Beneficial Image Preprocessing by Contrast Enhancement Techniquefor SEM Images

In this paper a morphological filtering algorithm using an exposure thresholding and measures of central tendency hasbeen proposed for solving the low contrast of Scanning Electron Microscopic (SEM) images of composite materials foraccurate Filler Content Estimation. SEM image of a composite material comprises visible morphological structures likefillers such as silica nanoparticles. The SEM image analysis via segmentation will assist in the study of distribution of thesestructures. The estimation of the filler content is more accurate only when the SEM images have proper contrast for analysisif not the results lead to less accuracy. To overcome this drawback, we have proposed a preprocessing technique to increasethe contrast of SEM images. So that the preprocessed image can be used for post processing namely segmentation and hencethe error is less for filler content estimation. We introduced the transformations using morphological processing to extractthe bright and darker features of the images. The optimum threshold value is determined by the image exposure. A detailedcomparative analysis with other existing techniques has been performed to prove the superior performance of the proposedmethod

Granite classification using machine learning and edge computing [version 1; peer review: 1 approved, 2 approved with reservations]

Background: The outlook and the aura of any place are highly dependent on how a place is decorated and what materials are used in designing it. Granite is such a kind of rock which is vastly used for this purpose. Granite flooring and counters have a major influence on the interior d ́ecor which is essential to set the mood and ambience of a house. A system is needed to help the end users differentiate between granites, which enhance the grandeur of their house and also check the frauds of different color granite being sent by the merchant as compared to what was selected by the end user. Several models have been developed for this cause using CNN and other image processing techniques. However, a solution for this purpose must be precise and computationally efficient. Methods: For this purpose,researchers in this work developed a machine learning based granite classifier using Edge Computing and a website to help users in choosing which granite would go well with their d ́ecor is also built. The developed system consists of a color sensor [TCS3200] integrated with an ESP8266 board. The data pertaining to RGB contrasts of different rocks is acquired by using the color sensor from a dealership.This data is used to train a Machine Learning algorithm to classify the rock into different granite types from a granite dealer and yield the category prediction. Results: The proposed system yields a result of 94% accuracy when classified using Random Forest Algorithm. Conclusion: Thus, this system provides an upper hand for the end users in differentiating between different types of granites

A Survey on Big Five Personality Traits Prediction Using Tensorflow

A personality trait is a specific pattern of thought, thinking, or performing that manages to be faithful over time and beyond essential places. The Big Five—Extraversion, Agreeableness, Conscientiousness, Neuroticism and Openness to Practice are a set of five broad, bipolar quality dimensions that establish the most extensively used design of personality construction. Earlier investigations revealed a growing interest in defining the personality and behavior of people in fields such as career development, personalized health assistance, counseling, mental disorder analysis, and the detection of physical diseases with personality shift symptoms. Modern methods of discovering the Big-Five personality types include completing a survey, that takes an impractical amount of time and cannot be used often. This paper provides a survey on detecting of big five personality traits based on facial features recognition using TensorFlow mechanism. And also, various methods to detect big five personality traits are discussed in this paper. Finally, the graph provides a comparison between various detection of big five personality traits on facial expressions

A Survey of DDOS Attacks Using Machine Learning Techniques

The DDoS attacks are the most destructive attacks that interrupt the safe operation of essential services delivered by the internet community’s different organizations. DDOS stands for Distributed Denial Of Service attacks. These attacks are becoming more complex and expected to expand in number day after day, rendering detecting and combating these threats challenging. Hence, an advanced intrusion detection system (IDS) is required to identify and recognize an- anomalous internet traffic behaviour. Within this article the process is supported on the latest dataset containing the current form of DDoS attacks including (HTTP flood, SIDDoS). This study combines well-known grouping methods such as Naïve Bayes, Multilayer Perceptron (MLP), and SVM, Decision trees

Role of social media in women's health - boon or bane

Global society is excelling with technological initiatives and social media's role is commendable in this ascent. To the other side of the coin, the adverse impact of social media usage in women's health is on rise and is less explored. The global research community is blowing alarm about the adverse consequences of social media usage on the health of adolescent girls. Hence this study intends to review whether social media is a bane or boon to the health of women. So, the opinion of a group of 150 randomly selected female is sought through questionnaire to know whether social media is a beneficiary artefact. Statistical techniques are employed to understand the role of social media on the women's health from the scrutinized 100 questionnaires. The outcome revealed that though social media fetches many benefits to the community, it is causing major damage for the adolescent girl's psychological and reproductive health

Role of social media in women's health - boon or bane

Global society is excelling with technological initiatives and social media's role is commendable in this ascent. To the other side of the coin, the adverse impact of social media usage in women's health is on rise and is less explored. The global research community is blowing alarm about the adverse consequences of social media usage on the health of adolescent girls. Hence this study intends to review whether social media is a bane or boon to the health of women. So, the opinion of a group of 150 randomly selected female is sought through questionnaire to know whether social media is a beneficiary artefact. Statistical techniques are employed to understand the role of social media on the women's health from the scrutinized 100 questionnaires. The outcome revealed that though social media fetches many benefits to the community, it is causing major damage for the adolescent girl's psychological and reproductive health

Research on efficacy of webinars organized for faculty during lockdown of COVID-19

Online Faculty Development Programs/Webinars are the two buzzing words, which have become viral next to corona among the teaching fraternity during the lockdown period of pandemic situation caused by COVID-19. This work intends to throw light on, the reason for the outbreak of FDPs/ Webinars, their efficiency and the attitude of the participating faculty during the lockdown period from 16th March to 15th June 20. Information is gathered through an online survey having 31 research questions answered by 683 participants across India. The new found tool of online teaching has become the accepted norm and the urge to lead the bandwagon by each and every stakeholder in the education sector resulted in a sudden spurt of webinars and FDPs in such a short period. Study observed that global reach at no cost plus freedom of working from home spurred many faculty to experiment this mode and 40% from them have been found to be juggling with many courses simultaneously for certificate sake only, 45.1% attended on mandatory instructions and 38% have not even initiated the work. Quizzes and Polls during sessions besides assignments were found to be suitable active learning mechanisms to improve the efficacy of the online knowledge transfer methods