scholarly journals Erratic server behavior detection using machine learning on streams of monitoring data

2020 ◽  
Vol 245 ◽  
pp. 07002
Author(s):  
Martin Adam ◽  
Luca Magnoni ◽  
Martin Pilát ◽  
Dagmar Adamová
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Distributed Applications ◽  
Machine Learning Algorithms ◽  
Single Server ◽  
Distributed Application ◽  
Behavior Detection ◽  
Processing Techniques ◽  
Standard Techniques ◽  
Erratic Behavior

With the explosion of the number of distributed applications, a new dynamic server environment emerged grouping servers into clusters, utilization of which depends on the current demand for the application. To provide reliable and smooth services it is crucial to detect and fix possible erratic behavior of individual servers in these clusters. Use of standard techniques for this purpose requires manual work and delivers sub-optimal results. Using only application agnostic monitoring metrics our machine learning based method analyzes the recent performance of the inspected server as well as the state of the rest of the cluster, thus checking not only the behavior of the single server, but the load on the whole distributed application as well. We have implemented our method in a Spark job running in the CERN MONIT infrastructure. In this contribution we present results of testing multiple machine learning algorithms and pre-processing techniques to identify the servers erratic behavior. We also discuss the challenges of deploying our new method into production.

scholarly journals Computerized Answer Grading

2021 ◽  
Vol 9 (VI) ◽  
pp. 618-619
Author(s):  
Anurag Langan
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computer Technology ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Grade Student ◽  
Processing Techniques

Grading student answers is a tedious and time-consuming task. A study had found that almost on average around 25% of a teacher's time is spent in scoring the answer sheets of students. This time could be utilized in much better ways if computer technology could be used to score answers. This system will aim to grade student answers using the various Natural Language processing techniques and Machine Learning algorithms available today.

scholarly journals Answer Script Evaluation using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 849-852
Author(s):  
Dr. K. Suresh
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Methods ◽  
Language Processing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Text Extraction ◽  
Processing Techniques

The current way of checking answer scripts is hectic for the college. They need to manually check the answers and allocate the marks to the students. Our proposed system uses Machine Learning and Natural Language Processing techniques to beat this. Machine learning algorithms use computational methods to find out directly from data without hopping on predetermined rules. NLP algorithms identify specific entities within the text, explore for key elements during a document, run a contextual search for synonyms and detect misspelled words or similar entries, and more. Our algorithm performs similarity checking and also the number of words associated with the question exactly matched between two documents. It also checks whether the grammar is correctly used or not within the student's answer. Our proposed system performs text extraction and evaluation of marks by applying Machine Learning and Natural Language Processing techniques.

scholarly journals Review on anomalous gait behavior detection using machine learning algorithms

2020 ◽  
Vol 9 (5) ◽  
pp. 2090-2096
Author(s):  
Hana’ Abd Razak ◽  
M. Ahmed M. Saleh ◽  
Nooritawati Md Tahir
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Gait Recognition ◽  
Learning Algorithms ◽  
Anomalous Behavior ◽  
Machine Learning Algorithms ◽  
Behavior Detection ◽  
Detection And Recognition

A review on anomalous behavior in crime by other researchers is discussed in this study that focused specifically on the linkage between anomalous behaviors. Next, comprehensive reviews related to gait recognition in utilizing machine learning algorithms for detection and recognition of anomalous behavior is elaborated too. The review begins with the conventional approach of gait recognition that includes feature extraction and classification using PCA, OLS, ANN, and SVM. Further, the review focused on utilization of deep learning namely CNN for anomalous gait behavior detection and transfer learning using pre-trained CNNs such as AlexNet, VGG, and a few more. To the extent of our knowledge, very few studies investigated and explored crime related anomalous behavior based on their gaits, hence this will be the next study that we will explore.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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