Cybersecurity strategies for smart cities M. Sindhuja, K. Alice, A. Maria Nancy, N. A. S. Vinoth Harnessing AI in Geospatial Technology for Environmental Monitoring and Management, 2024 In the era of rapid technological advancements, cities worldwide are rapidly evolving into “smart cities,” leveraging technology to enhance the lives of their residents. However, this digital transformation brings forth significant challenges and risks, particularly in terms of cybersecurity. This chapter comprehensively examines the realm of smart city cybersecurity, focusing on vital elements and essential security aspects. It begins with an introduction highlighting technology's pivotal role in modern urban development and identifying key components such as IoT devices and communication networks. The chapter delves into the spectrum of cyber threats that smart cities encounter, emphasizing risk mitigation to secure both infrastructure and data. It explores security challenges while elucidating the intricacies of technological integration. The chapter outlines proactive cybersecurity strategies tailored for smart cities and explores emerging trends, offering insights into the future landscape of cybersecurity within these cities.
SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation Maria Nancy A, K. Sathyarajasekaran Automatika, 2024 Brain Tumor Segmentation (BTS) and classification are important and growing research fields. Magnetic resonance imaging (MRI) is commonly used in the diagnosis of brain tumours owing to its low radiation exposure and high image quality. One of the current subjects in the field of medical imaging is how to quickly and precisely segment MRI scans of brain tumours. Unfortunately, most existing brain tumour segmentation algorithms use inadequate 2D picture segmentation methods and fail to capture the spatial correlation between features. In this study, we propose a segmentation model (SwinVNETR) Swin V-NetTRansformer-based architecture with a non-local block. This model was trained using the Brain Tumor Segmentation Challenge BraTS 2021 dataset. The Dice similarity coefficients for the enhanced tumour (ET), whole tumour (WT), and tumour core (TC) are 0.84, 0.91, and 0.87, respectively. By leveraging this methodology, we can segment brain tumours more accurately than ever before. In conclusion, we present the findings of our model through the application of the Grad-CAM methodology, an eXplainable Artificial Intelligence (XAI) technique utilized to elucidate the insights derived from the model, which helped in better understanding; doctors can better diagnose and treat patients with brain tumours.
Multi-Modal Explainability Evaluation for Brain Tumor Segmentation: Metrics MSFI International Journal of Intelligent Systems and Applications in Engineering, 2024
Secured Data Cluster Distribution and Propagation using IBBE and Attribute-based CPRE N. A. S. Vinoth, M. Sindhuja, A. Maria Nancy, R. Renuka Devi Proceedings 7th International Conference on Computing Methodologies and Communication Iccmc 2023, 2023 Cloud services are one of the key factors in many real time applications. It involves huge amounts of data transfer and thereby data privacy and security are the major concern in cloud computing applications. To address this issue, Secured Data Cluster Distribution and Propagation using IBBE and Attribute-Based CPRE (SDCDP), a model has been proposed in this manuscript where the data is being shared with a lot of users in groups with proprietary distribution data in which the owners of the data can simultaneously transmit the data that is being encrypted to a recipient batch by specifying the recipient's identity like a secure and easy way. The encryption technique used is a proxy kind of encryption which is a conditional based on merit and release time to ensure that only data distributors have access to the data access policy and distribute to other groups after release by providing a rewrite key to the cloud server. Data owners will have complete and timed-release controls used over distributed cipher texts, which are generated by the encryption conditions associated with the attributes and release time. The integrity and adequacy of proposed approach was demonstrated through theoretical analysis and experimental results to test this exhibit at each stage. Theoretical analysis and experimental results to assess the exhibition at each stage to show the adequacy of the proposed plan. To maintain the guarantee of the private resource batch transfer, Identity-Based Broadcast Encryption (IBBE) process will be used to perform data transmission. The owners of the data can publish their data that is encrypted for batch when the particular time arrives and the key that is needed that is given to the person will get an email or number that is different from others and name for a particular user. Therefore, there is a need to prove that the owner of the resource will transfer resources with other batch people in an easy and secure way.
Comparative Performance Analysis using Machine Learning for Churn Prediction in E-commerce R Alexander, A Maria Nancy, E Aswini, Parwaz Singh Sarao 2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023 New clients cost a business significantly more money in e-commerce than keeping its existing clients. Companies can boost consumer retention, which will result in more revenue and faster growth, by anticipating which customers will quit. There are several products and solutions in today’s competitive industry. Because of this, most clients are accustomed to quickly switching from one brand to another and from one supplier to another in their search for the best possible product or item to fulfill their requirements. This problem, known as “client churn,” affects e-commerce enterprises. Due to their ability to process large volumes of data and recognize complex patterns, machine learning algorithms have emerged as a powerful tool for predicting client churn in recent years. Using a publicly accessible dataset, the proposed model examines various machine learning methods for predicting customer churn in this study. Also, by using performance metrics, the proposed model compares how well different algorithms perform.
Revolutionizing Urban Mobility: An Overview of Intelligent Transportation Systems in Smart Cities A. Maria Nancy, N. A. S. Vinoth, K. Sargunan, A. Alex Rajesh, Kamal Alaskar, V Raviteja Kanakala Proceedings of the 5th International Conference on Inventive Research in Computing Applications Icirca 2023, 2023 In the present days, starting from phones to regularly utilized technologies all are getting smart. During this smart technological era, intelligent transportation system is one of the most expected project for developing a smart city. It is possible to assess the efficiency of Intelligent Transportation Systems (ITS) in a variety of urban environments by using statistical methods such as Analysis of Variance (ANOVA) and Regression Analysis in order to single out the factors that are most important. The primary objective of this study is to get an understanding of the impact that ITS has on travel times, congestion, and other metrics. According to the statistics, the population density of an area and the accessibility of road infrastructure are two of the most essential elements that must be present for an ITS to be effective. In light of these results, it is abundantly evident that prior to making any choice whatsoever, serious thought has to be given to both the advantages and the negatives of implementing ITS. The results of the research might be utilized by legislators and city planners in other nations to create transportation systems that are beneficial to the economic as well as the environment.
Enhanced Ransomware Detection Techniques using Machine Learning Algorithms G. Usha, P. Madhavan, Meenalosini Vimal Cruz, N A S Vinoth, Veena, Maria Nancy Proceedings of the 2021 4th International Conference on Computing and Communications Technologies Iccct 2021, 2021 A challenge that governments, enterprises as well as individuals are constantly facing is the growing threat of ransomware attacks. Ransomware is a type of malware that encrypts the user's files and then demands a huge sum of money from the user. This increasing complexity calls for more advancement and innovative ideas in defensive strategies used to tackle the problems. In this paper, firstly we discuss the existing research in the field of ransomware detection techniques and their shortcomings. Secondly, a juxtaposed study on various machine learning algorithms to detect ransomware attacks is compared for ransomware dataset. Thirdly, various behavioral data such as API Calls, Target files, Registry Operations, Signature, Network Accesses are collected for each ransomware and benign sample and the results are compared for various attributes to understand the behavior of the attack. In order to understand the behavior of the attack various Machine Learning Algorithms like KNN, Naïve Bayes, Random Forest, Decision Trees are used for training and testing the dataset.. Further optimization was done using hyper parameters to control the learning process. Finally, we have used the model(s) Accuracy, F1 Score, Precision and Recall to compare the results observed and suggesting how the roadmap for how efficiently the attacks can be prevented in future.
Hybrid approach for an efficient discovery of web services International Journal of Control Theory and Applications, 2016
Cost model for E-learning system International Journal of Control Theory and Applications, 2016
RECENT SCHOLAR PUBLICATIONS
Brain tumor segmentation and classification using transfer learning based CNN model with model agnostic concept interpretation AM Nancy, R Maheswari Multimedia Tools and Applications 84 (5), 2509-2538 , 2025 2025.0 Citations: 14
Cybersecurity Strategies for Smart Cities M Sindhuja, K Alice, AM Nancy, NAS Vinoth Harnessing AI in Geospatial Technology for Environmental Monitoring and … , 2025 2025.0 Citations: 2
SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation A Maria Nancy, K Sathyarajasekaran Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i … , 2024 2024.0 Citations: 5
An immersive method of loci with dynamic objects using virtual reality for learning KP Grandhi, A Singh, M Nancy AIP Conference Proceedings 3075 (1), 020093 , 2024 2024.0 Citations: 1
Detecting pattern in crime analysis using machine learning A Sharma, R Agarwal, AM Nancy AIP Conference Proceedings 3075 (1), 020235 , 2024 2024.0 Citations: 1
Comparative Performance Analysis using Machine Learning for Churn Prediction in E-commerce R Alexander, AM Nancy, E Aswini, PS Sarao 2023 2nd International Conference on Automation, Computing and Renewable … , 2024 2024.0 Citations: 5
Multi-modal explainability evaluation for brain tumor segmentation: metrics msfi Maria Nancy A, Sathyarajasekaran, K International Journal of Intelligent Systems and Applications in Engineering … , 2024 2024.0 Citations: 3
Revolutionizing Urban Mobility: An Overview of Intelligent Transportation Systems in Smart Cities AM Nancy, NAS Vinoth, K Sargunan, AA Rajesh, K Alaskar, VR Kanakala 2023 5th International Conference on Inventive Research in Computing … , 2023 2023.0 Citations: 3
Secured Data Cluster Distribution and Propagation using IBBE and Attribute-based CPRE NAS Vinoth, M Sindhuja, AM Nancy, RR Devi 2023 7th International Conference on Computing Methodologies and … , 2023 2023.0
The future of artificial intelligence in digital forensics: A revolutionary approach I Saxena, G Usha, NAS Vinoth, S Veena, M Nancy Artificial Intelligence and Blockchain in Digital Forensics, 133-151 , 2023 2023.0 Citations: 11
Enhanced ransomware detection techniques using machine learning algorithms G Usha, P Madhavan, MV Cruz, NAS Vinoth, M Nancy 2021 4th International Conference on Computing and Communications … , 2021 2021.0 Citations: 22
A secure cloud based image processing technique G Usha, NAS Vinoth, Veena, M Nancy, D Evangeline AIP Conference Proceedings 2277 (1), 130004 , 2020 2020.0
Fraud detection in credit card transaction using hybrid model AM Nancy, GS Kumar, S Veena, NAS Vinoth, M Bandyopadhyay AIP Conference Proceedings 2277 (1), 130010 , 2020 2020.0 Citations: 13
Effective system for software requirement management S Veena, NAS Vinoth, AM Nancy, GS Kumar, RT Teja AIP Conference Proceedings 2277 (1), 240010 , 2020 2020.0 Citations: 1
Recommendation of web services using implicit feedback and collaborative filtering technique GS Kumar, AM Nancy, S Veena, PK Harika, S Mukesh AIP Conference Proceedings 2277 (1), 130008 , 2020 2020.0
A review on unstructured data in medical data AM Nancy, R Maheswari J. Crit. Rev 7, 2202-2208 , 2020 2020.0 Citations: 25
Query Optimization in Universal Description Discovery and Integration for Effective Web Services Discovery GS Kumar, AM Nancy, S Soral, A Shrivastava Journal of Computational and Theoretical Nanoscience 15 (6-7), 2420-2424 , 2018 2018.0 Citations: 1
Audio based emotion recognition using Mel frequency Cepstral coefficient and support vector machine AM Nancy, GS Kumar, P Doshi, S Shaw Journal of Computational and Theoretical Nanoscience 15 (6-7), 2255-2258 , 2018 2018.0 Citations: 17
Cost model for E-learning system TS Angel, GS Kumar, S Selvakumarasamy, AM Nancy, S Veena International Journal of Control Theory and Applications 9, 65-70 , 2016 2016.0 Citations: 3
Enhanced Deep Learning Method for Detecting and Segmenting Multi Modality Brain Tumor in Volumetric Imaging A Maria Nancy Vellore , 0
MOST CITED SCHOLAR PUBLICATIONS
A review on unstructured data in medical data AM Nancy, R Maheswari J. Crit. Rev 7, 2202-2208 , 2020 2020.0 Citations: 25
Enhanced ransomware detection techniques using machine learning algorithms G Usha, P Madhavan, MV Cruz, NAS Vinoth, M Nancy 2021 4th International Conference on Computing and Communications … , 2021 2021.0 Citations: 22
Audio based emotion recognition using Mel frequency Cepstral coefficient and support vector machine AM Nancy, GS Kumar, P Doshi, S Shaw Journal of Computational and Theoretical Nanoscience 15 (6-7), 2255-2258 , 2018 2018.0 Citations: 17
Brain tumor segmentation and classification using transfer learning based CNN model with model agnostic concept interpretation AM Nancy, R Maheswari Multimedia Tools and Applications 84 (5), 2509-2538 , 2025 2025.0 Citations: 14
Fraud detection in credit card transaction using hybrid model AM Nancy, GS Kumar, S Veena, NAS Vinoth, M Bandyopadhyay AIP Conference Proceedings 2277 (1), 130010 , 2020 2020.0 Citations: 13
The future of artificial intelligence in digital forensics: A revolutionary approach I Saxena, G Usha, NAS Vinoth, S Veena, M Nancy Artificial Intelligence and Blockchain in Digital Forensics, 133-151 , 2023 2023.0 Citations: 11
SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation A Maria Nancy, K Sathyarajasekaran Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i … , 2024 2024.0 Citations: 5
Comparative Performance Analysis using Machine Learning for Churn Prediction in E-commerce R Alexander, AM Nancy, E Aswini, PS Sarao 2023 2nd International Conference on Automation, Computing and Renewable … , 2024 2024.0 Citations: 5
Multi-modal explainability evaluation for brain tumor segmentation: metrics msfi Maria Nancy A, Sathyarajasekaran, K International Journal of Intelligent Systems and Applications in Engineering … , 2024 2024.0 Citations: 3
Revolutionizing Urban Mobility: An Overview of Intelligent Transportation Systems in Smart Cities AM Nancy, NAS Vinoth, K Sargunan, AA Rajesh, K Alaskar, VR Kanakala 2023 5th International Conference on Inventive Research in Computing … , 2023 2023.0 Citations: 3
Cost model for E-learning system TS Angel, GS Kumar, S Selvakumarasamy, AM Nancy, S Veena International Journal of Control Theory and Applications 9, 65-70 , 2016 2016.0 Citations: 3
Cybersecurity Strategies for Smart Cities M Sindhuja, K Alice, AM Nancy, NAS Vinoth Harnessing AI in Geospatial Technology for Environmental Monitoring and … , 2025 2025.0 Citations: 2
An immersive method of loci with dynamic objects using virtual reality for learning KP Grandhi, A Singh, M Nancy AIP Conference Proceedings 3075 (1), 020093 , 2024 2024.0 Citations: 1
Detecting pattern in crime analysis using machine learning A Sharma, R Agarwal, AM Nancy AIP Conference Proceedings 3075 (1), 020235 , 2024 2024.0 Citations: 1
Effective system for software requirement management S Veena, NAS Vinoth, AM Nancy, GS Kumar, RT Teja AIP Conference Proceedings 2277 (1), 240010 , 2020 2020.0 Citations: 1
Query Optimization in Universal Description Discovery and Integration for Effective Web Services Discovery GS Kumar, AM Nancy, S Soral, A Shrivastava Journal of Computational and Theoretical Nanoscience 15 (6-7), 2420-2424 , 2018 2018.0 Citations: 1
Secured Data Cluster Distribution and Propagation using IBBE and Attribute-based CPRE NAS Vinoth, M Sindhuja, AM Nancy, RR Devi 2023 7th International Conference on Computing Methodologies and … , 2023 2023.0
A secure cloud based image processing technique G Usha, NAS Vinoth, Veena, M Nancy, D Evangeline AIP Conference Proceedings 2277 (1), 130004 , 2020 2020.0
Recommendation of web services using implicit feedback and collaborative filtering technique GS Kumar, AM Nancy, S Veena, PK Harika, S Mukesh AIP Conference Proceedings 2277 (1), 130008 , 2020 2020.0
Enhanced Deep Learning Method for Detecting and Segmenting Multi Modality Brain Tumor in Volumetric Imaging A Maria Nancy Vellore , 0
