Pravara Rural Engineering College

The rapid evolution of digital education has accelerated the adoption of cloud computing as a foundational infrastructure for e-learning systems. This paper presents a systematic review of e-learning-based cloud computing environments to analyze architectural models, cloud service utilization, deployment strategies, challenges, and emerging research opportunities. A total of 154 peer-reviewed studies published between 2010 and 2022 were systematically selected from IEEE Xplore, Springer, ACM Digital Library, and Elsevier databases. The reviewed studies were classified according to e-learning dimensions including architecture, software, performance, security, network, hardware, and virtualization. Results indicate that architectural design dominates existing research (27%), followed by general studies (21%), software-based implementations (19%), and performance optimization (18%). Public cloud deployment models are most prevalent (74%), while private (12%) and hybrid (3%) clouds remain underexplored. Although cloud computing enhances scalability, accessibility, and cost efficiency in e-learning environments, significant challenges persist in data security, privacy protection, bandwidth dependency, and interoperability. This review identifies critical research gaps and outlines future research directions, including artificial intelligence-driven personalization, private cloud adoption, blockchain-based security mechanisms, and green cloud computing for sustainable educational environments.

Phenol and its derivatives, commonly detected not only in industrial effluents but also in various wastewater treatment systems, pose significant environmental and health hazards due to their toxicity to humans and aquatic life. This review focuses on the recent advancements in hydrogel-based nanocomposites for sustainable phenol remediation. Various types of hydrogels, nanoparticles, and their integrated hydrogel–nanoparticle composites are discussed in terms of synthesis techniques and structural properties. This study also examines different degradation mechanisms, including adsorption, photocatalysis, and advanced oxidation processes, highlighting their effectiveness in phenol removal. Key influencing factors such as initial pH, catalyst type, and reactant concentrations are critically analysed for their roles in enhancing degradation efficiency. In addition, comprehensive characterisation techniques like high-resolution transmission electron microscopy, X-ray diffraction, and field emission scanning electron microscopy are reviewed to evaluate the morphology and crystallinity of nanoparticles. Reported degradation efficiencies of phenol using various nanomaterials are summarised to establish comparative insights. This review aims to provide a consolidated understanding of nanocomposite hydrogel-based systems for effective phenol degradation, contributing to the development of sustainable wastewater treatment technologies.

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.