The Importance of Data Visualization in Exploratory Data Analysis

Data analysis or data science is the most talked about and buzz world in recent time it is also the most research area. Exploratory data analysis also popularly known as EDA is a statistical method or process which helps you to get a better understanding of the data or dataset which you are working on. Exploratory data analysis is considered an essential process in any data science project life cycle. The better you understand your data the better report you will provide or you will able to build more robust and better models. The EDA is consisting of several steps or is a process of several steps that you need to perform on your dataset. The data visualization technics help you a better representation of your data. There n-numbers of way to visualize your data. In this work, we are going to see the importance of data visualization in exploratory data analysis and the graphs you look for in any EDA. There are many paperwork and books available on exploratory data analysis and the steps involved in it. But here we will only try to focus on the different types of visualization techniques involved in the EDA. All the examples we going to see here are built by using python. There many tools available in the market to perform exploratory data analysis but in python where you write your own code to perform anything and python is widely used in the data science field. We will segregate each and every stage of EDA and see the important role plays by data visualization in order to understand the data you are working on

Health Care Chatbot Assistant System

Rasa stack consists of many open source AI apparatuses solely utilized in plan to make a logical chatbot. It consists of incredible APIs embedded along the Rasa stack that incorporates  Natural language understanding. It incorporates the sack of words calculation helping in streamlining portrayl utilized in measurable displaying and AI stages and furthermore trend setting innovation. The proposed framework is to make an option in contrast to this ordinary strategy for visiting a clinic and making a meeting with a specialist to get analysis. From the user queries chatbot will, predicts the infection and prescribes treatment along with necessary medicine. It like wise support the utilization of this RASA stage for the client specific format according to their prerequisites and furthermore elevates in building up the system for better efficiency

CDLX: An Efficient novel approach for COVİD detection lung x-rays throw transfer learning based on State of the art deep learning image classification models

When compared to the general population, lung cancer patients have a higher incidence of COVID-19 infection, pulmonary problems, and poorer survival results. As a reference for prioritising cancer care issues during the epidemic, the world's main professional organisations issued new recommendations for the diagnosis, treatment, and follow-up of lung cancer patients. In today's world, we are fighting one of the greatest pandemics in human history, known as COVID-2019, which is caused by a coronavirus. The patient can be treated promptly if the infection is detected early (before it enters the lower respiratory tract). To observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs once the virus has reached the lungs. Artificial intelligence techniques can be utilised to detect the presence and degree of illness based on the major discrepancies between X-ray images of an infected and non-infected person. For this study, I employed feature extraction from Transfer Learning, which entails importing a pre-trained CNN model, such as Distributed Deep Convolutional VGGNet or Distributed Deep Convolutional with ResNet Model, and changing the last layer to meet my needs

Extracellular polymeric substances affecting pore-scale hydrologic conditions for bacterial activity in unsaturated soils

Soil bacterial cells are often found embedded in biosynthesized extracellular polymeric substances ( EPS) forming aggregates or stationary colonies attached to solid surfaces. Soil bacterial aggregation and pooling of resources offer a successful adaptation to variations in hydration status and in nutrient availability and enhance cooperative genetic and metabolic exchanges. The ubiquity of such microbially excreted exopolymeric substances across many different environmental conditions and habitats is attributed to their key role in environmental adaptation, including colony architecture and anchoring, nutrient entrapment, and maintenance of favorable hydration conditions. This review focuses on the hydrophysical properties of EPS and its primary constituent, exopolysaccharides, and their role as an interface between living cells and the harsh conditions common to the shallow vadose zone. We review water retention, diffusion, and hydraulic properties of EPS and postulate mechanisms conferring an advantage to embedded bacterial cells. The shrink-swell behavior of EPS for different water potentials affects mean pore size and passage of solutes and colloids of different sizes; we evaluate various water-related morphological transformations of EPS that influence diffusion behavior in unsaturated soils. We hypothesize that EPS low permeability results in hydraulic decoupling during rapid wetting or drying events, effectively shielding embedded bacterial cells from adverse effects of extreme fluctuations in hydration conditions. We show that the addition of minute amounts of EPS significantly alters the hydrological conditions experienced by microbial colonies and, in some case, may alter macroscopic hydrological and mechanical properties of the host porous medium

Optimization Studies on Improving the Dielectric Properties of Alkali Treated Fibers from Phaseolus Vulgaris Reinforced Polyester Composites by Central Composite Design

This study investigates the dielectric efficiency of a composite prepared using a fiber extracted from Phaseolus vulgaris. The extraction of fiber was statistically modeled using face-centered central composite design. The concentration of NaOH and extraction time was taken as the process variables and dielectric strength was taken as the response. The numerically optimized model for the extraction of fiber from Phaseolus vulgaris of alkali treatment showed 14.325 kV/cm as dielectric strength of initial NaOH concentration of 3% and time period of 51 min. The model was significant with R2 = 0.9323 and adjusted R2 = 0.884. The linear, quadratic & interactive relationship between the response (dielectric strength) and variables (NaOH & time) was also established. This study explains the importance of NaOH and time period in establishing the fiber extraction by the alkali treatment process

Investigation on the Physicochemical and Mechanical Properties of Novel Alkali-treated Phaseolus vulgaris Fibers

This investigation is aimed to analyze the effect of sodium hydroxide (NaOH) treatment on the physical, chemical, structural, thermal and surface topography of Phaseolus vulgaris fibers (PVFs). The surface of raw PVFs was modified by soaking with 5% NaOH solution for 15, 30, 45 and 60 min. The various functional groups of the alkali-treated PVFs (APVFs) were studied Fourier-transform infrared spectroscopy. The outcomes of thermogravimetric analysis evident that the optimum treatment time for 5% NaOH was 45 min. It was noticed that optimally treated PVFs have higher cellulose (69.48 wt.%), crystallinity index (52.27%) and lower hemicellulose (4.30 wt.%), lignin (7.02 wt.%) contents. The thermogravimetric analysis (TGA) of PVFs also revealing moderate thermal stability was observed; atomic force microscopy (AFM) investigation inveterates that the surface of the fiber is rough and it will be a potential reinforcement for polymer composites

Average travel time estimations for urban routes that consider exit turning movements

This paper presents a methodology for real-time estimation of exit movement-specific average travel time on urban routes by integrating real-time cumulative plots, probe vehicles, and historic cumulative plots. Two approaches, component based and extreme based, are discussed for route travel time estimation. The methodology is tested with simulation and is validated with real data from Lucerne, Switzerland, that demonstrate its potential for accurate estimation. Both approaches provide similar results. The component-based approach is more reliable, with a greater chance of obtaining a probe vehicle in each interval, although additional data from each component is required. The extreme-based approach is simple and requires only data from upstream and downstream of the route, but the chances of obtaining a probe that traverses the entire route might be low. The performance of the methodology is also compared with a probe-only method. The proposed methodology requires only a few probes for accurate estimation; the probe-only method requires significantly more probes

Actinobacterial community structure in the Polar Frontal waters of the Southern Ocean of the Antarctica using Geographic Information System (GIS): A novel approach to study Ocean Microbiome

Integration of microbiological data and geographical locations is necessary to understand the spatiotemporal patterns of the microbial diversity of an ecosystem. The Geographic Information System (GIS) to map and catalogue the data on the actinobacterial diversity of the Southern Ocean waters was completed through sampling and analysis. Water samples collected at two sampling stations viz. Polar Front 1 (Station 1) and Polar Front 2 (Station 2) during 7th Indian Scientific Expedition to the Indian Ocean Sector of the Southern Ocean (SOE-2012-13) were used for analysis. At the outset, two different genera of Actinobacteria were recorded at both sampling stations. Streptomyces was the dominanted with the high score (> 60%), followed by Nocardiopsis (< 30%) at both the sampling stations-Polar Front 1 and Polar Front 2-along with other invasive genera such as Agrococcus, Arthrobacter, Cryobacterium, Curtobacterium, Microbacterium, Marisediminicola, Rhodococcus and Kocuria. This data will help to discriminate the diversity and distribution pattern of the Actinobacteria in the Polar Frontal Region of the Southern Ocean waters. It is a novel approach useful for geospatial cataloguing of microbial diversity from extreme niches and in various environmental gradations. Furthermore, this research work will act as the milestone for bioprospecting of microbial communities and their products having potential applications in healthcare, agriculture and beneficial to mankind. Hence, this research work would have significance in creating a database on microbial communities of the Antarctic ecosystem. Keywords: Antarctica, Marine actinobacteria, Southern ocean, GIS, Polar Frontal waters, Microbiom