Green Cloud Computing: A Framework for Sustainable and Efficient Cloud Infrastructure
Authors:- Professor Dr. Angajala Srinivasa Rao, Professor Dr. Sudheer Pullagura

Abstract-As the demand for cloud computing services continues to soar, concerns about its environmental impact have become more pronounced. This research-oriented descriptive article aims to address this issue by proposing a comprehensive framework for Green Cloud Computing. The framework focuses on minimizing the environmental footprint of cloud computing by optimizing energy consumption and resource usage. Through an exploration of key principles, challenges, and real-world applications, this article provides insights into building a sustainable and efficient cloud infrastructure. Keywords, relevant studies, and references are included to serve as a valuable resource for researchers and practitioners in the field.

DOI: 10.61137/ijsret.vol.9.issue6.120

Published by: vikaspatanker

A Study to Know – Use of AI For Personalized Recommendation, Streaming Optimization, and Original Content Production at Netflix
Authors:-Komal Khandelwal, Sarvanaman Patel, Jarni Patel, Monika Pnachal

Abstract-Netflix has become a household name in the entertainment industry due to its innovative use of data science and artificial intelligence (AI) in its business strategy. This paper provides a comprehensive overview of how Netflix has leveraged data science to gain a competitive edge in the industry. The paper explores how Netflix uses personalized recommendations to enhance the user experience. Netflix’s recommendation system is powered by a collaborative filtering algorithm that analyses user data, such as viewing history and ratings, to suggest content that is likely to be of interest to the user. The recommendation system is continuously improved through machine learning algorithms, which learn from user behaviour and preferences to provide more accurate recommendations. The paper also discusses how Netflix uses streaming optimization to deliver high-quality video content to its users. Netflix’s AI-powered encoding system analyses each video and optimizes the encoding process to reduce file size without compromising video quality. This enables Netflix to deliver high-quality video content with minimal buffering time, even in areas with slow internet connectivity.Another aspect of Netflix’s success is its production of original content. Netflix uses data science to identify gaps in the market and understand audience preferences, enabling it to produce highly engaging original content. The company uses machine learning algorithms to analyse viewer data and identify trends and patterns that inform its content creation strategy.However, implementing data science in the entertainment industry comes with its challenges and limitations. Netflix faces issues such as bias in the recommendation system, privacy concerns, and the high cost of producing original content. Nevertheless, Netflix continues to invest in data science and AI to improve its services and stay ahead of its competitors. This paper provides a comprehensive understanding of how Netflix has implemented creative data science and AI in its business strategy to become a leader in the entertainment industry. The paper highlights the importance of personalized recommendations, streaming optimization, and original content production in Netflix’s success. It also emphasizes the challenges and limitations of using data science in the entertainment industry and the need for continuous improvement and innovation.

DOI: 10.61137/ijsret.vol.9.issue6.119

Published by: vikaspatanker

To What Extent Does Consumer Awareness Influence the Preferences of Individuals Towards Neo Banks in The Indian Banking Sector?
Authors:- Ansuman Ray, Ashish Singh, Nishtha Rastogi, Aanchal Agrawal

Abstract- This study investigates the landscape of neo banks in India, focusing on consumer awareness and preferences within the evolving digital banking sector. Acknowledging the global significance of neo banks and the transformative impact they pose to traditional banking, the research addresses a notable gap by examining their adoption in the Indian context. The study explores factors influencing consumer behavior, including convenience, efficiency, trust, and the integration of financial technologies. Employing a comprehensive research methodology, encompassing surveys, interviews, and demographic considerations, the research aims to provide nuanced insights into how neo banks are reshaping the banking experience for Indian consumers. By bridging global insights with specific Indian market nuances, the study contributes to both academic and practical understanding, informing strategies in the banking and fintech industry to better align with the preferences of Indian consumers in the digital era.

DOI: 10.61137/ijsret.vol.9.issue6.118

Published by: vikaspatanker

Authors:- M.Siva Leela, Shaik Hussain Vali

Abstract-In this study, we use a loop matrix to describe the reorganisation of the RDN's formulation. Calculation time is increased when an optimum reorganisation is determined analytically. More network buses means more time to calculate. Therefore, a technique of optimisation is required to determine the best reorganisation of the radial distribution system. The optimum reorganisation aims to reduce network losses to a minimum. Genetic algorithm (GA) and particle swarm optimisation (PSO) are the optimisation methods employed in this piece. In this piece, we look at how meta-heuristic optimisation may be applied for efficient rearranging. For the purpose of rearrangement, we use organic optimisation techniques such as the PSO approach. We describe and analyse the reorganisation issue in a typical large-scale 119 and 135-node network under both optimisation and non-optimization conditions. The outcomes are then compared with one another.

DOI: 10.61137/ijsret.vol.9.issue6.117

Published by: vikaspatanker

Authors:-Dr. Juhi Shamim

Abstract- Present paper discusses the determinants of food grains production in India. The Indian economy has changed fundamentally over time with the foreseen decrease in agriculture’s share in gross domestic product (GDP). There is high burden on agriculture to produce more and to raise the income of farmers. India’s manufacturing sector saw unpredictable growth and its share in GDP has nearly stayed steady at 15 percent over the most recent three decades. Under these conditions, it is valuable to investigate the determinants of agriculture growth. There are countless determinants that influence food grains production. Some of them are discussed in this paper.

DOI: 10.61137/ijsret.vol.9.issue6.116

Published by: vikaspatanker

 

Srinivasa Chakravarthy Seethala
Affilation:	Senior Data Engineer HCL America Inc, USA
Email-Id:	srinivasa.seethala@gmail.com
About Srinivasa Seethala has done Bachelor of Engineering in Mechanical from University of Madras. A Senior Data Engineer with over 25 years of experience, specializing in AI, Big Data, and data warehousing development. I have demonstrated expertise in designing and implementing advanced data solutions, integrating AI to enhance data quality and system performance. A recognized leader in the field, I excel at modernizing data architectures and leading cross-functional teams to deliver innovative solutions for complex challenges. As a published author with expertise in AI and Big Data, I am skilled at driving impactful projects across industries, including finance, healthcare, and e-commerce, while ensuring scalability and efficiency in data systems.
Projects & Publications: PNC Claims(Snowflake) Client: United Services Automobile Association (USAA) Project : Project OAK Client: United Technologies Corporation, USA Project : Broker Dealer Operations Reporting Client : Lincoln Financial Group, USA

 

Published by: vikaspatanker

Demand Forecasting Using MLR-ARIMA Hybrid Model
Authors:-Vaibhav R. A. Prasad, Anunita Bhattacharya

Abstract- Data analytics (DA) is becoming increasingly important in supply chain management (SCM) due to its ability to provide valuable insights that can improve efficiency and decision-making. One of the key applications of DA in SCM is demand forecasting, which involves predicting future demand for products or services. Accurate demand forecasting is crucial for ensuring that the right amount of inventory is maintained, reducing the risk of stock outs, and optimizing production and logistics processes. There are several algorithms that can be used for demand forecasting in SCM, and they can be broadly classified into two categories: time-series forecasting and causal forecasting. Time-series forecasting algorithms rely on historical data to make predictions. This study will Evaluate both time-series and casual algorithms and study their efficacy and uses.

DOI: 10.61137/ijsret.vol.9.issue6.115

Published by: vikaspatanker

Demand Forecasting Using MLR-ARIMA Hybrid Model
Authors:-Vaibhav R. A. Prasad, Anunita Bhattacharya

Abstract- Data analytics (DA) is becoming increasingly important in supply chain management (SCM) due to its ability to provide valuable insights that can improve efficiency and decision-making. One of the key applications of DA in SCM is demand forecasting, which involves predicting future demand for products or services. Accurate demand forecasting is crucial for ensuring that the right amount of inventory is maintained, reducing the risk of stock outs, and optimizing production and logistics processes. There are several algorithms that can be used for demand forecasting in SCM, and they can be broadly classified into two categories: time-series forecasting and causal forecasting. Time-series forecasting algorithms rely on historical data to make predictions. This study will Evaluate both time-series and casual algorithms and study their efficacy and uses.

DOI: 10.61137/ijsret.vol.9.issue6.115

Published by: vikaspatanker

Relationship between Electronic Banking and Customer Satisfaction
Authors:-Shaun Mendonsa, Akash Shukla, Akash, Venkata Veda Vyas Dega

Abstract- This research paper explores the relationship between electronic banking and customer satisfaction in the banking sector of India. The main objective of this study is to investigate the impact of e-service quality on customer satisfaction in the banking sector. The study uses a mixed research approach, comprising both descriptive and analytical research. A survey consisting of 12 questions was conducted, and the responses were collected from 59 respondents. The study found that e-service quality is the most significant factor impacting customer satisfaction in the banking sector. The study concludes that banks can gain a competitive advantage by focusing on the quality of electronic banking services, which helps attract and retain a strong customer base. Limitations of the study are also discussed, and suggestions for future research are provided.

DOI: 10.61137/ijsret.vol.9.issue6.114

Published by: vikaspatanker

Authors:-Bhavesh Gattani, Shamik Saha, Komal Gill

Abstract- In FMCG e-commerce, digital tactics are crucial in changing customer purchasing trends and behaviours. This study emphasises advertising strategies and the tactical application of consumer data as it investigates the significant effects of digital marketing and AI-driven tools on consumer patterns. By utilising cutting-edge technologies like chatbots, complex algorithms, and user behaviour analysis, businesses may gain profound insights into their clientele, facilitating customised and customer- focused online shopping experiences. This shift mostly depends on customised digital tactics that use customer data to design distinctive e-commerce experiences. This strategy also applies to advertising, using data-driven techniques to provide pertinent and compelling advertisements that are tailored to the unique requirements and tastes of FMCG customers. When it comes to FMCG e-commerce advertising, the use of digital and AI techniques has a big impact on customer engagement and purchasing behaviours. Businesses may maximise the impact of their ads by optimising the selection and delivery of their ads with the help of these tools’ insights. This study examines the impact of digital strategies on FMCG e-commerce customer behaviours by combining consumer data with insights from these strategies. Interestingly, these tactics—which are especially noticeable in social media postings and pop-up advertisements—stimulate instant wants, enable interactive interfaces, and encourage higher spending in the FMCG e-commerce space.

DOI: 10.61137/ijsret.vol.9.issue6.113

Published by: vikaspatanker