Search

Steel Slag (SS) is an industrial by product get from the steel manufacturing industries .It is produced in large quantities during the steel making operation which utilize electric arc furnaces .SS can also be produced by smelting iron ore in the basic oxygen furnace. SS can be used in construction industry as an aggregate in concrete by replacing natural aggregate. Natural aggregate are becoming increasingly scare and their production shipment is more difficult. SS is used for aggregate in hot as plat surface application. The majority of the aggregate is quantity of the concrete. Replacing all or a few of the natural aggregate with the SS will leads to some environmental benefits. For this present study M25 concrete is used. The one-sided substitute of the SS will be made for changing percentages, for example 0%,10%, 20%,30%,40%,50%. From this learn the compressive strength, flexural strength and split tensile strength will be made experimentally also to find the optimal level of SS at a particular replacement of SS as natural aggregate.

Fear of Missing Out (FoMO), often known as the worry of missing out on exciting or fascinating events, has garnered a lot of attention recently, but little study has been done on its relationship to personality in the Indian context. The term "Fear of Missing Out" (FoMO) was used to characterise a trend observed on social networking sites. The purpose of this study is to look into the relationship between the BigFive Personality Traits and FoMO. The questionnaire was administered to 248 emerging adults who were selected using convenience sampling technique. The collected data was analyzed using correlational research method. The tools used for this study were the Fear of Missing Out (FoMO) scale and the TenItem Personality Inventory (TIPI). The findings of this study reveals that all Big-Five Personality Traits had an impact on FoMO. FoMO and all Big-Five Personality Traits show a positive and significant relationship. Limitations and directions for further research were discussed.

A dataset that has massive features and imbalanced classes may be challenging for obtaining adequate accuracy in classification approaches of Machine Learning (ML). The purpose of this research is to find the optimal feature subset for cervical cancer diagnosis with efficient classification approach by estimating the performance of various Machine Learning predictive models. Filter-based feature selection techniques of Relief and Information Gain are applied in this study to calculate the rank for each feature that can be applied to order and select highest scoring features for feature selection. An optimal feature subset is generated with wrapper approach through Recursive Feature Elimination which uses a Random Forest procedure and Genetic Algorithm has been employed based on evolutionary principle. The predictive models are established with 10fold cross validation using prevalent classification algorithms like Random Forest, C5.0, K-Nearest Neighbour and Naïve Bayes. The results showed an enhancement in the average performance of these classifiers concurrently and the classification error for these classifiers decreases substantially. The experiments also exhibited that by employing this approach an optimal and reduced feature subset is desirable for the enrichment of classification accuracy with a lower computational cost. The features generated by fused approach of Relief and Genetic algorithm methods were able to predict the results in an efficient manner, hence an optimal feature subset has been nominated through this procedure. Maximum number of classifiers have shown good results in terms of performance outcomes. In addition, Random Forest method has shown advanced accuracy rate with an improved percentage of sensitivity and specificity results. Also, this work established that the best and optimal feature subset selection through Fused Feature Selection (FFS) approach could reduce the complexity of the predictive model.

An individual’s concern is striving for perfection, self-evaluation in critical situations and regarding other's evaluation. Most of the students think about getting evaluated for their own abilities. They also face situations where they don’t feel completely successful. This study tries to find the relationship of the Imposter Phenomenon with Perfectionism and Fear of Failure. Using the convenience sampling technique, 166 emerging adults were chosen and given the questionnaire. The correlational research methodology was used to analyse the acquired data. The Multidimensional Perfectionism Scale, The Performance Failure Appraisal Inventory (PFAI) and Imposter Profile (IPP31) were the instruments employed in this study. It was found that Competence Doubt, Frugality and Need For Sympathy were significantly correlated with Self-oriented Perfectionism, Other-oriented Perfectionism and Socially-oriented Perfectionism. Alienation and Other-Self Divergence were significantly and positively correlated with socially-oriented perfectionism. Fear of Failure was also found to have a significant relationship with all sub factors of Imposter Phenomenon.

Cognitive flexibility refers to understanding how to learn and having the flexibility to approach learning in different ways. The person can modify their cognitive processing methods in response to novel and unanticipated environmental circumstances. Previous studies showed that the cognitive flexibility of individuals changes according to their personality traits. The present study investigated the relationship between cognitive flexibility and Big Five personality traits to understand how individuals with different personality traits adapt to new surroundings. 221 college students were selected using the convenience sampling method. The cognitive flexibility scale and ten-item personality inventory were administered online using google forms. The data collected was analysed based on Pearson's bivariate correlational research design. The results of this research showed a significant correlation between cognitive flexibility and Big Five personality traits. Cognitive flexibilitywas found to have a significant and positive correlation with extraversion, conscientiousness, emotional stability and openness to experience. The results of this study corroborate earlier studies that show a strong relationship between personality traits and cognitive flexibility.

Homosexuality is an enduring pattern of emotional or sexual attraction to people of same-sex. There may be numerous individual differences have been linked to attitude towards homosexuality. The main objective of this study was to examine the association between the attitude of emerging adults towards homosexuality and dark triad personality traits which may help us to predict how individuals see homosexuals and what's their thought about homosexuals. A sample of 201 people was selected using the convenience sampling technique. The data collected using Google Forms was analysed based on a correlational research design. The finding of our study indicated a substantial negative correlation between attitude towards homosexuality and psychopathy. More studies have to be conducted including large sample size covering a larger population.

Procrastination and the fear of failure have been linked to major issues with achievement and health. To forecast how an individual may experience and interpret procrastination and fear of failure, this study attempts to analyse the relationship between procrastination and fear of failure with the big five personality traits. College student’s procrastination, fear of failure and personality traits were assessed using the procrastination scale, the PFAI inventory and the Ten Item Personality Inventory (TIPI) respectively. A sample of 176 was selected using the convenience sampling technique. The data collected using google forms were analysed based on a correlational research design. According to the present study, procrastination and fear of failure were found to be highly correlated with the Big Five personality traits. The limitations and suggestions for additional research were discussed.

The vehicle congestion on the road is increasing day by day and also the management of such large traffic by traditional approach isn’t adequate enough. To eliminate this problem, the project is developed using machine learning in which the testing model is trained to extract the needed image about traffic Information. Extracted information from image sequences of testing model can give us real information to create the database which is the captured images like accident, foggy places, collision of the vehicles, traffic signal, no traffic jam etc. take the image from testing model and processing the trained model which compares the new image and trained image and identify the reason for violation or reason for accident. Data processing will be done to determine the reason under the cause of the accident. This application is utilizing image processing methods designed and modified to the needs and constraints of traffic analysis. Therefore, it shows that it can reduce the traffic congestion and avoids the time being wasted.

A Wireless Body Area Network (WBAN) is the composition of a group of energy-efficient, miniature, invasive/non-invasive, light-weighted sensors that monitor human body health conditions for early detection and treatment for life-threatening diseases. Due to the stringent demands of WBAN, such as energy efficiency, reliability and low delay, the development of an efficient contention control algorithm is exceptionally crucial that aims to maximize throughput by reducing collisions. In this context, this paper proposes an adaptive algorithm, namely, Prioritization and Contention Control (PCC) algorithm, to minimize collisions, latency and energy consumption. The first phase of the proposed algorithm prioritizes sensors using run-time metrics to grant channel access only for the potential nodes to send their data. It leads to a lesser number of collisions among sensors, thereby reducing retransmission attempts. In the second phase, the Contention Window (CW) size is predicted using queue length and collision rate that accurately mimic the current channel status. The dynamic estimation of CW aids in minimizing channel access delay, collisions and energy consumption, thereby enhancing overall network performance. The performance of the proposed PCC algorithm is validated with the 2D Markov model and NS2 simulation in terms of throughput, packet delivery ratio, delay and remaining energy.

Now a days, meeting doctors is quite too frequent. People always meet doctors in order to get treatment for their symptoms. Existing clinic registration systems have to make an appointment with a doctor only when they visit the clinic or hospital and wait in queues for long hours in order to get treatment and everything was tedious too because everything was done manually. So many mistakes could have been made. Therefore, many online appointment systems have been developed in order to book appointments with doctors through mobile application. No priority based appointments and an emergency situation for appointments with doctors is considered. All these aspects are taken into consideration and an online appointment booking system with doctors is especially designed for the Madurai city, Tamil Nadu, India. We will design an android application that tracks the location of a user using particle swarm optimization algorithm and also it displays the details of all the hospitals and clinics in the Madurai district and the available specialists in each stream. This system uses java programming language as interface between the user and doctor and MySQL as the database.