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Optimizing Heart Attack Prediction Through OHE2LM: A Hybrid Modelling Strategy

Journal of Electrical Systems 2024年第1期20卷 66-75页

作者： Mall, Pawan Kumar Srivastava, Swapnita Patel, Mitul M. Kumar, Aniruddh Narayan, Vipul Kumar, Sanjay Singh, P.K. Singh, D.S. GL Bajaj Institute of Technology and Management India Department of Electronics and Communication Engineering Parul Institute of Engineering and Technology Parul University Vadodara India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Knowledge Park-2 Greater Noida India Galgotias University Uttar Pradesh Gautam Buddha Nagar India Rajkiya Engineering College Azamgarh India Computer Science and Engineering Department Madan Mohan Malaviya University Of Technology Uttar Pradesh Gorakhpur273010 India Computer Science and Engineering Department Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein Data science and machine learning (ML) offer invaluable tools. These methodologies aid in predicting heart attacks by considering various risk factors Just like high blood pressure, increased cholesterol levels, irregular pulse rates, and diabetes, this research aims to enhance the accuracy of predicting heart disease through machine learning *** study introduces a MLdriven approach, termed ML-ELM, dedicated to forecasting heart attacks by analysing diverse risk factors. The proposed ML-ELM model is compared with alternative Utilizing machine learning techniques like Support Vector Machines, Logistic Regression, Naïve Bayes, and XGBoost is a key aspect of this exploration into different approaches for predictive modeling., is part of the research strategy. The dataset utilized for heart disease symptoms is sourced from the UCI ML Repository. The outcomes reveal that our proposed ML-ELM model has demonstrated superior predictive performance among the ML techniques tested. ML models show notable efficiency in identifying heart attack symptoms, particularly with boosting algorithms. Accuracy assessments were employed to gauge the predictive ability, Our suggested model demonstrated an outstanding accuracy rate of 96.77%. © JES 2024 on-line: ***

关键词： Support vector machines

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Ensuring Safety Drive through a Smart Helmet design using IOT 7

Ensuring Safety Drive through a Smart Helmet design using IO...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Akshara, C.V. Aakash, S. Padma Suresh, L. Usha Nandini, D. Paul Selvan, Mercy Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

The smart helmet is a kind of protection gear that improves the rider's safety while operating a bike. The primary motive is to keep the motorcyclist safe. The attributes including alcohol detection, global positioning system, accelerometer, and fall detection can be employed to achieve this. As a consequence, it acts as a smart helmet. The first condition needs to be fulfilled for the bike to ignite, is that the person should have not consumed alcohol, followed by that the eye blink sensor detects the motion of eyeballs and if the driver falls is tired and closes eye for a period of time, then the sensor alerts the driver and when the accelerometer detects the change in motion, it alerts the GPS and GSM and it posts message to their nearby hospital and relatives. The advancement in our projects includes the temperature sensor which alerts during long hours of wearing helmet. Smart helmets can also help prevent head injuries. Some smart helmets can provide real-time information about the force, frequency and severity of head impacts. © 2024 IEEE.

关键词： Global positioning system

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An intrusion attack classification using bio-inspired optimization technique and ensemble learning model for edge computing environments

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Multimedia Tools and Applications 2024年 1-28页

作者： Rahamathulla, Mohemmed Yousuf Ramaiah, Mangayarkarasi School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamilnadu Vellore India

The phenomenal rise in network traffic across various sectors, driven by advancements in network communication, has led to an explosion of connected devices. While internet-based service providers have enhanced smart environments, these environments remain highly vulnerable to security risks. Due to their resource- constrained nature, Internet of Things (IoT) systems rely on edge servers for processing. Cutting-edge machine learning algorithms have significantly improved the security of edge computing systems by enabling early intrusion detection. However, the dynamic nature of our society demands more sophisticated Network Intrusion Detection Systems (NIDS) that can balance the challenge of managing the explosive growth of network traffic data with effectively responding to attacks. This paper proposes a novel NIDS that can train and learn from massive amounts of data in a centralized environment. To identify the best subset of features for improved data representation and classification, a bio-inspired algorithm, the Improved Firefly Optimization Algorithm (IFOA), is employed. Furthermore, a hyper-tuned Extremely Randomized Trees (ERT) attack detection model is developed to accurately identify malicious behavior while minimizing false positives. The experimental results demonstrate that the proposed approach achieves superior accuracy and execution speed compared to alternative solutions and its baseline versions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network intrusion

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MindCare: Machine Learning based Personalized Mental Health Monitor 4

MindCare: Machine Learning based Personalized Mental Health ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ushashree, P. Naik, Archana Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331540364

Mental health challenges are growing in recent years, emphasizing the need for effective monitoring and inter- vention systems. This paper presents a comprehensive approach to detect mental unstabilities by analyzing the text entered by the user. Utilizing a combination of sentiment analysis and machine learning techniques, the proposed system preprocesses text data to extract sentiment scores and other key features, which are then used to train models for accurate mental health assessments. The system also offers personalized recommendations based on the user's mental state. Experimental results demonstrate the effectiveness of the approach, with the model achieving accuracy of 82.31% for negative text classification. These results underscore the potential of the system to serve as a valuable tool in the early detection and management of mental health issues. © 2024 IEEE.

关键词： Electronic health record

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The Evolution of Labor Market: A Blockchain Based Decentralized Hiring System for Efficient Labor Acquisition 11

The Evolution of Labor Market: A Blockchain Based Decentrali...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Prasanna Kumar, Kuna Sai Manoj Kumar, Kodisana Sai Yogitha, R. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350350357

The significant impact that technological advancement has had on the evolution of the labour market has led to the emergence of innovative and imaginative approaches to hiring more workers more effectively. In order to address the issues with the current job market, this essay suggests a novel approach to hiring that is decentralised and blockchain-based. The proposed method increases labour procurement's efficacy and efficiency by leveraging blockchain technology's decentralisation, transparency, and immutability. Rather than using middlemen like traditional job boards or recruiting agencies, this approach eliminates middlemen by connecting companies and job seekers directly through blockchain technology. Smart contract automation of the hiring process guarantees the safe implementation of employment terms and conditions. The de centralised nature of the system ensures that individuals from all around the world can find work, and employers can locate talent from all over the world. Furthermore, the system ensures that both companies and job seekers have credible profiles by incorporating reputation management into a de centralised identification system. This helps to reduce problems caused by dishonesty or misrepresentation of qualifications. Furthermore, the entire process incorporates a grading and feedback system, allowing participants to assess one another's trustworthiness and performance, resulting in better hiring decisions. © 2024 IEEE.

关键词： Blockchain

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Smart Business with Intelligent Business Cards (SBIBC)

Smart Business with Intelligent Business Cards (SBIBC)

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Kirubakaran, N. Preethi, S.T. Parijatham, R. Pramithi, R. Chennai Institute of Technology Department of Computer Science and Business Systems Chennai India S.A. Engineering College Department of Computer Science and Engineering Chennai India Madha Institute Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

The research emphasizes the creation of a powerful and efficient system for the automaticextraction of contact information from physical calling cards through computer vision and information extraction techniques. This is achieved by integrating Tesseract Optical Character Recognition (OCR) with the YOLO (You Only Look Once) object detection algorithm, which acts as a dynamic combination for text and relevant elements identification and digitization from calling cards. The developed system uses client-server architecture to enable communication and data processing seamlessly. The server handles computationally intensive tasks such as object detection, OCR processing, and data validation while the client side has an interactive user interface that enables easy uploading of images and retrieval of structured contact information. The optimizations include this: YOLO is customized for detecting particular card elements (name, phone number, email) and enhancing the capabilities of Tesseract OCR to improve effectiveness for text extraction from varied card formats or fonts in the system. It is designed to be scalable and efficient, processing large datasets without affecting accuracy. This approach not only eases the digitization of calling card details into databases but also goes a long way in contributing to the domains of computer vision, object detection, and mechanized information extraction. It extends the scope to broad applications in automated data entry, customer relationship management (CRM), and digital archival. It takes forward for AI-driven sustaining technologies the integration of these techniques into practical solutions by overcoming the challenges of data extraction from real-world images. © 2024 IEEE.

关键词： Optical character recognition

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Analysis and Performance Evaluation of various Artificial Intelligence techniques for Disease Prediction 7

Analysis and Performance Evaluation of various Artificial In...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Refonaa, J. Jany Shabu, S.L. Rishi, S. Padma Suresh, L. Praveen, S. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

The framework of the proposed system utilizes AI, machine learning procedures and data handling methodologies to analyze, gather data and reports from clinics or patient facilities, and prognosticate reports that assist the outcomes used to foresee various kinds of infection and disease. The most recurrently occurring disease and its infection is the dominant source for the human's death. Clinical endlessly reports from different documents have been taken out to analyze and diagnose breast malignant growth or cancer, coronary or heart illness, and diabetes which are very frequent in present day people, and their wellbeing is significant in the finding. Today, propels in clinical innovation are assisting individuals with improving on the manner in which they give medical clinics and doctors' the most straightforward method for treating, oblige, and treat any infection they need. We have brought this sort of framework into our application, which can give positive and negative outcomes to make individuals' lives simpler to anticipate the sickness by bringing explicit data into their reports, and backing the theory of the infection with the choice of looking for exhortation good clinics and great specialists close to clients or guardians in our future enactments. © 2024 IEEE.

关键词： Logistic regression

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Using Contact Tracing technology for Communicable Disease Tracking

Using Contact Tracing Technology for Communicable Disease Tr...

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

作者： Dandavate, Prajkta Dutta, Atharva Kulkarni, Atharva Raut, Atharva Atkari, Darshan Auti, Sakshi Joshi, Atharva Vishwakarma Institute of Technology Computer Engineering - AI Pune India Vishwakarma Institute of Technology Computer Engineering Pune India Vishwakarma Institute of Technology Computer Science - AI/DS Pune India Vishwakarma Institute of Technology Computer Science Pune India Vishwakarma Institute of Technology Computer Engineering AIDS Pune India

ISBN: (纸本)9798331517953

Having recently tackled the COVID-19 pandemic, a new dimension to healthcare management has emerged. Irreparable damages were done due to the inadequacy of the existing systems and the lack of knowledge in most of the population in this field. Effective tracking and alerting of communicable diseases has never been of more significance. Our project harnesses advanced contact tracing technology to help users monitor and better manage their health and reduce the risk of exposure to communicable diseases. The app gives real time notifications to the user anytime they are near a potential disease carrier alerting them thereby avoiding potential infections. The innovated system has a questionnaire consisting of simple yes or no questions for symptom detection and provides precautionary measures based on the user's responses. The app also utilizes a radar giving the users better knowledge about their surroundings. Additionally, the app offers online consultations with doctors. Moreover, it provides emergency helpline access to the users. All these features help in more effective tracking of healthcare for not only emergency situations like pandemics, but further provides a complete solution for day-to-day management of healthcare also informing the users about safety measures so they can be well prepared if they ever develop an illness. © 2024 IEEE.

关键词： COVID-19

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Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

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Intelligent Automation & Soft Computing 2023年第3期35卷 3845-3860页

作者： P.N.Ramesh S.Kannimuthu Computer Science and Engineering Karpagam Institute of TechnologyCoimbatore641105India Information Technology Karpagam College of EngineeringCoimbatore641032India

The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn *** POJs have greater number of pro-gramming problems in their repository,learners experience information *** systems are a common solution to information *** recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difﬁculty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation *** system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and toﬁnd learners’learning ***ﬁltering(CF)and con-tent-basedﬁltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation *** sequence similarity measure is used toﬁnd the top k neighbors based on the sequence of problems solved by the *** experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.

关键词： Recommender systems skill level navigation pattern programming online judge collaborativeﬁltering content-basedﬁltering

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Magnifier: A Chiplet Feature-Aware Test Case Generation Method for Deep Learning Accelerators

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第7期44卷 2803-2816页

作者： Li, Boyu Zhu, Zongwei Liu, Weihong Cao, Qianyue Li, Changlong Ji, Cheng Li, Xi Zhou, Xuehai University of Science and Technology of China School of Computer Science and Technology Hefei230026 China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou215123 China University of Science and Technology of China School of Software Engineering Hefei230026 China University of Science and Technology of China China and Suzhou Institute for Advanced Research Suzhou215123 China East China Normal University School of Computer Science and Technology Shanghai200063 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

The development of deep learning has led to increasing demands for computation and memory, making multi-chiplet accelerators a powerful solution. Multi-chiplet accelerators require more precise consideration of hardware configurations and mapping schemes in terms of computation, memory, and communication patterns compared to monolithic designs, in order to avoid underutilization of performance. However, there is currently a lack of performance testing methods specifically tailored for multi-chiplet accelerators. Existing testing methods primarily focus on correctness testing and do not address potential performance issues from a hardware perspective. To address these issues, this paper proposes Magnifier: A test case generation method for performance testing of multi-chiplet accelerators. Firstly, we analyze typical multi-chiplet accelerator prototype from the perspectives of computation, memory, and communication patterns, and summarize a chiplet feature-Aware operator task set. Next, we define the test evaluation metric IPPstd and use a candidate operator set to construct a sampling space for model-level test cases. Finally, we build a GAN to learn the distribution of high-diversity test cases, enabling the rapid generation of high-quality test cases. We validate the proposed method on both simulated and real multi-chiplet accelerators. Experiments show that Magnifier can improve the metric of test cases by up to 3.42 times and significantly reduce generation time, providing valuable insights for optimizing the hardware and software of multi-chiplet accelerators. © 1982-2012 IEEE.

关键词： Acceleration

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