Browsing by Author "Saini, K"

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    Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram
    (2023-12) Gupta, C; Khullar, V; Goyal, N; Saini, K; Baniwal, R; Kumar, S; Rastogi, R
    In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early stages to stop the side effects. Early detection is critical to identify a variety of serious conditions. In order to provide safe and effective protection to MDD patients, it is crucial to automate diagnoses and make decision-making tools widely available. Although there are various classification systems for the diagnosis of MDD, no reliable, secure method that meets these requirements has been established to date. In this paper, a federated deep learning based multimodal system for MDD classification using electroencephalography (EEG) and audio datasets is presented while meeting data privacy requirements. The performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. The study began by extracting features from several pre-trained models and ultimately decided to use bidirectional short-term memory (Bi-LSTM) as the base model, as it had the highest validation accuracy of 91% compared to a convolutional neural network and LSTM with 85% and 89% validation accuracy on audio data, respectively. The Bi-LSTM model also achieved a validation accuracy of 98.9% for EEG data. The FL method was then used to perform experiments on IID and non-IID datasets. The FL-based multimodal model achieved an exceptional training and validation accuracy of 99.9% when trained and evaluated on both IID and non-IIID datasets. These results show that the FL multimodal system performs almost as well as the Bi-LSTM multimodal system and emphasize its suitability for processing IID and non-IIID data. Several clients were found to perform better than conventional pre-trained models in a multimodal framework for federated learning using EEG and audio datasets. The proposed framework stands out from other classification techniques for MDD due to its special features, such as multimodality and data privacy for edge machines with limited resources. Due to these additional features, the framework concept is the most suitable alternative approach for the early classification of MDD patients.
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    An efficient framework for secure data transmission using blockchain in IoT environment
    (2023-12) Bhattacharjee, S; Gangwar, S; Kumar, M; Saini, K; Saini, R; Chauhan, S; Pandey, K; Goyal, N
    The secure and efficient sharing of data has been recognised as a significant concern in Internet of Things (IoT)-enabled smart systems, including smart cities, smart agriculture, and smart health applications. Smart systems utilise a substantial quantity of IoT devices, which in turn generate a significant volume of data. Internet of Things (IoT) devices typically possess constrained storage and processing capacities, making the implementation of security measures on such devices a difficult task. This paper presents a framework for secure data transmission using blockchain (SDTUB) for blockchain-based IoT systems, with a focus on enhancing data security. The use of clustered authorization aims to enhance the interoperability of IoT authorization. The central blockchain is employed for permission purposes concerning cluster management nodes, whereas the regional blockchain suffices for authorization of regular nodes. The proposed mechanism is implemented using MATLAB, and the performance is analysed using performance metrics such as energy consumption and objective value. In the proposed mechanism, the energy consumption is low compared to the AuBWSN technique.
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    An efficient framework for secure data transmission using blockchain in IoT environment
    (2023-12) Bhattacharjee, S; Gangwar, S; Kumar, M; Saini, K; Saini, R; Chauhan, S; Pandey, K; Essah, R; Goya, N
    The secure and efficient sharing of data has been recognised as a significant concern in Internet of Things (IoT)-enabled smart systems, including smart cities, smart agriculture, and smart health applications. Smart systems utilise a substantial quantity of IoT devices, which in turn generate a significant volume of data. Internet of Things (IoT) devices typically possess constrained storage and processing capacities, making the implementation of security measures on such devices a difficult task. This paper presents a framework for secure data transmission using blockchain (SDTUB) for blockchain-based IoT systems, with a focus on enhancing data security. The use of clustered authorization aims to enhance the interoperability of IoT authorization. The central blockchain is employed for permission purposes concerning cluster management nodes, whereas the regional blockchain suffices for authorization of regular nodes. The proposed mechanism is implemented using MATLAB, and the performance is analysed using performance metrics such as energy consumption and objective value. In the proposed mechanism, the energy consumption is low compared to the AuBWSN technique.