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Sustainable Development Goals (SDGs) are a set of 17 interrelated goals adopted by the United Nations (UN) for a better and sustainable future for all. One of the SDGs, SDG 2, relates to achieving food security, and improved nutrition and promoting sustainable agriculture. Millets are a group of small seeded grasses rich in different nutrients, micronutrients and minerals. Apart from many types, finger millet, pearl millet, foxtail millet, barnyard millet, proso millet, kodo millet, and sorghum are the most important ones grown worldwide. Millets can contribute to dietary diversity and promote sustainability. The objective of this research has been to identify an optimal combination of intake food items to satisfy the daily nutritional requirements by including millets. This research uses a modified form of the classic Diet problem. Using this mathematical model, an optimal combination of food items to be included in daily meal is found out. Apart from satisfying the nutritional need optimally, this research also takes into consideration to include the dietary preference of people. After solving for multiple scenarios, it has been concluded that that daily Recommended Dietary Allowance (RDA) cannot be fulfilled without including millets in the meal. Millets can have transforming impact on the nutritional intake of people and they can be highly beneficial in combating dietary nutrient deficiency. [ABSTRACT FROM AUTHOR]

According to one of the most commonly acknowledged marketing principles, retaining customers is more profitable than acquiring new customers. Therefore, in order for restaurateurs to grow their sales, it is imperative that they find strategies to retain and increase the number of repeat customers. This study aims to analyse the factors that influence customers' revisit intention to restaurants. The success factors critical in improving the repeat visits of restaurants are identified through a comprehensive literature search and the opinion of domain experts. The domain experts comprised experienced stakeholders associated with the restaurant industry. The success factors were categorized into five (food quality, service quality, atmosphere, green practices, and price and value) perspectives. Further, the factors were prioritized using the fuzzy best worst method (FBWM) approach. Later, sensitivity analysis was performed to check the robustness and viability of the model. The results are useful for restaurant operators to take effective actions to attract more customers to revisit. [ABSTRACT FROM AUTHOR]

Education data mining is data mining on an education dataset with an emphasis to understand the underlying patterns in student learning and its performance. The present study pertains to an analysis of performance of schools in Delhi, India using a decision tree approach. This helps to classify the schools that according to their performance. Data for government schools in Delhi, India for grade XII has been procured from the Delhi government education portal and the Unified District Information System for Education (UDISE) portal. There are three measures of the school performance, namely, score in the individual subjects, quality index of a school and the pass percentage. Scores in a subject is the average score obtained by the students who have appeared in the given subject. Quality index of a student is the total score across the five subjects in which the student has appeared. Quality index of a school is the average value of quality index of the individual students of a school. Pass percentage is the percentage of students that have obtained the minimum passing score in all the subjects. The aim of schools is to maximize their pass percentage (typically, 100%) as well as the scores, however, first it is desired to improve the pass percentage to have minimum levels of attainment for all the students. The school input data used in the analysis pertains to its infrastructure that includes classrooms, playground, library, etc; its operations that comprises the medium of instruction, implementation of continuous and comprehensive evaluation, number of working days, working hours per day, number of inspections by external officials and other concerned authorities; school enrolment, and number and distribution of teachers; type of school (boys', girls' or co-educational) and school category (based on grades offered). The variables that are correlated have been removed to yield a reliable rule set. The schools are classified based on whether they achieve the minimum acceptable level of performance. The schools are classified with two target labels: (i) those that achieve minimum acceptable performance, (ii) those that do not have minimum acceptable performance. The decision rule set can be used by the policy makers to identify and take appropriate corrective actions in the schools to help them with minimum acceptable performance. The work can be extended to other classification algorithms as well. [ABSTRACT FROM AUTHOR]