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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2024年第1期86卷 155-166页

作者： Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana Dept. of Computer Science University of Technology Iraq School of Computing Universiti Utara Malaysia Malaysia

Biometrics systems utilizing hand geometry, fingerprint, iris, face, palm print, voice, gesture, and palm print have been utilised for authentication purposes. Through these templates, the face template is suggested as the strongest. However, problems still exist in face-matching implementation which affects biometric security. Thus, in this paper, a new cancellable face image algorithm using the Convolution Neural Network (CNN) feature extraction technique and Winner-Takes-All hashing method is proposed. The algorithm is to overcome the problem of matching which will enhance its security. ORL and Yale datasets were utilized in the evaluation of the proposed algorithm. Several common algorithms were utilised in the comparison based on Equal Error Ratio (ERR) measurement. The outcomes displayed the verification performance of the unimportant differences with EER = 0.95% and 3.25% for the extended version of the face dataset for the Yale and ORL face datasets, respectively. The acceptable implementation of the proposed algorithm demonstrates that it can be used for the security purpose of biometric trait tasks. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Convolution

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Accelerating Cloud Deployment Through Containerization Technologies

Accelerating Cloud Deployment Through Containerization Techn...

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2024 International Conference on Advances in Computing Research on science Engineering and technology, ACROSET 2024

作者： Lakkadwala, Aayushya Lakkadwala, Prashant Acropolis Institute of Technology and Research Dept. of Computer Science and Engineering Indore India Acropolis Institute of Technology and Research Dept. of Information Technology Indore India

ISBN: (纸本)9798350388800

This research investigates the effectiveness of containerization technologies, with a focus on Docker, in accelerating cloud application deployments. The study examines diverse methodologies aimed at optimizing container image size, refining container orchestration mechanisms for expedited deployments, and evaluating the integration of serverless functions within containers to reduce deployment durations. By synthesizing existing scholarly literature with empirical analyses, this paper aims to elucidate the efficacy of containerization in streamlining cloud deployment processes. It offers insights into optimal practices and identifies prospective avenues for future research and development in the field. © 2024 IEEE.

关键词： Cloud platforms

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Development of Extraction Technique for Diagnosing Kidney Abnormalities by Using Ultrasound Images 8

Development of Extraction Technique for Diagnosing Kidney Ab...

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8th International Conference on information technology, information Systems and Electrical Engineering, ICITISEE 2024

作者： Zain, Ruri Hartika Sumijan Defit, Sarjon Universitas Putra Indonesia Yptk Faculty of Computer Science Dept. Information Technology Padang Indonesia

ISBN: (纸本)9798350368970

Kidney disease is a health problem consisting of kidney failure, where it is caused by a complication of decreased kidney function. To prevent serious complications, kidney disorders must be diagnosed quickly and automatically. The aim of this study was to develop an extraction method to diagnose kidney abnormalities in ultrasound images from 102 patients. Researchers proposed the development of an extraction technique by using a combination of the Grey Level Co-Occurrence Matrix (GLCM), Scale Invariant Feature Transform (SIFT) method and the Canny method. The statistical-based GLCM feature extraction method used a pixel value matrix that had a certain score and forms the angle of the kidney image pattern. The research shows that the SIFT algorithm is effective for object recognition and it is robust to changes in rotation and scaling of kidney images. Canny is also known as optimal edge detection, localizing edge points and providing only one response for one edge of the image. Based on the tests, it is found that the process of detecting kidney abnormalities quickly and accurately. it increases the efficiency of the medical role in decision making. This automatic system analyses and identifies kidneys based on existing abnormalities with maximum accuracy such as cysts, kidney stones, cancer and tumours as abnormalities in the kidneys. In addition, the automated kidney diagnosis system will help radiologists and nephrologists in reducing inadequate nephrology problems and discrepancies. © 2024 IEEE.

关键词： Ultrasonic imaging

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Autoencoded Image Compression for Secure and Fast Transmission

Autoencoded Image Compression for Secure and Fast Transmissi...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Naveen, Aryan Kashyap Thunga, Sunil Murki, Anuhya Kalale, Mahati Anil, Shriya National Institute of Technology Karnataka Dept. of Information Technology Surathkal India National Institute of Technology Karnataka Dept. of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798350376876

With exponential growth in the use of digital image data, the need for efficient transmission methods has become imperative. Traditional image compression techniques often sacrifice image fidelity for reduced file sizes, challenging maintaining quality and efficiency. They also compromise security, leaving images vulnerable to threats such as man-in-the-middle attacks. This paper proposes an autoencoder architecture for image compression to not only help in dimensionality reduction but also inherently encrypt the images. The paper also introduces a composite loss function that combines reconstruction loss and residual loss for improved performance. The autoencoder architecture is designed to achieve optimal dimensionality reduction and regeneration accuracy while safeguarding the compressed data during transmission or storage. Images regenerated by the autoencoder are evaluated against three key metrics: reconstruction quality, compression ratio, and one-way delay during image transfer. The experiments reveal that the proposed architecture achieves an SSIM of 97.5% over the regenerated images and an average latency reduction of 87.5%, indicating its effectiveness as a secure and efficient solution for compressed image transfer. © 2024 IEEE.

关键词： Image compression

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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WellBe: An Intelligent Elderly Care and Well-Being Monitoring System Using Deep Learning 4

WellBe: An Intelligent Elderly Care and Well-Being Monitorin...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Raotole, Atharv Shirodkar, Sailee Sanjay Shukla, Rahul Sisodia, Jignesh Devadkar, Kailas Patel Institute of Technology Dept. of Information Technology Sardar Mumbai India Patel Institute of Technology Dept. of Computer Science and Engineering Sardar Mumbai India

ISBN: (纸本)9798350349900

Deep Learning refers to a subset of Machine Learning that utilizes deep neural networks to simulate the complex decision-making process of the human brain. In recent years, DL has made remarkable progress in addressing multiple unique challenges faced by most elderly individuals. While there have been significant contributions to independent systems catered for the elderly, there is the existence of an overall integrated AI platform that unifies the use of individual modules for an optimized solution. This paper introduces 'WellBe,' an innovative AI system that encompasses an alarm-based fall detection component, a facial emotion recognition (FER) system and a built-in personalized chatbot all under a user-friendly website. The novelty of the work extends beyond the aforementioned components, owing to the implementation of an optimized FER system able to handle wrinkles and changes in skin texture, thereby tailored exclusively for the elderly demographic. The system leverages a custom feature extraction module, fusing facial and wrinkle features, in addition to extending a convolutional neural network (CNN) architecture for accurate emotion classification, achieving promising performance with precision, recall, and F1-scores of 0.87, 0.91, and 0.89 for the 'Happy' (one of the predicted emotions) class, respectively. The fall detection component, on the other hand, utilizes a pre-trained YOLOv7 Pose model for the real-time identification of falls, sitting at a performance accuracy of 95.3%. Finally, the personalized chatbot is implemented using a rule-based approach enhanced with the Jaccard similarity algorithm, to facilitate prompt assistance and a sense of companionship to the end user. Overall, the integration of these components into a cohesive website offers a comprehensive solution for enhancing the quality of life for elderly individuals living alone, addressing loneliness, safety, and overall well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and Development of Cognitive Training Systems Based on Extended Reality and BCI technology 7

Design and Development of Cognitive Training Systems Based o...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Lin, Pei-Jung Ku, Hsiu-Chien Lin, Li-Lun Feng Chia University Dept. of Information Engineering and Computer Science Taichung Taiwan Hungkuang University Dept. of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350307207

Dementia is considered a Mild Cognitive Impairment (MCI), representing a blurred transition from normal cognitive function to dementia. MCI might be a precursor to dementia being caused by various neurodegenerative diseases, including Alzheimer's disease, stroke, metabolic disorders, and mental health conditions. According to the World Health Organization (WHO), over 55 million people worldwide are suffering from dementia, with nearly 10 million new cases each year. This number is expected to rise to 78 million by 2030. Numerous studies have proposed various training methods targeting specific cognitive domains in dementia. For instance, reminiscence therapy encourages individuals to recall buried memories, while reality orientation therapy focuses on strengthening orientation cues to help patients with dementia establish a stronger sense of control. With aging, changes occur in cognitive and physical functions. Research suggests that memory functions can be improved through training. Therefore, the primary goal of this study was to develop an Extended Reality (XR) cognitive system to assist MCI patients in enhancing logical thinking, increasing independence in daily life, and improving response accuracy. The application of extended reality technology in the medical field is expanding, covering areas such as surgical assistance, medical consultation, disease treatment, and medical training. We integrated mixed reality devices with brain-computer interface (BCI) equipment. By detecting brainwave signals in the frontal region of the brain through sensors, neural activity was quantified, and digital brainwave data was transmitted to the system. This data was then visualized on screen in real-time to analyze changes in brainwave data during training. Through multifaceted task training, the system enhanced cognitive response and sensory perception, stimulating brain activity with the goal of strengthening cognitive functions. © 2024 IEEE.

关键词： Mixed reality

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Fuzzy-rule-decided small cell offloading for rate-adaptive SVC-DASH video streaming over the vehicle environment

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computer Communications 2023年 212卷 1-20页

作者： Huang, Chung-Ming Wang, Han-I Dept. of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

This paper proposed an SVC-DASH video streaming method over the vehicular environment based on the Multi-access Edge Computing (MEC) architecture. For the streaming concern, a control scheme using the Segment-Set-based buffer-aware bitrate adaptation with the backward quality's increment control was proposed;for the mobility concern, a Small Cell Video Streaming Offloading (SC–VSO) control scheme adopting the multiple attribute fuzzy-based decision for deciding whether it can offload the video streaming traffic from a macro cell to a small cell or not was proposed. To have the suitable bitrate adaptation and stable quality variation, three factors that the proposed streaming control scheme considers are (i) estimated bandwidth, (ii) buffer occupancy and (iii) quality variation. Since the vehicle is moving across many types of Base Stations (BSs), the proposed mobility control scheme can decide whether to have SC-VSO or not before vehicle X entering into the ahead small cell's signal coverage based on (i) the network situations of the corresponding macro and small cell and (ii) the reported context from vehicle X using the proposed fuzzy logic mechanism. The performance evaluation results shown that the proposed method has the higher video quality and better quality stability for video streaming over the vehicle environment. © 2023 Elsevier B.V.

关键词： Video streaming

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Exploring Agile Methodologies in Educational Software Development- A Comparative Analysis and Project Management Insights 3

Exploring Agile Methodologies in Educational Software Develo...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Kate, Vandana Bhalerao, Shilpa Sharma, Vanshika Acropolis Institute of Technology and Research Dept. of Computer Science and Information Technology Indore India Acropolis Institute of Technology and Research Dept. of Computer Science and Technology Indore India

ISBN: (纸本)9798350343274

In the realm of academic research, the task of determining the most suitable Software Development Life Cycle (SDLC) becomes complex, owing to the diverse project requirements, rapidly changing technology, team skills and experience, organizational culture and readiness, flexibility and adaptability needs, and decision-making complexity. It requires a thorough analysis of various factors to make a wise decision that aligns with the project's goals and constraints. A variety of models, including Waterfall, Prototyping, and Spiral, offer distinct sets of advantages and disadvantages. Agile modeling is a flexible and iterative software development strategy that emphasizes collaborative modeling and rapid feedback. The agile philosophy promotes transparency, effective communication, and embracing change to meet evolving project needs efficiently and effectively. An In-dept. Examination of Agile Methodologies versus Conventional Approaches gives a deeper understanding of their respective strengths, weaknesses, and applicability. Also as the educational institutes increasingly recognize the importance of providing industry-relevant skills and preparing students for dynamic work environments, agile methodologies are likely to play a vital role in shaping the future of education and fostering the success of students in their careers. In this paper, we have explored the practical application of well-established Agile methodologies including Scrum, Kanban, Lean, Extreme Programming (XP), Feature Driven Development, and Dynamic Systems Development Method (DSDM) within the context of educational institutions and project management. © 2023 IEEE.

关键词： Project management

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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Enhancing VANET Efficiency with ACRR Technique for Improved ...

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2024 International Conference on science, technology, Engineering and Management, ICSTEM 2024

作者： Karthick, K. Talari, Praveen Nishadevi, V. Krishna Prakash, R. Asokan, R. Velusamy, A. Dept. of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Computer Science and Engineering Vignan Bharathi institute of technology Telangana Hyderabad India Dept. of Computer Science and Engineering Mahendra College of Engineering Tamilnadu Salem India Dept. of Information Technology Sona College of Technology Salem India Dept. of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Information Technology Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9798350376913

The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p

关键词： Traffic congestion

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