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Heart Disease Detection Using Machine Learning Models 2

Heart Disease Detection Using Machine Learning Models

引用

2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Singh, Amrit Mahapatra, Harisankar Biswal, Anil Kumar Mahapatra, Madhumita Singh, Debabrata Samantaray, Milan Berhampur India Berhampur India Dept. of Computer Science Udayanath Autonomous College of Science and Technology Cuttack India Dept. of Computer Science and Engineering Delhi Technical Campus UP Greater Noida India Bhubaneswar India Department of Computer Science and Engineering Silicon Institute of Technology Bhubaneswar India

An enormous number of deaths occur every year as a result of heart disease, making it a major concern in world health. Improving patient outcomes and lowering death rates, early detection and correct diagnosis of cardiac disease play a key role. When the heart's arteries become blocked, oxygen-poor blood cannot reach the heart properly, resulting in coronary heart disease. Early detection of cardiac disease is viable because it reduces medical costs and potentially saves the patient's life. Recently presented methods have improved heart failure detection accuracy on testing data without sacrificing accuracy on training data, yet most of these algorithms are suffering from the issue of overfitting. Models that were created end up fitting the test data too well. In this study, we create a novel diagnostic system to address this issue, and the resulting system demonstrates high intelligence and excellent performance on both training and testing data. Machine learning (ML) algorithms have demonstrated promising potential in assisting healthcare professionals with timely and accurate diagnosis. In this paper, is based on supervised machine learning methods are decision tree (DT), random forest (RF), Support vector Machine (SVM), Principal Component Analysis(PCA). We compare their accuracy with each other by using bar plot. © 2024 Elsevier B.V.. All rights reserved.

关键词： Blood pressure

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Adaptive Hybrid Fraud Detection System with Personalized Transaction Profiling 5

Adaptive Hybrid Fraud Detection System with Personalized Tra...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Musthafa, A. Syed Sree, Pokkuluri Kiran Fathima, H. Jayalakshmi.r, Angu Saranya, P. Murugesh, L. M.Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639113 India Department of Computer science and Engineering Vishnupur Andhra Pradesh Bhimavaram534202 India KSR College of Arts and Science Department of Computer Applications Tamil Nadu Tiruchengode637 215 India M. Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639113 India

ISBN: (纸本)9798331530013

This research study presents an Adaptive Fraud Detection System that uses personalized user behavior profiles and multiple layers of detection methods to spot fraud in real time. By clustering similar transaction patterns and applying machine learning techniques like ensemble models and autoencoders, the system can continuously improve its ability to detect unusual activity. Frequency of getting false alarm is reduced. Improved functional clarity in the system. © 2024 IEEE.

关键词： Adversarial machine learning

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Performance Evaluation of Blob Detection Techniques Using Image Processing 4

Performance Evaluation of Blob Detection Techniques Using Im...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sathva, S. Madhumathi, R. Manjula, D. MeenaKowshalya, A. Sri Ramakrishna Engineering College Department of Computer Science & Engineering Coimbatore India Department of Community Medicine Karpagam Faculty of Medical Sciences and Research Coimbatore India Government College of Technology Department of Computer Science & Engineering Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Blob detection is a primary requirement in computer vision and image processing tasks. Unique visual traits are obtained by identifying blobs in an image. Variations in colour, texture, intensity, or shape are just examples of these attributes, which make blob detection a flexible tool for a variety of image analysis applications. The technique of separating the objects in the binary image is called Binary Large Object (blob) extraction. Every representation technique provides distinct insights into the properties of the blob, which facilitates further processing and interpretation. Techniques for classifying blobs according to their size, shape, texture, and colour are presented, highlighting their importance involving object recognition and classification. The categorization of blob detection techniques presented in this work includes, encompassing Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), Difference of Hessian (DOH), and Connected Components (Labelling). The results demonstrate that LoG and DoG detect a higher number of blobs, with LoG detecting the most (1,366 blobs), while DoH focuses on larger blobs but with a smaller count (777 blobs). Moreover, DoH had the largest mean blob size and higher variability in blob size, while DoG exhibited the fastest detection time (4.27 seconds). © 2024 IEEE.

关键词： Binary images

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Modeling and Simulation of a Robot Arm with Conveyor Belt Using Matlab Simulink Model 13th

Modeling and Simulation of a Robot Arm with Conveyor Belt Us...

引用

13th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on information and Communication Technologies, WICT 2022

作者： Obagbuwa, Ibidun Christiana Mogorosi, Kutlo Baldwin Department of Computer Science and Information Technology Sol Plaatje University Kimberley South Africa

ISBN: (纸本)9783031274985

This work modeled and simulated a robotic arm with a conveyer belt that picks and places objects from one spot to another. Daily production is increasing and increasing production rates while increasing profit margins has become a priority for every organization. When large-scale production occurs, problems in the material handling system arise as a result of product counting, defective piece removal, and so on. Manufacturing units are more interested in automation via robots for their work because of these factors. Pick-and-place robots are a type of automation technology used in the manufacturing industry. They are planned and designed to eliminate human error and intervention, allowing for more precise work and faster production. The simulation of robotic arm in this work was carried out using MATLAB Simulink. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Belt conveyors

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Improving Diabetes Classification Accuracy by Incorporating External Factors in a Machine Learning Framework 16

Improving Diabetes Classification Accuracy by Incorporating ...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Mane, Navnath Patel, Samprit Athavale, Vijay Anant Lobo, L.M.R.J. Walchand Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India Walchand Institute of Technology Department of Information Technology Maharashtra Solapur India

ISBN: (纸本)9798331505264

Diabetes is a chronic condition that significantly increases the risk of developing serious health complications, including heart disease, renal failure, vision impairment, and nerve damage. Early prediction and accurate diagnosis are crucial to preventing these complications. In current clinical practices, a variety of tests are used to collect data for diagnosing diabetes. However, the large volume of available datasets makes it difficult to efficiently extract meaningful insights, often limiting the accuracy of predictions. Machine learning (ML) algorithms have shown promise in analyzing these complex datasets to uncover hidden patterns and predict diabetes-related health risks. This study proposes a novel diabetes prediction model that leverages a Random Forest Classifier to improve classification accuracy. Traditional factors such as age, body mass index (BMI), glucose levels, and insulin resistance are considered, but the study introduces an innovative approach by incorporating external factors (such as lifestyle and socio-economic factors) that could influence diabetes progression. An enforced pipeline model is used in this study, streamlining data preprocessing, feature selection, and algorithm optimization, which results in a more precise prediction framework. This approach brings the prediction accuracy for diabetes classification closer to 77%, a significant improvement over current models. © 2024 IEEE.

关键词： Diabetes Diabetes Prediction Accuracy Disease Prediction Health Care Machine Learning Random Forest Classifier

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HOG and Cloud Computing based Face Recognition for Attendance Monitoring 3

HOG and Cloud Computing based Face Recognition for Attendanc...

引用

3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sudha, V. Kalaiselvi, R. Jithenthiriya, C.K. Government Polytechnic College Dept. of Computer Engineering Coimbatore India Kumaraguru College of Technology Information Science and Engineering Coimbatore India Kumaraguru College of Technology Dept. Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665462167

Participation plays a key role in the classroom culture. When calling out the students' names at the beginning and end of a lecture, lecturers may miss a student, or someone may respond several times to substitute their peers. Face identification can be difficult when using low-definition video and other information systems with the assistance of facial recognition technologies. This necessitates the need to develop a novel device to quickly and accurately identify human faces from images and videos. Many algorithms and techniques have been recently developed to improve the facial recognition accuracy. Deep learning has been recently implemented in various real-time applications to identify and perceive multiple faces at the same time. When it comes to computers, however, it is unrealistic to attempt any of the human identity. The identification of the human identity is a critical aspect of biometrics. The biometric results are consistent with human characteristics. Remote and applied facial expressions can be used to enhance the results. computers that identify and recognize faces can be used for a variety of purposes, including criminal recognition, network surveillance, biometric authentication, and so on. In this paper, an automated system for attendance recognition is proposed. © 2023 IEEE.

关键词： Face recognition

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Cross Domain Collaborative Filtering: A Graph Neural Network Approach for Accurate and Diverse Recommendations

Cross Domain Collaborative Filtering: A Graph Neural Network...

引用

2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Goel, Chirag Sinha, Bam Bahadur Indian Institute of Information Technology Ranchi Department of Computer Science and Engineering Ranchi India

ISBN: (纸本)9798350348910

Collaborative filtering is a widely adopted technique in the field of recommender systems, aiming to predict users' preferences based on their historical interactions with items. Traditional collaborative filtering methods often face challenges when dealing with cross-domain recommendation scenarios, where user-item interactions are scattered across multiple domains and data sparsity is prevalent. This research paper proposes a novel Graph Neural Network (GNN) approach for Cross-Domain Collaborative Filtering Recommendations to address these issues. The proposed approach leverages the expressive power of GNNs to capture complex and non-linear patterns in user-item interactions while incorporating cross-domain knowledge. The proposed research paper models the recommendation system as a heterogeneous graph, where users and items are represented as nodes, and the interactions between users and items, as well as the relations between domains, are represented as edges. To train the GNN model, mean squared error loss function is used, which jointly optimizes for domain-specific recommendation performance while encouraging knowledge sharing across multiple domains. Experimental results demonstrate the supremacy of the GNN-based method compared to state-of-the-art collaborative filtering techniques, especially in situations where the cold start problem is a major concern. The GNN-based cross-domain collaborative filtering approach not only outperforms traditional collaborative filtering methods but also exhibits robustness in handling heterogeneous and sparse data across multiple domains. The ability to incorporate cross-domain knowledge makes the proposed model a valuable tool for building recommendation systems in complex and diverse environments. In conclusion, this research presents a comprehensive investigation of the application of Graph Neural Networks to cross-domain collaborative filtering recommendations. The results highlight the effectiveness and versatility of

关键词： Recommender systems

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Cryptographic Algorithm Identification through Machine Learning for Enhanced Data Security 2

Cryptographic Algorithm Identification through Machine Learn...

引用

2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Rachini, Ali Abi Assaf, Maroun Fares, Charbel Khatoun, Rida Department of Computer Science and Information Technology Jounieh Lebanon Telecom ParisTech Paris France

ISBN: (纸本)9798350373363

This paper discusses the significance of identifying encryption algorithms in today's digital era to ensure data security. The study uses machine learning (ML) techniques, including Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbors (KNN), to develop a classification model for distinguishing between encryption algorithms like Blowfish, AES, and 3DES. Results show distinct performances among the algorithms, with SVM achieving a robust 91 % accuracy rate, Random Forest excelling in precision with a 99 % accuracy rate, and KNN providing reasonable but comparatively lower accuracy at 34 %. The findings underscore the diverse capabilities of ML algorithms in encryption algorithm identification, offering valuable insights for enhancing data security practices and emphasizing the importance of selecting the most suitable ML approach based on specific security requirements. © 2023 IEEE.

关键词： Nearest neighbor search

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Border Control via Passport Verification using Fingerprint Authentication Technique

Border Control via Passport Verification using Fingerprint A...

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2023 International Conference on science, Engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Omolewa, Oladele Tinuke Adeioke, Emmanuel Jadesola Titilope, Oladele Oluwabunmi Sakarivan, Adewole Kayode Kehinde, Adeniyi Jide University of Ilorin Department of Computer Science Ilorin Nigeria United States International University Department of Computer Science Kenya University of Ilorin Depatment of Information Technology Ilorin Nigeria Landmark University Department of Computer Science Omuaran Nigeria

ISBN: (纸本)9798350324785

Security of lives and properties is of high essence to a nation's growth and development. With the increase of global terrorist invasions, levels of national internal border security need to be increased. Thus, the need to deploy technologies such as biometric for further fortification cannot be underrated. Research has shown that biometrics technology - as an authentication technique has a broad-spectrum application in fields ranging from banking, medicine, airlines and so on. Unlike the conventional means of security checks, which basically entail physical/manual verification. Biometric techniques such as Face recognition, Fingerprint, DNA are more reliable and capable of uniquely identifying individuals in the verification process. The porous nature of the nation's border enables unauthorized immigrants gain access to the country which poses a high level of security threat. In this paper, a biometric based border control system is developed to reduce the rate of illegal immigrants into the country. C# programming language was employed to implement the proposed biometric based border control system. The various stages of biometric authentication were incorporated. The proposed system is recommended for controlling illegal immigrants at the Nigerian border. © 2023 IEEE.

关键词： Biometrics

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DNA Transcription and Translation Inspired Deep Features for Classification-Based CBIR 23th

DNA Transcription and Translation Inspired Deep Features fo...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Pradhan, Jitesh Pal, Arup Kumar Islam, SK Hafizul Samanta, Debabrata Department of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India Jharkhand Dhanbad826004 India Department of Computer Science and Engineering Indian Institute of Information Technology West Bengal Kalyani741235 India Department of Computing and Information Technologies Rochester Institute of Technology Rochester Kosovo

ISBN: (纸本)9783031648465

DNA is a macromolecule that carries the genetic information of nearly all living things on the planet. They not only determine the characteristics and behavior of an organism but also pass the essential features to the next generation, ensuring that "like begets like." Because of their same genetic structure, organisms of the same species appear identical. Inspired by this property, a novel DNA-based scheme for class-based image retrieval has been proposed. The algorithm imitates the flow of genetic information, which is initially stored in the DNA and is transcripted and translated to RNA and amino-acid sequences, respectively, using genetic coding. Since similar images would generate a similar sequence, ensuring the preservation of salient features of the images required for retrieval. Thus, these amino-acid sequences are then deployed on a DNA-inspired ResNet-50 CNN architecture for performing image classification-based image retrieval. The proposed scheme has been extensively tested on six different datasets to demonstrate its performance. Comparative results reveal that the proposed scheme outperforms competing state-of-the-art Content-based Image Retrieval techniques in terms of retrieval performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： DNA

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