scholarly journals A College Student Behavior Analysis and Management Method Based on Machine Learning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoying Shen ◽  
Chao Yuan
Keyword(s):  
Machine Learning ◽  
College Students ◽  
Behavior Analysis ◽  
School Safety ◽  
Student Behavior ◽  
Learning Algorithm ◽  
Learning Ability ◽  
Learning Technology ◽  
Digital Campus ◽  
Management Policies

A digital campus will generate a large amount of student-related data. How to analyze and apply these data has become the key to improving the management level of students. The analysis of student behavior data can not only assist schools in early warning of dangerous events and strengthen school safety but also can use real data to describe student behavior, thereby providing quantitative data support for scholarship and grant evaluation. This paper takes a university student as the research object, collects various data in the digital campus platform, and uses an adaptive K -means algorithm in the machine learning algorithm to cluster the data. Analyze the behavior of college students from the clustering results, so as to provide a basis for the education management and learning ability improvement of college students. Specifically, the student’s study, life, and consumption data are selected as the data to describe the student’s behavior at school. This data is input into the adaptive K -means algorithm to obtain different types of student consumption habits, living habits, and learning habits. Through the analysis results, it can be found that the problem of the group of students with low financial ability, the problem of too long online time for students, and the number of books borrowed are too low. According to the characteristics of these problems, teachers and schools are provided with targeted management suggestions. The analysis of student behavior based on machine learning technology provides a reference for the formulation of students’ school management policies and provides teachers with information on students’ personality characteristics, which is conducive to improving teachers’ teaching effects. In short, the management of the results of student behavior analysis can provide a basis for the school to formulate reasonable management policies, thereby promoting precision management and scientific decision-making.

scholarly journals Segmentation of Affected Crops using Deep Learning

2019 ◽  
Vol 8 (5S3) ◽  
pp. 232-234
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Monitoring System ◽  
Learning Algorithm ◽  
Target Area ◽  
Learning Technology ◽  
Machine Learning Algorithm ◽  
Normal Human ◽  
Human Monitoring ◽  
Disease Attack

Deep Learning technology can accurately predict the presence of diseases and pests in the agricultural farms. Upon this Machine learning algorithm, we can even predict accurately the chance of any disease and pest attacks in future For spraying the correct amount of fertilizer/pesticide to elimate host, the normal human monitoring system unable to predict accurately the total amount and ardent of pest and disease attack in farm. At the specified target area the artificial percepton tells the value accurately and give corrective measure and amount of fertilizers/ pesticides to be sprayed.

scholarly journals Construction of Innovation and Entrepreneurship Platform Based on Deep Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jian Li ◽  
Yongyan Zhao
Keyword(s):  
Machine Learning ◽  
College Students ◽  
Neural Networks ◽  
Deep Learning ◽  
National Economy ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Computing Power ◽  
Deep Learning Algorithm ◽  
Innovation And Entrepreneurship

As the national economy has entered a stage of rapid development, the national economy and social development have also ushered in the “14th Five-Year Plan,” and the country has also issued support policies to encourage and guide college students to start their own businesses. Therefore, the establishment of an innovation and entrepreneurship platform has a significant impact on China’s economy. This gives college students great support and help in starting a business. The theory of deep learning algorithms originated from the development of artificial neural networks and is another important field of machine learning. As the computing power of computers has been greatly improved, especially the computing power of GPU can quickly train deep neural networks, deep learning algorithms have become an important research direction. The deep learning algorithm is a nonlinear network structure and a standard modeling method in the field of machine learning. After modeling various templates, they can be identified and implemented. This article uses a combination of theoretical research and empirical research, based on the views and research content of some scholars in recent years, and introduces the basic framework and research content of this article. Then, deep learning algorithms are used to analyze the experimental data. Data analysis is performed, and relevant concepts of deep learning algorithms are combined. This article focuses on exploring the construction of an IAE (innovation and entrepreneurship) education platform and making full use of the role of deep learning algorithms to realize the construction of innovation and entrepreneurship platforms. Traditional methods need to extract features through manual design, then perform feature classification, and finally realize the function of recognition. The deep learning algorithm has strong data image processing capabilities and can quickly process large-scale data. Research data show that 49.5% of college students and 35.2% of undergraduates expressed their interest in entrepreneurship. Entrepreneurship is a good choice to relieve employment pressure.

scholarly journals An End-to-End Rumor Detection Model Based on Feature Aggregation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Aoshuang Ye ◽  
Lina Wang ◽  
Run Wang ◽  
Wenqi Wang ◽  
Jianpeng Ke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Language Processing ◽  
Domain Knowledge ◽  
Learning Ability ◽  
Feature Engineering ◽  
Learning Technology ◽  
Detection Model ◽  
Feature Aggregation ◽  
Rumor Detection

The social network has become the primary medium of rumor propagation. Moreover, manual identification of rumors is extremely time-consuming and laborious. It is crucial to identify rumors automatically. Machine learning technology is widely implemented in the identification and detection of misinformation on social networks. However, the traditional machine learning methods profoundly rely on feature engineering and domain knowledge, and the learning ability of temporal features is insufficient. Furthermore, the features used by the deep learning method based on natural language processing are heavily limited. Therefore, it is of great significance and practical value to study the rumor detection method independent of feature engineering and effectively aggregate heterogeneous features to adapt to the complex and variable social network. In this paper, a deep neural network- (DNN-) based feature aggregation modeling method is proposed, which makes full use of the knowledge of propagation pattern feature and text content feature of social network event without feature engineering and domain knowledge. The experimental results show that the feature aggregation model has achieved 94.4% of accuracy as the best performance in recent works.

scholarly journals Load Balancing Algorithm of Controller Based on SDN Architecture Under Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 578-588
Author(s):  
Siyuan Liang ◽  
Wenli Jiang ◽  
Fangli Zhao ◽  
Feng Zhao
Keyword(s):  
Machine Learning ◽  
Load Balancing ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Network Control ◽  
Learning Technology ◽  
Related Services ◽  
Network Transmission ◽  
Optimal Action ◽  
Load Balancing Algorithm

Abstract With the rapid development of cloud computing and other related services, higher requirements are put forward for network transmission and delay. Due to the inherent distributed characteristics of traditional networks, machine learning technology is difficult to be applied and deployed in network control. The emergence of SDN technology provides new opportunities and challenges for the application of machine learning technology in network management. A load balancing algorithm of Internet of things controller based on data center SDN architecture is proposed. The Bayesian network is used to predict the degree of load congestion, combining reinforcement learning algorithm to make optimal action decision, self-adjusting parameter weight to adjust the controller load congestion, to achieve load balance, improve network security and stability.

scholarly journals Computer Network Attack Detection Using Enhanced Clustering Technologies

2022 ◽  
Vol 9 (6) ◽  
Author(s):  
Dhamyaa Salim Mutar
Keyword(s):  
Machine Learning ◽  
Computer Network ◽  
Learning Algorithm ◽  
Attack Detection ◽  
Learning Technology ◽  
Feed Forward Neural Network ◽  
Network Attack ◽  
The Road ◽  
Network Intrusions ◽  
Smart Machine

The need for security means has brought from the fact of privacy of data especially after the communication revolution in the recent times. The advancement of data mining and machine learning technology has paved the road for establishment an efficient attack prediction paradigm for protecting of large scaled networks. In this project, computer network intrusions had been eliminated by using smart machine learning algorithm. Referring a big dataset named as KDD computer intrusion dataset which includes large number of connections that diagnosed with several types of attacks; the model is established for predicting the type of attack by learning through this data. Feed forward neural network model is outperformed over the other proposed clustering models in attack prediction accuracy.

Detecting risk of suicide attempts among Chinese medical college students using a machine learning algorithm

2020 ◽  
Vol 273 ◽  
pp. 18-23
Author(s):  
Yanmei Shen ◽  
Wenyu Zhang ◽  
Bella Siu Man Chan ◽  
Yaru Zhang ◽  
Fanchao Meng ◽  
...  
Keyword(s):  
Machine Learning ◽  
College Students ◽  
Suicide Attempts ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Medical College

Machine Learning Technology Overview In Terms Of Digital Marketing And Personalization

2021 ◽  
Author(s):  
Anna Nikolajeva ◽  
Artis Teilans
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Digital Marketing ◽  
Learning Technology ◽  
Estimation Problems ◽  
Artificial Intelligence Technology

The research is dedicated to artificial intelligence technology usage in digital marketing personalization. The doctoral theses will aim to create a machine learning algorithm that will increase sales by personalized marketing in electronic commerce website. Machine learning algorithms can be used to find the unobservable probability density function in density estimation problems. Learning algorithms learn on their own based on previous experience and generate their sequences of learning experiences, to acquire new skills through self-guided exploration and social interaction with humans. An entirely personalized advertising experience can be a reality in the nearby future using learning algorithms with training data and new behaviour patterns appearance using unsupervised learning algorithms. Artificial intelligence technology will create website specific adverts in all sales funnels individually.

scholarly journals Customer Behavior Analysis and Revenue Prediction System

2020 ◽  
pp. 05-08
Keyword(s):  
Machine Learning ◽  
Behavior Analysis ◽  
Learning Algorithm ◽  
Customer Behavior ◽  
Gradient Boosting ◽  
Machine Learning Algorithm ◽  
Prediction System ◽  
Customer Behaviour ◽  
Boosting Method ◽  
Potential Customers

In the era of e-commerce there are many organizations that have implemented customer behaviour analytics for their growth in business. It is a crucial challenge for the organizations in the e-commerce world to study and analyse the behaviour of the online buyers. The success of every organization is within the satisfaction of the customers they have and to gain new customers as well, and this is done by targeting the potential customers that can generate revenue to the organizations. RFM analysis is used to indicate recently buying customers, frequently buying customers, and huge spending customers. It is one of the best methods to segment organization’s revenue generating customers around other customers. Also 80/20 rule is implemented which focuses on the 20 percent of the customers that generate 80 percent of the revenue for the organization. The model is developed using Light GBM (Gradient Boosting Method) which is a machine learning algorithm.

scholarly journals Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis

2019 ◽  
Vol 8 (8) ◽  
pp. 1241 ◽  
Author(s):  
Aryan Mobiny ◽  
Aditi Singh ◽  
Hien Van Nguyen
Keyword(s):  
Machine Learning ◽  
Skin Lesion ◽  
Learning Algorithm ◽  
Learning System ◽  
Clinical Settings ◽  
Learning Technology ◽  
Learning Classifier ◽  
Common Skin ◽  
Intraepithelial Carcinoma ◽  
Physician Team

Knowing when a machine learning system is not confident about its prediction is crucial in medical domains where safety is critical. Ideally, a machine learning algorithm should make a prediction only when it is highly certain about its competency, and refer the case to physicians otherwise. In this paper, we investigate how Bayesian deep learning can improve the performance of the machine–physician team in the skin lesion classification task. We used the publicly available HAM10000 dataset, which includes samples from seven common skin lesion categories: Melanoma (MEL), Melanocytic Nevi (NV), Basal Cell Carcinoma (BCC), Actinic Keratoses and Intraepithelial Carcinoma (AKIEC), Benign Keratosis (BKL), Dermatofibroma (DF), and Vascular (VASC) lesions. Our experimental results show that Bayesian deep networks can boost the diagnostic performance of the standard DenseNet-169 model from 81.35% to 83.59% without incurring additional parameters or heavy computation. More importantly, a hybrid physician–machine workflow reaches a classification accuracy of 90 % while only referring 35 % of the cases to physicians. The findings are expected to generalize to other medical diagnosis applications. We believe that the availability of risk-aware machine learning methods will enable a wider adoption of machine learning technology in clinical settings.

Application of wireless network and machine learning algorithm in entrepreneurship education of remote intelligent classroom

2020 ◽  
pp. 1-12
Author(s):  
Heping Lu
Keyword(s):  
Machine Learning ◽  
Distance Education ◽  
Learning Algorithm ◽  
Learning Technologies ◽  
Learning Technology ◽  
Expression Recognition ◽  
Computer Algorithms ◽  
The Status ◽  
Education Today ◽  
Intelligent Classroom

Educational information system is a hot topic in education today, and informatization is not only reflected in teaching methods. With the development of computer vision and deep learning technologies and the gradual maturity of related hardware, the application of computer algorithms and intelligent identification in distance education has become a norm. This research studies the entrepreneurial model of distance intelligent classrooms, uses machine learning technology as the basis, and combines intelligent image recognition technology to identify the status and expression of students in distance education classrooms. Moreover, this paper has carried out a more detailed study of face detection and expression recognition technology and tried to apply it to classroom teaching evaluation, which has shown certain feasibility in experiments. At the end of this article, the system was tested and analyzed with the collected data, which verified the feasibility and accuracy of the system.

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