Volume 9 | Issue 6 |

 Abstract

Personality is a parameter that makes one individual different from the other individual. Predicting personality has many applications in real world. The main objective of this paper is to take textual data as input from the user and then run the trained machine learning model on this data to predict his 4 personality traits which are Introversion vs Extroversion, Sensing vs Intuition, Thinking vs Feeling, Judging vs Perceiving. The main objective is to build an application where users can answer the questions which are processed and analyzed to output his personality traits. The output is a string of 4 characters where each character determines a personality trait, total of 16 personality types are possible. The Machine learning model XGboost is used to classify the text and output four personality traits. Processing of large textual data is to be done using Natural Language Processing (NLP) techniques with the help of nltk libraries to process and categorize the data. In order to increase the performance of the model hyper parameter tuning along with cross fold validation is done

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Logistic regression, Naive Bayes classifiers, XGBoost, Decision Tree

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Emotional Intelligence, SMT, Wives and Dual Career Families

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Dropout rates for students are increasing. We need to make certain measurements to find out the factors which are leading to the dropout of the student by using machine learning techniques with which we can choose an alternate way to select the dropout rates of the student from the course. We can make an analysis of each and every student by identifying the most related features. There are many factors which are responsible for the dropouts of the students like financial, economic, and many other problems. If the dropout rate of the students is increasing then the employment opportunities of the students will decrease gradually. Here we are going to implement some of the Machine Learning techniques to identify the students who are likely to drop out. Our main task is to apply different machine learning techniques to the selected features. A comparison is made for machine learning algorithms with each metric, and the model that gives the more appropriate result is considered the best one.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Dropouts, Identification, Features, Machine Learning Techniques.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This Study investigates the impact CSR has on the financial performance of companies using annual data ranging from 2014 to 2019 in India. Correlation analysis and Regression analysis have been used in this study to find the relationship and the impact on the variables. The results reveal that CSR has a positive significant influence on Net profits of the company.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CSR

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Credit Card fraud detection is considered as one of the major challenge that the digital world witnessed today. During this pandemic situation the use of credit card transactions has been increased tremendously. Hence fraud transactions also . The credit card fraudulent becomes a major security threat faced by the financial industry. Many modern techniques like genetic algorithm, machine learning, data mining, fuzzy logic etc has been used vividly used for detecting such fraud transactions. A clear analyzing and sound understanding about all these techniques will give an efficient credit card fraud detection system. This paper aims to presents survey of various techniques used in credit card fraud detection and evaluates each approach based on defined design criteria.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Prediction, Classification Technique, Accuracy,Hidden Markovt,Logistic Regression

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Multi-dimensional, Cyber crime, Cyber Stalking

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Dental caries is AN irreversible microbic sickness of the calcified tissues of the teeth, characterized by demineralization of the inorganic portion and destruction of the organic substance of the tooth, which frequently results in cavitation. Understanding the polymicrobial ecology in pulp-dentin complicated & periapical tissues is very important for the reduction and elimination of microorganism infection. The present study aims to spot the predominant aerobic and anaerobic microorganisms in varied stages of a dentinal cavity, pulpal and periapical lesions, thus providing a principle for sickness bar and higher treatment set-up AIMS AND OBJECTIVES Quantitative analysis of predominant microbial species in Enamel caries, Dentinal caries, and in pulpal infections without periapical lesion and with periapical lesion. METHODOLOGY Collection of samples categorized Into 4 groups to identify the predominant aerobic and anaerobic microorganisms in various stages of dentinal caries, pulpal and periapical lesions In each group 2 samples will be collected for aerobic and anaerobic culture. Under strict isolation with rubber dam, Dentinal caries and Enamel caries from the subjects teeth will be excavated using sterilised sharp spoon excavator . Superficial enamel and dentinal caries has to be removed before entering pulp chamber using high speed airotor hand piece. Under strict isolation with rubber damsamples from the subjects teeth will be excavated using sterile sharp spoon excavator and root canal instruments from the pulp chamber and root canal .Samples were collected in transporting medium and sent to laboratory CONCLUSION The study shows the quantity and prevalence of anaerobic and aerobic bacterias in each satge of dental caries.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Dental caries, Streptococcus spp, staphylococcus species ,pulpal infecton,

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Phishing, Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost Classifier

  License

Creative Commons Attribution 4.0 and The Open Definition