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Enhanced Heart Disease Classification: An Ensemble-Based Deep Learning Approach

Enhanced Heart Disease Classification: An Ensemble-Based Dee...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Nivaashini, M. Sathish Kumar, G. Uma Maheshwari, G. Soundariya, R.S. Kumar B, Arun Vijaya, G. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Karpagam Academy of Higher Education Faculty of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376135

The WHO's assessment indicates a rising prevalence of heart disease, exacerbated by challenges in detection and diagnosis in India due to population growth. Machine Learning (ML) is increasingly used for precise judgments in healthcare, aiming to accurately diagnose heart-related illnesses. This study proposes a Deep Learning (DL) ensemble method, combining ML classifiers to enhance accuracy. The ensemble model outperforms AdaBoost, XGBoost, Random Forest, Decision Tree, SVM, and Logistic Regression with 98.3% accuracy on a UCI dataset. Correlation-based Feature Selection (CFS) for relevant characteristic selection improves the model's accuracy, recall, precision, and f1-score. This research contributes to improved disease prediction and categorization in cardiovascular health, vital for effective healthcare interventions and outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Dementia Disorder Analysis Using Optimized Deep ResiNet Based on Cognitive Approach 1st

Dementia Disorder Analysis Using Optimized Deep ResiNet Base...

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1st International Conference on data engineering and Machine Intelligence, ICDEMI 2023

作者： Duraipandian, Kavitha Ambigapathy, Murugan Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9789819776153

Early warning signs of dementia may include memory loss, difficulty with problem-solving, confusion, changes in mood and behavior, and impaired communication skills. People impacted by dementia often make a loss on things, money, etc. In the social environment, the disease is considered as one of the serious issues. In order to correctly identify dementia, automated methods of diagnosis have grown essential. Conventional non-imaging components are capable of accurately identifying dementia as well as readily integrating the practice of medicine, despite the fact that neuro-images are currently fully utilized in the most recent technologies. To develop a powerful dementia screening tool, researchers combined machine learning with an enormous amount of non-imaging variables. In order to dramatically lessen the obstacles to employing the diagnostic tool, this project makes use of the predictive capability of non-imaging data. The suggested system was completely equipped to detect and evaluate cognitive signs associated with dementia using speech data, keystroke evaluation, and so on. The optimized Deep Residual Network (ODRN) is used to identify brain fluctuations, the sound information is subsequently analyzed with frequency extractors, and the voice pitch is further examined. A deep neural network is used to evaluate keystrokes while contrasting them to Kaggle data on odd stroke rates. On the ensemble method developed here, the system at hand has a precision of 96.66% performance. The overall processing delay of 52 s is achieved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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CLERC: An Intelligent Sign Language Translator for Improved Accessibility using Machine Learning 2

CLERC: An Intelligent Sign Language Translator for Improved ...

引用

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Vijayaganth, V. Santhosh Kumar, S. Davis, L. Pranava Darisan, S.V. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350369960

Clerc's system is a modern smart language interpreter using advanced methods like artificial intelligence, imagery recognition, and natural languages translation. The Clergs System includes these are just some of tools used. The system is able to do this by turning gestures in sign language into verbal and written outputs smoothly thus helping people with hearing impairment to communicate easily in real time. In this research, an object recognition SSD MobileNets and FDN feature extraction models are used for accurate gesture detection. A machine-learning model can also be used to predict accurate translations. The efficacy of the Clerc system has been tested and validated to the utmost extent in different settings such as public places, schools, hospitals amongst others. This research seeks to explore possibilities of improving the system's precision, efficacy, scalability, and use with regards to the practical application. © 2023 IEEE.

关键词： Clerc system computer vision FPN-lite feature extractor scalability SSD Mobile Net Object recognition model

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Enhancing the Security of Big data based on the New AI & its Techniques 5

Enhancing the Security of Big Data based on the New AI & its...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Singh, Uday Pratap Kumari, Bersha Saxena, Vivek Goyal, Dinesh Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur India Department of Computer Science & Engineering Poornima Institute of Engineering and Technology Jaipur India

ISBN: (纸本)9798400709418

Since that time i.e 1864 till today, research on different security level for the security of data which has been transferred or to be transfer within a network is on the boom day by day because with the advances in equipment innovations just as the quantity of gadgets associated day by day to the Internet, there are enormous data are gathered. Such data, whenever examined, will add significant qualities to the private just as governmental sectors. Simultaneously, Artificial Intelligences (AI) algorithms are demonstrated proficient in taking care of numerous unpredictable issues, particularly Deep Neural Networks (DNNs) on other Big data techniques. In this paper will have survey the efficiency of the system by applying different data value techniques and algorithms on the hardware tools. © 2023 ACM.

关键词： Deep neural networks

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A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning 25

A Case Study on Context-Aware Neural Machine Translation wit...

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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna India Wipro AI Lab45 London United Kingdom School of AI and Data Science Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781068690709

In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies (Li et al., 2020) have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context. We conduct experiments on cascade MTL architecture, which consists of one encoder and two decoders. Generation of the source from the context is considered an auxiliary task, and generation of the target from the source is the main task. We experimented with German-English language pairs on News, TED, and Europarl corpora. Evaluation results show that the proposed MTL approach performs better than concatenation-based and multi-encoder DocNMT models in low-resource settings and is sensitive to the choice of context. However, we observe that the MTL models are failing to generate the source from the context. These observations align with the previous studies, and this might suggest that the available document-level parallel corpora are not context-aware, and a robust sentence-level model can outperform the context-aware models. © 2024 The authors, © 2024 European Association for Machine Translation.

关键词： Neural machine translation

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Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass 1

Improving Heart Disease Prediction with Stacked Ensemble Lea...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Amirthaa, M. Mary, Gerardine Immaculate Julian, Anitha Selvarathinam, Anto Lourdu Xavier Raj School of Electronics Engineering Vellore Institute of Technology Vellore India Saveetha Engineering College Computer Science and Engineering Tamilnadu Chennai India Data Science and Analytics Grand Valley State University MI United States

ISBN: (纸本)9798331518943

Heart disease prediction remains a critical area of research due to its significant impact on public health. This paper, titled 'Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass,' presents a comprehensive study comparing the performance of binary and multiclass classification models for heart disease prediction. Utilizing two distinct datasets, one for binary classification and one for multiclass classification. The study evaluates various machine learning and deep learning models, with a focus on stacked ensemble methods. The Binary Classification Results leveraging the Preprocessing-and-PCA-on-Heart-Disease-dataset, the study evaluated several models including Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, Deep Learning model, and Logistic Regression. The results indicate that the Stacking Ensemble model achieved the highest accuracy of 90.2%, outperforming individual models where Random Forest (88.5%), SVM (89.1%), XGBoost (89.7%), Gradient Boosting (88.0%) and Deep Learning (89.1%). The Multiclass Classification study using the UCI Heart Disease dataset, when applied the same models, the Stacking Ensemble model achieved an accuracy of 64.1%, which, although superior to other individual models such as Random Forest (58.1%), SVM (54.3%), Gradient Boosting (59.2%) and Deep Learning model (57.6%) fell short compared to XGBoost, which attained an accuracy of 65.8%. This outcome highlights the challenges associated with multiclass classification in heart disease prediction. © 2024 IEEE.

关键词： Contrastive Learning

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Label Aggregation with Self-Supervision Enhanced Graph Transformer 26

Label Aggregation with Self-Supervision Enhanced Graph Trans...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Jiacheng Tang, Feilong Hou, Xiaofeng Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9781643684369

Aggregating noisy labels produced by the crowd of workers to generate true labels is a challenging problem in crowdsourcing. The key behind label aggregation is to effectively utilize the hidden information (e.g., characteristics of workers and questions which are often missing) in the labeling process. Existing methods mainly generated aggregation models based on the complicated Bayesian model or some strong assumptions. Recently, deep learning-based methods attempt to automate label aggregation but need various labels. These all make them hard to deploy to real-world applications. In fact, abundant information in the process of crowdsourcing itself can be extremely helpful to aggregate the labels. In this paper, we propose ATHENA (lAbel aggregaTion witH sElf-supervision eNhanced grAph transformer) to aggregate labels by utilizing the self-supervision signals in crowdsourcing. Firstly, we propose a transformer-based graph neural network that can learn from the crowdsourcing topology and features. Then, we use self-supervision signals inherently included in the dataset to help to aggregate the labels. To be specific, we identify the answer-based self-supervision signal that can predict the answer of any user given to different tasks. In our evaluations, we compare the proposed ATHENA with the other 11 representative methods on 10 datasets. Our experimental results demonstrate that ATHENA is highly effective in aggregating labels and obtains much better performance than existing methods. © 2023 The Authors.

关键词： Aggregates

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Fast Parallel Recovery for Transactional Stream Processing on Multicores 40

Fast Parallel Recovery for Transactional Stream Processing o...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhao, Jianjun Liu, Haikun Zhang, Shuhao Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Yu School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Transactional stream processing engines (TSPEs) have gained increasing attention due to their capability of processing real-time stream applications with transactional semantics. However, TSPEs remain susceptible to system failures and power outages. Existing TSPEs mainly focus on performance improvement, but still face a significant challenge to guarantee fault tolerance while offering high-performance services. We revisit commonly-used fault tolerance approaches in stream processing and database systems, and find that these approaches do not work well on TSPEs due to complex data dependencies. In this paper, we propose a novel TSPE called MorphStreamR to achieve fast failure recovery while guaranteeing low performance overhead at runtime. The key idea of MorphStreamR is to record intermediate results of resolved dependencies at runtime, and thus eliminate data dependencies to improve task parallelism during failure recovery. MorphStreamR further mitigates the runtime overhead by selectively tracking data dependencies and incorporating workload-aware log commitment. Experimental results show that MorphStreamR can significantly reduce the recovery time by up to 3.1 x while experiencing much less performance slowdown at runtime, compared with other applicable fault tolerance approaches. © 2024 IEEE.

关键词： Fault tolerance

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LLM4Decompile: Decompiling Binary Code with Large Language Models

LLM4Decompile: Decompiling Binary Code with Large Language M...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Tan, Hanzhuo Luo, Qi Li, Jing Zhang, Yuqun Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre for Data Science & Artificial Intelligence Hong Kong

ISBN: (纸本)9798891761643

Decompilation aims to convert binary code to high-level source code, but traditional tools like Ghidra often produce results that are difficult to read and execute. Motivated by the advancements in Large Language Models (LLMs), we propose LLM4Decompile, the first and largest open-source LLM series (1.3B to 33B) trained to decompile binary code. We optimize the LLM training process and introduce the LLM4Decompile-End models to decompile binary directly. The resulting models significantly outperform GPT-4o and Ghidra on the HumanEval and ExeBench benchmarks over 100% in terms of re-executability rate. Additionally, we improve the standard refinement approach to fine-tune the LLM4DecompileRef models, enabling them to effectively refine the decompiled code from Ghidra and achieve a further 16.2% improvement over the LLM4Decompile-End. LLM4Decompile demonstrates the potential of LLMs to revolutionize binary code decompilation, delivering remarkable improvements in readability and executability while complementing conventional tools for optimal results. © 2024 Association for Computational Linguistics.

关键词： Binary codes

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An Analysis of Human Factors in Malware Attacks

An Analysis of Human Factors in Malware Attacks

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2024 International Conference on computer and Applications, ICCA 2024

作者： Alotaibi, Jawza Al Ja'am, Jihad M. Hoda, Mohammed Oryx Universal College in Partnership with Liverpool John Moores University Dept. of Computing and Data Science Doha Qatar Kuwait College of Science and Technology Dept. of computer Science and Engineering Doha Kuwait

ISBN: (纸本)9798350367560

This research study investigates human responses to malware attacks, focusing on attacker motivations, individual reactions, and key factors influencing user vulnerability and resilience. Using a structured survey questionnaire, the study gathered data from 104 participants with diverse demographics across various occupations. The data was analyzed using SPSS statistical software and Python-based machine learning algorithms. The survey included demographic information and responses to cybersecurity-related questions about human behavior and experience. Machine learning techniques identified ten critical factors as significant predictors of vulnerability to malware attacks. The prediction model indicated that 55 out of 104 participants were vulnerable, with higher vulnerability observed among female participants and those in non-IT professions. This study contributes valuable insights into the human factors influencing cybersecurity resilience, providing a foundation for developing effective strategies to mitigate malware attack vulnerability. © 2024 IEEE.

关键词： Cyber attacks

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