University Publications
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This community contains all the publications of the university, including research papers. manuscripts, videos, conference papers, project reports, etc.
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Item Mu-eta Fusion Experimental Investigation of Wireless System Performance in Weibull Fading and Correlated Interference Scenarios(World Scientific, 2026) Upadhyay, Deepak; Chhabra, GunjanIn this paper, we have introduced the Mu-Eta fusion to compute wireless communication performance over Weibull fading and correlated interference. In contrast to typical fading models, the Weibull distribution provides adjustable parameters to represent various NLOS environments. Correlated interference, typical in high-density environments, has a severe influence on system reliability. Transmission performance analysis is presented in terms of throughput, BER, energy efficiency and secrecy capacity in an exact closed form. The robustness of the presented model is confirmed by simulation and SDR results. Results indicate that Mu-Eta can achieve significantly higher throughput than the conventional schemes and the achieved throughput can be substantially superior, with up to 36% higher throughput and 35% lower BER under high interference correlation. The framework is especially relevant to urban wireless systems, vehicular networks and the future 5G/6G generations.Item Emerging Role of Artificial Intelligence (AI) in Early Autism Diagnosis: Comprehensive Review(Springer, 2026) Pant, Shivani; Gehlot, AnitaSDG 10 aims to reduce inequalities and ensure no one is left behind. Autism, a complex neurological disorder, is found in around 1/100 children worldwide. Early diagnosis is crucial for effective treatment and intervention. Artificial Intelligence (AI) has emerged as a revolutionary tool in ASD research, enhancing early detection mechanisms and improving symptom management. ML, supervised, unsupervised, semi-supervised, and reinforcement learning algorithms are used for image recognition, language understanding, and speech processing. This paper aims to emphasize the role of AI in early autism diagnosis and to identify and evaluate various AI-based algorithms used for accurate autism detection. Autism diagnosis is challenging due to the lack of standard medical tests. AI interventions, such as natural language processing, machine learning and deep learning. Can improve early diagnosis by identifying complex patterns and detecting subtle symptoms. However, issues such as algorithm transparency and data privacy must be addressed. With continued research and ethical AI technology, these algorithms can transform early ASD diagnosis, enabling timely intervention and improved outcomes.Item Utilizing Fractal Analysis and Machine Learning to Enhance Natural Disaster Prediction and Foster Resilient Smart City Development(Bentham Science, 2026) Negi, Deepak; Sharma, Ravindra et al.Disasters, which occur in periodic intervals, are natural phenomena such as floods, landslides, etc. Nowadays, they are more frequent due to the effects of global warming. These natural phenomena possess complex dynamics, making their precise prediction a difficult task. The intention of smart cities is to use the advanced technologies for flexible and sustainable solutions to such threats. In this paper, we present a new and efficient approach for extending natural thread prediction using mathematical fractal analysis to identify self-similar, multi-scale information that can be incorporated into machine learning prediction models for natural disasters. In addition, we used fractal generation techniques to create virtual replicas of the metropolitan infrastructure in order to model reactions to extreme event forecasts. Our multimodal method, at the nexus of urban modeling, machine learning, and fractal mathematics, offers a groundbreaking way to improve climate preparedness for smart cities. Adaptive resilience planning and early warning systems can benefit from the application of data-driven infrastructure response techniques and catastrophe prediction methodology.Item Enhance Reliability and Security in VANET Using Clustering Based on ANT Colony Optimization and Fuzzy Logic(Wiley, 2026) Khan, Gulista; Kanauzia, Rohit et al.Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by enabling communication between vehicles and infrastructure. However, ensuring secure, reliable, and consistent data transfer remains challenging due to their dynamic nature. This paper proposes a Clustering-Based Ant Colony Optimization (CB-ACO) and Fuzzy Logic algorithm to address these issues. Clustering reduces network load and enhances stability, while ACO selects optimal cluster heads and routes, supported by Fuzzy Logic-based trust assessments for secure access control. Compared to existing algorithms, the proposed method improves packet delivery, reduces latency, and enhances security, offering a robust solution for next-generation VANETs.Item An Explainable 3D Graph-Transformer Framework for Hepatotoxicity Prediction with Scaffold-Aware Evaluation(IEEE, 2026) Mishra, Suraj; Kanauzia, Rohit et al.Drug-induced hepatotoxicity continues to be a major factor in post-market removal and late-stage drug attrition. While graph neural networks have shown promise in predicting chemical toxicity, many existing approaches rely on random data splits and offer limited interpretability, thereby reducing their reliability in real-world screening scenarios. This study presents an explainable 3D graph-Transformer framework for hepatotoxicity prediction that integrates geometric inductive bias, rigorous scaffold-based evaluation, and multiple complementary explainability approaches. Molecular structures are encoded, and spatial linkages are captured using a 3D-equivariant graph neural network. Next, a graph-level Transformer is used to model long-range atomic interactions. Five-fold scaffold cross-validation is used to assess the model on a curated hepatotoxicity dataset of 8,538 molecules obtained from Tox21 liver-related assays. We use three different explainability techniques to guarantee transparency: atom-level perturbation analysis, integrated gradients, and transformer attention visualization. Experimental results show that the proposed framework achieves 91.8% accuracy with perfect recall on the evaluated splits, while generating explanations that are chemically significant and consistent with recognized hepatotoxic substructures. The suggested method is appropriate for early-stage toxicity screening and decision support because it combines multi-view explainability with robust out-of-distribution evaluation.Item Future Trends: AI And Cloud Innovations In Healthcare(Bentham Science, 2026) Bhatt, Ashutosh; Aggarwal, Ambika et alAmalgamation of AI & cloud computing brings a significant relevant change in healthcare, where they are providing assistance to the doctor by examining huge amounts of data in the medical discipline. This means with the help of these emerging technologies, experts can easily diagnose the disease in the early stage, provide better personalised cure plans, speed up the treatment, and the discovery of new drugs. Cloud computing is dedicated to keeping track of data securely with accuracy while sharing it with others. But, here it is important to focus on some issues like algorithm selection, authenticity, integrity, and security of the patients’ data. Despite these concerns, AI and cloud machinery can really boost healthcare and help to create a better future with more proactive and personalised care for a healthy population. The operations of medical research, managing all aspects of patient care, and delivering the results with accuracy are evolving right now. This chapter highlights the impact of AI & cloud tech in the coming years to identify their fruitful effect in healthcare. In the bigger picture, these technologies strive to achieve remarkable changes with improvement in areas like instant drug discovery, medical imaging, virtual assistance, accurate prediction in smart health, early diagnosis, treatment, and cure. The healthcare cloud system is much more committed to delivering a structure that gives cost-effective, secure, and shareable services in healthcare along with advanced disaster recovery plans using AI tools and techniques. The real revolution with amazing possibilities can occur with the help of AI & CC (Cloud Computing) if we can provide better ways to handle population health using precise public health strategies, data processing in real-time and executing the services like AI-as-a-service (AIaaS).Item Remote Sensing Reveals Land Cover Dynamics at SIDCUL Industrial Estate, Haridwar: A Sacred City in the Himalayan Foothills, India (2018–2023)(Springer, 2026-04-01) Joshi, Pooja; Bhatt, AshutoshThis study employs Sentinel-2 satellite imagery and spectral indices (NDVI, MNDWI, NDBI) to quantify land cover changes in Haridwar, India, between 2018 and 2023. Using Google Earth Engine, we computed index differences and classified land cover into five categories: Barren, Sparse Vegetation, Dense Vegetation, Water, and Built-up. Results indicate a 5.5% expansion in built-up areas (+3.68 km2), a 2.9% decline in dense vegetation (−1.36 km2), and minor increases in water bodies (+14.1%) and barren land (+4.3%). These changes reflect urbanization pressures near critical Himalayan ecosystems, providing a baseline for sustainable land management.Item Design and Simulation of Small Scale RF MTF-MOSFET(Springer, 2026-05-01) Payal, Mohit; Sharma, Vibhor et al.In this study, trenches inside the epitaxial layer are proposed as structural alterations to the traditional planar metal-oxide semiconductor field-effect transistor (MOSFET) on silicon-on-insulator (SOI) substrates. The innovative multi-trench-finger MOSFET (MTF-MOSFET) features multiple channels in its p-base, achieved by placing several vertical gates in distinct trenches. These additional channels enable parallel conduction of the drain current. TaN is used as the gate electrode and silicon dioxide (SiO2) as the gate dielectric in the suggested MTF-MOSFET architecture. The inclusion of multiple channels significantly enhances the device’s electrical characteristics. Specifically, the transconductance (gm) and the drain current (ID) are substantially increased due to the concurrent conduction of many channels. This concurrent conduction leads to a marked improvement in the cut-off frequency (fT). Two-dimensional simulations were used to assess and contrast the MTF-MOSFET’s performance with that of a standard MOSFET. The results demonstrate that, with a gate length of 60 nm, the MTF-MOSFET exhibits: 6.5 times increase in ID, 3.7 times improvement in gm, 1.3 times enhancement in fT and superior control over short channel effects. These improvements underscore the significant advantages of the MTF-MOSFET design over the traditional planar MOSFET, particularly in terms of current handling, transconductance, frequency response, and mitigation of short channel effects. Proposed device is used for ICT Applications for Electrical and Intelligent Engineering.Item Unveiling the transformative role of artificial intelligence in improving business process performance(Emerald, 2026-06-30) Sharma, Priyanka; Dang, G.P.Purpose The present study intends to examine the role of AI in enhancing the process performance of manufacturing entities in India. It aims to explore factors that measure the business process performance, which are influenced by the adoption of AI within various business processes. Design/methodology/approach The research conducted an empirical survey on Indian Manufacturing organizations using a questionnaire-based survey method on those businesses that have adopted AI within their business processes. For this, the study targeted C-level technology managers in the select manufacturing businesses in India. The structural equation modelling (SEM) technique was applied for data analysis. Findings The findings of the study indicate that the adoption of AI has a significant positive role in improving the business process performance of manufacturing firms. It can bring manifold advantages to business firms, including accuracy, speed, operational cost reduction, improved quality, enhanced productivity and efficiency. These benefits can help organizations in India to gain a competitive advantage in the changing business world. Originality/value The present article explores the transformative role of AI in the manufacturing sector of a rapidly developing economy like India. It provides empirical evidence on certain crucial benefits of AI, including quality enhancement, cost efficiency and productivity enhancement. This research offers valuable insights for both business leaders and policymakers on leveraging AI to drive industrial growth and competitiveness. It contributes to the limited literature on the practical implications of AI in emerging markets, particularly within the Indian context.Item Leveraging Machine Learning in Digital Payroll and Payment Playforms Redifining HR Practices in the FinTech Era(IEEE, 2026) Rao, P.Venkateswara; Chhabra, Gunjan et al.The objective of this research paper is to explore how Machine Learning (ML) could potentially be used to transform the HR process within Digital Payroll & Payment Platforms in today's Fintech industry. We did this through development of a Reinforcement Learning (RL) model in a simulated environment, utilizing artificial data to measure various Performance Metrics including Efficiency, Fairness, Fraud Prevention and Employee Satisfaction. Our results indicated that our AI-enabled model outperformed the AI-free Baseline Model in terms of Reward Convergence, Satisfaction Index, Transaction Success Rate with Fraud Attempted, and Scalable Adaptability as the Workforce Size increased. Additionally, we conducted Ablation Testing for Features, which confirmed the importance of engineered features to produce desired outcomes. These results clearly indicate that Intelligent Payroll Platforms have the potential to reduce employee stress, increase employee trust and provide Human Resource Managers with tools to create Financial Operations that are both Sustainable and Employee-Centered.