scholarly journals COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR PURCHASING FORECAST

2021 ◽  
pp. 59-68
Author(s):  
Rabia ÖZDEMİR ◽  
Münevver TURANLI
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Network Concept ◽  
Vector Machines ◽  
Accuracy Ratio ◽  
Future Data ◽  
Technological Developments ◽  
Selection Of

With the development of computer technologies and invention of internet, many concepts have entered our lives. With the starting of wide usage of globalized internet network, concept of machine learning has emerged in time for smarter management of data flow in big dimensions. In line with technological developments, all activities began to be carried to digital environment and as a result of this, concept of e-commerce has entered our lives. E-commerce is one of the areas where machine learning is used most widely. By examining product purchasing situations in accordance with data available at the enterprises, various researches have been made for selection of most appropriate model in order to predict future data. In the study it was mentioned about concepts of e-commerce and machine learning and by applying Logistic Regression, Naïve Bayes and Support Vector Machines being machine learning classification algorithms, it has been aimed to determine the model having best accuracy ratio.

Machine Learning Classification Algorithms to Predict aGvHD following Allo-HSCT: A Systematic Review

2019 ◽  
Vol 58 (06) ◽  
pp. 205-212
Author(s):  
Cirruse Salehnasab ◽  
Abbas Hajifathali ◽  
Farkhondeh Asadi ◽  
Elham Roshandel ◽  
Alireza Kazemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Review ◽  
Predictor Variables ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbors ◽  
Hematopoietic Stem ◽  
Machine Learning Classification ◽  
Graft Versus Host ◽  
Meta Analyses

Abstract Background The acute graft-versus-host disease (aGvHD) is the most important cause of mortality in patients receiving allogeneic hematopoietic stem cell transplantation. Given that it occurs at the stage of severe tissue damage, its diagnosis is late. With the advancement of machine learning (ML), promising real-time models to predict aGvHD have emerged. Objective This article aims to synthesize the literature on ML classification algorithms for predicting aGvHD, highlighting algorithms and important predictor variables used. Methods A systemic review of ML classification algorithms used to predict aGvHD was performed using a search of the PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases undertaken up to April 2019 based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statements. The studies with a focus on using the ML classification algorithms in the process of predicting of aGvHD were considered. Results After applying the inclusion and exclusion criteria, 14 studies were selected for evaluation. The results of the current analysis showed that the algorithms used were Artificial Neural Network (79%), Support Vector Machine (50%), Naive Bayes (43%), k-Nearest Neighbors (29%), Regression (29%), and Decision Trees (14%), respectively. Also, many predictor variables have been used in these studies so that we have divided them into more abstract categories, including biomarkers, demographics, infections, clinical, genes, transplants, drugs, and other variables. Conclusion Each of these ML algorithms has a particular characteristic and different proposed predictors. Therefore, it seems these ML algorithms have a high potential for predicting aGvHD if the process of modeling is performed correctly.

scholarly journals Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

Science ◽  
2019 ◽  
Vol 363 (6424) ◽  
pp. eaau5631 ◽  
Author(s):  
Andrew F. Zahrt ◽  
Jeremy J. Henle ◽  
Brennan T. Rose ◽  
Yang Wang ◽  
William T. Darrow ◽  
...  
Keyword(s):  
Machine Learning ◽  
Catalyst Design ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Asymmetric Reaction ◽  
Stage Of Development ◽  
Chiral Phosphoric Acid ◽  
Vector Machines ◽  
Feed Forward Neural Networks ◽  
Selection Of

Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid–catalyzed thiol addition toN-acylimines.

scholarly journals Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms

2019 ◽  
Vol 11 (11) ◽  
pp. 1279 ◽  
Author(s):  
Pramaditya Wicaksono ◽  
Prama Ardha Aryaguna ◽  
Wahyu Lazuardi
Keyword(s):  
Machine Learning ◽  
Classification Scheme ◽  
Learning Algorithm ◽  
Classification Tree ◽  
Habitat Mapping ◽  
Support Vector ◽  
Benthic Habitat ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Mapping Model

This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemujan Island was used in developing the model, while Karimunjawa, Menjangan Besar, and Menjangan Kecil Islands served as test areas. The results obtained indicated that RF was more accurate than any other classification algorithm based on the statistics and benthic habitats spatial distribution. The maximum accuracy of RF was 94.17% (4 classes) and 88.54% (14 classes). The accuracies from RF, CTA, and SVM were consistent across different input bands for each classification scheme. The application of RF model in the classification of benthic habitat in other areas revealed that it is recommended to make use of the more general classification scheme in order to avoid several issues regarding benthic habitat variations. The result also established the possibility of mapping a benthic habitat without the use of training areas.

scholarly journals Diabetes Prediction Using Machine Learning Techniques

2021 ◽  
pp. 150-152
Author(s):  
Seyma Kiziltas Koc ◽  
Mustafa Yeniad
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
High Performance ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Machine Learning Classification

Technologies which are used in the healthcare industry are changing rapidly because the technology is evolving to improve people's lifestyles constantly. For instance, different technological devices are used for the diagnosis and treatment of diseases. It has been revealed that diagnosis of disease can be made by computer systems with developing technology.Machine learning algorithms are frequently used tools because of their high performance in the field of health as well as many field. The aim of this study is to investigate different machine learning classification algorithms that can be used in the diagnosis of diabetes and to make comparative analyzes according to the metrics in the literature. In the study, seven classification algorithms were used in the literature. These algorithms are Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Random Forest, Decision Trees, Support Vector Machine and Naive Bayes. Firstly, classification performance of algorithms are compared. These comparisons are based on accuracy, sensitivity, precision, and F1-score. The results obtained showed that support vector machine algorithm had the highest accuracy with 78.65%.

scholarly journals 5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

2018 ◽  
Vol 1 (1) ◽  
pp. 6 ◽  
Author(s):  
Lubna Farhi ◽  
Razia Zia ◽  
Zain Anwar Ali
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Classification Algorithms ◽  
Mr Images ◽  
Data Set ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Brain Mr Images ◽  
Artificial Neural Network Ann

Brain cancer has remained one of the key causes ofdeaths in people of all ages. One way to survival amongst patientsis to correctly diagnose cancer in its early stages. Recentlymachine learning has become a very important tool in medicalimage classification. Our approach is to examine and comparevarious machine learning classification algorithms that help inbrain tumor classification of Magnetic Resonance (MR) images.We have compared Artificial Neural Network (ANN), K-nearestNeighbor (KNN), Decision Tree (DT), Support Vector Machine(SVM) and Naïve Bayes (NB) classifiers to determine theaccuracy of each classifier and find the best amongst them forclassification of cancerous and noncancerous brain MR images.We have used 86 MR images and extracted a large number offeatures for each image. Since the equal number of images, havebeen used thus there is no suspicion of results being biased. Forour data set the most accurate results were provided by ANN. Itwas found that ANN provides better results for medium to largedatabase of Brain MR Images.

scholarly journals Human Papillomavirus Targeted Immunotherapy Outcome Prediction Using Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3598-3611
Author(s):  
Vidya Moni
Keyword(s):  
Machine Learning ◽  
Human Papillomavirus ◽  
Outcome Prediction ◽  
Performance Comparison ◽  
Gradient Boosting ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Nearest Neighbours ◽  
Vector Machines ◽  
Modern Machine

Warts caused by the Human Papillomavirus (HPV) is a highly contagious disease, and affects several million people across the globe every year, in the form of small lesions on the skin, commonly known as warts. Warts can be treated effectively with several methods, the most effective being Immunotherapy and Cryotherapy. Our research is focused on the performance comparison of modern Machine Learning classification techniques to predict the outcome (positive or negative) of Immunotherapy treatment given to a patient, by using patient data as input features to our classifiers. The precision, recall, f-measure and accuracy were used to compare the performance of the various classifiers considered in this study. We considered Logistic Regression, ZeroR, AdaBoost, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Gradient Boosting, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Decision Trees and Random Forests. The ZeroR classifier was used as a baseline to provide us with insights into the skewed nature of the data, so as to enable us to better understand the comparison in performance of the various classifiers.

scholarly journals An Ontology Driven System to Predict Diabetes with Machine Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 4005-4011
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Diagnostic Center ◽  
Mental Trauma

Diabetes Mellitus is considered one of the chronic diseases of humankind which causes an increase in blood sugar. Many complications are reported if DM remains untreated and unidentified. Identification of this disease requires a lot of physical and mental trauma and effort which involves visiting a doctor, blood and urine test at the diagnostic center which consumes more time. Difficulties can be over crossed using the trending technology of Machine learning. The idea of the model is to prognosticate the occurrence of a diabetic with high accuracy. Therefore, two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage. Therefore two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage.

scholarly journals Students’ Class Performance Prediction Using Machine Learning Classifiers

2021 ◽  
Vol 19 (1) ◽  
pp. 112-121
Author(s):  
Adeel Ahmed ◽  
Kamlesh Kumar ◽  
Mansoor A. Khuhro ◽  
Asif A. Wagan ◽  
Imtiaz A. Halepoto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Performance Measure ◽  
Faculty Members ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Vector Machines ◽  
Academic Records

Nowadays, educational data mining is being employed as assessing tool for study and analysis of hidden patterns in academic databases which can be used to predict student’s academic performance. This paper implements various machine learning classification techniques on students’ academic records for results predication. For this purpose, data of MS(CS) students were collected from a public university of Pakistan through their assignments, quizzes, and sessional marks. The WEKA data mining tool has been used for performing all experiments namely, data pre-processing, classification, and visualization. For performance measure, classifier models were trained with 3- and 10-fold cross validation methods to evaluate classifiers' accuracy. The results show that bagging classifier combined with support vector machines outperform other classifiers in terms of accuracy, precision, recall, and F-measure score. The obtained outcomes confirm that our research provides significant contribution in prediction of students’ academic performance which can ultimately be used to assists faculty members to focus low grades students in improving their academic records.

scholarly journals Current Trends and Applications of Machine Learning in Tribology—A Review

Lubricants ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 86
Author(s):  
Max Marian ◽  
Stephan Tremmel
Keyword(s):  
Machine Learning ◽  
Surface Engineering ◽  
Numerical Algorithms ◽  
Support Vector ◽  
Current Trends ◽  
Scientific Disciplines ◽  
Vector Machines ◽  
Applications Of Machine Learning ◽  
Published Research ◽  
Selection Of

Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes. Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies.

An analysis of PCOS disease prediction model using machine learning classification algorithms

2020 ◽  
Vol 15 ◽  
Author(s):  
Shivani Aggarwal ◽  
Kavita Pandey
Keyword(s):  
Machine Learning ◽  
Insulin Resistance ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Metabolic Abnormalities ◽  
Related Disorder ◽  
Machine Learning Classification

Background: Polycystic ovary syndrome is commonly known as PCOS and it is surprising that it affects up to 18% of women in reproductive age. PCOS is the most usually occurring hormone-related disorder. Some of the symptoms of PCOS are irregular periods, increased facial and body hair growth, attain more weight, darkening of skin, diabetes and trouble conceiving (infertility). It also came into light that patients suffering from PCOS also possess a range of metabolic abnormalities. Due to metabolic abnormalities, some disorder may occur which increase the risk of insulin resistance, type 2 diabetes and impaired glucose tolerance (a sign of prediabetes). Family members of women suffering from PCOS are also at higher hazardous level for developing the same metabolic abnormalities. Obesity and overweight status contribute to insulin resistance in PCOS. Objective: In the modern era, there are several new technologies available to diagnose PCOS and one of them is Machine learning algorithms because they are exposed to new data. These algorithms learn from past experiences to produce reliable and repeatable decisions. In this article, Machine learning algorithms are used to identify the important features to diagnose PCOS. Methods: Several classification algorithms like Support vector machine (SVM), Logistic Regression, Gradient Boosting, Random Forest, Decision Tree and K-Nearest Neighbor (KNN) are uses well organized test datasets for classify huge records. Initially a dataset of 541 instances and 41 attributes has been taken to apply the prediction models and a manual feature selection is done over it. Results: After the feature selection, a set of 12 attributes has been identified which plays a crucial role in diagnosing PCOS. Conclusion: There are several researches progressing in the direction of diagnosing PCOS but till now the relevant features are not identify for the same.

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