What books will be your bestseller? A machine learning approach with Amazon Kindle

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Seungpeel Lee ◽  
Honggeun Ji ◽  
Jina Kim ◽  
Eunil Park
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
User Satisfaction ◽  
Large Scale ◽  
Recommendation Systems ◽  
Feature Representation ◽  
Support Vector ◽  
Data Set ◽  
Content Type ◽  
Online Stores

Purpose With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions. Design/methodology/approach The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used. Findings The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%. Originality/value This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.

Different firm responses to the COVID-19 pandemic shocks: machine-learning evidence on the Vietnamese labor market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lam Hoang Viet Le ◽  
Toan Luu Duc Huynh ◽  
Bryan S. Weber ◽  
Bao Khac Quoc Nguyen
Keyword(s):  
Machine Learning ◽  
Labor Market ◽  
Large Scale ◽  
Government Support ◽  
Policy Implications ◽  
Machine Learning Techniques ◽  
Firm Characteristics ◽  
Data Set ◽  
Content Type ◽  
Firm Responses

PurposeThis paper aims to identify the disproportionate impacts of the COVID-19 pandemic on labor markets.Design/methodology/approachThe authors conduct a large-scale survey on 16,000 firms from 82 industries in Ho Chi Minh City, Vietnam, and analyze the data set by using different machine-learning methods.FindingsFirst, job loss and reduction in state-owned enterprises have been significantly larger than in other types of organizations. Second, employees of foreign direct investment enterprises suffer a significantly lower labor income than those of other groups. Third, the adverse effects of the COVID-19 pandemic on the labor market are heterogeneous across industries and geographies. Finally, firms with high revenue in 2019 are more likely to adopt preventive measures, including the reduction of labor forces. The authors also find a significant correlation between firms' revenue and labor reduction as traditional econometrics and machine-learning techniques suggest.Originality/valueThis study has two main policy implications. First, although government support through taxes has been provided, the authors highlight evidence that there may be some additional benefit from targeting firms that have characteristics associated with layoffs or other negative labor responses. Second, the authors provide information that shows which firm characteristics are associated with particular labor market responses such as layoffs, which may help target stimulus packages. Although the COVID-19 pandemic affects most industries and occupations, heterogeneous firm responses suggest that there could be several varieties of targeted policies-targeting firms that are likely to reduce labor forces or firms likely to face reduced revenue. In this paper, the authors outline several industries and firm characteristics which appear to more directly be reducing employee counts or having negative labor responses which may lead to more cost–effect stimulus.

scholarly journals Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hai-Bang Ly ◽  
Thuy-Anh Nguyen ◽  
Binh Thai Pham
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Soil Properties ◽  
Clay Content ◽  
Absolute Error ◽  
Experimental Methods ◽  
Liquid Limit ◽  
Support Vector ◽  
Data Set ◽  
Soil Cohesion

Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.

scholarly journals Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse

Recycling ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 65
Author(s):  
Ali Hewiagh ◽  
Kannan Ramakrishnan ◽  
Timothy Tzen Vun Yap ◽  
Ching Seong Tan
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Waste Management ◽  
Management System ◽  
Learning Algorithms ◽  
Fraud Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Set ◽  
Waste Management System

Online frauds have pernicious impacts on different system domains, including waste management systems. Fraudsters illegally obtain rewards for their recycling activities or avoid penalties for those who are required to recycle their own waste. Although some approaches have been introduced to prevent such fraudulent activities, the fraudsters continuously seek new ways to commit illegal actions. Machine learning technology has shown significant and impressive results in identifying new online fraud patterns in different system domains such as e-commerce, insurance, and banking. The purpose of this paper, therefore, is to analyze a waste management system and develop a machine learning model to detect fraud in the system. The intended system allows consumers, individuals, and organizations to track, monitor, and update their performance in their recycling activities. The data set provided by a waste management organization is used for the analysis and the model training. This data set contains transactions of users’ recycling activities and behaviors. Three machine learning algorithms, random forest, support vector machine, and multi-layer perceptron are used in the experiments and the best detection model is selected based on the model’s performance. Results show that each of these algorithms can be used for fraud detection in waste managements with high accuracy. The random forest algorithm produces the optimal model with an accuracy of 96.33%, F1-score of 95.20%, and ROC of 98.92%.

scholarly journals A Machine Learning Approach to Determine Maturity Stages of Tomatoes

2017 ◽  
Vol 10 (3) ◽  
pp. 683-690 ◽  
Author(s):  
Kamalpreet Kaur ◽  
O.P. Guptata
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Fruits And Vegetables ◽  
Support Vector ◽  
Large Set ◽  
Accuracy Score ◽  
Data Set ◽  
Maturity Stages ◽  
Total Data

Maturity checking has become mandatory for the food industries as well as for the farmers so as to ensure that the fruits and vegetables are not diseased and are ripe. However, manual inspection leads to human error, unripe fruits and vegetables may decrease the production [3]. Thus, this study proposes a Tomato Classification system for determining maturity stages of tomato through Machine Learning which involves training of different algorithms like Decision Tree, Logistic Regression, Gradient Boosting, Random Forest, Support Vector Machine, K-NN and XG Boost. This system consists of image collection, feature extraction and training the classifiers on 80% of the total data. Rest 20% of the total data is used for the testing purpose. It is concluded from the results that the performance of the classifier depends on the size and kind of features extracted from the data set. The results are obtained in the form of Learning Curve, Confusion Matrix and Accuracy Score. It is observed that out of seven classifiers, Random Forest is successful with 92.49% accuracy due to its high capability of handling large set of data. Support Vector Machine has shown the least accuracy due to its inability to train large data set.

scholarly journals Amino Acid Composition and Charge Based Prediction of Antisepsis Peptides by Random Forest Machine Learning Algorithm

2021 ◽  
Author(s):  
Aayushi Rathore ◽  
Anu Saini ◽  
Navjot Kaur ◽  
Aparna Singh ◽  
Ojasvi Dutta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Multiple Organ ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Set ◽  
Organ Systems

ABSTRACTSepsis is a severe infectious disease with high mortality, and it occurs when chemicals released in the bloodstream to fight an infection trigger inflammation throughout the body and it can cause a cascade of changes that damage multiple organ systems, leading them to fail, even resulting in death. In order to reduce the possibility of sepsis or infection antiseptics are used and process is known as antisepsis. Antiseptic peptides (ASPs) show properties similar to antigram-negative peptides, antigram-positive peptides and many more. Machine learning algorithms are useful in screening and identification of therapeutic peptides and thus provide initial filters or built confidence before using time consuming and laborious experimental approaches. In this study, various machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbour (KNN) and Logistic Regression (LR) were evaluated for prediction of ASPs. Moreover, the characteristics physicochemical features of ASPs were also explored to use them in machine learning. Both manual and automatic feature selection methodology was employed to achieve best performance of machine learning algorithms. A 5-fold cross validation and independent data set validation proved RF as the best model for prediction of ASPs. Our RF model showed an accuracy of 97%, Matthew’s Correlation Coefficient (MCC) of 0.93, which are indication of a robust and good model. To our knowledge this is the first attempt to build a machine learning classifier for prediction of ASPs.

scholarly journals Predicting speech discrimination scores from pure-tone thresholds—A machine learning-based approach using data from 12,697 subjects

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261433
Author(s):  
Hantai Kim ◽  
JaeYeon Park ◽  
Yun-Hoon Choung ◽  
Jeong Hun Jang ◽  
JeongGil Ko
Keyword(s):  
Machine Learning ◽  
Hearing Loss ◽  
Random Forest ◽  
Prediction Model ◽  
Pure Tone ◽  
Large Scale ◽  
Pure Tone Audiometry ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Speech Discrimination

Diagnostic tests for hearing impairment not only determines the presence (or absence) of hearing loss, but also evaluates its degree and type, and provides physicians with essential data for future treatment and rehabilitation. Therefore, accurately measuring hearing loss conditions is very important for proper patient understanding and treatment. In current-day practice, to quantify the level of hearing loss, physicians exploit specialized test scores such as the pure-tone audiometry (PTA) thresholds and speech discrimination scores (SDS) as quantitative metrics in examining a patient’s auditory function. However, given that these metrics can be easily affected by various human factors, which includes intentional (or accidental) patient intervention, there are needs to cross validate the accuracy of each metric. By understanding a “normal” relationship between the SDS and PTA, physicians can reveal the need for re-testing, additional testing in different dimensions, and also potential malingering cases. For this purpose, in this work, we propose a prediction model for estimating the SDS of a patient by using PTA thresholds via a Random Forest-based machine learning approach to overcome the limitations of the conventional statistical (or even manual) methods. For designing and evaluating the Random Forest-based prediction model, we collected a large-scale dataset from 12,697 subjects, and report a SDS level prediction accuracy of 95.05% and 96.64% for the left and right ears, respectively. We also present comparisons with other widely-used machine learning algorithms (e.g., Support Vector Machine, Multi-layer Perceptron) to show the effectiveness of our proposed Random Forest-based approach. Results obtained from this study provides implications and potential feasibility in providing a practically-applicable screening tool for identifying patient-intended malingering in hearing loss-related tests.

scholarly journals Malaria Prediction Model Using Machine Learning Algorithms

2021 ◽  
Vol 12 (10) ◽  
pp. 7488-7496
Author(s):  
Yusuf Aliyu Adamu, Et. al.
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Public Awareness ◽  
Health Sector ◽  
Weather Condition ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
African Countries ◽  
Data Set

Measures have been taking to ensure the safety of individuals from the burden of vector-borne disease but it remains the causative agent of death than any other diseases in Africa. Many human lives are lost particularly of children below five years regardless of the efforts made. The effect of malaria is much more challenging mostly in developing countries. In 2019, 51% of malaria fatality happen in Africa which it increased by 20% in 2020 due to the covid-19 pandemic. The majority of African countries lack a proper or a sound health care system, proper environmental settlement, economic hardship, limited funding in the health sector, and absence of good policies to ensure the safety of individuals. Information has to become available to the peoples on the effect of malaria by making public awareness program to make sure people become acquainted with the disease so that certain measure can be maintained. The prediction model can help the policymakers to know more about the expected time of the malaria occurrence based on the existing features so that people will get to know the information regarding the disease on time, health equipment and medication to be made available by government through it policy. In this research weather condition, non-climatic features, and malaria cases are considered in designing the model for prediction purposes and also the performance of six different machine learning classifiers for instance Support Vector Machine, K-Nearest Neighbour, Random Forest, Decision Tree, Logistic Regression, and Naïve Bayes is identified and found that Random Forest is the best with accuracy (97.72%), AUC (98%) AUC, and (100%) precision based on the data set used in the analysis.  

Predicting drug–disease associations by network embedding and biomedical data integration

2019 ◽  
Vol 53 (2) ◽  
pp. 217-229 ◽  
Author(s):  
Xiaomei Wei ◽  
Yaliang Zhang ◽  
Yu Huang ◽  
Yaping Fang
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Drug Repositioning ◽  
Heterogeneous Data ◽  
Feature Representation ◽  
Support Vector ◽  
Network Embedding ◽  
Data Set ◽  
Content Type ◽  
Disease Associations

PurposeThe traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue.Design/methodology/approachHere, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation.FindingsThis model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs.Originality/valueThis study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.

scholarly journals Evaluation of Light Gradient Boosted Machine Learning Technique in Large Scale Land Use and Land Cover Classification

Environments ◽  
2020 ◽  
Vol 7 (10) ◽  
pp. 84
Author(s):  
Dakota Aaron McCarty ◽  
Hyun Woo Kim ◽  
Hye Kyung Lee
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Support Vector Machines ◽  
Random Forest ◽  
Land Cover ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Light Gradient ◽  
Vector Machines

The ability to rapidly produce accurate land use and land cover maps regularly and consistently has been a growing initiative as they have increasingly become an important tool in the efforts to evaluate, monitor, and conserve Earth’s natural resources. Algorithms for supervised classification of satellite images constitute a necessary tool for the building of these maps and they have made it possible to establish remote sensing as the most reliable means of map generation. In this paper, we compare three machine learning techniques: Random Forest, Support Vector Machines, and Light Gradient Boosted Machine, using a 70/30 training/testing evaluation model. Our research evaluates the accuracy of Light Gradient Boosted Machine models against the more classic and trusted Random Forest and Support Vector Machines when it comes to classifying land use and land cover over large geographic areas. We found that the Light Gradient Booted model is marginally more accurate with a 0.01 and 0.059 increase in the overall accuracy compared to Support Vector and Random Forests, respectively, but also performed around 25% quicker on average.

scholarly journals Estimation of Precipitation Area Using S-Band Dual-Polarization Radar Measurements

2021 ◽  
Vol 13 (11) ◽  
pp. 2039
Author(s):  
Joon Jin Song ◽  
Melissa Innerst ◽  
Kyuhee Shin ◽  
Bo-Young Ye ◽  
Minho Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Support Vector ◽  
Classification Methods ◽  
Dual Polarization ◽  
Data Set ◽  
Spatial Classification ◽  
Precipitation Area ◽  
Radar Measurements

Estimating precipitation area is important for weather forecasting as well as real-time application. This paper aims to develop an analytical framework for efficient precipitation area estimation using S-band dual-polarization radar measurements. Several types of factors, such as types of sensors, thresholds, and models, are considered and compared to form a data set. After building the appropriate data set, this paper yields a rigorous comparison of classification methods in statistical (logistic regression and linear discriminant analysis) and machine learning (decision tree, support vector machine, and random forest). To achieve better performance, spatial classification is considered by incorporating latitude and longitude of observation location into classification, compared with non-spatial classification. The data used in this study were collected by rain detector and present weather sensor in a network of automated weather systems (AWS), and an S-band dual-polarimetric weather radar during ten different rainfall events of varying lengths. The mean squared prediction error (MSPE) from leave-one-out cross validation (LOOCV) is computed to assess the performance of the methods. Of the methods, the decision tree and random forest methods result in the lowest MSPE, and spatial classification outperforms non-spatial classification. Particularly, machine-learning-based spatial classification methods accurately estimate the precipitation area in the northern areas of the study region.

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