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Dharani, D.
- A Review of Data Classification Using various Classifiers Algorithm
Authors
1 Department of IT, PSG College of Technology Coimbatore, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 12, No 5 (2020), Pagination: 81-85Abstract
Machine-Learning (ML) methods have great importance in interdisciplinary domains. Besides many areas, healthcare domain is the most thriving area where the involvement of Machine Learning algorithms is relatively essential. The purpose of this research is to put together the various supervised learning algorithms such as Logistic Regression, Random Forest, XG boost and Support Vector Machine for the prediction of heart disease by considering relevant medical parameters in the dataset. It uses the training dataset to get better boundary conditions which could be used to determine each target class. Once the boundary conditions are determined, the validation will be done to predict the target class.The system also analyses the performance metrics of the algorithms in order to compare their effectiveness in real-time.Keywords
Healthcare Domain, Heart Disease, Supervised Learning Algorithms, Performance Analysis.
- A Review on Heart Disease Prediction Using Supervised Learning Techniques
Authors
1 Department of IT, PSG College of Technology Coimbatore, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 12, No 4 (2020), Pagination: 61-64Abstract
Nowadays Health care System using Internet of Things (IoT) provides better efficiency than the traditional health care systems. Health care using IoT provides the easiest way of communication between patients and doctors. The patient`s health is monitored continuously by the doctor through IoT devices which in turn produces the data pertaining to patient`s health. According to the data received from the IoT devices, the doctors can make a diagnosis of patient health on Real-time. It can be done through Machine Learning (ML) algorithms. This ML technique helps to minimize the disease recurrence by alerting the doctor by identifying the risk factors of a patient`s health. The system uses various supervised learning algorithms such as Logistic Regression, Support Vector Machine, Random Forest, XG Boost Algorithms for disease diagnosis and prediction. The algorithms are then compared using evaluation metrics.
Keywords
Internet of Things, Healthcare, Heart Disease Prediction, Supervised Learning Algorithms.- A Systematic Review on Predictive Analytics for Stock Management System
Authors
1 Department of IT, PSG College of Technology, Coimbatore, IN
Source
Software Engineering, Vol 12, No 5 (2020), Pagination: 85-91Abstract
The item list creates a major factor as a working capital for many commercial and industrial companies. For inventory, the system can include raw materials, finished goods, continuous operation, supplies and other accessories. To maintain the viability of business enterprises, the bottom engineering of stock is always required. Inventory management is designed to control the investment value of existing assets, the types of assets that are carried in a cell to meet production needs. Stock Exchange Management System is an application made using Python to provide easy-to-follow products, shares and inventory information as well as buying and selling information. This app also records shares currently available in the store. The controlling entity can view the sales report and purchase details of the products stored in the database. The classification and analysis involved in the system can help in predicting the sales details of a particular product in a specific database. This app can be used in any kind of store to modify the process of manually keeping records related to the stock keeping subject. All purchasing information can be automatically saved and provide instant access to archived records. It also highlights important reviews about the business so that growth can be easily measured and provides various reports that show related information so that important decisions can be taken. This application is supported to complete and, in some cases, reduces the complexity of the existing system. In addition, the program is designed in such a way that companies can perform tasks in a smooth and efficient manner. Applied machine learning techniques are used in this program that increase the effectiveness of tracking technology in inventory management and provide relevant information to aid in planning for the future.