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检索条件"主题词=Machine Learning Algorithms"

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Bollywood Movie Success Prediction using machine learning algorithms

Bollywood Movie Success Prediction using Machine Learning Al...

引用

International Conference on Circuits, Communication, Control and Computing

作者： Ashutosh Kanitkar Computer Science Department Pune Institute of Computer Technology Pune India

ISBN: (纸本)9781538684887

Hindi Film Industry also referred to as Bollywood has now become a multibillion dollar industry and has also surpassed Hollywood in terms of amount of ticket annually sold. With so much money now riding on Bollywood movies it has become imperative to make accurate predictions about success of Bollywood films. Today even if a Bollywood movie does not become a hit at box-office the producer of movie will still make profits through sale of satellite rights and music rights but it is Distributors who suffer losses. Hence it has now become imperative for distributors to purchase distribution rights of movies at reasonable prices such that they can obtain profits from it rather than just break even. This problem is a supervised learning problem and will review regression techniques discussed in the literature for predicting the lifetime net India collections of Bollywood films as well as use classification methods on our Bollywood movie dataset for multiclass classification. An evaluation of all the approaches is proposed in which the accuracy score will be reported.

关键词： Motion pictures machine learning algorithms Lead Classification algorithms Support vector machines Logistics Prediction algorithms

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Comparing Estimation Achievements by Determining Ideal Training Iteration Numbers in Supervised machine learning algorithms 2

Comparing Estimation Achievements by Determining Ideal Train...

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2017 International Conference on Computer Science and Engineering (UBMK)

作者： Bekmezci, Suleyman Yigit, Tuncay Suleyman Demirel Univ Fen Bilimleri Enstitusu Bilgisayar Muhendisligi Anabilim Dali Isparta Turkey Suleyman Demirel Univ Muhendisl Fak Bilgisayar Muhendisligi Bolumu Isparta Turkey

ISBN: (纸本)9781538609309

Data analysis has become important with the ever-increasing and diversified data from the past to the present. Supervised machine learning algorithms have been one of the preferred methods with time because of the ability to produce fast and effective solutions in data analysis. One of the most important problems of supervised machine learning algorithms is the inability to achieve the ideal predicted values which are obtained by adversely affecting the test values of little or over training. Therefore, determining the ideal number of training iterations for each algorithm is important for algorithm estimation success. In this study, it has been tried to obtain the best results in the estimation performance with the help of an application being developed in determining the ideal training iteration numbers of the supervised machine learning algorithms on a sample dataset and the prediction performances of the algorithms are compared with each others.

关键词： machine learning algorithms prediction success iteraton

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Detection of Distributed Denial of Service Attacks using machine learning algorithms in Software Defined Networks

Detection of Distributed Denial of Service Attacks using Mac...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Meti, Nisharani Narayan, D. G. Baligar, V. P. BV Bhoomaraddi Coll Engn & Technol Hubli Karnataka India KLE Technol Univ Hubli Karnataka India

ISBN: (纸本)9781509063673

Software Defined Networking (SDN) is a new promising networking concept which has a centralized control over the network and separates the data and control planes. This new approach provides abstraction of lower-level functionality and allows the network administrators to initialize, control, change, and manage network behavior programmatically. The centralized control, being the major advantage of SDN can sometimes also be a major security threat. If the intruder succeeds in attacking the central controller, he would get access to the entire system. The controller is highly vulnerable to Distributed Denial of Service (DDoS) attacks which lead to exhaustion of the system resources which causes non-availability of the services given by the controller. It is critical to detect the attacks in the controller at earlier stage. Many algorithms and techniques have been discovered for this purpose. But less work has been done in the field of SDN networks. Using machine learning algorithms for classifying the connections into legitimate and illegitimate is one such solution. We use two machine learning algorithms namely, the Support Vector machine (SVM) classifier and the Neural Network (NN) classifier to detect the suspicious and harmful connections.

关键词： SDN DDoS machine learning algorithms SVM Neural Network

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A SURVEY OF machine learning algorithms FOR BIG DATA ANALYTICS

A SURVEY OF MACHINE LEARNING ALGORITHMS FOR BIG DATA ANALYTI...

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International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

作者： Athmaja, S. Hanumanthappa, M. Kavitha, Vasantha Bangalore Univ Dept Comp Sci Bengaluru India Maharani Lakshmi Ammanni Coll Women Dept Comp Sci Bengaluru India

ISBN: (纸本)9781509032945

Big data analytics is a booming research area in computer science and many other industries all over the world It has gained great success in vast and varied application sectors. This includes social media, economy, finance, healthcare, agriculture, etc. Several intelligent machine learning techniques were designed and used to provide big data predictive analytics solutions. A literature survey of different machine learning techniques is provided in this paper. Also a study on commonly used machine learning algorithms for big data analytics is done and presented in this paper.

关键词： big data analytics machine learning algorithms technique prediction model

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Evaluation of machine learning algorithms for Intrusion Detection System 15

Evaluation of Machine Learning Algorithms for Intrusion Dete...

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15th IEEE International Symposium on Intelligent Systems and Informatics (SISY)

作者： Almseidin, Mohammad Alzubi, Maen Kovacs, Szilveszter Alkasassbeh, Mouhammd Univ Miskolc Dept Informat Technol H-3515 Miskolc Hungary Mutah Univ Dept Informat Technol Amman Jordan

ISBN: (纸本)9781538638552

Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate.

关键词： machine learning algorithms Intrusion detection Vegetation Algorithm design and analysis Prediction algorithms Testing Neural networks

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Novel Trends in Scaling Up machine learning algorithms 16

Novel Trends in Scaling Up Machine Learning Algorithms

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16th IEEE International Conference on machine learning and Applications (ICMLA)

作者： Lopes, Noel Ribeiro, Bernardete Polytech Guarda UDI Guarda Portugal Univ Coimbra CISUC Coimbra Portugal Univ Coimbra CISUC Dept Informat Engn Coimbra Portugal

ISBN: (纸本)9781538614174

Big Data has been a catalyst force for the machine learning (ML) area, forcing us to rethink existing strategies in order to create innovative solutions that will push forward the field. This paper presents an overview of the strategies for using machine learning in Big Data with emphasis on the high-performance parallel implementations on many-core hardware. The rationale is to increase the practical applicability of ML implementations to large-scale data problems. The common underlying thread has been the recent progress in usability, cost effectiveness and diversity of parallel computing platforms, specifically, the Graphics Processing Units (GPUs), tailored for a broad set of data analysis and machine learning tasks. In this context, we provide the main outcomes of a GPU machine learning Library (GPUMLib) framework, which empowers researchers with the capacity to tackle larger and more complex problems, by using high-performance implementations of well-known ML algorithms. Moreover, we attempt to give insights on the future trends of Big Data Analytics and the challenges lying ahead.

关键词： Graphics processing units machine learning algorithms Big Data Feature extraction Libraries Hardware Central Processing Unit

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Performance Analysis of machine learning algorithms on Diabetes Dataset using Big Data Analytics

Performance Analysis of Machine Learning Algorithms on Diabe...

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International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS)

作者： Kumar, P. Suresh Pranavi, S. Kakatiya Inst Technol & Sci Warangal Andhra Pradesh India Kakatiya Univ Warangal Andhra Pradesh India

ISBN: (纸本)9781538605141

New Technologies such as Big Data and Cloud is playing a vital role in providing solutions to Healthcare problems. Now-a-days healthcare data is growing very drastically day-by-day and it requires an efficient, effective and timely solution to reduce the mortality rate. One of the most critical chronic healthcare problems is diabetes. In Long run, this problem may leads to damage eyes, heart, kidneys and nerves of diabetes patient if improper medication is done which also leads to death. The aim of this paper is to analyze and compare different machine learning algorithms to identify a best predicting algorithm based on various metrics such as accuracy, kappa, precision, recall, sensitivity and specificity. A comprehensive study is done on diabetes dataset with Random Forest (RF), SVM, k-NN, CART and LDA algorithms. The achieved results shows that RF is giving more accurate predictions with compared to other algorithms.

关键词： Big data predictive analytics Hive and R Diabetes machine learning algorithms

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Assessing machine learning algorithms for Self-Management of Asthma 6

Assessing Machine Learning Algorithms for Self-Management of...

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6th IEEE International Conference on E-Health and Bioengineering (EHB)

作者： Kocsis, Otilia Arvanitis, Gerasimos Lalos, Aris Moustakas, Konstantinos Sont, Jacob K. Honkoop, Persijn J. Chung, Kian Fan Bonini, Matteo Usmani, Omar S. Fowler, Stephen Simpson, Andrew Univ Patras Dept Elect & Comp Engn Patras Greece Leiden Univ Med Ctr Leiden Netherlands Imperial Coll London Natl Heart & Lung Inst London England Univ Manchester Manchester Inst Biotechnol Manchester Lancs England

ISBN: (纸本)9781538603581

Control and monitoring of asthma progress is highly important for patient's quality of life and healthcare management. Emerging tools for self-management of various chronic diseases have the potential to support personalized patient guidance. This work presents the design aspects of the myAirCoach decision support system, with focus on the assessment of three machine learning approaches as support tools the first prototype implementation.

关键词： machine learning algorithms decision support system asthma self-management

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Assessment of machine learning algorithms for Determining Defective Classes in an Object-Oriented Software 6

Assessment of Machine Learning Algorithms for Determining De...

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6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

作者： Singh, Prabhpahul Malhotra, Ruchika JP Inst Informat Technol Dept Comp Sci Noida India Delhi Technol Univ Dept Comp Sci & Engn Delhi 110042 India

ISBN: (纸本)9781509030125

Software defect prediction is a well renowned field of software engineering. Determination of defective classes early in the lifecycle of a software product helps software practitioners in effective allocation of resources. More resources are allocated to probable defective classes so that defects can be removed in the initial phases of the software product. Such a practice would lead to a good quality software product. Although, hundreds of defect prediction models have been developed and validated by researchers, there is still a need to develop and evaluate more models to draw generalized conclusions. Literature studies have found machine learning (ML) algorithms to be effective classifiers in this domain. Thus, this study evaluates four ML algorithms on data collected from seven open source software projects for developing software defect prediction models. The results indicate superior performance of the Multilayer Perceptron algorithm over all the other investigated algorithms. The results of the study are also statistically evaluated to establish their effectiveness.

关键词： Defect Prediction machine learning algorithms Software Quality Improvement

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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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