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Predicting Cardiovascular Disease in Patients with machine learning and Feature Engineering Techniques

Predicting Cardiovascular Disease in Patients with Machine L...

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international conference on signal processing and Information Security (ICSPIS)

作者： Sapna Tyagi Preeti Sirohi Piyush Maheshwari IMT Ghaziabad Ghaziabad India IMT CDL Ghaziabad India The British University in Dubai Dubai Academic City Dubai United Arab Emirates

ISBN: (纸本)9781665492669

Cardiac disease prediction and detection are among the most difficult and important jobs encountered by medical practitioners. Heart disease can be caused by a range of factors, including a sedentary lifestyle, stress, alcohol, cigarette intake, and so on. the current prediction algorithms focus on forecasting the illness label though the likelihood of getting the condition is still unknown. this study is conducted to forecast the heart disease progression well in advance so that essential action can be taken before the condition becomes severe. As a result, the research proposes a model for predicting the likelihood of heart disease incidence using logistic regression capabilities.

关键词： Heart signal processing algorithms Medical services machine learning signal processing Predictive models Prediction algorithms

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A Novel Recommendation System for Vaccines Using Hybrid machine learning Model 4th

A Novel Recommendation System for Vaccines Using Hybrid Mach...

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4th international conference on machine Intelligence and signal processing, MISP 2022

作者： Hada, Nishant Singh Maloth, Sreenu Jatoth, Chandrashekar Fiore, Ugo Sharma, Sangeeta Chatharasupalli, Subrahmanyam Buyya, Rajkumar National Institute of Technology Hamirpur HP Hamirpur177005 India National Institute of Technology Raipur CG Raipur492010 India Federico II University of Naples Naples Italy Union Public Service Commission New Delhi India The University of Melbourne ParkvilleVIC 3010 Australia

ISBN: (纸本)9789819900848

Advancements in the medical field have brought almost all diseases within the realm of vaccinology. Multiple manufacturers produce vaccines for various diseases. the efficacy rate of these vaccines varies based on different factors. Over the years researchers have developed various recommendation systems for drugs using different techniques with the common goal to help doctors in prescribing drugs after considering different factors. the recommendation system for vaccines is an unexplored area that requires extensive consideration of factors to recommend the vaccine that provides a high efficacy rate. In our paper, we propose a recommendation system for vaccines that considers host-based factors such as age, sex, medical history and also, vaccine-based factors such as post-vaccination recovery rate, death rate and symptoms faced by recipients. the algorithm is score-based where each vaccine is given a score and ranked accordingly for the patient. We created a hybrid machine learning model to extract useful information from the medical data to score the vaccines that are suitable for a particular recipient. the results produced by the system when run on simulated patient data show significant changes in recommended vaccines for the recipient based on different factors. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vaccines

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A Composite Image processing Technique to Enhance Segmentation of Ultrasound Images 5

A Composite Image Processing Technique to Enhance Segmentati...

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5th international conference on Digital Medicine and Image processing, DMIP 2022

作者： Alzubaidi, Mahmood Agus, Marco Al-thelaya, Khaled Makhlouf, Michel Alyafei, Khalid Househ, Mowafa Hamad Bin Khalifa University Qatar Foundation Doha Qatar Sidra Medicine Qatar Foundation Doha Qatar

ISBN: (纸本)9781450397643

In ultrasound images, the speckle noise phenomenon is a typical issue that limits the accuracy of the images obtained from any quantitative measurement. Noise elimination in ultrasound images in the pre-processing stage is an important step to make the image fit for the following steps involved in image segmentation. However, various noises produce a range of significant impacts on the quality of the image and thus tend to affect the interpretation of neural networks. In this study, 35 different image processing techniques, including denoising, feature descriptor, edge detection, miscellaneous, clustering, morphological, sharpening, and composite techniques were used to determine the most optimal ultrasound image in order to improve the segmentation of ultrasound images. the performance of image processing techniques was compared using a statistical parameter, peak signal-To-noise ratio (PSNR). All techniques were examined on deep learning segmentation approach. the segmentation performance was evaluated using mean intersection over union (mIoU) and mean pixel accuracy (mPA). the results showed that the composite image technique (FancyPCA + Bilateral + Gabor) slightly enhanced the segmentation performance, with the most ideal mIoU 0.96893%, mPA 0.97831%, and average PSNR 53.034 dB. © 2022 ACM.

关键词： Image segmentation

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A Fault Diagnosis Method for Power Transformer Using Canonical Variate Analysis and Support Vector machine 1

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11th international conference on Communications, signal processing, and Systems, CSPS 2022

作者： Luo, Long Li, Yan Shi, Yan Han, Ting Yang, Wencui Jin, Xiaojun Han, Di Extra High Voltage Company of State Grid Qinghai Electric Power Company Beijing China Advanced Smart Electric Information Technology Hangzhou310000 China

ISBN: (数字)9789819923625

ISBN: (纸本)9789819923618

the transformer is one of the most important units in the power grid. Due to the potential failures and costs of the power system, it is necessary to pay attention to the fault diagnosis of power transformers. this paper proposes a fault diagnosis method based on Canonical Variate Analysis and Support Vector machine (CVA-SVM). As a system identification method, CVA is widely used for fault detection because of its ability to identify multivariate state space models using experimental data. the support vector machine is a new machine learning method and is a powerful tool for solving problems with nonlinear and non-Gaussian distributed data. Dissolved gas analysis (DGA) has shown great potential for detecting faults in power transformers. For fault diagnosis based on DGA, a CVA model is first constructed for the process variables to generate a series of feature vectors, and then the fault types are classified using SVM. A real power transformer process is employed to verify the effectiveness of the proposed method. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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OptTTA: Learnable Test-Time Augmentation for Source-Free Medical Image Segmentation Under Domain Shift 5

OptTTA: Learnable Test-Time Augmentation for Source-Free Med...

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5th international conference on Medical Imaging with Deep learning (MIDL)

作者： Tomar, Devavrat Vray, Guillaume thiran, Jean-Philippe Bozorgtabar, Behzad Ecole Polytech Fed Lausanne Signal Proc Lab LTS5 5 Lausanne Switzerland Univ Lausanne UNIL Lausanne Switzerland Ctr Hosp Univ Vaudois CHUV Radiol Dept Lausanne Switzerland Ctr Biomed Imaging CIBM Lausanne Switzerland

As distribution shifts are inescapable in realistic clinical scenarios due to inconsistencies in imaging protocols, scanner vendors, and across different centers, well-trained deep models incur a domain generalization problem in unseen environments. Despite a myriad of model generalization techniques to circumvent this issue, their broad applicability is impeded as (i) source training data may not be accessible after deployment due to privacy regulations, (ii) the availability of adequate test domain samples is often impractical, and (iii) such model generalization methods are not well-calibrated, often making unreliable overconfident predictions. this paper proposes a novel learnable test-time augmentation, namely OptTTA, tailored specifically to alleviate large domain shifts for the source-free medical image segmentation task. OptTTA enables efficiently generating augmented views of test input, resembling the style of private source images and bridging a domain gap between training and test data. Our proposed method explores optimal learnable test-time augmentation sub-policies that provide lower predictive entropy and match the feature statistics stored in the BatchNorm layers of the pretrained source model without requiring access to training source samples. thorough evaluation and ablation studies on challenging multi-center and multi-vendor MRI datasets of three anatomies have demonstrated the performance superiority of OptTTA over prior-arts test-time augmentation and model adaptation methods. Additionally, the generalization capabilities and effectiveness of OptTTA are evaluated in terms of aleatoric uncertainty and model calibration analyses. Our PyTorch code implementation is publicly available at https://***/devavratTomar/OptTTA.

关键词： Learnable test-time augmentation domain shift medical image segmentation

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Performance Comparison of Credit Card Fraud Detection System using machine learning

Performance Comparison of Credit Card Fraud Detection System...

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international conference on signal processing and Information Security (ICSPIS)

作者： Alavikunhu Panthakkan Najiya Valappil Majida Appathil Seema Verma Wathiq Mansoor Hussain Al-Ahmad University of Dubai Dubai United Arab Emirates Banasthali Vidyapith Rajasthan India

ISBN: (纸本)9781665492669

Cyber security is becoming an integral part of modern life, tackling abnormal activities being the main challenge in the domain. To find and abort a fishy transaction is the procedure for maintain the security in credit cards. With the availability of past datasets, machine learning algorithms have made it possible to detect abnormal activities among the transactions. the aim of the paper is to create a balanced data from a pre-existing dataset and to apply six different machine learning algorithms like Decision tree, K-Nearest Neighbor, Logistic regression, SVM, Random Forest and XGBoost to detect fraud activities. Later, the results are compared and discussed on the basis of evaluation matrices which include precision, recall, f1-score and accuracy. All the algorithms are applied on three sets of data, where the ratio of fraud to valid transactions is varied. three cases are considered with ratios as 1:100, 1:10 and 1:1. A balanced dataset showed greater performance in the terms of all evaluation matrices.

关键词： Support vector machines machine learning algorithms signal processing algorithms Information security signal processing Credit cards Fraud

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Vehicle Localization with Surveillance Cameras 5

Vehicle Localization with Surveillance Cameras

引用

5th international conference on Vision, Image and signal processing (ICVISP)

作者： Kong, Qi Zhang, Liangliang Xu, Xin JD com Mountain View CA 94043 USA JD com Beijing Peoples R China

ISBN: (纸本)9781665407700

Vehicle localization is an important problem in autonomous driving research. Main stream methods use sensors on the vehicle with a SLAM algorithm, yet it has disadvantage on safety, cost and global intelligence comparing to sensing from infrastructure, especially surveillance cameras which are already pervasive in public urban area. To better address these challenges, this paper, for the first time, uses multiple surveillance cameras for vehicle localization. In this paper, a flexible two-stage framework for vehicle localization with surveillance cameras is introduced. Flexible deploying is its advantage. With first stage deploying on the camera local computing and second stage on the cloud, it can runs on limited bandwidth and computing conditions. Two potential directions of solutions under the framework are proposed. One is using instance mask as intermediate information, another is using key points of the vehicle.

关键词： Autonomous Driving Vehicle Localization Surveillance Cameras machine learning

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machine learning Based 5G RAN Slicing for Channel Evaluation in Mobile State 9th

Machine Learning Based 5G RAN Slicing for Channel Evaluation...

引用

9th international conference on signal and Information processing, Network and Computers, ICSINC 2021

作者： Zhang, Fangpei Jing, Xiaojun Mu, Junsheng Zhu, Jia Li, Bohan Information Science Academy of China Electronic Technology Group Corporation Beijing100086 China School of Information and Communication Engineering Beijing University of Post and Telecommunications Beijing100876 China University of Southampton Southampton United Kingdom

ISBN: (纸本)9789811947742

the number of mobile broadcast users has increased significantly because of evolved multi-media broadcast multicast services (eMBMS). According to the above reasons, machine learning technology is introduced into 5G RAN slices to forecast the state of communication channel in a mobile scene. We propose a new architecture not only including convolutional neural network (CNN), but also fusing long short-term memory network (LSTM) to implement channel estimation, simultaneously demonstrate the performance of our proposed scheme with simulation results. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Channel estimation

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A Review of Image Enhancement Methods Based on Stochastic Resonance of Neuronal Networks

A Review of Image Enhancement Methods Based on Stochastic Re...

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Pattern Recognition and machine learning (PRML), IEEE international conference on

作者： Ximin Sun Pingjuan Niu Yong Li Xiaoming Li Bingzhen Zhao Bin Zhang Dan Liu Bo Sun Tiangong University Laboratory of Metaverse in Electric Power Industry State Gride Ecommerce Technology CO.LTD Tian'jin China Laboratory of Metaverse in Electric Power Industry Tiangong University Tian'jin China Laboratory of Metaverse in Electric Power Industry State Gride Ecommerce Technology CO.LTD Tian'jin China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Stochastic resonance has always been a research hotspot in the field of image processing. the ability of the nervous system to detect signals is closely related to nonlinear and collective behavior in neural mediators. Neuronal noise is the most significant factor influencing this ability, and it is a natural endogenous fluctuation in brain activity. Experimental and theoretical studies have shown that noise is beneficial, not harmful, for the normal functioning of the nervous system. In this regard, it is generally agreed that noise of appropriate intensity can participate in the rhythmic activity of the brain, and that noise-induced oscillations enhance the performance of weak signal processing, especially when the frequency of the signal is approximately noise-induced rhythmic oscillations. this behavior in the biological nervous system can be explained by the concept of “stochastic resonance”. Correspondingly, the stochastic resonance mechanism of neurons is used to denoise the image, which has a good effect. In this paper, we first introduce an image processing method using random resonance of neurons. then, the commonly used evaluation indicators in the field of image processing are sorted out. Finally, the development trend of image processing using random resonance of neurons is prospected.

关键词： Reviews Stochastic resonance Noise Neurons Resonant frequency machine learning Market research Pattern recognition Oscillators Image enhancement

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Fast Node Selection of Networked Radar Based on Transfer Reinforcement learning 5th

Fast Node Selection of Networked Radar Based on Transfer R...

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5th IFIP TC 12 international conference on Intelligence Science, ICIS 2022

作者： Cao, Yanjun Wang, Yuan Guo, Jingjing Han, Li Zhang, Chao Zhu, Jin Zhang, Tianyang Zhang, Xiangrong The 54th Research Institute of China Electronics Technology Group Corporation Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Shaanxi Xian710071 China National Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9783031149023

the networked radar system can synthesize different echo signals received by various radars and realize the cooperative detection of multiple radars, becoming more and more critical for data fusion sharing and network collaboration. However, due to the large number and wide range of nodes in the networked radar system, there exists a redundancy problem in radar node assignment, which causes additional resource consumption and slows down the task execution speed of radar node selection. To solve the above problem, this paper proposes a fast radar node selection method based on transfer reinforcement learning to quickly select the optimal and minimum node resources. the proposed method devises a novel reward function for the Monte Carlo Tree and a different termination state of iteration to select the minimize the number of radar nodes. In order to further accelerate the selection of radar nodes, transfer reinforcement learning is presented to fully leverage the previous knowledge. Experimental results show that our proposed method can quickly select the optimal and minimum radar nodes in a brief period, significantly improving the speed of radar node selection in the networked radar. © 2022, IFIP international Federation for Information processing.

关键词： Reinforcement learning

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