A Comparative Study of Diagnosis of Lower Back Pain Based on Classification and Imaging Techniques | Chapter 14 | Current Topics on Mathematics and Computer Science Vol. 3

 Different classification approaches are compared in this research for the accurate diagnosis of Lower Back Pain using base and meta (Combination of Multiple Classifier for Training) level classifiers. Different imaging modalities based on radiology are also evaluated for diagnosing Lower Back Pain, such as Computed Tomography (CT) scans and Magnetic Resonance Imaging (MRI) (MRI). Lower back pain gets persistent as people age, so it's important to get a proper diagnosis early on. At the base and meta levels, five separate classifiers were implemented. Five distinct classifier combinations were developed at the meta level, using a voting mechanism. The overall classification utilising Nave Bayes and Multilayer Perceptron had the highest efficiency of 83.87 percent, according to the results. The goal of this study is to efficiently diagnose healthy people. to investigate the symptoms of lower back pain The dataset comes from Kaggle, a well-known predictive modelling site. The studies were conducted using the WEKA (Waikato Environment for Knowledge Analysis) software suite [1].

Author (S) Details

Dr. Mittal Desai
MCA Department, CMPICA, CHARUSAT University, Changa, India.

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An Approach of Short Term Road Traffic Flow Forecasting Using Artificial Neural Network | Chapter 13 | Recent Studies in Mathematics and Computer Science Vol. 2

In recent days, road traffic management and congestion control has become major problems in any busy junction in Hyderabad city. Hence short term traffic flow forecasting has gained greater importance in Intelligent Transport System (ITS). Artificial Neural Network (ANN) models have been fruitfully applied for classification and prediction of time series. In this chapter, an attempt has been made to model and forecast short-term traffic flow at 6.no. junction in Amberpet, Hyderabad, Telangana state, India applying Neural Network models. The traffic data has been considered for peak hours in the morning for 8A.M to 12 Noon, for 5 days. Multilayer Perceptron (MLP) network model is used in this study. These results can be considered to monitor traffic signals and explore methods to avoid congestion at that junction.

Author(s) Details

V. Sumalatha 
Department of Statistics, OSMANIA University, Hyderabad, India.

Manohar Dingari
Department of Mathematics School of Technology, GITAM University, Hyderabad 502329, India.

Prof. C. Jayalakshmi
Department of Statistics, OSMANIA University, Hyderabad, India.

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