Computer Engineering, Artificial Intelligence, Computer Science, Cancer Research
8
Scopus Publications
14
Scholar Citations
2
Scholar h-index
Scopus Publications
An Innovative Approach for Detecting the Freshness of Fruits and Vegetables using Transfer Learning Techniques V Subrahmanyam, Polasi Sudhakar, B. Madhav Rao, Ventrapati Jaya Ramakrishna, Manoj Kumar Vemula, G.Satyanarayana Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications Icimia 2025, 2025 Freshness of fruits and vegetables is essential for food safety, customer satisfaction and reducing food waste. To automatically assess the freshness of fruits and vegetables using image data, this study proposes a new deep learning based framework that uses transfer learning techniques. The freshness level is spoiled as fresh and moderately fresh, spoiled as something rotten in folding networks -CNN model such as ResNet50, MobileTv2, and EfficiencyTB0. High resolution photos of different fruits and vegetables under different fresh conditions come from carefully selected data records. Compared to traditional computer vision techniques, the proposed model related to accuracy, accuracy and speed of inference improves performance. Additionally, the model is implemented in a thin mobile application interface that allows realtime mobile phone detection in grocery stores, supply chains and homes. By using AI-controlled automation, this creative method not only improves quality control, but also supports sustainable food practices.
Efficient Heart Failure Prediction using PSO Optimized Wrapper Feature Selection and Ensemble Models Penuganti Harika Devi, Polasi Sudhakar, Shaga Anoosha, K. Hanuman Sai, Bigul Sunitha Devi, G. Satyanarayana Proceedings of the 6th International Conference on Smart Electronics and Communication Icosec 2025, 2025 Heart failure is a predominant cause of mortality globally, underscoring the critical necessity for robust predictive models for early diagnosis and efficient treatment approaches. This paper introduces a novel ensemble machine learning framework enhanced by wrapper-based feature selection utilizing Particle Swarm Optimization (PSO) in order to improve heart failure prediction. Inspired from swarm intelligence, PSO finds an optimal subset of features, which it does by trading off between exploration and exploitation to overcome challenges such as feature redundancy and irrelevance. The selected features are then evaluated with robust ensemble classifiers like Random Forest, Extreme Gradient Boosting, stacking, and AdaBoost in order to increase the performance and reliability of the model. Comprehensive experiments demonstrated the heart failure dataset on the PSO-driven feature selection, thereby reducing dimensionality with much improved or maintained values of predictive metrics. Superior accuracy, precision, recall, and F1 score are achieved by ensemble models compared to baseline models that were trained on full sets of features. This paper underlines the crucial role of efficient feature selection in improving the accuracy and interpretability of machine learning models for healthcare applications. The proposed framework provides scalability in solving complex health problems in general and, more specifically, leads to improved patient outcomes and better optimization of healthcare resources in clinical settings.
Quantum Machine Learning for Detecting Known Cyber-Attacks in IoT Networks Polasi Sudhakar, D Ratnagiri, Chokkapu Bhargavi, Sape Chittibabulu, G. Satyanarayana, Amanulla Mohammad Proceedings of the 9th International Conference on Inventive Systems and Control Icisc 2025, 2025 The rapid growth of Internet of Things (IoT) devices has exposed weaknesses in network infrastructures, so efficient and effective cyberattack detection is absolutely important. The application of Quantum Machine Learning (QML), more especially Quantum Support Vector Machines (QSVM), to raise the detection accuracy of known cyberthreats in IoT environments is investigated in this work. To better manage the enormous dimensionality and complexity of IoT network traffic data than conventional approaches, QSVM uses quantum computing ideas including improved quantum feature encoding and kernel techniques. With an accuracy of 99.12% against 92.00%, along with improved precision, recall, and F1-score metrics, experimental data show that QSVM greatly beats the classical Support Vector Machine (SVM). An essential first step toward integrating quantum computing into practical cybersecurity applications, our results show the potential of quantum-enhanced classifiers to deliver strong, scalable, and highly accurate intrusion detection systems for IoT networks.
Quantum Convolutional Neural Network Framework for Efficient and Accurate Plant Disease Diagnosis Gumpula Jhansi, Polasi Sudhakar, Chennam Chandrika Surya, Appana Kiran Kumar, Madhava Rao Chunduru, G. Satyanarayana Proceedings 2025 2nd International Conference on Electronic Circuits and Signaling Technologies Icecst 2025, 2025 Plants are susceptible to numerous illnesses during their growth stages, rendering early diagnosis a significant challenge in contemporary agriculture. Not being able to find infections early on can greatly lower agricultural yields and hurt farmers' incomes. There are several new methods that use Machine Learning and Deep Learning to solve this problem; however they generally have low classification accuracy or need millions of trainable parameters, which makes them less efficient. To overcome these limitations, this study proposes a Quantum Convolutional Neural Network (QCNN) model for the automated detection of plant diseases. To the best of our knowledge, there is currently no literature that has investigated QCNNs for this purpose. This work employs the proposed approach to detect Bacterial Spot disease in peach plants utilizing leaf photos from the publicly accessible Plant Village dataset. The system only needs 9,914 trainable parameters to work, and it gets 94.96 % accuracy. These results show that QCNNs are good at predicting plant diseases and could be useful in other areas of agriculture as well.
Wrapper-based Feature Selection for Enhanced Intrusion Detection Using Random Forest Classification Polasi Sudhakar, Durga Prasanna N, Sreedhar Bhukya, Mohammad Azhar, G R Suresh, Mohan Ajmeera Proceedings of 5th International Conference on Iot Based Control Networks and Intelligent Systems Icicnis 2024, 2024 In network environments, detecting and mitigating cyber-attacks necessitates the creation of an effective Intrusion Detection System. This research describes an IDS framework that combines Random Forest (RF) classification and a wrapper-based feature selection strategy to increase performance. The wrapper-based strategy iteratively picks the most relevant features by lowering dimensionality and removing redundant or irrelevant data, increasing the RF classifier's efficiency. The framework is assessed against three well-known benchmark datasets: NSL-KDD, CICIDS-2019, and Bot-IoT. Experimental results show that when used in conjunction with wrapper-based feature selection, the RF classifier improves detection accuracy, precision, and recall significantly. This solution handles the complexity and variety of modern network traffic, resulting in a more accurate and efficient IDS than older systems.
A novel skin cancer Detection based transfer learning with optimization algorithm using Dermatology Dataset Polasi Sudhakar, Suresh Chandra Satapathy Eai Endorsed Transactions on Pervasive Health and Technology, 2023 Detecting skin cancer at the preliminary stage is a challenging issue, and is of high significance for the affected patients. Here, Fractional Gazelle Optimization Algorithm_Convolutional Neural Network based Transfer Learning with Visual Geometric Group-16 (FGOA_CNN based TL with VGG-16) is introduced for primary prediction of skin cancer. Initially, input skin data is acquired from the database and it is fed to the data preprocessing. Here, data preprocessing is done by missing value imputation and linear normalization. Once data is preprocessed, the feature selection is done by the proposed FGOA. Here, the proposed FGOA is an integration of Fractional Calculus (FC) and Gazelle Optimization Algorithm (GOA). After that, skin cancer detection is carried out using CNN-based TL with VGG-16, which is trained by the proposed FGOA and it is an integration of FC and GOA. Moreover, the efficiency of the proposed FGOA_ CNN-based TL with VGG-16 is examined based on five various metrics, like accuracy, Positive Predictive Value (PPV), True Positive Rate (TPR), True Negative Rate (TNR), and Negative Predictive Value (NPV) and the outcome of experimentation reveals that the devised work is highly superior and has attained maximal values of metrics is 92.65%, 90.35%, 91.48%, 93.56%, 90.77% respectively.
Quantum Convolutional Neural Network Framework for Efficient and Accurate Plant Disease Diagnosis G Jhansi, P Sudhakar, CC Surya, AK Kumar, MR Chunduru, ... 2025 2nd International Conference on Electronic Circuits and Signaling … , 2025 2025 Citations: 1
Efficient Heart Failure Prediction using PSO Optimized Wrapper Feature Selection and Ensemble Models PH Devi, P Sudhakar, S Anoosha, KH Sai, BS Devi, G Satyanarayana 2025 6th International Conference on Smart Electronics and Communication … , 2025 2025
An Innovative Approach for Detecting the Freshness of Fruits and Vegetables using Transfer Learning Techniques V Subrahmanyam, P Sudhakar, BM Rao, VJ Ramakrishna, MK Vemula, ... 2025 4th International Conference on Innovative Mechanisms for Industry … , 2025 2025 Citations: 1
Quantum machine learning for detecting known cyber-attacks in iot networks P Sudhakar, D Ratnagiri, C Bhargavi, S Chittibabulu, G Satyanarayana, ... 2025 9th International Conference on Inventive Systems and Control (ICISC … , 2025 2025 Citations: 5
Wrapper-based Feature Selection for Enhanced Intrusion Detection Using Random Forest Classification P Sudhakar, S Bhukya, M Azhar, GR Suresh, M Ajmeera 2024 International Conference on IoT Based Control Networks and Intelligent … , 2024 2024 Citations: 1
A novel skin cancer Detection based transfer learning with optimization algorithm using Dermatology Dataset P Sudhakar, SC Satapathy EAI Endorsed Transactions on Pervasive Health and Technology 9 , 2024 2024 Citations: 2
Feature Selection with Binary Differential Evolution for Microarray P Sudhakar, SC Satapathy Intelligent Systems and Sustainable Computing: Proceedings of ICISSC 2022, 193 , 2023 2023
OBJECT DETECTION AND ALERT SYSTEM FOR VISUALLY IMPAIRED PEOPLE MP Sudhakar, KSD Ushasri, KN Bhavani, MM Kumar, MA Babu 2023
Feature selection with binary differential evolution for microarray datasets P Sudhakar, SC Satapathy International Conference on Intelligent Systems and Sustainable Computing … , 2022 2022 Citations: 1
A new adaptive artificial bee colony (AABC) technique in cellular automata data clustering G Srinivasa Rao, P Sudhakar Smart Intelligent Computing and Applications: Proceedings of the Third … , 2019 2019 Citations: 2
Application of An Encrypted Data Search Across Mobile Clouds PI Sowjanya, P Sudhakar, DVS Narayana International Journal for Innovative Engineering & Management Research 8 (05) , 2019 2019 Citations: 1
Failure Prediction Resource Allocation for Dynamic Load in Virtual Machines using Hybrid Cloud P Sudhakar, R Vinoth Asian Journal of Research in Social Sciences and Humanities 7 (2), 1047-1057 , 2017 2017
MOST CITED SCHOLAR PUBLICATIONS
Quantum machine learning for detecting known cyber-attacks in iot networks P Sudhakar, D Ratnagiri, C Bhargavi, S Chittibabulu, G Satyanarayana, ... 2025 9th International Conference on Inventive Systems and Control (ICISC … , 2025 2025 Citations: 5
A novel skin cancer Detection based transfer learning with optimization algorithm using Dermatology Dataset P Sudhakar, SC Satapathy EAI Endorsed Transactions on Pervasive Health and Technology 9 , 2024 2024 Citations: 2
A new adaptive artificial bee colony (AABC) technique in cellular automata data clustering G Srinivasa Rao, P Sudhakar Smart Intelligent Computing and Applications: Proceedings of the Third … , 2019 2019 Citations: 2
Quantum Convolutional Neural Network Framework for Efficient and Accurate Plant Disease Diagnosis G Jhansi, P Sudhakar, CC Surya, AK Kumar, MR Chunduru, ... 2025 2nd International Conference on Electronic Circuits and Signaling … , 2025 2025 Citations: 1
An Innovative Approach for Detecting the Freshness of Fruits and Vegetables using Transfer Learning Techniques V Subrahmanyam, P Sudhakar, BM Rao, VJ Ramakrishna, MK Vemula, ... 2025 4th International Conference on Innovative Mechanisms for Industry … , 2025 2025 Citations: 1
Wrapper-based Feature Selection for Enhanced Intrusion Detection Using Random Forest Classification P Sudhakar, S Bhukya, M Azhar, GR Suresh, M Ajmeera 2024 International Conference on IoT Based Control Networks and Intelligent … , 2024 2024 Citations: 1
Feature selection with binary differential evolution for microarray datasets P Sudhakar, SC Satapathy International Conference on Intelligent Systems and Sustainable Computing … , 2022 2022 Citations: 1
Application of An Encrypted Data Search Across Mobile Clouds PI Sowjanya, P Sudhakar, DVS Narayana International Journal for Innovative Engineering & Management Research 8 (05) , 2019 2019 Citations: 1
Efficient Heart Failure Prediction using PSO Optimized Wrapper Feature Selection and Ensemble Models PH Devi, P Sudhakar, S Anoosha, KH Sai, BS Devi, G Satyanarayana 2025 6th International Conference on Smart Electronics and Communication … , 2025 2025
Feature Selection with Binary Differential Evolution for Microarray P Sudhakar, SC Satapathy Intelligent Systems and Sustainable Computing: Proceedings of ICISSC 2022, 193 , 2023 2023
OBJECT DETECTION AND ALERT SYSTEM FOR VISUALLY IMPAIRED PEOPLE MP Sudhakar, KSD Ushasri, KN Bhavani, MM Kumar, MA Babu 2023
Failure Prediction Resource Allocation for Dynamic Load in Virtual Machines using Hybrid Cloud P Sudhakar, R Vinoth Asian Journal of Research in Social Sciences and Humanities 7 (2), 1047-1057 , 2017 2017
