A Novel Approach to Analyze Pranayama through Machine Learning Techniques | Chapter 07 | New Approaches in Engineering Research Vol. 12

 Pranayama (breathing exercises) is an integral part of yoga practise. When practising pranayama, it's important to keep track of how many cycles you've done. The most important thing is to keep track of how long someone is inhaling and exhaling. Maintaining a proper ratio throughout the inhalation: exhalation cycle is critical. The counting procedure is so taxing for a beginning that it is difficult to retain awareness of the breathing process, and the standard of pranayama practise suffers as a result. The goal of the proposed system is to develop a new method for analysing the quality of Pranayama using machine learning techniques. The main goal of the proposed project is to develop an application that can count inhalations and exhalations. It guarantees that users receive feedback based on their breathing and exhalation patterns. It analyses each breath and exhalation pattern and uses Clustering algorithms to classify inhalation and exhalation. This paper's proposed structure aids in improving the uniformity of pranayama. As a result, respiratory performance improves, which reduces melancholy and anxiety. To test the validity of breathing patterns, the KNN, SVM, Random Forest, and Decision Tree algorithms are used.

Author(S) Details

A. Parkavi
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

V. Sangeetha
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

G. R. Amith
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

B. K. Harini
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

M. Supriya
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

K. N. Tejasvini
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

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Determination of Chennai Annual Rainfall Using Datamining Technique | Chapter 01 | Recent Recent Advances in Mathematical Research and Computer Science Vol. 8

 India is predominantly an agricultural country, and weather forecasting has always relied on model-based methods. Water is a necessary requirement for human living, and rainfall is one of the primary sources of this resource. Rainfall forecasting is a critical responsibility for agricultural and meteorological departments, yet it is also one of the most difficult. The demand for water has increased as a result of the growing population. As a result, the availability of water has decreased as groundwater levels have been depleted. Chennai, in particular, is significantly reliant on rains to replenish its groundwater resources. As a result, accurate rainfall forecasting is critical. For rainfall prediction, many techniques such as ARIMA, ANN, Regression analysis, Genetic Algorithm, Fuzzy logic, SVM, and others are used. This research shows how to use the k-nearest neighbour data mining technique to estimate Chennai rainfall. The error measure Mean Absolute Percentage Error (MAPE) is used to validate the model, and analysis shows that k-nearest neighbour for k = 3 produced the best results when compared to k = 5.

Author(S) Details

M. Mallika
Sathyabama Institute of Science and Technology, Chennai, India.

M. Nirmala
Sathyabama Institute of Science and Technology, Chennai, India.

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The Maximum Euclidean Distance in a Class Defines the Boundary of Neighborhood and Leads to a New Machine Learning Algorithm | Chapter 06 | Recent Advances in Mathematical Research and Computer Science Vol. 7

 When we are given a data set, we assign a class to each data point based on the values and/or characteristics of attributes. In machine learning, the k-Nearest Neighbor (kNN) method is a relatively basic and powerful tool for performing this. It is built on the concept of data points belonging to the same class being neighbours. In kNN, one evaluates the Euclidean distances of the test data or unknown data from all the data points of all the classes in the training data to discover the class to which it belongs. The class to which test data or unknown data is closest the most number of times, out of the k nearest distances, where k is any number higher or equal to 1, is the class assigned to the test data or unidentified data. In this chapter, I offer an alternative to kNN, which I refer to as the ANN technique (Alternative Nearest Neighbor). The defining aspect of ANN that distinguishes it from kNN is the concept of neighbour. The unknown data is neighboured to the class whose maximum Euclidean distance from its data points is smaller than or equal to the maximum Euclidean distance between all of the class's training data points in ANN. As a result, each unknown data will always receive a unique solution from ANN. The solution in kNN, on the other hand, may vary depending on the number of nearest neighbours k. As a result, the performance of kNN may vary as k is changed. This is not the case with ANN, whose performance is tailored to a given training dataset.

The fundamental goal for developing the ANN machine learning method was to improve on the traditional kNN method by making it independent of the parameter k and eliminating the necessity for the user to select k neighbours based on experience or other criteria.

The ANN produces a 100 percent accurate result for the training data [1] used in this article.


Author(S) Details

Pushpam Kumar Sinha
Department of Mechanical Engineering, Netaji Subhas Institute of Technology, Amhara, Bihta, Patna, India.

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Classification of Brain Tumor Types on Magnetic Resonance Images Using Hybrid Deep Learning Approach with Radial Basis Function Neural Network | Chapter 05 | Recent Advances in Mathematical Research and Computer Science Vol. 7

 In general, classification or segmentation refers to the division of an image into smaller sections in order to identify or pinpoint the abnormality region. Even though it remains a difficult task in medical applications due to the complicated structure of brain tumours, due to opposing image, local observations of an image, noise image, and non uniform texture of the images, and so on. Although there are numerous image segmentation techniques accessible, it is still necessary to introduce efficient and quick medical picture segmentation approaches. This research paper presents an effective picture classification method based on K-means clustering and a K-means clustering algorithm. The following are the primary contributions of this study paper: The first machine learning classification model for brain tumours was built on a Hybrid PSO-WCA (Particle Swarm Optimization-Water Cycle Algorithm) based Radial Basis Function Neural Network (RBFNN). Second, the GLCM (Gray Level Co-occurrence Matrix) approach was utilised to extract features. Finally, the performance of the clustering classification was demonstrated, with malignant and benign tumours being identified using Fast fuzzy c-means, KNN (Nearest Neighbor) method, Fuzzy c means algorithm, and K-Means algorithm with features as input for visual localization. Finally, the extracted tumour images from the proposed hybrid model PSO-WCA-RBFNN are segmented to produce 99.62 percent accurate representations of brain tumours. The suggested technique was also compared to various current segmentation algorithm models such as PSO-RBFNN, WCA-RBFNN, and LMS-RBFNN. The results suggest that the proposed method has a higher accuracy rate. 255 MRI brain scans from Harvard Medical School are used in the investigation.

Author(S) Details

T. Gopi Krishna
Department of CSE, SoEEC, Adama Science and Technology University, Ethiopia.

Satyasis Mishra
Department of ECE, SoEEC, Adama Science and Technology University, Ethiopia.

Sunita Satapathy
Department of Zoology, School of Applied Science, Centurion University of Technology & Management, Odisha, India.

K. V. N. Sunitha
Department of CSE, BVRIT Women’s Engineering College, Hyderabad, India

Mohamed A. Abdelhadi
Department of Information Technology, Tripoli University, Libya

View Book:- https://stm.bookpi.org/RAMRCS-V7/article/view/5490