A Comprehensive Examination, Execution, and Evaluation of Machine Learning Algorithms Applied to a Breast Cancer Dataset Utilizing the WEKA Software Tool | Chapter 7 | Mathematics and Computer Science: Contemporary Developments Vol. 8

In the contemporary world, cancer ranks as one of the most critical diseases. The factors contributing to cancer include modern lifestyle choices, environmental influences, and genetic predispositions. It has emerged as the leading cause of mortality in developed nations. Among various types of cancer, Breast Cancer is the most prevalent among women and is a significant contributor to female mortality. Cancer screening methods are typically experimental or natural in their approach. Machine Learning algorithms play a crucial role in identifying significant patterns within data, particularly in the context of disease prediction. In this research, a new Machine Learning approach is developed in predicting breast cancer at an early stage. The objective is to assess the effectiveness of the ML algorithm based on metrics such as precision, specificity, sensitivity, and accuracy. A real-time Breast Cancer dataset was collected from the UCI Machine Learning repository for performing research. The data was firstly preprocessed to identify the missing values of the features in the dataset and replaced with the mean of the relevant variable. Then the dataset was projected on popular Machine Learning algorithms, including Sequential Minimal Optimization (SMO), Naive Bayes (NB), J48 (C4.5 decision tree), K-Means, and K-Nearest Neighbours (k-NN). From the analysis, it is observed that the SMO Machine Learning algorithm provides 96.90% accuracy which is very high when compared to the accuracy of existing approaches. The results indicate that SMO outperforms the others in terms of accuracy and low error rate.

 

Author (s) Details

 

K. SrikanthA
Department of Computer Science & Engineering, G. Pulla Reddy Engineering College (A), Kurnool, Andhra Pradesh, India.

 

S. Zahoor Ul Huq
Department of Computer Science & Engineering, G. Pulla Reddy Engineering College (A), Kurnool, Andhra Pradesh, India.

 

A.P. Siva Kumar
Department of Computer Science & Engineering, JNTUA, Anantapuramu, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v8/2986 

Hybrid Neural Network for Human Activity Recognition | Chapter 10 | Emerging Trends in Engineering Research and Technology Vol. 1

 A actual time for action or event detection of human movements is a proficient solution to monitor old people or mentally disputed people accompanying the permission of their family. Household character is needed to monitor the elder and differently abled public. Instead of monitoring their exercises with the help of other society, smart phones are used as a remote to monitor their projects and simultaneously please the message to their family appendages. The accelerometer sensor placed in the travelling phones. It is used to identify the activities of the woman who holds the mobile phones. The most usually used classifier method is Naive Bayes classifier which has a limitation of handle accompanying the large set of dossier. To overcome this defect, the proposed system categorizes the data using k-most forthcoming neighbor (K-NN) technique and Neuroevolution. This arrangement recognize some representative human campaigns such as promenading, climbing upstairs, crawling downstairs, standing, sitting and running ,utilizing a conventional travelling equipped with a distinct tri-axial accelerometer sensor.

Author(s) Details:

K. N. Apinaya Prethi,
Department of Computer Science and Engineering, Coimbatore Institute of Technology, India.

M. Sangeetha,
Department of Computer Science and Engineering, Coimbatore Institute of Technology, India.

S. Nithya,
Department of Computer Science and Engineering, Coimbatore Institute of Technology, India.

Please see the link here: http://bp.bookpi.org/index.php/bpi/catalog/book/147

Application of Data Mining Algorithms for the Detection of Non-functional Bore Wells | Chapter 1 | Research Developments in Science and Technology Vol. 9

India uses the most groundwater, with over 230 cubic kiloliters produced annually by its 27.5 million wells. Vertical artesian wells are dug in the surface's subsurface aquifer to gather water for a number of uses. of objectives. Due to the rising need for artesian wells, more are being drilled each year. factors including decreased rainfall, water scarcity, and groundwater depletion etc. The motor is removed as the water dries, and the exterior is not adequately sealed or covered. The borewell's diameter is big enough for a kid to fall into. into the interior of the at present disused or inoperative well. Apparently, one Sadly, more than 40 kids have fallen into wells since 2009, according to a report. portion of Rescue efforts have fallen short. The present study includes an analysis. of the Kaggle dataset derived from the drill holes. The purpose of this work is aims to create predictive models using basic machine learning techniques. ANN models, logistic regression, and bays to "forecasting existing equipment failure wells that will soon be reported and confiscated." i.e., to identify non-operational bore wells that demand quick intervention. The effectiveness of the measures were used to assess the model. Good results from logistic regression performance in identifying deadly inactive wells.

Author (s) Details

K. V. Uma
Department of Information Technology, Thiagarajar College of Engineering, Madurai, India.

T. Janani Priya
Department of Information Technology, Thiagarajar College of Engineering, Madurai, India.

K. P. Sabari Priya
Department of Information Technology, Thiagarajar College of Engineering, Madurai, India.

Jennifer Priscilla Vincent
Department of Information Technology, Thiagarajar College of Engineering, Madurai, India.

View Book:  https://stm.bookpi.org/RDST-V9/article/view/7546