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检索条件"主题词=Machine Learning Algorithms"

共 25782 条记录，以下是561-570 订阅

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Comparative analysis of prediction results of multiple machine learning algorithms on LT-FS-Identity: Intrusion detection Dataset in Nuclear power plant systems 10

Comparative analysis of prediction results of multiple machi...

引用

10th International Forum on Electrical Engineering and Automation, IFEEA 2023

作者： Ye, Shengnian Clermont Auvergne University Polytech Clermont-Ferrand 63100 China

ISBN: (纸本)9798350309379

In this paper, we propose an intrusion detection dataset for nuclear power plant systems, which consists of processed and labeled network traffic data collected from real nuclear power plant systems. Through Pearson correlation analysis of the data set, it is found that there is a strong positive correlation between the number of obstacles (target variable), the perceived distance, the propagation distance and the number of sensor nodes, and the correlation coefficient is 0.84. The results show that the decision tree regression model has better prediction performance and smaller model parameters. The second is the random forest regression model, whose regression results rank second among the five models. The prediction effect of BP neural network regression model is poor. The data set can be used to train and test intrusion detection systems, and provide valuable reference for developing effective intrusion detection systems for nuclear power plants. At the same time, the research results of this paper also provide a certain theoretical basis and technical support for improving the safety of nuclear power plant system. The data set and model evaluation results presented in this paper are applicable not only to nuclear power plant systems, but also to other industrial control system intrusion detection research. In addition, the Pearson correlation analysis and regression model evaluation methods used in this paper can also provide references for other related fields. © 2023 IEEE.

关键词： Decision Tree machine learning algorithms Random Forest

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E-Commerce User Purchase Prediction Based on Improved machine learning algorithms 3

E-Commerce User Purchase Prediction Based on Improved Machin...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Si, Jiabin Hainan College of Software Technology Hainan Province Qionghai City571400 China

ISBN: (纸本)9798350319125

The role of consumer shopping demand in the operation forecast of e-commerce platforms is very important, but there is the problem of incomplete data estimation. The k-NN nearest neighbor algorithm cannot accurately predict the problem of user purchasing power in e-commerce platform data, and the sample is unbalanced, and the problem is handled in a single way. Therefore, this paper proposes a machine learning algorithm to analyze the shopping needs of e-commerce enterprises. Firstly, the Markov chain theory is used to evaluate the purchase behavior of users, and indicators are carried out according to the requirements of purchase historical data Divide and reduce distractions in purchasing behavior. Then, Markov chain theory evaluates the purchasing power of consumers, forms a consumer shopping demand program, and has a consumer shopping demand The results were comprehensively analyzed. MATLAB simulation shows that under certain evaluation criteria, the machine learning algorithm is accurate to the purchasing power of e-commerce platform users The range of consumer demand is better than the k-NN nearest neighbor algorithm. © 2023 IEEE.

关键词： consumer shopping needs E-commerce platform users Historical data machine learning algorithms Markov chain theory

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An Impact of Poisoning Attacks on machine learning algorithms 3

An Impact of Poisoning Attacks on Machine Learning Algorithm...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Lourdu Mahimai Doss, P. Gunasekaran, M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350317022

machine learning models, ubiquitous in domains like natural language processing and image recognition, are vulnerable to adversarial poisoning attacks, where malicious actors manipulate training data to induce erroneous predictions. This study delves into the susceptibility of Support Vector machine (SVM) models to such attacks, employing the Gradient Ascent Poisoning (GAP) approach. The findings reveal a substantial impact of GAP on SVM performance, leading to a notable decline in accuracy from 81% to 73%. Beyond presenting empirical evidence, the paper highlights the broader challenges of detecting and mitigating poisoning attacks, underscoring the need for robust security measures in machine learning systems. By illuminating the effectiveness of adversarial poisoning techniques and their ramifications, this research contributes to the ongoing endeavor to enhance the resilience of machine learning models in practical applications. © 2023 IEEE.

关键词： Adversarial machine learning Gradient ascent Incorrect predictions machine learning algorithms Perturbations Poisoning attacks Training data manipulation

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An Extensive Review and Comparison of Different machine learning algorithms for Customer Behaviour Pattern Analysis 10

An Extensive Review and Comparison of Different Machine Lear...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Garg, Sheena Ajmani, Prerna Singh, Tanya Aggarwal, Deepti Jain, Prachi Kohli, Diksha Vivekananda Institute of Professional Studies Delhi India

ISBN: (纸本)9798350382471

Consumer shopping behavior is changing due to the explosive growth of e-commerce, which brings both benefits and challenges. E-commerce enterprises must comprehend the shopping behavior of their clients to enhance user experiences and increase consumer satisfaction. This emphasizes how crucial it is to research consumer behavior in the context of online shopping. Customer transactions on e-commerce platforms produce enormous data from clicking, buying histories, demographics, and beyond. Integrating the findings from investigations offers a comprehensive understanding of buyers. In this paper, we have thoroughly analyzed and used various machine learning algorithms (Apriori algorithm, Ensemble learning, KNN, Naive Bayes, NLP) to analyze customer behavior. We have also evaluated the performance of various algorithms in terms of accuracy and time taken to analyze the data of consumers' shopping behavior. © 2023 IEEE.

关键词： Apriori algorithm Customer behavior E-commerce Ensemble learning KNN machine learning algorithms Naïve Bayes NLP Pattern analysis

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Empirical Evaluation of machine learning algorithms based on EMG, ECG and GSR Data to Classify Emotional States

Empirical Evaluation of Machine Learning Algorithms based on...

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作者： Amare Ketsela Tesfaye Amrit Pandey Blekinge Institute of Technology

学位级别：硕士

The peripheral psychophysiological signals (EMG, ECG and GSR) of 13 participants were recorded in the well planned Cognition and Robotics lab at BTH University and 9 participants data were taken for further processing. Thirty(30) pictures of IAPS were shown to each participant individually as stimuli, and each picture was displayed for five- second intervals. Signal preprocessing, feature extraction and selection, models, datasets formation and data analysis and interpretation were done. The correlation between a combination of EMG, ECG and GSR signal and emotional states were investigated. 2- Dimensional valence-arousal model was used to represent emotional states. Finally, accuracy comparisons among selected machine learning classification algorithms have performed. Context: Psychophysiological measurement is one of the recent and popular ways to identify emotions when using computers or robots. It can be done using peripheral signals: Electromyography (EMG), Electrocardiography (ECG) and Galvanic Skin Response (GSR). The signals from these measurements are considered as reliable signals and can produce the required data. It is further carried out by preprocessing of data, feature selection and classification. Classification of EMG, ECG and GSR data can be conducted with appropriate machine learning algorithms for better accuracy results. Objectives: In this study, we investigate and analyzed with psychophysiological (EMG, ECG and GSR) data to find best classifier algorithm. Our main objective is to classify those data with appropriate machine learning techniques. Classifications of psychophysiological data are useful in emotion recognition. Therefore, our ultimate goal is to provide validated classified psychological measures for the automated adoption of human robot performance. Methods: We conducted a literature review in order to answer RQ1. The sources used are Inspec/ Compendex, IEEE, ACM Digital Library, Google Scholar and Springer Link. This helps us to id

关键词： Psychophysiological Measures machine learning algorithms Emotions Classification

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Evaluation of machine learning algorithms for Classification of Short-Chain Dehydrogenase/Reductase Protein Sequences

Evaluation of Machine Learning Algorithms for Classification...

引用

作者： Sandra Olanders Blekinge Institute of Technology

学位级别：硕士

The classification of protein sequences is a subfield in the area of Bioinformatics that attracts a substantial interest today. machine learning algorithms are here believed to be able to improve the performance of the classification phase. This thesis considers the application of different machine learning algorithms to the classification problem of a data set of short-chain dehydrogenases/reductases (SDR) proteins. The classification concerns both the division of the proteins into the two main families, Classic and Extended, and into their different sub- families. The results of the different algorithms are compared to select the most appropriate algorithm for this particular classification problem.

关键词： machine learning algorithms Bioinformatics Sequence analysis SDR proteins

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Improving the matching of registered unemployed to job offers through machine learning algorithms

Improving the matching of registered unemployed to job offer...

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作者： Cruz, Paula Isabel Moura Meireles Universidade Nova de Lisboa

学位级别：硕士

Due to the existence of a double-sided asymmetric information problem on the labour marketcharacterized by a mutual lack of trust by employers and unemployed people, not enough job matchesare facilitated by public employment services (PES), which seem to be caught in a low-end equilibrium. In order to act as a reliable third party, PES need to build a good and solid reputation among their mainclients by offering better and less time consuming pre-selection services. The use of machine-learning, data-driven relevancy algorithms that calculate the viability of a specific candidate for a particular jobopening is becoming increasingly popular in this field. Based on the Portuguese PES databases (CVs, vacancies, pre-selection and matching results), complemented by relevant external data published byStatistics Portugal and the European Classification of Skills/Competences, Qualifications andOccupations (ESCO), the current thesis evaluates the potential application of models such as RandomForests, Gradient Boosting, Support Vector machines, Neural Networks Ensembles and other tree-basedensembles to the job matching activities that are carried out by the Portuguese PES, in order tounderstand the extent to which the latter can be improved through the adoption of automatedprocesses. The obtained results seem promising and point to the possible use of robust algorithms suchas Random Forests within the pre-selection of suitable candidates, due to their advantages at variouslevels, namely in terms of accuracy, capacity to handle large datasets with thousands of variables, including badly unbalanced ones, as well as extensive missing values and many-valued categoricalvariables.

关键词： Job Matching Registered Unemployed with PES machine learning algorithms Social Data ESCO

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A Data Analysis and Market Price Prediction of Ethiopian Commodity Market with machine learning algorithms

A Data Analysis and Market Price Prediction of Ethiopian Com...

引用

作者： Selam, Damtew Addis Ababa University

学位级别：硕士

The current Ethiopian market is conducted in a traditional manner and market drivers are still not used for prediction of future market price. Although, large amount of market data have been gathered throughout years by both governmental and non-governmental organizations, yet little have been done to analyze the data for future market price prediction. Moreover, the analysis methods were often manual creating inefficiency in time and quality of market prediction. Analyzing valuable data will show us what the future holds and accelerate the development goals of the country in the sector. The study examines features of current Ethiopian market attributes to find out most valuable features for predicting market price. Eighteen technical indicators are taken and tested for their individual ability of prediction and redundancy. From the feature selection of commodity marke, we have found that features like Stochastic %K, Stochastic %D, Close gain/loss, High, close price, Opening Price, Low, RSI, Ton and Moving Average Convergence/ divergence (MACD) founded to be in the top ten of individual performance evaluation. Moreover features namely Stochastic %K, Relative Strength Index (RSI), Bollinger Bands-Upper, Highest-High, close gain/loss, Simple Moving Average (SMA), Closing price, MACD-Fast, Exponential Moving Average (EMA), MACD-Slow and Low founded to be less redundant. The study also compares four machine learning models for their prediction ability of Ethiopian commodity market price. The outcomes of feature selection were used to compare the models. Two experiments were conducted; the first was comparison of the models with 10 fold cross validation using feature of high individual predictive ability and less redundancy. The second one was a comparison of models with separate train and test data using features of high individual predictive ability and less redundancy. From the models (Support Vector machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (

关键词： Feature selection Technical Indicators Price prediction machine learning algorithms

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Predicting Countermovement Jump Heights by Time domain, Frequency domain, and machine learning algorithms

Predicting Countermovement Jump Heights by Time domain, Freq...

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10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Zhou, Zhaoxian Shakya, Sarbagya Sha, Zhanxin Univ Southern Mississippi Sch Comp Hattiesburg MS 39406 USA Univ Southern Mississippi Sch Kinesiol Hattiesburg MS USA

ISBN: (纸本)9781538636749

In this paper, we introduce an experiment evaluating performance of football players in countermovement jumps (CMJs). Three methods including time domain, frequency domain, and machine learning algorithms are proposed for performance evaluation. Correlation coefficients and p-values are given for time domain and frequency domain methods, and prediction errors are given for different machine learning algorithms.

关键词： Sports performance evaluation Vertical ground reaction force Countermovement jump time domain method frequency domain method machine learning algorithms

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An In₂O₃ Nanotubes based Gas Sensor Array combined with machine learning algorithms for Trimethylamine Detection

An In<sub>2</sub>O<sub>3</sub> Nanotubes based Gas Sensor Ar...

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16th IEEE International Conference on Nano/Micro Engineered and Molecular Systems (IEEE-NEMS)

作者： Ren, Wenjie Zhao, Changhui Liu, Yingming Wang, Fei Southern Univ Sci & Technol Sch Microelect Shenzhen 518055 Peoples R China

ISBN: (纸本)9780738124292

This work focuses on developing an electronic nose system with machine learning algorithm for detection of trimethylamine (TMA). Pure and Ga-doped In2O3 nanotubes are synthesized by a simple electrospinning method, and four kinds of gas sensors (pristine, 1% Ga, 10% Ga, and 20% Ga-doped In2O3) are fabricated to form a sensor array. Results show that the sensor array can classify TMA effectively from interference gases (xylene, ethanol, hydrogen sulfide) by a support vector machine (SVM) algorithm. Several algorithms, including radial basis function neural network (RBFNN), back propagation neural network (BPNN) and principal component analysis combined with linear regression (PCA-LR), are used to predict the concentration level of each gas. For TMA gas, the trained algorithms can predict its concentration with average relative errors of 1.22% for RBFNN, 2.5% for BPNN and 13.34% for PCA-LR. Furthermore, the binary mixtures of TMA and ethanol are measured and used to train the above algorithms, and the lowest average relative error of 1.74% is achieved in the case of RBFNN algorithm.

关键词： Support vector machines machine learning algorithms Radial basis function networks Nanotubes Interference Prediction algorithms Electronic noses

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