Assessment Land Changes in the Area of Kahrizak Waste Disposal Center by Using Spatial Information System and Remote Sensing Methods | Chapter 7 | Novel Perspectives of Geography, Environment and Earth Sciences Vol. 9

 For as well 50 years, Kahrizak waste center has happened a neighbor of Tehran metropolis. With the happening of the Tehran city and the increase in waste production, it's hurtful effects on the environment. Land use wholes that are always changeful under the influence of everyday events, human conduct, social and financial issues, especially about big capitals like Tehran is obvious. In these changes, various lands are convinced into built-up lands, either residential, marketing, industrial, or drive network, and in some cases, they are abandoned as barren lands. In this research, by utilizing landsat8 satellite concepts and remote sensing method, land use changes of Kahrizak waste center have been examined in thematic maps that is more available for consumers, Five vegetation indications NDVI, EVI, SAVI, LAI and VCI and six supervised categorization methods maximum prospect, parallel networks, minimum distance from the mean, neural network, Mahalanobis distance and support heading machines with four circular, linear, polynomial kernels .and branching with the alike training and test dossier on the image of 2022 were examined by using two limits of overall accuracy and Kappa cooperative. For this research 4classes was selected soil, water, construction (urban area) and land lands, the results showed that the maximum possibility classification pattern is the best design with overall veracity of 90.99% and Kappa coefficient of 0.85 and the extreme similarity of the create user map classes to the original subsidiary image. Then, a consumer map was produce from all the images from 2011 to 2022 utilizing the maximum likelihood categorization method. After manipulative the area of the classes, the results demonstrated that the area of the building class raised by 71% and the agricultural land class depreciated by 80%.

Author(s) Details:

Vahid Hatamzadeh,
Islamic Azad University Branches, Iran.

Sara Vahidi,
Islamic Azad University Branches, Iran.

Paniz Nouri,
Islamic Azad University Branches, Iran.

Niloofar Karbalaei,
Tehran University, Iran.

Please see the link here: https://stm.bookpi.org/NPGEES-V9/article/view/11628

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.

View Book :- https://stm.bookpi.org/CTMCS-V3/article/view/2054