Volume 13 | Issue 5 |

 Abstract

This review explores the integration of platform automation in e-commerce product management, focusing on its role in enhancing operational efficiency. As e-commerce businesses continue to evolve, the need for faster, more accurate, and cost-effective solutions has driven the adoption of advanced automation technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and cloud-based systems. The review proposes a new model for platform automation that combines these technologies to optimize key operational functions including inventory management, pricing strategies, supply chain coordination, and customer experience management. A comparative analysis with existing models reveals that the proposed framework improves predictive performance, scalability, and decision-making speed. Furthermore, the paper discusses the implications of these advancements for practitioners and policymakers, offering recommendations for future research. This review serves as a foundational resource for researchers, decision-makers, and industry professionals seeking to understand and apply platform automation in e-commerce product management.

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 Keywords

Platform Automation, Operational Efficiency, E-commerce, Artificial Intelligence, Machine Learning, Robotic Process Automation, Cloud Computing, Supply Chain Optimization, Demand Forecasting, Pricing Strategies, Inventory Management, Customer Experience Management, Predictive Models, E-commerce Product Management.

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Creative Commons Attribution 4.0 and The Open Definition

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As cloud adoption accelerates, optimizing operational costs in platforms like Amazon Web Services (AWS) has become a critical priority for organizations. This review paper synthesizes current research and practices surrounding cost optimization in AWS, focusing on three key components: EC2/RDS scheduling, AWS Compute Optimizer, and billing analysis. Despite the availability of individual tools and techniques, existing approaches often operate in silos, resulting in suboptimal outcomes. We propose a Unified Cost-Aware Scheduling and Forecasting Model (U-CSFM) that integrates performance telemetry, usage patterns, and cost data to enable predictive, automated, and business-aligned optimization. Through a comparative analysis of the proposed model with existing frameworks, we demonstrate superior performance in cost savings, resource efficiency, and anomaly response times. This review provides actionable insights for researchers, practitioners, and policymakers, while also outlining future directions for cross-cloud interoperability, compliance-aware optimization, and intelligent feedback systems. The findings aim to advance the development of more robust and proactive cost governance strategies in cloud environments.

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 Keywords

AWS Cost Optimization, EC2 Scheduling, RDS Scheduling, Compute Optimizer, Billing Analysis, Cloud Governance, Predictive Modeling, Resource Management, Anomaly Detection, Unified Framework

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Creative Commons Attribution 4.0 and The Open Definition

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Artificial Intelligence (AI) has emerged as a transformative force in Research and Development (R&D), reshaping traditional approaches to innovation, productivity, and creativity. This paper examines how AI-driven technologies--such as machine learning, natural language processing, and generative algorithms--are redefining the R&D landscape across industries. The study adopts a mixed-method approach, combining an extensive review of scholarly literature, industry case studies, and secondary data from leading organizations. Findings indicate that AI significantly accelerates innovation by enabling rapid data analysis, predictive modeling, and automated design processes, thereby shortening product development cycles and enhancing decision-making accuracy. In terms of productivity, AI reduces costs and research timelines through automation of repetitive tasks, real-time simulations, and optimized resource allocation. Moreover, AI tools have shown the potential to stimulate creativity by generating novel ideas, fostering cross-disciplinary collaboration, and supporting complex problem-solving. However, the research also highlights challenges such as overreliance on algorithms, ethical concerns, data privacy issues, and the risk of diminishing human originality. The paper concludes that while AI is not a replacement for human ingenuity, it serves as a powerful collaborator that augments researchers' capabilities. Recommendations emphasize the need for balanced integration of AI, ethical guidelines, and continuous human oversight to maximize its potential in advancing R&D outcomes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Research and Development, Innovation, Productivity, Creativity, Machine Learning, Generative AI, Human-AI Collaboration, Automation, Ethical AI, Scientific Discovery, Digital Transformation, Technological Advancement.

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Creative Commons Attribution 4.0 and The Open Definition

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Web content mining is a core component of web mining that focuses on extracting meaningful information from the unstructured or semi-structured content of web pages. As the volume of web data continues to grow, effective classification and information retrieval techniques have become essential. This paper presents a comparative study of three widely used machine learning algorithms in web content mining: Naive Bayes (NB), Support Vector Machine (SVM), and Neural Networks (NN). These algorithms are evaluated based on key factors such as classification accuracy, scalability, training time, and suitability for high-dimensional web data. The findings of this study aim to guide researchers and practitioners in selecting appropriate models for tasks such as web page classification, sentiment analysis, and content filtering.

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Creative Commons Attribution 4.0 and The Open Definition

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Pain is a complex sensory and emotional experience influenced by biological, psychological, and cultural factors. It represents a significant clinical challenge due to its varied etiology and individual response. In Ayurveda, pain (Shoola) is predominantly linked to Vata dosha, and Basti (medicated enema) is acknowledged as the most effective therapy for Vata vitiation. This article provides an in-depth analysis of the classical understanding and modern applicability of Basti therapy in managing pain conditions like dysmenorrhea, post-surgical pain, and chronic musculoskeletal discomfort. It highlights the types, formulations, and protocols of Basti, supported by classical references and recent clinical data, along with extensive tabular presentation of mechanisms, ingredients, and outcomes.

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 Keywords

Basti, Shoola, Pain, Ayurveda, Vata, Panchakarma, Dysmenorrhea, Post-operative Pain

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Creative Commons Attribution 4.0 and The Open Definition

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Cloud operating systems face challenges in managing dynamic workloads for applications like e-commerce and streaming, processing over 2.5 quintillion bytes daily. Traditional static and heuristic-based resource allocation methods often lead to 40% resource wastage or 20-30% latency spikes, failing to adapt to fluctuating demands. This paper proposes a hybrid framework integrating reinforcement learning and large language models to dynamically allocate CPU, memory, and network resources. A lightweight large language model, similar to DistilBERT, analyzes system logs and user requests to predict resource demands with 92% accuracy, updated every 10 seconds. A reinforcement learning component, using a Deep Q- Network with a three-layer neural network, optimizes allocations based on these predictions and real-time metrics. Simulations on a 10-server testbed with 100 virtual machines demonstrate a 32% improvement in resource utilization, 18% reduction in latency to 75 ms, and 12% decrease in energy consumption to 0.70 kWh compared to heuristic methods. Throughput increased by 31%, handling 12,500 requests per second. The framework outperforms static, heuristic, and reinforcement learning-only approaches, particularly under high loads of 15,000 requests per second. Challenges include computational overhead of large language models, consuming 10% CPU, and model interpretability, critical for 80% of administrators. Future work focuses on lightweight algorithms and multi-cloud scalability to enhance efficiency and practicality in dynamic cloud environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cloud Computing, Resource Management, Reinforcement Learning, Large Language Models, Dynamic Allocation, Artificial Intelligence

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Hate crimes are bias-motivated offenses targeting individuals or groups based on caste, religion, ethnicity, gender, or other protected identities. These acts inflict not only direct harm on victims but also create a climate of fear and exclusion within communities. While countries like the United States and members of the European Union have enacted comprehensive hate crime legislation and developed specialized enforcement mechanisms, India continues to rely on fragmented provisions under general criminal law and selective statutes such as the Scheduled Castes and Scheduled Tribes (Prevention of Atrocities) Act, 1989. This paper critically examines the limitations of India's existing legal framework and compares it with international best practices to highlight systemic gaps in definition, enforcement, and victim protection. It further explores the role of civil society organizations, the tension between hate speech regulation and free expression, and the need for institutional reforms. The study calls for the enactment of a dedicated hate crime law in India, supported by specialized training, reliable data systems, and victim-centric mechanisms. Addressing hate crimes requires not only legal reform but also a holistic, rights-based approach that affirms constitutional values and social justice.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Hate crimes, Indian Penal Code, SC/ST Act, bias-motivated violence, legal reform, enforcement challenges, human rights, freedom of expression

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Creative Commons Attribution 4.0 and The Open Definition

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Government's microfinance initiatives, increases financial inclusion, business performance and socio-economic empowerment of entrepreneurs among small scale enterprises in average income regions and thereby provides boost to economy. This research studies the effectiveness of microfinance initiatives in empowering entrepreneurs in Amravati District, Maharashtra. Using a mixed-methods approach with survey data and strategic analysis, the findings indicate significant impacts on financial inclusion (R2 = 0.298), business performance (R2 = 0.040), and socio-economic empowerment (R2 = 0.223). On other hand high and variable interest rates, documentation delays, and poor skill development programs pose challenges that obstruct microfinance initiatives from reaching their full potential. The scope of study includes 120 women entrepreneurs who have undertaken loan under Pradhan Mantri Mudra Yogna. The study suggests enhanced outreach, simple loan processes, and effective training programs to optimize benefits for women entrepreneurs.

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Creative Commons Attribution 4.0 and The Open Definition

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Key Words: Microfinance Initiatives, Financial inclusion, Business Performance, Socio-Economic Empowerment.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In recent years, artificial intelligence and analytics have dominated all new inventions and technology. With the advent of Internet and mobile banking platforms, digital transactions gained considerable acceptance, resulting in a progressive decrease in "footfall in branches." Customers of today need a flawless experience across all platforms, and highly customized services are urgently needed. However, communicating with and comprehending the distinct needs of these clients spread across different regions has grown to be a significant obstacle for all Indian banks. The banking industry is now capable of making use of Artificial Intelligence (AI) and seamlessly integrating it with operational requirements thanks to the shifting dynamics of an app-driven environment. The adoption of artificial intelligence will continue to advance, enabling a digital financial infrastructure, as the nation's banking environment continues to expand rapidly.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Banking industry, AI in banking, Digital Banking

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Fast disintegrating tablets have possible advantages over usual dosage forms, with improved patient observance, convenience, bioavailability and rapid onset of action. They are good substitute for drug delivery to geriatric and paediatric patients. They have major advantages of both solid and liquid dosage forms, as they remain solid during storage, which assist in stability of dosage forms and transform into liquid form within few seconds after its administration Thus FDT has great scope for being immediate drug delivery. The Loratidine are used as a model drug in the preparation of formulation. It is generally used for the treatment of Asthma. It is safe well tolerated. The study began with the preformulation characterization of drug which involves determination of melting point and development of suitable analytical method for the estimation of drug. The direct compression approach was used to create fast-dissolving tablets. It was discovered that the disintegration time of medication tablets made via direct compression was between 30 and 40 seconds. The best results were obtained from tablets manufactured with the highest amount of Crospovidone, or F15. According to the dissolving study's findings, almost half of the medication was released in the first five minutes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Loratidine, Fast Disintegrating Tablets, Crospovidone

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Creative Commons Attribution 4.0 and The Open Definition