Data Analytics, Artificial Intelligence, Information Systems
264
Scopus Publications
Scopus Publications
Design of an intelligent IoT enabled healthcare responsive framework for emergency scenarios Sushruta Mishra, Hrudaya Kumar Tripathy, Himansu Das, Mohammad Shabaz, Surbhi Bhatia Khan, Ahlam Almusharraf Scientific Reports, 2026 Timely assessment and response to critical health scenarios are important for survival of patients. Smart wearables help in non-interrupted patient tracking whereas advanced intelligent models enhance early risk detection. But nowadays, heavy road traffic is causing delays in arrival of ambulance service thereby decreasing emergency service efficiency. Existing frameworks either address patient health monitoring or traffic control in a separate manner. Thus, a model which can integrate risk analysis and adaptive traffic management for ambulance service is lacking. The aim of this research is to design an intelligence based responsive health model for patients needing emergency help by tracking vital metrics with an advanced risk predictive model. Real time traffic support is also desirable to reduce ambulance service delay. The framework consists of a smart wristband 'BioTrace-G' to collect patient's vital signs. This data is sent to the patient's smartphone where an application 'E-response' is configured. The application hosts GA-DNN (Genetic algorithm-Deep neural network) model used for feature optimization and critical risk level prediction. When the detected risk is high or mid type, emergency ambulance service is automatically triggered which is supported by a traffic unit to facilitate faster emergency service. The model upon evaluation recorded a promising outcome. The mean risk prediction accuracy with GA-DNN was 95.2% in context to sensor readings while it is 94.2% when number of patients are considered. The computed mean inference latency was only 57.8 s. Also, the GA-DNN generated the least mean false negatives and false positives of 6.9% and 13.4% respectively. The framework optimized the patients prioritization and ambulance dispatch delay as compared to conventional approach. The model with integrated traffic support showed better results when validated against metrics like response delay, number of signal stops and ambulance speed. Hence, the integrated responsive framework serves as a prototype for early risk identification and categorization with reduced response delay and enhanced patient care.
Examining circular economy principles to transform public sector pharmaceutical supply chains for flexibility: A multi-method approach Angel Singh, Bharti Ramtiyal, Vipul Jain, Lokesh Vijayvargy, Surbhi B. Khan Technological Forecasting and Social Change, 2026 Digital transformation enhances sustainability in public sector pharmaceutical supply chains by optimizing sources, improving traceability, and promoting circular economy principles. This research converges on the circular economies principles to transform the Indian public sector pharmaceutical supply chains for flexibility by a mixed method approach. This study uses the Technology-Organization-Environment (TOE) Framework integrated with a Delphi study to identify and prioritize the key enablers of digital transformation and circular economy principles to enhance supply chain flexibility in the Indian public sector pharmaceutical services. The relative importance of each identified enablers was measured using the best worst method to confirm focused resource allocation and strategic prioritization for optimal impact in public sector pharmaceutical services. The study further uses the Bayesian network approach to determine the most prominent enabler of digital transformation and circular economy principles, which enhance supply chain flexibility in the Indian public sector pharmaceutical services. This research highlights that technology-driven investments enhance supply chain flexibility, supplier collaboration is critical for continuous circular economy transitions, and strong regulatory policies are needed for effective adoption. These insights provide policymakers and practitioners with a comprehensive framework to drive sustainable transformation and operational excellence in the public sector pharmaceutical sector services. • Mixed methods integrate TOE & Delphi to identify and classify digital transformation & circular economy enablers in pharma SCs. • The Best-Worst Method evaluates the strategic prioritization of each enabler in public pharma services for better SCF. • Bayesian analysis reveals tech investments and supplier collaboration as key enablers for sustainable change. • Strong regulatory frameworks drive effective adoption of circular economy practices in public pharma services.
TransDiff-HiSeg: An Adaptive Transformer-Diffusion Framework for Medical Image Segmentation in Sustainable Healthcare Pratishtha Verma, Hema Latha Undam, Naween Kumar, Surbhi Bhatia Khan, Oumaima Saidani, Mohammad Tabrez Quasim Computational Intelligence, 2026 Medical image segmentation is pivotal in clinical diagnosis and treatment planning. However, conventional CNN‐based methods often struggle with capturing global context and handling noise, especially in complex or ambiguous anatomical regions. To address these limitations, we propose a hybrid framework that synergistically combines Transformer and diffusion models, capitalizing on their strengths in long‐range dependency modeling and denoising. In this work, we introduce TransDiff‐HiSeg, a novel Transformer‐guided Diffusion segmentation framework that integrates a conditioned diffusion model, binarized cross transformer, and adaptive feature fusion blocks. The framework comprises a parallel encoder built with convolution and transformer blocks for robust feature extraction and noise suppression, and a decoder of stacked convolutional blocks to reconstruct high‐resolution segmentation. Our model emphasizes sustainable healthcare by achieving improved segmentation accuracy with reduced computational overhead, making it suitable for long‐term clinical integration. Extensive experiments on multi‐organ and brain tumor segmentation tasks demonstrate that TransDiff‐HiSeg consistently outperforms state‐of‐the‐art methods, achieving superior Dice, Accuracy, and HD95 scores while maintaining a lightweight impact. These results validate the efficacy and sustainability of our approach in real‐world medical image segmentation scenarios.
Proactive Zero-Trust Intrusion Detection for Consumer IoT Applications Using Lightweight Ensemble Learning With Anomaly Analysis Bipasha Guha Roy, Deepsubhra Guha Roy, Piyali Datta, Surbhi Bhatia Khan, Asma Alshuhail, Oumaima Saidani IEEE Transactions on Consumer Electronics, 2026 The rapid proliferation of consumer IoT devices from smart home hubs to wearables has expanded the attack surface, introducing new security and privacy challenges. Traditional Intrusion Detection Systems (IDS) often rely on implicit trust and heavyweight computation, making them unsuitable for resource-constrained consumer electronics. This paper presents a proactive, lightweight zero-trust IDS tailored for consumer applications. Our two-layer architecture integrates a supervised stacked ensemble classifier (Random Forest, XGBoost, Light-GBM) to detect known threats and an unsupervised DBSCAN (Density-Based Spatial Clustering of Applications with Noise)-based anomaly detector to identify zero-day attacks. We introduce a feature reduction pipeline driven by correlation and variance analysis, reducing the feature set by over 50% to fit edge hardware constraints. Evaluated on the CICIDS collection dataset (over 9 million flows), the framework achieves 98.48% accuracy while maintaining real-time processing capability on Raspberry Pi-class hardware. By continuously scrutinizing both malicious and benign traffic, our system delivers proactive, trust-enhancing defense critical to modern consumer IoT applications.
EfficientNetB7-Based Deep Learning Framework for Enhanced Classification of Lung and Colon Cancer Histopathological Images Pai H. Aditya, T. R. Mahesh, J. V. Muruga Lal Jeyan, Surbhi Bhatia Khan, Shakila Basheer, Ali Algarni Journal of Visualized Experiments, 2026 Early diagnosis of lung cancer plays a pivotal role in ensuring improved treatment and survival of patients. This remains a major focus in clinical research. Artificial intelligence (AI) has transformed pathology by significantly improving diagnostic accuracy and efficiency. This study presents a robust deep learning model in the shape of the pretrained EfficientNetB7 model to classify colon and lung tissue histopathological images with an extremely high accuracy of 96%. The model's performance was optimized using advanced preprocessing methods, fine-tuning, and domain-specific data augmentation techniques. These strategies help reduce problems such as class imbalance and subtle histological variations. To address the issue of overfitting, multiple data augmentation techniques were combined, and an early stopping criterion was incorporated. This approach enabled efficient and cost-effective training. Robust validation of the model demonstrates high utility for clinical applications and enables pathologists to deliver timely and accurate diagnoses. Integrating advanced deep learning models into medical imaging workflows holds great promise for early and accurate cancer diagnosis, ultimately improving patient outcomes.
Driving Digital Transformation in Quick Service Laboratory Supply Chains Through Statistical Anomaly Detection Saeed Alzahrani, Surbhi B. Khan, Mohammed Alojail, Nidhi Bhatia Transactions on Emerging Telecommunications Technologies, 2026 Quick Service Laboratories (QSL) provide the necessary diagnostic services that have to be performed within limited time frames and rely on coordinated solutions across its supply chain to operate successfully. The application of standard supply chain management approaches often fails to recognize the variable and unpredictable nature of QSL operations, which significantly contributes to stockouts, delays, or surplus inventory. This study looks into a different approach to the traditional methodologies of supply chain management by investigating the means when machine learning algorithms with the purpose of discovering anomalous behavior patterns are applied to QSL supply chain practices and generate value. In examining and evaluating the historical demand forecasting patterns, inventory levels, and operational performance metrics will be more easily identifiable as anomalous behaviors or dissenting levels such as demand spikes, unanticipated inventory shortfall levels, and atypical arrival patterns of inventory to generate disruption to laboratory operations. Machine learning models can be supervised or unsupervised to learn normal operation behaviors, and even detect anomalies in real time through model training. These models facilitate proactive interventions that would improve inventory management and distribution planning, as well as service delivery in general. When building on the results of our detection modeling, we found that machine learning anomaly detection could provide actionable suggestions and improved supply chain resiliency, and reduce stockouts and excess inventory, all while maintaining more controlled service levels. Our comparative evaluation of conventional monitoring and forecasting methods demonstrates superior capabilities over traditional methods in our results, by resorting to fully utilizing the complexity of simple linear and rare events found in QSL supply chains and their digital transformation story.
Improved Alzheimer's Detection with a Modified Multi-Focus Attention Mechanism using Computational Techniques Purushottam Kumar Pandey, Jyoti Pruthi, Surbhi Bhatia Khan, Nora A. Alkhaldi, Daniel Saraee Recent Patents on Engineering, 2026 Alzheimer disease is a common type of dementia which shrinks the brain cells and eventually causes death. It disturbs the life quality of patients with progressive symptoms such as memory loss, conversation, etc. It is vital to identify the disease earlier to get precise treatment. Besides, it is significant to locate the forms of Alzheimer's such as AD (Alzheimer Disease), CN (Cognitive Normal), and MCI (Mild Cognitive Impairment). Traditionally, manual screening of Alzheimer's is carried out by qualified physicians, which is a time-consuming mechanism, expensive, and prone to human error. To resolve the issue, several conventional researches attempted to attain better efficiency in the Alzheimer classification but were limited through accuracy, speed, and inefficacy. To address the challenge of classifying Alzheimer's in its various forms (AD, CN, and MCI), the proposed system utilizes the Modified Multi-Focus Attention and Hierarchical Scalerated Convolutional Neural Network (HSCN) mechanisms within the ResNet-101 model. The system undergoes testing with custom datasets such as OASIS, AIBL, and ADNI, and the classification performance is assessed using efficiency factors to gauge the effectiveness of the research. Background: Alzheimer is a century-old disease, still there is no concrete method to diagnose the disease. Many time diagnosis takes large time and the patient has been referred to many doctors. Objective: The objective of the study is to create a prediction model using deep learning which will be able to classify the patient into three different classes, CN, MCI,, and AD. The model is trained on hetero dataset, ADNI, AIBL,, and OASIS. Method: For the deep learning model, we have used Resnet 101 in which the convolution layer is changed to Hierarchical Scalerated CNN and the bottleneck layer is changed to modified multi-focus attention. The preprocessing of the image is also done as the initial step of process. Results: Our model accuracy is more than 99% for all three datasets used for the research. Conclusion: The model is trained for MRI from different datasets, the same model should be used for PET scans for Alzheimer's diagnosis, and the same model can be used to diagnose other disease patients which will be very useful for mankind.
Preface AI Modernisation Techniques for Contemporary Trends, 2026
AI Modernisation Techniques for Contemporary Trends Pramod Singh Rathore, Abhishek Kumar, Surbhi B. Khan, Faheem Masoodi, Fatima Asiri, T. Rajasanthosh Kumar AI Modernisation Techniques for Contemporary Trends, 2026
An Adaptive Xception Model for Classification of Brain Tumors Arastu Thakur, T. R. Mahesh, Surbhi Bhatia Khan, Shivakumara Palaiahnakote, V. Vinoth Kumar, Ahlam Almusharraf, Arwa Mashat International Journal of Pattern Recognition and Artificial Intelligence, 2024
Sentiment analysis using deep learning Parul Gandhi, Surbhi Bhatia, Norah Alkhaldi Computer Vision and Recognition Systems Using Machine and Deep Learning Approaches Fundamentals Technologies and Applications, 2021
Conclusions Saneh Lata Yadav, Ritika Dhaiya, Surbhi Bhatia Researches and Applications of Artificial Intelligence to Mitigate Pandemics History Diagnostic Tools Epidemiology Healthcare and Technology, 2021
An algorithmic approach based on principal component analysis for aspect-based opinion summarization Proceedings of the 2019 6th International Conference on Computing for Sustainable Global Development Indiacom 2019, 2019
Opinion target extraction with sentiment analysis International Journal of Computing, 2018
Opinion score mining: An algorithmic approach Banasthali University/ CS Department, Rajasthan, India, Surbhi Bhatia, Manisha Sharma, Komal Kumar Bhatia International Journal of Intelligent Systems and Applications, 2017
Customer churn analysis in telecom industry Kiran Dahiya, Surbhi Bhatia 2015 4th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions Icrito 2015, 2015
Strategies for mining opinions: A survey 2015 International Conference on Computing for Sustainable Global Development Indiacom 2015, 2015
