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检索条件"主题词=Machine Learning Algorithms"

共 25782 条记录，以下是461-470 订阅

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A fault sensitivity analysis for anomaly detection in water distribution systems using machine learning algorithms 14

A fault sensitivity analysis for anomaly detection in water ...

引用

14th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Predescu, Alexandru Mocanu, Mariana Lupu, Ciprian Univ Politehn Bucuresti Fac Automat Control & Comp Bucharest Romania

ISBN: (纸本)9781538684450

The introduction of machine learning in large scale utility networks extends the room for improvement in the quality of service and maintenance costs. The ever expanding network of smart meters allows for a more accurate estimation of the state of the water distribution systems, at the same time requiring modern data processing solutions. By fusion with the more traditional approach in this field of research it is possible to enhance the existing capabilities for network analysis and to extend the algorithms to the level of cognitive abilities that form a basis for more efficient decision support system. In this paper we extend the fault sensitivity analysis for water distribution systems with the insights provided by state-of-the-art machine learning algorithms for data clustering and anomaly detection.

关键词： Anomaly detection Clustering algorithms machine learning algorithms Smart meters Maintenance engineering Correlation

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Sentiment Analysis on Reviews of Amazon Products Using Different machine learning algorithms 2nd

Sentiment Analysis on Reviews of Amazon Products Using Diffe...

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2nd International Conference on Forthcoming Networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Tasci, Merve Esra Rasheed, Jawad Ozkul, Tarik Istanbul Sabahattin Zaim Univ Dept Software Engn Istanbul Turkiye Istanbul Sabahattin Zaim Univ Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9783031628801;9783031628818

There are thousands of products with hundreds of reviews on major e-commerce sites such as Amazon and eBay. Customers often browse through positive and negative reviews before making a purchase decision. Reading hundreds of reviews for a single product can be time-consuming and overwhelming for customers. Sentiment analysis approach has been identified to address this issue. The study aspires to use several machine learning algorithms to do sentiment analysis on Amazon product reviews. For this purpose, supervised learning, online learning, and ensemble learning algorithms have been applied to Amazon product reviews obtained from the Kaggle database. Natural language processing and data mining techniques were applied to the dataset. Firstly, natural language processing techniques were applied for data preprocessing. The dataset was separated into 20% for testing and 80% for training. Term Frequency-Inverse Document Frequency (TF-IDF) vectorization was employed to create word vectors. Passive Aggressive (PA), SupportVector machine (SVM), Random Forest (RF), AdaBoost, K-Nearest Neighbor (KNN), and XGBoost algorithms were employed in model implementation, which was the crucial step. Accuracy rates, cross-validation scores, confusion matrices, and classification report results were compared. The Random Forest algorithm provided the highest accuracy rate with a prediction accuracy of 96.13%.

关键词： Natural Language Processing Data Mining Sentiment Analysis machine learning algorithms

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Effective Selection of machine learning algorithms for Big Data Analytics Using Apache Spark 2nd

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2nd International Conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Hafez, Manar Mohamed Shehab, Mohamed Elemam El Fakharany, Essam Hegazy, Abd El Ftah Abdel Ghfar Arab Acad Sci Technol & Maritime Transport Dept Informat Syst Cairo Egypt Arab Acad Sci Technol & Maritime Transport Cairo Egypt Arab Acad Sci Technol & Maritime Transport Coll Comp & Informat Technol Cairo Egypt

ISBN: (纸本)9783319483085;9783319483078

Big Data appears with not only the increasing size of data but also complex and different processing and analytical tools. This research aims to compare some selected machine learning algorithms on datasets of different types and sizes using Apache spark tool in order to make a fair judgment about which one is the best fitting in. The algorithms were compared based on few parameters including mainly accuracy and training time. The algorithms were applied on three datasets of different fields: marketing, packing and statistics, and security datasets. The findings of this experiment show that the decision tree algorithm is the most suitable algorithm for marketing and security datasets. Additionally, logistic regression algorithm had the highest accuracy for packing and statistics dataset.

关键词： Big Data Apache spark machine learning algorithms Decision tree Naive Bayes Logistic regression Gradient boosted trees Random forest

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Extracting Indicators Model for the Development of Undergraduates in Guangdong Province Based on machine learning algorithms 10

Extracting Indicators Model for the Development of Undergrad...

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10th International Conference on Information and Education Technology (ICIET)

作者： Zhuang, Mandan Chen, Zhuohui Huang, Haozhang Huang, Bin Wang, Shiyi Cai, Xing Beijing Inst Technol Sch Appl Sci & Civil Engn Zhuhai Peoples R China

ISBN: (纸本)9781665480482

The aim is to use machine learning methods to select indicators of undergraduates' development. This paper adopts questionnaire survey. Then, we collected nearly 3000 pieces of data through 2 months and used the lasso regression model (LASSO), ridge regression model (RRM) and partial least squares regression model (PLSRM) of machine learning to select more relevant indicators among 15 indicators. According to the results, these indicators are main indicator of the development of undergraduate, included score, business project, paper, patent, competitions in academic, honor, public service, the level of English and professional qualification certificate. It can not only lay the foundation for the subsequent research but also help mentor to guide them in the right way.

关键词： Principal Components Analysis (PCA) Extracting Indicators Sigmoid Function machine learning algorithms

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Exploring machine learning algorithms and Protein Language Models Strategies to Develop Enzyme Classification Systems 10th

Exploring Machine Learning Algorithms and Protein Language M...

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10th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Fernandez, Diego Olivera-Nappa, Alvaro Uribe-Paredes, Roberto Medina-Ortiz, David Univ Magallanes Dept Ingn Computac Ave Pdte Manuel Bulnes Punta Arenas 01855 Chile Univ Chile Dept Ingn Quim Biotecnol & Mat Beauche 851 Santiago Chile Univ Chile Ctr Biotechnol & Bioengn Beauchef 851 Santiago Chile

ISBN: (纸本)9783031349522;9783031349539

Discovering functionalities for unknown enzymes has been one of the most common bioinformatics tasks. Functional annotation methods based on phylogenetic properties have been the gold standard in every genome annotation process. However, these methods only succeed if the minimum requirements for expressing similarity or homology are met. Alternatively, machine learning and deep learning methods have proven helpful in this problem, developing functional classification systems in various bioinformatics tasks. Nevertheless, there needs to be a clear strategy for elaborating predictive models and how amino acid sequences should be represented. In this work, we address the problem of functional classification of enzyme sequences (EC number) via machine learning methods, exploring various alternatives for training predictive models and numerical representation methods. The results show that the best performances are achieved by applying representations based on pre-trained models. However, there needs to be a clear strategy to train models. Therefore, when exploring several alternatives, it is observed that the methods based on CNN architectures proposed in this work present a more outstanding facility for learning and pattern extraction in complex systems, achieving performances above 97% and with error rates lower than 0.05 of binary cross entropy. Finally, we discuss the strategies explored and analyze future work to develop integrated methods for functional classification and the discovery of new enzymes to support current bioinformatics tools.

关键词： machine learning algorithms protein language models EC number classifications convolutional neural networks enzyme discovery

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Anomaly detection in medical wireless sensor networks using machine learning algorithms 4th

Anomaly detection in medical wireless sensor networks using ...

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4th International Conference on Eco-Friendly Computing and Communication Systems (ICECCS)

作者： Pachauri, Girik Sharma, Sandeep Gautam Buddha Univ Sch Informat & Commun Technol Greater Noida India

Wireless sensor networks suffer from a wide range of faults and anomalies which hinder their smooth working. These faults are even more significant for medical wireless sensor networks, which simply cannot afford such inconsistencies. To combat this issue, various fault detection mechanisms have been developed. We tried enhancing the performance of one such mechanism, and our findings are presented in this paper. Using machine learning algorithms, we will show through our experiments on real medical datasets that our approach gives more accurate results than other existing fault detection mechanisms. This research will be critical in detecting sensor faults quickly, accurately and with a low false alarm ratio. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/)

关键词： Wireless Sensor Networks machine learning algorithms Sensor Faults Healthcare and patient monitoring

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Cloud Computing Threats Classification Model Based on the Detection Feasibility of machine learning algorithms 40

Cloud Computing Threats Classification Model Based on the De...

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40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Masetic, Z. Hajdarevic, K. Dogru, N. Int Burch Univ Sarajevo Bosnia & Herceg Univ Sarajevo Fac Elect Engn Sarajevo Bosnia & Herceg

ISBN: (纸本)9789532330922

Cloud computing became very popular in past few years, and most of the business and home users rely on its services. Because of its wide usage, cloud computing services became a common target of different cyber-attacks executed by insiders and outsiders. Therefore, cloud computing vendors and providers need to implement strong information security protection mechanisms on their cloud infrastructures. One approach that has been taken for successful threat detection that will lead to the successful attack prevention in cloud computing infrastructures is the application of machine learning algorithms. To understand how machine learning algorithms can be applied for cloud computing threat detection, we propose the cloud computing threat classification model based on the feasibility of machine learning algorithms to detect them. In this paper, we addressed three different criteria types, where we considered three types of classification: a) type of learning algorithm, b) input features and c) cloud computing level. Results proposed in this paper can contribute to further studies in the field of cloud threat detection with machine learning algorithms. More specifically, it will help in selecting appropriate input features, or machine learning algorithms, to obtain higher classification accuracy.

关键词： Cloud computing machine learning algorithms Computational modeling Telecommunication traffic Computer crime Support vector machines

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Predicting and Analyzing Absenteeism at Workplace Using machine learning algorithms 43

Predicting and Analyzing Absenteeism at Workplace Using Mach...

引用

43rd International Convention on Information, Communication and Electronic Technology (MIPRO)

作者： Rista, Amarildo Ajdari, Jaumin Zenuni, Xhemal SEE Univ Fac Contemporary Sci & Technol Tetovo North Macedonia

ISBN: (纸本)9789532330991

Absenteeism is the usual or recurrent absence from work is continuously causing disruption in the smooth running of business, affecting the organizational performance and productivity and impacting on the employees' morale. The Oil Refinery in Albania (ARMO), employing 1200 employees is facing high rate of absences. If necessary measures are not being serious dealt with, the issue of absenteeism may jeopardize the operation and production. Prediction of absenteeism is too complex influenced by many factors. Usage of data mining and machine learning algorithms is a good solution to predict and analyze it. The aim of this paper is to identify and evaluate the appropriate ML algorithms to predict and analyses absenteeism at workplace. The dataset taken into account consists of some attributes such as: age, education, employment category, day, month, length of service ect, and 125000 records are considered. Analysis and comparison of various algorithms in terms of accuracy, precision and sensitivity are done in Weka tool.

关键词： data mining absenteeism machine learning algorithms prediction

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Hannes Prosthesis Control Based on Regression machine learning algorithms

Hannes Prosthesis Control Based on Regression Machine Learni...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Di Domenico, D. Marinelli, A. Boccardo, N. Semprini, M. Lombardi, L. Canepa, M. Stedman, S. Bellingegni, A. Dellacasa Chiappalone, M. Gruppioni, E. Laffranchi, M. De Michieli, L. Ist Italiano Tecnol Rehab Technol Lab I-16163 Genoa Italy Univ Genoa Dept Informat Bioengn Robot & Syst Engn I-16145 Genoa Italy INAIL Prosthet Ctr I-40054 Bologna Italy

ISBN: (纸本)9781665417143

The quality of life for upper limb amputees can be greatly improved by the adoption of poly-articulated myoelectric prostheses. Typically, in these applications, a pattern recognition algorithm is used to control the system by converting the recorded electromyographic activity (EMG) into complex multi-degrees of freedom (DoFs) movements. However, there is currently a trade-off between the intuitiveness of the control and the number of active DoFs. We here address this challenge by performing simultaneous multi-joint control of the Hannes system and testing several state-of-the-art classifiers to decode hand and wrist movements. The algorithms discriminated multi-DoF movements from forearm EMG signals of 10 healthy subjects reproducing hand opening-closing, wrist flexion-extension and wrist pronation-supination. We first explored the effect of the number of employed EMG electrodes on device performance through the classifiers optimization in terms of F1Score. We further improved classifiers by tuning their respective hyperparameters in terms of the Embedding Optimization Factor. Finally, three mono-lateral amputees tested the optimized algorithms to intuitively and simultaneously control the Hannes system. We found that the algorithms performances were similar to that of healthy subjects, particularly identifying the Non-Linear Regression classifier as the ideal candidate for prosthetic applications.

关键词： Wrist Performance evaluation machine learning algorithms Control systems Electromyography Classification algorithms Optimization

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Autonomous Parameter Optimization of A Heterogeneous Wireless Network Aggregation System using machine learning algorithms

Autonomous Parameter Optimization of A Heterogeneous Wireles...

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IEEE Consumer Communications and Networking Conference (CCNC)

作者： Kon, Yohsuke Ito, Masato Hassel, Nico Hasegawa, Mikio Ishizu, Kentaro Harada, Hiroshi Tokyo Univ Sci Chiyoda Ku 1-14-16 Kudan Kita Tokyo 1020073 Japan Hsch Wismar Univ Appl Sci Technol Business & Design D-23952 Wismar Germany Natl Inst Informat & Commun Technol Yokosuka Kanagawa 2390847 Japan

ISBN: (纸本)9781457720710

By increase of various radio access network (RAN) services, available spectrum resources for mobile communications get decrease, and efficient use of the radio resource is becoming a very important issue. In order to optimize the radio resource usage and maxmize the throughput and quality of service (QoS), the link aggregation technologies to utilize multiple different available RANs have been studied. However, in such heterogeneous wireless networks, it is difficult to improve the throughput by their aggregation because of the differences among the QoSs of the different RANs. In this paper, we propose an autonomous parameter optimization scheme using a machine learning algorithm, which maximize the throughput of the heterogeneous RAN aggregation system. We evaluate the performance of the proposed scheme implemented on a cognitive wireless network system called Cognitive Wireless Cloud (CWC) system, connected to real wireless network services, such as HSDPA, WiMAX and W-CDMA. Our experimental results of the proposed system show that the aggregation throughput can be improved with increase of the training samples, which are collected autonomously.

关键词： machine learning algorithms Wireless networks Parameter optimization Throughput heterogeneous wireless network WCDMA agglomeration mobile communication Telecommunications Quality of service

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