Seeds of Intelligence: Generative AI and the Rise of Collaborative Robots in Agriculture 6.0 Lingala Thirupathi, Mamatha Bontha, Ravi Aavula, K. Sreerama Murthy, Vinay Kumar Nyatha, Santhosh Reddy Thuraga Exploring Generative AI for Collaborative Robots in Agriculture 6 0, 2025 Agriculture 6.0 marks a transformative era where generative AI and collaborative robots (cobots) reshape farming into an adaptive, intelligent system. Unlike previous technological shifts, this evolution is rooted in co-creation between humans and machines. Generative AI acts as a thinking partner, designing crop plans, predicting pests, and simulating outcomes while cobots execute tasks safely alongside humans, learning and sharing knowledge collectively. Case studies from India, Kenya, and the Netherlands illustrate how these technologies enhance yields, preserve resources, and respect local traditions. Yet challenges remain: data ownership, equitable access, and job displacement demand thoughtful solutions. Looking ahead, regenerative intelligence will enable farms to restore ecosystems while maintaining productivity. Agriculture 6.0 is not just innovation; it's a respectful alliance between technology, nature, and human wisdom, sowing the seeds for a resilient, inclusive, and intelligent future of food.
Machine Learning: AI Meets Blockchain: Machine Learning-driven Carbon Footprint Analysis for Next-Gen Sustainable Supply Chains S. Sakthi RAADHA, J. Pavithra, R. Kaviyaraj, Ravi AAVULA, Sakthitharan SUBRAMANIAN, S. VENKATESWARAN Sustainable Supply Chains and Carbon Footprint Reduction the Blockchain Advantage, 2025 This chapter presents an intelligent, scalable and transparent system to provide carbon responsibility and establish sustainability targets for future-proofed supply chains. It proves that machine learning (ML) algorithms can be subject to carbon footprint analysis. Implementation of these algorithms on blockchain platforms can help in the development of future-proof green supply chains that are cost-effectiveness driven and sustainable. Blockchain–AI integration provides a green alternative for carbon footprint supply chain sharing. The chapter shows the integration of smart contracts and blockchain nodes within the carbon tracking system. The hybrid ML–blockchain model is tested using a set of performance metrics. To demonstrate the model's applicability, a use-case simulation is carried out on a supply chain network of a consumer goods company. The integrated model showed a consistent reduction in carbon emissions across different supply chain stages.
PMiner: Process Mining using Deep Autoencoder for Anomaly Detection and Reconstruction of Business Processes Veluru Chinnaiah, Vadlamani Veerabhadram, Ravi Aavula, Srinivas Aluvala International Journal of Electrical and Computer Engineering Systems, 2024 We proposed a deep learning-based process mining framework known as PMiner for automatic detection of anomalies in business processes. Since there are thousands of business processes in real-time applications such as e-commerce, in the presence of concurrency, they are prone to exhibit anomalies. Such anomalies if not detected and rectified, cause severe damage to businesses in the long run. Our Artificial Intelligence (AI) enabled framework PMiner takes business process event longs as input and detects anomalies using a deep autoencoder. The framework exploits a deep autoencoder technique which is well-known for Its ability to discriminate anomalies. We proposed an algorithm known as Intelligent Business Process Anomaly Detector (IBPAD) to realize the framework. This algorithm learns from historical data and performs encoding and decoding procedures to detect business process anomalies automatically. Our empirical results using the BPI Challenge dataset, released by the IEEE Task Force on Process Mining, revealed that PMiner outperforms state-of-the-art methods in detecting business process anomalies. This framework helps businesses to identify process anomalies and rectify them in time to leverage business continuity prospects.
ENERGY EFFICIENT ROUTING USING SUPPORT VECTOR MACHINE IN WIRELESS SENSOR NETWORKS Journal of Theoretical and Applied Information Technology, 2024
XBPF: An extensible breast cancer prognosis framework for predicting susceptibility, recurrence and survivability International Journal of Engineering and Advanced Technology, 2019
A fuzzy logic based hybrid approach for disease interpretation and prediction Journal of Theoretical and Applied Information Technology, 2018
A machine learning based fine-tuned and stacked model: Predictive analysis on cancer dataset Ravi Aavula, R. Bhramaramba International Journal of Advanced Computer Science and Applications, 2018 The earlier forecast and location of disease cells can be useful in curing the illness in medical applications. Knowledge discovery is having many significant roles in health sector, bioinformatics etc. Plenty of hidden information is available in the datasets present in the various domains like medical information, textual analysis, image attributes exploration etc. Predictive analytics and modeling encompasses a variety of statistical methodologies from machine learning that can analyze the present along with historical facts to make the predictions about the future events. Breast cancer research already has involved with the good amount of progress in recent decade, but due to advancement in technologies, there is still some possibilities for an improvement. In this paper, the fine-tuned and stacked model procedure is presented which is experimented on standard breast cancer dataset. The obtained results show the improvement over stateof-the-art algorithms with improved performance parameters e.g. disease prediction accuracy, sensitivity and better F1 score etc. Keywords—Machine learning; Cancer prediction; Data mining and Knowledge discovery; Supervised learning; Neural Networks
Seeds of Intelligence: Generative AI and the Rise of Collaborative Robots in Agriculture 6.0 L Thirupathi, M Bontha, R Aavula, KS Murthy, VK Nyatha, SR Thuraga Exploring Generative AI for Collaborative Robots in Agriculture 6.0, 1-24 , 2026 2026
Chaos and Gaussian Strategy with Crisscross Optimization Algorithm for Parkinson Disease Classification D Saidulu, R Aavula, P Rajeshwari, A Singh 2025
PMiner: Process mining using deep autoencoder for anomaly detection and reconstruction of business processes V Chinnaiah, V Veerabhadram, R Aavula, S Aluvala International journal of electrical and computer engineering systems 15 (6 … , 2024 2024 Citations: 7
The Improvement Prediction Model Using Anfis for Medical Dataset S Sumarlinda, AB Rahmat, ZBA Long, W Lestari, HE ZHENGFANG, ... Journal of Theoretical and Applied Information Technology 102 (5), 1663-1672 , 2024 2024 Citations: 2
Pattern Recognition-An Approach towards Machine Learning A Ravi Lambert Publications , 2022 2022 Citations: 24
Multiple object detection and classification based on Pruning Using YOLO S Banoth, R Aavula, LKS Kazi, D Lokhande 2022 Citations: 2
Design and Implementation of sensor and IoT based Remembrance system for closed one R Aavula, A Deshmukh, VA Mane, GH Chavhan, KK Liyakat Telematique 21 (1), 2769-2778 , 2022 2022 Citations: 33
Privacy cloud storage with data dynamics using private network coding techniques G Kotikam, R Aavula Journal of Innovation in Computer Science and Engineering 11 (1), 38-41 , 2021 2021
Towards a framework for breast cancer prognosis: risk assessment R Aavula, R Bhramaramba ICCCE 2020: Proceedings of the 3rd International Conference on … , 2020 2020 Citations: 2
An Extensible Breast Cancer Prognosis Framework for Predicting Susceptibility, Recurrence and Survivability R Aavula, R Bhramaramba International Journal of Engineering and Advanced Technology (IJEAT) Volume … , 2019 2019
XBPF: an extensible breast cancer prognosis framework for predicting susceptibility, recurrence and survivability R Aavula, R Bhramaramba Int. J. Eng. Adv. Technol 8 (5), 2249-8958 , 2019 2019 Citations: 19
A comprehensive study on data mining techniques used in bioinformatics for breast cancer prognosis R Aavula, R Bhramaramba, US Ramula Journal of Innovation in Computer Science and Engineering 9 (1), 34-39 , 2019 2019 Citations: 10
A Machine Learning based Fine-Tuned and Stacked Model: Predictive Analysis on Cancer Dataset R AAVULA CSA) International Journal of Advanced Computer Science and Applications, 9 … , 2018 2018 Citations: 1
A Fuzzy Logic and Roughset based Hybrid Approach for Disease Interpretation and Prediction RB RAVI AAVULA Journal of Theoretical and Applied Information Technology 96 (16), 5217-5226 , 2018 2018
Dumping and storing data in local file system using pig A Koluguri, R Aavula, R Sravani Journal of Innovation in Computer Science and Engineering 8 (1), 41-44 , 2018 2018
A survey on latest academic thinking of breast cancer prognosis R Aavula, R Bhramaramba Int J Appl Eng Res 13, 5207-5215 , 2018 2018 Citations: 6
Innovations in Computer Science and Engineering: Proceedings of the Fourth ICICSE 2016 HS Saini, R Sayal, SS Rawat Springer , 2017 2017 Citations: 4
Smart Health Consulting Android System R Aavula, M Kruthini, N Raviteja, K Shashank International Journal of Innovative Research in Science, Engineering and … , 2017 2017 Citations: 6
Hooked on Hadoop to Read And Write Sequence File Using Map Reduce R Aavula, D Saidulu, BH Chandana Journal of Innovation in Computer Science and Engineering 7 (1), 53-57 , 2017 2017
Minimization of Congestion by Local Route Repair And Dynamic Path Finding Using TCP Vegas and Hybrid Approach With AOMDV Routing Protocol in MANET R Aavula, SK Bhadar, R Mande, TS Kumar Journal of Innovation in Computer Science and Engineering 6 (1), 33-40 , 2016 2016
MOST CITED SCHOLAR PUBLICATIONS
Design and Implementation of sensor and IoT based Remembrance system for closed one R Aavula, A Deshmukh, VA Mane, GH Chavhan, KK Liyakat Telematique 21 (1), 2769-2778 , 2022 2022 Citations: 33
Pattern Recognition-An Approach towards Machine Learning A Ravi Lambert Publications , 2022 2022 Citations: 24
XBPF: an extensible breast cancer prognosis framework for predicting susceptibility, recurrence and survivability R Aavula, R Bhramaramba Int. J. Eng. Adv. Technol 8 (5), 2249-8958 , 2019 2019 Citations: 19
A comprehensive study on data mining techniques used in bioinformatics for breast cancer prognosis R Aavula, R Bhramaramba, US Ramula Journal of Innovation in Computer Science and Engineering 9 (1), 34-39 , 2019 2019 Citations: 10
PMiner: Process mining using deep autoencoder for anomaly detection and reconstruction of business processes V Chinnaiah, V Veerabhadram, R Aavula, S Aluvala International journal of electrical and computer engineering systems 15 (6 … , 2024 2024 Citations: 7
A survey on latest academic thinking of breast cancer prognosis R Aavula, R Bhramaramba Int J Appl Eng Res 13, 5207-5215 , 2018 2018 Citations: 6
Smart Health Consulting Android System R Aavula, M Kruthini, N Raviteja, K Shashank International Journal of Innovative Research in Science, Engineering and … , 2017 2017 Citations: 6
Innovations in Computer Science and Engineering: Proceedings of the Fourth ICICSE 2016 HS Saini, R Sayal, SS Rawat Springer , 2017 2017 Citations: 4
The Improvement Prediction Model Using Anfis for Medical Dataset S Sumarlinda, AB Rahmat, ZBA Long, W Lestari, HE ZHENGFANG, ... Journal of Theoretical and Applied Information Technology 102 (5), 1663-1672 , 2024 2024 Citations: 2
Multiple object detection and classification based on Pruning Using YOLO S Banoth, R Aavula, LKS Kazi, D Lokhande 2022 Citations: 2
Towards a framework for breast cancer prognosis: risk assessment R Aavula, R Bhramaramba ICCCE 2020: Proceedings of the 3rd International Conference on … , 2020 2020 Citations: 2
A Machine Learning based Fine-Tuned and Stacked Model: Predictive Analysis on Cancer Dataset R AAVULA CSA) International Journal of Advanced Computer Science and Applications, 9 … , 2018 2018 Citations: 1
Effect of supplementation of sweet potato ( Ipomoea batatus ) vine in the diet of crossbred boars (LWY × Desi) on the digestibility of nutrients. A Ravi, M Nedunzhiyan, KS Rao 2001 Citations: 1
Seeds of Intelligence: Generative AI and the Rise of Collaborative Robots in Agriculture 6.0 L Thirupathi, M Bontha, R Aavula, KS Murthy, VK Nyatha, SR Thuraga Exploring Generative AI for Collaborative Robots in Agriculture 6.0, 1-24 , 2026 2026
Chaos and Gaussian Strategy with Crisscross Optimization Algorithm for Parkinson Disease Classification D Saidulu, R Aavula, P Rajeshwari, A Singh 2025
Privacy cloud storage with data dynamics using private network coding techniques G Kotikam, R Aavula Journal of Innovation in Computer Science and Engineering 11 (1), 38-41 , 2021 2021
An Extensible Breast Cancer Prognosis Framework for Predicting Susceptibility, Recurrence and Survivability R Aavula, R Bhramaramba International Journal of Engineering and Advanced Technology (IJEAT) Volume … , 2019 2019
A Fuzzy Logic and Roughset based Hybrid Approach for Disease Interpretation and Prediction RB RAVI AAVULA Journal of Theoretical and Applied Information Technology 96 (16), 5217-5226 , 2018 2018
Dumping and storing data in local file system using pig A Koluguri, R Aavula, R Sravani Journal of Innovation in Computer Science and Engineering 8 (1), 41-44 , 2018 2018
Hooked on Hadoop to Read And Write Sequence File Using Map Reduce R Aavula, D Saidulu, BH Chandana Journal of Innovation in Computer Science and Engineering 7 (1), 53-57 , 2017 2017
