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检索条件"主题词=Machine Learning Algorithms"

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An Interactive Web Tool for Classification Problems Based on machine learning algorithms Using Java Programming Language: Data Classification Software

An Interactive Web Tool for Classification Problems Based on...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： İbrahim PERÇİN Fatma Hilal YAĞIN A. Kadir ARSLAN Cemil ÇOLAK Dept. of Biostatistics and Medical Informatics Faculty of Medicine Inonu University Malatya TURKEY

Classification analysis is a frequently used approach in fields such as biomedical, bioinformatics, medical and engineering. In the field of health, it has become common to classify diseases based on risk factors by machine learning methods and to determine the effect sizes of these risk factors on the disease. There are many analysis tools used to guide researchers in classification analysis. While some of these tools are commercial and provide basic methods for classification analysis, some offer advanced analysis techniques and are desktop applications such as the WEKA environment. The WEKA environment includes comprehensive tools for classification analysis. However, use of the WEKA environment can be difficult and time-consuming, especially when a quick assessment is essential for users who do not have WEKA tool on their computer (doctors, etc.). Therefore; fast, comprehensive, free and easy to use analysis tool is required. The purpose of this study is to develop a user-friendly web tool (Data Classification Software; DCS) based on the classification algorithms of WEKA tool in Java programming language. The data classification software can be used on any device with an internet connection, which is independent of the any operating systems. In the developed web-based tool, data preprocessing module consists of missing value assignment, variable type conversion and normalization-standardization methods. Classification module encapsulates random forest, Naive Bayes, Bayes Network, j48, sequential minimal optimization, a rule and attribute selected classifier algorithms. This web tool can be accessed free of charge at http://***/DCS/.

关键词： Tools Classification algorithms Random forests machine learning algorithms Software Training

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Performance Evaluation of machine learning algorithms for Hypertext Transfer Protocol Distributed Denial of Service Intrusion Detection

Performance Evaluation of Machine Learning Algorithms for Hy...

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International Conference on Electronics, Computer and Computation (ICECCO)

作者： Rukayya Umar Morufu Olalere Ismaila Idris Raji Abdullahi Egigogo G Bolarin Department of Cyber Security Science Federal University of Technology Minna Nigeria Department of Computer Science Federal University of Technology Minna Nigeria

ISBN: (数字)9781728151601

ISBN: (纸本)9781728151618

As this paper has expounded, the techniques against DDoS attacks borrow greatly from the already tested traditional techniques. However, no technique has proven to be perfect towards the full detection and prevention of DDoS attacks. Intrusion detection system (IDS) using machine learning approach is one of the implemented solutions against harmful attacks. However, achieving high detection accuracy with minimum false positive rate remains issue that still need to be addressed. Consequently, this study carried out an experimental evaluation on various machine learning algorithms such as Random forest J48, Naïve Bayes, IBK and Multilayer perception on HTTP DDoS attack dataset. The dataset has a total number of 17512 instances which constituted normal (10256) and HTTP DDoS (7256) attack with 21 features. The implemented Performance evaluation revealed that Random Forest algorithm performed best with an accuracy of 99.94% and minimum false positive rate of 0.001%.

关键词： DDoS IDS machine learning Random Forest Distributed Denial of Service machine learning algorithms intelligent decision support system Performance Evaluation Artificial Intelligence machine learning Hypertext Transfer Protocol Denial (Psychology) Dataset

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Physiological-Based Smart Stress Detector using machine learning algorithms

Physiological-Based Smart Stress Detector using Machine Lear...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Marife A. Rosales Argel A. Bandala Ryan Rhay Vicerra Elmer P. Dadios Electronics and Communications Engineering Department De La Salle University Manila Manila Philippines Manufacturing Engineering amd Management Department De La Salle University Manila Manila Philippines

ISBN: (数字)9781728130446

ISBN: (纸本)9781728130453

This paper is focused on the development of an intelligent system to identify if one person is stress or not stress using physiological parameters through machine learning. In this study, the dataset was acquired from three hundred (300) male and female participants ages 18 to 25. The gathered dataset is composed of five (5) features (i.e. heart rate, systolic blood pressure, diastolic blood pressure, galvanic skin response and gender). An intelligent system was developed using machine learning algorithms for classification such as Linear Regression (LR), K-Nearest Neighbor (KNN), and Support Vector machine (SVM) using Python IDE with sci-kit learn machine learning libraries. Google Colaboratory (Colab) was utilized to perform optimization using Gridsearch to identify the best parameters of each algorithm. Feature selection methods are implemented to identify the most significant features related to stress condition of one person. After optimization, the results showed that SVM has the best performance to classify if one person is stress or not stress with optimized training-testing accuracy score of 95.00% - 96.67%.

关键词： Stress Support vector machines Heart rate Blood pressure machine learning algorithms machine learning Skin

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Sentiment Analysis in Turkish Text with machine learning algorithms

Sentiment Analysis in Turkish Text with Machine Learning Alg...

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Innovations in Intelligent Systems and Applications Conference

作者： Merve Rumelli Deniz Akkuş Özge Kart Zerrin Isik Computer Engineering Departmen Dokuz Eylül University İzmir Turkey

ISBN: (纸本)9781728128696

With the developing technology, the number of comments made on the internet is increasing day by day. It has become almost impossible to make a manual sentiment analysis on these comments. Therefore, new algorithms should be developed to automatically perform sentiment analysis on these texts. In this study, a sentiment analysis model has been developed for Turkish texts. While developing this model, lexicon-based methods and machine learning algorithms were used together. As a naïve method of sentiment analysis, the root of each word in a sentence takes a score from a dictionary and the final polarity score of the relevant sentence is calculated by using additive score-based models. machine learning models are trained to perform accurate sentiment annotations by using features based on polarity scores of texts. The final supervised machine learning model can achieve sentiment annotations of new Turkish texts within a 73% success rate without any human intervention.

关键词： Sentiment analysis Dictionaries machine learning algorithms machine learning Support vector machines Tools

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Predicting Bug Estimation Time for Newly Reported Bug Using machine learning algorithms

Predicting Bug Estimation Time for Newly Reported Bug Using ...

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International Conference for Convergence of Technology (I2CT)

作者： Rucha Sawarkar Naresh Kumar Nagwani Sanjay Kumar Department of Information Technology NIT Raipur Chattisgarh India Department of Computer Science & Engineering NIT Raipur Chattisgarh India

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

In commercial projects, number of defects raised directly depends upon the releases. In order to get the idea of progress of the project it is necessary to estimate the average time required to fix the bug. This time is referred as bug estimation time. It is essential to estimate the time of software bug for a proper project planning. In this paper, a new algorithm is proposed to determine bug estimation time with the help of machine learning algorithms. New developer is predicted and its average bug estimation time is calculated based on the bug estimation time of already existing developer. A comparative study for accuracy for different machine learning algorithms is carried out.

关键词： Computer bugs Estimation machine learning algorithms Forestry Support vector machines Kernel

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Using of machine learning algorithms without Preliminary Training in Unconstant Game Systems

Using of Machine Learning Algorithms without Preliminary Tra...

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International Scientific-Practical Conference Problems of Infocommunications Science and Technology

作者： Hleb Shpyta Yaroslaw Dorogyy ACTS FICT National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine

ISBN: (数字)9781728141848

ISBN: (纸本)9781728141855

This article contains a comparative analysis of the effectiveness of machine learning algorithms in finding optimal strategy by result in a competitive environment without the possibility of prior training using an example game based on the dilemma of the prisoner. Results of using of adaptive algorithms in comparison with constant strategies are considered from the point of view of game design. Based on the data obtained, the article offers a set of approaches for implementing an adaptive gaming environment as an alternative to the decision trees which are often used in videogame programming.

关键词： Games Training machine learning algorithms Heuristic algorithms machine learning Task analysis Genetic algorithms

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Prediction of Casing Damage in Unconsolidated Sandstone Reservoirs Using machine learning algorithms

Prediction of Casing Damage in Unconsolidated Sandstone Rese...

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IEEE International Conference on Computation, Communication and Engineering (ICCCE)

作者： Qing Tang Hua Wu Guoquan Teng Hongguang Bu Chaodong Tan Jiankang Liu Xiongying Zhang Yanlong Zhang Wei Yan Jingen Deng PetroChina Dagang Oilfield Production Technology Research Institute Tianjin China China University of Petroleum Beijing China Beijing Yadan Petroleum Technology Co. Ltd Beijing China CNPC Engineering Technology R&D Company Limited Beijing China

ISBN: (数字)9781728131122

ISBN: (纸本)9781728131139

Despite numerous studies in the subject matter, there is no mature casing damage prediction method based on historical casing damage data in the oil and water well production stage for unconsolidated sandstone reservoirs. We use two popular algorithms to establish a prediction model for the sand-sand casing damage area, eXtreme Gradient Boosted Trees (XGBoost) and Light Gradient Boosted Trees (LGBM). According to data analysis and casing damage mechanism, we selected 19 casing damage factors for oil wells and 18 for water injection wells. Geological, reservoir, completion and historical production/operation data for 653 production layers and 212 injection layers in Gangxi Oilfield are collected to form dataset. Among them, the casings of 91 production layers and 22 injection layers were damaged. The dataset is split into 80% training and 20% holdout datasets. A training dataset is split into 10-fold cross validation. Two machine learning algorithms are evaluated predicting casing damage and their performance is compared. For production wells, the prediction accuracy of LGBM model is higher, up to 95.4%. For injection wells, the prediction accuracy of LGBM model is higher, up to 100%. Therefore, we can use more accurate model to predict casing damage in unconsolidated sandstone reservoirs, and determine main controllable factors of casing damage in risk wells, so as to provide technical guidance for technicians to take preventive measures.

关键词： Production Predictive models Oils Reservoirs machine learning algorithms Computational modeling

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Performance comparative study of machine learning algorithms for automobile insurance fraud detection

Performance comparative study of machine learning algorithms...

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International Conference on Intelligent Computing in Data Sciences (ICDS)

作者： Bouzgarne Itri Youssfi Mohamed Qbadou Mohammed Bouattane Omar SSDIA Laboratory ENSET Mohammedia University Hassan 2 Casablanca Morocco

ISBN: (纸本)9781728100043

Nowadays, fraud is a major problem facing the insurance industry that Big Data and machine learning are trying to solve. This paper deals with the evaluation of the effectiveness and the verifiability of the best-known machine learning algorithms for fraud prediction. We adopted the supervised method applied to automobile data claims of an anonymous insurance company. We aim to propose an approach that improves the relevance of the results of artificial intelligence. The study has demonstrated that RandomForest works better among all algorithms compared.

关键词： Insurance Automobiles Random forests Accidents machine learning algorithms Companies

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Performance Analysis of machine learning algorithms for Fall Detection

Performance Analysis of Machine Learning Algorithms for Fall...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Anita Ramachandran Adarsh Ramesh Piyush Pahwa A. Prahalad Atreyaa Shivam Murari K. R. Anupama Dept. of Computer Science & Information Systems BITS Pilani Bangalore India Dept. of Electrical & Electronics Engg BITS Pilani India

ISBN: (数字)9781728104027

ISBN: (纸本)9781728104034

Intelligent IoT-based ambient assisted living systems (AALS) have been a major research focus area in recent times. Application of machine learning in areas of AALS such as fall detection has the potential to have huge social impact. There has been active research in the application of machine learning in fall detection, using data generated by various means such as wearable devices, environment sensors and vision based systems. The main challenge is to create a model that detects falls accurately, while keeping the design of the fall detection system minimal and non-intrusive. Wearable devices equipped with inertial motion unit (IMU) sensors and vital signs sensors are commonly used to enable analysis around performance of machine learning (ML) models. In this paper, we analyze the impact of using IMU sensor parameters in combination with vital signs parameters, on the performance of ML algorithms for fall detection. We present details on the data set we have generated for this purpose, and compare the performance of various ML algorithms on the collected dataset, with features from IMU sensors vis-à-vis those from IMU sensors in combination with vital signs sensors. We also apply machine learning algorithms on two public datasets, one with only IMU sensor parameter values and the second with only vital signs parameter values, and summarize their performance.

关键词： Fall detection machine learning wearable systems machine learning algorithms Vital Signs Artificial Intelligence machine learning Sensors falls Fall Computer Programming Languages Autumn

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Usage of machine learning algorithms in Flexible Use of Airspace Concept

Usage of Machine Learning Algorithms in Flexible Use of Airs...

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Conference on Digital Avionics Systems (DASC)

作者： Emre Osman Birdal Serdar Üzümcü Technologies Division HAVELSAN Ankara TURKEY

ISBN: (数字)9781728106496

ISBN: (纸本)9781728114972

Civil Aviation Authorities (CAA), Air Navigation Service Providers (ANSP) and Military Forces share the common airspace and operate regarding their own needs. With an effective usage of airspace which is the use of military restricted and limited zones for civil flights, national boundaries without constraints will be decreased the cost of fuel of aircrafts, increase the airspace capacity and lead to less time consumption for flights. Implementation of Flexible Use of Airspace (FUA) concept to national Air Traffic Management in three levels that ICAO and EUROCONTROL determined in their standards is considered as a complete system solution that is now available in various countries. The FUA concept is based on Level-1 Strategic, Level-2 Pre-tactical and Level-3 Tactical levels. After civil and military organizations determined their strategic requirements according to the Level-1 Strategic Management of Airspace, Level-2 and Level-3 activities could be achieved based on this high-level requirements using by advanced technologies. In Level-1 Strategic Management of Airspace level, machine-learning algorithms are used for ensuring the reliability and availability of services to get continuous and daily allocation readiness for Level-2 and Level-3 activities. Actual and historical data of the flights and airspace information gathered from civil and military parts of the concept can be used as an input of machine learning training for development of the Level-1 Strategical Management of Airspace proposed application. When the rules from the Level-l implemented on the training data, FUA specified computers start categorizing the FIRs as per capacity, traffic density, weather conditions, fuel consumption, distance and time characteristics of routes and proposed applications start learning how to allocate airspace according to predetermined user requirements. machine learning algorithms will be used as an assistant for Flight Planning and it leads to optimize air traffic ma

关键词： Flexible Use of Airspace (FUA) Air Traffic Management (ATM) Reinforcement learning (RL) Airspace Structure Air Traffic Services Route Planning ATS) Civil-Military Coordination machine learning algorithms Strategic Planning Civil Air traffic management airspace structure Military Personnel Health Services Accessibility International Civil Aviation Organisation national boundaries Aircraft advanced technologies

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