scholarly journals Intelligent Diagnosis of Cardiac Disease Prediction using Machine Learning

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 ◽  
Neural 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

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

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 ◽  
Neural Network Algorithm

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.

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

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 ◽  
Neural 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.

Transfer Learning Based Recurrent Neural Network Algorithm for Linguistic Analysis

2021 ◽  
Vol 20 (3) ◽  
pp. 1-16
Author(s):  
Peipei Jiang ◽  
Liailun Chen ◽  
Min-Feng Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
The Core ◽  
Machine Learning Methods ◽  
Neural Network Algorithm ◽  
Comparative Results

Each language is a system of understanding and skills that allows language users to interact, express thoughts, hypotheses, feelings, wishes, and all that needs to be expressed. Linguistics is the research of these structures in all respects: the composition, usage, and sociology of language, in particular, are the core of linguistics. Machine Learning is the research area that allows machines to learn without being specifically scheduled. In linguistics, the design of writing is understood to be a foundation for many distinct company apps and probably the most useful if incorporated with machine learning methods. Research shows that besides text tagging and algorithm training, there are major problems in the field of Big Data. This article provides a collaborative effort (transfer learning integrated into Recurrent Neural Network) to analyze the distinct kinds of writing between the language's linear and non-computational sides, and to enhance granularity. The outcome demonstrates stronger incorporation of granularity into the language from both sides. Comparative results of machine learning algorithms are used to determine the best way to analyze and interpret the structure of the language.

scholarly journals A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Multilayer Perceptron ◽  
Computer Aided Design ◽  
Machine Learning Algorithms ◽  
Regression Equations ◽  
Market Demand ◽  
Product Modeling ◽  
Network Algorithm ◽  
Neural Network Algorithm

Products no longer exist simply as carriers of useful functions, but more and more consumers are beginning to pay attention to the spiritual aspects of the feelings brought by products. This paper brings machine learning algorithms to the discipline of industrial design and proposes a method to evaluate the design of product shapes using a multilayer perceptron genetic algorithm neural network (GA-MLP-NN) algorithm, quantifying the product shape, using computer-aided design technology to achieve shape optimization, shape, and color scheme generation, and using interactive feedback with users to finally generate a product shape with market demand. In this paper, we use the combinatorial innovation method to arrange and combine the detail elements in the solution library to generate the modeling solution, combine the multilayer perceptron genetic algorithm neural network algorithm with product modeling, and establish the interactive genetic modeling system for the product, use this system to design the product modeling solution, and finally get the product modeling solution satisfied by the target users; using the multilayer perceptron genetic algorithm neural network method to evaluate the product modeling items. The mapping relationship model between morphological feature space and imagery cognitive space was constructed based on multiple linear regression equations, and the multiple regression model for each affective dimension was ideal. The results show that the model performance is reliable. The weights are calculated, and the appropriate people are selected to score and calculate the modeling scheme, and finally, the satisfactory product modeling scheme is obtained.

scholarly journals Reduction of transients in switches using embedded machine learning

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 ◽  
Neural Network Algorithm ◽  
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>

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

2020 ◽  
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 ◽  
Neural Network Algorithm ◽  
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.

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