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Journal articles on the topic 'Classification used machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Sabeti, Behnam, Hossein Abedi Firouzjaee, Reza Fahmi, Saeid Safavi, Wenwu Wang, and Mark D. Plumbley. "Credit Risk Rating Using State Machines and Machine Learning." International Journal of Trade, Economics and Finance 11, no. 6 (2020): 163–68. http://dx.doi.org/10.18178/ijtef.2020.11.6.683.

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Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed sta

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Carpenter, Chris. "Dynamometer-Card Classification Uses Machine Learning." Journal of Petroleum Technology 72, no. 03 (2020): 52–53. http://dx.doi.org/10.2118/0320-0052-jpt.

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Hall, Brendon. "Facies classification using machine learning." Leading Edge 35, no. 10 (2016): 906–9. http://dx.doi.org/10.1190/tle35100906.1.

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There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But now that large, complex data sets are widely available, there has been a proliferation of tools and techniques for analyzing them. Many free and open-source packages now exist that provide powerful additions to the geoscientist's toolbox, much of which used to be only available in proprietary (and expensive) software platf

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Hang, Weiqiang, and Timothy Banks. "Machine learning applied to pack classification." International Journal of Market Research 61, no. 6 (2019): 601–20. http://dx.doi.org/10.1177/1470785319841217.

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Pack or product classification is a quite common task in market research, particularly for sales tracking audits and related services. Electronic data sources have led to increased volumes, both in the sales volume being tracked and also the number of packs (or stock keeping units). The increase in packs needing to be classified presents a problem, in that, it needs to be done accurately and quickly. Traditional solutions using people for the classifications can be costly, due to the large number of people required to process the classifications in a timely and accurate manner. Reducing the ma

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Parhusip, Hanna Arini, Bambang Susanto, Lilik Linawati, Suryasatriya Trihandaru, Yohanes Sardjono, and Adella Septiana Mugirahayu. "Classification Breast Cancer Revisited with Machine Learning." International Journal on Data Science 1, no. 1 (2020): 42–50. http://dx.doi.org/10.18517/ijods.1.1.42-50.2020.

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The article presents the study of several machine learning algorithms that are used to study breast cancer data with 33 features from 569 samples. The purpose of this research is to investigate the best algorithm for classification of breast cancer. The data may have different scales with different large range one to the other features and hence the data are transformed before the data are classified. The used classification methods in machine learning are logistic regression, k-nearest neighbor, Naive bayes classifier, support vector machine, decision tree and random forest algorithm. The ori

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Butler, Brooks A., Spencer Wadsworth, Dallen Stark, et al. "Feature reduction of crowd noise used for machine learning classification." Journal of the Acoustical Society of America 146, no. 4 (2019): 2906. http://dx.doi.org/10.1121/1.5137086.

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Litman, D. J. "Cue Phrase Classification Using Machine Learning." Journal of Artificial Intelligence Research 5 (September 1, 1996): 53–94. http://dx.doi.org/10.1613/jair.327.

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Cue phrases may be used in a discourse sense to explicitly signal discourse structure, but also in a sentential sense to convey semantic rather than structural information. Correctly classifying cue phrases as discourse or sentential is critical in natural language processing systems that exploit discourse structure, e.g., for performing tasks such as anaphora resolution and plan recognition. This paper explores the use of machine learning for classifying cue phrases as discourse or sentential. Two machine learning programs (Cgrendel and C4.5) are used to induce classification models from sets

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Nikmon, Marcel, Roman Budjač, Daniel Kuchár, Peter Schreiber, and Dagmar Janáčová. "Convolutional Networks Used to Classify Video and Audio Data." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 27, no. 45 (2019): 113–20. http://dx.doi.org/10.2478/rput-2019-0034.

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Abstract Deep learning is a kind of machine learning, and machine learning is a kind of artificial intelligence. Machine learning depicts groups of various technologies, and deep learning is one of them. The use of deep learning is an integral part of the current data classification practice in today’s world. This paper introduces the possibilities of classification using convolutional networks. Experiments focused on audio and video data show different approaches to data classification. Most experiments use the well-known pre-trained AlexNet network with various pre-processing types of input

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B.Meena, Preeth, and Radha, P. "Disease Classification and Prediction using Ensemble Machine Learning Classification Algorithm." International Journal of Recent Technology and Engineering 9, no. 6 (2021): 202–14. http://dx.doi.org/10.35940/ijrte.f5507.039621.

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In today’s scenario, disease prediction plays an important role in medical field. Early detection of diseases is essential because of the fast food habits and life. In my previous study for predicting diseases using radiology test report , and to classify the disease as positive or negative three classifiers Naïve Bayes (NB), Support Vector Machine (SVM) and Modified Extreme Learning Machine (MELM was used to increase the accuracy of results. To increase the efficiency of predicting the disease and to find which disease pricks the society, ensemble machine learning algorithm is used. The huge

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Punia, Sanjeev Kumar, Manoj Kumar, Thompson Stephan, Ganesh Gopal Deverajan, and Rizwan Patan. "Performance Analysis of Machine Learning Algorithms for Big Data Classification." International Journal of E-Health and Medical Communications 12, no. 4 (2021): 60–75. http://dx.doi.org/10.4018/ijehmc.20210701.oa4.

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In broad, three machine learning classification algorithms are used to discover correlations, hidden patterns, and other useful information from different data sets known as big data. Today, Twitter, Facebook, Instagram, and many other social media networks are used to collect the unstructured data. The conversion of unstructured data into structured data or meaningful information is a very tedious task. The different machine learning classification algorithms are used to convert unstructured data into structured data. In this paper, the authors first collect the unstructured research data fro

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Dias Canedo, Edna, and Bruno Cordeiro Mendes. "Software Requirements Classification Using Machine Learning Algorithms." Entropy 22, no. 9 (2020): 1057. http://dx.doi.org/10.3390/e22091057.

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The correct classification of requirements has become an essential task within software engineering. This study shows a comparison among the text feature extraction techniques, and machine learning algorithms to the problem of requirements engineer classification to answer the two major questions “Which works best (Bag of Words (BoW) vs. Term Frequency–Inverse Document Frequency (TF-IDF) vs. Chi Squared (CHI2)) for classifying Software Requirements into Functional Requirements (FR) and Non-Functional Requirements (NF), and the sub-classes of Non-Functional Requirements?” and “Which Machine Lea

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Wu, Jiande, and Chindo Hicks. "Breast Cancer Type Classification Using Machine Learning." Journal of Personalized Medicine 11, no. 2 (2021): 61. http://dx.doi.org/10.3390/jpm11020061.

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Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Seq

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Graf, Arnulf B. A., Olivier Bousquet, Gunnar Rätsch, and Bernhard Schölkopf. "Prototype Classification: Insights from Machine Learning." Neural Computation 21, no. 1 (2009): 272–300. http://dx.doi.org/10.1162/neco.2009.01-07-443.

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We shed light on the discrimination between patterns belonging to two different classes by casting this decoding problem into a generalized prototype framework. The discrimination process is then separated into two stages: a projection stage that reduces the dimensionality of the data by projecting it on a line and a threshold stage where the distributions of the projected patterns of both classes are separated. For this, we extend the popular mean-of-class prototype classification using algorithms from machine learning that satisfy a set of invariance properties. We report a simple yet genera

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Et. al., Sarthika Dutt,. "Comparison of Classification Methods used in Machine Learning for Dysgraphia Identification." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (2021): 1886–91. http://dx.doi.org/10.17762/turcomat.v12i11.6142.

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Dysgraphia is a disorder that affects writing skills. Dysgraphia Identification at an early age of a child's development is a difficult task. It can be identified using problematic skills associated with Dysgraphia difficulty. In this study motor ability, space knowledge, copying skill, Visual Spatial Response are some of the features included for Dysgraphia identification. The features that affect Dysgraphia disability are analyzed using a feature selection technique EN (Elastic Net). The significant features are classified using machine learning techniques. The classification models compared

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Silva, Adrielle A., Mônica W. Tavares, Abel Carrasquilla, Roseane Misságia, and Marco Ceia. "Petrofacies classification using machine learning algorithms." GEOPHYSICS 85, no. 4 (2020): WA101—WA113. http://dx.doi.org/10.1190/geo2019-0439.1.

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Carbonate reservoirs represent a large portion of the world’s oil and gas reserves, exhibiting specific characteristics that pose complex challenges to the reservoirs’ characterization, production, and management. Therefore, the evaluation of the relationships between the key parameters, such as porosity, permeability, water saturation, and pore size distribution, is a complex task considering only well-log data, due to the geologic heterogeneity. Hence, the petrophysical parameters are the key to assess the original composition and postsedimentological aspects of the carbonate reservoirs. The

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Turior, Rashmi, Pornthep Chutinantvarodom, and Bunyarit Uyyanonvara. "Automatic Tortuosity Classification Using Machine Learning Approach." Applied Mechanics and Materials 241-244 (December 2012): 3143–47. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.3143.

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Retinopathy of Prematurity (ROP) is a vital cause of vision loss in premature infants, but early detection of its symptoms enables timely treatment and prevents blindness. Tortuosity is the major indicator of ROP that can potentially be automatically quantified. In this paper, which focuses on automatic tortuosity quantification and classification in images from infants at risk of ROP, we present a series of experiments on preprocessing, feature extraction, image feature selection and classification using nearest neighbor classifier. Fisher linear Discriminant analysis is used as a feature sel

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HOUFANI, Djihane, Sihem SLATNIA, Okba KAZAR, Noureddine ZERHOUNI, Hamza SAOULI, and Ikram REMADNA. "Breast cancer classification using machine learning techniques: a comparative study." Medical Technologies Journal 4, no. 2 (2020): 535–44. http://dx.doi.org/10.26415/2572-004x-vol4iss2p535-544.

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Background: The second leading deadliest disease affecting women worldwide, after lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed. These approaches show their effectiveness in data classification in many fields, especially in healthcare. Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kern

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Garcia-Dias, Rafael, Carlos Allende Prieto, Jorge Sánchez Almeida, and Ignacio Ordovás-Pascual. "Machine learning in APOGEE." Astronomy & Astrophysics 612 (April 2018): A98. http://dx.doi.org/10.1051/0004-6361/201732134.

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Context. The volume of data generated by astronomical surveys is growing rapidly. Traditional analysis techniques in spectroscopy either demand intensive human interaction or are computationally expensive. In this scenario, machine learning, and unsupervised clustering algorithms in particular, offer interesting alternatives. The Apache Point Observatory Galactic Evolution Experiment (APOGEE) offers a vast data set of near-infrared stellar spectra, which is perfect for testing such alternatives. Aims. Our research applies an unsupervised classification scheme based on K-means to the massive AP

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Badanina, Natalya Dmitriyevna, and Vladimir Anatolievich Sudakov. "Machine learning models for bank reviews classification." Keldysh Institute Preprints, no. 50 (2021): 1–14. http://dx.doi.org/10.20948/prepr-2021-50.

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Using the banking products and services review corpus, analysis is conducted to establish different text classification models. The paper explores different approaches to the processing of unstructured textual information. Based on the selected approaches, the review corpus on banking products and services received during the COVID-19 pandemic is analyzed. An automatic Internet resources parser has been developed to obtain the required training sample. Software has been developed that implemens basic methods for the classification models construction. This model can be used to create system fo

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Bickler, Simon H. "Machine Learning Arrives in Archaeology." Advances in Archaeological Practice 9, no. 2 (2021): 186–91. http://dx.doi.org/10.1017/aap.2021.6.

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OverviewMachine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are particularly suited toward rapid identification and classification of archaeological features and objects. The results of these new studies include identification of many new sites around the world and improved classification of large archaeological datasets. ML fits well with more traditional methods used in archaeological analysis, and it remains subject to both the benefits and difficulties of those

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Sami, Khan Nasik, Zian Md Afique Amin, and Raini Hassan. "Waste Management Using Machine Learning and Deep Learning Algorithms." International Journal on Perceptive and Cognitive Computing 6, no. 2 (2020): 97–106. http://dx.doi.org/10.31436/ijpcc.v6i2.165.

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Waste Management is one of the essential issues that the world is currently facing does not matter if the country is developed or under developing. The key issue in this waste segregation is that the trash bin at open spots gets flooded well ahead of time before the beginning of the following cleaning process. The isolation of waste is done by unskilled workers which is less effective, time-consuming, and not plausible because of a lot of waste. So, we are proposing an automated waste classification problem utilizing Machine Learning and Deep Learning algorithms. The goal of this task is to ga

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Qin, Yuping, Hamid Reza Karimi, Dan Li, Shuxian Lun, and Aihua Zhang. "A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm." Abstract and Applied Analysis 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/894246.

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A Mahalanobis hyperellipsoidal learning machine class incremental learning algorithm is proposed. To each class sample, the hyperellipsoidal that encloses as many as possible and pushes the outlier samples away is trained in the feature space. In the process of incremental learning, only one subclassifier is trained with the new class samples. The old models of the classifier are not influenced and can be reused. In the process of classification, considering the information of sample’s distribution in the feature space, the Mahalanobis distances from the sample mapping to the center of each hy

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Pilania, G., P. V. Balachandran, J. E. Gubernatis, and T. Lookman. "Classification ofABO3perovskite solids: a machine learning study." Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials 71, no. 5 (2015): 507–13. http://dx.doi.org/10.1107/s2052520615013979.

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We explored the use of machine learning methods for classifying whether a particularABO3chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, theAandBionic radii relative to the radius of O, and the bond valence distances between theAandBions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2–3 percentage points over using any one pair. We als

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Patel, Nimesh V., and Hitesh Chhinkaniwala. "Investigating Machine Learning Techniques for User Sentiment Analysis." International Journal of Decision Support System Technology 11, no. 3 (2019): 1–12. http://dx.doi.org/10.4018/ijdsst.2019070101.

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Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Naïve Bayes and Support Vector Machin

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Dankovičová, Zuzana, Dávid Sovák, Peter Drotár, and Liberios Vokorokos. "Machine Learning Approach to Dysphonia Detection." Applied Sciences 8, no. 10 (2018): 1927. http://dx.doi.org/10.3390/app8101927.

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This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological sp

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Salman, Hayder Mahmood. "Text Classification Based on Weighted Extreme Learning Machine." Ibn AL- Haitham Journal For Pure and Applied Science 32, no. 1 (2019): 203. http://dx.doi.org/10.30526/32.1.1978.

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The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM).

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Babhulkar, Mr Shubham. "Application of Machine Learning for Emotion Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1567–72. http://dx.doi.org/10.22214/ijraset.2021.36459.

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In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creat- ing a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training pro- cedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also in

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Assis, Carlos A. S., Eduardo J. Machado, Adriano C. M. Pereira, and Eduardo G. Carrano. "Hybrid deep learning approach for financial time series classification." Revista Brasileira de Computação Aplicada 10, no. 2 (2018): 54–63. http://dx.doi.org/10.5335/rbca.v10i2.7904.

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This paper proposes a combined approach of two machine learning techniques for financial time series classification. Boltzmann Restricted Machines (RBM) were used as the latent features extractor and Support Vector Machines (SVM) as the classifier. Tests were performed with real data of five assets from Brazilian Stock Market. The results of the combined RBM + SVM techniques showed better performance when compared to the isolated SVM, which suggests that the proposed approach can be suitable for the considered application.

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Hong, Qing, Peifei Feng, and Zhichao Cheng. "Clothing Product Reviews Mining Based on Machine Learning." International Journal of Online Engineering (iJOE) 11, no. 9 (2015): 71. http://dx.doi.org/10.3991/ijoe.v11i9.5069.

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This paper used the method of machine learning to study clothing product reviews classification based on big enterprise data. Taking Taobao clothing reviews as the object, it firstly excavated review themes from reviews corpus by association rules, and then searched review themes related to the categories by a method of mutual information to enrich the review themes. In the process of building classification models, commonly used SVM classifiers were studied in the beginning. After training and verification of a large amount of data, the classification accuracy reached 90.597%. In order to fur

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Nidhi Sengar and Amita Goe, Nishant Bansal. "Predicting Breast Cancer Classification Using Various Machine Learning Classification Algorithm." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 282–85. http://dx.doi.org/10.46501/ijmtst061252.

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Cancer diagnosis is one among the foremost studied problems within the medical domain. Several researchers have focused so as to enhance performance and achieve to get satisfactory results. Breast cancer[1] represents the second primary explanation for cancer deaths in women today and has become the foremost common cancer among women both within the developed and therefore the developing world in the last years. Breast cancer diagnosis is used to categorize the patients among benign (lacks ability to invade neighbouring tissue) from malignant (ability to invade neighbouring tissue) categories.

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Sihombing, Pardomuan Robinson. "HOW MACHINE LEARNING METHOD PERFORMANCE FOR IMBALANCED DATA." TEKNOKOM 4, no. 2 (2021): 48–52. http://dx.doi.org/10.31943/teknokom.v4i2.64.

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This study will examine the application of several classification methods to machine learning models by taking into account the case of imbalanced data. The research was conducted on a case study of classification modeling for working status in Banten Province in 2020. The data used comes from the National Labor Force Survey, Statistics Indonesia. The machine learning methods used are Classification and Regression Tree (CART), Naïve Bayes, Random Forest, Rotation Forest, Support Vector Machine (SVM), Neural Network Analysis, One Rule (OneR), and Boosting. Classification modeling using resample

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Arora, Vinay, Rohan Leekha, Raman Singh, and Inderveer Chana. "Heart sound classification using machine learning and phonocardiogram." Modern Physics Letters B 33, no. 26 (2019): 1950321. http://dx.doi.org/10.1142/s0217984919503214.

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This research pertains to classification of the heart sound using digital Phonocardiogram (PCG) signals targeted to screen for heart ailments. In this study, an existing variant of the decision tree, i.e. XgBoost has been used with unsegmented heart sound signal. The dataset provided by PhysioNet Computing in Cardiology (CinC) Challenge 2016 has been used to validate the technique proposed in this research work. The said dataset comprises six databases (A–F) having 3240 heart sound recordings in all with the duration lasting from 5–120 s. The approach proposed in this paper has been compared w

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Srikanth, Phani, Amarjot Singh, Devinder Kumar, Aditya Nagrare, and Vivek Angoth. "A Comparison of Machine Learning Classifiers." Advanced Materials Research 271-273 (July 2011): 149–53. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.149.

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A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and Local SVM applied to a cancer dataset. The comparison is made on the basis of precision and accuracy along with the training time analysis. Finally, the efficacy of the classifiers is found.

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Islam, S. M. Mohidul, Suriya Islam Bani, and Rupa Ghosh. "Content-based Fish Classification Using Combination of Machine Learning Methods." International Journal of Information Technology and Computer Science 13, no. 1 (2021): 62–68. http://dx.doi.org/10.5815/ijitcs.2021.01.05.

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Fish species recognition is an increasing demand to the field of fish ecology, fishing industry sector, fisheries survey applications, and other related concerns. Traditionally, concept-based fish specifies identification procedure is used. But it has some limitations. Content-based classification overcomes these problems. In this paper, a content-based fish recognition system based on the fusion of local features and global feature is proposed. For local features extraction from fish image, Local Binary Pattern (LBP), Speeded-Up Robust Feature (SURF), and Scale Invariant Feature Transform (SI

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Pouliakis, Abraham, Periklis Foukas, Konstantinos Triantafyllou, et al. "Machine Learning for Gastric Cancer Detection." International Journal of Reliable and Quality E-Healthcare 9, no. 2 (2020): 48–58. http://dx.doi.org/10.4018/ijrqeh.2020040104.

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The objective of this study is the investigation of the potential value of a logistic regression model for the classification of gastric cytological data. The model was based on the morphological features of cell nuclei. The aim was the discrimination of benign from malignant nuclei and subsequently patients. Cytological images of gastric smears were analyzed by an image analysis system capable to extract cell nuclear features. Measurements from 50% of the patients were selected as a training set for model creation, while the measurements from the remaining patients were used as test set to va

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Özdemir, E., F. Remondino, and A. Golkar. "AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 843–49. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-843-2019.

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Abstract. With recent advances in technology, 3D point clouds are getting more and more frequently requested and used, not only for visualization needs but also e.g. by public administrations for urban planning and management. 3D point clouds are also a very frequent source for generating 3D city models which became recently more available for many applications, such as urban development plans, energy evaluation, navigation, visibility analysis and numerous other GIS studies. While the main data sources remained the same (namely aerial photogrammetry and LiDAR), the way these city models are g

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Gong, Mingxing. "A Novel Performance Measure for Machine Learning Classification." International Journal of Managing Information Technology 13, no. 1 (2021): 11–19. http://dx.doi.org/10.5121/ijmit.2021.13101.

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Machine learning models have been widely used in numerous classification problems and performance measures play a critical role in machine learning model development, selection, and evaluation. This paper covers a comprehensive overview of performance measures in machine learning classification. Besides, we proposed a framework to construct a novel evaluation metric that is based on the voting results of three performance measures, each of which has strengths and limitations. The new metric can be proved better than accuracy in terms of consistency and discriminancy.

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De Sio, Chiara. "Machine Learning in KM3NeT." EPJ Web of Conferences 207 (2019): 05004. http://dx.doi.org/10.1051/epjconf/201920705004.

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The KM3NeT Collaboration is building a network of underwater Cherenkov telescopes at two sites in the Mediterranean Sea, with the main goals of investigating astrophysical sources of high-energy neutrinos (ARCA) and of determining the neutrino mass hierarchy (ORCA). Various Machine Learning techniques, such as Random Forests, BDTs, Shallow and Deep Networks are being used for diverse tasks, such as event-type and particle identification, energy/direction estimation, source identification, signal/background discrimination and data analysis, with sound results as well as promising research paths

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Cheng, Ting-Yun, Christopher J. Conselice, Alfonso Aragón-Salamanca, et al. "Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging." Monthly Notices of the Royal Astronomical Society 493, no. 3 (2020): 4209–28. http://dx.doi.org/10.1093/mnras/staa501.

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ABSTRACT There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual clas

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KUTSCHENREITER-PRASZKIEWICZ, Izabela. "MACHINE LEARNING IN SMED." Journal of Machine Engineering 18, no. 2 (2018): 31–40. http://dx.doi.org/10.5604/01.3001.0012.0923.

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The paper discusses Single Minute Exchange of Die (SMED) and machine learning methods, such as neural networks and a decision tree. SMED is one of lean production methods for reducing waste in the manufacturing process, which helps to reorganize a conversion of the manufacturing process from current to the next product. SMED needs set-up activity analyses, which include activity classification, working time measurement and work improvement. The analyses presented in the article are focused on selecting the time measurement method useful from the SMED perspective. Time measurement methods and t

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Tanone, Radius, and Arnold B. Emmanuel. "Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang." AITI 17, no. 1 (2020): 42–55. http://dx.doi.org/10.24246/aiti.v17i1.42-55.

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Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classifi

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Wu, Guorong, Zichen Liu, and Xuhui Chen. "Weighted Classification of Machine Learning to Recognize Human Activities." Complexity 2021 (April 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/5593916.

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This paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be used during benchmarking the system. Unlike previous methods that need size or length of shapes mainly to represent the cues when m

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Tiitta, Markku, Valtteri Tiitta, Jorma Heikkinen, Reijo Lappalainen, and Laura Tomppo. "Classification of Wood Chips Using Electrical Impedance Spectroscopy and Machine Learning." Sensors 20, no. 4 (2020): 1076. http://dx.doi.org/10.3390/s20041076.

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Wood chips are extensively utilised as raw material for the pulp and bio-fuel industry, and advanced material analyses may improve the processes in utilizing these products. Electrical impedance spectroscopy (EIS) combined with machine learning was used in order to analyse heartwood content of pine chips and bark content of birch chips. A novel electrode system integrated in a sampling container was developed for the testing using frequency range 42 Hz–5 MHz. Three electrode pairs were used to measure the samples in x-, y- and z-direction. Three machine learning methods were used: K-nearest ne

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Ray, S. S. "EXPLORING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR CROP CLASSIFICATION USING SENTINEL 2 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 573–78. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-573-2019.

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<p><strong>Abstract.</strong> Crop Classification and recognition is a very important application of Remote Sensing. In the last few years, Machine learning classification techniques have been emerging for crop classification. Google Earth Engine (GEE) is a platform to explore the multiple satellite data with different advanced classification techniques without even downloading the satellite data. The main objective of this study is to explore the ability of different machine learning classification techniques like, Random Forest (RF), Classification And Regression Trees (CAR

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Wang, Xian-Fang, Peng Gao, Yi-Feng Liu, Hong-Fei Li, and Fan Lu. "Predicting Thermophilic Proteins by Machine Learning." Current Bioinformatics 15, no. 5 (2020): 493–502. http://dx.doi.org/10.2174/1574893615666200207094357.

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Background: Thermophilic proteins can maintain good activity under high temperature, therefore, it is important to study thermophilic proteins for the thermal stability of proteins. Objective: In order to solve the problem of low precision and low efficiency in predicting thermophilic proteins, a prediction method based on feature fusion and machine learning was proposed in this paper. Methods: For the selected thermophilic data sets, firstly, the thermophilic protein sequence was characterized based on feature fusion by the combination of g-gap dipeptide, entropy density and autocorrelation c

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Budjač, Roman, Marcel Nikmon, Peter Schreiber, Barbora Zahradníková, and Dagmar Janáčová. "Automated Machine Learning Overview." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 27, no. 45 (2019): 107–12. http://dx.doi.org/10.2478/rput-2019-0033.

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Abstract This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several da

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Ben Salem, Yassine, and Mohamed Naceur Abdelkrim. "Texture classification of fabric defects using machine learning." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 4390. http://dx.doi.org/10.11591/ijece.v10i4.pp4390-4399.

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In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for su

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S G, Deekshith. "Twitter Bots Detection Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1536–41. http://dx.doi.org/10.22214/ijraset.2021.36637.

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The social network, a crucial part of our life is plagued by online impersonation and fake accounts. Fake profiles are mostly used by the intruders to carry out malicious activities such as harming person , identity theft and privacy intrusion in Online Social Network(OSN). Hence identifying an account is genuine or fake is one of the critical problem in OSN .In this paper we proposed many classification algorithm like Support Vector Machine algorithm ,KNN, and Random Forest algorithm. It also studies the comparison of classification methods on Spam User dataset which is used to select the bes

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Nepacina, M. R. J., J. R. F. Foronda, K. J. F. Haygood, et al. "Differentiation of Rubber Cup Coagulum Through Machine Learning." Scientia Agriculturae Bohemica 50, no. 1 (2019): 51–55. http://dx.doi.org/10.2478/sab-2019-0008.

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Abstract A support vector machine classification algorithm was formulated to differentiate rubber cup coagulum according to the type of acid coagulant used. Two classification models were established, a binary classification algorithm and a model that can identify if formic, acetic, sulfuric acid, or no acid was used to induce coagulation. The models were based on the properties of the rubber cup coagulum that are easy to measure, such as tensile strength, water contact angle, and density. The binary classification model, which differentiates the industry-accepted formic acid-coagulated rubber

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Assiri, Adel S., Saima Nazir, and Sergio A. Velastin. "Breast Tumor Classification Using an Ensemble Machine Learning Method." Journal of Imaging 6, no. 6 (2020): 39. http://dx.doi.org/10.3390/jimaging6060039.

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Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer clas

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