• A multi-modal attentive framework that can interpret text (MMAT) 

  Sharma, Yashvardhan (IEEE, 2025-07)

  Deep learning algorithms have demonstrated exceptional performance on various computer vision and natural language processing tasks. However, for machines to learn information signals, they must understand and have enough ...
• Control-data plane intelligence trade-off in SDN 

  Sinha, Yash (IEEE, 2025)

  With the decoupling of network control and data planes, the upcoming Software Defined Networking (SDN) paradigm advocates better network control and manageability. It introduces logical centralized control, network ...
• Comparative study of preprocessing and classification methods in character recognition of natural scene images 

  Sinha, Yash (Springer, 2025-01)

  This paper presents an approach to character recognition in natural scene images. Recognizing such text is a challenging problem in the field of Computer Vision, more than the recognition of scanned documents due to several ...
• Studying the role of kinect as a multi-sensory learning platform for children 

  Sinha, Yash (ACM Digital Library, 2018)

  According to the theory of Embodied Cognition, our behavior is a result of real-time interaction with surroundings, our cognitive skills, and the nervous system. From this perspective, researchers are considering a learning ...
• Lung cancer detection: a deep learning approach 

  Sinha, Yash (Springer, 2018-10)

  We present an approach to detect lung cancer from CT scans using deep residual learning. We delineate a pipeline of preprocessing techniques to highlight lung regions vulnerable to cancer and extract features using UNet ...
• Distill to delete: unlearning in graph networks with knowledge distillation 

  Sinha, Yash (2023-09)

  Graph unlearning has emerged as a pivotal method to delete information from a pre-trained graph neural network (GNN). One may delete nodes, a class of nodes, edges, or a class of edges. An unlearning method enables the GNN ...
• Towards robust concept erasure in diffusion models: unlearning identity, nudity and artistic styles 

  Sinha, Yash (2024-11)

  Diffusion models have achieved remarkable success in generative tasks across various domains. However, the increasing demand for content moderation and the removal of specific concepts from these models has introduced the ...
• Unstar: unlearning with self-taught anti-sample reasoning for ILMS 

  Sinha, Yash (2024-10)

  The key components of machine learning are data samples for training, model for learning patterns, and loss function for optimizing accuracy. Analogously, unlearning can potentially be achieved through anti-data samples ...
• Multi-modal recommendation unlearning for legal, licensing, and modality constraints 

  Sinha, Yash (Association for the Advancement of Artificial Intelligence, 2025)

  User data spread across multiple modalities has popularized multi-modal recommender systems (MMRS). They recommend diverse content such as products, social media posts, TikTok reels, etc., based on a user-item interaction ...
• Orgaccess: a benchmark for role based access control in organization scale llms 

  Sinha, Yash (2025-05)

  Role-based access control (RBAC) and hierarchical structures are foundational to how information flows and decisions are made within virtually all organizations. As the potential of Large Language Models (LLMs) to serve ...
• Step-by-step reasoning attack: revealing 'erased' knowledge in large language models 

  Sinha, Yash (2025-06)

  Knowledge erasure in large language models (LLMs) is important for ensuring compliance with data and AI regulations, safeguarding user privacy, mitigating bias, and misinformation. Existing unlearning methods aim to make ...
• Weak links in Linkedin: enhancing fake profile detection in the age of llms 

  Agarwal, Vinti (2025-07)

  Large Language Models (LLMs) have made it easier to create realistic fake profiles on platforms like LinkedIn. This poses a significant risk for text-based fake profile detectors. In this study, we evaluate the robustness ...
• Asynchronous deep reinforcement learning for semantic communication and digital-twin deployment in transportation networks 

  Gupta, Shashank (IEEE, 2025-08)

  The dynamically evolving and technologically-driven hybrid landscape of transportation networks integrated with advanced edge computing capabilities has demonstrated efficient communication and computation techniques to ...
• An efficient federated transfer learning approach for Multi-UAV systems 

  Joshi, Sandeep; Rajya Lakshmi, L. (IEEE, 2025-05)

  Recent advances in multi-unmanned aerial vehicle (UAV) based federated learning do not take into consideration the massive computational requirements of modern deep learning models on mobile UAV s. Additionally, there has ...
• SAC: a framework for measuring and inducing personality traits in llms with dynamic intensity control 

  Challa, Jagat Sesh; Kumar, Dhruv (2025-06)

  Large language models (LLMs) have gained significant traction across a wide range of fields in recent years. There is also a growing expectation for them to display human-like personalities during interactions. To meet ...
• Digital medicine assistant-health application for expiry tracking 

  Rao, Shreyas Suresh (IEEE, 2021)

  Medicines play an important part in each and everyone's life where some of them relieve us from diseases, disorders or pains and some of them are very essential lifesaving ones. Some of them are rarely available and need ...
• An analysis of the psychological implications of covid-19 pandemic on undergraduate students and efforts on mitigation 

  Rao, Shreyas Suresh (Springer, 2022-02)

  The whole world is combating the COVID-19 pandemic, which has affected mankind in enormous ways. To limit its pervasive expansion, many measures were taken up by the Indian government, as a result of which colleges were ...
• Ontology enabled disaster management web service using data integration 

  Rao, Shreyas Suresh (IJCSE, 2022)

  Meteorological disasters caused by extreme weather may result in damage to infrastructure, transport, livelihood, agro-industries and affects the overall economy of a country. To reduce losses due to disasters, an effective ...
• AIOT and big data analytics for smart healthcare applications 

  Rao, Shreyas Suresh (Bentham Science, 2023)

  AIoT (Artificial Intelligence of Things) and Big Data Analytics are catalyzing a healthcare revolution. This book is an accessible volume that summarizes the information available. In this book, researchers explore how ...
• A review on WSN based resource constrained smart IoT systems 

  Haribabu, K. (Springer, 2025)

  In Wireless Sensor Network (i.e. WSN) based resource constrained Internet of Things (i.e. IoT) environments, efficient data forwarding is achieved through cluster based mechanisms, where cluster heads facilitate communication ...

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