Shabana Samsher Pathan

@stvincentngp.edu.in

Assistant Professor and Department of Information Technology
St. Vincent Pallotti College of Engineering &Technology

Shabana Samsher Pathan


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https://researchid.co/spathan123

EDUCATION

BE(CE), MTech(CSE), Phd (CSE)

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Engineering
9

Scopus Publications

57

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Integrating AI and Digital Twins for Real- Time Fault Identification in Wind Turbines
    Deepak Gupta, K Balakrishna Reddy, K. Neelima, Shabana Samsher Pathan, Bali Ram Gupta, P. Rajeshwari, Pranda P. Gupta
    AI Powered Analysis Modeling and Monitoring of Wind Energy Systems, 2026
    The integration of artificial intelligence (AI) and digital twin technologies has revolutionized condition monitoring and fault detection in industrial systems, particularly in wind energy applications. This chapter explores the synergistic relationship between AI algorithms and digital twin frameworks for enhanced predictive maintenance strategies. Digital twins create virtual replicas of physical systems, enabling real-time monitoring, simulation, and analysis of operational conditions. When combined with AI techniques such as machine learning, deep learning, and neural networks, these systems can predict failures, optimize maintenance schedules, and reduce operational costs. The chapter examines current methodologies, implementation challenges, and future prospects of AI-enhanced digital twins in condition monitoring. Case studies from wind turbine applications demonstrate the practical benefits of this integrated approach, showing improvements in fault detection accuracy, reduced downtime, and enhanced system reliability.
  • Smart Healthcare Ecosystems A Deep Dive into Applications, Advancements, and Ethical Considerations of Deep Learning Technologies
    Prashant Khobragade, Samir N. Ajani, Ashwini Yerlekar, Abhijit Titarmare, Shabana S. Pathan
    Applied Machine Learning in Healthcare Case Based Approach, 2025
    Research into deep learning technologies analyses developments in neural network structures, natural language processing, edge computing, and integrating wearable devices, and the Internet of Things. Ethics is important, addressing concerns about biased algorithms, privacy, data ownership, and legal compliance. The chapter offers a comprehensive view of competent care complex nature by critically reviewing challenges like technical difficulties and financial factors. The chapter demonstrates the real impacts of competent healthcare on patients and healthcare efficiency by combining real examples, success stories, and applications. A look at upcoming changes, new trends, and how artificial intelligence will affect healthcare is included. After introducing innovative healthcare systems, the chapter explores the fundamental parts and development of these systems, creating a base to thoroughly analyze deep learning’s uses in healthcare. This chapter explains the various ways deep learning is changing medicine, ranging from drug creation and customized care to more accurate diagnoses and predictive analysis. This chapter provides a complete overview of the applications, advances, and important considerations of including deep learning technologies in today’s innovative healthcare systems.
  • DESIGN OF AN ITERATIVE PREDICTIVE CARBON-AWARE RESOURCE SCHEDULING METHOD FOR CLOUD–FOG–EDGE ECOSYSTEMS THROUGH MULTI-STAGE ENERGY AND THERMAL OPTIMIZATIONS
    Harshala Shingne
    International Journal of Applied Mathematics, 2025
    Growing computational demand across cloud, fog, and edge infrastructures is intensifying energy consumption and carbon emissions, yet existing scheduling frameworks typically treat power draw as a static, short-term metric. This narrow view struggles with the fluid geography of modern workloads bursty, migratory, and thermally entangled leading to inefficient energy use and weak carbon accountability. To confront these gaps, we introduce a five-stage resource–energy orchestration model that explicitly intertwines spatio-temporal energy prediction, quantum Inspired optimization, live task migration, thermal dynamics, and carbon-economic feedback. The pipeline begins with Multi-Modal Spatio-Temporal Energy Profiler (MSTEP), which continuously profiles heterogeneous nodes through tensor decomposition and graph-temporal convolution, forecasting per-node energy use over 5-second horizons and improving power-capping accuracy by about 12%. Its predictive map drives the Energy-Aware Quantum Inspired Resource Orchestrator (EQUIRO), a classical Hamiltonian optimizer borrowing from quantum annealing to escape local minima, cutting energy consumption by roughly 18% while lowering latency. The resulting plan is enacted by Reinforcement-Driven Adaptive Task Migrator (R-ATM), where policy-gradient agents treat migration as a continuous-time control problem, reducing unnecessary moves by ~20%. To prevent thermal hotspots created by such migrations, Cross-Layer Thermal-Aware Cooling Optimizer (CLTACO) applies physics Informed neural networks to couple micro-scale thermal diffusion with macro cooling strategy, yielding around 15% better power usage effectiveness. Finally, Carbon Impact Feedback and Economic Optimizer (CIFEO) close the loop by translating operational data into carbon-weighted pricing and deferral schedules, achieving near-neutral or positive margins with an estimated 25% cut in carbon footprint. This integrated architecture demonstrates how predictive, cross-layer intelligence can transform cloud-fog-edge scheduling from reactive energy management into proactive carbon-aware economics, pointing toward greener and more economically resilient distributed computing.
  • Pirate Math Quest: An Engaging Educational Game for Teaching basic Mathematics to Young Learners
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Comparative Analysis of Decision Tree with Existing Approaches for Improving Prediction Performance
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • Self-Supervised Transformer Networks: Unlocking New Possibilities for Label-Free Data
    Bhagyashree S. Madan
    Panamerican Mathematical Journal, 2024
    In machine learning, self-supervised transformer networks have become a new way of doing things, especially when it comes to handling and understanding huge amounts of data that hasn't been labeled. This way of doing things uses the structure of the data itself to make representations that make sense without the need for specific names. With the help of advanced techniques like masked language modeling and contrastive learning, these networks can find complex patterns and connections in the data. Self-supervised learning is flexible enough to be used in many areas, such as natural language processing, computer vision, and voice processing. This shows how adaptable and useful it is. The most interesting thing about self-supervised transformer networks is that it can make supervision signs right from the data. This feature makes it much less important to use named datasets, which are hard to find and cost a lot. So, the framework not only makes strong machine learning methods easier for everyone to use, but it also makes jobs easier to do when labeled data is scarce or not available at all. Using transformer structures, which are known for being able to handle long-range relationships and environmental knowledge, makes self-supervised learning even more useful. New developments in self-supervised transformer networks have made big improvements in a number of measures, showing that it can compete with or even beat standard supervised methods. This amazing progress is due to new ways of designing models, training them, and finding the best ways to use the data. For better understanding, using techniques like transfer learning and fine-tuning has made it easier for models that have already been trained to adapt to specific tasks, which has led to better performance in a wide range of applications. Self-supervised transformer networks are very important for dealing with the problems that come up with adapting to new domains and generalizing. These models are better at handling changes in the distribution of inputs because it learn stable representations that catch the underlying structure of data. This trait is especially useful in the real world, where data may be very different from the training set. Self-supervised transformer networks have effects that go beyond just making them work better. These networks open up new areas for study and use by making label-free data useful. Researcher make it possible for progress to be made in areas like active learning and semi-supervised learning. This area of machine learning is always changing, and studying self-supervised transformer networks could help find new ways to solve hard problems.
  • Design an Optimal Decision Tree based Algorithm to Improve Model Prediction Performance
    Shabana Pathan, Sanjeev Kumar Sharma
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
    Performance of decision trees is assessed by prediction accuracy for unobserved occurrences. In order to generate optimised decision trees with high classification accuracy and smaller decision trees, this study will pre-process the data. In this study, some decision tree components are addressed and enhanced. The algorithms should produce precise and ideal decision trees in order to increase prediction performance. Additionally, it hopes to create a decision tree algorithm with a tiny global footprint and excellent forecast accuracy. The typical decision tree-based technique was created for classification purposes and is used with various kinds of uncertain information. Prior to preparing the dataset for classification, the uncertain dataset was first processed through missing data treatment and other uncertainty handling procedures to produce the balanced dataset. Three different real-time datasets, including the Titanic dataset, the PIMA Indian Diabetes dataset, and datasets relating to heart disease, have been used to test the proposed algorithm. The suggested algorithm's performance has been assessed in terms of the precision, recall, f-measure, and accuracy metrics. The outcomes of suggested decision tree and the standard decision tree have been contrasted. On all three datasets, it was found that the decision tree with Gini impurity optimization performed remarkably well.
  • An advanced cloud based framework for privacy and security in medical data using cryptographic method
    Rashmi Welekar, Farhadeeba Shaikh, Abhijit Chitre, Kirti Wanjale, Shabana Pathan, Anil Kumar
    Journal of Discrete Mathematical Sciences and Cryptography, 2023
    The exchange of medical information has been drastically altered by patient-centered developments such as personal health records (PHR). By giving patients a place to handle their own PHR on a unified transactional platform, personal health record (PHR) services increase the efficiency with which medical information may be kept, accessed, and transferred. With the ultimate objective of providing patients with total surveillance under data, our findings is focused on creating a state-of-the-art infrastructure for the safe transfer of personal health data via cloud computing. Patients have the option of encrypting their PHR files, which provides an additional layer of security and allows them to set access control limits such as who has access to their files and to what degree. When data is encrypted in the cloud, only approved users may access it. Using cloud-based platforms to share health records raises concerns over confidentiality and privacy, which are addressed by the proposed method. Patients may still benefit from data interchange for the goal of better healthcare thanks to the framework’s provision of an encrypted PHR file option. This framework may accommodate attribute-based encryption (ABE) and other kinds of granular security. These measures ensure that people may continue to have access to, and make changes to, their own medical data, even when they are stored on the cloud. This article presents research that attempts to meet the demands of patients while also providing a safe method of transferring individual health information through cloud computing.
  • An Integrative Approach to Healthcare Enhancement through Internet of Things, Artificial Intelligence and Smart City Innovations
    Et al. Archana V. Potnurwar
    Journal of Electrical Systems, 2023
    In order to improve healthcare outcomes, this study investigates the synergistic potential of combining Internet of Things (IoT), artificial intelligence (AI), and smart city innovations. The study examines how the confluence of various technologies creates a cohesive ecosystem, enhancing patient care, accessibility, and overall system efficiency against the backdrop of current healthcare difficulties. Using a conceptual framework, the study tackles privacy and data security issues in this networked healthcare environment. Methodologically, case studies and surveys are used in conjunction with quantitative and qualitative methods to examine the effects of this integration. Initial results show better patient outcomes, more accessibility to healthcare, and higher operational effectiveness. The consequences, difficulties, and moral issues surrounding the integration are all covered throughout the conversation. In addition to offering insightful information to the healthcare field, the research suggests directions for further investigation. In summary, this research proposes a holistic strategy for improving healthcare by carefully combining IoT, AI, and smart city innovations.

RECENT SCHOLAR PUBLICATIONS

  • A Smart QR-based Food Ordering System with Dynamic Menu and Checkout Integration.
    S Pathan, S Daultani, O Dhoble, V Pillai
    Grenze International Journal of Engineering & Technology (GIJET) 12 (Part2 … , 2026
    2026
  • Digital twins in metaverse
    S Pathan, S Dubey, A Katre, Y Sharma
    Technological Innovations & Applications in Industry 4.0, 116-120 , 2025
    2025
  • The Creative Web: Merging Design and Development for Engaging Online Content
    S Pathan, S Daultani, O Dhoble, V Pillai
    International Journal of Innovative Research in Engineering 6 (2), 86-89 , 2025
    2025
    Citations: 1
  • Comparative Analysis of Decision Tree with Existing Approaches for Improving Prediction Performance.
    S Pathan, SK Sharma
    Grenze International Journal of Engineering & Technology (GIJET) 10 , 2024
    2024
  • An Interactive approach to Healthcare Enhancement through Internet of Things,Artifical Intelligence and Smart city Innovations
    S Pathan
    Journal of Electrical Systems 19 (2), 9-17 , 2024
    2024
  • An Integrative Approach to Healthcare Enhancement through Internet of Things, Artificial Intelligence and Smart City Innovations.
    AV Potnurwar, VK Bongirwar, SS Pathan, PM Kothoke, S Dongre, ...
    Journal of Electrical Systems 19 (2) , 2023
    2023
    Citations: 7
  • An advanced cloud based framework for privacy and security in medical data using cryptographic method
    S Pathan
    Journal of Discrete Mathematical Sciences & Cryptography 26 (5), 1585-1596 , 2023
    2023
  • Design an Optimal Decision Tree based Algorithm to Improve Model Prediction Performance
    S Pathan
    International Journal on Recent and Innovation Trends in Computing and … , 2023
    2023
    Citations: 3
  • Are AI and chat bots services effects the psychology of users in banking services and financial sector
    SO Kediya, S Dhote, DK Singh, VS Bidve, S Pathan, A Suchak
    Journal for ReAttach Therapy and Developmental Diversities 6 (2), 191-197 , 2023
    2023
    Citations: 40
  • Comparative Analysis of Scalability Approaches using Data Mining Methods on Health Care Datasets.
    SS Uparkar, SN Dhote, SS Pathan, PD Shobhane, D Das
    International Journal of Next-Generation Computing 13 (5) , 2022
    2022
  • Comparative Analysis of Scalability Approaches using Data Mining Methods on Health Care Datasets
    S Pathan
    2nd International Conference on Innovative Computing and Applications(ICICA’22) , 2022
    2022
  • A Review: Classification Based Decision Tree Induction
    S Pathan
    NeuroQuantology 20 (19), 2764-2769 , 2022
    2022
  • A Framework For Uncertainty Identification And Classification Using Decision Tree
    S Pathan
    Journal of Northeastern University 25 (4), 1608-1617 , 2022
    2022
  • Word Phrase Alignment Techniques
    S Pathan
    International Journal for Research in EngineeringApplication and Management … , 2022
    2022
  • AN APPROACH TO DECISION TREE INDUCTION FOR CLASSIFICATION
    S Pathan
    International Conference on Sustainable Innovation in Science and Technology … , 2021
    2021
    Citations: 5
  • Cake Ordering Online Application
    S Pathan
    International Journal of All Research Education & Scientific Methods(IJARESM … , 2021
    2021
  • ECommerce Application
    S Pathan
    International Journal of All Research Education & Scientific Methods(IJARESM … , 2021
    2021
  • Desktop Voice Assistant
    S Pathan
    International Research Journal of Engineering &technology(IRJET), 7 (4), 913-916 , 2020
    2020
  • Android Application Service Call Management System
    S Pathan
    International Research Journal of Engineering &technology(IRJET), 7 (4) , 2020
    2020
  • A Survey on Creation of Hindi-Spell Checker to Improve the Processing of OCR
    S Pathan, N Khuje, P Kolhe
    International Journal of Research in Engineering, Science and Management 2 … , 2019
    2019
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Are AI and chat bots services effects the psychology of users in banking services and financial sector
    SO Kediya, S Dhote, DK Singh, VS Bidve, S Pathan, A Suchak
    Journal for ReAttach Therapy and Developmental Diversities 6 (2), 191-197 , 2023
    2023
    Citations: 40
  • An Integrative Approach to Healthcare Enhancement through Internet of Things, Artificial Intelligence and Smart City Innovations.
    AV Potnurwar, VK Bongirwar, SS Pathan, PM Kothoke, S Dongre, ...
    Journal of Electrical Systems 19 (2) , 2023
    2023
    Citations: 7
  • AN APPROACH TO DECISION TREE INDUCTION FOR CLASSIFICATION
    S Pathan
    International Conference on Sustainable Innovation in Science and Technology … , 2021
    2021
    Citations: 5
  • Design an Optimal Decision Tree based Algorithm to Improve Model Prediction Performance
    S Pathan
    International Journal on Recent and Innovation Trends in Computing and … , 2023
    2023
    Citations: 3
  • The Creative Web: Merging Design and Development for Engaging Online Content
    S Pathan, S Daultani, O Dhoble, V Pillai
    International Journal of Innovative Research in Engineering 6 (2), 86-89 , 2025
    2025
    Citations: 1
  • A Survey on Creation of Hindi-Spell Checker to Improve the Processing of OCR
    S Pathan, N Khuje, P Kolhe
    International Journal of Research in Engineering, Science and Management 2 … , 2019
    2019
    Citations: 1
  • A Smart QR-based Food Ordering System with Dynamic Menu and Checkout Integration.
    S Pathan, S Daultani, O Dhoble, V Pillai
    Grenze International Journal of Engineering & Technology (GIJET) 12 (Part2 … , 2026
    2026
  • Digital twins in metaverse
    S Pathan, S Dubey, A Katre, Y Sharma
    Technological Innovations & Applications in Industry 4.0, 116-120 , 2025
    2025
  • Comparative Analysis of Decision Tree with Existing Approaches for Improving Prediction Performance.
    S Pathan, SK Sharma
    Grenze International Journal of Engineering & Technology (GIJET) 10 , 2024
    2024
  • An Interactive approach to Healthcare Enhancement through Internet of Things,Artifical Intelligence and Smart city Innovations
    S Pathan
    Journal of Electrical Systems 19 (2), 9-17 , 2024
    2024
  • An advanced cloud based framework for privacy and security in medical data using cryptographic method
    S Pathan
    Journal of Discrete Mathematical Sciences & Cryptography 26 (5), 1585-1596 , 2023
    2023
  • Comparative Analysis of Scalability Approaches using Data Mining Methods on Health Care Datasets.
    SS Uparkar, SN Dhote, SS Pathan, PD Shobhane, D Das
    International Journal of Next-Generation Computing 13 (5) , 2022
    2022
  • Comparative Analysis of Scalability Approaches using Data Mining Methods on Health Care Datasets
    S Pathan
    2nd International Conference on Innovative Computing and Applications(ICICA’22) , 2022
    2022
  • A Review: Classification Based Decision Tree Induction
    S Pathan
    NeuroQuantology 20 (19), 2764-2769 , 2022
    2022
  • A Framework For Uncertainty Identification And Classification Using Decision Tree
    S Pathan
    Journal of Northeastern University 25 (4), 1608-1617 , 2022
    2022
  • Word Phrase Alignment Techniques
    S Pathan
    International Journal for Research in EngineeringApplication and Management … , 2022
    2022
  • Cake Ordering Online Application
    S Pathan
    International Journal of All Research Education & Scientific Methods(IJARESM … , 2021
    2021
  • ECommerce Application
    S Pathan
    International Journal of All Research Education & Scientific Methods(IJARESM … , 2021
    2021
  • Desktop Voice Assistant
    S Pathan
    International Research Journal of Engineering &technology(IRJET), 7 (4), 913-916 , 2020
    2020
  • Android Application Service Call Management System
    S Pathan
    International Research Journal of Engineering &technology(IRJET), 7 (4) , 2020
    2020