AUDIBLOC: A POST-QUANTUM AUDIO SECURITY SYSTEM WITH FORENSIC WATERMARKING CHAOTIC ENCRYPTION AND BLOCKCHAIN VERIFICATION Hamza Tabti, Fahd Jarad Journal of Theoretical and Applied Information Technology, 2025 Our aim in this paper is to explore the existence of nonnegative solutions for a boundary value problem involving nonlinear Caputo fractional differential equations. The analysis begins with the formulation of superlinear and sublinear conditions, under which the Guo-Krasnosel'skii fixed point theorem is applied in a cone to get the existence of positive solutions. To facilitate this, the corresponding Green's function is constructed, and its essential properties are explored. A number of illustrative examples are included to show the applicability of the theoretical results and emphasize their effectiveness.
Deep Spoil: A Deep Learning-Based model for Accurate Food Spoilage Detection Maithili Chigurupati, Anupama Gopavarapu, Naresh Sammeta 2nd International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2025, 2025 A systematic study demonstrated deep learning algorithm performance when detecting food spoilage during its implementation. Within the proposed methodology, deep learning algorithms, particularly convolutional neural networks (CNNs), perform automatic food image analysis to detect spoilage status. The research group operates deep learning models through testing them on extensive food image collection featuring various types of foods at different spoilage phases. The VGG CNN model operated as a transfer learning approach during food spoilage system construction. Scientists prepared the models through training them with spoiled food images to enable the identification of specific qualities in spoiled food. The research presented multiple variations of model parameters and architectural elements during its exploration of deep learning systems for food spoilage identification. The deep learning algorithms demonstrated precise food spoilage detection capabilities because they delivered dependable results per experimental findings.
Automated Anomaly Detection and Threat Classification in Network Traffic Krishnaja Venkata Naga Sri Lasya P, Munisaiteja Sharan Tupakula, Naresh Sammeta 2nd International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2025, 2025 Organizations need automated detection technology since security threats become increasingly complicated. The automatic anomaly detection framework established by research work provides organizations with real-time anomaly detection capabilities for their network traffic records analysis. The study performs performance testing for evaluating four machine learning systems. The research determined Random Forest(RF) Classifier and Logistic Regression as well as Decision Tree(DT) Classifier and K-Nearest Neighbors(KNN) Classifier as suitable options for threat classification frameworks. F1-score joins with accuracy, recall and precision to serve as the main evaluation metrics throughout the testing phase. The system employs a log reader automation that automatically handles unmarked test data for threat identification requirements with integrated security threat detection capabilities. Model choice requirements for security needs differ according to practical situations which leads to significant variations in performance based on strengths and weaknesses. Real-time corporate implementation becomes feasible through this top-performing model since it shows exceptional detection ability together with reliable operational behavior. Businesses need to select specific security models based on their personal security requirements according to the research findings. The research findings enable practitioners to select appropriate machine learning platforms which strengthen corporate cybersecurity infrastructure by running them on intrusion detection systems and malware recognition and network security operations.
Bird Species Classification: Using CNN Models Uppara Nithin, Ambati Somnath, Vadla Dileep, Naresh Sammeta 3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics Icdcece 2024, 2024 This research employs Convolutional Neural Networks (CNNs) to identify bird species. A dataset of 1701 curated images, featuring Abbotts booby, Abbotts babbler, Abyssinian ground hornbill, African crowned crane, African firefinch, African oyster catcher, African pied hornbill, African pygmy goose, and albatross forms a comprehensive image library. Utilizing deep learning architectures (ResNet, VGG16, VGG19, AlexNet, GoogleNet, LeNet), the trained neural networks achieved remarkable accuracies, with some models exceeding 99%, demonstrating robust bird species classification. The study's 91% to over 99% accuracy rate in identifying different bird species emphasizes its usefulness in real-world situations. The fields of computer vision and ornithology benefit from this research, which helps classify bird species. Offering a thorough description of the model's efficacy and possible implications for conservation monitoring tools or apps for identifying bird species, the study describes the experimental design, evaluation measures, dataset characteristics, and concluding thoughts
Integration of IoT and AI for Enhanced Efficiency and Control in Smart Energy Management Systems S. Naganandhini, S.Saravanan, Naresh Sammeta, S.Ramkumar 2nd International Conference on Emerging Research in Computational Science Icercs 2024, 2024 This paper presents a detailed analysis of how the integration of Internet of Things (IoT) and artificial intelligence (AI) to energy management systems (EMS) has a possibility of enhancing energy efficiency and control across various sectors. Therefore, this paper seeks to analyze the new developments in smart EMS through analyzing the possibilities of the combination of IoT and AI. Hence, the research utilises the hybrid approach with regard to the applicable methodology that includes numerical and analytical description, qualitative case and example analysis, and the actual implementation. Sophisticated quantitative analysis was also performed to analyze how the sensors of IoTs and the AI algorithm would affect the real-time energy monitoring, the demand forecasting, and the automated energy control systems. This paper presented case studies which offered more details on implementation including the processes, operations and such gains as experienced from employing IoT and AI. It is evident from the analysis that IoT enables detailed data acquisition and real-time observation and monitoring of devices, whereas AI optimises predictive modelling and decision-making. These result to imperative upgrades in energy efficiency, cuts in operation costs and strengthening in system performance. This paper provides an insight into the subject of smart EMS’ application of IoT and AI in the context of energy management and presents recommendations to the practitioners and scholars. The implications also brought out future research directions in terms of improving the AI models and extending the use of IoT across a range of energy management scenarios.
Blockchain-based Scalable and Secure EHR Data Sharing using Proxy Re-Encryption Naresh Sammeta, Latha Parthiban International Arab Journal of Information Technology, 2023 Electronic Health Record (EHR) includes highly sensitive data like medical images, prescriptions, medical test result, medical history of patients, etc., These sensitive data cannot be transmitted in its original form in the network due to security issues. Hence, encryption is done prior to transmission. To increase the speed of data transfer and to overcome the storage issues, data is usually transferred through the cloud. Hence, to ensure the security and scalability of the data, a third-party encryption called re-encryption is performed at the proxy cloud. This re-encryption ensures that the data can be reliably transmitted through the network. In this research, a novel scheme called block-chain based EHR data sharing using chaotic re-encryption (BC-EDS-CR) is proposed. In the proposed scheme, re-encryption is performed using chaos theory. The proposed re-encryption scheme ensures that the cloud administrator cannot access the medical data. Metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index (SSIM), entropy and correlation coefficient are used in evaluating this scheme. It was found that the proposed scheme outperforms the existing methods by achieving a PSNR of 57.66, SSIM of 0.985 and MSE of 0.058.
Data Ownership and Secure Medical Data Transmission using Optimal Multiple Key-Based Homomorphic Encryption with Hyperledger Blockchain Naresh Sammeta, Latha Parthiban International Journal of Image and Graphics, 2023 Recent healthcare systems are defined as highly complex and expensive. But it can be decreased with enhanced electronic health records (EHR) management, using blockchain technology. The healthcare sector in today’s world needs to address two major issues, namely data ownership and data security. Therefore, blockchain technology is employed to access and distribute the EHRs. With this motivation, this paper presents novel data ownership and secure medical data transmission model using optimal multiple key-based homomorphic encryption (MHE) with Hyperledger blockchain (OMHE-HBC). The presented OMHE-HBC model enables the patients to access their own data, provide permission to hospital authorities, revoke permission from hospital authorities, and permit emergency contacts. The proposed model involves the MHE technique to securely transmit the data to the cloud and prevent unauthorized access to it. Besides, the optimal key generation process in the MHE technique takes place using a hosted cuckoo optimization (HCO) algorithm. In addition, the proposed model enables sharing of EHRs by the use of multi-channel HBC, which makes use of one blockchain to save patient visits and another one for the medical institutions in recoding links that point to EHRs stored in external systems. A complete set of experiments were carried out in order to validate the performance of the suggested model, and the results were analyzed under many aspects. A comprehensive comparison of results analysis reveals that the suggested model outperforms the other techniques.
Medical data analytics for secure multi-party-primarily based cloud computing utilizing homomorphic encryption Journal of Scientific and Industrial Research, 2021
Reinforcement Learning Based Adaptive Traffic Signal Control Using Deep Q-Network Variants R Nalla, VT Madhavareddy, BA Sajjala, N Sammeta 2026 Third International Conference on Networking and Communications (ICNWC … , 2026 2026
Hybrid Machine Learning Framework for Match Outcome and Player Performance Prediction in Team Sports VT Madhavareddy, R Nalla, N Sammeta 2026 Third International Conference on Networking and Communications (ICNWC … , 2026 2026
SECURE CLOUD DATA STORAGE USING CONTEXTUAL ENTROPIC CIPHERTEXT-POLICY ATTRIBUTE-BASED ENCRYPTION NS DHULIPUDI, B SAVADAM, A ZINENKO, N KRASNOSTANOVA, ... Journal of Theoretical and Applied Information Technology 103 (23) , 2025 2025
Automated Anomaly Detection and Threat Classification in Network Traffic MS Tupakula, N Sammeta 2025 2nd International Conference on Research Methodologies in Knowledge … , 2025 2025
Blockchain-based privacy-preserving electronics healthcare records in healthcare 4.0 using proxy re-encryption L Parthiban, N Sammeta, ACJ Malathi, BE Samuel Integrating Blockchain and Artificial Intelligence for Industry 4.0 … , 2023 2023 Citations: 1
Blockchain-Based Privacy-Preserving L Parthiban, N Sammeta, ACJ Malathi, BE Samuel Integrating Blockchain and Artificial Intelligence for Industry 4.0 … , 2023 2023
Blockchain-based scalable and secure EHR data sharing using proxy re-encryption. N Sammeta, L Parthiban Int. Arab J. Inf. Technol. 20 (5), 702-710 , 2023 2023 Citations: 13
Data ownership and secure medical data transmission using optimal multiple key-based homomorphic encryption with hyperledger blockchain N Sammeta, L Parthiban International Journal of Image and Graphics 23 (03), 2240003 , 2023 2023 Citations: 10
Enhancing Reliability in Multi-Path Mobile Wireless Sensor Network J Visumathi, S Gurusubramani, SK Mouleeswaran, N Sammeta 2023 Third International Conference on Artificial Intelligence and Smart … , 2023 2023 Citations: 2
Deep learning enabled cross‐lingual search with metaheuristic web based query optimization model for multi‐document summarization M Gangathimmappa, N Subramani, V Sambath, RAM Ramanujam, ... Concurrency and Computation: Practice and Experience 35 (2), e7476 , 2023 2023 Citations: 30
Evolutionary Algorithm with Machine Learning Enabled Color Texture Image Segmentation and Classification Model GU Maheswari, L Selvam, N Sammeta L. and SAMMETA, NARESH, Evolutionary Algorithm with Machine Learning Enabled … , 2022 2022
RETRACTED: An optimal elliptic curve cryptography based encryption algorithm for blockchain-enabled medical image transmission N Sammeta, L Parthiban Journal of Intelligent & Fuzzy Systems 43 (6), 8275-8287 , 2022 2022 Citations: 10
Convolutional neural network-based MRI brain tumor classification system M Amanullah, J Visumathi, N Sammeta, M Ashok AIP Conference Proceedings 2519 (1), 030019 , 2022 2022 Citations: 3
Hyperledger blockchain enabled secure medical record management with deep learning-based diagnosis model N Sammeta, L Parthiban Complex & Intelligent Systems 8 (1), 625-640 , 2022 2022 Citations: 84
Hyperledger blockchain enabled secure medical record management with deep learning-based diagnosis model. Complex Intell. Syst. 8, 625–640 (2022) N Sammeta, L Parthiban doi. org/1 0 1 (0), 0 , 2022 2022 Citations: 5
Ensemble Artificial Intelligence based Programmable Digital Billboards with Automatic Variation of Images and Text N Sammeta IN Patent App. 202,141,035,052 , 2021 2021
Medical data analytics for secure multi-party-primarily based cloud computing utilizing homomorphic encryption N Sammeta, L Parthiban Journal of Scientific and Industrial Research (JSIR) 80 (08), 692-698 , 2021 2021 Citations: 5
Vision Platform Lesion Quality Measurement of Accessible Endoscopic Images Using Machine Learning Techniques N Sammeta, N Duraichi, P Velmurugan, V Lalitha Journal of Physics: Conference Series 1964 (4), 042096 , 2021 2021 Citations: 1
A Supply Chain Framework for Identified Internet Services Based on Blockchain N Duraichi, N Sammeta, V Lalitha, P Velmurugan Journal of Physics: Conference Series 1964 (6), 062043 , 2021 2021
REDUCEMENT OF GLASS BOTTLE DEFECT USING IOT N Sammeta IN Patent App. 202,141,016,344 , 2021 2021
MOST CITED SCHOLAR PUBLICATIONS
Hyperledger blockchain enabled secure medical record management with deep learning-based diagnosis model N Sammeta, L Parthiban Complex & Intelligent Systems 8 (1), 625-640 , 2022 2022.0 Citations: 84
Deep learning enabled cross‐lingual search with metaheuristic web based query optimization model for multi‐document summarization M Gangathimmappa, N Subramani, V Sambath, RAM Ramanujam, ... Concurrency and Computation: Practice and Experience 35 (2), e7476 , 2023 2023.0 Citations: 30
Blockchain-based scalable and secure EHR data sharing using proxy re-encryption. N Sammeta, L Parthiban Int. Arab J. Inf. Technol. 20 (5), 702-710 , 2023 2023.0 Citations: 13
Data ownership and secure medical data transmission using optimal multiple key-based homomorphic encryption with hyperledger blockchain N Sammeta, L Parthiban International Journal of Image and Graphics 23 (03), 2240003 , 2023 2023.0 Citations: 10
RETRACTED: An optimal elliptic curve cryptography based encryption algorithm for blockchain-enabled medical image transmission N Sammeta, L Parthiban Journal of Intelligent & Fuzzy Systems 43 (6), 8275-8287 , 2022 2022.0 Citations: 10
Hyperledger blockchain enabled secure medical record management with deep learning-based diagnosis model. Complex Intell. Syst. 8, 625–640 (2022) N Sammeta, L Parthiban doi. org/1 0 1 (0), 0 , 2022 2022.0 Citations: 5
Medical data analytics for secure multi-party-primarily based cloud computing utilizing homomorphic encryption N Sammeta, L Parthiban Journal of Scientific and Industrial Research (JSIR) 80 (08), 692-698 , 2021 2021.0 Citations: 5
Convolutional neural network-based MRI brain tumor classification system M Amanullah, J Visumathi, N Sammeta, M Ashok AIP Conference Proceedings 2519 (1), 030019 , 2022 2022.0 Citations: 3
Enhanced trusted third party for cyber security in multi cloud storage N Sammeta, R Jagadeesh Kannan, L Parthiban ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention … , 2014 2014.0 Citations: 3
Enhancing Reliability in Multi-Path Mobile Wireless Sensor Network J Visumathi, S Gurusubramani, SK Mouleeswaran, N Sammeta 2023 Third International Conference on Artificial Intelligence and Smart … , 2023 2023.0 Citations: 2
Blockchain-based privacy-preserving electronics healthcare records in healthcare 4.0 using proxy re-encryption L Parthiban, N Sammeta, ACJ Malathi, BE Samuel Integrating Blockchain and Artificial Intelligence for Industry 4.0 … , 2023 2023.0 Citations: 1
Vision Platform Lesion Quality Measurement of Accessible Endoscopic Images Using Machine Learning Techniques N Sammeta, N Duraichi, P Velmurugan, V Lalitha Journal of Physics: Conference Series 1964 (4), 042096 , 2021 2021.0 Citations: 1
Scheduling on Heterogeneous Hadoop System in Eucalyptus Private Cloud S Karthikeyan, N Sammeta, B Saravanan IJSRD-International Journal for Scientific Research & Development 3 (2 … , 2015 2015.0 Citations: 1
SECURED DATA STORAGE ENVIRONMENT IN CLOUD COMPUTING N Sammeta, DL Parthiban International Journal of Science Technology & Management, I SSN (Print … , 0 Citations: 1
Reinforcement Learning Based Adaptive Traffic Signal Control Using Deep Q-Network Variants R Nalla, VT Madhavareddy, BA Sajjala, N Sammeta 2026 Third International Conference on Networking and Communications (ICNWC … , 2026 2026.0
Hybrid Machine Learning Framework for Match Outcome and Player Performance Prediction in Team Sports VT Madhavareddy, R Nalla, N Sammeta 2026 Third International Conference on Networking and Communications (ICNWC … , 2026 2026.0
SECURE CLOUD DATA STORAGE USING CONTEXTUAL ENTROPIC CIPHERTEXT-POLICY ATTRIBUTE-BASED ENCRYPTION NS DHULIPUDI, B SAVADAM, A ZINENKO, N KRASNOSTANOVA, ... Journal of Theoretical and Applied Information Technology 103 (23) , 2025 2025.0
Automated Anomaly Detection and Threat Classification in Network Traffic MS Tupakula, N Sammeta 2025 2nd International Conference on Research Methodologies in Knowledge … , 2025 2025.0
Blockchain-Based Privacy-Preserving L Parthiban, N Sammeta, ACJ Malathi, BE Samuel Integrating Blockchain and Artificial Intelligence for Industry 4.0 … , 2023 2023.0
Evolutionary Algorithm with Machine Learning Enabled Color Texture Image Segmentation and Classification Model GU Maheswari, L Selvam, N Sammeta L. and SAMMETA, NARESH, Evolutionary Algorithm with Machine Learning Enabled … , 2022 2022.0
Publications
Hyperledger blockchain enabled secure medical record management with deep learning-based diagnosis model
Sammeta, Naresh and Parthiban, Latha
Article Complex & Intelligent Systems, Volume 8, Year 2022, Pages 625-640
Data Ownership and Secure Medical Data Transmission using Optimal Multiple Key-Based Homomorphic Encryption with Hyperledger Blockchain
Sammeta, Naresh and Parthiban, Latha
Article International Journal of Image and Graphics, Volume , Year 2021
Medical data analytics for secure multi-party-primarily based cloud computing utilizing homomorphic encryption
Sammeta, Naresh and Parthiban, Latha
Article Journal of Scientific and Industrial Research , Volume 80, Year 2021, Pages 692-698
