arXiv subjects

Muhammad Aslam Jarwar

Publications and source records attributed to Muhammad Aslam Jarwar.

8 recordsLinked to original sources

arXiv2026

Privacy-Preserving ECG-HRV Emotion Classification for Mood Swing Detection using Federated Learning on Constrained Edge Devices

This paper presents a privacy-preserving mood monitoring framework that classifies emotional states from ECG-derived heart rate variability (HRV) features. The framework is evaluated on three public datasets comprising 74 participants and further validated using a wearable device in a pilot study. Leave-One-Participant-Out cross-validation (LOPO-CV) is used to assess generalization to unseen individuals. A series of experiments investigates the impact of temporal windowing, HRV feature selection, and class balancing on both four-class valence-arousal and binary valence classification. Results show that class balancing substantially improves minority-class recall, while Logistic Regression consistently outperforms five alternative classifiers and achieves competitive performance against HRV-based baselines. Pilot validation using a Polar H10 wearable achieves 75% binary valence accuracy on an unseen individual without calibration, demonstrating the feasibility of real-world deployment. To preserve privacy, the framework is also implemented using federated learning on a six-node Raspberry Pi cluster. Federated Logistic Regression achieves performance comparable to centralized training without sharing raw physiological data. These results demonstrate the potential of interpretable and privacy-preserving mood monitoring for continuous mental health assessment in clinical and community settings.

cs.LG↗

arXiv2026

Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals

Seizure prediction and detection, essential for clinical treatment of epilepsy, remain the crucial task of this neurological disease. EEG and iEEG are widely used to analyze epilepsy due to its capability of capturing discriminative brain activities at high time resolution. However, existing works generally analyze either EEG or iEEG seizure activity separately, where EEG analysis has been well-researched, while more and more works consider iEEG for its higher specificity at the spatial level and signal quality. Currently, most of the existing seizure prediction systems are single-modal in nature, which limits its application to a variety of clinical conditions. Therefore, a common framework to analyze seizures with both EEG and iEEG signals needs to be developed. This paper presents a new framework of multimodal seizure classification integrating the heterogeneous EEG and iEEG recordings from different subjects and sources. A specific framework, called Unified Multimodal Signal Harmonization (UMSH), is developed for harmonizing signals with varied sampling rates and recording conditions. Moreover, to handle the extremely class-imbalanced problem in the data sets, we use a Conditional Generative Adversarial Network (cGAN) based data augmentation technique to generate synthetic samples to balance the class distribution. The proposed framework uses the EEGNet-Vision Transformer (EEGNet-ViT) as the feature extraction and classification network to learn the local discriminative patterns as well as global contextual information in the multimodal seizure data. Experimental results demonstrate the effectiveness of the proposed framework for robust seizure classification, emphasizing the potential of integrated EEG-iEEG multimodal learning in advancing next-generation epilepsy monitoring and clinical decision-support systems.

cs.LG↗

arXiv2026

LightHybridNet: A Lightweight Hybrid Deep Learning Framework for Efficient Multi-Label Ophthalmic Disease Classification in Imbalanced Fundus Images

Eye diseases are a major global cause of blindness and vision impairment, highlighting the need for accurate and early diagnosis to prevent further deterioration. Despite advances in medical imaging, classifying retinal diseases from fundus images remains challenging due to the complexity and subtlety of visual features. This study aims to develop and validate LightHybridNet, a novel lightweight hybrid deep learning framework, to improve the classification of eye diseases from fundus images. The model incorporates architectural features, such as depthwise separable convolutions and compound scaling mechanisms, to enhance diagnostic accuracy and robustness. To validate the model, the ODIR-5K dataset, comprising 8 disease categories, was used. To enhance image quality, pre-processing approaches were applied, including histogram equalisation, colour space conversion, and contrast adjustment, resulting in high-resolution images. Furthermore, image augmentation was employed to increase the dataset size, thereby improving the model's ability to generalise during training. The hybrid architecture helps the model concentrate on relevant image regions while capturing and integrating details at different scales, significantly improving classification performance. To evaluate its effectiveness, the model was compared with transfer-learning models. The results show that LightHybridNet outperforms the baselines, achieving a weighted F1-score of 91.51%, a macro AUC of 98.26%, and a Cohen's Kappa of 90.14% on the validation set. Furthermore, model interpretability was enhanced using explainable AI methods, including Grad-CAM, to identify and emphasise features influencing the decision-making process. LightHybridNet represents a promising advancement in automated ocular disease detection, offering improved diagnostic reliability for clinical use.

eess.IV↗

arXiv2022

Get your Foes Fooled: Proximal Gradient Split Learning for Defense against Model Inversion Attacks on IoMT data

The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 14.0$\%$, 17.9$\%$, and 36.9$\%$ gains in accuracy over reconstructed and adversarial attacked images, respectively.

cs.CR↗

arXiv2021

Provenance, Anonymisation and Data Environments: a Unifying Construction

The Anonymisation Decision-making Framework (ADF) operationalizes the risk management of data exchange between organizations, referred to as "data environments". The second edition of ADF has increased its emphasis on modeling data flows, highlighting a potential new use of provenance information to support anonymisation decision-making. In this paper, we provide a use case that showcases this functionality more. Based on this use case, we identify how provenance information could be utilized within the ADF framework, and identify a currently un-met requirement which is the modeling of \textit{data environments}. We show how data environments can be implemented within the W3C PROV in four different ways. We analyze the costs and benefits of each approach, and consider another use case as a partial check for completeness. We then summarize our findings and suggest ways forward.

cs.DB↗

arXiv2019

Microservices based Framework to Support Interoperable IoT Applications for Enhanced Data Analytics

Internet of things is growing with a large number of diverse objects which generate billions of data streams by sensing, actuating and communicating. Management of heterogeneous IoT objects with existing approaches and processing of myriads of data from these objects using monolithic services have become major challenges in developing effective IoT applications. The heterogeneity can be resolved by providing interoperability with semantic virtualization of objects. Moreover, monolithic services can be substituted with modular microservices. This article presents an architecture that enables the development of IoT applications using semantically interoperable microservices and virtual objects. The proposed framework supports analytic features with knowledge-driven and data-driven techniques to provision intelligent services on top of interoperable microservices in Web Objects enabled IoT environment. The knowledge-driven aspects are supported with reasoning on semantic ontology models and the data-driven aspects are realized with machine learning pipeline. The development of service functionalities is supported with microservices to enhance modularity and reusability. To evaluate the proposed framework a proof of concept implementation with a use case is discussed.

cs.DC↗

arXiv2019

Microservices based Linked Data Quality Model for Buildings Energy Management Services

During the production, distribution, and consumption of energy, a large quantity of data is generated. For efficiently using of energy resources other supplementary data such as building information, weather, and environmental data etc. are also collected and used. All these energy data and relevant data is published as linked data in order to enhance the reusability of data and maximization of energy management services capability. However, the quality of this linked data is questionable because of wear and tears of sensors, unreliable communication channels, and highly diversification of data sources. The provision of high-quality energy management services requires high quality linked data, which reduces billing cost and improve the quality of the living environment. Assessment and improvement methodologies for the quality of data along with linked data needs to process very diverse data from highly diverse data sources. Microservices based data-driven architecture has great significance to processes highly diverse linked data with modularity, scalability, and reliability. This paper proposed microservices based architecture along with domain data and metadata ontologies to enhance and assess energy-related linked data quality.

cs.CY↗

arXiv2019

A Framework for Detecting Event related Sentiments of a Community

Social media has revolutionized human communication and styles of interaction. Due to its easiness and effective medium, people share and exchange information, carry out discussion on various events, and express their opinions. For effective policy making and understanding the response of a community on different events, we need to monitor and analyze the social media. In social media, there are some users who are more influential, for example, a famous politician may have more influence than a common person. These influential users belong to specific communities. The main object of this research is to know the sentiments of a specific community on various events. For detecting the event based sentiments of a community we propose a generic framework. Our framework identifies the users of a specific community on twitter. After identifying the users of a community, we fetch their tweets and identify tweets belonging to specific events. The event based tweets are pre-processed. Pre-processed tweets are then analyzed for detecting sentiments of a community for specific events. Qualitative and quantitative evaluation confirms the effectiveness and usefulness of our proposed framework.

cs.SI↗