Adaptive Multi-Level Cloud Service Selection and Composition Using AHP–TOPSIS V. N. V. L. S. Swathi, G. Senthil Kumar, A. Vani Vathsala Applied Sciences Switzerland, 2025 The growing diversity of cloud services has made evaluating their relative merits in terms of price, functionality, and availability increasingly complex, particularly given the wide range of deployment alternatives and service capabilities. Cloud manufacturing often requires the integration of multiple services to accomplish user tasks, where the effectiveness of resource utilization and capacity sharing is closely tied to the adopted service composition strategy. This complexity, intensified by competition among providers, renders cloud service selection and composition an NP-hard problem involving multiple challenges, such as identifying suitable services from large pools, handling composition constraints, assessing the importance of quality-of-service (QoS) parameters, adapting to dynamic conditions, and managing abrupt changes in service and network characteristics. To address these issues, this study applies the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) in conjunction with Multi-Criteria Decision Making (MCDM) to evaluate and rank cloud services, while the Analytic Hierarchy Process (AHP) combined with the entropy weight method is employed to mitigate subjective bias and improve evaluation accuracy. Building on these techniques, a novel Adaptive Multi-Level Linked-Priority-based Best Method Selection with Multistage User-Feedback-driven Cloud Service Composition (MLLP-BMS-MUFCSC) framework is proposed, demonstrating enhanced service selection efficiency and superior quality of service compared to existing approaches.
A Cloud Integrity Verification and Validation Model Using Double Token Key Distribution Model V. Swathi, G. Kumar, A. Vathsala Mathematical and Computational Applications, 2025 Numerous industries have begun using cloud computing. Among other things, this presents a plethora of novel security and dependability concerns. Thoroughly verifying cloud solutions to guarantee their correctness is beneficial, just like with any other computer system that is security- and correctness-sensitive. While there has been much research on distributed system validation and verification, nobody has looked at whether verification methods used for distributed systems can be directly applied to cloud computing. To prove that cloud computing necessitates a unique verification model/architecture, this research compares and contrasts the verification needs of distributed and cloud computing. Distinct commercial, architectural, programming, and security models necessitate distinct approaches to verification in cloud and distributed systems. The importance of cloud-based Service Level Agreements (SLAs) in testing is growing. In order to ensure service integrity, users must upload their selected services and registered services to the cloud. Not only does the user fail to update the data when they should, but external issues, such as the cloud service provider’s data becoming corrupted, lost, or destroyed, also contribute to the data not becoming updated quickly enough. The data saved by the user on the cloud server must be complete and undamaged for integrity checking to be effective. Damaged data can be recovered if incomplete data is discovered after verification. A shared resource pool with network access and elastic extension is realized by optimizing resource allocation, which provides computer resources to consumers as services. The development and implementation of the cloud platform would be greatly facilitated by a verification mechanism that checks the data integrity in the cloud. This mechanism should be independent of storage services and compatible with the current basic service architecture. The user can easily see any discrepancies in the necessary data. While cloud storage does make data outsourcing easier, the security and integrity of the outsourced data are often at risk when using an untrusted cloud server. Consequently, there is a critical need to develop security measures that enable users to verify data integrity while maintaining reasonable computational and transmission overheads. A cryptography-based public data integrity verification technique is proposed in this research. In addition to protecting users’ data from harmful attacks like replay, replacement, and forgery, this approach enables third-party authorities to stand in for users while checking the integrity of outsourced data. This research proposes a Cloud Integrity Verification and Validation Model using the Double Token Key Distribution (CIVV-DTKD) model for enhancing cloud quality of service levels. The proposed model, when compared with the traditional methods, performs better in verification and validation accuracy levels.
Medical Imaging and Radiology: AI-Powered Diagnostics Swathi V. N. V. L. S., S. Sugapriya, Suraj Garg, Amruta Mahalle, Atharva Umakant Yewale, P. Selvakumar, Manjunath T. C. Applied AI and Computational Intelligence in Diagnostics and Decision Making, 2025 Medical imaging has become a cornerstone of human body without the need for invasive procedures. This non-invasive capability allows for early detection, precise diagnosis, and personalized worldwide. medical imaging in diagnostics, its wide range of applications, the numerous benefits it future potential of this technology. of imaging modalities, each designed to offer unique insights into the human body. Early X-ray technology, while groundbreaking at the time, was limited in its ability to provide detailed images of soft tissues. As a result, subsequent innovations (PET) have dramatically expanded the capabilities of medical imaging, allowing healthcare professionals to visualize bones, soft tissues, organs, blood vessels, and even cellular and molecular structures in unprecedented detail.The advent of digital imaging, which replaced film-based techniques, has further enhanced diagnostic accuracy, allowing for faster image acquisition, enhanced image clarity, and easier storage and sharing of medical images.
Resource-Efficient Named Entity Recognition: Evaluating Classical, BiLSTM-CRF, and Distilled Transformer Architectures K Deepthi Reddy, V.D.S Krishna, Priyanka, M Archana, Swathi V. N. V. L. S 2025 International Conference on Networks and Cryptology Netcrypt 2025, 2025 Named Entity Recognition (NER) is a critical task in the field of Natural Language Processing (NLP) which is aimed at recognizing and giving categories to the entities such as names, dates, and locations from raw text. While deep learning approaches, especially transformer models like BERT and RoBERTa, have set new benchmarks in performance, they often come with substantial computational costs, making them less feasible for real-time or resource-constrained environments. This research investigates both traditional and deep learning-based NER models, aiming to strike a balance between accuracy and computational efficiency. In particular, we compare conventional machine learning techniques, such as Conditional Random Fields (CRF) and Support Vector Machines (SVM), with more sophisticated neural network architectures, including Bidirectional Long Short-Term Memory (BiLSTM) combined with CRF, and compact transformer models like DistilBERT and ALBERT. Our evaluation utilizes the CoNLL-2003 dataset, a widely recognized benchmark featuring various entity categories across diverse contexts. To optimize the performance of classical models, we incorporate FastText embeddings for enhanced feature representation, while fine-tuning transformer models for domain-specific adaptations. The results indicate that BiLSTM-CRF with FastText embeddings achieves an F1-score of 88.5% on the CoNLL-2003 dataset, offering a promising solution for efficiency-oriented applications. On the other hand, DistilbERT shows a commendable F1-score of 91.2 % on CoNLL-2003 and 87.4% on OntoNotes 5.0, effectively balancing computational efficiency and high performance. Additionally, ALBERT, through its parameter-sharing strategy, attains an F1-score of 90.1 % on CoNLL-2003, with notably reduced memory consumption, further solidifying its potential for largescale, real-time NER tasks. This study highlights the trade-offs involved in selecting the right NER model for diverse practical scenarios, where both speed and accuracy are paramount.
Bio-Inspired Secure Routing in IoT-Enabled Smart Healthcare Networks Bandi Rambabu, Mallareddy Adudhodla, M Archana, K Deepthi Reddy, Vnvls Swathi Proceedings of 3rd International Conference on Sustainable Computing and Data Communication Systems Icscds 2025, 2025 The integration of Internet of Things in smart healthcare has introduced complex challenges in routing, where ensuring secure, energy-efficient, and low-latency data transmission is paramount. Traditional algorithms often fall short in addressing dynamic network conditions and emerging security threats. This research proposes a novel hybrid routing framework that synergizes the Adaptive Artificial Bee Colony (AABC) algorithm with a lightweight blockchain architecture to enhance routing intelligence and data security in IoT-enabled healthcare environments. The AABC algorithm dynamically optimizes routing paths based on energy, trust, and link quality metrics, while the blockchain layer provides decentralized trust management, immutable audit trails, and secure authentication with minimal resource overhead. To validate its effectiveness, the proposed method is benchmarked against three recent algorithms—Trust-based Secure Routing Protocol, Firefly Algorithm-based Secure Routing, and Quantum-Inspired Ant Colony Optimization. Simulation results demonstrate that this hybrid approach achieves a 10% higher packet delivery ratio, reduces end-to-end delay by 22.7%, and extends network lifetime by 29%, outperforming the comparative models across all key performance metrics. Furthermore, it exhibits resilience against common routing attacks such as blackhole and sinkhole, affirming its robustness. The proposed solution offers a scalable and secure routing paradigm well-suited for next-generation, mission-critical IoT healthcare systems.
Bio-Inspired Adaptive Starfish Optimization Approach to Efficient Load Balancing in Cloud Computing V. N. V. L. S. Swathi, Naseema Shaik, Baby Janaki Ketineni, S. Thillai Nayagi, Komuravelli Manisha Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2025, 2025 Effective load balancing is essential in cloud computing to optimize resource consumption, reduce reaction time, and improve system reliability. Conventional methods frequently falter under fluctuating workloads, resulting in suboptimal task distribution and heightened energy usage. This work presents an Improved Starfish Optimization Algorithm (ISFO) for cloud load balancing, motivated by the regenerative flexibility of starfish. The algorithm adaptively reallocates tasks based on real-time variations, guaranteeing optimal workload distribution and energy efficiency. The performance assessment of ISFO in comparison to Particle Swarm Optimization and Artificial Bee Colony reveals a 28.5% decrease in response time, a 48.5% enhancement in load variance, and a 25% reduction in energy consumption. These findings confirm ISFO as a scalable and efficient method for managing cloud workloads, improving overall system performance while decreasing operational expenses.
Secure Federated Learning Framework for Anomaly Detection in IoT Networks using Lightweight Cryptographic Hashing Mallareddy Adudhodla, M Archana, K. Deepthi Reddy, VNVLS Swathi, Bandi Rambabu Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications Icimia 2025, 2025 The increasing deployment of Internet of Things (IoT) devices in critical infrastructures has made anomaly detection a cornerstone for maintaining system integrity and security. However, centralized data processing introduces privacy risks and communication overheads. This paper proposes a secure federated learning (FL) framework designed for distributed IoT environments, enabling collaborative anomaly detection without exposing raw sensor data. To address privacy and integrity concerns, this work proposes to integrate a lightweight cryptographic hashing mechanism that ensures data authenticity and resistance against adversarial attacks during model aggregation. The framework is tailored for low-power IoT devices, optimizing both communication efficiency and model accuracy. Through extensive simulations on benchmark IoT anomaly datasets, the proposed approach demonstrates superior detection performance while maintaining low computational and communication costs. The results confirm that our framework strikes a practical balance between privacy preservation, scalability, and real-time responsiveness for secure IoT network monitoring.
An Energy-Efficient and Privacy-Preserving Routing Scheme for Sustainable IoT Health Systems M Archana, K. Deepthi Reddy, V N V L S Swathi, Bandi Rambabu, Mallareddy Adudhodla Proceedings of 3rd International Conference on Sustainable Computing and Data Communication Systems Icscds 2025, 2025 The growing dependence on IoT-based health systems for continuous patient monitoring and real-time diagnostics necessitates routing protocols that ensure both energy efficiency and strong privacy guarantees. In E2PR-HNet—an Energy-Efficient and Privacy-Preserving Routing scheme tailored for sustainable IoT Health Networks. E2PR-HNet integrates reinforcement learning for adaptive route selection, dynamic clustering to reduce redundant transmissions, and lightweight homomorphic encryption to secure sensitive medical data. Unlike existing protocols, it considers patient mobility and real-time context to enhance routing decisions without compromising on computational efficiency. To validate its effectiveness, compare E2PR-HNet against three state-of-the-art algorithms LEACH-C, PEGASIS, and SPEEDY within a simulated healthcare IoT environment featuring wearable and ambient biosensors. Experimental results demonstrate that E2PR-HNet outperforms state-of-the-art protocols by achieving a 30.6% improvement in network lifetime, a 35.6% reduction in average energy consumption, and a 23% enhancement in privacy preservation, as evaluated through a comparative privacy score. These gains are attributed to the use of reinforcement learning for intelligent route selection and lightweight homomorphic encryption for secure data transmission. Furthermore, it demonstrates reduced packet loss and improved latency under dynamic network conditions. These results highlight the superiority of E2PR-HNet in meeting the dual demands of sustainability and data security in next-generation e-health systems. The proposed approach not only contributes to the advancement of secure routing in IoT health applications but also lays the groundwork for future intelligent, privacy-aware health communication infrastructures.
Adaptive Multi-Level Cloud Service Selection and Composition Using AHP–TOPSIS V Swathi, G Senthil Kumar, A Vani Vathsala Applied Sciences 15 (20), 11010 , 2025 2025 Citations: 2
A Cloud Integrity Verification and Validation Model Using Double Token Key Distribution Model V Swathi, GS Kumar, AV Vathsala Mathematical and Computational Applications 30 (5), 114 , 2025 2025 Citations: 1
Optimized Neural Architecture for Wearable Health Devices using Edge-Level TinyML PKR Manellore, M Archana, KD Reddy, V Swathi, B Rambabu, ... 2025 4th International Conference on Innovative Mechanisms for Industry … , 2025 2025 Citations: 1
A Hybrid Deep Reinforcement and Swarm Optimization Strategy for Intelligent Cloud Service Composition VS Swathi, B Rambabu, M Adudhodla, M Archana, KD Reddy 2025 4th International Conference on Innovative Mechanisms for Industry … , 2025 2025
Secure Federated Learning Framework for Anomaly Detection in IoT Networks using Lightweight Cryptographic Hashing M Adudhodla, M Archana, KD Reddy, V Swathi, B Rambabu 2025 4th International Conference on Innovative Mechanisms for Industry … , 2025 2025
An Energy-Efficient and Privacy-Preserving Routing Scheme for Sustainable IoT Health Systems M Archana, KD Reddy, V Swathi, B Rambabu, M Adudhodla 2025 3rd International Conference on Sustainable Computing and Data … , 2025 2025 Citations: 3
Bio-Inspired Secure Routing in IoT-Enabled Smart Healthcare Networks B Rambabu, M Adudhodla, M Archana, KD Reddy, V Swathi 2025 3rd International Conference on Sustainable Computing and Data … , 2025 2025
Revolutionizing Blood Group Prediction: Machine Learning and Deep Learning-Driven Fingerprint Analysis V Swathi, Y Sneha, B Ramasree, PA Kumar, KS Prasad, B Sneha 2025 International Conference on Engineering Innovations and Technologies … , 2025 2025
Bio-Inspired Adaptive Starfish Optimization Approach to Efficient Load Balancing in Cloud Computing V Swathi, N Shaik, BJ Ketineni, ST Nayagi, K Manisha 2025 Third International Conference on Augmented Intelligence and … , 2025 2025
Design and Implementation of Theft Detection Using YOLO Based Object Detection Methodology and Gen AI for Enhanced Security Solutions KUK Reddy, F Shaik, V Swathi, P Sreevidhya, A Yashaswini, ... 2025 International Conference on Inventive Computation Technologies (ICICT … , 2025 2025 Citations: 4
Crayfish Optimization Algorithm for Enhanced Feature Selection Accuracy MA Bandi Rambabu, V Swathi, GVR Lakshmi, JR Reddy Computing and Machine Learning: Proceedings of CML 2024, Volume 2 2, 437 , 2025 2025
A metaheuristic optimization model based on corporate hierarchical dynamics for efficient and scalable feature selection in high-dimensional data B Rambabu, S Anupkant, M Archana, V Swathi, S Nimmala, A Mallareddy 2025 6th International Conference on Mobile Computing and Sustainable … , 2025 2025 Citations: 4
Energy-efficient and sustainable cluster-based routing in IoT based WSNs using metahueristic optimization M Archana, V Swathi, AV Vathsala, KD Reddy, B Rambabu, JR Reddy 2025 6th International Conference on Mobile Computing and Sustainable … , 2025 2025 Citations: 28
Heap-based Optimization with Corporate Rank Hierarchy for Enhanced Cluster Head Selection in IoT-Enabled Wireless Sensor Networks B Rambabu, S Anupkant, M Archana, V Swathi, A Mallareddy, S Nimmala 2024 4th International Conference on Technological Advancements in … , 2024 2024 Citations: 9
Enhancing post-quantum cryptography security with biocrypt quantum shield through nature-inspired machine learning K Venkatesh Sharma, R Betala, V Swathi International Conference on Multi-disciplinary Trends in Artificial … , 2024 2024 Citations: 2
Dynamic framework for optimized cloud service selection using adaptive weighting and enhanced TOPSIS V Swathi, V Nakka, S Farhana, M Archana, KD Reddy, AV Vathsala 2024 5th international conference for emerging technology (INCET), 1-6 , 2024 2024 Citations: 6
YOLO-SpectraWatch: A novel architecture for rapid suspect detection in crowded urban environments M Archana, M Supriya, JR Reddy, V Swathi, KD Reddy, AV Vathsala 2024 International Conference on Expert Clouds and Applications (ICOECA … , 2024 2024 Citations: 17
Crayfish optimization algorithm for enhanced feature selection accuracy for datasets B Rambabu, M Archana, V Swathi, GVR Lakshmi, JR Reddy International Conference on Computing and Machine Learning, 437-450 , 2024 2024 Citations: 4
AI-POWERED INTELLIGENT NUTRIENT DELIVERY SYSTEM FOR IOT-ENABLEHYDROPONIC FARMING BR M.Archana, VNVLS Swathi, A.Vani Vathsala IN Patent App. 202441009041 A , 2024 2024
SECURE STUDENT PROFILE MANAGEMENT WITH PRIVATE BLOCKCHAIN NFTS VS Bandi Rambabu, M.Archana, Mallareddy adudhodla IN Patent App. 202341085508 A , 2024 2024
MOST CITED SCHOLAR PUBLICATIONS
Energy-efficient and sustainable cluster-based routing in IoT based WSNs using metahueristic optimization M Archana, V Swathi, AV Vathsala, KD Reddy, B Rambabu, JR Reddy 2025 6th International Conference on Mobile Computing and Sustainable … , 2025 2025.0 Citations: 28
Deep learning-based brain tumor detection: An MRI segmentation approach V Swathi, K Sinduja, V Ravi Kumar, A Mahendar, GV Prasad, B Samya MATEC Web of Conferences 392, 01157 , 2024 2024.0 Citations: 18
YOLO-SpectraWatch: A novel architecture for rapid suspect detection in crowded urban environments M Archana, M Supriya, JR Reddy, V Swathi, KD Reddy, AV Vathsala 2024 International Conference on Expert Clouds and Applications (ICOECA … , 2024 2024.0 Citations: 17
New extractive method development of sitagliptin phosphate in API and its unit dosage forms by spectrophotometry N Monila, RP Pulla, H Shabad, V Swathi, J Rajasekhar, A Ramesh, ... IOSR Journal of Pharmacy and Biological Sciences 1 (6), 37-40 , 2012 2012.0 Citations: 11
Heap-based Optimization with Corporate Rank Hierarchy for Enhanced Cluster Head Selection in IoT-Enabled Wireless Sensor Networks B Rambabu, S Anupkant, M Archana, V Swathi, A Mallareddy, S Nimmala 2024 4th International Conference on Technological Advancements in … , 2024 2024.0 Citations: 9
Cloud Service Selection System Approach based on QoS Model: A Systematic Review V Swathi, V.N.V.L.S., Senthil Kumar, G. International Journal on Recent and Innovation Trends in Computing and … , 2023 2023.0 Citations: 9
Dynamic framework for optimized cloud service selection using adaptive weighting and enhanced TOPSIS V Swathi, V Nakka, S Farhana, M Archana, KD Reddy, AV Vathsala 2024 5th international conference for emerging technology (INCET), 1-6 , 2024 2024.0 Citations: 6
Real-time driver distraction detection using OpenCV and machine learning algorithms V Swathi, D Akhilesh, G Senthil Kumar, AV Vathsala Smart Computing Techniques and Applications: Proceedings of the Fourth … , 2021 2021.0 Citations: 6
Design and Implementation of Theft Detection Using YOLO Based Object Detection Methodology and Gen AI for Enhanced Security Solutions KUK Reddy, F Shaik, V Swathi, P Sreevidhya, A Yashaswini, ... 2025 International Conference on Inventive Computation Technologies (ICICT … , 2025 2025.0 Citations: 4
A metaheuristic optimization model based on corporate hierarchical dynamics for efficient and scalable feature selection in high-dimensional data B Rambabu, S Anupkant, M Archana, V Swathi, S Nimmala, A Mallareddy 2025 6th International Conference on Mobile Computing and Sustainable … , 2025 2025.0 Citations: 4
Crayfish optimization algorithm for enhanced feature selection accuracy for datasets B Rambabu, M Archana, V Swathi, GVR Lakshmi, JR Reddy International Conference on Computing and Machine Learning, 437-450 , 2024 2024.0 Citations: 4
An Energy-Efficient and Privacy-Preserving Routing Scheme for Sustainable IoT Health Systems M Archana, KD Reddy, V Swathi, B Rambabu, M Adudhodla 2025 3rd International Conference on Sustainable Computing and Data … , 2025 2025.0 Citations: 3
The oceans—unlocking the treasured drugs V Swathi, PR Pratap, N Monila, S Harshini, J RajaSekhar, A Ramesh International Journal of Pharmaceutical and Chemical Sciences 1, 1098-1105 , 2012 2012.0 Citations: 3
Adaptive Multi-Level Cloud Service Selection and Composition Using AHP–TOPSIS V Swathi, G Senthil Kumar, A Vani Vathsala Applied Sciences 15 (20), 11010 , 2025 2025.0 Citations: 2
Enhancing post-quantum cryptography security with biocrypt quantum shield through nature-inspired machine learning K Venkatesh Sharma, R Betala, V Swathi International Conference on Multi-disciplinary Trends in Artificial … , 2024 2024.0 Citations: 2
Development of educational model of the shake table to assess the building response under earthquake KP Reddy, V Swathi Citations: 2
A Cloud Integrity Verification and Validation Model Using Double Token Key Distribution Model V Swathi, GS Kumar, AV Vathsala Mathematical and Computational Applications 30 (5), 114 , 2025 2025.0 Citations: 1
Optimized Neural Architecture for Wearable Health Devices using Edge-Level TinyML PKR Manellore, M Archana, KD Reddy, V Swathi, B Rambabu, ... 2025 4th International Conference on Innovative Mechanisms for Industry … , 2025 2025.0 Citations: 1
A Hybrid Deep Reinforcement and Swarm Optimization Strategy for Intelligent Cloud Service Composition VS Swathi, B Rambabu, M Adudhodla, M Archana, KD Reddy 2025 4th International Conference on Innovative Mechanisms for Industry … , 2025 2025.0
Secure Federated Learning Framework for Anomaly Detection in IoT Networks using Lightweight Cryptographic Hashing M Adudhodla, M Archana, KD Reddy, V Swathi, B Rambabu 2025 4th International Conference on Innovative Mechanisms for Industry … , 2025 2025.0
