Computer public course teaching based on improved machine learning and neural network algorithm

Computer Courses

With the continuous progress of the times, the development of college education is also constantly tending to enrich and diversify. In the course of curriculum setting in many colleges and universities, more and more attention is paid to the teaching of computer courses for college students. In the course of setting up and teaching, we still follow the traditional teaching mode and do not pay more attention to students’ practical practice. By observing the computer course materials selected by many colleges and universities for students, we can see that most of the textbooks still focus on arranging some exercises from the point of view that science and engineering are students, and lack the basic knowledge of the curriculum. Because the understanding and research of computer course is not deep enough, the teaching effect obtained since the course is not ideal. By studying the relevant knowledge of machine learning and some important problems in the development of neural network algorithm theory, this paper puts forward some viewpoints based on the current curriculum system in colleges and universities in order to improve the learning quality of computer courses. And hope to build a variant learning model to improve students’ interest in computer courses. The exploration and inference of some knowledge in this paper are mostly my own views, some places are not professional enough, the majority of experts and scholars can criticize and correct at will.

Software Reliability Growth Fault Correction Model Based on Machine Learning and Neural Network Algorithm

Microprocessors and Microsystems ◽

10.1016/j.micpro.2020.103538 ◽

2021 ◽

Vol 80 ◽

pp. 103538

Author(s):

Liya Li

Keyword(s):

Neural Network ◽

Machine Learning ◽

Software Reliability ◽

Reliability Growth ◽

Correction Model ◽

Growth Fault ◽

Model Based ◽

Network Algorithm ◽

Software Reliability Growth

Enterprise supply chain risk assessment based on improved neural network algorithm and machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189532 ◽

2020 ◽

pp. 1-12

Author(s):

Shaoqin Lu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Risk Assessment ◽

Supply Chain ◽

Semantic Network ◽

System Structure ◽

Supply Chain Risk ◽

Network Algorithm ◽

Risk Knowledge

It is of practical significance to study the decision-making subject in the supply chain under the influence of risk aversion to make a decision and make the supply chain compete in an orderly market environment. In order to improve the effect of enterprise supply chain risk assessment, this paper improves the traditional neural network algorithm, combines machine learning methods and supply chain risk assessment time requirements to set system function modules, and builds the overall system structure. Considering the multiple relationship attributes of supply chain risk knowledge, this paper uses a multi-element semantic network to represent the network structure of supply chain risk knowledge, and proposes a multi-level inventory control modelThis is based on the inventory of the coordination center and other retailers’ procurement/relocation strategy models. After building the model, this paper designs a simulation test to verify and analyze the model performance. The research results show that the model proposed in this paper has a certain effect.

Understanding Neural Networks for Machine Learning using Microsoft Neural Network Algorithm

International Journal of Computer Applications ◽

10.5120/ijca2016911481 ◽

2016 ◽

Vol 150 (3) ◽

pp. 32-38

Author(s):

Nagesh Ramprasad

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Network Algorithm ◽

Reduction of transients in switches using embedded machine learning

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v11.i1.pp235-241 ◽

2020 ◽

Vol 11 (1) ◽

pp. 235

Author(s):

P. Suresh ◽

S. George Fernandez ◽

S. Vidyasagar ◽

V. Kalyanasundaram ◽

K. Vijayakumar ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Feedback System ◽

Network Algorithm ◽

Non Linear ◽

Electronic Switches

<p>Non-linear loads can cause transients in electronic switches. They also result in a fluctuating output when the device is switched ON or OFF. These transients can harm not only the switches but also the devices that they are connected to, by passing excess currents or voltages to the devices. By applying machine learning, we can improve the gate drive voltages of the switches and thereby reduce switch transients. A feedback system is built that measures the output transients and then feeds it to a neural network algorithm that then gives a proper gate drive to the device. This will reduce transients and also improve performances of switch based devices like inverters and converters.</p>

Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution

10.1101/2020.02.13.948414 ◽

2020 ◽

Cited By ~ 2

Author(s):

Aravind Akella ◽

Vibhor Kaushik

Keyword(s):

Neural Network ◽

Machine Learning ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Network Model ◽

Neural Network Model ◽

Network Algorithm ◽

The Neural Network ◽

Artery Disease

AbstractThe development of Coronary Artery Disease (CAD), one of the most prevalent diseases in the world, is heavily influenced by several modifiable risk factors. Predictive models built using machine learning (ML) algorithms may assist healthcare practitioners in timely detection of CAD, and ultimately, may improve outcomes. In this study, we have applied six different ML algorithms to predict the presence of CAD amongst patients listed in an openly available dataset provided by the University of California Irvine (UCI) Machine Learning Repository, named “the Cleveland dataset.” All six ML algorithms achieved accuracies greater than 80%, with the “Neural Network” algorithm achieving accuracy greater than 93%. The recall achieved with the “Neural Network” model is also highest of the six models (0.93). Additionally, five of the six algorithms resulted in very similar AUC-ROC curves. The AUC-ROC curve corresponding to the “Neural Network” algorithm is slightly steeper implying higher “true positive percentage” achieved with this model. We also extracted the variables of importance in the “Neural Network” model to help in the risk assessment. We have released the full computer code generated in this study in the public domain as a preliminary effort toward developing an open solution for predicting the presence of coronary artery disease in a given population and present a workflow model for implementing a possible solution.

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

Intelligent Diagnosis of Cardiac Disease Prediction using Machine Learning

10.35940/ijitee.j9765.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1417-1421

Keyword(s):

Neural Network ◽

Machine Learning ◽

Cardiac Disease ◽

Disease Diagnosis ◽

Machine Learning Algorithms ◽

Public Health Issue ◽

Cardiac Diseases ◽

Performance Measurements ◽

Network Algorithm ◽

Cardiac disease have become worldwide common public health issue, mainly due to lack of awareness of health, poor lifestyle and poor consumption. Practitioners may have different concerns when it comes to disease diagnosis, which result in different decisions and actions. On the other hand, even in the specific case of a typical disease the amount of information available is so massive that it can be difficult to make accurate and reliable decisions. With adequate patient and non-patient medical constraints, it is possible to accurately predict how likely it is that a person with heart disease and to obtain potential information from these systems. A mechanized framework for therapeutic analysis would also dramatically increase medical considerations and reduce costs. We developed a framework in this exploration that can understand the principles of predicting the risk profile of patients with the clinical data parameters. In this article, four machine learning algorithms and one neural network algorithm were used to compare performance measurements to cardiac diseases identification. We evaluated the algorithms with respect to accuracy, precision, recall and F1 settings to achieve the ability to predict cardiac attacks. The results show our method achieved 98 percent accuracy by neural network algorithm to predict cardiac diseases

Simulation Analysis of Collaborative Efficiency Improvement of Innovative Talents Management in Colleges and Universities Based on BP Neural Network

Tobacco Regulatory Science ◽

10.18001/trs.7.5.2.11 ◽

2021 ◽

Vol 7 (5) ◽

pp. 4449-4462

Author(s):

Xiyin Chang ◽

Yuchun Sun

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Simulation Analysis ◽

Talent Management ◽

Colleges And Universities ◽

Management Model ◽

Analysis Model ◽

Calculation Time ◽

Network Algorithm ◽

Objectives: In recent years, it is more and more difficult to manage innovative talents. In order to improve the collaborative efficiency of innovative talents management, this paper presents a simulation analysis of collaborative efficiency of innovative talents management in Colleges and Universities Based on BP neural network algorithm. Methods: Data simulation technology is used to establish talent management model. This model puts forward the optimization scheme from the algorithm flow, and improves the synergy of talent management by using data transformation technology. This model is analyzed from two aspects of universities and talents. BP neural network algorithm is added to the calculation of management efficiency to realize the sequence optimization of data. Results: In order to test the authenticity and efficiency of the algorithm in the talent management model, a comparative experiment is set up to analyze the results. The test results show that the accuracy of the optimized data analysis model is generally above 95%, while the accuracy of the traditional algorithm is generally below 80%, the collaborative efficiency calculation time of talent management model is the shortest, averaging only about 15 seconds; the traditional model calculation time is very unstable, from short 12 seconds to long 45 seconds, the calculation span is very large, and the accuracy rate is low. Conclusion: The research shows that BP neural network algorithm can improve the synergy of management and optimize the management mode of innovative talents, which is worthy of further promotion.

Online Shopper Intention Analysis Using Conventional Machine Learning And Deep Neural Network Classification Algorithm

Jurnal Penelitian Pos dan informatika ◽

10.17933/jppi.v11i1.341 ◽

2021 ◽

Vol 11 (1) ◽

pp. 89-100

Author(s):

Cucu Ika Agustyaningrum ◽

Muhammad Haris ◽

Riska Aryanti ◽

Titik Misriati

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Neural Network ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Market Potential ◽

Continuous Increase ◽

Network Algorithm ◽

Conventional Machine ◽

The use of e-commerce throughout the world in recent years is very rapid. The continuous increase in sales shows that e-commerce has huge market potential. Store profits are derived from the process of assessing data to identify and classify online shopper intentions. The process of assessing the data uses conventional machine learning algorithms and deep neural networks. Comparison of algorithms in this study using the python programming language by knowing the value of Accuracy, F1-Score, Precision, Recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 98.48%, the F1 score is 95.06%, precision is 97.36%, recall is 96.81% and AUC is 96.81%. So, based on this research, deep neural network data mining techniques can be an effective algorithm for online shopper intention data sets with cross-validation folds of 10, six hidden layer decoder-encoder variations, relu-sigmoid activation function, adagrad optimizer, and learning rate of 0.01 and no dropout. The value of this deep neural network algorithm is quite dominant compared to conventional machine learning algorithms and related research.