Computer Engineering, Computer Science, Computer Vision and Pattern Recognition
6
Scopus Publications
22
Scholar Citations
3
Scholar h-index
Scopus Publications
Artificial Intelligence Driven Drug Delivery Systems: Recent Advances and Emerging Trends Sudha Y, Radhika K R, Chethana C, Anoop G L, Gadhiraju Tej Varma, et al. International Journal of Drug Delivery Technology, 2026 Drug Delivery Systems (DDS) play a critical role in ensuring the therapeutic efficacy and safety of pharmaceutical agents. Conventional drug delivery approaches often suffer from limitations such as poor bioavailability, nonspecific targeting, and systemic toxicity. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the design and optimization of drug delivery platforms. AIdriven methods enable predictive modeling, intelligent nanocarrier design, and personalized therapeutic strategies by analyzing large biomedical datasets. These technologies facilitate optimized drug formulation, controlled release mechanisms, and targeted delivery, thereby improving treatment outcomes. AI algorithms such as Support Vector Machines (SVM), random forests, Convolutional Neural Networks (CNN), and reinforcement learning are increasingly applied in nanoparticle design, pharmacokinetic modeling, and clinical decision support systems. Additionally, emerging concepts such as self-driving laboratories, autonomous drug delivery systems, and AIguided nanomedicine are reshaping pharmaceutical research. This review provides a comprehensive analysis of recent advances in AI-driven drug delivery systems, covering computational techniques, nanocarrier optimization, clinical applications, and emerging research trends. Comparative analysis tables summarize key algorithms, delivery platforms, and research developments reported in the literature. Finally, major challenges including data quality, regulatory issues, and interpretability of AI models are discussed along with future directions for the integration of AI in precision medicine and smart therapeutics.
Transfer Learning-based Optimal Feature Selection with DLCNN for Shrimp Recognition and Classification Gadhiraju Tej Varma, A Sowmya Sri, M Ahmed, T Aurpa, M Azad, et al. International Journal of Intelligent Engineering and Systems, 2022 Shrimp is a main international food item with a significant economic value, as well as one of the most vital animal protein sources. The variety of shrimps can be found in aquaculture. Thus, it is necessary to categorize each shrimp. The conventional machine learning approaches are failed to classify the multiple classes of shrimp, which causes large economic losses in the shrimp farming industry. Therefore, this article proposes a hybrid mechanism for shrimp recognition and classification (SRC), which is named as transfer learning-based optimal feature selection (TLOFS) with deep learning convolutional neural network (DLCNN). Initially, transfer learning based AlexNet is used to extract the features from the shrimp samples. Then, machine learning based iterative random forest algorithm (IRFA) is utilized to select the optimal features from the AlexNet extracted features, which can also identify the relationship between various shrimp classes. Finally, DLCNN is trained and tested with the optimal features and classifies the various shrimp categories, hereafter the proposed hybrid model is named as TLOFS with DLCNN. Obtained simulations discloses the effectiveness of proposed TLOFS with DLCNN model with 99.98% of accuracy, and 99.97% of F1-score as compared to state-of-art SRC approaches.
SDNet: Integrated Unsupervised Learning with DLCNN for Shrimp Disease Detection and Classification Gadhiraju Tej Varma, Adusumilli Sri Krishna IEEE International Conference on Data Science and Information System Icdsis 2022, 2022 Shrimp is a main international food item with a significant economic value, as well as one of the most vital animal protein sources. However, the production of shrimps is directly affected by the different types of shrimp diseases. Thus, it is necessary to identify the shrimp diseases in primary stage to avoid the losses. Therefore, this article is implemented the shrimp disease network (SDNet) using deep learning architectures. Initially, K-means clustering (KMC) is applied on the test images to localize the region of disease or virus location. Then, machine learning based iterative random forest algorithm (IRFA) is utilized to extract the features from segmented images and it also develops the optimal features. Finally, deep learning convolution neural network (DLCNN) is used to perform the multi class classification of shrimp diseases by training the optimal features. The proposed SDNet method resulted in superior performance as compared to state of art approaches with respect to both subjective and objective metrics in terms of classification metrics such as sensitivity, specificity, accuracy, precision, recall, and F1-socre.
RECENT SCHOLAR PUBLICATIONS
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Transfer Learning-based Optimal Feature Selection with DLCNN for Shrimp Recognition and Classification. GT Varma, AS Krishna International Journal of Intelligent Engineering & Systems 15 (5) , 2022 2022.0 Citations: 2
SDNet: Integrated unsupervised learning with DLCNN for shrimp disease detection and classification GT Varma, AS Krishna 2022 IEEE International Conference on Data Science and Information System … , 2022 2022.0 Citations: 7
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MOST CITED SCHOLAR PUBLICATIONS
SDNet: Integrated unsupervised learning with DLCNN for shrimp disease detection and classification GT Varma, AS Krishna 2022 IEEE International Conference on Data Science and Information System … , 2022 2022.0 Citations: 7
Privacy-enhanced heart stroke detection using federated learning and homomorphic encryption VS Naresh, GT Varma Smart Health 37, 100594 , 2025 2025.0 Citations: 6
Shrimp Surfacing Recognition System in the Pond Using Deep Computer Vision G Tej Varma, SK Adusumalli Intelligent Computing and Applications: Proceedings of ICDIC 2020, 217-226 , 2022 2022.0 Citations: 4
Enhancing Privacy in Collaborative Breast Cancer Diagnosis: A Federated Learning Approach with Homomorphic Encryption VS Naresh, GT Varma, D Ayyappa Algorithms in Advanced Artificial Intelligence, 305-310 , 2025 2025.0 Citations: 3
Transfer Learning-based Optimal Feature Selection with DLCNN for Shrimp Recognition and Classification. GT Varma, AS Krishna International Journal of Intelligent Engineering & Systems 15 (5) , 2022 2022.0 Citations: 2
Contemporary Energy Optimization for Mobile and Cloud Environment PK Vadrevu, RK Suggala, TV Gadhiraju
