Pravara Rural Engineering College (PREC), a private college in India, is affiliated to the University of Pune, India and is recognized by All India Council for Technical Education, New Delhi. The institute provides simulating academic environment for new technology. Academic study combined with industrial graduate research projects prepare the student for professional practice in engineering..
This paper presents the design and implementation of an integrated business intelligence prediction system using machine learning techniques to support data-driven decision making in organizations. The proposed system combines three key predictive modules: sales forecasting based on historical transactional data, credit risk prediction to evaluate loan eligibility of customers, and sentiment analysis to classify customer reviews as positive or negative.In this system, first the data is prepared before using it. Raw data is not always clean, so some cleaning is done. Unnecessary values are removed and only useful data is taken. After that, the model is trained using supervised learning. This helps the system to give better results.The system is made in a simple and flexible way. Different parts are connected, but still they can work separately if needed. Because of this, it becomes easy to manage and also changes can be done later.When we look at the output, the system is working okay. Sales prediction is close to expected values. The risk checking part is also giving proper idea about customers. Sentiment analysis is also working fine in most cases, though sometimes it may not be exact.So overall, the system is useful. It takes normal data and converts it into something meaningful. This helps in making decisions in business. It is not perfect, but still it gives good support.
Gait recognition has emerged as a powerful biometric technique thanks to its capability to identify individuals from afar, eliminating the need for physical interaction or high-resolution imagery. However, the performance of gait recognition models largely depends on the quality and discriminative power of the features extracted from gait patterns. This paper presents a comprehensive study aimed at identifying the prominent features that most substantially enhance accurate gait recognition. Both traditional handcrafted features and modern deep-learning-based representations are examined across appearance-based, model-based, and spatio-temporal approaches. Through a systematic review and comparative analysis of state-of-the-art methods, this work highlights key gait attributes such as silhouette shape cues, joint–angle trajectories, limb motion dynamics, periodicity of gait cycles, and deep spatio-temporal embeddings. The findings reveal that robust gait recognition is typically achieved by combining multi-level features—capturing both structural and temporal characteristics—while ensuring invariance to covariates such as view angle, clothing, and walking speed. This study provides a consolidated understanding of the most effective feature categories and offers insights for developing next-generation gait recognition systems with improved accuracy and robustness.
This study investigates the mechanical and tribological performance of LM26 aluminum alloy and its hybrid composites reinforced with almandine garnet and MoS2. The composites were fabricated using a bottom-pouring, two-step stir casting method with almandine garnet reinforcement ranging from 5 to 20 wt% and MoS2 from 1 to 4 wt%. Sliding wear tests performed on a Linear reciprocating tribometer, analyzed through Taguchi optimization, confirmed that this composition also achieved the lowest wear rate. Microstructural examination revealed uniform reinforcement dispersion, almandine garnet and MoS2 surface coating formation, and temperatureinduced oxidation influencing wear progression. Among the developed composites, the specimen reinforced with 5 wt% almandine garnet demonstrated the most balanced combination of mechanical strength and overall performance. However, Taguchi optimization of tribological parameters identified the composite containing 20 wt% almandine garnet and 4 wt% MoS2 as the optimum configuration for minimizing wear. The optimal parameter combination was obtained at A1B1C1D1E4, corresponding to Load (A1) = 20 N, Frequency (B1) = 20 Hz, Stroke Length (C1) = 2 mm, Temperature (D1) = 50 degrees C, and Filler Content (E4) = 20 wt%, achieving an optimization accuracy of 98.76%.
