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.

scholarly journals Tauke and Emotional Network Model in Oil Palm Marketing: Getting Fresh Fruit from Smallholders in Indonesia

2020 ◽  
Vol 55 (4) ◽  
Author(s):  
Rizabuana Ismail ◽  
Slamet Haryono ◽  
Ira Maya Sofa Harahap ◽  
Ria Manurung
Keyword(s):  
Oil Palm ◽  
Marketing Channel ◽  
Marketing Channels ◽  
Fresh Fruit ◽  
Network Marketing ◽  
Marketing Model ◽  
Marketing Models ◽  
Cash Payment ◽  
Fresh Fruit Bunches ◽  
Depth Interviews

This article describes how fresh fruit bunches grown by oil palm smallholders are incorporated into oil palm marketing models in Indonesia. This emotional network marketing model is a supplementary model of marketing models in Malaysia which is called factory centered and middleman model. This research uses a descriptive qualitative method. The data was collected by conducted in-depth interviews with 28 informants coming from 4 (four) categories of oil palm smallholders: oil palm tauke (middleman) that included big tauke and small tauke, workers in the loading ramps, and workers in the oil palm factories who were involved in oil palm marketing channels. The result of the research showed that the oil palm marketing channel between smallholders and either small tauke and big tauke was based on an emotional network with a strong bond of friendship, brotherhood, dwelling location, cash payment, giving loan with reasonable requirements, and providing transportation for fresh fruit bunches. In contrast, oil palm marketing channel among smallholders, loading ramp buyers, and POF was based on regulations. This writing presented a different perspective of oil palm marketing channels in general by involving the emotional network of the existing actors for getting fresh fruit bunches and the advantages of oil palm marketing. In this marketing model, there is a longer marketing channel and actors with their varied roles.

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.

Machine Learning Classification of Head Impact Sensor Data

2019 ◽  
Author(s):  
Tyler F. Rooks ◽  
Andrea S. Dargie ◽  
Valeta Carol Chancey
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
External Validation ◽  
Classification Algorithm ◽  
Sensor Data ◽  
Environmental Sensors ◽  
Head Acceleration ◽  
Machine Learning Classification ◽  
Environmental Sensor ◽  
Validation Set

Abstract A shortcoming of using environmental sensors for the surveillance of potentially concussive events is substantial uncertainty regarding whether the event was caused by head acceleration (“head impacts”) or sensor motion (with no head acceleration). The goal of the present study is to develop a machine learning model to classify environmental sensor data obtained in the field and evaluate the performance of the model against the performance of the proprietary classification algorithm used by the environmental sensor. Data were collected from Soldiers attending sparring sessions conducted under a U.S. Army Combatives School course. Data from one sparring session were used to train a decision tree classification algorithm to identify good and bad signals. Data from the remaining sparring sessions were kept as an external validation set. The performance of the proprietary algorithm used by the sensor was also compared to the trained algorithm performance. The trained decision tree was able to correctly classify 95% of events for internal cross-validation and 88% of events for the external validation set. Comparatively, the proprietary algorithm was only able to correctly classify 61% of the events. In general, the trained algorithm was better able to predict when a signal was good or bad compared to the proprietary algorithm. The present study shows it is possible to train a decision tree algorithm using environmental sensor data collected in the field.

scholarly journals Fake Job Detection and Analysis Using Machine Learning and Deep Learning Algorithms

2021 ◽  
Vol 11 (2) ◽  
pp. 642-650
Author(s):  
C.S. Anita ◽  
P. Nagarajan ◽  
G. Aditya Sairam ◽  
P. Ganesh ◽  
G. Deepakkumar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Cleaning ◽  
Personal Information ◽  
Learning Algorithms ◽  
Classification Algorithm ◽  
Paper Machine ◽  
Machine Learning Classification ◽  
Accuracy And Precision ◽  
Processing Step

With the pandemic situation, there is a strong rise in the number of online jobs posted on the internet in various job portals. But some of the jobs being posted online are actually fake jobs which lead to a theft of personal information and vital information. Thus, these fake jobs can be precisely detected and classified from a pool of job posts of both fake and real jobs by using advanced deep learning as well as machine learning classification algorithms. In this paper, machine learning and deep learning algorithms are used so as to detect fake jobs and to differentiate them from real jobs. The data analysis part and data cleaning part are also proposed in this paper, so that the classification algorithm applied is highly precise and accurate. It has to be noted that the data cleaning step is a very important step in machine learning project because it actually determines the accuracy of the machine learning as well as deep learning algorithms. Hence a great importance is emphasized on data cleaning and pre-processing step in this paper. The classification and detection of fake jobs can be done with high accuracy and high precision. Hence the machine learning and deep learning algorithms have to be applied on cleaned and pre-processed data in order to achieve a better accuracy. Further, deep learning neural networks are used so as to achieve higher accuracy. Finally all these classification models are compared with each other to find the classification algorithm with highest accuracy and precision.

scholarly journals COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHM ON HOTEL REVIEW SENTIMENT ANALYSIS (CASE STUDY: LUMINOR HOTEL PECENONGAN)

2020 ◽  
Vol 16 (1) ◽  
pp. 59-64
Author(s):  
Jaja Miharja ◽  
Jordy Lasmana Putra ◽  
Nur Hadianto
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Business Decisions ◽  
Machine Learning Classification ◽  
Business People ◽  
Auc Value ◽  
The Right

Analysis of hotel review sentiment is very helpful to be used as a benchmark or reference for making hotel business decisions today. However, all the review information obtained must be processed first by using an algorithm. The purpose of this study is to compare the Classification Algorithm of Machine Learning to obtain information that has a better level of accuracy in the analysis of hotel reviews. The algorithm that will be used is k-NN (k-Nearest Neighbor) and NB (Naive Bayes). After doing the calculation, the following accuracy level is obtained: k-NN of 60,50% with an AUC value of 0.632 and NB of 85,25% with an AUC value of 0.658. These results can be determined by the right algorithm to assist in making accurate decisions by business people in the analysis of hotel reviews using the NB Algorithm.

scholarly journals Prediction of Heart Disease using Machine Learning

2020 ◽  
pp. 150-166
Author(s):  
Ankit Singh
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Mean Squared Error ◽  
Classification Algorithm ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Machine Learning Classification ◽  
Squared Error ◽  
Clinical Data Analysis ◽  
Learning Technique

Cardiovascular Disease is the leading cause of death (Approximately, 17 million people every year) in the all the area of the world. Prediction of heart disease is the critical challenge in the area of the clinical data analysis. The objective of paper is to build the model for predicting the Heart Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of heart disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model. The result of the study indicates that KNN algorithm is effective in predicting the model with the accuracy of the 85.71% and having a very low mean squared error.

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