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Activity Monitoring for ICU Patients Using Deep Learning and Image Processing

sN computer science 2020年第3期1卷 1-8页

作者： Magi, Nikitaa Prasad, b.G. Department of Computer Science and Engineering B. M. S. College of Engineering Bull Temple Road Basavanagudi Bangalore 560019 India

A patient in coma condition needs intense monitoring and care, but nowadays shortage of intensivist and critical care nurses is the major problem faced by the hospitals. Traditional method of monitoring ICU patients requires dedicated proactive observer to look at the patient all day long. One of the issues with traditional methods is that the observer has to be available all 24 h of the day and one observer will be able to monitor only one patient at a time. This paper aims to develop a system which overcomes these issues faced by traditional methods. The proposed system uses image processing and deep learning approach, with which the system can monitor the patients in critical care unit and can report to the doctor or nurse. The proposed system is capable of detecting target object (patient) in real time, further detect patient’s activity if any and notify the doctor or nurse. © 2020, springer Nature singapore Pte Ltd.

关键词： Artificial intelligence Deep learning Human activity analysis Image processing Neural network Object detection Patient monitoring

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Robust Gray-scale Image Watermarkingusing symlet Wavelet Decomposition using Hybrid Approach

Robust Gray-Scale Image Watermarkingusing Symlet Wavelet Dec...

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International Conference on Futuristic Technologies (INCOFT)

作者： Dayanand savakar Pandurang biradar Anand Ghuli shreya shirur Department of Computer Science Rani Channamma University Belagavi Karnataka India Department of M.C.A BLDEA's V.P.Dr.P.G.Halakatti College of Engineering & Technology Vijayapur Karnataka India

Amongst digital requirements, gray-scale image watermarking-capacity is the crucial issue that has to be effectively handled. A method of utilizing a blind watermarking scheme to insert a gray-scale image into another gray-scale image of a different size is proposed in order to mark the same. This method achieves a logically good perceptual analogy by measuring the appropriate (PsNR) and (ssIM) index. To extract LL bands, the technique uses sym 4 discrete wavelet transformation at a single level (DWT), where most of the image's features are kept. A trustworthy rectangular matrix of computationally viable areas of the updated image is then obtained using a singular value decomposition (svD), first utilized to obtain a set of elements sufficient to describe the full image. The technique is resistant to attacks from motion blur, JPEG compression, pepper and salt, Gaussian, speckle, median, and Gaussian lowpass filters, as well as attacks from sharpening and histogram equalization. An increasingly popular strategy for securing copyright protection and identifying data owners is watermarking. The representation of the image determines the watermark embedding techniques, and each domain has its own set of advantages and disadvantages.

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Experimental Analysis of Malware Detection and Classification system using Intelligent Deep Learning Methodology

Experimental Analysis of Malware Detection and Classificatio...

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smart Technologies in Computing, Electrical and Electronics (ICsTCEE), International Conference on

作者： K. Indumathi R. Krishnaprasanna G Chamundeeswari M. Preetha Tamilvani K senthilkumar b Department of IT Rajalakshmi Engineering College Chennai Tamil Nadu India Department of ECE Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of ECE Saveetha Engineering College Chennai Tamil Nadu India Department of CSE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Biosciences Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of EEE V.S.B. Engineering College Karur Tamil Nadu India

ISBN: (数字)9798350384628

ISBN: (纸本)9798350384635

computer system security relies heavily on the ability to identify and eliminate malware. signature-based approaches are widely used, although they are not able to effectively identify zero-day assaults or polymorphic infections. This is why there is a demand for detection methods based on deep learning and categorization. computers, smartphones, and apps are all fair game for malicious software. Malware is software that has to be watched out for since it might be a hazard for both computer users and internet-based systems. Malware was developed with the intention of stealing sensitive data from a user's computer or taking remote control of a user's device. Malware is software designed to disrupt or even destroy data on a user's computer. Malicious software can enter a computer in a number of ways, one of which occurs over a network that already has the virus installed. This project will investigate and deploy high-precision classification models using deep learning technologies from the discipline of computational intelligence to enhance the efficiency and accuracy of malware detection. Learning based Classification scheme for Improved Malware Detection (LCsIMD) is a unique technique described in this work and utilized as the primary training model. The suggested LCsIMD framework is trained with malware visualization tools. Models with higher classification accuracy can be found by transforming malware features into images and then modifying their characteristics and input procedures; similarly, prototypes with proposed model can benefit from the use of suitable characteristics and preprocessing methods. The final step is to generate characteristics for network-generated instances, which improves the precision of small-scale training. Every one of us desire to utilize malware that impacts various devices as a teaching example, as described above. The output efficiency of the suggested technique is cross-validated with the classic Neural Network (NN) model to assess

关键词： Deep learning Training Accuracy Computational modeling Prototypes Malware computer security

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Correction to: A transformer based real-time photo captioning framework for visually impaired people with visual attention

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Multimedia Tools and Applications 2024年第41期83卷 88879-88879页

作者： Kunju, Abubeker Kiliyanal Muhammed baskar, s. Zafar, sherin R, bushara A. s, Rinesh A, shafeena Karim Department of Electronics and Communication Engineering Amal Jyothi College of Engineering (Autonomous) Kanjirapally India Faculty of Engineering Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer and Electronics Engineering SEST- Jamia Hamdard New Delhi India Department of Electronics and Communication Engineering KMEA College of Engineering Cochin India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India

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Distributed Task-Oriented Communication Networks with Multimodal semantic Relay and Edge Intelligence

arXiv

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arXiv 2024年

作者： Guo, Jie Chen, Hao song, bin Chi, Yuhao Yuen, Chau Yu, Fei Richard Li, Geoffrey Ye Niyato, Dusit The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The Hangzhou Institute of Technology Xidian University Hangzhou311231 China The School of Electrical and Electronics Engineering The School of Computer Science and Engineering Respectively Nanyang Technological University 639798 Singapore The Department of Systems and Computer Engineering Carleton University OttawaK1S 5B6 Canada The Faculty of Engineering Imperial College London LondonSW7 2AZ United Kingdom

In this article, we present a novel framework, named distributed task-oriented communication networks (DTCN), based on recent advances in multimodal semantic transmission and edge intelligence. In DTCN, the multimodal knowledge of semantic relays and the adaptive adjustment capability of edge intelligence can be integrated to improve task performance. specifically, we propose the key techniques in the framework, such as semantic alignment and complement, a semantic relay scheme for deep joint source-channel relay coding, and collaborative device-server optimization and inference. Furthermore, a multimodal classification task is used as an example to demonstrate the benefits of the proposed DTCN over existing methods. Numerical results validate that DTCN can significantly improve the accuracy of classification tasks, even in harsh communication scenarios (e.g., low signal-to-noise regime), thanks to multimodal semantic relay and edge intelligence. © 2024, CC bY-NC-sA.

关键词： signal to noise ratio

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Digital Twin-Assisted space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long sun, Geng sun, Zemin Wang, Jiacheng Du, Hongyang Niyato, Dusit Liu, Jiangchuan Leung, victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada R&D Department Jiangxing Intelligence Inc. Nanjing210000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

The emergence of space-air-ground integrated multi-access edge computing (sAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous nature of sAGIMEC networks, coupled with the stringent computational and communication requirements of diverse applications in the LAE, introduces considerable challenges in integrating sAGIMEC into the LAE. In this work, we first present a digital twin-assisted sAGIMEC paradigm for LAE, where digital twin enables reliable network monitoring and management, while sAGIMEC provides efficient computing offloading services for Internet of Things sensor devices (IsDs). Then, a joint satellite selection, computation offloading, communication resource allocation, computation resource allocation and UAv trajectory control optimization problem (JsC4OP) is formulated to maximize the quality of service (Qos) of IsDs. Given the complexity of JsC4OP, we propose an online decentralized optimization approach (ODOA) to address the problem. specifically, JsC4OP is first transformed into a real-time decision-making optimization problem (RDOP) by leveraging Lyapunov optimization. Then, to solve the RDOP, we introduce an online learning-based latency prediction method to predict the uncertain system environment and a game theoretic decision-making method to make real-time decisions. Finally, theoretical analysis confirms the effectiveness of the ODOA, while the simulation results demonstrate that the proposed ODOA outperforms other alternative approaches in terms of overall system performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Quality of service

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Medical Data Classification using a Gravitational search Algorithm and Artificial Intelligence

Medical Data Classification using a Gravitational Search Alg...

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International Conference on smart systems and Inventive Technology (ICssIT)

作者： P. Dileep Kumar Reddy M. Umaselvi Devikanniga Devarajan swati Jha Amuthaguka Raj Kumar Gupta Department of CSE Narsimha Reddy Engineering College (Autonomous) Secunderabad Telangana India Department of CSE P. A. College of Engineering and Technology Pollachi Coimbatore Tamilnadu India Department of Computer Science and Engineering Jain (Deemed-to-be) University Bangalore Karnataka India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Physics Sardar Vallabhai Patel College Bhabua (V.K.S.U Ara)

Current years have exhibited the prosperity of several types of medical data, for the first time presenting the likelihood to expose the interior mechanism of illness through excavating the huge amount of monitored data for one such individual. Over the past decades, machine learning (ML) approaches were broadly implied for detecting distinct diseases. This enables an initial diagnosis and raises the probability of survival. Many medical data sets are unstable. because of this, ML classification methods provide biased classification with the majority class. This manuscript derives a Gravitational search Algorithm with Artificial Intelligence Driven Medical Data Classification (GsAAI-MDC) model. The presented technique majorly intends to accomplish reliable and accurate data classification processes in the medical sector. In order to attain this, the GsAAI-MDC technique applied Deep support vector Machine (DsvM) method for the classification of medical data. In addition, the GsA is utilized to enhance the performance of the DsvM method. The simulation analysis of the GsAAI-MDC technique is carried out using benchmark dataset and the results highlighted the superior outcomes of the GsAAI-MDC model over recent approaches.

关键词： support vector machines Analytical models Machine learning algorithms benchmark testing Data models Classification algorithms Reliability

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Personalized Meal Planning in Inpatient Clinical Dietetics Using Generative Artificial Intelligence: system Description

Personalized Meal Planning in Inpatient Clinical Dietetics U...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Leon Kopitar Gregor stiglic Leon bedrac Jiang bian Faculty of Health Sciences Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Faculty of Health Sciences University of Maribor Maribor Slovenia The NU B.V. Leiden The Netherlands Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida FL USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

This study addresses the limitations of traditional prescribed meal plans, which lack personalization and often prove monotonous and challenging for patients to follow. We propose a novel approach employing generative artificial intelligence in the context of a learning health system, with an emphasis on inpatient clinical dietetics. The system incorporates two key models: the Meal Plan Generation Model, MeaIGM, and the Meal Plan Image Generation Model, MealImageGM, leveraging state-of-the-art large language models. Patient information from electronic health records and clinical dietetics guidelines are incorporated into prompts for MeaIGM, which is refined through nutritionist validations and users' feedback. On the other hand, MealImageGM generates visual representations of meal plans to enhance patient engagement, utilizing crowd-sourced feedback to optimize image generation prompts. The overall system process includes extracting data from electronic health records, pre-designed user meal generation prompts, and the generation of personalized meal plans and images. Nutritionists play a crucial role in monitoring patient adherence and preferences, contributing to a continuous learning health system cycle. The proposed framework ensures clinically appropriate and personalized meal plans, aligning with dynamic dietary recommendations. The study emphasizes the importance of patient-physician co-creation for constant optimization and highlights the potential positive impact on health outcomes.

关键词： visualization Image synthesis Generative AI Medical services Learning (artificial intelligence) Planning Electronic medical records

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video based Car Parking Management and Monitoring Using computer vision and Machine Learning

Video Based Car Parking Management and Monitoring Using Comp...

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Multi-Agent systems for Collaborative Intelligence (ICMsCI), International Conference on

作者： bulla sujitha Anitha Ponraj Chakkarapani v sriram Parabrahmachari T. v. Hyma Lakshmi T. Annamani Department of ECE V. R. Siddhartha Engineering College Vijayawada AP India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Kalasalingam University Krishnankoil Tamil Nadu India School of Electrical and Electronics Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of CSE Guru Nanak institutions Technical Campus Ibrahimpatnam Telangana India Department of ECE S.R.K.R. Engineering College Bhimavaram A.P India Department of ECE Anurag University Hyderabad Telangana India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Each day, countless individuals around the globe are affected by the extensive and complex problem of traffic congestion. This raising challenge is becoming increasingly substantial across the country, driven by factors such as rising population numbers, speedy urban development, insufficient infrastructure, and changing socioeconomic requirements. This paper depicts a novel global video-based parking approach designed to boost the supervising and management of parking spaces using real-time aerial statistics. Utilizing advanced image processing and computer vision procedures, the system essentially detects and examines the occupancy position of parking slots. In our realization, we assessed a total of 48 parking spaces, positively identifying 7 as unused while 41 were occupied. The application of Python, OpenCv, and machine learning assisted real-time monitoring, allowing precise counting and expose of parking availability. The procedure contained critical procedures, including Gaussian blur filtering to minimize noise and improve edge detection, followed by binary thresholding to describe vehicles and parking spots from the background. Furthermore, a median filter was utilized to boost the clarity of vehicle contours. These image enhancement methods significantly expanded detection accuracy and facilitated precise recognition of the Region of Interest (ROI). The results show the system's ability to provide reliable, real-time data for dynamic parking administration, thereby focusing the challenges of urban parking.

关键词： computer vision Thresholding (Imaging) Accuracy Filtering streaming media Real-time systems Car parking vehicle dynamics Monitoring Traffic congestion

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A survey on Resource Management in Joint Communication and Computing-Embedded sAGIN

arXiv

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arXiv 2024年

作者： Chen, Qian Guo, Zheng Meng, Weixiao Han, shuai Li, Cheng Quek, Tony Q.s. The School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China The School of Engineering Science Simon Fraser University BurnabyBCV5A 1S6 Canada Electrical and Computer Engineering Memorial University St. John'sNLA1B 3X5 Canada The Singapore University of Technology and Design Singapore487372 Singapore The Yonsei Frontier Lab Yonsei University Korea Republic of

The advent of the 6G era aims for ubiquitous connectivity, with the integration of non-terrestrial networks (NTN) offering extensive coverage and enhanced capacity. As manufacturing advances and user demands evolve, space-air-ground integrated networks (sAGIN) with computational capabilities emerge as a viable solution for services requiring low latency and high computational power. Resource management within joint communication and computing-embedded sAGIN (JCC-sAGIN) presents greater complexity than traditional terrestrial networks. This complexity arises from the spatiotemporal dynamics of network topology and service demand, the interdependency of large-scale resource variables, and intricate tradeoffs among various performance metrics. Thus, a thorough examination of resource management strategies in JCC-sAGIN is crucial, emphasizing the role of non-terrestrial platforms with processing capabilities in 6G. This paper begins by reviewing the architecture, enabling technologies, and applications in JCC-sAGIN. Then, we offer a detailed overview of resource management modeling and optimization methods, encompassing both traditional optimization approaches and learning-based intelligent decision-making frameworks. Finally, we outline the prospective research directions in JCC-sAGIN. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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