Haroon R. Lone

Aditya Mishra, Haroon R. Lone, Aayush Mishra

Energy prediction in buildings plays a crucial role in effective energy management. Precise predictions are essential for achieving optimal energy consumption and distribution within the grid. This paper introduces a Long Short-Term Memory (LSTM) model designed to forecast building energy consumption using historical energy data, occupancy patterns, and weather conditions. The LSTM model provides accurate short, medium, and long-term energy predictions for residential and commercial buildings compared to existing prediction models. We compare our LSTM model with established prediction methods, including linear regression, decision trees, and random forest. Encouragingly, the proposed LSTM model emerges as the superior performer across all metrics. It demonstrates exceptional prediction accuracy, boasting the highest R2 score of 0.97 and the most favorable mean absolute error (MAE) of 0.007. An additional advantage of our developed model is its capacity to achieve efficient energy consumption forecasts even when trained on a limited dataset. We address concerns about overfitting (variance) and underfitting (bias) through rigorous training and evaluation on real-world data. In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability.

Aditya Sneh, Nilesh Kumar Sahu, Anushka Sanjay Shelke et al.

Anxiety disorders impact millions globally, yet traditional diagnosis relies on clinical interviews, while machine learning models struggle with overfitting due to limited data. Large-scale data collection remains costly and time-consuming, restricting accessibility. To address this, we introduce the Hyperbolic Curvature Few-Shot Learning Network (HCFSLN), a novel Few-Shot Learning (FSL) framework for multimodal anxiety detection, integrating speech, physiological signals, and video data. HCFSLN enhances feature separability through hyperbolic embeddings, cross-modal attention, and an adaptive gating network, enabling robust classification with minimal data. We collected a multimodal anxiety dataset from 108 participants and benchmarked HCFSLN against six FSL baselines, achieving 88% accuracy, outperforming the best baseline by 14%. These results highlight the effectiveness of hyperbolic space for modeling anxiety-related speech patterns and demonstrate FSL's potential for anxiety classification.

Sharmad Kalpande, Saurabh Shirke, Haroon R. Lone

Mood instability is a key behavioral indicator of mental health, yet traditional assessments rely on infrequent and retrospective reports that fail to capture its continuous nature. Smartphone-based mobile sensing enables passive, in-the-wild mood inference from everyday behaviors; however, deploying such systems at scale remains challenging due to privacy constraints, uneven sensing availability, and substantial variability in behavioral patterns. In this work, we study mood inference using smartphone sensing data in a cross-country federated learning setting, where each country participates as an independent client while retaining local data. We introduce FedFAP, a feature-aware personalized federated framework designed to accommodate heterogeneous sensing modalities across regions. Evaluations across geographically and culturally diverse populations show that FedFAP achieves an AUROC of 0.744, outperforming both centralized approaches and existing personalized federated baselines. Beyond inference, our results offer design insights for mood-aware systems, demonstrating how population-aware personalization and privacy-preserving learning can enable scalable and mood-aware mobile sensing technologies.

Om Govind Jha, Harsh Shukla, Haroon R. Lone

Federated Learning (FL) faces major challenges in real-world deployments due to statistical heterogeneity across clients and system heterogeneity arising from resource-constrained devices. While clustering-based approaches mitigate statistical heterogeneity and pruning techniques improve memory and communication efficiency, these strategies are typically studied in isolation. We propose CA-AFP, a unified framework that jointly addresses both challenges by performing cluster-specific model pruning. In CA-AFP, clients are first grouped into clusters, and a separate model for each cluster is adaptively pruned during training. The framework introduces two key innovations: (1) a cluster-aware importance scoring mechanism that combines weight magnitude, intra-cluster coherence, and gradient consistency to identify parameters for pruning, and (2) an iterative pruning schedule that progressively removes parameters while enabling model self-healing through weight regrowth. We evaluate CA-AFP on two widely used human activity recognition benchmarks, UCI HAR and WISDM, under natural user-based federated partitions. Experimental results demonstrate that CA-AFP achieves a favorable balance between predictive accuracy, inter-client fairness, and communication efficiency. Compared to pruning-based baselines, CA-AFP consistently improves accuracy and lower performance disparity across clients with limited fine-tuning, while requiring substantially less communication than dense clustering-based methods. It also shows robustness to different Non-IID levels of data. Finally, ablation studies analyze the impact of clustering, pruning schedules and scoring mechanism offering practical insights into the design of efficient and adaptive FL systems.

Anushka Sanjay Shelke, Aditya Sneh, Arya Adyasha et al.

Fairness in AI-driven stress detection is critical for equitable mental healthcare, yet existing models frequently exhibit gender bias, particularly in data-scarce scenarios. To address this, we propose FairM2S, a fairness-aware meta-learning framework for stress detection leveraging audio-visual data. FairM2S integrates Equalized Odds constraints during both meta-training and adaptation phases, employing adversarial gradient masking and fairness-constrained meta-updates to effectively mitigate bias. Evaluated against five state-of-the-art baselines, FairM2S achieves 78.1% accuracy while reducing the Equal Opportunity to 0.06, demonstrating substantial fairness gains. We also release SAVSD, a smartphone-captured dataset with gender annotations, designed to support fairness research in low-resource, real-world contexts. Together, these contributions position FairM2S as a state-of-the-art approach for equitable and scalable few-shot stress detection in mental health AI. We release our dataset and FairM2S publicly with this paper.

Manjeet Yadav, Nilesh Kumar Sahu, Mudita Chaturvedi et al.

Improving mental health support in developing countries is a pressing need. One potential solution is the development of scalable, automated systems to conduct diagnostic screenings, which could help alleviate the burden on mental health professionals. In this work, we evaluate several state-of-the-art Large Language Models (LLMs), with and without fine-tuning, on our custom dataset for generating concise summaries from mental state examinations. We rigorously evaluate four different models for summary generation using established ROUGE metrics and input from human evaluators. The results highlight that our top-performing fine-tuned model outperforms existing models, achieving ROUGE-1 and ROUGE-L values of 0.810 and 0.764, respectively. Furthermore, we assessed the fine-tuned model's generalizability on a publicly available D4 dataset, and the outcomes were promising, indicating its potential applicability beyond our custom dataset.

Nilesh Kumar Sahu, Nandigramam Sai Harshit, Rishabh Uikey et al.

Social Anxiety Disorder (SAD) significantly impacts individuals' daily lives and relationships. The conventional methods for SAD detection involve physical consultations and self-reported questionnaires, but they have limitations such as time consumption and bias. This paper introduces video analysis as a promising method for early SAD detection. Specifically, we present a new approach for detecting SAD in individuals from various bodily features extracted from the video data. We conducted a study to collect video data of 92 participants performing impromptu speech in a controlled environment. Using the video data, we studied the behavioral change in participants' head, body, eye gaze, and action units. By applying a range of machine learning and deep learning algorithms, we achieved an accuracy rate of up to 74\% in classifying participants as SAD or non-SAD. Video-based SAD detection offers a non-intrusive and scalable approach that can be deployed in real-time, potentially enhancing early detection and intervention capabilities.

Nilesh Kumar Sahu, Snehil Gupta, Haroon R Lone

Social Anxiety Disorder (SAD) is a widespread mental health condition, yet its lack of objective markers hinders timely detection and intervention. While previous research has focused on behavioral and non-verbal markers of SAD in structured activities (e.g., speeches or interviews), these settings fail to replicate real-world, unstructured social interactions fully. Identifying non-verbal markers in naturalistic, unstaged environments is essential for developing ubiquitous and non-intrusive monitoring solutions. To address this gap, we present AnxietyFaceTrack, a study leveraging facial video analysis to detect anxiety in unstaged social settings. A cohort of 91 participants engaged in a social setting with unfamiliar individuals and their facial videos were recorded using a low-cost smartphone camera. We examined facial features, including eye movements, head position, facial landmarks, and facial action units, and used self-reported survey data to establish ground truth for multiclass (anxious, neutral, non-anxious) and binary (e.g., anxious vs. neutral) classifications. Our results demonstrate that a Random Forest classifier trained on the top 20% of features achieved the highest accuracy of 91.0% for multiclass classification and an average accuracy of 92.33% across binary classifications. Notably, head position and facial landmarks yielded the best performance for individual facial regions, achieving 85.0% and 88.0% accuracy, respectively, in multiclass classification, and 89.66% and 91.0% accuracy, respectively, across binary classifications. This study introduces a non-intrusive, cost-effective solution that can be seamlessly integrated into everyday smartphones for continuous anxiety monitoring, offering a promising pathway for early detection and intervention.

Sharmad Kalpande, Nilesh Kumar Sahu, Haroon Lone

Electrocardiograms (ECGs) are essential for monitoring cardiac health, allowing clinicians to analyze heart rate variability (HRV), detect abnormal rhythms, and diagnose cardiovascular diseases. However, ECG signals, especially those from wearable devices, are often affected by noise artifacts caused by motion, muscle activity, or device-related interference. These artifacts distort R-peaks and the characteristic QRS complex, making HRV analysis unreliable and increasing the risk of misdiagnosis. Despite this, the few existing studies on ECG noise detection have primarily focused on a single dataset, limiting the understanding of how well noise detection models generalize across different datasets. In this paper, we investigate the generalizability of noise detection in ECG using a novel HRV-based approach through cross-dataset experiments on four datasets. Our results show that machine learning achieves an average accuracy of over 90\% and an AUPRC of more than 0.9. These findings suggest that regardless of the ECG data source or the type of noise, the proposed method maintains high accuracy even on unseen datasets, demonstrating the feasibility of generalizability.

Kunwar Arpit Singh, Ankush Prakash, Haroon R Lone

Despite having hundreds of millions of speakers, handwritten Devanagari text remains severely underrepresented in publicly available benchmark datasets. Existing resources are limited in scale, focus primarily on isolated characters or short words, and lack controlled lexical content and writer level diversity, which restricts their utility for modern data driven handwriting analysis. As a result, they fail to capture the continuous, fused, and structurally complex nature of Devanagari handwriting, where characters are connected through a shared shirorekha (horizontal headline) and exhibit rich ligature formations. We introduce DohaScript, a large scale, multi writer dataset of handwritten Hindi text collected from 531 unique contributors. The dataset is designed as a parallel stylistic corpus, in which all writers transcribe the same fixed set of six traditional Hindi dohas (couplets). This controlled design enables systematic analysis of writer specific variation independent of linguistic content, and supports tasks such as handwriting recognition, writer identification, style analysis, and generative modeling. The dataset is accompanied by non identifiable demographic metadata, rigorous quality curation based on objective sharpness and resolution criteria, and page level layout difficulty annotations that facilitate stratified benchmarking. Baseline experiments demonstrate clear quality separation and strong generalization to unseen writers, highlighting the dataset's reliability and practical value. DohaScript is intended to serve as a standardized and reproducible benchmark for advancing research on continuous handwritten Devanagari text in low resource script settings.

Aditya Mishra, Akshay Agarwal, Haroon Lone

Traffic signboards are vital for road safety and intelligent transportation systems, enabling navigation and autonomous driving. Yet, recognizing traffic signs at night remains challenging due to visual noise and scarcity of public nighttime datasets. Despite advances in vision architectures, existing methods struggle with robustness under low illumination and fail to leverage complementary mutlimodal cues effectively. To overcome these limitations, firstly, we introduce INTSD, a large-scale dataset comprising street-level night-time images of traffic signboards collected across diverse regions of India. The dataset spans 41 traffic signboard classes captured under varying lighting and weather conditions, providing a comprehensive benchmark for both detection and classification tasks. To benchmark INTSD for night-time sign recognition, we conduct extensive evaluations using state-of-the-art detection and classification models. Secondly, we propose LENS-Net, which integrates an adaptive image enhancement detector for joint illumination correction and sign localization, followed by a structured multimodal CLIP-GCNN classifier that leverages cross-modal attention and graph-based reasoning for robust and semantically consistent recognition. Our method surpasses existing frameworks, with ablation studies confirming the effectiveness of its key components. The dataset and code for LENS-Net is publicly available for research.

Vimaleswar A, Prabhu Nandan Sahu, Nilesh Kumar Sahu et al.

Mental health plays a crucial role in the overall well-being of an individual. In recent years, digital platforms have been increasingly used to expand mental health and emotional support. However, there are persistent challenges related to limited user accessibility, internet connectivity, and data privacy, which highlight the need for an offline, smartphone-based solution. To address these challenges, we propose EmoSApp (Emotional Support App): an entirely offline, smartphone-based conversational app designed for mental health and emotional support. The system leverages Large Language Models (LLMs), specifically fine-tuned, quantized and deployed using Torchtune and Executorch for resource-constrained devices, allowing all inferences to occur on the smartphone. To equip EmoSApp with robust domain expertise, we fine-tuned the LLaMA-3.2-1B-Instruct model on our custom curated ``Knowledge dataset'' of 14,582 mental-health QA pairs, along with the multi-turn conversational data. Through qualitative human evaluation with the student population, we demonstrate that EmoSApp has the ability to respond coherently, empathetically, maintain interactive dialogue, and provide relevant suggestions to user's mental health problems. Additionally, quantitative evaluations on nine standard commonsense and reasoning benchmarks demonstrate the efficacy of our fine-tuned, quantized model in low-resource settings. By prioritizing on-device deployment and specialized domain adaptation, EmoSApp serves as a blueprint for future innovations in portable, secure, and highly tailored AI-driven mental health solutions.

Aditya Mishra, Haroon Lone

In academic settings, the demanding environment often forces students to prioritize academic performance over their physical well-being. Moreover, privacy concerns and the inherent risk of data breaches hinder the deployment of traditional machine learning techniques for addressing these health challenges. In this study, we introduce RiM: Record, Improve, and Maintain, a mobile application which incorporates a novel personalized machine learning framework that leverages federated learning to enhance students' physical well-being by analyzing their lifestyle habits. Our approach involves pre-training a multilayer perceptron (MLP) model on a large-scale simulated dataset to generate personalized recommendations. Subsequently, we employ federated learning to fine-tune the model using data from IISER Bhopal students, thereby ensuring its applicability in real-world scenarios. The federated learning approach guarantees differential privacy by exclusively sharing model weights rather than raw data. Experimental results show that the FedAvg-based RiM model achieves an average accuracy of 60.71% and a mean absolute error of 0.91--outperforming the FedPer variant (average accuracy 46.34%, MAE 1.19)--thereby demonstrating its efficacy in predicting lifestyle deficits under privacy-preserving constraints.

Nilesh Kumar Sahu, Snehil Gupta, Haroon R Lone

Anxiety-provoking activities, such as public speaking, can trigger heightened anxiety responses in individuals with anxiety disorders. Recent research suggests that physiological signals, including electrocardiogram (ECG) and electrodermal activity (EDA), collected via wearable devices, can be used to detect anxiety in such contexts through machine learning models. However, the generalizability of these anxiety prediction models across different activities and diverse populations remains underexplored-an essential step for assessing model bias and fostering user trust in broader applications. To address this gap, we conducted a study with 111 participants who engaged in three anxiety-provoking activities. Utilizing both our collected dataset and two well-known publicly available datasets, we evaluated the generalizability of anxiety detection models within participants (for both same-activity and cross-activity scenarios) and across participants (within-activity and cross-activity). In total, we trained and tested more than 3348 anxiety detection models (using six classifiers, 31 feature sets, and 18 train-test configurations). Our results indicate that three key metrics-AUROC, recall for anxious states, and recall for non-anxious states-were slightly above the baseline score of 0.5. The best AUROC scores ranged from 0.62 to 0.73, with recall for the anxious class spanning 35.19% to 74.3%. Interestingly, model performance (as measured by AUROC) remained relatively stable across different activities and participant groups, though recall for the anxious class did exhibit some variation.

Arijit Samal, Haroon R Lone

Non-invasive temperature monitoring of individuals plays a crucial role in identifying and isolating symptomatic individuals. Temperature monitoring becomes particularly vital in settings characterized by close human proximity, often referred to as dense settings. However, existing research on non-invasive temperature estimation using thermal cameras has predominantly focused on sparse settings. Unfortunately, the risk of disease transmission is significantly higher in dense settings like movie theaters or classrooms. Consequently, there is an urgent need to develop robust temperature estimation methods tailored explicitly for dense settings. Our study proposes a non-invasive temperature estimation system that combines a thermal camera with an edge device. Our system employs YOLO models for face detection and utilizes a regression framework for temperature estimation. We evaluated the system on a diverse dataset collected in dense and sparse settings. Our proposed face detection model achieves an impressive mAP score of over 84 in both in-dataset and cross-dataset evaluations. Furthermore, the regression framework demonstrates remarkable performance with a mean square error of 0.18$^{\circ}$C and an impressive $R^2$ score of 0.96. Our experiments' results highlight the developed system's effectiveness, positioning it as a promising solution for continuous temperature monitoring in real-world applications. With this paper, we release our dataset and programming code publicly.

Nandigramam Sai Harshit, Nilesh Kumar Sahu, Haroon R. Lone

Depression is one of the most common mental disorders affecting an individual's personal and professional life. In this work, we investigated the possibility of utilizing social media posts to identify depression in individuals. To achieve this goal, we conducted a preliminary study where we extracted and analyzed the top Reddit posts made in 2022 from depression-related forums. The collected data were labeled as depressive and non-depressive using UMLS Metathesaurus. Further, the pre-processed data were fed to classical machine learning models, where we achieved an accuracy of 92.28\% in predicting the depressive and non-depressive posts.

Pranay Jaiswal, Haroon R. Lone

This study investigates the potential of using smartwatches with built-in microphone sensors for monitoring coughs and detecting various cough types. We conducted a study involving 32 participants and collected 9 hours of audio data in a controlled manner. Afterward, we processed this data using a structured approach, resulting in 223 positive cough samples. We further improved the dataset through augmentation techniques and employed a specialized 1D CNN model. This model achieved an impressive accuracy rate of 98.49% while non-walking and 98.2% while walking, showing smartwatches can detect cough. Moreover, our research successfully identified four distinct types of coughs using clustering techniques.