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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Mishra, Amit Kumar Sharma, Rahul Singh, Jagendra Singh, Prabhishek Diwakar, Manoj Tiwari, Mohit Department of Computer Science & Engineering Graphic Era Hill University Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Dehradun India Bharati Vidyapeeth's College of Engineering Dept. of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350344837

This article presents a distinctive framework that utilises blockchain technology to enable smart contracts, revolutionising the administration of electronic medical health records (EMHRs). The framework utilises the Ethereum blockchain and contemporary user interfaces to tackle significant concerns in healthcare data management, ensuring enhanced security, transparency, and user engagement. The device employs smart contracts to automate data access and permission management, reducing administrative complexities and enhancing data integrity. Utilising MetaMask and IPFS, user-friendly interfaces provide medical physicians and patients with quick access to EMHRs, promoting enhanced patient care and medical decision-making. The performance benefits of the framework are evaluated by performance evaluation, which utilises many metrics like transaction processing times, data retrieval times, system uptime, data storage efficiency, and user adoption rates. The results pertain to the device's level of responsiveness and durability, highlighting its capacity to facilitate the interchange of healthcare data management. This study establishes the basis for a future in which medical information is effectively exchanged and controlled, resulting in more accurate diagnoses, more efficient patient care, and adherence to regulatory standards. This technology plays an important part in advancing the development of safe and personalised EMHR control, as the healthcare industry continues to progress. © 2024 IEEE.

关键词： Smart contract

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An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model 4

An Innovative Method for Brain Stroke Prediction based on Pa...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kavitha, K. Devi, Vanjangi Jyotsna Alam, Mohammad Shabbir Sindhu, V. Gandhewar, Nisarg Kalra, Gourav Gmr Institute of Technology Department of CSE-AI&ML Andhra Pradesh Razam India Aditya Institute of Technology and Management Department of Information Technology Andhra Pradesh Tekkali India College of Engineering and Computer Science Jazan University Department of Computer Science Jizan Saudi Arabia Christ University Department of Computer Science Karnataka Bangalore India Shri Ramdeobaba College of Engineering and Management Department of Aiml Nagpur India Department of Mechanical Engineering Mullana Ambala India

ISBN: (纸本)9798331540364

Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%. © 2024 IEEE.

关键词： Contrastive Learning

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Fake Product Identification Using Deep Learning

Fake Product Identification Using Deep Learning

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Setty, Tejas Adarsh Rag, S. Mohapatra, Saumendra Kumar Nair, Vishnu G Dsa, Joyline G Sahyadri College of Engineering and Management Department of Computer Science and Engineering Mangalore India Sri Sri University Faculty of Engineering & Technology Odisha Cuttack India Manipal Institute of Technology Department of Aeronautical & Automobile Engineering Manipal India

ISBN: (纸本)9798331517892

Digital images play a critical role across various domains, including journalism, criminal forensics, and medical imaging. However, the growing availability of advanced image editing tools has raised concerns regarding the authenticity of digital photographs, particularly in contexts where integrity is paramount. Image forgery, a predominant form of manipulation, involves altering or concealing key image details by copying and pasting portions of the image, often without visible signs of tampering. Such manipulations pose significant challenges for digital forensics in validating image authenticity. This poses significant challenges for digital forensics in authenticating images. To address this, we propose a deep learning-based approach for detecting image forgeries. Using a Convolutional Neural Network (CNN), the method extracts image features and classifies them as authentic or manipulated. The proposed approach addresses the growing sophistication of digital forgery techniques, offering enhanced accuracy and reliability in image authentication and contributing to the advancement of forensic analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Oral Lesion Cell Segmentation and Classification using Convolution Neural Network Technique 3

Oral Lesion Cell Segmentation and Classification using Convo...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Amutha, J. Priyadarsini, S. Prasanth, S. Senkumar, M.R. Ramnath, M. Jasperline, T. P.S.R Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Dr.G.U.POPE College of Engineering Sawyerpuram Department of Computer Science and Engineering Tamil Nadu Tuticorin India

ISBN: (纸本)9798350384598

Oral Cancer of the mouth kills millions of people, Oral cancer is better treated and more often survived when caught early. Convolutional neural networks (CNNs) have recently shown remarkable promise for cancer diagnosis and other medical image processing applications. In this study, we train deep convolutional neural networks (CNNs) to detect oral cancer early. The suggested approach makes use of a large database of pictures of the mouth and its cavities. Develop and refine a convolutional neural network (CNN) model using the use of radiographs, histology slides, and intraoral images. Oral carcinogenic tumours, both precancerous and malignant, may be identified and classified automatically using this approach. The process begins with picture pre-processing, continues with dataset supplementation for better model generalizability, and culminates in the use of complex neural networks for feature extraction and learning. Transfer learning methods modify previously taught models for this medical imaging task. The system's performance, sensitivity, and accuracy are evaluated using a set of images that have pre-existing diagnoses. Deep learning CNNs were able to identify oral cancer with an efficiency and accuracy of 93.62%, according to the research. The trained model is a valuable tool for early cancer screening because of its high sensitivity and specificity. By automating the process, oral cancer may be detected and treated more quickly, which improves patient outcomes and decreases the disease burden. © 2024 IEEE.

关键词： Convolutional neural networks

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SSAT: Scientific Storyboarding Framework Using Artificial Intelligence Techniques

SSAT: Scientific Storyboarding Framework Using Artificial In...

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International Conference on Data science, Computation and Security, IDSCS 2023

作者： Hariharan, Nitin Deepak, Gerard Department of Computer Science and Engineering Alagappa Chettiar Government College of Engineering and Technology Tamil Nadu Karaikudi India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819709748

This paper introduces a scientific storyboarding framework that uses artificial intelligence for learning and inferencing. It works by classifying a dataset of documents using the Bi-LSTM classifier to identify different instances. The results are then used to create metadata, which is further classified using deep belief networks. Next, the user's topic of interest is pre-processed, and an intermediate ontology model is generated. This model helps build knowledge by gathering information from the World Wide Web using recurrent neural networks. The framework then combines the results from two distinct classifiers—one from the dataset and the other from the user's query. These results are analyzed through explicit semantic analysis to select relevant entities. The monkey algorithm is utilized for optimization, resulting in an optimal solution set. To ensure semantic reasoning and entity regulation, Morisita index and explicit semantic analysis is applied. The proposed SSAT model achieves high percentages of precision, recall, accuracy, and F-measure, specifically 95.58, 96.83, 96.205, and 96.2009397%, respectively. Additionally, the model achieves a low value of FDR (False Discovery Rate) at 0.05%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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aMLP-ReID: A Vision MLP Architecture Mixed Linear Attention for Person Reid 12th

aMLP-ReID: A Vision MLP Architecture Mixed Linear Attention ...

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12th International Conference on Frontier Computing, FC 2022

作者： Lei, Guangyu Lei, Jingsheng College of Computer Science and Technology Shanghai University of Power Shanghai China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China

ISBN: (纸本)9789819914272

The Vision Transformer architecture based on Self Attention has been found performing well in feature extracting compared with the CNNs, but there were simply few studies on downstream detecting tasks. The person re-identification task lies on judging whether there is a specific person by extracting image features. There is inevitable loss when convolution kernels are operated with pooling and downsampling with traditional methods based on convolutional network. Therefore, we considered replacing Multihead Attention in Vision Transformer mechanism with a MAL layer based on Vision MLP. The attention mechanism aggregates spatial information. Specifically, the constructed MAL layer explored the MLP structure fitting spatial information. Based on the MLP structure, aMLP-reid mixed linear attention with which was proposed for person re-identification. The improvement was obvious compared with the CNNs on several popular datasets after plenty of experiments. The experiments also demonstrated the effectiveness of Vision MLP architecture for image matching tasks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Prediction of Employee Attrition Classification and Retention Strategies using Gradient Boosting with Binary Logistic Regression 2

Prediction of Employee Attrition Classification and Retentio...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Mamatha Bai, B.G. Hassan, Muntather Muhsin Sri, B. Aruna Latha, P. Vinola, F. Anitha Florence Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Maisammaguda Hyderabad India Department of Information Technology SSM College of Engineering Namakkal India Department of Mathematics New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

A voluntary or involuntary parting of an employee from an organization is known as and the strategies to maintain an employee are known as Employee attrition (turnover) and retention. Employee attrition and retention are crucial Human Resource (HR) concerns, where attrition impacts the organizations performance, profitability, and productivity. Several methods have been proposed to predict attrition of employees, but the existing methods have overfitting, class imbalance, interpretability, and generalizability problems. To overcome these issues, a novel Gradient Boosting-Binary Logistic Regression approach is proposed to predict the employee’s turnover in an early stage. Initially, an IBM-HR dataset is considered as the input for preprocessing, which is performed using Standard Scalar to preserve data distributions. Next, One-hot encoding is assessed for data encoding to convert categorical variable into numerical variables. Followed by, feature extraction using Principal Component Analysis (PCA) for faster training and enhancing the performance of model. Later, Gradient Boosting-Binary Logistic Regression (GB-BLR) is employed for classification to analyze and predict attrition of employee’s. The proposed GB-BLR method obtained better performance when compared with other existing Extra Trees Classifier (ETC) method in terms of accuracy, precision, recall, and f1 score of 97.48%, 98.57%, 96.25%, and 98.77% respectively. © 2024 IEEE.

关键词： Logistic regression

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Computational Approach for Automated Segmentation and Classification of Region of Interest in Lateral Breast Thermograms

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computers, Materials & Continua 2024年第9期80卷 4749-4765页

作者： Dennies Tsietso Abid Yahya Ravi Samikannu Basit Qureshi Muhammad Babar Department of Electrical Computer and Telecommunications EngineeringBotswana International University of Science and TechnologyPalapyePrivate Bag 16Botswana College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh11586Saudi Arabia

Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems *** computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this ***,accurately segmenting the Region of Interest(ROI)fromthermograms remains *** paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree *** proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground *** features were extracted from each view’s ROI,with significant features selected via Mutual Information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)*** findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of *** consistency of the method across both sides of the breast suggests its viability as an auto-segmentation ***,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.

关键词： Breast cancer CAD machine learning ROI segmentation thermography

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Protection of Routing in WSN: Efficient Path Planning Using Block Chain-Assisted Dynamic Waterwheel Plant Optimization Technique for Applications of Cybersecurity 1

Protection of Routing in WSN: Efficient Path Planning Using ...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Balamurugan, K.S. Manimaran, A. Attabi, Kassem Al Fatima Alsalamy, H.A. Aljawaheri, Karar Hariz, Hussein M. Karpaga Vinayaga College of Engineering and Technology Department of ECE Tamil Nadu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Altoosi University College Department of Computer Science Najaf Iraq Mazaya University College Department of Computer Techniques Engineering DhiQar Iraq

ISBN: (纸本)9798350367003

Efficient routing techniques are critical in a wireless sensor network (WSN) to extend network lifetime and preserve energy, providing uninterrupted data transfer across a wide range of applications. In this research, we intend to develop a novel dynamic waterwheel plant optimization (DWPO) strategy for efficient path planning with blockchain (BC) for enhancing cybersecurity in WSN. Our proposed approach utilizes a fitness function that incorporates inter cluster distance along with other relevant parameters. This function strategically contributes to the selection of ideal routes, contributing to the efficacy of our method for improving cybersecurity in WSN contexts. The experimental validation of the DWPO model is conducted using the MATLAB program and examined in terms of the network lifetime (93.6%), packet delivery ratio (90.2%), and energy consumption (1 8. 2J). The experimental results illustrate that the proposed DWPO approach performed better than the other existing approaches for protecting the routing nodes through efficient path planning with BC-assisted DWPO in a WSN. © 2024 IEEE.

关键词： Cybersecurity

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Emergence of Quantum-Enabled 6G Model for Computational Efficiency Retention 5th

Emergence of Quantum-Enabled 6G Model for Computational Effi...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Shukla, Aastha Verma, Tanisha Mishra, Sushruta Rathore, Rajkumar Singh Alkhafaji, Mohammed Ayad Kalinga Institute of Industrial Technology Bhubaneswar India Department of Computer Science Cardiff Metropolitan University Cardiff United Kingdom College of Technical Engineering National University of Science and Technology DhiQar Nasiriyah64001 Iraq

ISBN: (纸本)9789819771776

With the exponential growth of mercantile 5G installation, experimentation into B5G and 6G has kicked off solemnly. However, it is too ahead of time to say what will 6G resemble to, it is assured that 6G systems will hold up creative instances with complicated Key Performance Indicators (KPIs) that can be assured through enabling new advances and network designs. In connection to expansion of 5G, QIT is also surging in the field of quantum computing. QIT is awaited to heighten the approaching 6G network with regard to computing and connectivity. The privacy offered by 6G can be improved by using reliable quantum communications including the transmission of quantum keys. The motive of this study is to present a technologically-inclined and futuristic explanation of potential applications of QIT for next-generation 6G networks. This study defined a quantum enabled model for 6 g networks with an optimum mean accuracy of 92.3% and a least mean response delay of only 2.1 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum computers

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