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Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review

Multimedia Tools and Applications 2024年第39期83卷 86895-86922页

作者： Bhavekar, Girish Shrikrushnarao Das Goswami, Agam Vasantrao, Chafle Pratiksha Gaikwad, Amit K. Zade, Amol V. Vyawahare, Harsha Department of Artificial Intelligence & Data Science CSMSS Chh. Shahu College of Engineering Aurangabad431136 India Department of SENSE VIT-AP University Andhra Pradesh 522237 India Department of AI & DS CSMSS Chh. Shahu College of Engineering Maharashtra Aurangabad431136 India Department of Computer Science & Engineering GH Raisoni University Maharashtra Amravati444701 India Department of Computer Science & Engineering Sipna College of Engineering & Technology Maharashtra Amravati444701 India

Cardiovascular disease holds the position of being the foremost cause of death worldwide. Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with better experience. Moreover, it encounters numerous significant challenges in clinical data analysis. While many researchers have focused on predicting heart disease, the performance metric, namely prediction accuracy, remains suboptimal. The accurate HDP can help the person to prevent himself from life threats and at the same time, inaccurate prediction can prove to be fatal. To solve these issues, in this review work several Deep Learning (DL), Machine Learning (ml) and optimization based HDP techniques are discussed. In recent times, many researchers have been utilizing different DL and ml algorithms to help the professionals and health care industry for the prediction of heart disease. Further, it discussed about various optimization-based algorithms and its performance analysis. Therefore, this review paper suggests that the optimization-based HDP algorithm could assist doctors in predicting the occurrence of heart disease in advance and offering suitable treatment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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A Novel Approach to Optimizing Spark: Detecting Misconfigurations for Enhanced Efficiency

A Novel Approach to Optimizing Spark: Detecting Misconfigura...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Sanjana S V Rachana Uday Umesh Suraj Rao R. Jayashree Department of Computer Science and Engineering PES University Bengaluru India Department of Computer Science and Engineering (AI & ML) PES University Bengaluru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Configuration errors constitute 60% of contemporary system failures in distributed computing platforms. These errors lead to data breaches, system crashes, or silent failures requiring extensive technical support and knowledge of the application architecture. This research work introduces a dataset that can be used to detect misconfigurations in a Spark cluster. The dataset encompasses detailed system performance metrics such as CPU usage and memory allocation that were recorded by running CPU intensive and memory intensive Spark jobs under various configuration settings to capture performance variations. Analysis of this dataset provides insights into optimizing the values of Spark cluster configurations and mitigating the impact of configuration errors. By using machine learning techniques on this dataset, it is possible to construct automated methods for proactively detecting and mitigating misconfigurations, which improves the stability and efficiency of distributed computing environments.

关键词： Measurement System performance Machine learning Data breach Stability analysis computer crashes Sparks Resource management Distributed computing Intelligent systems

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Enhancing Real-Time Stream Processing with Apache Kafka by integrating Scaled-Hidden Markov Models

Enhancing Real-Time Stream Processing with Apache Kafka by i...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： V Rachana Sanjana S R Jayashree Department of Computer Science and Engineering PES University Bangalore India Department of Computer Science and Engineering(AI & ML) PES University Bangalore India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This research study explores the integration of Kafka, a distributed streaming platform, in the context of enhancing speech recognition systems utilizing Hidden Markov Models (HMMs). Speech recognition is a vital aspect of ai and Machine Learning which plays an important role in the interaction between humans and computers. Hidden Markov models (HMMs) are a popular choice for speech recognition systems due to sequential data modeling. In this paper, we conduct a comprehensive study integrating Big Data technologies like Kafka into Hidden Markov Model (HMM) based speech recognition, aiming to recognize spoken words from audio wave files and classify them into predefined word classes. The integration involves a multifaceted approach, employing Kafka for efficient data processing and communication between distinct system modules. To prevent underflow during HMM training, a Scaled HMM Module is employed. We address challenges posed by unseen data, by incorporating smoothing techniques such as add-delta smoothing which mitigates zero probabilities and improves the generalization of the model. We also experiment the use of pruning techniques which disregards the low-probability paths during the Viterbi algorithm, which results in improvement in the accuracy. By incorporating the integration of Big Data technologies like Kafka, along with advanced techniques such as pruning, smoothing, and scaling of Hidden Markov Models (HMMs), we delve into a comprehensive strategy for advancing speech recognition capabilities.

关键词： Adaptation models Analytical models Smoothing methods Accuracy Viterbi algorithm Hidden Markov models Speech recognition Big Data Data models Robustness

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Classifying Network Abnormalities Into Faults and Attacks in Iot-Based Cyber-Physical Systems using Machine Learning 2

Classifying Network Abnormalities Into Faults and Attacks in...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kumar, P. Jagdish Alzubaidi, Laith H. Jasim Nagarjuna, N. Deshmukh, Ram Madhura, G.K. AVN Institute of Engineering and Technology Department of Computer Science and Engineering Telangana India The Islamic University Najaf Iraq CVR College of Engineering Department of CSIT Telangana India SR University Department of Electrical and Electronics Engineering Telangana Warangal India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350317558

Amidst the proliferation of Industry 4.0, the integration of artificial intelligence and smart techniques has emerged as a focal point in discussions surrounding industrial cyber-physical systems (CPS). It is still very difficult to detect anomalies in a way that protects security and productivity, especially when there isn't much labeled data available for cyber-physical security protection. A novel method called the Few-Shot Learning model with Siamese Neural Network (FSL-SNN) is presented in this paper with the goal of improving the accuracy of intelligent anomaly detection in industrial CPS and reducing the over-fitting problem. To calculate the distances between input samples using their optimum feature representations, a Siamese encoding network is developed. To bolster the efficiency of the training process, a robust cost function is designed, encompassing three specific losses. The culmination of these efforts results in the development of an intelligent anomaly detection algorithm. The results of experiments conducted on two datasets - one with sparse labels and the other fully labels shows the notable improvements that the proposed FSL-SNN achieves when it comes to lowering false alarm rate (FAR) of 0.041 and raising F1 score of 0.975 for intrusion signal identification in the context of industrial CPS security protection compared to the existing Siamese Convolutional Autoencoder. © 2024 IEEE.

关键词： Anamoly Cyber Physical System Few Shot Learning Intrusion Detection Siamese Neural Network

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Detecting Objects in Surveillance Videos with Deep Neural Networks for Crime Scene Analysis

Detecting Objects in Surveillance Videos with Deep Neural Ne...

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

作者： Sudha, I. Kirubasri, G. Deivakani, M. Singh, Vijendra Pratap Kumar, T.Rajesh Niranjanamurthy, M. Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India Sona College of Technology Department of Computer Science and Engineering Salem India Psna College of Engineering and Technology Department of Ece Dindigul India Mahatma Gandhi Kashi Vidyapith Department of Computer Science & Applications U.P Varanasi India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Department of Ai and Ml Karnataka Bangalore India

ISBN: (纸本)9798350348910

Surveillance systems are critical components of modern security and forensic investigation, and their efficacy is strongly reliant on precise object detection in video recordings. This study looks into the use of deep neural networks to improve object detection in surveillance films for crime scene analysis. This study investigates the capabilities of cutting-edge deep learning architectures in recognizing and classifying objects in various surveillance contexts. The study's findings are extensive, including detection accuracy metrics for numerous item classes such as 'Person,' 'Vehicle,' and 'Suspicious Item.' Precision values range from 0.78 to 0.92, recall values range from 0.82 to 0.90, and F1 scores range from 0.80 to 0.90, demonstrating the models' ability to recognize objects accurately, but with variances among item categories. This study also looks into computational performance, offering information about inference times and GPU utilization. Inference times for ResNet-50 and YOLOv3 are 15 ms and 20 ms, respectively, with GPU use percentages of 75% and 90%. These findings provide useful information for picking models that fulfill real-time processing needs while optimizing computational resources. The research also examines the connection between video resolution, detection speed (up to 30 frames per second), and average detection accuracy. Lower resolutions allow for faster processing, but at the expense of accuracy, whilst higher resolutions provide finer details at the expense of larger computational needs. These trade-offs are critical considerations when building surveillance systems to meet certain operational requirements. © 2023 IEEE.

关键词： Object detection

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Comparative Analysis of Dimensionality Reduction Techniques in Machine Learning Models for Liver Disease Detection

Comparative Analysis of Dimensionality Reduction Techniques ...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Manish Rai Hemlata Parmar Shivangi Singh Department of Computer Science and Engineering(AI&ML) Manipal University Jaipur Jaipur India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Liver disease is a significant global health concern that necessitates accurate and timely diagnosis. This review paper explores the application of machine learning techniques, specifically focusing on the impact of dimensionality reduction methods, in liver disease prediction. This study evaluates both linear and non-linear dimensionality reduction techniques to assess their performance, accuracy, and computational efficiency in the context of liver disease prediction. Through a comprehensive review of existing research and experimental results, this study reveals that while non-linear methods offer advantages in data visualization, linear methods like PCA generally outperform them in terms of model accuracy and computational efficiency. This review provides valuable insights for researchers and healthcare professionals seeking to optimize machine learning models for liver disease diagnosis. By understanding the strengths and limitations of different dimensionality reduction techniques, researchers can select the most appropriate methods to improve the accuracy and efficiency of liver disease prediction models.

关键词： Dimensionality reduction Analytical models Accuracy Liver diseases Reviews Computational modeling Medical services Predictive models Computational efficiency Principal component analysis

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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

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Framework for Evaluating the Carbon Footprint of Academic Machine Learning Endeavor

Framework for Evaluating the Carbon Footprint of Academic Ma...

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Communications and computer science (InCCCS), International Conference on

作者： Karthik K Pai Monika Goyal Department of Computer Science And Engineering (AI&ML) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350358858

ISBN: (纸本)9798350358865

Carbon foot printing has gained massive importance in our tryst to move towards sustainability. With machine learning seen as a solution for many real world problems, sustainability in machine learning is important. Hence the motivation to develop this framework which can aid in foot printing current activities irrespective of architecture and platform. Through various iterations of preliminary analysis and through research, framework has been implemented and successfully tested on multiple architecture models to accurately compute the same.

关键词： Training Printing Analytical models Publishing Computational modeling Machine learning computer architecture

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A Novel Architecture for a Medical Image Recognition System Using Deep Learning-Based Multiple Regression Evaluation 4th

A Novel Architecture for a Medical Image Recognition System ...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Parashar, Jyoti Upreti, Kamal Fatima, Iram Gangwar, Divya Saini, Ashok Kumar Vats, Prashant Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Computer Science Christ University Delhi NCR Campus Ghaziabad India Department of AI & ML Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Management Babu Banarasi Das Institute of Technology & Management New Delhi India Department of Computer Science & Engineering School of Computer Science & Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819751990

Models based on machine learning are optimization models that collect data, assess it, and deliver the reports required by specialists and management to make the best decisions. The application of contemporary machine learning allows the organization to quickly analyze photographs, differentiate voices assist in providing customer service, assess the information that is at hand, and uncover connections to aid in decision-making processes. The results of this investigation use quantitative methodologies to collect data and analyze it using mathematical procedures such as regression modeling as well as analysis of variance. Deep learning techniques applied to digital imaging, particularly in medical treatment, can increase picture quality, aid in modeling, aid in making the best possible diagnosis, and successfully address demands from patients. To analyze the hypothesis, investigators intend to utilize statistical approaches such as descriptive data analysis, regression evaluation, and analysis of variance (ANOVA). The authors employ the purposive sample approach to choose respondents from the healthcare industry. Purpose sampling is a non-probability sampling approach. Researchers collected data from 193 respondents working at hospitals that are privately owned in Southern Asia. As stated by the study, all factors, including efficiently meeting patient needs, have a probability value of under 0.05, indicating that they are statistically noteworthy. Following the study, the coefficient of variance (R squared) is 0.744, or 74.4%. According to the study, there is a high association between better image quality and ml-based digital picture identification systems. The recognition of patterns and the application of artificial intelligence to computerized recognition of pictures also have a close link. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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