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Heart Failure Prediction using Machine Learning Techniques: A Comparative Analysis 15

Heart Failure Prediction using Machine Learning Techniques: ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rithish Krishnendu, S. Sarath, S. Niveditha, S. Sai, Bayana Mohith Siva Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Heart failure is a prevalent cardiovascular disease with significant global mortality rates. Accurate prediction methods are imperative for predicting cardiovascular disease and identifying individuals at risk of adverse outcomes, highlighting the crucial need for precise prediction tools. This study employs advanced machine learning techniques to enhance heart failure prediction. We conduct a comparative analysis of various machine learning models, including Logistic Regression, Support Vector Machine (SVM), Random Forest, and Adaptive Boosting (AdaBoost), on three diverse datasets. The datasets encompass clinical characteristics, personal habits, and mortality outcomes, providing a comprehensive understanding of heart failure determinants. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess model performance. The results reveal patterns in model behavior across datasets, highlighting the importance of selecting appropriate models for specific data characteristics. Our findings contribute to the building of proper heart failure prediction models, supporting medical institutions in resource allocation and early therapeutic customization. © 2024 IEEE.

关键词： Adaboost Classification Comparison Heart failure Logistic regression Machine learning Random forest SVM

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RLAD: Reinforcement Learning based Anomaly Detection system for IoT devices in smart homes 15

RLAD: Reinforcement Learning based Anomaly Detection system ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chadha, Angela Raj Reddy, Kethamreddy Karthikeya Sountharrajan, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

In the era of the Internet of Things (IoT), smart homes have become a cornerstone of modern living, offering convenience, energy efficiency, and security. The extensive connectivity within these networks brings forth security challenges, making the detection of anomalies a critical aspect of IoT network security. Anomalies encompass a wide array of unexpected events, from irregular data patterns to communication anomalies, power fluctuations, behavioral deviations, security breaches, and environmental variations. This paper underscores the pivotal role of anomaly detection in securing IoT networks, highlighting its significance in identifying both known threats and emerging risks. Anomaly detection in IoT relies on machine learning and statistical techniques, encompassing data collection, preprocessing, feature extraction, model training, real-time anomaly detection, and responsive actions. Reinforcement Learning(RL), a subset of machine learning, emerges as a formidable tool in enhancing IoT network security. RL models continuously observe and adapt to device behavior, identifying anomalies and security breaches in real-time. RL offers adaptability in rapidly changing IoT environments, proactive responses to anomalies, and the ability to learn and evolve against new threats. RL strengthens network security without compromising individual device data. Our advanced security system that refines its understanding of the smart home environment, collectively learns from anomalies, and bolsters network security. Synergy of IoT and RL is reshaping IoT network security, promising not only seamless device functionality but also the safeguarding of sensitive data and personal spaces. © 2024 IEEE.

关键词： Anomaly Intrusion Metadata Stream Reinforcement learning

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Advancing Medical Imaging: A Comprehensive Synthetic dataset for Infant Brain MRI Analysis 2

Advancing Medical Imaging: A Comprehensive Synthetic Dataset...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Yashasvi, B.N. Kavya, N.P. R N S Institute of Technology VTU Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350371314

Accurate diagnosis and treatment planning are consistently challenging tasks in the medical field, especially when reliant on image diagnosis. The availability of sufficient and high-quality datasets becomes crucial in addressing this challenge. To tackle this issue, synthetic datasets can play a vital role in augmenting the number of samples while maintaining good quality. This study focuses on utilizing Deep Convolutional GANs (DCGANs) and Wasserstein GANs (WGANs) to generate synthetic datasets, specifically for Infant Brain MRI imaging. The Human Connectome Project (dHCP) dataset, comprising 800 samples categorized into four classes, was used for this study. Several key metrics, including the Similarity Index (SSI), Inception Score (IS), and Fréchet Inception Distance (FID), were employed to evaluate the performance of the generated synthetic datasets. The Inception Score was calculated for each class individually as well as for the overall synthetic dataset. For the normal class (first class), DCGAN achieved an Inception Score of lS.45, while WGAN scored 15.13. In the second class, DCGAN scored 19.70 compared to WGAN's 16.21. Similarly, in the third class, DCGAN scored lS.65 and WGAN 15.65, and in the abnormal class (fourth class), DCGAN scored 17.58 against WGAN's 16.00. The overall synthetic dataset scored 19.29 for DCGAN and 16.47 for WGAN. As the higher Inception Scores across different classes and for the overall dataset denoted that DCGAN performed well compare to WGAN in generating synthetic images for the Infant Brain MRI dataset. © 2024 IEEE.

关键词： Generative adversarial networks

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An In-dept. Exploration of Image Captioning Training Approaches and Performance Analysis 9

An In-Depth Exploration of Image Captioning Training Approac...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Kushal, K. Manoj, Madhav Reddy, Kevansh Nair, Priyanka C. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the rapidly evolving landscape of computer vision, image captioning has emerged as a challenging task. This report explores the techniques involved in image captioning using deep learning techniques, especially the encoder-decoder framework. Models such as CNN-LSTM, CNN-GRU,Xception - YOLO v4, GIT Based Model are used. Recognition is given to the significance of ample and well-annotated datasets in teaching the algorithms to understand the complex relationships between visual elements and textual descriptions. Along with traditional evaluation metrics like BLEU score, this study also employs metrics such as METEOR, ROUGE-L, and SPICE to compare performance between models. The findings highlight on the impact of deep learning in enabling computers to generate captions for diverse visual content. © 2024 IEEE.

关键词： Long short-term memory

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Study on Networking Models of SDN using Mininet and MiniEdit 2

Study on Networking Models of SDN using Mininet and MiniEdit

引用

2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Bhaskaran, Sreebha Supriya, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350327533

Software Defined Networking (SDN) has brought about a revolutionary shift in network management and control. The driving force behind SDN's development is the need for enhanced network programmability and scalability to accommodate rapidly changing applications and services. By decoupling the control plane from the data plane, network managers gain centralized control and programmability over resources. This study explores the architecture, encompassing application, control layer, and data layer, while highlighting the pivotal role of controllers. In addition, this work also explores SDN using Mininet and MiniEdit emulators for designing custom topologies built based on real time scenarios. Consequently, the research aims to pinpoint the optimal topology suited for various applications. © 2024 IEEE.

关键词： Controllers

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Machine Learning Approaches for Predictive Channel State Information in MIMO Systems Using LSTM 2

Machine Learning Approaches for Predictive Channel State Inf...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Upadhyay, Deepak Sharma, Amulya Gupta, Mridul Devgan, Divyansh Garg, Shobhit Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Management Studies Dehradun India

ISBN: (纸本)9798331519056

In this paper, we analyse machine learning models, including Long Short-Term Memory networks, to predict the CSI and evaluate their impact on adaptive transmission in MIMO systems. To achieve this objective, we use simulation to generate CSI data in a MIMO environment and compare the performance of ML models to traditional methods. Our simulation reveals using our dataset that LSTM models improve the SNR values significantly, which means that their predictions are more accurate. At the same time, these models require greater computational and other resources. In contrast, despite its relative inefficiency in comparison with LSTM, Decision Trees improve the SNR moderately while consuming fewer resources but needing a substantial amount of time. Analysis of the source tries to examine the balance between performance and resource usage may validate the use of ML-based methods, and especially LSTM, to predict the CSI advancedly, but the actual application must take constraints into account. © 2024 IEEE.

关键词： Contrastive Learning

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Swarm-Optimized Deep Convolutional Networks for Precise Detection and Grading of Diabetic Retinopathy 4

Swarm-Optimized Deep Convolutional Networks for Precise Dete...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sadhana, U. Singh, Tripty Beena, B.M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350364729

Diabetic Retinopathy (DR) is a very critical problem of diabetes that can lead to blindness if not detected and treated in the early stage. This research was conducted on this difficult disease based on image processing and Machine intelligence. In this research, our model detects and classifies the DR using the swarm-optimized deep convolutional networks. The proposed model integrated the convolutional neural networks (CNNs) along with Particle Swarm Optimization (PSO) to enrich the performance of the model execution and results with fine-tuned hyperparameters. This custom-optimized CNN model, designed for image classification, was evaluated on the Kaggle diabetic retinopathy dataset. The swarm-optimized deep CNN model presented balanced performance across all DR classes and achieved a precision of 0.93, recall of 0.97, and F1-score of 0.95 for all classes. The model's overall accuracy reached 0.84 on the selected dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Image Encryption and Decryption Based on 3D Chaos Maps and DNA Encoding 6

Image Encryption and Decryption Based on 3D Chaos Maps and D...

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Chethan, Kosaraju Sai Sumanth, Kodur Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidypeetham Bengaluru India

ISBN: (纸本)9798350306613

High information redundancy and strong correlation of neighboring pixel values are characteristics of encryption methods like DES (data Encryption Standard), AES (Advanced Encryption Standard) and other traditional data encryption methods. There is a need for more robust methods to overcome these challenges. This paper discusses an image encryption method that uses DNA encoding rules to improve image security and combination of 3D chaotic maps like Lorenz, Rossler, Chen and Lu systems to create unpredictable sequences using chaotic dynamics. First, the image is divided into its RGB color channels, and then DNA rules are used to encode each RGB channels. Using a key derivation function, a secure key is generated from the image content to modulate the chaotic sequences. The chaos systems are utilized to create chaotic sequence. Sequence scrambling and XOR operations improve security even more. After that, an encrypted image is obtained and saved. Using the key, the chaotic sequences are recreated during decryption, and the original image is recovered by repeating the process in reverse. © 2024 IEEE.

关键词： Cryptography

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Incremental Learning for Enhancing Misbehavior Detection in Multi-Access Vehicular Networks 100

Incremental Learning for Enhancing Misbehavior Detection in ...

引用

100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Nguyen, Lan-Huong Nguyen, Van-Linh Hwang, Ren-Hung College of Artificial Intelligence Tainan Taiwan Dept. of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798331517786

Autonomous cars are already the driving force behind achieving the goal of intelligent mobility in smart cities. However, providing internet access for automobiles introduces additional risks of distributing false information. By broadcasting fake sharing data, an attack vehicle has the potential to mislead surrounding cars and trigger catastrophic accidents. This work introduces a novel incremental learning model for misbehavior detection in vehicular communications. Unlike previous methods which mostly relied on the pre-trained model, in this work, we develop accumulative learning capability for the misbehavior detection engines at vehicles or distributed roadside units. The model can assist in obtaining new knowledge from continuous learning. The simulation results indicate that the proposed model outperforms the state-of-the-art studies by up to 12% in terms of detection accuracy and 45% faster in continuous processing, particularly in the single detector mode. © 2024 IEEE.

关键词： 5G mobile communication systems

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Intelligent Accident Detection and Severity Analysis System for Enhanced Emergency Response 1

Intelligent Accident Detection and Severity Analysis System ...

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

作者： Harini, P. Chidambarathanu, K. Ramachandran, Lalitha R.M.K Engineering College Dept. Computer Science and Business Systems Kavarapettai India R.M.K Engineering College Dept. Science and Humanities Kavarapettai India

ISBN: (纸本)9798350365337

Road accidents cause serious injuries and deaths worldwide. The previous year's Ministry of Road Transport and Highways Annual Report on Road Accidents in India showed that many road accidents were fatal. A large percentage of Indian deaths occur without rapid medical intervention or first assistance. Delays in reporting incidents to emergency services increased fatalities. A sophisticated method using deep learning and analytics to locate CCTV accidents, classify their severity, and speed up emergency response. Accident detection and analysis are efficient with cutting-edge technologies. The accident detection phase uses YOLOv7, a deep-learning object detection model, to evaluate CCTV footage in real time. A stratified K-fold deep neural network classifies accidents as major or minor based on collision severity. Real-time GPS tracking alerts ambulance drivers within three km of the accident using the mobile app. This expedites emergency response, lowering mortality by reducing the time between the accident and medical assistance. © 2024 IEEE.

关键词： Object detection

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