No abstract

For a healthy lifestyle, foods are not just expected to be taken but taken as required by the body system (that is balanced nutrition and diets). Cardiovascular diseases (CVD) has been identified as number one cause of death worldwide and twice as many deaths due to CVD can be attributed to risk factors such as hypertension and obesity. It is of a vital importance to monitor the dietary need of hypertensive, obese and diabetic patients, major cause of morbidity and mortality, in relation to various foods in order to recommend healthy diets. In this paper, a diet advisory system was developed using ontological approach, a form of knowledge representation. The system serves as a platform to assist users to proffer appropriate dietary solution through provision of advice as to whether a particular food is good enough after the health condition of such user has been considered.

Human personality plays a vital role in individual's life as well as in the development of an organization. Common ways to evaluating human personality is by using standard questionnaires or by analyzing the Curriculum Vitae (CV). Traditionally, recruiters manually shortlist/filters a candidate’s CV as per their requirements. In this work, a system that automates the eligibility check and aptitude evaluation of candidates in a recruitment process is developed. To meet this need an automated system module is developed for the analysis of aptitude or personality test based on candidate’s CV. The work presented in this paper determines the personality trait of applicants through CV analysis using Python upon which the Personality prediction Model is built. The result helps in evaluating the qualities in the candidates by analyzing personality trait and character of such candidate. The system provides serves as a better option  for the recruitment process so that candidate’s data can extracted from CV and shortlisted  for the best decision via fair judgment.

Computing power has been increasing exponentially, meaning that processing power can be harnessed to solve more complex tasks. Two fields that have emerged alongside this rapid growth are data analytics, machine learning. Data analytic and Machine learning algorithms are two terms used interchangeably in the world of big data and statistical analysis, the line that divide these two related terms is so tiny that most data analyst forget about the existence of such line dividing and providing blur differences between data analytic and machine learning algorithms. In this study, the underlying the difference between these two related terms and their common and different applications is studied in a concise manner. Their impact in decision making for firms, organizations and cooperate bodies, their approaches to problem solutions, as well as their limitations.