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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Maula, Shabana Wagh, Aparna Vyas, Vibha Patil, Prakash Sridhar, P. Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India College of Engineering Pune Department of Electronics and Telecommunication Pune India Department of Radio-Diagnosis Karad India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350360707

Artificial Intelligence usually comprises a system that is composed of hardware and software. Machine learning is a subset of artificial intelligence (AI). The study discovered that the environment and contemporary technology on the current healthcare services have created new demands, which has led to the formation of these five categories of research. By matching eligible patients with clinical trials, AI technologies in the healthcare industry may also be beneficial for medical research. There are numerous other fascinating uses for AI in the biomedical field. It is evident that AI is becoming more and more relevant in the field of biomedicine. AI technologies may be able to assist in addressing significant health issues, but its potential may be constrained by the caliber of the health data that is now available and by the fact that AI cannot exhibit certain human traits, like empathy. Numerous ethical and societal concerns are brought up by the use of AI, many of which are also brought up by the use of data and healthcare technologies more generally. © 2024 IEEE.

关键词： Adversarial machine learning

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Augmented Reality Shopping and IoT-Enabled Virtual Try-On with Cloud Services for Interactive Product Displays 10

Augmented Reality Shopping and IoT-Enabled Virtual Try-On wi...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Sridevi, V. Azath, H. Vijayakumar, R. Anbuselvan, N. Amirthalingam, V. Arunkumar, S. Academy of Maritime Education and Training Deemed To Be University Electrical and Electronics Engineering Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Bhopal India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Adhiparasakthi Engineering College Department of Computer Applications Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The research focuses on how augmented reality (AR) and the Internet of Things (IoT) might work together to create a novel retail environment. This integrated system revolutionizes how customers engage with products by fusing the digital and real worlds. Smartphones and other devices with AR capabilities allow customers to virtually try on products before purchasing. Smart mirrors and sensor-enabled shelving are just two examples of the IoT-powered interactive displays available today. These fusions of AR and the IoT improve the retail experience and facilitate choice. Capturing data from the physical environment, sensors allow for customized experiences. The extensive product data stored by cloud services paves the way for rapid data processing. However, there are obstacles, such as ensuring data privacy and security and coordinating AR-IoT smoothly. Finding a happy medium between technological complexity and ease of use is essential for a satisfying buying experience. The combination of AR and the IoT broadens the retail experience and has the potential to revolutionize the way people purchase. © 2024 IEEE.

关键词： Internet of things

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Contextual Evaluation of Segmentation Models using Spatial Reasoning 26

Contextual Evaluation of Segmentation Models using Spatial R...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Porter, Victoria Styles, Iain Curtis, Tim M. Taggart, Michael J. Albargothy, Mona J. Gault, Richard Centre for Intelligent Sustainable Computing School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Wellcome-Wolfson Institute for Experimental Medicine Queen's University Belfast United Kingdom Biosciences Institute Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781837242672

Image segmentation models are often evaluated using measures of overlap and boundary deviation between a ground truth and a prediction. These measures do not indicate whether a prediction is an overestimation or underestimation of the ground truth. This contextual information is critical in medical imaging applications such as tumor detection where a model's tendency to overestimate a prediction would be preferred to avoid overlooking malignant cells. Spatial reasoning provides context on a model's segmentation performance in terms of its tendency to over- or underestimate a region of interest. Such context can highlight a model's decision-making trends and can be applied to inform targeted improvements. In this work, we provide a Python module1,2 that implements a model-agnostic spatial reasoning pipeline for the contextual evaluation of segmentation methods. We apply this pipeline to the output of the Segment Anything model on 3 electron microscopy (EM) datasets and demonstrate the meaningful inferences that can be made. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Image segmentation

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Determination of Parameters of Time-delayed Embedding Algorithm Using Koopman Operator-based Model Predictive Frequency Control

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CSEE Journal of Power and Energy Systems 2021年第6期7卷 1140-1151页

作者： Xiawen Li Chetan Mishra Shichao Chen Yajun Wang Jaime De La Ree Dominion Energy RichmondVAUSA Thorlabs Quantum Electronics. JessupMDUSA Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State UniversityBlacksburgVAUSA

Power systems around the world have been registering a degenerating inertial response in view of the growth of inverter-based resources along with the withdrawal of conventional coal ***,there is a need for swift frequency support and its control,preferably by means of power electronic-interfaced storage devices,owing to their beneficial *** being particularly efficient,pragmatically,the traditional model-based non-linear control techniques are not highly popular in power system control design,primarily due to the complications faced in obtaining accurately suitable models for certain power system ***,the modelfree Koopman operator-based model predictive control(KMPC)has proven to be highly conducive for data-driven non-linear control *** principle behind KMPC is to change the coordinates in a manner to get an approximately linear model,which can then be controlled using a linear model predictive *** this study,we employed time-delayed embedding of measurements to reconstruct a new set of preferable coordinates,thereby suggesting an approach for finding the optimal number of time lags and the embedding dimensions which are the key parameters of this *** efficacy of this KMPC framework is established by adopting a decentralized frequency control problem through a decoupled synchronous machine system,which we proposed for both the Kundur two-area system as well as the IEEE 39-bus test system.

关键词： False nearest neighbors koopman operator model predictive control non-linear control time-delayed embedding

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Multi-Positive Sample quantum Contrastive Learning for Human Activity Recognition

Multi-Positive Sample Quantum Contrastive Learning for Human...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ren, Yanhui Wang, Di An, Lingling Mao, Shiwen Wang, Xuyu Xidian University Guangzhou Institute of Technology Guangzhou China Xidian University School of Computer Science and Technology Xi'an China Auburn University Department of Electrical and Computer Engineering AuburnAL United States Florida International University Knight Foundation School of Computing & Information Sciences MiamiFL United States

ISBN: (纸本)9798350351255

Human activity recognition (HAR) based on wearable devices has become an active research direction in the field of ubiquitous computing, and has a wide range of Internet of Things (IoT) applications. Unfortunately, it is challenging to obtain large amounts of labeled sensing data, and manual annotation is time-consuming and labor-intensive, making it impossible for the extensive deployment of HAR systems. Consequently, self-supervised learning has emerged to address this challenge by training on unlabeled data. However, traditional contrastive learning fails to simulate more sample diversity problems caused by environmental heterogeneity and sensor heterogeneity. In this paper, we propose a multi-positive sample quantum contrastive learning (MPSQCL) framework. By increasing the positive samples for contrastive learning and leveraging the advantages of quantum machine learning (QML) techniques, the richer features of input samples are extracted to improve the robustness and generalization of the model. Moreover, we design a new contrastive loss function to adapt to multiple positive sample contrastive learning scenarios. Finally, we validate the effectiveness of the proposed framework on several publicly available HAR datasets. © 2024 IEEE.

关键词： Contrastive Learning

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Tiruveedula Gopi Krishna Nune Sreenivas M. Ajay Kumar Chandra Sekhar Reddy Davinder Sing Rathee Teklu Urgessa Department of Computer Science and Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronics Engineering Andhra Loyala institute of Engineering and Technology Vijayawada India Department of Electrical Power and Control Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of ECE College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods.

关键词： Support vector machines Image segmentation Adaptation models Accuracy Magnetic resonance imaging Machine learning Feature extraction Brain modeling Data models Numerical models

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RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection

RBAD: A Dataset and Benchmark for Retinal Vessels Branching ...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Wang, Hao Zhu, Wenhui Qin, Jiayou Li, Xin Dumitrascu, Oana Chen, Xiwen Qiu, Peijie Razi, Abolfazl Wang, Yalin School of Computing Clemson University Clemson United States School of Computing and Augmented Intelligence Arizona State University Tempe United States Stevens Institute of Technology Department of Electrical and Computer Engineering Hoboken United States Mayo Clinic Department of Neurology Phoenix United States McKeley School of Engineering Washington University in St. Louis St. Louis United States

ISBN: (纸本)9798350351552

Detecting retinal image analysis, particularly the geometrical features of branching points, plays an essential role in diagnosing eye diseases. However, existing methods used for this purpose often are coarse-level and lack fine-grained analysis for efficient annotation. To mitigate these issues, this paper proposes a novel method for detecting retinal branching angles using a self-configured image processing technique. Additionally, we offer an open-source annotation tool and a benchmark dataset comprising 40 images annotated with retinal branching angles. Our methodology for retinal branching angle detection and calculation is detailed, followed by a benchmark analysis comparing our method with previous approaches. The results indicate that our method is robust under various conditions with high accuracy and efficiency, which offers a valuable instrument for ophthalmic research and clinical applications. The dataset and source codes are available at: https://***/Retinal-Research/RBAD. © 2024 IEEE.

关键词： Ophthalmology

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A Washing Machine is All You Need? On the Feasibility of Machine Data for Self-Supervised Human Activity Recognition

A Washing Machine is All You Need? On the Feasibility of Mac...

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Activity and Behavior computing (ABC), International Conference on

作者： Harish Haresamudram Irfan Essa Thomas Plötz School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA School of Interactive Computing Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798350375503

ISBN: (纸本)9798350375510

Learning representations via self-supervision has emerged as a powerful framework for deriving features for automatically recognizing activities using wearables. The current de-facto protocol involves performing pre-training on (large-scale) data recorded from human participants. This requires effort as recruiting participants and subsequently collecting data is both expensive and time-consuming. In this paper, we investigate the feasibility of an alternate source of data for its suitability to lead to useful representations, one that requires substantially lower effort for data collection. Specifically, we examine whether data collected by affixing sensors on running machinery, i.e., recording non-human movements/vibrations can also be utilized for self-supervised human activity recognition. We perform an extensive evaluation of utilizing data collected on a washing machine as the source and observe that state-of-the-art methods perform surprisingly well relative to when utilizing large-scale human movement data, obtaining within 5-6 % Fl-score on some target datasets, and exceeding on others. In scenarios with limited access to annotations, models trained on the washing-machine data perform comparably or better than end-to-end training, thereby indicating their feasibility and potential for recognizing activities. These results are significant and promising because they have the potential to substantially lower the efforts necessary for deriving effective wearables-based human activity recognition systems.

关键词： Training Annotations Transfer learning Self-supervised learning Data models Sensors Human activity recognition

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Analyzing the Domain Shift Immunity of Deep Homography Estimation

Analyzing the Domain Shift Immunity of Deep Homography Estim...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Mingzhen Shao Tolga Tasdizen Sarang Joshi Kahlert School of Computing University of Utah Scientific Computing and Imaging Institute University of Utah Department of Electrical & Computer Engineering University of Utah Department of Biomedical Engineering University of Utah

Homography estimation serves as a fundamental technique for image alignment in a wide array of applications. The advent of convolutional neural networks has introduced learning-based methodologies that have exhibited remarkable efficacy in this realm. Yet, the generalizability of these approaches across distinct domains remains underexplored. Unlike other conventional tasks, CNN-driven homography estimation models show a distinctive immunity to domain shifts, enabling seamless deployment from one dataset to another without the necessity of transfer learning. This study explores the resilience of a variety of deep homography estimation models to domain shifts, revealing that the network architecture itself is not a contributing factor to this remarkable adaptability. By closely examining the models’ focal regions and subjecting input images to a variety of modifications, we confirm that the models heavily rely on local textures such as edges and corner points for homography estimation. Moreover, our analysis underscores that the domain shift immunity itself is intricately tied to the utilization of these local textures. 1

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Analyzing the function of smart security in the context of the internet of things 3

Analyzing the function of smart security in the context of t...

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3rd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naidu, K. Venkatagurunatham Pillai, Anchu Krishnan Rajaprabha, C.E. Sharma R, Rajesh Ul Islam, Atowar Khasimbee, Shaik Dr M.G.R Educational and Research Institute Dept. of Computer Science and Engineering Chennai India Xavier Institute of Engineering Applied Science and Humanities Maharashtra Mumbai India Hindusthan Institute of Technology Department of Computer Science and Engineering Coimbatore India Adama Science and Technology University School of Electrical Engineering and Computing Department of Computer Science and Engineering Nazareth1888 Ethiopia University of Science and Technology Meghalaya Department of Computer Science and Electronics Meghalaya Baridua793101 India Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India

ISBN: (纸本)9798350399264

Technology is getting better day by day and human beings are using the latest technology possible for their needs and safety. Many devices have been invented that use the internet and with this human safety issues are also taken care of up to a great extent. GPS tracking devices, SHE (Society Harnessing Equipment), AESHS (Advanced Electronic System for Human Safety), ILA Systems are one of the most useful applications and devices that are used for human safety. Human safety and especially the safety of girls have become an emerging and sensitive issue in the present world scenario. This article deals with the role of the Internet of Things in advancing smart security. The issues are taken care of with the help of these upgraded devices that are easily accessible from a remote location with an internet connection. © 2023 IEEE.

关键词： Internet of things

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