit (GloRe), SegFormer, Segmenter, BiSeNet V2, and short-Term dense concatenate network (STDC)) on two fetal HC measurement datasets. SegFormer achieves the best results in Dice similarity coefficient (DSC), Hausdorff distance (HD), and absolute Difference (ADF). The performance of Attention U-Net is slightly worse than that of SegFormer. © 2022 ACM.

关键词： Ultrasonic imaging

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Robust Model Evaluation over Large-scale Federated Networks

arXiv

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arXiv 2024年

作者： Najafi, Amir Sani, Samin Mahdizadeh Farnia, Farzan Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Hong Kong

In this paper, we address the challenge of certifying the performance of a machine learning model on an unseen target network, using measurements from an available source network. We focus on a scenario where heterogeneous datasets are distributed across a source network of clients, all connected to a central server. Specifically, consider a source network’A’ composed of K clients, each holding private data from unique and heterogeneous distributions, which are assumed to be independent samples from a broader meta-distribution µ. Our goal is to provide certified guarantees for the model’s performance on a different, unseen target network "B," governed by another meta-distribution µ′, assuming the deviation between µ and µ′ is bounded by either the Wasserstein distance or an f-divergence. We derive theoretical guarantees for the model’s empirical average loss and provide uniform bounds on the risk CDF, where the latter correspond to novel and adversarially robust versions of the Glivenko-Cantelli theorem and the Dvoretzky-Kiefer-Wolfowitz (DKW) inequality. Our bounds are computable in polynomial time with a polynomial number of queries to the K clients, preserving client privacy by querying only the model’s (potentially adversarial) loss on private data. We also establish non-asymptotic generalization bounds that consistently converge to zero as both K and the minimum client sample size grow. Extensive empirical evaluations validate the robustness and practicality of our bounds across real-world tasks. © 2024, CC BY.

关键词： Federated learning

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Enhanced Security for IOMT Devices: Deep learning Techniques for Network Intrusion Detection

Enhanced Security for IOMT Devices: Deep learning Techniques...

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Communication, computing and Industry 4.0 (C2I4), International Conference on

作者： Gopala Krishnan D M Diviya Jyoshika Shri K M. Subramanian Department of Computer Science and Engineering Amrita School of Computing Chennai India Department of Mechanical Engineering St Joseph’s College of Engineering Chennai India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

The increasing popularity of Internet of Medical Things (IoMT) devices, like wearable sensors, has greatly improved patient care by allowing continuous monitoring and real-time data transfer to the cloud. However, this progress also raises security concerns, as the data sent from these devices can be at risk of tampering or hacking during transmission. Keeping this sensitive medical data safe is crucial for patient safety. To address these issues, a fused CNN and LSTM are developed to check whether the data from wearable IoMT devices has been altered or attacked during transmission to the cloud. The CNN part of the model helps to pull out important features from the data, while the LSTM part looks for patterns to spot any signs of tampering. The model uses two convolutional layers each of which is followed by a MaxPooling layer. Our model, tested on a large dataset, achieved an accuracy of 97.06%, showing its ability to protect the integrity of transmitted medical data.

关键词： Cloud computing Network intrusion detection Internet of Medical Things Data models Real-time systems Safety Security Convolutional neural networks Long short term memory Wearable sensors

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Quality of Service (QoS)-Driven Edge computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions 1

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NIELIT's International Conference on Communication, Electronics and Digital Technology, NICE-DT 2023

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer and Information Sciences The University of Hyderabad Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

ISBN: (数字)9789819916993

ISBN: (纸本)9789819916986

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in health care and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses Edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage, and monitor the hospital systems in real time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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An Early Lung Disease Detection and Classification Using Bidirectional Deep Learning Over Masked Autoencoder

An Early Lung Disease Detection and Classification Using Bid...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Ramadevi M MO. Ramkumar D. Jayakumar R. Rajmohan A. Arunkumar Computer Science and Engineering IFET college of Engineering Villupuram India Department of Computing Technology SRM Institute of Science and Technology Tamilnadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Around the world., lung diseases like Pneumonia., lung nodule infiltration., pneumothorax are the main cause of death and severe health problems. In this digital world., many new technologies were developed for early and accurate detection of lung disease but many of the technologies are not able to predict the lung disease accurately and required large amount of labelled data. This paper describes a new approach by combining bidirectional deep learning and masked autoencoders for lung disease detection and classification from medical images. The masked autoencoder learns meaningful representations by reconstructing masked regions in lung images., while bidirectional deep learning model context from both directions., improving disease detection accuracy This algorithm doesn”t required large labelled data it can learns from unlabelled data. The main purpose of this algorithm is used to learn effective representation of medical images by masking parts of input data and reconstructing them. which are crucial for accurate disease detection and classification. The proposed Bidirectional deep learning framework combines forward & Backward propagation process to enchance the learning efficiency and robustness of the lung images. This methodology detect and classify the disease like pneumonia., lung nodule., pneumothorax., infiltration and other lung diseases. Experimental results show significant improvements in sensitivity and specificity., especially in cases with subtle abnormalities in the lung tissue.

关键词： Deep learning Accuracy Pneumonia Lungs Autoencoders Real-time systems Classification algorithms Medical diagnostic imaging Image reconstruction X-ray imaging

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Safeguarding the Homeless Using ViTransformer in Violence Detection

Safeguarding the Homeless Using ViTransformer in Violence De...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： R Bhuvaneswari Sai Lasya M B Natarajan Tamilselvi M K Geetha M Velammal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering VIT University Chennai Tamilnadu India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu India Department of Computer Science and Engineering Karpagam college of Engineering India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

In recent years, the issue of homelessness has gained significant attention as societies grapple with finding solutions to alleviate the plight of those without stable shelter. To address this pressing concern, this research utilizes advanced video analysis techniques, specifically Vision Transformer, to detect the presence of individuals in homeless populations. By leveraging state-of-the-art technology, the research aims to not only identify individuals but also discern their actions and activities within the monitored environment. The methodology involves the initial detection of persons using Vi Transformer, an innovative deep learning model renowned for its accuracy in object recognition tasks. Once a person is successfully identified, this research further scrutinizes their activities, with a particular focus on detecting instances of conflict or fighting. This step holds immense potential or the safety and well-being of homeless individuals, as it enables the swift notification of nearby authorities or support services in cases of altercation. Crucially, this research delves into the performance metrics of both person detection and activity detection experiments, providing valuable insights into the efficacy of the Vi Transformer model in real-world applications. By assessing the system's accuracy, speed, and reliability, this study contributes to the development of proactive strategies for assisting homeless populations, emphasizing the importance of technology in addressing social challenges.

关键词： Surveillance Sociology Smart homes Transformers Safety Security Personnel

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Evaluating Automated Information Retrieval Techniques for Improved Data Accessibility

Evaluating Automated Information Retrieval Techniques for Im...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mishra, Vaishali A. Srivastava, Manish Nithya, M.S. Mishra, Smita Kannimuthu, S. Divya, N. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Business Management Maharishi University of Information Technology UP India Karpagam College of Engineering Department of Information Technology Coimbatore641032 India Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India

ISBN: (纸本)9798350329773

This looks at targets to assess numerous automated fact retrieval strategies with appreciation for their potential to provide advanced data accessibility. The diverse methods might be evaluated by exploring how nicely they offer green statistics access, person-friendly systems, and the potential to handle massive datasets. The unique techniques include SEO (SEO), herbal language processing (NLP), content-primarily based retrieval, and disbursed databases. The impact of every method in the records accessibility procedure may be evaluated based on the extent of personal pleasure, typical performance, and scalability of the gadget. Sooner or later, the results can be used to offer pointers for improving data accessibility. © 2023 IEEE.

关键词： Scalability

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