Cancer is now a day's one of the main diseases which has widely affected among the peoples. A molecular pathologist selects a list of genetic variations of interest that he/she wants to analyze. The molecular pathologist searches for evidence in the medical literature that somehow is relevant to the genetic variations of interest Finally this molecular pathologist spends a huge amount of time detecting the evidence which is related to each of the variations to classify them. The ultimate goal is to replace step 3 by a machine learning model. The molecular pathologist will still have to decide which variations area of interest, and also collect the relevant evidence. In this paper, we apply machine learning methods especially logistic regression (which is more accurate) on the datasets to determine and examine whether there are any signs or possibilities of cancer and if the person is examined as cancerous then the stage of cancer is also determined. Cancer disease is classified into four types named type 1, type 2, type 3 and type 4. Id, Gene, variation, and class are the fields used.
Plastics having number of applications all over the
world and its great usage lead to rapid increase in plastic
production and disposal. Around 400 million tons of plastics
produced per year worldwide, out of this only 18% of plastics
recycled that has led to its poor disposal practice because
discharged plastics overcome in the environment for several 100
years either in their original or fragmented form. The
fragmentation of particles are caused by several factors like wind
currents, wave currents, abrasions etc., leading to various sizes
which are classified as macro- (≥25mm), meso-(<25mm–5mm)
micro- (<5mm–1μm) and further Nano-plastics (<1μm).This
study manly focusses on quantification of Micro plastics which
can be identified in different shapes such as spherical beads
(pellets), films, fragments, foam, fibers etc., and are generally
composed of polyethylene (0.91-0.97 g/mL), polypropylene (0.94
g/mL), polyvinyl chloride (1.4 g/mL), and polystyrene (1.05 g/mL).
For this analysis of micro plastic existence, the water samples
have been collected from two locations such as Adyar and Cooum
estuaries. 10 samples have been collected each location and
carried over to the laboratory for FTIR- Spectrometer (Fourier
Transform Infrared Spectrometer) analysis along with the
protocol laid by the NOAA. Typical infrared spectrum covers
between 2.5 µm to 25 µm (4000 to 400 )
The city administration values the district attraction rating since it can aid in extracting the desirability of the location and hence support the officials in making smart city development decisions. Traditional urban planning tactics mostly rely on Gross Domestic Product, rate of employment, number of people per unit area, and district statistics gleaned through surveys and questionnaires, among other factors. As a point of reference, such knowledge becomes less and less helpful over time. The volume of urban data is growing at an exponential rate. Furthermore, these tactics suffer from a fatal flaw: they are unsuccessful. Independent representations of a district's appeal, as well as inter-district interactions, are not taken into account. It is now feasible to use urban data efficiently for urban planning thanks to advances in urban computing. To that end, this paper proposes PageRank, a district attractiveness rating algorithm based on taxi big data, which is the first to do so. A working software is constructed for visualization motives. To begin, the total area is split into numerous parts using the k-means algorithm.
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