A Brief Overview on Artificial Intelligence in Dentistry | Chapter 10 | Research Developments in Medicine and Medical Science Vol. 4

 Artificial intelligence has transformed the health care system with its vast application in various fields of medicine. AI has many subfields, of which machine learning has the major contribution in improving the delivery of health care to the population.

Medical AI has taken center stage in modern research. Medical AI applications range from the detection of various cancerous tumors to the prediction of arrhythmia attacks. The augmentation of such AI embedded techniques into the medical profession has streamlined analysis as well as aided professionals in reaching a more efficient and accurate diagnosis. This has also translated in the field of dentistry where strong deep learning architectures such as Convolutional neural networks in the form of Resnet, Inception, GoogleNet etc have been used to process raw images and detect a range of dental diseases.

This chapter aims to provide an overview of progress made in the field of Machine Learning, with a particular emphasis on its medical and dental applications. Different aspects of Machine Learning are discussed in terms of their strengths, weaknesses, and how artificial intelligence has been used to solve problems in the medical field.  A descriptive overview about state-of-the-art machine learning-based applications used in various dental subfields is discussed, along with current challenges the industry faces today.

Author(s) Details:

Iman Baig,
Islamabad Medical and Dental College, Pakistan.

Saima Azam,
Islamabad Medical and Dental College, Pakistan.

Talha Bin Mushtaq,
Islamabad Medical and Dental College, Pakistan.

Please see the link here: https://stm.bookpi.org/RDMMS-V4/article/view/9839

A Handbook on Healthcare Applications | Book Publisher International

 With the rise in great dossier, machine intelligence has enhance specifically main for answering questions. Machine learning uses two types of techniques: directed education and alone education. Clustering is ultimate prevailing alone education method. Classification and Regression are directed education methods. Clustering algorithms attempt two broad groups: Hard grouping and soft grouping. K-Means, K-Mediods, Hierarchical grouping, Self-arranging Map are few of the hard assembling arrangements. Fuzzy C- Means, Gaussian Mixture Model are gentle grouping forms. In categorization question, the classes grant permission be twofold or multiclass. A multiclass categorization problem is mainly challenging cause it demands a more intricate model. Most ordinary categorization algorithms involve Logistic Regression, k Nearest Neighbor (kNN), Support Vector Machine (SVM), Neural Network, Naïve Bayes, Discriminant Analysis, Decision Tree, Bagged and Boosted Decision Trees. Regression algorithms contain Gaussian Process Regression Model, SVM Regression, Generalized Linear Model and Regression Tree.Depends on the request, few questions demand pre-handle and addition. Real-world datasets maybe dirty, unfinished and in a difference of layouts. Hence Pre-treat should before resolving the question. Machine learning is an persuasive system for verdict patterns in large datasets. But more generous data influences additional complicatedness. As datasets become larger, it is owned by decrease the number of facial characteristics. The three most usually secondhand range decline methods are: Principal Component Analysis (PCA), Factor Analysis and Nonnegative form factorization. The acting of the method seemingly increases when machine intelligence algorithms is secondhand. Selecting a machine intelligence treasure is a process of experimental approach. The particular traits of the algorithms contain Speed of preparation, Memory custom, Predictive veracity on new dossier, Transparency or interpretability.

Author(s) Details:

S. Sowmyayani,
Department of Computer Science (SF), St. Mary’s College (Autonomous), Thoothukudi, Tamilnadu, India.

Please see the link here: https://stm.bookpi.org/AHHA/article/view/8699

Study on Auto Sector Stock Price Trend Prediction by using Decision Tree| Chapter 7 | New Approaches in Engineering Research Vol. 5

 The auto sector stock price trend is influenced by a number of national and worldwide unknown elements. It takes into account both local and global influences. The influence of such a factor on the stock price trend is difficult to forecast because the impact of the same factor fluctuates over time and due to the nonlinear character of the financial stock market. One of the strategies for capturing trends in historical data is machine learning. This feature is what inspired us to apply it in this study to forecast the auto sector stock price trend. It suggests a thorough investigation into the auto sector's stock price trend forecasting. This study focuses on decision tree classifier as a method for forecasting trend in the auto industry price prediction since it can handle multidimensional data and does not require domain knowledge.

 
Author(s) Details

Manish M. Goswami
Department of Information Technology, Rajiv Gandhi College of Engineering & Research, Nagpur, India.

Sachin Jambhulkar
Department of Electronics & Communication, Vidarbha Institute of Technology, Nagpur, India.

View Book :- https://stm.bookpi.org/NAER-V5/article/view/2088