(SoE) School of Engineering

Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.

Shiv Nadar University

Selecting a business site is a significant decision in managing a commercial organization. It becomes more complex in countries like India, where population density is high, and it is tough to distribute services fairly. With this difficulty, investing in a location without a thorough survey is challenging. This study aims to provide an information map that can help choose the optimal neighborhood for a particular business. The research aims to use data science powers to provide an unsupervised machine-learning approach to offer a recommendation-based solution that suggests the best location for a given use case. The objective of this paper is the in-depth analysis for site-finding that includes clustering regions based on various factors such as geographical locations (like schools, bus and railway stations, and hospitals), sanitation, distance, footfall, and other entities. The idea is to obtain the user's use case query and then create hotspots on the map that show the most suitable locations where the specific use case will benefit the stakeholders the most. We need to identify neighborhoods where there may be an unsatisfied need for Indian restaurants. Additionally, we require places that are uncrowded and have little competition. If the first two requirements are satisfied, we would also like a location close to a prominent city neighborhood. The advantages of each area will then be clearly expressed so that stakeholders can choose the best possible final location.

Computer Science and Engineering

2024 IEEE International Conference on Contemporary Computing and Communications

Location-Based Ideal Site Selection using Clustering

This research explores the intricate relationship between weather-related variables and the transmission dynamics of two pivotal diseases: COVID-19 and dengue. Our primary goal is to advance our understanding of how climate factors influence the occurrence and spread of these diseases through a comparative analysis. In the context of the pressing need for accurate prediction systems, our research aims to contribute to the refinement of public health planning and decision-making strategies. In this work, our findings underscore the superiority of certain models in predicting epidemic outbreaks. Notably, Random Forest consistently demonstrated exceptional performance across all metrics for both COVID-19 and Dengue predictions. The model exhibited superior accuracy, precision, and correlation with actual values.

Asian Conference on Intelligent Information and Database Systems

Predicting Epidemic Outbreak Using Climatic Factors

World University root node

Mechanical Engineering

Tuijin Jishu/Journal of Propulsion Technology

Application of Artificial Intelligence to Measure the Productivity of an Assembly - Line Worker

Diffusion tensor imaging (DTI) is a non-invasive technique for analyzing the movement of water in the brain. However, the precision of measurements required for tracking white matter pathways can lead to long scan times, which can be challenging for some patient populations such as pediatric patients. To address this issue, researchers have been experimenting with deep learning techniques for faster estimation of DTI parameters, which are helpful in neurological diagnosis, of diffusion-weighted images. Our proposed solution is a transformer neural network-based approach for fast estimation of diffusion tensor parameters using sparse measurements. While there have been attempts to address this problem, our proposed model handles both scalable and generalized estimation of DTI parameters using multiple sparse measurements. Through experimentation on the Human Connectome Project (HCP) Young Adult benchmark dataset, our proposed model demonstrated state-of-the-art results in terms of fractional anisotropy (FA), axial diffusivity (AD), and mean diffusivity (MD) when compared to traditional linear least square (LLS) fitting and 3D U-Net model with input size (3D U-Net16).

Neural Computing and Applications

SwinDTI: Swin transformer based generalized fast estimation of diffusion tensor parameters from sparse data

9th International Conference on Internet of Things, Big Data and Security (IoTBDS 2024)

Industrial Internet of Things for Assembly Line Worker’s Work Fatigue Recognition

IFAC-PapersOnLine

Controlling and Optimization of On-time Delivery for Assemble-to-order Production System

Alzheimer disease is one of the most common neuro-degenerative diseases, with an estimated 6.2 million cases in the United States. This research article investigates the potential of Transformer-based deep learning techniques to accelerate the processing of diffusion tensor imaging (DTI) measures and improve the early diagnosis of Alzheimer disease (AD) using sparse data. Diffusion Weighted Imaging (DWI) is a time-consuming process, with each diffusion direction taking between 2-5 minutes, and at least 40 diffusion directions are needed for routine clinical diagnosis, which needs scanning duration exceeding 3 hours for each patient. By leveraging the attention mechanism, our proposed model generates quantitative measures of fractional anisotropy (FA), axial diffusivity (AxD), and mean diffusivity (MD) using 5 and 21 diffusion directions, making it useful for clinical diagnosis through reduced scanning time of more than half. Our experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed model outperforms the traditional linear least square method, achieving accurate quantitative measurement of FA, AxD, and MD scores for early diagnosis of AD patients from healthy controls using sparse diffusion directions. Our analysis highlights the potential of Swin-Transformer attention-based deep learning framework to improve the early diagnosis and treatment of Alzheimer’s disease.

Proceedings of Machine Learning Research

Early Diagnosis of Alzheimer through Swin-Transformer-Based Deep Learning Framework using Sparse Diffusion Measures

Frontotemporal dementia (FTD) is a devastating neurodegenerative disorder that primarily affects the frontal and temporal lobes of the brain, leading to cognitive decline and behavioral changes. Early and accurate diagnosis of FTD is crucial for initiating timely interventions and providing appropriate care to patients. In the opinion of the experts, about 12-22 persons out of the population of 100,000 persons experience FTD. That means between 1.2 million and 1.8 million people have it worldwide. This research paper proposes a novel deep learning framework that utilizes sparse diffusion measures extracted from neuroimaging data to aid in the early diagnosis of Frontotemporal Dementia. The proposed model leverages the power of deep learning techniques to automatically learn relevant features from the data and effectively distinguish between healthy individuals and those with FTD. The experimental results demonstrate the promising potential of the proposed approach in improving FTD diagnosis and paving the way for future research in this area.

IEEE/CVF International Conference on Computer Vision

Deep Learning Framework using Sparse Diffusion MRI for Diagnosis of Frontotemporal Dementia

This paper aims to improve the identification of taxi hotspots in spatio-temporal space. We propose an approach that combines ensemble-weighted degree and node entropy measures within a dominating set to enhance the identification of taxi hotspots. First, we construct a graph representation of the spatio-temporal space, where nodes represent potential taxi hotspots. The weighted degree of each node is computed by considering the weights of its adjacent edges, which correspond to the distances between the two adjacent nodes on the road network. This measure quantifies the importance and connectivity of a node. We calculate node entropy to capture the level of uncertainty and randomness associated with each node. The entropy measure provides insights into the diversity of each potential hotspot, helping to distinguish between highly active and less predictable areas. We then integrate the ensemble weighted degree and node entropy measures into a dominating set approach. By iteratively selecting the nodes with the highest combined scores, we construct an optimal set of taxi hotspots that effectively cover the spatio-temporal space while considering both connectivity and uncertainty. The proposed approach enables a more comprehensive identification of taxi hotspots, taking into account the connectivity and importance of potential locations and their level of uncertainty. To evaluate the effectiveness of the proposed approach, we conducted experiments using New York taxi trip data. The results demonstrate that the ensemble weighted degree and node entropy measures enhance the identification of taxi hot spots compared to traditional dominating set methods.

<a href="https://link.springer.com/chapter/10.1007/978-3-031-48316-5_31">Enhancing Taxi Placement in Urban Areas Using Dominating Set Algorithm with Node and Edge Weights</a>