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Machine learning Models for Advancing Heart Disease Prediction and Diagnosis

Machine Learning Models for Advancing Heart Disease Predicti...

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Santosh Reddy Kruthi K Shetty Apeksha G S Department of Computer Science and Engineering B N M Institute of Technology Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Cardiovascular diseases are still among the leading causes of deaths worldwide, thus calling for early detection and precise risk assessment. Traditional methods of diagnosing cardiovascular disease often rely on standardized risk factors, which might not always catch the condition in its early stages. Machine learning and AI are revolutionizing CVD prediction by examining complex data in healthcare to find previously unknown patterns. The paper discusses advances in ML techniques such as supervised and unsupervised learning, deep learning, and ensemble methods. It emphasizes hyperparameter tuning, feature engineering, and model interpretability. AI models like Random Forest, SVM, and Neural Networks have shown better accuracy in predicting cardiovascular disease risk, enabling earlier and more personalized interventions. AI's potential reaches from wearable devices to predictive cardiac event models, promising huge advances in cardiovascular care. Still, there are data privacy issues, model interpretability, and regulatory problems that will continue to pose significant challenges. Future advancements in multimodal imaging, precision medicine, and collaboration between clinicians and data scientists will be pivotal to the realization of AI's full potential in transforming the prediction and prevention of CVD.

关键词： Support vector machines Adaptation models Medical services Predictive models Prediction algorithms Data models Real-time systems Cardiovascular diseases Wearable devices Random forests

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Vehicle Routing Problem Using Reinforcement learning: Recent Advancements 3rd

Vehicle Routing Problem Using Reinforcement Learning: Recent...

引用

3rd International conference on Machine Intelligence and Signal Processing, MISP 2021

作者： Raza, Syed Mohib Sajid, Mohammad Singh, Jagendra Department of Computer Science Aligarh Muslim University Aligarh India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9789811908392

In the realization of smart cities, the most important component is the smart logistics in which the vehicle routing problem (VRP) plays a significant role. The VRP has been proven to be NP-hard, and this combinatorial optimization problem requires efficiently serving the demands of geographically distributed customers using vehicles with limited capacities in order to optimize travel time or traveled distance. In general, VRP and its variants have been solved using OR-Tools, meta-heuristic as well as local search algorithms. However, these methods need high computational efforts and may offer poor-quality solutions in case of large problem sizes. The deep learning models can also be employed to solve the VRP. This paper explores the recent advancements in solving VRP using reinforcement learning (RL). The paper surveys the different RL approaches used to solve VRP and its variants. The paper also presents the issues and challenges that emerged with the use of RL to solve the VRP variants. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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An Efficient Application of Hybrid optimization with Deep learning Approach in the Prediction of Cardiovascular Disease

An Efficient Application of Hybrid Optimization with Deep Le...

引用

Pervasive Computing and Social Networking (ICPCSN), International conference on

作者： P. Saikrishna K. Abdul Basith Department of CSE Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad Department of CSE Marri Laxman Reddy Institute of Technology and Management Hyderabad

The incidence of arteries and cardiovascular diseases, which result in heart failure and early deaths in the form of myocardial infarction, stroke, and fainting, is currently one of the primary causes of mortality in every country in the globe. Deep learning (DL) has lately attracted research interest in a variety of fields, especially medical treatment, where it is being used to diagnose the cardiovascular disease early on via image processing. The methodology proposed in this work is revolutionary using a deep learning method for the premature heart disease identification and diagnosis. To reduce noise and improve image quality, the dataset must first be pre-processed using the Weiner-Frost filter. Then, Feature extraction is carried out using k-means clustering technique to retrieve the exact features for further process. The following phase involves selecting the most crucial features for predicting CVD using a hybrid lion-grey-wolf optimization algorithm, which incorporates the advantages of both techniques. A deep learning model which incorporates a convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) architecture completes the categorization of CVD. The suggested method is highly accurate at identifying CVD and is a dependable technique for identifying and diagnosing CVD early.

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Root Cause Analysis in Power Transformer Failure with Improved intelligent Methods 3

Root Cause Analysis in Power Transformer Failure with Improv...

引用

3rd IEEE International Power and Renewable Energy conference (IPRECON)

作者： Baiju, Sreelakshmi S. Adarsh, S. TKM Coll Engn Ctr Aritificial Intelligence Kollam Kerala India TKM Coll Engn Dept Civil Engn Kollam Kerala India

ISBN: (纸本)9781665491754

Root cause investigation, diagnosis and fault classification in power transformers are the essential features to look for when trying to ensure dependability and power quality with minimum interruptions. To achieve greater diagnostic precision, a novel method that combines Stacked Denoising Auto Encoder and Bidirectional Long - Short Term Memory (SDAE-BiLSTM) is indicated in this work. Dissolved gas analysis is the most effective method for determining the cause of electric power transformers(DGA) problems. All forms of faults can be differentiated by separating samples from power transformers. The SDAE-BiLSTM method has much research potential because it uses the dissolved gas in the transformers for analysis and fault diagnosis. A comparative study has been done using various machine learning models, such as Support Vector Machine, Random Forest and Convolutional Neural Network. Compared to the performance of these models, it is clear that the SDAE-BiLSTM model possesses superior accuracy because it has more parameters.

关键词： Fault diagnosis Root cause analysis Dissolved gas analysis

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Advance in 3D printing defect detection technology based on deep learning

Advance in 3D printing defect detection technology based on ...

引用

Power Electronics, Computer Applications (ICPECA), IEEE International conference on

作者： Yizhou Su Huaiguang Guan Xunwei Wang Guanghao Qi Baozhen Lei Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China CATARC (Tianjin) Automotive Engineering Research Institute Co. Ltd. Tianjin China Beijing Intelligent Machinery Innovation Design Service Engineering Technology Research Center Beijing Union University Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Intelligent Machinery Innovation Design Service Engineering Technology Research Center Beijing Union University Beijing China

With the development of manufacturing industry, defect detection is more and more widely used in industrial production, and there are more and more strict requirements for the accuracy and efficiency of detection. Compared with traditional manufacturing methods, 3D printing, with its unique manufacturing method, has unique advantages in small batch and multi batch manufacturing. This review aims to introduce some of the mainstream methods of industrial defect detection. First, it introduces the background and characteristics of industrial defect detection, introduces the industrial CT of classical nondestructive testing methods, and introduces the classical methods of defect detection and deep learning methods. The 3D reconstruction of defects is also introduced, and the methods of obtaining defect detection data and optimizing industrial parameters are introduced, and the commonly used defect detection data sets are summarized. Finally, the future development trend and potential research directions in this field are prospected.

关键词： Deep learning Manufacturing industries Electric potential Three-dimensional displays Nondestructive testing Production Three-dimensional printing

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Design of An intelligent Transfer Robot with Color Recognition 3

Design of An Intelligent Transfer Robot with Color Recogniti...

引用

3rd International conference on Applied Machine learning, ICAML 2021

作者： Tan, Hanhong Song, Jinye Guangdong University of Science and Technology Dongguan523000 China

ISBN: (纸本)9781665421256

With the STM32F103C8T6 chip as the core, an intelHgent transfer robot with color recognition function is designed. By using PWM to control the motor and the steering gear, the robot's crawler and manipulator can work;through the grayscale sensor, the robot's color recognition function is realized;through the TCRT5000 infrared reflection sensor, the robot's two-way tracking function is realized. The design is expected to exert its unique advantages in a variety of industries and provide a theoretical reference for the design of related robots. It has certain practical value and research significance. © 2021 IEEE

关键词： Machine design

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Design and optimization of Deep Immune Network Algorithm for Smart Grid Fault Data Analysis

Design and Optimization of Deep Immune Network Algorithm for...

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Power Electronics, Computer Applications (ICPECA), IEEE International conference on

作者： Wenzhe Zhou Tianjin University of Technology Tianjin China

In order to solve the problem that the effect of traditional neural network in fault data diagnosis of power system is not obvious, inspired by the application of deep learning image recognition, this paper combines immune algorithm and deep learning, and proposes a new smart grid fault data analysis method based on deep immune network. Deep immune network is an immune-improved deep learning network, which extracts features from data through the multi-layer structure of convolutional neural network, and inputs feature samples into the immune-optimized radial basis network to classify faults. By analogy with the immune process of biological immune system, a fault diagnosis immune model suitable for power grid fault data analysis is proposed and established. By establishing a one-to-one mapping relationship with biological immune system, the model structure was improved and optimized accordingly. Finally, the experiment proves that the fault diagnosis immune model has excellent learning ability and the fault recognition efficiency is improved compared with the traditional fault diagnosis method.

关键词： Fault diagnosis Deep learning Data analysis Biological system modeling Neural networks Feature extraction Power electronics

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WildGuard: Automated Barrier System for Animal Attack Prevention

WildGuard: Automated Barrier System for Animal Attack Preven...

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Automation, Computing and Renewable Systems (ICACRS), International conference on

作者： S Ariffa Begum N. Subbulakshmi A Balakrishna Reddy Battu Aravind Kumar B Sandeep Kumar Reddy B Manikanta Reddy Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnan Koil Taminadu India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

In areas that are close to forests, human-wildlife conflict poses serious threats to both human life and property. In order to detect and categorise animals in real-time CCTV camera video feeds, this study presents an intelligent system that makes use of deep learning techniques, particularly Convolutional Neural Networks (CNNs). Using Internet of Things components, the system classifies animals as either harmful or non-threatening and initiates automated reactions for the latter. A red warning sign is triggered in the village, a sound-based trap is set up to draw the animal within the camera’s field of view, and notifications are sent to the appropriate authorities when a dangerous animal is spotted. By improving safety by early detection and real-time response, this proactive system seeks to safeguard communities that are close to woods.

关键词： Deep learning Renewable energy sources Animals Prevention and mitigation Wildlife Streaming media Real-time systems Safety Internet of Things intelligent systems

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Detection of electricity theft based on ISSA-DELM

Detection of electricity theft based on ISSA-DELM

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Power Electronics, Computer Applications (ICPECA), IEEE International conference on

作者： Liangbin Dong Weixing Hu Shaobin Lin Zhong Zheng Qili Wu State Grid Fujian Markting Service Center Measuring Center and Integrated Capital Center Fuzhou China State Grid Fujian Yongding Distric Electric Power Supply Company Longyan China

In order to improve the identification accuracy of electricity theft by smart grid users, a deep extreme learning machine optimized by improved sparrow search algorithm (ISSA-DELM) is proposed. The population diversity and global optimization performance of the traditional sparrow search algorithm (SSA) are improved by modifying the location of the finder and the watchman. Due to the random initialization of weights and thresholds, the traditional deep extreme learning machines are prone to fall into local optima. The weights and thresholds of DELM are optimized by the improved Sparrow search algorithm to improve the convergence accuracy. Then, the ISSA-DELM is used to establish the identification model of electric theft. Simulation results show that the ROC and AUC of the proposed method are better than the traditional DELM and K-means, which verifies the effectiveness.

关键词： Extreme learning machines Simulation Sociology Power electronics Classification algorithms Behavioral sciences Smart grids

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Bias Correction in Urban Building Energy Modeling for Chicago Using Machine learning 3

Bias Correction in Urban Building Energy Modeling for Chicag...

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3rd International conference on intelligent Data Science Technologies and Applications, IDSTA 2023

作者： Chowdhury, Shovan Li, Fengqi Stubbings, Avery New, Joshua Garg, Ankur Correa, Santiago Bacabac, Kevin The University of Tennessee The Bredesen Center for Interdisciplinary Research KnoxvilleTN37996 United States Oak Ridge National Laboratory Electrification and Energy Infrastructures Division Oak RidgeTN37831 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616-3793 United States BlocPower BrooklynNY11210 United States

ISBN: (纸本)9798350339253

Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making. © 2023 IEEE.

关键词： Mapping

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