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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 IEEE.

关键词： Encryption algorithms

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Deep Learning Classification Techniques on Detecting Diabetic Retinopathy Dataset 7

Deep Learning Classification Techniques on Detecting Diabeti...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Revathi, B. Usharani, C. Kezial Elizabeth, S.K. Nagaraj, P. Nithya, D. Ramco Institute of Technology Department of Artificial Intelligence And Data Science Virudhunagar India Mangayarkarasi College of Engineering Department of Computer Science And Engineering Madurai India Kalasalingam Academy of Research And Education Department of Computer Science And Engineering Virudhunagar Krishnankoil India Theni Kammavar Sangam College of Technology Department of Computer Science And Engineering Theni India

ISBN: (纸本)9798350359299

Deep learning algorithms can summarize images to understand how to carry out necessary tasks. The purpose of this study is to compare several deep learning methods. Both experience-based and explanation-based learning are possible in deep learning. The most widely utilized algorithms, such as Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), Generative Adversarial Networks (GAN), Radial Basis Function Networks (RBFN), and Deep Belief Networks (DBN), and the Diabetic Retinopathy dataset is utilized in this study to evaluate the effectiveness of the algorithms. A comparative study of the classifiers reveals that CNN performs more accurately than the other approaches. © 2024 IEEE.

关键词： Classification (of information)

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Touchless User Interface for Sketching Using Hand Gesture Recognition 2

Touchless User Interface for Sketching Using Hand Gesture Re...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Babu, Tharunaa Shoban Fathima, G. Shubhakar, B.C. Kumaresan, Bhuvanesh Adhiyamaan College of Engineering Department of Computer Science and Engineering Hosur India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram India

ISBN: (纸本)9798350308846

Artificial intelligence (AI) technologies have a significant impact on developments in research and creative processes. With the quick development of computer vision, the extensive need for interaction with machines and humans is growing. Hand gesture recognition is often used in intelligent furniture, robot control and other meaningful use gestures using hand can be represented to give meaningful data or information. With current development in computing, the traditional writing and teaching methods are insufficient because it still uses mouse, keyboard, and touch pens. This paper proposes a solution by creating a Touchless User Interface (TUI). TUI uses hand gestures to sketch on the air without using the keyboard or mouse which is captured and reflected on the screen. The app developed for the same consists of tools such as circles, rectangles, lines, freehand drawing, and eraser which can be selected and drawn in the air, the system captures the respective shape or structure and reflects it on the screen. Human hands may be split from a complex background and identified in real time which is done using convolutional neural network. Thus, touchless user interface is developed with experimental accuracy of 98.48%. © 2023 IEEE.

关键词： Artificial Intelligence computer Vision Hand gesture recognition Touchless User Interface

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Wrist joint synovial hypertrophy and effusion detection in musculoskeletal ultrasound images using self-attention U-Net

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Multimedia Tools and Applications 2024年第41期83卷 89317-89334页

作者： Chang, Chuan-Wang Chang, Chuan-Yu Zhu, Yu-Xian Wang, Sz-Tsan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Douliu Taiwan Division of Allergy Immunology and Rheumatology Dalin Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation Dalin Chiayi Taiwan

Skeletal muscle ultrasound has emerged as a pivotal imaging modality in rheumatology clinics, offering unparalleled advantages such as radiation-free imaging, safety, and dynamic examination capabilities. However, its reliance on operator expertise often leads to inconsistent interpretations and diagnostic variability. In this study, we present a novel diagnostic system aimed at detecting rheumatoid arthritis (RA) in the wrist joint, with a focus on identifying synovial hypertrophy and effusion using musculoskeletal ultrasound images. Leveraging deep learning techniques, specifically semantic segmentation models, we introduce SEAT-UNet, which combines the U-Net architecture with a self-attention mechanism to enhance the accuracy of lesion classification and localization. SEAT-UNet addresses the challenge of discontinuous dispersion encountered in conventional segmentation models, particularly when delineating lesion areas. Our experimental results demonstrate exceptional performance, achieving a sensitivity and Dice coefficient of 100% and 84%, respectively, in synovial hypertrophy detection, and 86% sensitivity with an 84% Dice coefficient in effusion detection. These findings underscore the potential of SEAT-UNet as a valuable tool for early RA diagnosis, offering improved patient outcomes and facilitating more effective disease management strategies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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GENOCARE PROGNOSTICATOR MODEL: HOST GENETICS PREDICT SEVERITY OF INFECTIOUS DISEASE

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Scalable Computing 2025年第2期26卷 924-939页

作者： DUBEY, SHIVENDRA SINGH, SHWETA VERMA, DINESH KUMAR LODHI, SUDHEER KUMAR DUBEY, SAKSHI Department of Artificial Intelligence and Machine Learning Manipal University Rajasthan Jaipur303007 India School of Engineering and Technology Jagran Lakecity University Madhya Pradesh Bhopal462042 India Department of Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Gujarat Vadodara391760 India

Scientific community understanding of the variance in severity of infectious disease like COVID-19 across patients is an important area of focus. The article presents an innovative voting ensemble GenoCare Prognosticator (GCP) model that incorporates XGBoost and Random Forest classifiers, two cutting-edge machine learning approaches. A large dataset that incorporates medical covariates like gender and age along with biological WES (Whole Exome Sequencing) data was used to train these models. Five-fold stratified cross-validation was used to process the dataset in order to improve model stability and avoid overfitting. Two medical covariates and sixteen recognized candidate gene variants were among the eighteen major features on which our GCP model had been verified using data from earlier studies. Specific post-hoc clarification of the model's predictions was provided by ExplainerDashboard, a Python open-source library, to improve interpretability. Furthermore, we utilized OpenTarget and Enrichr, two bioinformatic resources, to establish connections between the discovered variations in genetics and pertinent ontologies, biological pathways, and possible drug/disease relationships. Unsupervised clustering of SHAP key feature values was included in the analysis, which revealed intricate genetic interactions that affect the severity of the disease. Our results show that although gender and age are the main factors influencing the severity of COVID-19, complex genetic interactions cause severe symptoms in a specific subset of patients. This work contributes to our comprehension of the biological variables influencing the severity of COVID-19 and offers a reliable, comprehensible model that can help recognize patients at high risk and guide individualized treatment plans. © (2025), (West University of Timisoara). All rights reserved.

关键词： COVID-19

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Hybrid blockchain based medical data sharing with the optimized CP-ABE for e-Health systems

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International Journal of Information Technology (Singapore) 2024年第1期16卷 121-130页

作者： Mishra, Anil Kumar Mohapatra, Yogomaya School of Computer Science and Engineering Trident Academy of Technology Odisha Bhubaneswar India Department of Computer Science and Engineering C.V. Raman Global University Odisha Bhubaneswar India

While e-Health systems must prioritise security and privacy, sharing medical data can help improve diagnostic accuracy. Because of its immutability, Blockchain (BC) is now being suggested as an exciting solution for sharing Personal Health Information (PHI). However, sharing PHI must improve patient privacy and security. As a result, this paper describes a secure medical data sharing system. Private BC, Consortium BC (CBC), and optimized cryptography are all included in the proposed system. To ensure patient privacy, PHI is encrypted using optimised ciphertext policy attribute based encryption (CP-ABE). In particular, replicated attributes amendment (RAA) is proposed to improve CP-ABE performance. For each replicated attribute, this RAA achieves a single share value. PHI ciphertexts are stored as blocks in the hospital’s private BC. Furthermore, the PHI keyword is saved to the CBC. By validating the keyword and getting access to the private BC, the doctor may retrieve all patient’s PHI. The article’s findings show that the proposed system achieves lower storage overhead, encryption time, and decryption time. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Blockchain (BC) CBC e-Health system Replicated attributes amendment

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An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

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computer Systems science & engineering 2023年第1期44卷 143-156页

作者： M.Hema S.Kanaga Suba Raja Department of Information Technology Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India

In cloud data centers,the consolidation of workload is one of the phases during which the available hosts are allocated *** phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement(SLA).To consolidate the workloads,the hosts are segregated into three categories:normal hosts,under-loaded hosts,and over-loaded hosts based on their *** is to be noted that the identification of an extensively used host or underloaded host is challenging to ***-old values were proposed in the literature to detect this *** current study aims to improve the existing methods that choose the underloaded hosts,get rid of Virtual Machines(VMs)from them,andfinally place them in some other *** researcher proposes a Host Resource Utilization Aware(HRUAA)Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud *** mechanism presented in this study is contrasted with existing mechanisms *** results attained from the study estab-lish that numerous hosts can be shut down,while at the same time,the user's workload requirement can also be *** proposed method is energy-efficient in workload consolidation,saves cost and time,and leverages active hosts.

关键词： Workload consolidation energy consumption under load server resource utilization

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Facial Emotion Detection Using Deep Learning: A Survey 1

Facial Emotion Detection Using Deep Learning: A Survey

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Ansari, Saif Kulkarni, Praveen Rajesh, Tm Gurudas, V.R. Department of Computer Science and Engineering Bangalore India Dayanand Sagar University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. © 2023 IEEE.

关键词： Emotion Recognition

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Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency 23

Analysing zero-shot temporal relation extraction on clinical...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Kougia, Vasiliki Sedova, Anastasiia Stephan, Andreas Zaporojets, Klim Roth, Benjamin Faculty of Computer Science University of Vienna Vienna Austria UniVie Doctoral School Computer Science Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Vienna Austria Department of Computer Science Aarhus University Aarhus Denmark

ISBN: (纸本)9798891761308

This paper presents the first study for temporal relation extraction in a zero-shot setting focusing on biomedical text. We employ two types of prompts and five Large Language Models (LLMs;GPT-3.5, Mixtral, Llama 2, Gemma, and PMC-LLaMA) to obtain responses about the temporal relations between two events. Our experiments demonstrate that LLMs struggle in the zero-shot setting, performing worse than fine-tuned specialized models in terms of F1 score. This highlights the challenging nature of this task and underscores the need for further research to enhance the performance of LLMs in this context. We further contribute a novel comprehensive temporal analysis by calculating consistency scores for each LLM. Our findings reveal that LLMs face challenges in providing responses consistent with the temporal properties of uniqueness and transitivity. Moreover, we study the relation between the temporal consistency of an LLM and its accuracy, and whether the latter can be improved by solving temporal inconsistencies. Our analysis shows that even when temporal consistency is achieved, the predictions can remain inaccurate.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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COVID-Pneumonia Classification using Chest X-Ray 14

COVID-Pneumonia Classification using Chest X-Ray

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

作者： Manjunath, Apeksh S. Monis, Derryl Kevin Gurukiran, D. Shastry, Sushil K. Ravikumar, V. Vidyavardhaka College of Engineering Computer Science and Engineering Mysore India Vidyavardhaka College of Engineering Dean Faculty Computer Science and Engineering Mysore India

The traditional methods of examining the reports physically is time consuming and chances of missing out smaller details are more. In the present world, Image Processing in the medical field has a significant impact in making the job of medicos easy. To make this job easy it is expected to have an automated system for medical image analysis related Covid-19. In this paper we are analyzing various approaches available for medical image analysis and to propose an efficient approach to increase the current system's performance. © Grenze Scientific Society, 2023.

关键词： Image classification

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