A Study to Know “Impact of AI on Sustainable Agriculture in India”
Authors:- Rahil Shah, Nikhil Kumar Menaria, M. Pradyumna, Rajdeep Singh Thakur Lodhi

Abstract- Artificial intelligence (AI) has the potential to revolutionize sustainable agriculture practices by enhancing building performance, energy efficiency, and reducing carbon emissions. In India, where the demand for sustainable building design is growing due to increasing energy costs and environmental concerns, AI can play a significant role in optimizing building performance. This study examines the impact of AI on sustainable agriculture in India and explores the potential benefits and challenges associated with the integration of AI in building design. Using a qualitative research approach, the study analyzes the existing literature on AI and sustainable agriculture in India. The findings reveal that AI can optimize building performance by providing real-time feedback on energy consumption, predicting future energy demand, and optimizing building systems. However, the integration of AI in sustainable agriculture also presents challenges, such as the need for specialized skills and knowledge, and potential privacy concerns associated with the collection of data. The study concludes that AI has the potential to significantly impact sustainable agriculture in India and recommends further research to explore the feasibility of AI integration in sustainable building design.

DOI: 10.61137/ijsret.vol.9.issue6.121

Published by: vikaspatanker

Authors: Research Scholar Sunil Chandolu, Professor Dr.Pankaj Khairnar

Abstract: Cloud computing and IoT devices are actually growing very fast, and this definitely makes cyber attacks more complex and common. Traditional systems for catching cyber attacks actually have problems with new threats and keeping data safe. These old methods definitely cannot handle big amounts of data spread across many places. This paper gives a complete study review regarding federated machine learning for keeping privacy safe in cyber threat detection as per distributed network systems. The study examines how cyber threat detection methods have evolved from basic rule-based systems to advanced machine learning approaches. It further analyzes how the field itself has progressed from simple anomaly detection to complex deep learning techniques. These methods surely make detection more accurate, but they depend too much on processing data in one central place. Moreover, this creates problems with privacy protection and handling large amounts of data. Federated learning actually solves these problems by letting different computers work together to train models using their own data. The computers definitely learn together but never actually share their raw information with each other. As per this method, data privacy gets better regarding protection, and the system becomes more scalable and strong. The review actually looks at important methods in federated learning like secure combining, privacy protection, and coding systems that definitely make the system more safe. Also, this study actually looks at the main problems in federated learning like different types of data, too much communication, and attacks from bad actors. These challenges definitely make the system harder to work with. Basically, the study shows the same research gaps and says we need good communication methods, strong security systems, and scalable designs for real-world use. We are seeing that federated learning can only change cybersecurity by helping different systems work together to find threats while keeping data safe and private.

DOI: https://doi.org/10.5281/zenodo.20049435

Published by: vikaspatanker

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: Research Scholar Chintu Kodanda Ramu, Professor Dr.Pankaj Khairnar

Abstract: AI technology is actually growing very fast and has definitely created big computer programs that can understand difficult written information. Real-world information actually comes in different forms like text, images, and speech, but traditional systems definitely cannot combine these forms effectively. As per this study, we review all research papers regarding Multimodal Large Language Models that can understand text, images, and speech together. This review surely examines how multimodal learning has evolved from old rule-based and machine learning methods to modern deep learning approaches. Moreover, it specifically looks at the shift towards transformer-based architectures in recent years. The study shows that early systems used handcrafted features and could not adapt further, while machine learning methods performed better but were itself limited by manual feature extraction. Deep learning methods like CNNs and RNNs helped machines learn features automatically, but they faced problems in understanding long connections and interactions between different types of data itself. Further research was needed to solve these limitations. Transformer models solved these problems using attention mechanisms, which further led to MLLMs that combine different data types in one framework itself. The review also studies different ways to combine multiple data types, shared spaces for embedding, and cross-modal attention methods as per enhancing better understanding and reasoning abilities. Despite good progress, challenges like data alignment, computational complexity, scalability, and need for large multimodal datasets remain critical problems that require further attention. These issues itself create barriers for better implementation. As per the study findings, there are important research gaps regarding the need for better system designs, improved data combining methods, and practical solutions that can work on a larger scale. Basically, this review gives a complete picture of how MLLMs are developing, what challenges they face, and where they're heading, showing they can bridge the same gap between how humans think and machine intelligence.

DOI: https://zenodo.org/records/20049642

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