Machine Learning Based Prediction and Prevention of Malicious Inventory Occupied Orders

2014 ◽  
Vol 6 (4) ◽  
pp. 56-72
Author(s):  
Qinghong Yang ◽  
Xiangquan Hu ◽  
Zhichao Cheng ◽  
Kang Miao
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Case Studies ◽  
Best Practice ◽  
Learning Technology ◽  
Prediction And Prevention ◽  
Sample Data ◽  
Logistic Regression Algorithm ◽  
Technical Solutions

MIOOs are orders created temporarily for the purpose of occupying the inventories of sellers. MIOOs disrupt normal business activities and harm both sellers and consumers. This study aims to determine the best practice and model of the technical solutions that can effectively and systematically limit malicious inventory occupied orders (MIOOs), using the methods of analytical mining and case studies. This work contains three contributions. Firstly, this work solves MIOOs problem by using machine learning technology. The result of the study indicates that 93% of MIOOs from the sample data are actually predictable and preventable. Secondly, this work presents a methodology of solving MIOOs problem which can be applied by other companies. The methodology in this paper consists of four major steps, namely doing statistics concerning MIOOs, using logistic regression algorithm to train a mode, optimizing the model, and applying the model. Finally, this work finds unique features of MIOOs, and they can help better understanding the behind logic of MIOO producers.

scholarly journals Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) For Data Privacy in the Context of Big Data

2021 ◽  
Vol 2083 (3) ◽  
pp. 032059
Author(s):  
Qiang Chen ◽  
Meiling Deng
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Privacy Protection ◽  
Data Privacy ◽  
Absolute Error ◽  
Average Absolute Error ◽  
Regression Algorithms ◽  
Hadoop Platform ◽  
Logistic Regression Algorithm ◽  
Computing Speed

Abstract Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied. This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm.

Using a Machine Learning Logistic Regression Algorithm to Classify Nanomedicine Clinical Trials in a Known Repository

2019 ◽  
pp. 98-110
Author(s):  
Charles M. Pérez-Espinoza ◽  
Nuvia Beltran-Robayo ◽  
Teresa Samaniego-Cobos ◽  
Abel Alarcón-Salvatierra ◽  
Ana Rodriguez-Mendez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Logistic Regression ◽  
Logistic Regression Algorithm

scholarly journals Deep Learning Technique to Predict Heart Disease using IoT Based ECG Data

2019 ◽  
Vol 9 (2) ◽  
pp. 2559-2562
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Learning Techniques ◽  
Learning Technique ◽  
Logistic Regression Algorithm ◽  
Target Output

Scientific Knowledge and Electronic devices are growing day by day. In this aspect, many expert systems are involved in the healthcare industry using machine learning algorithms. Deep neural networks beat the machine learning techniques and often take raw data i.e., unrefined data to calculate the target output. Deep learning or feature learning is used to focus on features which is very important and gives a complete understanding of the model generated. Existing methodology used data mining technique like rule based classification algorithm and machine learning algorithm like hybrid logistic regression algorithm to preprocess data and extract meaningful insights of data. This is, however a supervised data. The proposed work is based on unsupervised data that is there is no labelled data and deep neural techniques is deployed to get the target output. Machine learning algorithms are compared with proposed deep learning techniques using TensorFlow and Keras in the aspect of accuracy. Deep learning methodology outfits the existing rule based classification and hybrid logistic regression algorithm in terms of accuracy. The designed methodology is tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal beats. The proposed approach based on deep learning technique offered a better performance, improving the results when compared to machine learning approaches of the state-of-the-art

scholarly journals Please don’t put your phones away: the application of learning technology in UK Higher Education, presenting a Framework for Implementation

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Xue Zhou ◽  
Peter Wolstencroft ◽  
Stella-Maris Izegbua Orim
Keyword(s):  
Higher Education ◽  
Case Studies ◽  
Critical Review ◽  
Best Practice ◽  
Positive Impact ◽  
Learning Technologies ◽  
Learning Technology ◽  
Implementation Framework ◽  
Teaching Learning ◽  
Uk Higher Education

The aim of this research is to identify best practice when adopting new learning technologies in UK higher education. Although technology is widely used in institutions and often has a positive impact on the students’ learning experiences, there is only limited research designed to help lecturers with its implementation. This research presents a critical review and assessment of the practices being incorporated in higher education teaching, learning from both students’ and lecturers’ experiences. The outcome of two case studies are presented where Tophat and Socrative learning technology tools have been used in the classroom. The findings highlight the challenges and best practice.. Based on the case studies and the critical review of other, similar research, a Learning Technology Conceptual Implementation Framework has been developed, which offers guidance on the implementation of learning technology in the classroom.

scholarly journals Machine Learning Algorithm’s Measurement and Analytical Visualization of User’s Reviews for Google Play Store

2020 ◽  
Author(s):  
Abdul Karim ◽  
Azhari Azhari ◽  
Samir Brahim Belhaouri ◽  
Ali Adil Qureshi
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Ve Bayes ◽  
Android Apps ◽  
Online Marketplace ◽  
Document Frequency ◽  
Logistic Regression Algorithm ◽  
Google Play

The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.

scholarly journals Perbandingan Algoritma Machine Learning dalam Menilai Sebuah Lokasi Toko Ritel

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Kristiawan Kristiawan ◽  
Andreas Widjaja
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Pearson Correlation ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Learning Technology ◽  
K Nearest Neighbor ◽  
Store Location

Abstract  — The application of machine learning technology in various industrial fields is currently developing rapidly, including in the retail industry. This study aims to find the most accurate algorithmic model so that it can be used to help retailers choose a store location more precisely. By using several methods such as Pearson Correlation, Chi-Square Features, Recursive Feature Elimination and Tree-based to select features (predictive variables). These features are then used to train and build models using 6 different classification algorithms such as Logistic Regression, K Nearest Neighbor (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM) and Neural Network to classify whether a location is recommended or not as a new store location. Keywords— Application of Machine Learning, Pearson Correlation, Random Forest, Neural Network, Logistic Regression.

scholarly journals Application of the Preoperative Assistant System Based on Machine Learning in Hepatocellular Carcinoma Resection

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Shouyun Lv ◽  
Shizong Li ◽  
Zhiwei Yu ◽  
Kaiqiong Wang ◽  
Xin Qiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Liver Volume ◽  
Surgical Margin ◽  
Extensive Hepatectomy ◽  
Significant Difference ◽  
Logistic Regression Algorithm

To conduct better research in hepatocellular carcinoma resection, this paper used 3D machine learning and logistic regression algorithm to study the preoperative assistance of patients undergoing hepatectomy. In this study, the logistic regression model was analyzed to find the influencing factors for the survival and recurrence of patients. The clinical data of 50 HCC patients who underwent extensive hepatectomy (≥4 segments of the liver) admitted to our hospital from June 2020 to December 2020 were selected to calculate the liver volume, simulated surgical resection volume, residual liver volume, surgical margin, etc. The results showed that the simulated liver volume of 50 patients was 845.2 + 285.5 mL, and the actual liver volume of 50 patients was 826.3 ± 268.1 mL, and there was no significant difference between the two groups (t = 0.425; P  > 0.05). Compared with the logistic regression model, the machine learning method has a better prediction effect, but the logistic regression model has better interpretability. The analysis of the relationship between the liver tumour and hepatic vessels in practical problems has specific clinical application value for accurately evaluating the volume of liver resection and surgical margin.

scholarly journals Flight Delay Classification Prediction Based on Stacking Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jia Yi ◽  
Honghai Zhang ◽  
Hao Liu ◽  
Gang Zhong ◽  
Guiyi Li
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Civil Aviation ◽  
Learning Technology ◽  
Algorithm Selection ◽  
Classification Prediction ◽  
Flight Delay ◽  
Delay Prediction

With the development of civil aviation, the number of flights keeps increasing and the flight delay has become a serious issue and even tends to normality. This paper aims to prove that Stacking algorithm has advantages in airport flight delay prediction, especially for the algorithm selection problem of machine learning technology. In this research, the principle of the Stacking classification algorithm is introduced, the SMOTE algorithm is selected to process imbalanced datasets, and the Boruta algorithm is utilized for feature selection. There are five supervised machine learning algorithms in the first-level learner of Stacking including KNN, Random Forest, Logistic Regression, Decision Tree, and Gaussian Naive Bayes. The second-level learner is Logistic Regression. To verify the effectiveness of the proposed method, comparative experiments are carried out based on Boston Logan International Airport flight datasets from January to December 2019. Multiple indexes are used to comprehensively evaluate the prediction results, such as Accuracy, Precision, Recall, F1 Score, ROC curve, and AUC Score. The results show that the Stacking algorithm not only could improve the prediction accuracy but also maintains great stability.

scholarly journals Application of Logistic Regression Methods to Retinal Damage Detection on Digital Fundus Images

2020 ◽  
pp. 103-109
Author(s):  
Umniy Salamah
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Damage Detection ◽  
Service Providers ◽  
Learning Algorithm ◽  
Care Service ◽  
Diabetic Patients ◽  
Number Of Patients ◽  
Classification Evaluation ◽  
Logistic Regression Algorithm

The predictions about the number of people with diabetes will be increased which leads to a reduced balanced ratio between the quality of the eye care service providers with the number of patients. The alternative to solve this problem is to provide early detection service for the last condition of eye health in the diabetic patients. To detect the damage of the retina can be done help machine learning algorithm of the logistics regression. The justification for selection the logistic regression algorithm for retina damage detection in this research is that it has been widely used in a variety of machine learning problems where LR can describe the response variables with one or more variables predictors well. The methodology of research contained five phases, including preparation, feature extraction, normalization, classification, evaluation for processing dataset of digital fundus image were provided by EyePACS using scikit-learn as machine learning library and the Python as programming language. As result, we found the accuracy of retina damage detection using logistic regression is 0.7392 with following by F1-score 0.6317, Recall 0.7392, Precision 0.6043 and Kappa 0.0051.

scholarly journals Early Stage Identification of COVID-19 Patients in Mexico Using Machine Learning: A Case Study for the Tijuana General Hospital

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 490
Author(s):  
Cristián Castillo-Olea ◽  
Roberto Conte-Galván ◽  
Clemente Zuñiga ◽  
Alexandra Siono ◽  
Angelica Huerta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Logistic Regression ◽  
General Hospital ◽  
Early Stage ◽  
Individual Characteristics ◽  
Supervised Learning Algorithms ◽  
Neural Network Algorithm ◽  
The Individual ◽  
Logistic Regression Algorithm

Background: The current pandemic caused by SARS-CoV-2 is an acute illness of global concern. SARS-CoV-2 is an infectious disease caused by a recently discovered coronavirus. Most people who get sick from COVID-19 experience either mild, moderate, or severe symptoms. In order to help make quick decisions regarding treatment and isolation needs, it is useful to determine which significant variables indicate infection cases in the population served by the Tijuana General Hospital (Hospital General de Tijuana). An Artificial Intelligence (Machine Learning) mathematical model was developed in order to identify early-stage significant variables in COVID-19 patients. Methods: The individual characteristics of the study subjects included age, gender, age group, symptoms, comorbidities, diagnosis, and outcomes. A mathematical model that uses supervised learning algorithms, allowing the identification of the significant variables that predict the diagnosis of COVID-19 with high precision, was developed. Results: Automatic algorithms were used to analyze the data: for Systolic Arterial Hypertension (SAH), the Logistic Regression algorithm showed results of 91.0% in area under ROC (AUC), 80% accuracy (CA), 80% F1 and 80% Recall, and 80.1% precision for the selected variables, while for Diabetes Mellitus (DM) with the Logistic Regression algorithm it obtained 91.2% AUC, 89.2% accuracy, 88.8% F1, 89.7% precision, and 89.2% recall for the selected variables. The neural network algorithm showed better results for patients with Obesity, obtaining 83.4% AUC, 91.4% accuracy, 89.9% F1, 90.6% precision, and 91.4% recall. Conclusions: Statistical analyses revealed that the significant predictive symptoms in patients with SAH, DM, and Obesity were more substantial in fatigue and myalgias/arthralgias. In contrast, the third dominant symptom in people with SAH and DM was odynophagia.

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