The aim of the current study was to suggest a multi-hazard probability assessment in Fars Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and used the Boruta algorithm to prioritize the impact of each respective factor on the occurrence of each hazard. Subsequently, flood, landslides, and forest fire susceptibility maps prepared using a Random Forest (RF) model in the R statistical software. Results indicate that 42.83% of the study area are not susceptible to any hazards, while 2.67% of the area is at risk of all three hazards. The results of the multi-hazard map in Shiraz City indicate that 25% of Shiraz city is very susceptible to flooding, while 16% is very susceptible to landslide occurrences. For four strategic watersheds, it is notable that in the Dorodzan Watershed, landslides and floods are the most important hazards; whereas, flood occurrences cover the largest area of the Maharlou Watershed. In contrast, the Tashk-Bakhtegan Watershed is so sensible to floods and landslides, respectively. Finally, in the Ghareaghaj Watershed, forest fire ranks as the strongest hazard, followed by floods. The validation results indicate an AUC of 0.834, 0.939, and 0.943 for the flood, landslide, and forest fire susceptibility maps, respectively. Also, other accuracy measures including, specificity, sensitivity, TSS, CCI, and Gini coefficient confirmed results of the AUC values. These results allow us to forecast the spatial behavior of such multi-hazard events, and researchers and stakeholders alike can apply them to evaluate hazards under various mitigation scenarios.

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Assessing and mapping multi-hazard risk susceptibility using a machine learning technique.

Cancer is a deadly disease that plagues mankind. 12.5%of women acquire breast cancer in their lifetime. Though, the mortality rate in women due to breast cancer is the second highest behind lung cancer, periodic clinical checkups and self-tests help in early detection and thereby significantly increases the odds of survival. Mammography is considered as the “Gold Standard” of non-invasive breast cancer detection technique. Screening for cancer through manual analysis of the medical images is tedious, time-consuming and impractical for large data. Thus, an automated, accurate and efficient detection system is needed to diagnose breast cancer within a short period of time. Deep learning techniques can revolutionize modern medicine by automating the detection process without compromising its accuracy. In this work, an automated system is proposed for achieving error-free detection of breast cancer using a Sparse Autoencoder (SAE) which learns feature representations from the mammogram and a classifier which is cascaded with the SAE performs the classification based on these learned features. Different classifiers like decision trees, K-nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest classifier are used and their performances are compared. From the analysis, it is observed that the Random Forest Classifier performs better compared to other methods.

Performance analysis of various classifiers on deep learning network for breast cancer detection

Neural Network is utilized as a developing analytic tool for the diagnosis of breast cancer. The goal of this research is to determine breast tumor from digital mammograms with a machine learning technique in view of RF and combination of RF-ELM classifier. For digital mammogram images, MIAS database is used. Preprocessing is usually needed to enhance the low quality of the image. The region of interest (ROI) is determined in line with the scale of suspicious region. After the suspicious area is sectioned, features are extracted by texture analysis. GLCM is used as a texture attribute to extract the suspicious area. From all extracted features best features are selected with the help of CBF method. To enhance the exactness of classification, only six features are selected. These features are mean, standard deviation, kurtosis, variance, entropy and correlation coefficient. RF and RF-ELM are used as a classifier. The outcomes of present work show that the CAD system with the usage of RF-ELM classifier may be very powerful and achieves the exceptional results in the prognosis of breast cancer.

Detection and classification of breast cancer from digital mammograms using RF and RF-ELM algorithm

Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

Segmentation and classification of breast cancer using novel deep learning architecture

Breast cancer is the second most common cancer among the women and the major victim for the breast cancer is the women. In the USA, one out of eight is diagnosed as breast cancer among the other cancers. Medical images can be analysed for identification. Image pre-processing is an essential procedure used for reducing image noise, highlighting edges, or displaying digital images. Mammogram is the best way for screening the breast. Applying medical image techniques could help in identifying and classifying the abnormalities present in the breast. The features which are extracted from medical images can also be given as input to the classifier for classification. Mammogram has been given as input to the proposed system. Mammograms are pre-processed before given to the classifier. The features are extracted through GLCM and then decision tree classifier is used in this paper for classifying the breast abnormality as benign and malignant.

Classification of breast abnormality using decision tree based on GLCM features in mammograms

Breast cancer is one of the crucially prevailing cancer among women Early detection and diagnosis of breast cancer can be facilitating with mammography images since they are most cost effective and a good chances of recovery Classification is an identification technique used to organize the data into categories Classification algorithm identifies the severity of lymph's present in the breast The entire study focuses on different classifier techniques which can be used after pre-processing and segmentation process to improve the accuracy result of the image and can be categorized as well We made a study on suitable techniques for mammogram images such as decision tree, K-nearest Neighbour, Fuzzy K-Nearest Neighbor, Nave Bayes, Artificial Neural Network, Ensemble and Support vector Machine For each classification, we consider the factor such as sensitivity, specificity and accuracy which are chosen according to their suitable scenarios

Study on different classification technique for mammogram image

To classify forest fires dataset w.r.t burned area of the forests, we have brought data mining approach. Forest fires are a major environmental issue, causing damage in terms of economy as well as ecology, besides endangering human frailty and lives. Forest fires are usually unrestrained, and they occur over widespread forest areas taking the shape and vigor of destruction. Data mining is a way to extract the knowledge patterns and information, useful for our work; data being put and taken from the database. Classification has been applied to search and locate the particular or distinct where respective data instances have some relation in one way or the other, confined in the produced dataset.

Forest Fire Prediction to Prevent Environmental Hazards Using Data Mining Approach

The breast cancer is diagnosed using many ways for past two decades. The Studies have proved that the early detection of cancer will increase the life span of the patients. The breast cancer detection requires double reading of mammogram by radiologist, hence the radiologist need to have support from CAD which includes different image processing techniques. We are in urge to improve the CAD systems that detects the abnormalities such as micro calcification, mass, etc. than existing. Firstly, This paper focus on the preprocessing which removes noise from the mammogram and it is followed by segmentation of the image which helps to partition the image and to identify the abnormalities which could cause cancer. The segmentation is made by OTSU's method which helps us further to classify the abnormalities from the normal.

Identification of abnormility from digital mammogram to detect breast cancer

Testing gives means pertaining to assuring software performance. As it has been proved that the software testing phase is one of the most critical and important phases in the software development lifecycle as it lead to increase in cost, time and effort. The total aim of software industry is actually to make a certain start associated with high quality software for the end user. Software testing has quite a few underlying concerns, which are very important and need to pay attention on these issues which are effectively generating, prioritization of test cases, etc. To over come from these issues we need to pay attention and focus. In this paper we are discussing various techniques which are used to improve automated test case generation and comparing it with other existing techniques. In this we are also proposing new work which can be done in future.

https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042035/pdf

J. Kamalakannan

Papers

Classification of breast abnormality using decision tree based on GLCM features in mammograms

Study on different classification technique for mammogram image

Forest Fire Prediction to Prevent Environmental Hazards Using Data Mining Approach

Identification of abnormility from digital mammogram to detect breast cancer

Comparative study on test case generation: a survey