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In school, infrastructure development is an important aspect that needs to be taken into account. The term infrastructure is comprehensive, and several elements are included in it. These include playgrounds, library facilities, laboratories, computer centers, technology, machinery, tools, equipment, and so forth. The members of the educational institutions need to invest resources to bring about infrastructure improvements. The study aimed to investigate the current status of the availability and utilization of infrastructure in Eklavya Model Residential School. The investigator purposively selected one Eklavya Model Residential School at Rayagada in Odisha. This school provides quality education to merit-based tribal students, especially in tribal areas. The researcher has followed the purposive sampling technique for selecting the key informants of the case. The researcher used the classroom Observation Schedule, Questionnaire, checklist, and interview schedule for data collection. The data was analyzed by applying both quantitative and qualitative techniques, i.e., percentage; thick description. The study revealed that the infrastructure facilities are available as per the guideline of Eklavya Model Residential School.

This thesis explores Formula 1 pit stop strategies through advanced analytics, with a focus on driver clustering in relation to performance, tactical, and behavioural aspects. Our approach led to the identification of four distinct driver categories, providing a framework to investigate various pit stop strategies. By integrating these driver profiles into predictive models, the study delves into the impact of driver characteristics on team strategy and pit stop efficiency. We introduce a novel dimension by developing a binary prediction model for pit stop timing, thoroughly evaluated within a simulation environment. This research contributes to a more refined understanding of strategic elements in Formula 1, demonstrating the role of tailored analytic methods in optimizing racing tactics and decision-making processes.

This thesis explores the optimization of Formula 1 pit stop strategies, integrating advanced analytics and machine learning to predict tire compound decisions. A novel aspect of this study is the clustering of driver profiles based on performance, tactical, and behavioral metrics, which provides a deeper understanding of driver characteristics and their impact on race strategy. By analyzing data from the FastF1 API and employing various machine learning techniques, we developed predictive models that not only forecast compound decisions with higher accuracy but also highlight the significance of personalized strategies tailored to different driver clusters. The findings demonstrate the potential of combining driver clustering with predictive analytics to refine pit stop strategies, offering teams a competitive edge through data-driven decision-making.

Separation of the foreground from background on a processed image, namely background modelling, positively affects performance of certain computer vision applications. It has considered as preprocess for many tasks including moving object recognition, person tracking, traffic monitoring, motion capturing, teleconference and security surveillance systems. Video backgrounds can be considered in two categories as static and dynamic backgrounds. To improve the performance of background subtraction, we have developed four different methods by using different tools in case of distance computation between test and background frame and integrating a feedback mechanism that works beyond dynamic controller parameters. These methods are called as Background Modelling Using Common Vector Approach (BMCVA), Background Modelling Using Common Matrix Approach (BMCMA), Sliding Window-Based Change Detection (SWCD) and Common Vector Approach Based Background Subtraction (CVABS). Various experiments have conducted on different problem types related to dynamic backgrounds over CDnet2014 and Wallflower datasets. Several types of metrics calculated over the results of True-Positive (TP), True-Negative (TN), False-Positive (FP) and False-Negative (FN) counts, have utilized as objective measures and the obtained visual results are judged subjectively. Once the obtained results inspected, it has observed that the proposed methods generate successful results for different challenges.