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Machine Learning in Cardiovascular Disease: Clinical Applications and Relevance to Cardiac Imaging

Machine Learning in Cardiovascular Disease: Clinical Applica...

引用

Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： Tanishq Soni Deepali Gupta Mudita Uppal Ankita Kumari Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Cardiovascular diseases (CVDs) persist as significant contributors to global morbidity and death rates. Innovative methods are required in order to improve early identification, risk assessment, and individualized treatment. The use of machine learning, artificial intelligence, and others has become a significant advancement in the domain of medicine in recent times. These technological advancements have the potential to bring about a dramatic shift in the way cardiovascular diseases are treated. This in-depth research study investigates how Artificial Intelligence (AI) and Machine Learning (ML) can have an effect on various areas of cardiovascular illness, including risk prediction, early identification, therapy optimization, and prognosis evaluation.

关键词： Deep learning Precision medicine Medical specialties Real-time systems pattern recognition Cardiovascular diseases Risk management

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Recognizing 3D Orientation of a Two-RFID-Tag Labeled Object in Multipath Environments Using Deep Transfer Learning 41

Recognizing 3D Orientation of a Two-RFID-Tag Labeled Object ...

引用

41st IEEE international conference on Distributed computing Systems (ICDCS)

作者： Wang, Zhongqin Xu, Min Xiao, Fu Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia Nanjing Univ Posts & Telecommun Coll Comp Nanjing Peoples R China

ISBN: (纸本)9781665445139

State-of-the-art battery-free RFID systems attach multiple RFID tags to an object and exploit their RF phase to estimate its three-dimensional (3D) orientation. However, the measured RF phase may be inaccurate because each tag's signal fingerprint (i.e., RSSI and RF Phase) is distorted by multipath interference and electromagnetic interaction between neighboring tags. In this paper, we propose RF-Orien3D that minimizes these interferences for accurate 3D orientation recognition only using two RFID tags. The electromagnet interference modifies the radiation pattern and modulation factor of each tag in the two-element tag array, which can be estimated to compensate for the distortion in RFID fingerprints. To deal with the multipath impact, we simulate multipath noise to generate huge amounts of RFID fingerprints and use them to pre-train a convolutional neural network (CNN). Then we only collect dozens of actual samples to fine-tune the CNN for multipath-tolerant orientation recognition. The experiments show RF-Orien3D recognizes a two-tag labeled object's 2D orientation with the angular error of about 16 degrees and its 3D orientation (azimuth and elevation) with the errors of about 29 degrees and 11 degrees in low/rich multipath scenarios.

关键词： RFID Mutual Coupling Radiation pattern Multipath Interference CNN Transfer Leaning

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Deep Learning Approach for Karnataka Snacks recognition

Deep Learning Approach for Karnataka Snacks Recognition

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IEEE international conference on Cloud computing in Emerging Markets (CCEM)

作者： H. S. Gowri Yaamini Swathi K. J Sindhu H. R Vidya K Manohar N Tian Jipeng Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Computer Science College Zhongyuan University of Technology Zhengzhou Henan Province china

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360066

Food being an inseparable part of human's life, it is difficult for people to recognize and to find the recipe of traditional snacks descended by their ancestors. In this paper, a food recognition system is proposed where it recognizes the specific Karnataka snack inclusive of challenges like inter-class similarities, intra-class dissimilarities, diverse background, noise, and etc. using ResNet50 transfer learning model which has achieved a training accuracy of 99.8% and testing accuracy of 94.5%. Karnataka snacks are prepared through similar techniques which makes identification based on texture, color and pattern more challenging as well as the use of artificial intelligence techniques in recognizing snacks originated in Karnataka region needs exploration. The dataset consists of twelve classes with 150 images in each has been collected manually from various platforms.

关键词： Deep learning Training Accuracy Image color analysis Shape Transfer learning Feature extraction pattern recognition Residual neural networks Testing

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Application of Wave Cluster Algorithm Based on Kernel Optimization in pattern recognition of High-Frequency Financial Data

Application of Wave Cluster Algorithm Based on Kernel Optimi...

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Cloud computing, Big Data Applications and software Engineering (CBASE), international conference on

作者： Dazhi Yue Business School Xiamen Institute of Technology Xiamen China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

High-frequency financial data has the characteristics of high nonlinearity, non-stationarity and noise, which brings great challenges to traditional data mining algorithms. In this study, we propose a Wave Cluster algorithm based on kernel function optimization, which aims to improve its performance in pattern recognition of high-frequency financial data. The Wave Cluster algorithm has attracted much attention for its robustness to noise and multiresolution analysis capability, but it still has limitations when dealing with complex nonlinear patterns. In this paper, kernel-based functions are introduced to map the original high-dimensional financial data to a higher-dimensional feature space. This nonlinear transformation can effectively capture the potentially complex relationships in the data. Subsequently, the modified Wave Cluster algorithm is applied in the transformed feature space, and high-frequency trading data from the China stock market, including price, trading volume and order book information, are used in the empirical study. Experimental results show that compared with traditional methods, the proposed algorithm achieves significant improvement in the tasks of identifying market microstructure, detecting abnormal trading patterns and predicting short-term price movements. Especially during periods of high volatility, the algorithm shows greater stability and accuracy.

关键词： Accuracy software algorithms Noise Time series analysis Clustering algorithms Prediction algorithms Stability analysis Kernel Stock markets Optimization

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4th international Symposium on Intelligent computing Systems, ISICS 2022

4th International Symposium on Intelligent Computing Systems...

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4th international Symposium on Intelligent computing Systems, ISICS 2022

ISBN: (纸本)9783030984564

The proceedings contain 11 papers. The special focus in this conference is on Intelligent computing Systems. The topics include: On the Performance of Preconditioned Methods to Solve Lp -Norm Phase Unwrapping;Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption;DDoS Attack Preventing and Detection with the Artificial Intelligence Approach;iris recognition Using Supervised Learning Based on Matching Features;convolutional Neural Network for Segmentation of Single Cell Gel Electrophoresis Assay;Evaluation of Human SCD Test by Digital Image Analysis;machine Vision-Based Expert System for Automated Skin Cancer Detection;AI Used to Identify Car Accident Injury and Mortality Risk Factors in Dubai UAE;toward Automatic Water Pollution Analysis: A Machine Learning Approach for Water-Quality Monitoring Through pattern Classification of Water Crystallization.

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A CNN Based Air-Writing recognition Framework for Linguistic Characters 3rd

A CNN Based Air-Writing Recognition Framework for Linguistic...

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3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

作者： Kumar, Prabhat Chaudhary, Abhishek Sharma, Abhishek DSPM International Institute of Information Technology Naya Raipur Naya Raipur India

ISBN: (纸本)9783031057663

Air writing is a practice of writing the linguistic characters in free space utilizing the six degrees of freedom of hand motion. We propose a system that uses a generic webcam to detect and recognize the virtually written characters by a user as per their will. This system performs detection using morphological operations for masking the tracker or writing object. This system gives the user the freedom to select a writing object of any color, shape, or material for tracking purposes. The trajectory of the object’s mask is tracked and rendered on a virtual window. The air-written character is recognized using the Convolutional Neural Network (CNN). The CNN is trained on a dataset of English handwritten characters of 26 different classes (A-Z). The accuracy achieved by the proposed system for an isolated character is 99.75%. © 2022, Springer Nature Switzerland AG.

关键词： Computer vision

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Development of Gait Classification System Using Six-Axis Inertial Sensors by CNN-LSTM

Development of Gait Classification System Using Six-Axis Ine...

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international Symposium on soft computing and Intelligent Systems (SCIS)

作者： Shotaro Ohno Kouki Nagamune Graduated School of Engineering University of Fukui Fukui Japan Graduated School of Engineering University of Hyogo Hyogo Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Restoration of walking function is the most important goal of rehabilitation. However, the evaluation of post-stroke rehabilitation lacks quantitative elements. In this study, we used an IMU to acquire the angles of three axes (rotation of x-axis, rotation of y-axis, and rotation of z-axis) of the knee during walking motion as gait parameters. Using this data, machine learning analysis is performed. In this article, we discuss the gait parameters, the angular data of the three axes of the knee, and examine which of the three angular parameters increases or de-creases the generalization ability of the model. As a result, we identify the gait parameters to be used as a dataset. Time series data of the knee angle of one step in the gait measurement was used as the dataset. CNN - LSTM algorithm was used to construct the model. Five subjects were measured with the knee joint motion measurement device attached to both knees. A total of four types of gait movements, including normal gait and three types of gait movements that reproduce hemiplegia, were meas-ured for analysis. Out of one hundred thirty-six steps of data from five subjects, four subjects were used as training data and one unknown subject's data, which was not used during training, was applied to the test data. The results of the prediction of the estimated data show that rotation of z-axis was a parameter that reduced the generalization ability of the model, while rotation of y-axis was a parameter that increased the generalization ability of the model. Prediction results for the rotation of x-axis data alone showed poor accuracy, but when combined with the rotation of y-axis data, the generalization ability was improved.

关键词： Knee Legged locomotion Training Inertial sensors Time series analysis Training data Predictive models Data models pattern recognition Motion measurement

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pattern recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features

Pattern Recognition in Lung Disease Classification: Leveragi...

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international conference on computing and Networking Technology (ICCNT)

作者： M Ramya Sree Mettu Siddhartha Poli Vamsi Vardhan Reddy Tripty Singh Rekha R Nair Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and Local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction.

关键词： Visualization Accuracy Quantization (signal) Pneumonia Lungs Computational modeling Feature extraction Convolutional neural networks X-ray imaging Medical diagnostic imaging

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Compiler Design for recognizing different Programming Languages

Compiler Design for recognizing different Programming Langua...

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international conference on computing and Networking Technology (ICCNT)

作者： Ch Rahul A N Sharma Bedadala Mohaneeswar Reddy Dutta Swetchana Niharika Panda Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Compiler design for programming language recognition is a tedious process with crucial phases. These phases include lexical analysis, syntax parsing, semantic validation, intermediate code generation, optimization, and final code generation. The initial step involves linguistic analysis, breaking source code into elemental components like keywords, identifiers, literals, operators, etc. This step is tailored to each programming language, considering the specific constructs and syntax rules of that particular language. Leveraging the Python-Lex-Yacc library, this research seeks to design a compiler to identify multiple programming languages, primarily focusing on the abovementioned lexical analysis phase. The proposed compiler is engineered to recognize various programming languages, including C++, Java, and Python. The need for a compiler to recognize multiple programming languages concurrently arises from the evolving land of software development. With modern applications often written in various languages, a unified compiler capable of recognizing multiple languages is essential. This research aims to tackle this challenge by proposing a compiler that identifies multiple programming languages concurrently.

关键词： Java Program processors Codes Source coding Semantics Syntactics Tokenization pattern recognition Python software development management

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Estimating Human Strategies by Collision Avoidance Experiments with Meta-strategy Agents

Estimating Human Strategies by Collision Avoidance Experimen...

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international Symposium on soft computing and Intelligent Systems (SCIS)

作者： Norifumi Watanabe Kensuke Miyamoto Graduate School of Data Science Musashino University Tokyo JAPAN Graduate School of Media and Governance Keio University Kanagawa JAPAN

In human cooperative behavior, there are some strategies: a passive behavioral strategy based on others’ behaviors and an active behavioral strategy based on the objective-first. However, it is unclear how to acquire a meta-strategy to switch those strategies. In this study, we conduct a collision-avoidance experiment with agents taking multiple strategies in a grid-like corridor to see whether the subject’s behavior changes when the agent’s strategy changes. Furthermore, we compare the behavior selected by the subjects with the behavior of the agents acquired by reinforcement learning. The experimental results show that subjects can read the change in strategy from the behavior of the opposing agent.

关键词： Switches Reinforcement learning Behavioral sciences pattern recognition Collision avoidance Robots

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