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Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Jacobo González de Frutos Mia Sandra Nicole Siemon Kamal Nasrollahi Dept. of Electronic Systems Aalborg University Aalborg Denmark Dept. of Architecture Design and Media Technology Aalborg University and Milestone Systems Aalborg Denmark

ISBN: (数字)9798331541842

ISBN: (纸本)9798331541859

Unintentional injury deaths, primarily caused by simple falls among elderly individuals, have consistently ranked as the second most common cause of death over the years, as documented by the World Health Organization. This not only poses a significant health concern but also imposes a substantial financial burden on hospitals, as insurance companies typically do not cover such events. This project aims to address this challenge by developing a simple Artificial Intelligence system based on Video Anomaly Detection to detect fall situations that require less computational power and protect patients' privacy. The system comprises a Keypoint Detector for estimating the patient's pose and a discrete Bayesian Network to detect whether a person has fallen or not. The approach has been trained and tested under the public dataset CAUCAFall achieving an average accuracy of 90.62%, Area Under the ROC Curve of 88.71%, Region-Based Detection Criterion of 86.23% and Track-Based Detection Criterion of 98.39%. The obtained results demonstrate highly competitive performance, approaching the levels achieved by the most recent State-of-the-art works tested on CAUCAFall. Our unsupervised methodology to fall detection is privacy-preserving, computationally lightweight, and learns patterns from the training data in seconds on a conventional 30GB memory Google Cloud Virtual Machine with 8 AMD EPYC 7B13s Intel Broadwell CPUs.

关键词： Privacy Graphical models Hospitals Image processing Training data Insurance Probabilistic logic Fall detection Older adults Injuries

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting 17

An Image Analysis-Based Automated Method using Deep Learning...

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17th IEEE International Conference on Industrial and Information systems, ICIIS 2023

作者： Ajikaran, Ramesh Hewarathna, Ashen Iranga Palanisamy, Vigneshwaran Joseph, Charles Thuseethan, Selvarajah Sabaragamuwa University of Sri Lanka Dept. of Computing and Infromation Systems Sri Lanka Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sri Lanka Fau Dept. of Computer Science Germany School of Information Technology Deakin University Australia

ISBN: (纸本)9798350323634

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst. © 2023 IEEE.

关键词： Grain (agricultural product)

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Software Defined Network Based Next Generation Mobile Communication Network architecture

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第23期18卷 138-148页

作者： Kumar, Sumit Ben Sujin, B. Bhavani, K. Srilatha, Doddi Anand, Ketan Dept. of CSE Haridwar University Uttarakhand Roorkee India University of Technology and Applied Sciences Nizwa Oman Department of Computer Science and Business Systems Panimalar Engineering College Tamil Nadu Chennai India Department of CSE Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Sreenidhi Institute of Science and Technology Telangana Hyderabad India

As mobile networks and network speeds become more prevalent, the demand for marketing strategies increases. The operators are thinking about this, and the development of 5G communication networks is one of their main concerns. As the need for higher transmission speeds increases, 5G networks face challenges of scalability and adaptability. The next-generation mobile network (NMCN-SDN) architecture proposed in this study is based on Software-Defined Networks (SDN). A new network model called computer-defined networking allows for dynamic network definition and programming. A network simulator is created to examine the efficiency of the built infrastructure under different network conditions, including throughput, latency, and resource consumption. In this paper, the comparison of end-toend latency between the standard communication architecture and the proposed NMCN-SDN architecture is done. The results show that the proposed architecture has less space in various conditions compared to existing communication architectures in various conditions. © 2024 by the authors of this article.

关键词： 5G end-to-end latency mobile communication resource utilization Software-Defined Networks (SDN) throughput

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Taxonomy of Proactive Detection Methods of Drunk Driving for Enhancing Traffic Safety 17

Taxonomy of Proactive Detection Methods of Drunk Driving for...

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Swiley, Richard Preston Alsulieman, Razan Sherif, Ahmed Elsersy, Mohamed Khalil, Kasem University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States Higher Colleges of Technology Computer and Information Systems Dept. AlAin Abu Dhabi United Arab Emirates University of Mississippi Electrical and Computer Engineering Department MS United States

ISBN: (纸本)9798331530471

Drunk driving continues to be a substantial public health issue, leading to a multitude of accidents and deaths on a global scale. Conventional techniques for identifying drunk driving, such as breathalyzers and field sobriety tests, tend to be reactive rather than proactive. This paper surveys the existing methods for detecting drunk driving to enhance traffic safety. The technologies studied can proactively detect impaired driving behaviors by combining machine learning (ML) approaches with real-time data processing. The study outlines various classifications based on measured metrics and driving behavior. © 2024 IEEE.

关键词： Artificial Intelligence Drunk Driving Detection Traffic Safety

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Pedestrian Behavior Recognition via a Smart Graph-based Optimization 19

Pedestrian Behavior Recognition via a Smart Graph-based Opti...

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19th International Bhurban Conference on Applied Sciences and technology, IBCAST 2022

作者： Akhter, Israr Javeed, Madiha Hamdard University Dept. of Computer Science Islamabad Pakistan School of Systems and Technology University of Management and Technology Lahore Pakistan

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

Innovative technology and improvements in intelligent machinery, transport networks, emergency mechanisms, and educational facilities define the modern world. It is difficult to comprehend the settings, do crowd monitoring, and observe people. In this research, we present an effective method for understanding crowd data across a sustainable framework and for identifying pedestrian anomalous and normal behavior. Preprocessing is conducted initially, followed by person detection and contour separation. Then, the features are extracted using a force flow matrix and deep flow, and the graph mining algorithm is used to optimize the feature space. Finally, we utilized CNN to distinguish between regular and irregular pedestrian behavior. For the ADOC and UMN collections, the suggested technique has an acceptable performance percentage of 83.30 and 83.83 percent, correspondingly. In conclusion, the acquired results are shown to be more effective than other approaches described in the scientific literature. © 2022 IEEE.

关键词： Graphic methods

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Auction based Resource Allocation in Cloud Computing using Blockchain Techniques 14

Auction based Resource Allocation in Cloud Computing using B...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Kamaleshwar, T. Jayaraj, R. Vijayaraj, N. Sundaranarayana, D. Dr.Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of Computer Science & Engineering Chennai India SRM Institute of Science and technology Dept. of Data Science and Business systems Kattankulathur Chennai India

As the spine innovation of decentralized cryptocurrencies, blockchain has additionally proclaimed numerous applications in different fields, for example, resource allocation in cloud computing, Internet of Things (IoT) and resource offloading. The database state is decentrally kept up, and any part hub in the overlay blockchain organize is allowed to partake in the state support without character confirmation. The exchanges among part hubs are recorded in cryptographic hash-connected information structures known as squares. A progression of affirmed blocksis orchestrated in ordered request to shape a consecutive chain, henceforth named blockchain. All part hubs in the system are required to pursue consensus protocol on the value-based information, cryptographic hashes and computerized marks put away in the block to ensure trustworthiness of the blockchain. © Grenze Scientific Society, 2023.

关键词： Blockchain

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Allocating and Routing Resources in an IoT Network with an Efficient Hybrid Optimization Approach 1

Allocating and Routing Resources in an IoT Network with an E...

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

作者： Umar, Syed Reddy, N Linga Yadesa, Tariku Birhanu Deressa, Tadele Debisa Fikadu, Etana College of Engineering & Technology Wollega University Dept. of Computer Science Nekemte Ethiopia Dr Ysr Architecture and Fine Arts University Dept. of Computer Science Andhra Pradesh Kadapa India

ISBN: (纸本)9781665497107

technology, economics, government, people, management, and infrastructure all play a role in creating a Smart City. This indicates that a Smart City may have different communication requirements than a traditional city. The introduction of wireless technologies like WiFi, Bluetooth, ZigBee, WiMax, and 4G/LTE has made them a viable option for facilitating communication in Smart City projects. As long as they are using unlicensed interference, coexistence and band problems will continue to be a problem. Smart cities utilize the Internet of Things (IoT) to address this issue. Using hybrid optimization methods, this article tackles the problems of resource allocation and routing for the Internet of Things (IoT) by proposing the ECRR protocol (energy efficient, congestion aware resource allocation and routing). Data clustering and met heuristic algorithms are usedin the first step of the ECRR method to better distribute IoT devices and gateways throughout the network. It also proposes a method for selecting the best future route by using a queue-based swarm optimization algorithm, which enhancesthe route discovery process. With Network Simulator (NS-2), the proposed ECRR approach is simulated and compared to existing state-of-the-art methods in terms of energy consumption, node lifetime, throughput, end-to end latency, packet delivery ratio, and packet overheads. © 2022 IEEE.

关键词： Internet of things

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A Demonstrative Study on Prediction of Disease Using Bayesian Machine Learning Techniques

A Demonstrative Study on Prediction of Disease Using Bayesia...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Dhanalakshmi, R. Vijayaraghavan, N. Narasimhan, S. Rajesh, R. School. of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Kcg College of Technology Dept. of Mathematics Chennai India School. of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350315127

With the rapid advancement of biological information, accurate analysis of treatment data aids in early disease detection. To uncover knowledge for medical research, advanced Machine Learning algorithms are applied. Here in this research paper, to determine if a patient is affected by any Infectious disorders, we use the Naive Bayes Algorithm method. © 2023 IEEE.

关键词： Machine learning

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Enhancing Facial Recognition in Visual Prostheses using Region of Interest Magnification and Caricaturing 5

Enhancing Facial Recognition in Visual Prostheses using Regi...

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5th International Conference on computer and Applications, ICCA 2023

作者： Moneer, Hesham M. Elnabawy, Reham H. Eldawlatly, Seif German University in Cairo Digital Media Eng. and Technology Dept. Cairo Egypt Ain Shams University Computer and Systems Eng. Dept. Cairo Egypt The American University in Cairo Computer Science and Eng. Depr. Cairo Egypt

ISBN: (纸本)9798350303254

Blindness is a prevalent disability with significant personal and societal consequences. While medical advancements offer treatment options, severe damage to the retina, optic nerve, or brain may remain untreated. Visual prostheses are implantable medical devices that aim at providing limited vision to such individuals. However, such prostheses offer low spatial resolution making activities like reading, facial recognition, and navigation challenging. This work aims to enhance implantees' ability to recognize faces through real-time scene preprocessing, machine learning and computer vision techniques. Virtual-reality visual models simulating prosthetic vision were tested on normally/corrected sighted subjects, investigating the use of histogram equalization for contrast enhancement, facial region magnification, and caricaturing of facial features. Results revealed that histogram equalization with magnification increases facial recognition accuracy by 60%, distinguishability accuracy by 50%, and accuracy of seeing facial details by 90%. In contrast, adding facial caricaturing improved the accuracies by 66.66%, 25%, and 75% for recognizability, distinguishability, and seeing facial details, respectively. Consequently, the combination of visual field histogram equalization, face magnification, and optional caricaturing can be considered as a promising enhancement approach that could enhance the quality of vision perceived through visual prostheses. © 2023 IEEE.

关键词： Prosthetics

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Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive Learning in Sri Lanka

Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive...

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Advancements in Computing (ICAC), International Conference on

作者： P.S.D. Fernando H.P.D.E.R. Amarasekara M.D.B. Rathnakumara B.W. Amarakoon Samantha Rajapaksha Jenny Krishara Dept. of Information Technology Malabe Sri Lanka Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Proficiency in mathematics is essential for both academic and professional success, yet many students find the subject difficult, especially when preparing for important exams like Sri Lanka's General Certificate of Education (G.C.E.) Ordinary Level examination. In Sri Lanka, the students show poor pass rates in Ordinary Level mathematics examination. In order to improve mathematics education for O/L students, this research suggests an innovative mobile application that makes use of adaptive learning strategies. This application features components like a comprehensive knowledge bank that is curriculum-mapped, pre-assessment tools for identifying knowledge shortages, a weaknesses based lesson recommendation for personalized lesson suggestions, a custom developed chat assistant to interact with the knowledge bank, interactive 3D visualizations and justifications of complex concepts, and platforms for problem-solving, writing, and time management skill development. The suggested adaptive learning strategy makes use of multimedia tools and datadriven insights to tailor instruction to each student's unique needs, improving math comprehension and readiness for the O/L examination in Sri Lanka.

关键词： Adaptive learning Visualization Three-dimensional displays Writing Mathematics Natural language processing Mobile applications Problem-solving Stakeholders Research and development

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