scholarly journals Construction of the Luxury Marketing Model Based on Machine Learning Classification Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qiaoshan Chen ◽  
Shousong Cai ◽  
Xiaomin Gu
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Consumer Market ◽  
Luxury Goods ◽  
Machine Learning Classification ◽  
Marketing Model ◽  
Strategy Model ◽  
Sales And Marketing ◽  
Targeted Marketing ◽  
Marketing Methods

China has become the world’s largest luxury goods consumer market due to its population base. In view of the bright prospects of the luxury consumer market, major companies have entered and want to get a share. For the luxury goods industry, traditional mass marketing methods are not able to serve corporate sales and marketing strategies more effectively, and targeted marketing is clearly much more efficient than randomized marketing. Therefore, in this paper, based on consumer buying habits and characteristics data of luxury goods, the paper uses a machine learning algorithm to build a personalized marketing strategy model. And the paper uses historical data to model and form deductions to predict the purchase demand of each consumer and evaluate the possibility of customers buying different goods, including cosmetics, jewelry, and clothing.

Classifying Restatements: An Application of Machine Learning and Textual Analytics

2021 ◽  
Author(s):  
Louise Hayes ◽  
J. Efrim Boritz
Keyword(s):  
Machine Learning ◽  
Audit Quality ◽  
Learning Algorithm ◽  
Reporting Quality ◽  
Financial Statements ◽  
Net Income ◽  
Machine Learning Classification ◽  
Large Samples ◽  
Automated Methods ◽  
Manual Classification

Restatements of audited financial statements are used for evaluating reporting quality and audit quality, and for other evaluative purposes. We constructed a machine learning algorithm to classify restatements by management intent based on the language in restatement announcements. Our machine learning classification is as reliable as other commonly used automated methods such as those based on market reaction, restatement direction, and magnitude. Our method does not require a dictionary of words and is applicable when other automated methods are not, for example, when restatements are announced contemporaneously with financial results and when net income is not restated. For large samples, the use of such a classification algorithm is less tedious and less time-consuming, and more consistent, replicable and scalable than manual classification.

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 Intelligent SMOTE Based Machine learning Classification for Fetal State on Cardiotocography Dataset

2021 ◽  
Author(s):  
Pijush Dutta ◽  
Shobhandeb Paul ◽  
Madhurima Majumder
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Monitoring Tool ◽  
Statistical Parameters ◽  
Machine Learning Classification ◽  
High Risk Women ◽  
Previous State ◽  
The University ◽  
Intrapartum Complications

Abstract A major contributor to under-five mortality is the death of children in the 1st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high-risk women during labor. The objective of this study was to study the precision of machine learning algorithm techniques on CTG data in identifying high-risk fetuses. CTG data of 2126 pregnant women were obtained from the University of California Irvine Machine Learning Repository. Out of 2126 CTG dataset 78% of them were normal, 14% were suspect, and 8 % had a pathological fetal state. To improve data imbalance SMOTE is applied followed by five different machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 score for each class and overall accuracy of each model were obtained to predict normal, suspect, and pathological fetal states. For the model validity two statistical parameters MCC & kappa (k) are used. SMOTE based all the classification algorithm provides the higher degree of accuracy with minimum value is 96% and RF algorithm had the highest prediction accuracy about 98.01% which is quite satisfactory. Model validation statistical parameters MCC & kappa is maximum achieved by RF about 0.968 & 1 and for SVC is 0.977 & 1 respectively. Finally proposed work also compared with previous state of art techniques.

scholarly journals Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation. 

2020 ◽  
Author(s):  
Chris Toh ◽  
James Brody
Keyword(s):  
Machine Learning ◽  
Risk Score ◽  
Genetic Risk ◽  
Learning Algorithm ◽  
Genetic Risk Score ◽  
Severe Reaction ◽  
Length Variation ◽  
Uk Biobank ◽  
Machine Learning Classification ◽  
Scale Length

Abstract Introduction.The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection. Methods.We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.Results. We found that the XGBoost classifier could differentiate between the two classes at a significant level (I as measured against a randomized control and (n as measured against the expected value of a random guessing algorithm (AUC=0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.Conclusion.Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.

scholarly journals Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics

Molecules ◽  
2019 ◽  
Vol 24 (15) ◽  
pp. 2811 ◽  
Author(s):  
Rácz ◽  
Bajusz ◽  
Héberger
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Classification Problems ◽  
Machine Learning Classification ◽  
Learning Tasks ◽  
Sum Of Ranking Differences ◽  
Multi Level

Machine learning classification algorithms are widely used for the prediction and classification of the different properties of molecules such as toxicity or biological activity. the prediction of toxic vs. non-toxic molecules is important due to testing on living animals, which has ethical and cost drawbacks as well. The quality of classification models can be determined with several performance parameters. which often give conflicting results. In this study, we performed a multi-level comparison with the use of different performance metrics and machine learning classification methods. Well-established and standardized protocols for the machine learning tasks were used in each case. The comparison was applied to three datasets (acute and aquatic toxicities) and the robust, yet sensitive, sum of ranking differences (SRD) and analysis of variance (ANOVA) were applied for evaluation. The effect of dataset composition (balanced vs. imbalanced) and 2-class vs. multiclass classification scenarios was also studied. Most of the performance metrics are sensitive to dataset composition, especially in 2-class classification problems. The optimal machine learning algorithm also depends significantly on the composition of the dataset.

scholarly journals Application of Machine Learning Classification Algorithm to Cybersecurity Awareness

2018 ◽  
Vol 21 ◽  
pp. 45-48
Author(s):  
Shilpa Balan ◽  
Sanchita Gawand ◽  
Priyanka Purushu
Keyword(s):  
United States ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Identity Theft ◽  
The United States ◽  
Vital Role ◽  
Classification Model ◽  
Smart Devices ◽  
Machine Learning Classification ◽  
The Common

Cybersecurity plays a vital role in protecting the privacy and data of people. In the recent times, there have been several issues relating to cyber fraud, data breach and cyber theft. Many people in the United States have been a victim of identity theft. Thus, understanding of cybersecurity plays an important role in protecting their information and devices. As the adoption of smart devices and social networking are increasing, cybersecurity awareness needs to be spread. The research aims at building a classification machine learning algorithm to determine the awareness of cybersecurity by the common masses in the United States. We were able to attain a good F-measure score when evaluating the performance of the classification model built for this study.

scholarly journals Machine-learning classification of pronunciation difficulty for learners of English as a Foreign Language

2018 ◽  
pp. 1-8 ◽  
Author(s):  
Katsunori Kotani ◽  
Takehiko Yoshimi
Keyword(s):  
Machine Learning ◽  
Foreign Language ◽  
Language Learning ◽  
Learning Algorithm ◽  
Reliability And Validity ◽  
Measuring Method ◽  
Computer Assisted ◽  
Linguistic Features ◽  
Machine Learning Classification ◽  
Learner Corpus

This study compiled and assessed a learner corpus to measure the difficulty of pronouncing a sentence (henceforth, pronounceability). The method of measuring pronounceability is useful for computer-assisted language learning of English as a Foreign Language that employs online materials as a resource for pronunciation training. An advantage of this resource is that learners can select materials depending on their interest, a disadvantage being that pronounceability is unknown to learners. If pronounceability is automatically measured, learners can independently access materials appropriate for their proficiency levels without teachers’ assistance. The pronounceability assessment demonstrated moderate reliability and partial validity when it was measured by learners’ subjective judgment on a five-point Likert scale. Given the reliability and validity, this study developed a pronounceability measuring method utilizing a machine learning algorithm that automatically predicts the pronounceability of a sentence based on the linguistic features of the sentences and learners’ features (i.e. learners’ scores for an English proficiency test). The proposed measuring method demonstrated a higher classification accuracy (53.7 percent) than the majority class baseline (46.0 percent).

scholarly journals Evaluation of a Genetic Risk Score for Severity of COVID-19 Using Human Chromosomal-scale Length Variation

2020 ◽  
Author(s):  
Chris Toh ◽  
James Brody
Keyword(s):  
Machine Learning ◽  
Risk Score ◽  
Genetic Risk ◽  
Learning Algorithm ◽  
Genetic Risk Score ◽  
Severe Reaction ◽  
Length Variation ◽  
Uk Biobank ◽  
Machine Learning Classification ◽  
Scale Length

Abstract Introduction.The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection. Methods.We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.Results. We found that the XGBoost classifier could differentiate between the two classes at a significant level (I as measured against a randomized control and (n as measured against the expected value of a random guessing algorithm (AUC=0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.Conclusion.Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.

scholarly journals An Approach to detect multiple diseases using machine learning algorithm

2021 ◽  
Vol 2089 (1) ◽  
pp. 012009
Author(s):  
Indukuri Mohit ◽  
K. Santhosh Kumar ◽  
Uday Avula Kumar Reddy ◽  
Badhagouni Suresh Kumar
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Web Application ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Early Recognition ◽  
Heart Diseases ◽  
Potential Threat ◽  
Medical Test ◽  
Machine Learning Classification

Abstract There are multiple techniques in machine learning that can in a variety of industries, do predictive analytics on large amounts of data. Predictive analytics in healthcare is a difficult endeavour, but it can eventually assist practitioners in making timely decisions regarding patients’ health and treatment based on massive data. Diseases like Breast cancer, diabetes, and heart-related diseases are causing many deaths globally but most of these deaths are due to the lack of timely check-ups of the diseases. The above problem occurs due to a lack of medical infrastructure and a low ratio of doctors to the population. The statistics clearly show the same, WHO recommended, the ratio of doctors to patients is 1:1000 whereas India’s doctor-to-population ratio is 1:1456, this indicates the shortage of doctors. The diseases related to heart, cancer, and diabetes can cause a potential threat to mankind, if not found early. Therefore, early recognition and diagnosis of these diseases can save a lot of lives. This work is all about predicting diseases that are harmful using machine learning classification algorithms. In this work, breast cancer, heart, and diabetes are included. To make this work seamless and usable by the mass public, our team made a medical test web application that makes predictions about various diseases using the concept of machine learning. In this work, our aim to develop a disease-predicting web app that uses the concept of machine learning-based predictions about various diseases like Breast cancer, Diabetes, and Heart diseases.

scholarly journals Brand Digital Marketing under Intranet Security Control Based on the Machine Learning Classification Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yishu Liu ◽  
Xiaoyan Huang
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Hot Spot ◽  
Classification Algorithm ◽  
Digital Marketing ◽  
Network Marketing ◽  
Machine Learning Classification ◽  
Marketing Model ◽  
Marketing Models ◽  
Security Control

With the advent of the information age, digital marketing models have begun to receive attention and to have applications in many industries. Although the digital marketing model has thus become a hot spot in the sales world, there is still not enough research on digital marketing. In order to optimize brand digital marketing under internal and external security control based on the machine learning classification algorithm, this paper uses fuzzy system theory to perform fuzzy analysis on various experimental data studied, convert it into a fuzzy set, obtain the fuzzy solution of the related function, establish related models of machine learning classification algorithms, and identify and collect relevant experimental data in an intelligent way, saving time for data collection. This paper collects the customer characteristics, customer sensitivity, brand promotion, and brand revenue of a brand within seven days; then uses the classification algorithm and collected data to predict and analyze the future data results; and uses the machine learning classification algorithm model formula to solve the correlation function. The final experimental results show that, in the digital marketing mode, network marketing brings 75% of the benefits to the brand, which is the highest among the four digital marketing models, and it has the best brand publicity level, 45%. At the same time, customers’ sensitivity to the brand reaches 50% under the network marketing model.

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