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Coronary Artery Disease Diagnosis Using Feature Selection Based Hybrid Extreme learning machine

Coronary Artery Disease Diagnosis Using Feature Selection Ba...

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international conference on Information and Computer Technologies (ICICT)

作者： Afzal Hussain Shahid Maheshwari Prasad Singh Bishwajit Roy Aashish Aadarsh Computer Science and Engineering National Institute of Technology Patna India Arcade Business College Patna India

ISBN: (数字)9781728172835

ISBN: (纸本)9781728172842

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) that cause millions of deaths worldwide due to heart failure, heart attack, and angina. The symptoms of the CAD do not appear in the early stage of the disease and it causes deadly conditions; therefore, accurate and early diagnosis of CAD is necessary to take appropriate and timely action for preventing or minimizing such conditions. Angiography, being the most accurate method for diagnosis of CAD, is often used by the clinicians to diagnose the CAD but this is an invasive procedure, costly, and may cause side effects. Therefore, researchers are trying to develop alternative diagnostic modalities for the efficient diagnosis of CAD. To that end, machine learning and data mining techniques have been widely employed. This paper proposes and develops hybrid Particle swarm optimization based Extreme learning machine (PSO-ELM) for diagnosis of CAD using the publicly available Z-Alizadeh sani dataset. To enhance the performance of the proposed model, a feature selection algorithm, namely Fisher, is used to find more discriminative feature subset. In the training period, the PSO algorithm is used to calibrate the ELM input weights and hidden biases. Further, the performance of the proposed model is compared with the basic ELM in terms of accuracy, Pearson correlation coefficient (R2) and Root mean square error (RMSE) goodness-of-fit functions. The results show that the performance of the proposed model is better than the basic ELM. The obtained CAD classification performance in terms of sensitivity, accuracy, specificity, and F1-measure is competitive to the known approaches in the literature.

关键词： Solid modeling Arteries Feature extraction Classification algorithms Prediction algorithms Neurons Mathematical model

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Heart Disease Prediction Using machine learning Algorithms: Performance Analysis

Heart Disease Prediction Using Machine Learning Algorithms: ...

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international conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

作者： Subrata Saha Md. Motinur Rahman Tahmid Tamrin Suki Md. Mahbub Alam Md. Shah Alam Mohammod Abu Sayid Haque Institute of Electronics Bangladesh Atomic Energy Commission Dhaka Department of Computer Science and Engineering Bangladesh University of Professionals Dhaka

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Cardiovascular disease (CVD) is one of the major causes of death worldwide. Early detection of this disease can decrease the mortality rate significantly and it can also reduce the costs of medical treatment. Researchers are trying to develop automated systems using data mining techniques that can help in this regard. Although they would be extremely difficult to build, machine learning (ML) based methods for heart disease prediction might be very useful in clinical settings. Moreover, the majority of nations lack doctors specialized in cardiovascular treatments, and a high percentage of cases are misdiagnosed. These issues could be resolved by creating an accurate and effective early-stage heart disease prediction system using ML. In this paper, we propose an ML technique to identify cvd patients depending on different features. We collected a heart disease dataset from the Center for Disease Control and Prevention (CDC) then applied different pre-processing steps, and transformed several attributes to make them readable for the ML algorithms. 5 supervised ML algorisms were implemented and the Random Forest algorithm provided the best outcomes in all the performance metrics that we studied, with maximum accuracy of 91% and, ROC-AUC (Area under the ROC Curve) of 0.91. It also surpasses the performance of other algorithms with a maximum True Positive Rate (TPR) of 0.922 and a minimum False Negative Rate (FNR) of.078.

关键词： Heart Measurement machine learning algorithms Accuracy Medical treatment Forestry Prediction algorithms

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Evaluation Model and Algorithm Based on Financial data 2021

Evaluation Model and Algorithm Based on Financial Data

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Shuyu Hu Ming Huang Hunan Open University China

ISBN: (纸本)9781450385046

The rapid development of the domestic financial market has increased the enthusiasm of various investors to participate, the amount of data and the frequency of transactions have increased sharply, the demand for transaction speed has increased, the correlation of data between markets has become more complex, and the proportion of quantitative exchanges has increased. Correspondingly, the proportion of quantitative transactions that use model algorithms in finance is increasing, making it possible to use pattern recognition to analyze financial data to guide quantitative transactions. The purpose of this article is to study the evaluation model and its algorithm based on financial data. Aiming at the shortcomings of the algorithm in the future financial market and the specific environment of the domestic financial market, this paper proposes to use the continuous hidden Markov model to analyze and predict financial data. This paper conducts a comprehensive research on the development and algorithm theory of the entire continuous hidden Markov model system, decomposes the model's identification, decoding, and learning processes, and conducts research on the application of the three types of processes in finance. At the same time, according to the specific financial data and the algorithmic process of the model, the stock and futures data are extracted and processed, and the model system sample data and various parameters are set using classification selection, wavelet denoising and stable index construction to improve the model under financial data. This paper uses actual transaction data to verify the entire model system, and then uses the recognition and decoding process to study the adaptability of the model, and concludes that the Hidden Markov Model can well identify the pattern features in financial data. Experimental research shows that the result of the hidden state is close to 80% similar to the original state. The continuous hidden Markov model can restore

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A Sub-Linear Scalable MapReduce-based Apriori Algorithm 19

A Sub-Linear Scalable MapReduce-based Apriori Algorithm

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Proceedings of the 3rd international conference on Big data Research

作者： Gantaphon Chalumporn Phongphun Kijsanayothin Rattikorn Hewett Department of Computer Science Texas Tech University TTU Lubbock USA Department of Electrical and Computer Engineering Naresuan University NU Phitsanulok Thailand

ISBN: (纸本)9781450372015

Association Rule mining is one of the most popular data analytic algorithms where the well-known Apriori algorithm is its core. Like most machine learning and data mining algorithms, Apriori algorithm is designed for in-memory data. One natural way to cope with this limitation and emerging Bigdata challenges is to adapt the algorithm to parallel and distributed computing infrastructures, particularly a widely used Map-Reduce model. Much research has developed a variety of MapReduce-based Apriori algorithms. However, most have focused on either improving performance over that of the original Apriori algorithm or mechanisms MapReduce infrastructure. This paper presents yet another MapReduce-based Apriori algorithm. Unlike most traditional MapReduce-based Apriori that mimics incremental level-wise computation of the original Apriori, our MapReduce-based algorithm opportunistically exploits the map's keys for non-level-wise parallelism to fully benefit of parallel processing to gain efficiency. The paper describes our proposed approach and shows an empirical evaluation of its performance compared with that of the representative traditional MapReduce-based algorithm. The results show significant improvement with an average reduction of the execution time of about 70%, over 50,000-200,000 transactions on one to three machines, with 10% of support threshold. In fact, the execution time of the proposed MapReduce-based Apriori algorithm is empirically shown to scale sub-linearly (better than linear) in the number of transactions.

关键词： MapReduce-based Apriori Parallel computing Association rules mining Big data Analytics Algorithms

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A Novel Fishing Finder Technique Based on VMS data in Indonesia 19

A Novel Fishing Finder Technique Based on VMS Data in Indone...

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Proceedings of the 3rd international conference on Advances in Artificial Intelligence

作者： Pujianto Yugopuspito Samuel Lukas I. Made Murwantara Steven Albert Hery Informatics Department Universitas Pelita Harapan Information System Department Universitas Pelita Harapan UPH Tower Lippo Karawaci Tangerang Indonesia

ISBN: (纸本)9781450372534

Indonesian waters are very wide and have very high fisheries potential. This clearly benefits the Indonesian fishing industry. Tuna is a primary trading commodity in this industry, but catching this fish is not easy. In addition, the movement of tuna follows the pattern of changes in Earth's temperature, so determining its position clearly requires adequate equipment. The original purpose of Vessel Monitoring System (VMS) is for enforcement and control of vessel sailing. In this paper, we use VMS to find the position of tuna to reduce fishing costs for Indonesia tuna fishermen such as fuel and labor costs. This study does three things. Firstly, conduct tracking modeling of the movements of each type of fishing vessel in Indonesian waters, and use cross-reference with weather data and earth conditions, then filtering the data for fishing tuna vessel only. Secondly, develop visualization techniques resulting from algorithms by programming Python Dash. Finally, develop fish position prediction techniques using the machine learning ensemble by Support Vector machine technology. It concludes that system can predict the location of tuna correctly 97.6%.

关键词： Tuna Support Vector machine Vessel Monitoring System

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Sign Language recognition Using Hand Gestures

Sign Language Recognition Using Hand Gestures

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Advancement in Technology (ICONAT), international conference for

作者： Pravin Baban More Amolkumar N. Jadhav Irfan Khatik Sonam Singh Vishal Kisan Borate Yogesh Kisan Mali Department of CSE Annasaheb Dange Colleg of Engineering & Technology Ashta India Fergusson College Pune India Department of AIML Dr D Y Patil Institute of Technology Pune India Department of Computer Engineering Dr D Y Patil College of Engineering & Innovation Talegaon Pune India Department of AIML G.H Raisoni College of Engineering and Management Wagholi Pune India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Communication is an essential part of life, but it can be a significant challenge for those who cannot speak. That’s why we are working on a research project to develop a real-time American Sign Language (ASL) detector using computer vision and machine learning. Our goal is to create a solution that has the potential to transform the lives of people who rely on sign language. Imagine the frustration of wanting to express yourself or use voice-activated technology without the ability to speak. While sign language interpreters are a valuable resource, their cost can be a significant barrier to consistent access.

关键词： Training Sign language Technological innovation Computer architecture Transforms Feature extraction data models Real-time systems Communications technology Convolutional neural networks

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A Study on the Characteristics of College Students' Negative Emotion and the Strategy of Dredging from the Perspective of Large data

A Study on the Characteristics of College Students' Negative...

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2017 3rd international conference on Management Science and Innovative Education(MSIE 2017)

作者： YONG LI YUHUA HE YU ZHENG MUNI ZHUANG School of economics and management Changsha University School of Public Administration Xiangtan University

The extreme social events caused by the negative emotions of college students have been widely concerned by the community and have become an important topic of concern to researchers. This article based on the "Baidu public opinion", "Ali public event analysis", "Sina public opinion" and other commercial large data platform, and then the basic characteristics of the audience and college students negative emotional timing changes were analyzed by using the machine learning and data mining tools,the reasons for the negative emotions of college students were analyzed by using the questionnaire. The research found that the negative emotions of college students are influenced by social public opinion, study pressure and employment pressure. On these grounds, we put forward the dredge strategy, what is used by "opinion leaders" to guide public opinion, improve the psychological ability and improve the physical and mental health of college students.

关键词： Large data Negative emotions of college students Feature perception Dredge strategy

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Precision Countermeasures for GPU Side Channels Using Shader Execution pattern Analysis

Precision Countermeasures for GPU Side Channels Using Shader...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Nelson Lungu Simon Tembo Gopal Charan Sasmal Suchismita Rout Mahendra Kumar Gourisaria Sudhansu Shekhar Patra Electrical and Electronical Engineering University of Zambia Lusaka Zambia MCA Department Trident Academy of Creative Technology Bhubaneswar India School of Computer Engineering KIIT Deemed To be University Bhubaneswar India School of Computer Applications KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Graphics Processing Units (GPUs) have become increasingly susceptible to side-channel attacks, compromising the security of sensitive information processed on these devices. This paper presents a novel approach to mitigating GPU side-channel vulnerabilities using shader execution pattern analysis. The proposed technique enables the development of precision countermeasures that effectively obscure the timing and power consumption patterns exploited by adversaries. By leveraging insights gained from analysing shader execution patterns, we develop targeted countermeasures that combine data-dependent execution balancing, power consumption smoothing, and dynamic noise injection to provide comprehensive protection against a wide range of side-channel attacks. Experimental results demonstrate the efficacy of the proposed method in significantly reducing the leakage of sensitive information through GPU side channels while incurring minimal performance overhead. The findings of this research contribute to the advancement of GPU security and the protection of sensitive data in graphics-intensive applications, paving the way for more secure and trustworthy computing environments.

关键词： Power demand Noise Graphics processing units Side-channel attacks Power smoothing machine learning Timing

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A Novel Robustness Image Watermarking Scheme Based on Fuzzy Support Vector machine

A Novel Robustness Image Watermarking Scheme Based on Fuzzy ...

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2010 3rd IEEE international conference on Computer Science and Information Technology—ICCSIT 2010

作者： Lei Li 1,Wen-Yan Ding 2,Jin-Yan Li 3 1,2,3 pattern recognition and Intelligence System,College of Automation,Nanjing University of Post and Telecommunications,Nanjing 210003,China

In this paper,a novel fuzzy support vector machine based image watermarking scheme is *** the application of support vector machine in the process of watermarking technology is only a simple classification of the ***,the fuzzy support vector machines by selecting the appropriate degree of membership to reflect the different importance of the different sample *** this article,Firstly,we split the given image into 8 * 8 block,then calculated for each sub-block of the texture features as input vectors to train the support vector *** image sub-block is divided into two categories( one category is "-1" represents a weak texture,the other is "+1" represents a strong texture),and we make the strong texture as more important category in this ***,given its larger fuzzy membership than the weak texture'*** the relevant theory of watermarking technology we know that a strong local image texture can tolerate more of the watermark *** order to enhance watermark robustness and embedding more watermark information in the host image,we improve the accuracy of classifying one class(a strong texture).If the data points belonging to the class of strong texture are incorrectly categorized into the weak texture class,the amount of embedded watermarks are reduced and robustness(the ability of resisting attacks) of the image is *** show that this algorithm has good robustness against common image attacks.

关键词： digital watermarking fuzzy support vector machine robustness

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Fraud Detection Using machine learning: An Evaluation of Logistic Regression and Random Forest

Fraud Detection Using Machine Learning: An Evaluation of Log...

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Smart City Challenges & Outcomes for Urban Transformation (SCOUT)

作者： Xijie Zhang Computing Software & Technologies Hong Kong Baptist University Hong Kong China

ISBN: (数字)9798331528638

ISBN: (纸本)9798331528645

With the increasing importance of digital security in the current world of finance, it is a must to find ways to implement artificial intelligence techniques to detect financial fraud behaviours. This paper explores the possibility of implementing machine learning algorithms to detect credit card fraud among bank transactions. Whether algorithms could correctly classify transactions into fraud and non-fraud categories was experimented. dataset containing the results of Principle Component Analysis (PCA)-preprocessed real-world transaction information was extracted from Kaggle. Then, the dataset is separated into a training set and test set. As fraud transactions only account for less than 1/500 of the whole dataset, Synthetic Minority Oversampling Technique was implemented to the training set to mitigate the imbalance generated by the huge difference in quantity. However, it was not applied to the test set, so that a reality situation could be simulated. After that, Logistic Regression and Random Forest models were used for model training. Finally, trained models were applied to the test set for model testing. Their performance was evaluated by Confusion Matrix, Area under Precision-Recall Curve and the classification report. Outcomes showed that Random Forest model surpassed Logistic Regression in terms of minimizing fraud cases that were incorrectly identified as but performed slightly worse in detecting true fraud cases. Nevertheless, both algorithms showed satisfactory results in classifying fraud among all transactions. Thus, it is proved that machine learning techniques can be implemented in detecting credit card fraud.

关键词： Training Logistic regression machine learning algorithms Finance Credit cards Fraud Classification algorithms data mining Random forests Testing

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