


IJRRA ISSN: 2349-7688 is a scholarly online, open access, peer-reviewed, interdisciplinary and fully refereed journal oriented on the fact "Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction and skillful execution; it represents the wise choice of many alternatives". It is an international scientific journal that endeavors to donate for continuous scientific research and training, so as to promote research in multiple fields.
Waste Management and Circular Economy
Bhumi Sharma, Shrejal Agrahari, Deepti Kushwaha
Voice Based Interfaces In Fast Food Industry Using Soundhound
Deepti Kushwaha, Aditya Jha, Divyanshu Yadav
Integrating Waste Management Systems into the Circular Economy: A Framework for Sustainable Resource Utilization
Mayank Katiyar, Nikhil Rexwal
Adaptive Honeypots for HTTPS
Sneha Mishra, Ishika Dhiman, Kalidindi Sowmya
Multi-Agent AI Systems for Advanced Data Analytics and Decision-Making in Supply Chain Networks
Md Waheduzzaman Tuhin
Cloud-Based Accounting: Transforming Financial Management In The Digital Era
Nikhil Bhardwaj
Marketing Analytics & Consumer Behavior: The ECEM + Model for Emotional–Cognitive Engagement
Nishita Aggarwal, Aakriti Khanduri
Advancements In Agricultural Automation: Implementing Ai and IOT for Sustainable Farming Practices
Tirtha Raj Kandel
IoT-Enabled Predictive Framework for Tomato Crop Disease Detection: An Implementation-Oriented Approach
Himanshu Singh Rajput
Renewable Energy Adoption in Emerging Economies
P Ganesh, Ms. Deepti Kushwaha
Towards Smart Farming: An Implementation-Oriented Approach Leveraging Data-Driven Agricultural Systems
Siya Sharma
Object Detection Using Modified CNN with Novel Data Preprocessing Strategy in Deep Learning
Srinivasa Raju Birudaraju
Detection And Prediction of IOT Cyber Network Attacks Using Machine Learning
Kanak Ahlawat, Keerti Thakur
Emotional Intelligence Integration in Dialogue Systems for Empathetic Responses
Sneha Santra, Punit Kumar, Deepti Kushwaha
Adaptive Optimizer Strategies for Deep Neural Networks in Machine Learning
Srinivasa Raju Birudaraju