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A Novel Approach to Design Secured and Privacy Enabled Health Data Linkage System based on Cyber Security Principles

A Novel Approach to Design Secured and Privacy Enabled Healt...

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2024 IEEE International Conference on Big Data and machine Learning, ICBDmL 2024

作者： Gayathri, S. Jaya mabel Rani, A. Anushalin, P.S. Ravi, C.N. Jeyalaxmi, m. Samuthira Pandi, V. R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.Sakunthala Engineering College Department of Biomedical Engineering Vel Tech Multi Tech Dr.Rangarajan Avadi Chennai India CMR Engineering College Department of Computer Science and Engineering Medchal Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Physics Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350374100

medical records from a variety of sources must be integrated into healthcare data repositories for knowledge discovery to be possible. An essential area of study is how to keep records linked when merging medical data. Researchers have proposed many solutions to this issue, all of which are relevant to industrialized nations that use patient identification numbers such as social security numbers, health insurance numbers, universal patient identifiers (UPIs), etc., to keep track of electronic health records. Because of issues with missing data, unclear patient identification, and excessive noise in patient information, these systems are not suitable for record linking of health data in underdeveloped nations. There is a serious concern that patients' personally identifiable information stored in electronic health records poses a threat to their privacy and leaves health information systems open to intrusion. We have examined the real-world challenges of healthcare data collection and integration for a national health data warehouse in this article. To accommodate the limited health data of poor nations like Bangladesh, we have developed privacy-preserving secured record linkage architecture we call Secured Health Data Handler (SHDH). For the purpose of facilitating the knowledge discovery process, our approach may anonymize identifying private data of patients while preserving record linkage in integrated health repositories. Even without a standard ID, our suggested strategy was able to connect records with sufficient accuracy for noisy data, according to the experimental results. © 2024 IEEE.

关键词： Data warehouses

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Automated semantic segmentation and Diagnosis of Coconut Tree Infection Using Drone Imagery and machine Learning approach for smart agriculture

Automated semantic segmentation and Diagnosis of Coconut Tre...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Vasumathy, m. Devakanth, J. Jude moses Anto Thirugnanam, mythili Roselinkiruba, R. Hamsaveni, R. Nandhini, R. Kingston Engineering College Computer Science and Engineering Tamilnadu Vellore India Madanapalle Institute of Technology & Science Department ofComputer Applications Andhra Pradesh Madanapalle India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Computer Science and Engineering Tamilnadu Chennai India Computer Science and Engineering Andhra Pradesh Chittoor India D.K.M College for Women Computer Science and Applications Tamilnadu Vellore632001 India

ISBN: (纸本)9798331539948

The coconut industry of our Indian nation contributes an extensive range of economy to the national GDP. About 19,247 million coconuts, or 31.45% of the world's production, were produced in India in the year 2021-2022 making it the top producer. However, the coconut farmers face major challenges from a plethora of pests and diseases of coconut trees, and yield loss, which affects the farmers economically, standing around at 10-15%. major pests, such as Rhinoceros beetles and red palm weevils, are highly destructible, in some scenarios leading even to the mortality of crops. In most of the cases, when trees that are affected need a cure, farmers have to climb the trees to put chemicals in crowns infested with the pests, exposing the farmers to harmful chemicals and risking severe injuries or permanent disabilities. As a solution, the proposed methodology involves the collection of extensive drone imagery from coconut plantations, followed by the annotation and preprocessing of the data. This work utilizes state-of-the-art deep learning models, including Unet, convolutional neural networks (CNNs) for semantic segmentation, to differentiate coconut trees from the background and other vegetation. Subsequently, machine learning classifiers are employed to diagnose diseases by analyzing visual symptoms present in the segmented trees. The proposed approach offers significant improvements in accuracy and efficiency compared with other traditional manual methods, providing real-time insights into tree health and disease management. This system has the potential to enhance crop monitoring, optimize resource allocation, and ultimately increase agricultural productivity. © 2024 IEEE.

关键词： Drones

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KNN-Powered Online Voting System to Improve Accuracy and Transparency of Election Process

KNN-Powered Online Voting System to Improve Accuracy and Tra...

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2025 International Conference on multi-Agent Systems for Collaborative Intelligence, ICmSCI 2025

作者： Jaswanth, Ullamparthy muthukumar, D. Surendiran Abitha, R. Ganesh, Pamu Naveen, Nandyala Kavitha, G. Kalasalingam Academy of Research and Education Department Computer Science and Engineering Tamil Nadu Virudhunagar India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Artificial Intelligence and Data science Tamil Nadu Chennai India Dr.M.G.R Educational andResarch Institute Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

The proposed system aims to create a safe online voting system. Nowadays voting remains a serious safety and security issue so this system provides high performance and security. The System seeks to develop a face recognition-based voting system that authenticates voters using machine learning, especially K-Nearest Neighbors (KNN). Entering the voter's unique username and Password is the first step in the online voting system. If the voter's Username and password are valid, the system proceeds to the next step, requesting permission to turn on the webcam and take a picture of them. Once the photo is taken, it verifies that the voter has only taken it once. The website allows the voter to cast his vote only once. Voter access is denied if the same voter picture is entered again. By logging into the website voter can cast their vote from any place. Every minute the website updates the results of the election. So, voters do not need to wait in long queues to cast their votes. To give voters real-time information on their voting progress. The system also features dynamic voice feedback to guide users through voting, ensuring clarity and engagement. This initiative uses facial recognition technology to enhance the security and efficiency of voting systems. © 2025 IEEE.

关键词： Face recognition

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Home Security Voice based Alert System using IoT 5

Home Security Voice based Alert System using IoT

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Pandiaraja, P. Venkatramani, R. Arunraj, R. Rajesh Kanna, P. mahendran, G. Suresh, T. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Tamilnadu Thalavapalayam639113 India K. Ramakrishnan college of Engineering Department of Information Technology Tamil Nadu Samayapuram621112 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode638401 India K. Ramakrishnan college of Engineering Department of Aiml Tamilnadu Samayapuram621112 India

ISBN: (纸本)9798331504403

Every home has an issue, such as the inability to determine if a gas leak is occurring or not. Similarly, when a water storage tank overflows, we are unable to determine the level of the condition until it causes a spill. An existing system is employed to address this issue;however, it has a low level of attentiveness, making it difficult for users to correctly identify the problem. To solve all of these issues in every home, we have developed a voice-based Internet of Things device. This instrument's voice-based output enables the user to readily understand the situation. When a gas leak occurs, a gas detector that can identify LPG gas sends a signal to a microcontroller, which then sends the signal to a voice bank to generate a speech-based warning message. Additionally, it comes into touch with water when the water is expected to flow out, sends a signal to the Arduino, and produces a voice-based output. This will cut down significantly on accidents and product waste. © 2024 IEEE.

关键词： microcontrollers

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A Way of making Smart Health Through Collaborating machine Learning with the Bibliometrics 4

A Way of Making Smart Health Through Collaborating Machine L...

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4th IEEE International Conference on Advance Computing and Innovative technologies in engineering, ICACITE 2024

作者： Vignesh, T. Sruthi, P.S. mohanraj, T. Kumar, Gaurav Varkale, Akshay Sheela, m.Sahaya Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Nehru Institute of Technology Department of Computer Science and Business System Tamil Nadu T.M. Palayam India Karpagam Academy of Higher Education Department of Computer Science Engineering India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India M.P Bhopal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

It datasets are widely utilized, shared, and repurposed without providing enough context for understanding the decision-making processes that led to their creation. It is necessary to modify system creation and use procedures to handle the concrete effects of how knowledge for model building is envisioned and used in real-world applications as AI systems assume more and more important roles. Documents are created at every phase of the data generation lifecycle to improve communication, aid in decision-making, and highlight the need of careful data work. By putting out this paradigm, we want to shed light on the choices and duties that are often disregarded while creating datasets, which is a crucial first step in bridging the duty of care gap in AI. This framework provides an essential and important resource for guaranteeing ethical AI activities, and it is in line with current developments in auditing procedures. © 2024 IEEE.

关键词： Adversarial machine learning

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Facial Emotion Analysis using Deep Learning for Seamless Human-computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd IEEE International Conference on Networks, multimedia and Information technology, NmITCON 2024

作者： Sangeetha, S. Saini, Poonam Banupriya, R. Srimathi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or human-computer intelligent interaction, is a new academic subject that tries to make computer use more intuitive. Some say machines need human-level social abilities before interacting with people. These skills include reading emotions. Due to its exponential expansion, facial emotion recognition is becoming essential to face recognition. According on photographs, machine learning can recognise expressions. This paper discusses expression recognition model training datasets. Deep learning for Facial Emotion Recognition (FER) is used in this research to teach robots to recognise and adapt to human emotions. An extensive review of deep learning models, including CNNs and CNNR, emphasises their ability to extract spatial and temporal data needed for emotion classification. FER pipeline components like data collection, preprocessing, and model architecture are examined in the paper. Transfer learning, pre-trained models, and data augmentation are studied to address low-labeled dataset concerns. Detailed model training, validation, and evaluation metrics emphasise performance assessment. This study uses the enlarged CohnKanade (CK+) dataset from a face emotion recognition experiment. The system averaged 92.81% accuracy. Eight emotions were successfully detected by the algorithm. The proposed method for emotion recognition has proven useful. © 2024 IEEE.

关键词： Contrastive Learning

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Assessing Secure Cloud Information Sharing Through Authentication and Encoded Indexing 8

Assessing Secure Cloud Information Sharing Through Authentic...

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8th International Conference on I-SmAC (IoT in Social, mobile, Analytics and Cloud), I-SmAC 2024

作者： Pandiaraja, P. Karthik, K. Rajesh Kanna, P. Ushapriya, C. mahesh, P.C.Senthil muthumanickam, K. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600 062 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Tamilnadu Thalavapalayam639113 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Erode638401 India St. Martin's Engineering College Department of Ai & Ml Telangana Secunderabad500100 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India

ISBN: (纸本)9798350376425

The high usage of storage in cloud, ensuring the privacy and security of information stored in the cloud has come up with a difficult concern. One approach to address this issue is the use of techniques which are encrypted, where data is already encrypted being uploaded to the storage of cloud. However, traditional encryption methods hinder the ability to find and retrieve particular information from the secured information, as the information needs to be decrypted first. This limitation hampers the usability and efficiency of storage of cloud systems. To overcome the challenge, this research says a novel approach for keyword search on secured information in storage of cloud using an AES approach. The algorithm of AES is a maximum adopted symmetric encryption method familiar for efficiency and robustness. By leveraging the AES encryption approach, the solution assures the confidentiality of information stored while enabling efficient keyword-based search operations without the need for decryption. The proposed approach utilizes a combination of cryptographic techniques such as AES encryption and index structures to access efficient information findings on the secured information. A secure index is created during encryption process that enables for potent retrieval of encrypted data related to specific keywords. The encrypted data remains confidential, as only authorized users possess the necessary decryption keys to retrieve the plaintext information. During data outsourcing, data owner can set the access permission based on their user identity. Users should have specific identity with decryption key for accessing data from cloud. Here data manager can also provide particular period for accessing information from cloud. The results demonstrate that the keyword findings on secured information in storage of using the AES approach achieve a balance between data privacy and search functionality. The solution offers an effective means to securely store and retrieve data

关键词： Cloud storage

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An Efficient Investigation of Cloud Computing Security with machine Learning Algorithm

An Efficient Investigation of Cloud Computing Security with ...

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International Conference on Inventive Computation technologies (ICICT)

作者： R. Senthilkumar S. Yasotha P. m. manochithra J. Senthil Dr. G. Sivakumar Computer Science and Engineering Shree Venkateswara Hi Tech Engineering College Erode Tamilnadu India Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India Computer Science and Engineering Erode Sengunthar Engineering College Erode Tamilnadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Due to developments in technologies like Cloud Computing (CC), the Internet of Things (IoT), etc., the data volume transmitted across communication infrastructures has skyrocketed recently. In order to make network systems susceptible, attackers have increased their determinations. Improving the security of such network systems is very crucial. Using Deep Learning (DL) methods with an Intrusion Detection System (IDS) framework is put into action in this research. The Gated Recurrent Unit (GRU) as well as Support Vector machine (SVm) are used in this study for attack detection. Therefore, the implementation of Softmax in the GRU model's last output layer, this study introduces a linear SVm. The NSL-KDD dataset is taken into account to evaluate the proposed IDS framework's performance. In addition, when the number of features increases, current IDSs have poor test accuracy ratings when it comes to identifying new attacks. Each dataset's feature space was reduced using an XGBoost (XGB)-based feature selection approach in this research. After that, 22 detailed characteristics were selected from the NSL-KDD dataset using XGB. The results show that, the proposed IDS paradigm outperformed other approaches.

关键词： Support vector machines machine learning algorithms Computational modeling Intrusion detection Logic gates Feature extraction Vectors

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OpenCV Based Real-Time Traffic Analyzer 1

OpenCV Based Real-Time Traffic Analyzer

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Pandi, C. Ponnuviji, N.P. Srinivasan, R. Parthasarathy, C. Karthick, P. Rohith Kumar, V. Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India R.M.K College of Engineering and Technology Puduvoyal India Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi India Rajalakshmi Engineering College Department of Computer Science and Engineering Thandalam India

ISBN: (纸本)9798350365337

The report focuses on the development and implementation of a real-time traffic analysis system using OpenCV (Open Source computer Vision Library). The project aims to leverage computer vision techniques to analyze and monitor traffic patterns, providing valuable insights for traffic management and urban planning. The system utilizes video streams from surveillance camerasplaced at strategic locations such as traffic intersections or highways. These video streams are processed in real-time usingOpenCV algorithms to extract relevant information about vehicles, traffic flow, and congestion levels. The extracted data is then analyzed and visualized to provide meaningful insights for traffic management authorities and urban planners. The paper involves several key steps, including video input processing, vehicle detection and tracking, traffic flow analysis, and congestion detection. Each step employs specific OpenCV functionalities and techniques to achieve accurate and efficient results. Throughout the report, we will discuss the various components and methodologies used in developing the system. We will explore the algorithms and techniques employed for vehicle detection and tracking, as well as the methods used to analyze traffic flow and detect congestion. Additionally, we willaddress challenges encountered during the project and present potential future enhancements for the system. The findings of this paper demonstrate the feasibility and effectiveness of using OpenCV-based systems for real-time traffic analysis. The insights gained from this research have the potential to contribute to more efficient traffic management strategies and informed decision-making in urban planning. © 2024 IEEE.

关键词： Traffic congestion

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Optimizing Food Image Classification Using Black Widow Algorithm and Deep Learning techniques 2

Optimizing Food Image Classification Using Black Widow Algor...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Jamalpur, Bhavana Priya, Sakthi G. Venkat, S. Aarthi, R. Kavitha, S. Babu, G. Charles Department of Computer Science and Artificial Intelligence S R University Telangana Hanamkonda India Computer Science and Engineering Ramco Institute of Technology Rajapalayam India Department of ECE S.A. Engineering College Chennai India Department of ECE R.M.D Engineering College India Department of CSE Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College India Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798350383867

Food image classification is a key application of computer vision and machine Learning (mL) devoted to mechanically classifying and labelling different food items depending on visual content seized in images. Using sophisticated Convolutional Neural Networks (CNNs) and deep learning (DL) models, this skill permits detection of different dishes, culinary creations, ingredients and raises many practical uses. From supporting nutritional choices and recipe recommendations to menu analysis and food quality control, food image detection updates procedures of food identification, providing valuable insights for users, food experts as well as food industry. Its high potential is to power detection and classification of food items provide benefits in upholding better eating habits, cooking improvement, and enhancing food creation and management procedures. This research proposed Food Image Classification Using Black Widow Algorithm and Deep Learning (FIC-BWODL) model. The developed FIC-BWODL method combines a multifaceted model starting with Contrast Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing, certifying improved image quality and optimum feature extraction. Extraction of features executed by utilizing Capsule Networks (CapsNet) which is famous for their capability to capture difficult classified image patterns. Classification performed via Convolutional Autoencoders (CAE), exactly classifying food images. moreover, model controls Black Widow Optimization Algorithm (BWO) for hyperparameter tuning that enhances the act and supports it with exclusive features of a dataset. FIC-BWODL methodology provides an important leap in food image detection that provides a strong and exact solution for automated food detection. © 2024 IEEE.

关键词： Food chemistry

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