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Hybrid machine learning system for solving fraud detection tasks 3

Hybrid machine learning system for solving fraud detection t...

引用

3rd IEEE international conference on Data Stream Mining and processing, DSMP 2020

作者： Vynokurova, Olena Peleshko, Dmytro Bondarenko, Oleksandr Ilyasov, Vadim Serzhantov, Vladislav Peleshko, Marta GeoGuard Kharkiv Ukraine Lviv State University of Life Safety Lviv Ukraine

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132143

In parallel with technological development the problem of fraud detection is becoming more and more important. Increasing number of electronic transactions in various business environments, on the one hand, and software and technology development, on the other hand, lead to an active increase in electronic crime. In the paper the hybrid system of machine learning for solving tasks of anomalies detection has been proposed. This hybrid system consists of two subsystems - anomalies detection subsystem (based on unsupervised learning) and the interpretation subsystem of anomaly type (based on supervised system). The advantage of proposed hybrid system is the high-speed data processing when the data are fed in real time. The effectiveness of the proposed approach was confirmed during the solution of the detecting anomalies problem based on real data streams. © 2020 IEEE.

关键词： Hybrid systems

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Smart Surveillance of Driver Using machine learning

Smart Surveillance of Driver Using Machine Learning

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international conference on signal processing and Communication (ICSPC)

作者： T. P. Rani Sai Kaavya Sree. M P. Sharmila Sri Sairam Engineering College

Road accidents are often caused by drunk driving and drowsiness. This paper detects the drowsiness of the driver. Besides that, intake of alcohol by the driver is also detected. The main goal of this proposed system is to reduce the number of accidents due to driver's Drowsiness and alcohol intake. This increases the transportation safety. This system also makes use of a USB camera and Alcohol sensor (MQ-135)by which alcohol intake is detected and drowsiness of the driver is also monitored. OpenCV, a machine learning software examines vision-based applications. It is the one which detects the driver's drowsiness. The idea comes with an application which helps to track the drowsiness and also alert the passenger and the owner if the driver is drunk. This will perform tasks like notifying and the customer with alarm by a message notification. The ultimate aim of this system is to design a feasible system that decreases the fatal accidents caused by the drowsiness of the driver and can also display the percentage of alcohol consumed by the driver.

关键词： Road accidents Surveillance machine learning signal processing Universal Serial Bus Fatigue Software

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Classification of parkinson's disease using speech attributes with parametric and nonparametric machine learning techniques 3

Classification of parkinson's disease using speech attribute...

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3rd international conference on Intelligent Sustainable Systems, ICISS 2020

作者： Sharanyaa, S. Renjith, P.N. Ramesh, K. Hindustan Institute of Technology Science Chennai Tamilnadu India

ISBN: (纸本)9781728170893

Parkinson's disease is a neurological disorder that affects the central nervous system which results in improper functioning of body movements including difficulty in speaking, walking, rigidity, and so on. The aim is to classify Parkinson's disease based on acoustic voice features in dysphonic speech disorder in Parkinson's patients using state of art machine learning algorithms. Some of the parametric and nonparametric machine learning techniques was used on the voice features dataset. This research aims to evaluate the performance of state of art algorithms such as the Naïve Bayes Algorithm, Logistic regression, K-Nearest Neighbors, and Random Forest Algorithm to detect Parkinson's disease which gives higher classification accuracy. The performance is evaluated by pre-processing the data based on speech attributes and fed into the machine learning models for further processing. Various performance metrics like precision, recall, F1 score are computed for all four machine learning techniques and the results show that nonparametric models using Random Forest and K-Nearest Neighbors produce higher classification accuracy of 87.2% and 90.2% compared to parametric models. © 2020 IEEE.

关键词： Decision trees

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machine learning approaches for accurate image recognition and detection for plant disease 3rd

Machine learning approaches for accurate image recognition a...

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3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

作者： Vashisht, Swati Kumar, Praveen Trivedi, Munesh C. Computer Science and Engineering Amity University NoidaUttar Pradesh India Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789811553448

In recent world where world population has reached to 7.7 billion and still increasing at a rapid rate, and it would reach to approx 9.8 billion by 2050. With the increase of population, several concerns also grow side by side in parallel, like environment, development, and food security and availability and increase in population leads to increase in demand of food. Technically, there is only 510.1 million km2 of area on earth in total. And about which very small portion is used for crop cultivations. Hence, it becomes important to make best possible use of the land available for growing crops and feed our population accordingly [1]. With the fast improvement of Internet and information technology, maintaining large records about any specific dataset and processing it according to the requirements has become very easy and efficient [2]. The recent survey predicts that 30% of jobs will be extinguished in the next nine years as the machine learning algorithms are going to replace these jobs with automation and zero human work interface machineries. Many technologies come and go in the timeline of years but these are some basic technologies which cannot be replaced or can never extinguish till the final rock on this planet [3]. In this paper, we will be analyzing different approaches of machine learning which are involved in plant disease detection. This paper will also explain the working of various approaches or algorithms and their comparative measures with other methods and technologies. © Springer Nature Singapore Pte Ltd 2021.

关键词： Cultivation

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Citation Classification Prediction Implying Text Features Using Natural Language processing and Supervised machine learning Algorithms 3rd

Citation Classification Prediction Implying Text Features Us...

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3rd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2020

作者： Porwal, Priya Devare, Manoj H. Amity University Mumbai410206 India

ISBN: (纸本)9789811605062

Citation count of any research paper published is valuable to researcher’s career. Millions of research papers are available and it keeps growing fast. These published articles referred by upcoming research, but not all papers having same impact because of huge depository of research papers, only few publications reach researchers. So it will be interesting to study which articles are getting more citation, so in this paper citation count are considered as quantification parameter. To bring quality literature, it is important to analyze text in the research paper. With this motivation, this paper focuses on different technology to predict citation count considering text and structure features from research articles. For implementation purpose, title, abstract and conclusion fields are abstracted from research papers as the main content to analyze. The linguistic analysis of the corpus of research papers having high and low citation count is done using Natural Language processing and machine learning. A system is implemented using supervised classification model, which takes input few features of a particular publication and gives output as it belongs to either high or low citation category after 9 to 10 years of its publication. There are few classification models considered to evolve learning process and appraise its performance using few performance measures. Experimental results on dataset shows performance accuracy of 60.67% by Random Forest model. The comprehensive experiments on dataset exhibits that proposed models outperform and achieve convincing results. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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The Effect of Different Feature Selection Methods for Classification of Melanoma 1

引用

3rd international Symposium on signal and Image processing, ISSIP 2020

作者： Maiti, Ananjan Chatterjee, Biswajoy Department of Information Technology Techno International New Town Kolkata India Kolkata India

ISBN: (数字)9789813369665

ISBN: (纸本)9789813369658

Features of skin cancer have a certain impact on computer-aided diagnosis (CAD) systems. Researchers had used different techniques to experience with patterns. The melanoma lesion could also be identified with a different texture, shape, and clinical features. The proposed study has used 22 features of texture, 12 features of shape. The study has exposed three feature selection (FS) techniques like gradient boosting (GB), particle swarm optimization (PSO), and statistical approach. The features are evaluated with these methods and highlighted the effectiveness of each feature for the classification of melanoma. Selected key features have less than the cost of computation. The reduced feature set can make classification better than per the selection of the model. The random forest has the highest performance based on accuracy as it got the highest accuracy of 97.1% on GB feature sets. Decision tree and K-nearest neighbors have shown a decent accuracy of 96.8 and 93.3% on GB feature sets. The study rewards upcoming explorations to select an effective subset of features for machine learning and deep learning techniques. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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machine learning Model Based Expert System for Pig Disease Diagnosis 3rd

Machine Learning Model Based Expert System for Pig Disease D...

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3rd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2020

作者： Robindro, Khumukcham Singh, Ksh. Nilakanta Singh, Leishangthem Sashikumar Department of Computer Science Manipur University Imphal795003 India

ISBN: (纸本)9789811604928

This paper describes the importance of machine learning (ML) algorithms in the design and development of diagnostic expert systems. The designed system is intended on how ML techniques can be used in the prediction of pig diseases through visible symptoms and their past behaviours. These types of systems are very useful in situations where the domain experts in the field are not readily available. It is important to make an accurate diagnosis of pig diseases to provide control strategies for any symptom shows in a pig to prevent related health issues. The main concerned in the study begins with the collection of disease symptoms and related information of pigs during their life span from the domain experts and literatures in the domain. The acquired knowledge and information are then pre-processed to be used for being trained different ML algorithms. The system development involves the training and analysis of different ML classifier models and their performances. It has thus been found in the study that SVM classifier can produce more accurate results in disease prediction. The paper finally presents the design and development of an expert system for the diagnosis of Pig diseases using SVM classifier which is trained with pig disease dataset. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Expert systems

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Pre-processing Task for Classifying Satire in Indonesian News Headline 3

Pre-processing Task for Classifying Satire in Indonesian New...

引用

3rd international conference on Information and Communications Technology, ICOIACT 2020

作者： Imrona, Mas Siti Widyawan Nugroho, Lukito Edi Faculty of Engineering Universitas Gadjah Mada Departement of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9781728173566

One of challenges in the topic of natural language processing is detecting satire sentences. In the news headlines, satire commonly used to criticize the goverment. This study aims to know the impact of preprocessing in the classification of satire sentences in Indonesian news headlines. Besides, feature extraction was conducted by using Term Frequency Inverse Document frequency (TF-IDF) employing machine learning: Naive Bayes to classify the satire in Indonesian news headline. In thisstudy, there were six preprocessing combinations used which were;breaking down the word into tokens (tokenizing), changing word into base form (stemming), removing stopword, and removing punctuation. The result showed that preprocessing affected the accuracy of sarcastic sentence classification which are not on the dataset of Indonesian news headlines. The highest accuracy of 90.38% obtained by combination of standardized preprocessing which were tokenizing and lowercasing. © 2020 IEEE.

关键词： machine learning

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Two approaches to machine learning classification of time series based on recurrence plots 3

Two approaches to machine learning classification of time se...

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3rd IEEE international conference on Data Stream Mining and processing, DSMP 2020

作者： Kirichenko, Lyudmyla Radivilova, Tamara Bulakh, Vitalii Zinchenko, Petro Saif Alghawli, Abed Kharkiv National University of Radio Electronics Department of Applied Mathematics Kharkiv Ukraine Kharkiv National University of Radio Electronics V.V. Popovskyy Dept. of Infocommunication Engineering Kharkiv Ukraine Prince Sattam Bin Abdulaziz University Department of Computer Science Aflaj Saudi Arabia

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132143

The article considers the task of classifying fractal time series based on the construction of their recurrence plots. Short realizations of EEG signals were used as input data. Two classification machine learning methods were considered: in the first case, quantitative fractal and recurrent characteristics of the time series were classification features, in the second case, image recognition of recurrence plots was carried out. The results showed fairly high classification quality for both methods, and relative advantage of the image classification method. © 2020 IEEE.

关键词： Time series

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Region-based Birdcall Recognition Using signal processing

Region-based Birdcall Recognition Using Signal Processing

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international conference on machine learning, Big Data and Business Intelligence (MLBDBI)

作者： Yunqi Zhang University of California Santa Barbara California United States

ISBN: (纸本)9781665417914

The unique trait of birdsong provides a method for the investigation of bird population based on birdcall recognition. Due to the living habit and geographical distribution of bird species, the recognition of birdcall might be affected by birds' location. The effect of location on a different type of models' prediction performance is investigated in this paper. This research implements three types of predictive models - convolutional neural network (CNN), recurrent neural network (RNN), and support vector machine (SVM) - over three countries: Japan, Sweden, and Norway. signal processing is used for pre-processing of the dataset to obtain Mel Frequency Cepstrumas, the input of three models. Based on the evaluation of model performances, including training time, accuracy, AUC score, F1 score, recall, and precision, the best model of each country is selected: SVM for Japan with 93% accuracy, CNN for Sweden and Norway with 98% and 94% accuracy respectively. The difference in the performance of models in different countries indicates that a country-based prediction of birdsong using varied models may help improve the performance of birdcall recognition.

关键词： Support vector machines Training Recurrent neural networks Sociology machine learning Predictive models signal processing

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