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.
Advancements in Machine Learning for Healthcare Diagnostics Prathab Kumar K, Shanker Shalini, Chethana C, Prasanth Kamma, P Moniya 2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025 The healthcare diagnostics market has shown growing interest in machine learning (ML), which promises to improve diagnostic accuracy and transform conventional practices. The paper discusses the state-of-the-art in ML techniques relevant to health, focusing more on deep learning techniques such as Convolutional Neural Networks (CNNS) and transformer-based approaches. It also covers newer methods like federated learning. These techniques were applied to benchmark healthcare datasets for MIMIC-III (EHRS), CheXpert (chest X-rays), and Diabetic Retinopathy, used in this study. The proposed methodologies were evaluated based on conventional metrics: precision, recall, accuracy, F1-score, and Area Under the Curve (AUC). CNNS achieved 93.4% retinal imaging accuracy and an AUC of 0.96, whereas transformer-based approaches achieved 91.2% ECG classification accuracy and an F1-score of 0.89. Federated learning reached 88.6% for chest X-ray information, matching centralised approaches. These findings suggest that federated learning and deep learning-based techniques enhance diagnostic performance while protecting user privacy. Explainability techniques like LIME and SHAP improved the decision-making process to ensure clinical feasibility. ML may revolutionise healthcare diagnostics by addressing data privacy, interpretability, and clinical workflow integration.
Virtual Aerial View Projection of Vehicular Surrounding 14th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2023, 2023
Analysis of Credit Card Fraud Data Using Various Machine Learning Methods C. Chethana, Piyush Kumar Pareek Big Data Cloud Computing and Iot Tools and Applications, 2023 The credit card details of customers are very important. Due to online transactions, fraud is also increasing by those who have unauthorized access. Various machine learning algorithms are used to perform an analysis on the credit card fraud information. The analysis of the transaction details can be done during the transaction, or the details can be fetched from stored databases. In this chapter, different tree methods are used to perform the analysis and evaluate the performance. An accuracy of about 99.9% without Principal Component Analysis (PCA) and 99.8% of accuracy with PCA is obtained. The analysis is performed using various algorithms like the K-nearest neighbor method (KNN), the support vector machine (SVM) method, the Gaussian naive Bayesian algorithm, and the logistic regression (LR) algorithm. The analysis was done using the option of principal component analysis and without principal component analysis with good results of about 99.9% accuracy obtained for the SVM, KNN, and LR algorithms.
Deep Learning Technique Based Intrusion Detection in Cyber-Security Networks Chethana C, Piyush Kumar Pareek, Victor Hugo Costa de Albuquerque, Ashish Khanna, Deepak Gupta Mysurucon 2022 2022 IEEE 2nd Mysore Sub Section International Conference, 2022 As a result of the inherent weaknesses of the wireless medium, ad hoc networks are susceptible to a broad variety of threats and assaults. As a direct consequence of this, intrusion detection, as well as security, privacy, and authentication in ad-hoc networks, have developed into a primary focus of current study. This body of research aims to identify the dangers posed by a variety of assaults that are often seen in wireless ad-hoc networks and provide strategies to counteract those dangers. The Black hole assault, Wormhole attack, Selective Forwarding attack, Sybil attack, and Denial-of-Service attack are the specific topics covered in this thesis. In this paper, we describe a trust-based safe routing protocol with the goal of mitigating the interference of black hole nodes in the course of routing in mobile ad-hoc networks. The overall performance of the network is negatively impacted when there are black hole nodes in the route that routing takes. As a result, we have developed a routing protocol that reduces the likelihood that packets would be lost as a result of black hole nodes. This routing system has been subjected to experimental testing in order to guarantee that the most secure path will be selected for the delivery of packets between a source and a destination. The invasion of wormholes into a wireless network results in the segmentation of the network as well as a disorder in the routing. As a result, we provide an effective approach for locating wormholes by using ordinal multi-dimensional scaling and round trip duration in wireless ad hoc networks with either sparse or dense topologies. Wormholes that are linked by both short route and long path wormhole linkages may be found using the approach that was given. In order to guarantee that this ad hoc network does not include any wormholes that go unnoticed, this method is subjected to experimental testing. In order to fight against selective forwarding attacks in wireless ad-hoc networks, we have developed three different techniques. The first method is an incentive-based algorithm that makes use of a reward-punishment system to drive cooperation among three nodes for the purpose of vi forwarding messages in crowded ad-hoc networks. A unique adversarial model has been developed by our team, and inside it, three distinct types of nodes and the activities they participate in are specified. We have shown that the suggested strategy that is based on incentives prohibits nodes from adopting an individualistic behaviour, which ensures collaboration in the process of packet forwarding. To guarantee that intermediate nodes in resource-constrained ad-hoc networks accurately convey packets, the second approach proposes a game theoretic model that uses non-cooperative game theory. This model is based on the idea that game theory may be used. This game reaches a condition of desired equilibrium, which assures that cooperation in multi-hop communication is physically possible, and it is this state that is discovered. In the third algorithm, we present a detection approach that locates malicious nodes in multihop hierarchical ad-hoc networks by employing binary search and control packets. We have shown that the cluster head is capable of accurately identifying the malicious node by analysing the sequences of packets that are dropped along the path leading from a source node to the cluster head. A lightweight symmetric encryption technique that uses Binary Playfair is presented here as a means of safeguarding the transport of data. We demonstrate via experimentation that the suggested encryption method is efficient with regard to the amount of energy used, the amount of time required for encryption, and the memory overhead. This lightweight encryption technique is used in clustered wireless ad-hoc networks to reduce the likelihood of a sybil attack occurring in such networks
Improved Domain Generation Algorithm To Detect Cyber-Attack With Deep Learning Techniques Chethana C, Piyush Kumar Pareek, Victor Hugo Costa de Albuquerque, Ashish Khanna, Deepak Gupta Mysurucon 2022 2022 IEEE 2nd Mysore Sub Section International Conference, 2022 Deep learning is a subfield of machine learning (ML) that focuses on the development of artificial intelligence. It is also often referred to by its acronym, DL (AI). This technique lays an emphasis on the use of big capacity, scalable models that are able to construct distributed representations depending on the input data set. This proposed illustrates the generalizability of these methods and the usage of them in a broad range of cyber security investigations that are peculiar to their environment. The neural network models have been continuously refined and extended during the whole of this research in order to achieve greater adaptability. The following is a list of the important contributions that this proposed makes, in order from most significant to least significant: Work is currently being done to create a comprehensive database for the identification of domain names that have been generated by a domain generation algorithm (DGA), as well as a one-of-a-kind architecture that will increase the overall effectiveness of DGA domain name detection. Both of these will help increase overall efficiency. The creation of a hybrid intrusion detection warning system that is founded on a deep neural network (DNN) and that has the capability to monitor network and host-level activities inside an Ethernet local area network (LAN) (LAN). The examination of information gathered from social media platforms, electronic mail (email), and uniform resource locators in order to design a unified, DL-based framework for the detection of spam and phishing (URL). The creation of a technique based on DL for the study of secure shell (SSH) traffic, the categorization of application network traffic, the classification of malicious traffic, and the detection of harmful traffic is being worked on. The name of the new framework that has been suggested, which is called ScaleMalNet, reflects how hybrid and scalable it is. In the first stage, the executables file is classified as malware or genuine by using static and dynamic analysis. In the second stage, the malicious executables _le are grouped into corresponding malware families. This is a two-step process. For the aim of conducting investigations into Android ransomware and malware, an analogous hybrid DL framework is now in the process of being developed. This framework is better in its capacity to detect dangerous software and ransomware on Android when compared to the typical ML-based techniques that are presently in use. These approaches are already in widespread usage. The development of a framework for DNS-based botnet detection and DL-based network intrusion detection is now being worked on in the context of the Internet of things (IoT) and smart cities
Neural Network Approach to Human Brain by Reverse Engineering Brijendra Gupta, C. Chethana, Subbiah Swaminathan, S. Sharanyaa, Anuradha Thakare, G. Naga Rama Devi Mysurucon 2022 2022 IEEE 2nd Mysore Sub Section International Conference, 2022 The biological systems of the brain were fine for their ability to function reliably and effectively in high-noise environments. Biological pathways were built from brain components of highly adaptive or flexible interactions that could be low-precision, unpredictable, or extremely simultaneous. The capacity of brain networks to organize themselves and their pattern structure are two of the most intriguing features. Current findings in the addition of Neural Networks (NN) have revealed some intriguing ideas on Artificial Neural networks (ANN) and Convolutional Neural networks (CNN). Only largescale advanced neural simulations could be built, making it possible to understand these ideas and apply them to real problems. Large-scale network simulators are achievable with the latest enhancements to low-cost multiprocessor systems. Conceptual paradigms of NN methods for designing, generating, and evaluating advanced neural systems were discussed in this study.
Computation Approach in Steel Manufacturing Industries Through Artificial Neural Network Hemalatha C, C. Chethana, Gopala Rao Thellaputta, K Hemapriya, Rajeev Ranjan, Sanjay A. Jadhav Mysurucon 2022 2022 IEEE 2nd Mysore Sub Section International Conference, 2022 Current work aims to modify process variables for steel production by using an Artificial Neural Network (ANN) based architecture and an integrated optimization module. The concept was originally designed to be used throughout the creation of inorganic nanoparticles in laser ablation processors. It was originally conceived for adjusting the control of material manufacturing processes to achieve the desired outputs. Further research has resulted in more universal techniques that can be used in a wide variety of different manufacturing and treatment scenarios. Based on the knowledge of the experts and the built system, an example of optimization of process conditions in a coherent steel casting was provided. More progress has been made in modeling the whole production line of the steelworks to mold process. The frameworks were stages of growth results are presented in the framework used to conduct certain statistical surveys.
Forecasting the Future: The Role of Digital Transformation in the Evolution of Industry 5.0 in Developing Economies S Chandra, M Bhutani, C Chethana, MS Ramaratnam, D Misra, R Kaur International Conference On Innovative Computing And Communication, 267-286 , 2025 2025
Analysis of credit card fraud data using various machine learning methods C Chethana, PK Pareek Big Data, Cloud Computing and IoT, 103-116 , 2023 2023 Citations: 28
Process Optimization in Small Software Firms C Chethana, M Shaik, P Pareek SSRN , 2023 2023 Citations: 4
Neural Network Approach to Human Brain by Reverse Engineering B Gupta, C Chethana, S Swaminathan, S Sharanyaa, A Thakare, ... 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-6 , 2022 2022
Computation approach in steel manufacturing industries through artificial neural network C Hemalatha, C Chethana, GR Thellaputta, K Hemapriya, R Ranjan, ... 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-4 , 2022 2022 Citations: 1
Improved domain generation algorithm to detect cyber-attack with deep learning techniques C Chethana, PK Pareek, VHC de Albuquerque, A Khanna, D Gupta 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-8 , 2022 2022 Citations: 21
Deep learning technique based intrusion detection in cyber-security networks C Chethana, PK Pareek, VHC de Albuquerque, A Khanna, D Gupta 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-7 , 2022 2022 Citations: 32
Survey of graph neural network for learning complex problems C C https://www.sciencedirect.com/science/article/abs/pii/S0065245822000717?via … , 2022 2022
Deep Learning Model to reveal new Healthcare concepts and Improve Performance Bulletin of Environment, Pharmacology and Life Sciences , 2022 2022
AN EXPLORATORY STUDY TO REDUCE LEAD TIME IN SMALL AND MEDIUM LEVEL ENTERPRISES - IT SECTOR MCC Dr. Aditya Pai H,, Dr Piyush Kumar Pareek https://www.amazon.com/dp/B09QFFZWTH/ref … , 2022 2022
An Exploratory Study for Secure Software Development Life cycle with specificity towards Small and Medium Software firms in Bengaluru CC Dr. Shantappa G Gollagi ,Dr.Piyush Kumar LULU, https://www.lulu.com/en/us/shop/mrs-chet , 2022 2022
Application Of Reverse Engineering in the Process of Utilization of Human Brain in Artificial Intelligence C Chethana, S Swaminathan, S Sharanyaa, E Sathish, R Prathipa, ... Journal of Optoelectronics Laser 41 (3), 89-93 , 2022 2022 Citations: 4
Determination of Efficient and Secured Healthcare Monitoring Based on IoT Along with Machine Learning M Chethana C New Visions in Science and Technology Vol. 3 3, 144-149 , 2021 2021 Citations: 2
Tree based Predictive Modelling for Prediction of the Accuracy of Diabetics. M Chethana C IEEE Conference , 2021 2021 Citations: 3
Prediction of heart disease using different KNN classifier C Chethana 2021 5th international conference on intelligent computing and control … , 2021 2021 Citations: 33
A Compact multiband patch antenna for 5G applications using rectangular slotted DGS R Banuprakash, M Madhushree, C Chethana, M RD 2020 7th International Conference on Smart Structures and Systems (ICSSS), 1-6 , 2020 2020 Citations: 6
Sentence Similarity C C International Journal of Advanced Research in Computer and Communication … , 2020 2020
A miniaturized dual band microstrip antenna for Worldwide interoperability microwave access and C band radio navigation R Banuprakash, C Chethana, N Lekhya Sindura, M Madhushree 2019 1st International Conference on Advances in Information Technology … , 2019 2019 Citations: 2
Cyber Attack Prevention using Machine Learning CC Megha S M International Journal of Engineering Science and Computing, 9 (5), 22523-22525 , 2019 2019
Analysis of Various Clustering Algorithm on Job Events Data of Google Cloud Tracelog M Chethana C International Journal of Advanced Research in Computer and Communication … , 2018 2018
MOST CITED SCHOLAR PUBLICATIONS
Prediction of heart disease using different KNN classifier C Chethana 2021 5th international conference on intelligent computing and control … , 2021 2021 Citations: 33
Deep learning technique based intrusion detection in cyber-security networks C Chethana, PK Pareek, VHC de Albuquerque, A Khanna, D Gupta 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-7 , 2022 2022 Citations: 32
Analysis of credit card fraud data using various machine learning methods C Chethana, PK Pareek Big Data, Cloud Computing and IoT, 103-116 , 2023 2023 Citations: 28
Improved domain generation algorithm to detect cyber-attack with deep learning techniques C Chethana, PK Pareek, VHC de Albuquerque, A Khanna, D Gupta 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-8 , 2022 2022 Citations: 21
A Compact multiband patch antenna for 5G applications using rectangular slotted DGS R Banuprakash, M Madhushree, C Chethana, M RD 2020 7th International Conference on Smart Structures and Systems (ICSSS), 1-6 , 2020 2020 Citations: 6
Process Optimization in Small Software Firms C Chethana, M Shaik, P Pareek SSRN , 2023 2023 Citations: 4
Application Of Reverse Engineering in the Process of Utilization of Human Brain in Artificial Intelligence C Chethana, S Swaminathan, S Sharanyaa, E Sathish, R Prathipa, ... Journal of Optoelectronics Laser 41 (3), 89-93 , 2022 2022 Citations: 4
Tree based Predictive Modelling for Prediction of the Accuracy of Diabetics. M Chethana C IEEE Conference , 2021 2021 Citations: 3
Study of 3D Barcode with Steganography for Data Hiding CC Megha S M IRJET 5 (6), 250-254 , 2018 2018 Citations: 3
Determination of Efficient and Secured Healthcare Monitoring Based on IoT Along with Machine Learning M Chethana C New Visions in Science and Technology Vol. 3 3, 144-149 , 2021 2021 Citations: 2
A miniaturized dual band microstrip antenna for Worldwide interoperability microwave access and C band radio navigation R Banuprakash, C Chethana, N Lekhya Sindura, M Madhushree 2019 1st International Conference on Advances in Information Technology … , 2019 2019 Citations: 2
Computation approach in steel manufacturing industries through artificial neural network C Hemalatha, C Chethana, GR Thellaputta, K Hemapriya, R Ranjan, ... 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-4 , 2022 2022 Citations: 1
An Legal Trust and Reputation Calculation and Management System for Cloud and Sensor Networks Integration KRCN Mrs Chethana C International Journal of Innovations & Advancement in Computer Science 5 (7) , 2016 2016 Citations: 1
Forecasting the Future: The Role of Digital Transformation in the Evolution of Industry 5.0 in Developing Economies S Chandra, M Bhutani, C Chethana, MS Ramaratnam, D Misra, R Kaur International Conference On Innovative Computing And Communication, 267-286 , 2025 2025
Neural Network Approach to Human Brain by Reverse Engineering B Gupta, C Chethana, S Swaminathan, S Sharanyaa, A Thakare, ... 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-6 , 2022 2022
Survey of graph neural network for learning complex problems C C https://www.sciencedirect.com/science/article/abs/pii/S0065245822000717?via … , 2022 2022
Deep Learning Model to reveal new Healthcare concepts and Improve Performance Bulletin of Environment, Pharmacology and Life Sciences , 2022 2022
AN EXPLORATORY STUDY TO REDUCE LEAD TIME IN SMALL AND MEDIUM LEVEL ENTERPRISES - IT SECTOR MCC Dr. Aditya Pai H,, Dr Piyush Kumar Pareek https://www.amazon.com/dp/B09QFFZWTH/ref … , 2022 2022
An Exploratory Study for Secure Software Development Life cycle with specificity towards Small and Medium Software firms in Bengaluru CC Dr. Shantappa G Gollagi ,Dr.Piyush Kumar LULU, https://www.lulu.com/en/us/shop/mrs-chet , 2022 2022
Sentence Similarity C C International Journal of Advanced Research in Computer and Communication … , 2020 2020
