arXiv subjects

Hari Mohan Pandey

Publications and source records attributed to Hari Mohan Pandey.

11 recordsLinked to original sources

arXiv2026

Coronavirus Optimization Algorithm: A Success-History Adaptive Evolutionary Framework with Archive-Assisted Search and Stagnation Recovery for Global Optimization

This paper proposes the Coronavirus Optimization Algorithm (COA), a SARS-CoV-2-inspired success-history adaptive evolutionary optimizer for box-constrained continuous global optimization. COA does not model disease transmission; instead, it maps selected coronavirus mechanisms to explicit search operators, including elite-guided attraction, trial-vector generation, adaptive parameter variation, stagnation recovery, and population-size scheduling. The algorithm combines opposition-based initialization, current-to-pbest mutation, binomial crossover, an external archive, success-history adaptation, population reduction, and partial restart. COA is evaluated on 29 CEC 2017 benchmark functions at 10, 30, and 50 dimensions against 15 competitive optimizers. Results show that COA achieves the best overall Friedman rank across all dimensions, with particularly strong performance on composition functions. The findings demonstrate that COA is a compact, transparent, and competitive adaptive evolutionary optimizer, while also highlighting limitations on some hybrid functions and the need for further high-dimensional validation.

cs.NE↗

arXiv2025

GEMMA-SQL: A Novel Text-to-SQL Model Based on Large Language Models

Text-to-SQL systems enable users to interact with structured databases using natural language, eliminating the need for specialized programming knowledge. In this work, we introduce GEMMA-SQL, a lightweight and efficient text-to-SQL model built upon the open-source Gemma 2B architecture. Unlike many large language models (LLMs), GEMMA-SQL is fine-tuned in a resource-efficient, iterative manner and can be deployed on low-cost hardware. Leveraging the SPIDER benchmark for training and evaluation, GEMMA-SQL combines multiple prompting strategies, including few-shot learning, to enhance SQL query generation accuracy. The instruction-tuned variant, GEMMA-SQL Instruct, achieves 66.8% Test-Suite accuracy and 63.3% Exact Set Match accuracy, outperforming several state-of-the-art baselines such as IRNet, RYANSQL, and CodeXDavinci. The proposed approach demonstrates that effective prompt design and targeted instruction tuning can significantly boost performance while maintaining high scalability and adaptability. These results position GEMMA-SQL as a practical, open-source alternative for robust and accessible text-to-SQL systems.

cs.CL↗

arXiv2024

Harnessing Large Language Models for Mental Health: Opportunities, Challenges, and Ethical Considerations

Large Language Models (LLMs) are transforming mental health care by enhancing accessibility, personalization, and efficiency in therapeutic interventions. These AI-driven tools empower mental health professionals with real-time support, improved data integration, and the ability to encourage care-seeking behaviors, particularly in underserved communities. By harnessing LLMs, practitioners can deliver more empathetic, tailored, and effective support, addressing longstanding gaps in mental health service provision. However, their implementation comes with significant challenges and ethical concerns. Performance limitations, data privacy risks, biased outputs, and the potential for generating misleading information underscore the critical need for stringent ethical guidelines and robust evaluation mechanisms. The sensitive nature of mental health data further necessitates meticulous safeguards to protect patient rights and ensure equitable access to AI-driven care. Proponents argue that LLMs have the potential to democratize mental health resources, while critics warn of risks such as misuse and the diminishment of human connection in therapy. Achieving a balance between innovation and ethical responsibility is imperative. This paper examines the transformative potential of LLMs in mental health care, highlights the associated technical and ethical complexities, and advocates for a collaborative, multidisciplinary approach to ensure these advancements align with the goal of providing compassionate, equitable, and effective mental health support.

cs.CY↗

arXiv2024

Artificial Intelligence in Mental Health and Well-Being: Evolution, Current Applications, Future Challenges, and Emerging Evidence

Artificial Intelligence (AI) is a broad field that is upturning mental health care in many ways, from addressing anxiety, depression, and stress to increasing access, personalization of treatment, and real-time monitoring that enhances patient outcomes. The current paper discusses the evolution, present application, and future challenges in the field of AI for mental health and well-being. From the early chatbot models, such as ELIZA, to modern machine learning systems, the integration of AI in mental health has grown rapidly to augment traditional treatment and open innovative solutions. AI-driven tools provide continuous support, offering personalized interventions and addressing issues such as treatment access and patient stigma. AI also enables early diagnosis through the analysis of complex datasets, including speech patterns and social media behavior, to detect early signs of conditions like depression and Post-Traumatic Stress Disorder (PTSD). Ethical challenges persist, however, most notably around privacy, data security, and algorithmic bias. With AI at the core of mental health care, there is a dire need to develop strong ethical frameworks that ensure patient rights are protected, access is equitable, and transparency is maintained in AI applications. Going forward, the role of AI in mental health will continue to evolve, and continued research and policy development will be needed to meet the diverse needs of patients while mitigating associated risks.

cs.CY↗

arXiv2024

A Prototype Model of Zero-Trust Architecture Blockchain with EigenTrust-Based Practical Byzantine Fault Tolerance Protocol to Manage Decentralized Clinical Trials

The COVID-19 pandemic necessitated the emergence of decentralized Clinical Trials (DCTs) due to patient retention, accelerate trials, improve data accessibility, enable virtual care, and facilitate seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the Zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done to evaluate the quality of the system.

cs.CR↗

arXiv2023

A Novel Two Stream Decision Level Fusion of Vision and Inertial Sensors Data for Automatic Multimodal Human Activity Recognition System

This paper presents a novel multimodal human activity recognition system. It uses a two-stream decision level fusion of vision and inertial sensors. In the first stream, raw RGB frames are passed to a part affinity field-based pose estimation network to detect the keypoints of the user. These keypoints are then pre-processed and inputted in a sliding window fashion to a specially designed convolutional neural network for the spatial feature extraction followed by regularized LSTMs to calculate the temporal features. The outputs of LSTM networks are then inputted to fully connected layers for classification. In the second stream, data obtained from inertial sensors are pre-processed and inputted to regularized LSTMs for the feature extraction followed by fully connected layers for the classification. At this stage, the SoftMax scores of two streams are then fused using the decision level fusion which gives the final prediction. Extensive experiments are conducted to evaluate the performance. Four multimodal standard benchmark datasets (UP-Fall detection, UTD-MHAD, Berkeley-MHAD, and C-MHAD) are used for experimentations. The accuracies obtained by the proposed system are 96.9 %, 97.6 %, 98.7 %, and 95.9 % respectively on the UP-Fall Detection, UTDMHAD, Berkeley-MHAD, and C-MHAD datasets. These results are far superior than the current state-of-the-art methods.

cs.CV↗

arXiv2023

An Efficient Deep Convolutional Neural Network Model For Yoga Pose Recognition Using Single Images

Pose recognition deals with designing algorithms to locate human body joints in a 2D/3D space and run inference on the estimated joint locations for predicting the poses. Yoga poses consist of some very complex postures. It imposes various challenges on the computer vision algorithms like occlusion, inter-class similarity, intra-class variability, viewpoint complexity, etc. This paper presents YPose, an efficient deep convolutional neural network (CNN) model to recognize yoga asanas from RGB images. The proposed model consists of four steps as follows: (a) first, the region of interest (ROI) is segmented using segmentation based approaches to extract the ROI from the original images; (b) second, these refined images are passed to a CNN architecture based on the backbone of EfficientNets for feature extraction; (c) third, dense refinement blocks, adapted from the architecture of densely connected networks are added to learn more diversified features; and (d) fourth, global average pooling and fully connected layers are applied for the classification of the multi-level hierarchy of the yoga poses. The proposed model has been tested on the Yoga-82 dataset. It is a publicly available benchmark dataset for yoga pose recognition. Experimental results show that the proposed model achieves the state-of-the-art on this dataset. The proposed model obtained an accuracy of 93.28%, which is an improvement over the earlier state-of-the-art (79.35%) with a margin of approximately 13.9%. The code will be made publicly available.

cs.CV↗

arXiv2022

DroneAttention: Sparse Weighted Temporal Attention for Drone-Camera Based Activity Recognition

Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is comprised of two parts. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively.

cs.CV↗

arXiv2022

SWTF: Sparse Weighted Temporal Fusion for Drone-Based Activity Recognition

Drone-camera based human activity recognition (HAR) has received significant attention from the computer vision research community in the past few years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Fusion (SWTF) module to utilize sparsely sampled video frames for obtaining global weighted temporal fusion outcome. The proposed SWTF is divided into two components. First, a temporal segment network that sparsely samples a given set of frames. Second, weighted temporal fusion, that incorporates a fusion of feature maps derived from optical flow, with raw RGB images. This is followed by base-network, which comprises a convolutional neural network module along with fully connected layers that provide us with activity recognition. The SWTF network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a significant margin.

cs.CV↗

arXiv2022

Accessible Survey of Evolutionary Robotics and Potential Future Research Directions

This paper reviews various Evolutionary Approaches applied to the domain of Evolutionary Robotics with the intention of resolving difficult problems in the areas of robotic design and control. Evolutionary Robotics is a fast-growing field that has attracted substantial research attention in recent years. The paper thus collates recent findings along with some anticipated applications. The reviewed literature is organized systematically to give a categorical overview of recent developments and is presented in tabulated form for quick reference. We discuss the outstanding potentialities and challenges that exist in robotics from an ER perspective, with the belief that these will be have the capacity to be addressed in the near future via the application of evolutionary approaches. The primary objective of this study is to explore the applicability of Evolutionary Approaches in robotic application development. We believe that this study will enable the researchers to utilize Evolutionary Approaches to solve complex outstanding problems in robotics.

cs.RO↗

arXiv2021

Robustness analytics to data heterogeneity in edge computing

Federated Learning is a framework that jointly trains a model \textit{with} complete knowledge on a remotely placed centralized server, but \textit{without} the requirement of accessing the data stored in distributed machines. Some work assumes that the data generated from edge devices are identically and independently sampled from a common population distribution. However, such ideal sampling may not be realistic in many contexts. Also, models based on intrinsic agency, such as active sampling schemes, may lead to highly biased sampling. So an imminent question is how robust Federated Learning is to biased sampling? In this work\footnote{\url{https://github.com/jiaqian/robustness_of_FL}}, we experimentally investigate two such scenarios. First, we study a centralized classifier aggregated from a collection of local classifiers trained with data having categorical heterogeneity. Second, we study a classifier aggregated from a collection of local classifiers trained by data through active sampling at the edge. We present evidence in both scenarios that Federated Learning is robust to data heterogeneity when local training iterations and communication frequency are appropriately chosen.

cs.DC↗