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Breast cancer is type of tumor that occurs in the tissues of the breast. It is most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This paper presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyze different existing Machine Learning and Data Mining techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this review is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this paper provides the all necessary information to the beginners who want to analyze the machine learning algorithms to gain the base of deep learning.

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Currently, conversion of coal into alternative fuel and non-fuel valuable products is in demand and growing interest. In the present study, humic acid was extracted from two different ranks of coal, i.e., low rank and high rank (lignite and bituminous), through chemical pretreatment by nitric acid. Samples of lignite and bituminous coal were subjected to nitric acid oxidation followed by extraction using KOH and NaOH gravimetric technique. The chemical pretreatment of both types of coal led to enhanced yield of humic acid from 21.15% to 57.8% for lignite low-rank coal and 11.6% to 49.6% bituminous high rank coal. The derived humic acid from native coal and nitric acid treated coal was analyzed using elemental analysis, E4/E6 ratio of absorbance at 465 nm and 665 nm using UV-Visible spectrophotometry and Fourier transformed infrared spectroscopy FTIR. The chemical characteristics of coal treated with nitric acid have shown increased molecular weight and improved aromaticity with more oxygen and nitrogen and lower C, H, and sulphur content. The E4/E6 ratio of nitric acid-treated low and high ranks of coal were high. The FTIR spectroscopic data of nitric acid-treated lignite coal indicates an intensive peak of carboxyl group at 2981.84 cm, while bituminous coal was shown in cooperation of N-H group at 2923.04 cm. SEM was performed to detect the morphological changes that happen after producing humic acid from HNO3 treatment and native coal. The humic acid produced from HNO3 treated coal had shown clear morphological changes and some deformations on the surface. SEM-EDS detected the major elements, such as nitrogen, in treated humic acid that were absent in raw coal humic acid. Hence, the produced humic acid through HNO3 oxidation showed a more significant number of humic materials with improved efficiency as compared to native coal. This obtained humic acid can be made bioactive for agriculture purposes, i.e., for soil enrichment and improvement in growth conditions of plants and development of green energy solution.

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