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A quantitative gait assessment system is crucial for clinical analysis and decision‐making. Such rigorous evaluation involves costly clinical setups and domain experts for observation and analysis. To circumvent such constraints, the proposed work is conducted in a markerless environment and divided into three stages: First, we prepared a markerless gait database using videos from MNIT RAMAN LABORATORY in Jaipur. Second, we adapt the skeletal landmark data to generate kinematic gait characteristics comparable to gold‐standard marker‐based techniques. We provide a novel set of parameters based on video sequences and spatiotemporal and sagittal kinematic parameters to optimize accuracy and reliability. Third, we develop multi‐feature based gait analysis, an ensemble model based on Convolutional Neural Networks + LSTM (Long‐Short Term Memory), for gait classification. In addition, we deployed transfer learning models to correlate with our ensemble model. The findings indicate that gait analysis can be used successfully in a low‐cost clinical gait monitoring system in a constraint‐free environment. While considering the multiple gait variables, our proposed model attained an accuracy of 95.3%. Our model for quantifying gait analysis will improve access to quantitative gait analysis in clinics and rehabilitation centers and enable researchers to conduct large‐scale studies for gait‐related disorders. [ABSTRACT FROM AUTHOR]

The study was conducted at Post Graduate Institute of Veterinary Education and Research (PGIVER), Jaipur, Rajasthan, India during July-December 2020 to evaluate the effects of non-genetic factors on semen characteristics of bulls. The data of 4608 ejaculates during the period of 2017-2020 from Tharparkar and its cross with Holstein, Jersey and its cross with Sahiwal cows was maintained at Frozen Semen Bank (FSB), India. The Semen characteristics were recorded and stored in Information Network for Animal Productivity & Health (INAPH) application maintained at FSB and National Dairy Development Board (NDDB). The average semen volumes were estimated as 4.18±0.02 ml. Non-genetic factors viz. season, period, breed, ejaculation order and age of bulls showed significant effects on various semen parameters. Higher estimates of semen concentration, motility, Total Ejaculation Volume (TEV) and Post-thaw Motility (PTM) were recorded in Jersey Sahiwal crossbred bull whereas semen motility was observed as higher for Tharparkar bull. Seasonal analysis revealed that winter and autumn were favorable seasons for higher quantity and quality semen production. Age wise analysis presented significantly increased values with the increasing age except semen motility. Non-genetic factors under semi-arid climatic conditions revealed significant effects on semen characteristics of exotic, crossbred and indigenous genotypes and need to be accounted for quality semen production in a semen production centre. Present findings indicate that Jersey Sahiwal cross bulls were found better for quality semen production under semi-arid climatic condition than exotic and other crossbreed bulls. [ABSTRACT FROM AUTHOR]