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Heart Attack Prediction Using Ensemble Learning

Heart Attack Prediction Using Ensemble Learning

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Mali, Saurabh Veeramani, Karthika School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

Heart attacks are a leading cause of death worldwide, making it a necessity to develop accurate prediction systems for timely intervention and prevention. This paper addresses the challenge of heart attack prediction through ensemble learning methodologies. Previous research has primarily focused on individual machine learning algorithms, often overlooking the potential of ensemble techniques to improve predictive accuracy. In this study, we employed a dataset comprising various clinical features and medical indicators. After preprocessing steps such as encoding categorical variables, scaling, and balancing data using SMOTE, we explored multiple machine learning models, including logistic regression, SVM, KNN, decision trees, random forests, gradient boosting, and artificial neural networks. Additionally, feature selection techniques and hyperparameter optimization were utilized to enhance model performance. Finally, we employed ensemble learning, specifically Stacking, to combine the strengths of individual models, resulting in a higher predictive accuracy of 93%. The results demonstrate the high efficiency of ensemble learning in heart attack prediction, showcasing a significant improvement over individual models. © 2024 IEEE.

关键词： Heart

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Optimizing Bone Fracture Diagnosis: A Hybrid Approach using EfficientNet-B3 and SVM for Accurate Classification 2

Optimizing Bone Fracture Diagnosis: A Hybrid Approach using ...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Jesher Joshua, M. Spoorthi, J.S. Keerthi, K. Divya Sankari, S. Pandiyaraji, V. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

The identification of osseous fractures has significant importance for defining precise medical diagnosis and con- structing treatment plans, especially in emergent and orthopedic contexts. This work proposes a new classification framework for different types of hand fractures through the integration of the EfficientNet-B3 deep learning model with an SVM classifying kernel. The feature extractor uses the pre-trained and then fine-tuned EfficientNet-B3 model on hand X-ray images to obtain both higher level and fine-grained features required to accurately detect the fractures. Subsequent features are then again passed to an SVM classifier which defines an ideal decision boundary mapping which increases the model's accuracy and interpretability. The dataset utilized in this study consists of 1,000 hand X-ray images obtained from different medical centers, which went through different preprocessing to enhance the image quality, as outlined below: Conversion of the images to grayscale benzylpenicilloyl hydrochloride - Gaussian blurring of the images, and edge detection on the images. In assessing the efficacy of the model, a variety of evaluative metrics, including accuracy, precision, recall, F1-score, and additional analytical tools, were employed, with the accuracy percentage reaching an impressive 93.5 percent in classification. A comparison of the proposed model using existing approaches is promising when it comes to the adaption of the data set into an automated system with a highly accurate classification of bone fracture, making it ideal for future clinical application. © 2024 IEEE.

关键词： Support vector machines

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Flexible U-Net with Transfer Learning for Optic Disc Segmentation in Glaucoma Detection 3

Flexible U-Net with Transfer Learning for Optic Disc Segment...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Kalyanasundaram, V. Keerthi, A.J. Prethija, G. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

Glaucoma, a progressive optic neuropathy leading to irreversible visual field defects and heightened intraocular pressure, underscores the critical need for early detection to prevent vision loss. Automating glaucoma detection can significantly reduce diagnosis time for ophthalmologists. Optic disc segmentation is an essential step in detection and diagnosis of glaucoma. In the experimental study, a pre-trained deep learning model called Flexible U-Net is utilized. This model incorporates EfficientNetB0 and EfficientNetB1 as the encoder components within the U-Net architecture. The dataset used for the evaluation of their efficacy is ORIGA. By analyzing model performance, it is found that EfficientNetB1 demonstrates superior accuracy in glaucoma detection compared to other models, emphasizing its potential for enhancing diagnostic efficiency in clinical practice. The results indicate that EfficientNetB1 U-Net has achieved impressive segmentation performance based on several metrics, including a dice coefficient of 0.9476, a Jaccard score of 0.9005, an accuracy of 0.9984, and an AUC of 0.99 on the ORIGA dataset. © 2024 IEEE.

关键词： EfficientNet Glaucoma Detection Optic Disc Segmentation U-Net

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Prediction of Knee Osteoarthritis Severity from X-Ray Images Using Ensemble Learning 5

Prediction of Knee Osteoarthritis Severity from X-Ray Images...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Navyeesh, Kothoju Hinduja, Kanakala Jani Anbarasi, L. Charitha, Nagavarapu Divya Varshitha, Pasala Sri Varshitha, Uppuluri Venkata Sai School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385649

Millions of people worldwide are affected by knee osteoarthritis (OA), a prevalent condition involving the deterioration of joint cartilage and underlying bone. Consequently, it causes discomfort, restricted movement, and diminished mobility. It is crucial to accurately and promptly assess the severity grading of the disease for effective clinical management. In this study, the important issue of predicting knee OA severity using X-ray images is addressed through the utilization of deep learning models, specifically convolutional neural networks (CNNs). An ensemble model that combines the strengths of different architectures was proposed to show the enhanced accuracy in Knee OA. The proposed work used the best features extracted using EfficientNet, DenseNet and ensemble models to attain an accuracy rate of 94.76%. The exceptional performance of the ensemble model underscores its reliability in classifying knee OA severity and provides a comprehensive and precise solution for health assessment. The proposed systematic approach demonstrates how deep learning can enhance diagnostic capabilities for knee osteoarthritis and improve clinical decision-making. © 2024 IEEE.

关键词： Grading

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Enhancing Sentiment Analysis of Customer Reviews Using Deep Learning, Data Augmentation and Explainable AI 4

Enhancing Sentiment Analysis of Customer Reviews Using Deep ...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Gokul Ram, K. Rajakumar, R. Ilavendhan, A. Padmanaban, R. Vijayaprabakaran, K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

Sentiment analysis of customer reviews is crucial for understanding consumer feedback and improving business strategies in the e-commerce sector. This study proposes an enhanced deep learning framework for sentiment analysis, utilizing Data Augmentation to improve prediction accuracy. Additional layers are integrated into the sentiment analysis model to better differentiate emotions in customer feedback. Furthermore, Explainable AI (XAI) techniques, specifically LIME, enhance transparency and provide clear insights into the model's predictions, thereby increasing trust in the results. © 2024 IEEE.

关键词： Adversarial machine learning

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Crime Mapping and Predictive Analysis of Crimes in Maryland, USA

Crime Mapping and Predictive Analysis of Crimes in Maryland,...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Thomas, Beryl Abey Raja, Sivakami School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

The issue of crime has long existed, both nationally and internationally. Several research have looked at previous crimes and provided appropriate preventive measures. However, even in recent years, there has been a high prevalence of crime. Therefore, the primary objective of this study is to analyze the crimes that took place in Maryland, USA, between 2016 and 2020. After pre-processing and cleaning the dataset, numerous analysis and visualization findings were drawn. Improving public safety and crime prevention in the state of Maryland requires first developing a predictive analytic and crime mapping technology. By examining past trends and data, it was possible to ascertain the days of the week, time of the day, and months of the year that crimes were most likely to occur. In addition, the location of these occurrences and the most frequent form of crime each year could be inferred. The three primary machine learning algorithms that were employed to predict the kind of crime that would occur given other factors were K Neighbors Classifier, Random Forest Classifier, and Logistic Regression. The accuracy was moderate to low. The Random Forest Classifier, Logistic Regression, and K Neighbors Classifier displayed an accuracy of 52.55%, 43.86% and 38.39%. Nevertheless, by displaying past crime data trends, law enforcement officials can identify crime hotspots, identify emerging trends, and allocate resources efficiently. This would enable law enforcements to anticipate and prevent crimes before they occur, allowing for proactive interventions. © 2024 IEEE.

关键词： Crime

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A Smart Agricultural Assistant for Crop Recommendation using Machine Learning 15

A Smart Agricultural Assistant for Crop Recommendation using...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, D. Kiranteja, P. Shridharan, V.J. Venkhat Desigan, P.K. Raghav, N.G. Pranav, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331300579

This paper presents a Smart Agriculture Assistant that uses data analytics and machine learning to assist farmers in making informed decisions about various farming practices. The assistant offers a holistic approach to enhance agricultural decision-making process by recommending crops based on soil attributes, climate, and local weather conditions, as well as providing precise fertilizer and pesticide guidance tailored to Indian contexts. Data is collected from various sources, such as weather forecasts, groundwater data, soil maps, and crop prices. Machine learning algorithms are trained on that data to generate optimal recommendations for farmers. The study demonstrates the effectiveness of smart agricultural techniques over conventional methods in improving crop selection accuracy and optimizing fertilizer application, thereby enhancing agricultural productivity. Furthermore, the integration of image analysis technology using computer vision enables the system to assess crop quality parameters, aiding in the detection of grade, size, colour, and disease presence. It also provides insights of estimated real-time crop prices by taking into account the fickle market dynamics. This offers valuable information to the farmers for increasing profit margins. The assistant is implemented as a web application that can be accessed by farmers from anywhere. This research provides a framework for the integration of data-driven technology to solve the unique issues encountered by Indian farmers. The Smart Agriculture Assistant presented here represents the power of data science and deep learning for a more prosperous and sustainable future for Indian agriculture. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Identifying Artists from Paintings Using Deep Learning Models

Identifying Artists from Paintings Using Deep Learning Model...

引用

2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Krishnaraja, Shanmukapriya Prasad, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350378177

Identifying artists from paintings automatically has become more crucial as art collections go digital. This research compares how effective ResNet 50, ResNet 152, MobileNet v2 and Inception v3 architectures are for this purpose. In order to stress the importance of transfer learning in the classification of artworks, we use a dataset that is scraped from 'Art Challenge-A Quiz Game of Famous Painters'. By conducting experiments, we assess each model's accuracy, computational efficiency and interpretability. Our discoveries help progress automated art analysis and provide insights, for researchers and professionals dealing with digital art collections. © 2024 IEEE.

关键词： Contrastive Learning

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Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion Detection

Enhanced Convolution Neural Network with Optimized Pooling a...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Patel, Sourav Wakchaure, Supriya Bharat Sharma, Ayush Kumar Abirami, S. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540821

Network Intrusion Detection Systems (NIDS) are essential for protecting computer networks from malicious activities, including Denial of Service (DoS), Probing, User-to-Root (U2R), and Remote-to-Local (R2L) attacks. Without effective NIDS, networks are vulnerable to significant security breaches and data loss. Machine learning techniques provide a promising approach to enhance NIDS by automating threat detection and improving accuracy. In this research, we propose an Enhanced Convolutional Neural Network (EnCNN) for NIDS and evaluate its performance using the KDDCUP'99 dataset. Our methodology includes comprehensive data preprocessing, exploratory data analysis (EDA), and feature engineering. We compare EnCNN with various machine learning algorithms, including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and ensemble methods like Random Forest, AdaBoost, and Voting Ensemble. The results show that EnCNN significantly improves detection accuracy, with a notable 10% increase over state-of-art approaches. This demonstrates the effectiveness of EnCNN in real-time network intrusion detection, offering a robust solution for identifying and mitigating security threats, and enhancing overall network resilience. © 2024 IEEE.

关键词： Network intrusion

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Advancing E-commerce Recommendations: A Comparative Study of BERT-Enhanced Collaborative Filtering and Alternative Approaches

Advancing E-commerce Recommendations: A Comparative Study of...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Dazzle, A.J. Singh, Shashank Naik, Dishant Kanmani, Sujithra R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331517953

In e-commerce, recommendation systems are critical to increasing user engagement and conversion. However, traditional collaborative filtering methods require user feedback and do not take into consideration semantic understand of the items. In order to eliminate these barriers, this work suggests a novel solution of fusing collaboration technique with BERT-based embeddings for e-commerce suggestions. Besides this we include a variety of other models for comparison namely standalone BERT, Collaborative filtering approach, Roberta + Collaborative Filtering and Deberta+Collaborative Filtering. We are using collaborative filtering and item title, reviews (via BERT-based embeddings) to model user preferences. Through experiments on a real Flipkart dataset, we validate the effectiveness of our approach. Recommendation in the merged model results, is our proposed model achieved higher accuracies. These results hold valuable insights for enhancing e-commerce platform recommendation systems. This study helps improve the state-of-the-art in e-commerce recommendation systems by combining collaborative filtering and more advanced natural language processing methods. © 2024 IEEE.

关键词： Collaborative filtering

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