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A Comparative of Classification Models for Predicting of Heart Diseases

A Comparative of Classification Models for Predicting of Hea...

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2023 international conference on science, Engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Arowolo, Micheal Olaolu Adesina, Ruth Omorinsola Adebiyi, Marion Olubunmi Adebiyi, Ayodele Ariyo University of Missouri Department of Electrical Engineering and Computer Science Columbia United States Landmark University Department of Computer Science Omu-Aran Nigeria

ISBN: (纸本)9798350324785

To slow the rising mortality toll, early detection of heart illness is essential, as it is one crucial organ in human;if it stops beating, life ends. To put it simply, cardiovascular diseases (CVDs) are the important global killer. In 2019, CVDs were responsible for the deaths of 17.9 million individuals worldwide. The medical field has benefited greatly from technological advancements, but there are still some challenges associated with machine learning, such as data that is noisy, error-prone, or contains outliers. Due to their often-late diagnosis, these diseases are principal reason of bereavement in heart patients, making it imperative to learn more about effective algorithms for disease prognosis. Accurate predictions of such results with machine learning algorithms can support in the initial diagnosis of diseases, mitigation of their effects, and even prevention of their occurrence by providing crucial information to both medical professionals and patients. This study proposes a model that can rapidly and cheaply diagnose cardiac problems. Therefore, this research employs SVM, Naive Bayes, and Random Forest to detect cardiac illnesses using a data set from Kaggle, with 93%, 85%, and 95% accuracy, respectively;however, the Random Forest attained the best accuracy of 96.9%. © 2023 IEEE.

关键词： Forecasting

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Quantifying Inter-Well Connectivity and Sweet-Spot Identification through Wavelet Analysis and machine learning Techniques

Quantifying Inter-Well Connectivity and Sweet-Spot Identific...

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2024 Abu Dhabi international Petroleum Exhibition and conference, ADIPEC 2024

作者： Kalule, Ramanzani Iskandarov, Javad Al-Shalabi, Emad Walid Abderrahmane, Hamid Ait Markovic, Strahinja Farmanov, Ravan Al-Farisi, Omar Gibrata, Muhammad A. Eldali, Magdi Lozano, Jose Huang, QingFeng Rouis, Lamia Ameish, Giamal Rondon, Aldrin Chemical and Petroleum Engineering Department Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Research and Innovation Center on CO2 and Hydrogen Chemical and Petroleum Engineering Department Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Mechanical and Nuclear Engineering Department Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Dragon Oil Dubai United Arab Emirates

ISBN: (纸本)9781959025498

This study leverages wavelet analysis and machine learning (ML) techniques, including a 1D Convolutional Neural Network (1D CNN), to analyze inter-well connectivity and pinpoint an optimal new drilling location (sweet spot) based on datasets from five wells. The dataset utilized in this work includes well logging data of porosity, permeability, and water saturation at different depths of the wells. A 1D CNN was used to extract important features from the dataset. Wavelet analysis and correlation techniques were applied to the feature space extracted by the 1D CNN, revealing inter-well connectivity. Well-pairs with the highest correlation scores indicated enhanced inter-well communication. For identifying a sweet spot, machine learning regression models, including Gaussian Process (GPR), K-Nearest Neighbours (KNN), Gradient Boosting (GB), and Extreme Randomized Trees (ERT), were trained and tested to predict properties across the field. Locations with high porosity, high permeability, and low water saturation were assessed to identify sweet spots. The Wavelet analysis was then used to detect and analyze inter-well communication between identified locations and existing wells, aiding in identifying a new optimal drilling location relative to the five wells. Copyright 2024, Society of Petroleum Engineers.

关键词： Convolutional neural networks

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Deep Hashing with Triplet Loss of Hash Centers and Dissimilar Pairs for Image Retrieval 27

Deep Hashing with Triplet Loss of Hash Centers and Dissimila...

引用

27th international conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Liu, Ye Pan, Yan Yin, Jian Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Artificial Intelligence Zhuhai Peoples R China Lizhi Inc Artificial Intelligence Dept Beijing Peoples R China Lizhi Inc Big Data Dept Beijing Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Image hash coding learning based on deep neural network have a wide range of application scenarios and become a research hotspot in recent years. In the framework of most existing deep hashing methods, it is necessary to first design a loss function that can converge quickly, and then select an appropriate pre-training deep network and tune it. In order to balance global and local constraints, this paper combines the design of loss functions required by the processes of image feature learning and image hash coding, and adds the constraints of triple loss of hash centers and dissimilar pairs. The proposed novel loss function can give consideration to the characteristics of global hash centers, local dissimilarity of images and image classification labels in the training process. The pre-trained large models have been incorporated into the framework, which can extract image embeddings to assist in the generation of hash codes. The comparative experiments on several image datasets show that the DTLH method proposed in this paper can achieve better results than the traditional hashing methods and the deep hashing methods under different coding lengths.

关键词： deep hashing image retrieval hash center triplet loss pre-trained large model

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Benchmarking data Augmentation Techniques for Tabular data 23rd

Benchmarking Data Augmentation Techniques for Tabular Data

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23rd international conference on Intelligent data Engineering and Automated learning (IDEAL)

作者： Machado, Pedro Fernandes, Bruno Novais, Paulo Univ Minho ALGORITMI Ctr Braga Portugal

ISBN: (纸本)9783031217524;9783031217531

Imbalanced learning and small-sized datasets are usual in machine learning problems, even with the increased data availability provided by recent developments. The performance of learning algorithms in the presence of unbalanced data and significant class distribution skews is known as the "imbalanced learning problem". The models' performance on such problems can drastically decrease for certain classes with an uneven distribution because the models do not learn the distributive features of the data and present accuracy too favorable for a specific set of classes of data. As an example, this can have negative consequences when talking about cancer detection since the model may poorly identify unhealthy patients. Hence, data augmentation techniques are usually conceived to evaluate how models would behave in non-data-scarce environments, generating synthetic data that mimics the characteristics of real data. By applying those techniques, the amount of available data can be increased, balancing the class distributions. However, there are no standardized data augmentation processes that can be applied to every domain of tabular data. Therefore, this study aims to identify which characteristics of a dataset provide a better performance when synthesizing samples by a data augmentation technique in a tabular data environment.

关键词： data augmentation Imbalanced data machine learning

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DPLE: A Privacy-Enhanced and Straggler-Resilient Distributed learning Framework for Smart Cloud 9

DPLE: A Privacy-Enhanced and Straggler-Resilient Distributed...

引用

9th IEEE international conference on Smart Cloud, SmartCloud 2024

作者： Xue, Yilei Li, Jianhua Wu, Jun Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

ISBN: (纸本)9798350389500

Distributed machine learning (DML) encounters issues related to privacy and the presence of straggling nodes in smart cloud systems. Lagrange coded computing offers a partial solution to mitigate these concerns. Nonetheless, the privacy of the system becomes vulnerable when the number of semi-trusted nodes surpasses a specific limit, or when external eavesdroppers are present. To confront this hurdle, we introduce a novel framework for distributed learning called DPLE (Differentially Private Lagrange Encoding). This framework employs Lagrange interpolation polynomials to obscure the original data while introducing redundancy, thus improving privacy safeguards and increasing robustness to straggling nodes. It also incorporates artificial noise into local computation outcomes to protect confidential data from potential exposures. Furthermore, we perform theoretical analyses to identify the necessary variance of this noise to maintain desired privacy levels. Experimental validations confirm the efficacy of DPLE and examine how different settings of system parameters impact the accuracies of the results. © 2024 IEEE.

关键词： Interpolation

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Power System Fault Classification and Prediction Based on Intelligent Algorithms 4

Power System Fault Classification and Prediction Based on In...

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4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Hu, Hongbin Wang, Yuqiang Han, Junfei Zhong, Ming Zhang, Yifan An, Haijun Inner Mongolia Power Research Institute Inner Mongolia Enterprise Key Laboratory of Smart Grid Simulation of Electrical Power System Hohhot China Inner Mongolia Tianda Tiancai Information Technology Co. Ltd. Hohhot China

ISBN: (纸本)9798350308082

Power system fault classification and prediction based on intelligent algorithms is a method that utilizes machine learning and data mining techniques to classify and predict fault conditions in power systems. This method can effectively prevent and avoid the occurrence of faults in the power system by collecting a large amount of power system data and using intelligent algorithms for processing to predict the type and location of faults that may occur in the power system, thereby improving the reliability and stability of the power system. This paper mainly applies the method of algorithm comparison and experiment to analyze the correlation of power system fault classification and prediction. Experimental data show that the recall rate and accuracy of the adaptive moment estimation algorithm mentioned in this article are both above 0.9, ranking first. © 2023 IEEE.

关键词： machine learning

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Quality-Driven machine learning-based data science Pipeline Realization: a software engineering approach 22

Quality-Driven Machine Learning-based Data Science Pipeline ...

引用

44th ACM/IEEE international conference on Software Engineering - New Ideas and Emerging Results (ICSE-NIER)

作者： d'Aloisio, Giordano Univ Aquila Laquila Italy

ISBN: (纸本)9781665495981

The recently wide adoption of data science approaches to decision making in several application domains (such as health, business and even education) open new challenges in engineering and implementation of this systems. Considering the big picture of data science, machine learning is the wider used technique and due to its characteristics, we believe that a better engineering methodology and tools are needed to realize innovative data-driven systems able to satisfy the emerging quality attributes (such as, debias and fariness, explainability, privacy and ethics, sustainability). This research project will explore the following three pillars: i) identify key quality attributes, formalize them in the context of data science pipelines and study their relationships;ii) define a new software engineering approach for data-science systems development that assures compliance with quality requirements;iii) implement tools that guide IT professionals and researchers in the realization of ML-based data science pipelines since the requirement engineering. Moreover, in this paper we also presents some details of the project showing how the feature models and model-driven engineering can be leveraged to realize our project.

关键词： machine learning pipelines software quality product-line architecture model-driven

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Quantifying Energy Security and Influencing Factors Based on Entropy Weighted TOPSIS and machine learning 8th

Quantifying Energy Security and Influencing Factors Based on...

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8th international conference on Advances in Energy Resources and Environment Engineering, ICAESEE 2022

作者： Cai, Qiyuan Chen, Han Zheng, Nan Chen, Hanzi Shi, Pengjia Li, Yinan Zheng, Yulan Economic and Technological Research Institute State Grid Fujian Electric Power Co. Ltd. Fuzhou China School of Economics and Management Fuzhou University Fuzhou China

ISBN: (纸本)9783031425622

Prior to putting energy security plans into action, it is crucial to conduct a scientific and methodical assessment of energy security. The energy security level of 30 Chinese provinces from 2008 to 2019 is measured quantitatively using an energy security index system. Secondly, two machine learning methods, random forest and KNN model, are applied to fit the energy security level of each province. Finally, using the random forest technique, the key risk factors affecting energy security are mined. Our study shows that (1) Energy security levels are inversely correlated with energy load centers and vary widely within China, albeit the disparity is closing. (2) Supply security is the bottom line of energy security, while energy carbon intensity and raw coal and crude oil consumption are also important indicators affecting the level of energy security. (3) The machine learning approach can more accurately match and forecast the level of energy security. These findings provide theoretical support and practical guidance to optimize energy evolution security management and risk notification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Energy security

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Network Analysis of Multisource Packet Capture Files Using machine learning 5

Network Analysis of Multisource Packet Capture Files Using M...

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5th international conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Khanum, Farheen Lakshmi, P Sree Reddy, Harsha Vardhan School of Computer Science and Applications Reva University Karnataka Bengaluru India

ISBN: (纸本)9798331528539

The study uses machine learning methods to identify and detect Distributed Denial of Service (DDoS) attacks. The dataset used in this research was captured from the Packet capture (PCAP) files of the CTU-13, CIC-DDoS2019, and CIC-DoS2017 datasets, which contain different kinds of DDoS attacks such as ICMP, UDP, TCP, and HTTP flooding. data features were extracted from attack packets classified using time windows, with the packet windows being of different lengths to correct the distribution of packets into the classes. Network analysis helps conceptualize and solve DDoS-related problems by analyzing traffic. To detect DDoS attacks, the proposed systems use machine learning classifiers: Logistic Regression, Decision Tree, Random Forest, K-NN (K5), XGBoost (XGB), and Light GBM (LGBM). The investigation of the 'PKT_COUNT' attribute led to a positive correlation and subsequent feature selection process, while only outliers were 'cleaned out' to yield higher gains in model accuracy. The target variable 'PKT_COUNT' was then transformed into a binary form to detect the analyzed malware easily. To create a robust model, significant efforts were made to clean the dataset with processing, such as detecting and removing the outliers, ensuring the model's reliability. The analysis of models was assessed concerning the area under the Receiver Operating Characteristic - Accuracy (ROC-AUC), mean Average Precision (AP), and Accuracy Scores. Therefore, the novel machine learning-based system presented in this paper introduces a robust method of DDoS attack detection that overcomes the drawbacks of existing steps to analyze network traffic. The study raises the possibility of developing more sophisticated classifiers to be incorporated into an environment to optimize DDoS attack detection and improve network security. © 2024 IEEE.

关键词： HTTP

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SGCL: Semantic-aware Graph Contrastive learning with Lipschitz Graph Augmentation 40

SGCL: Semantic-aware Graph Contrastive Learning with Lipschi...

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40th IEEE international conference on data Engineering, ICDE 2024

作者： Cui, Jinhao Chai, Heyan Yang, Xu Ding, Ye Fang, Binxing Liao, Qing Dongguan University of Technology Dongguan China Harbin Institute of Technology Shenzhen Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350317152

Graph contrastive learning (GCL) has gained increasing interest as a solution for graph representation learning. In GCL, graph augmentation is essential to generate contrastive samples used for contrastive learning. Recently, most existing methods employ learnable graph view generators to augment graphs based on the node probability distribution adaptively. However, these methods cannot ensure that semantic-related nodes are preserved during graph augmentation, leading to performance degradation. To tackle this issue, we propose a novel approach called Semantic-aware Graph Contrastive learning (SGCL), which can generate high-quality contrastive samples by only augmenting semantic-unrelated nodes so as to facilitate the performance of GCL on downstream tasks. Specifically, we first design a Lipschitz constant generator to compute the Lipschitz constants that measure the semantic relevance of each node. Then, we propose the Lipschitz graph augmentation to augment graphs while only dropping these semantic-unrelated nodes with small Lipschitz constants. Furthermore, we propose semanticaware contrastive learning to obtain more refined representations by contrasting the graph-level representation of anchor graphs and high-quality generated samples. Experimental results on unsupervised learning and transfer learning demonstrate the effectiveness of SGCL compared to state-of-the-art methods. © 2024 IEEE.

关键词： Semantics

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