School of Science & Technology (SST)

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    Emerging Role of Artificial Intelligence (AI) in Early Autism Diagnosis: Comprehensive Review
    (Springer, 2026) Pant, Shivani; Gehlot, Anita
    SDG 10 aims to reduce inequalities and ensure no one is left behind. Autism, a complex neurological disorder, is found in around 1/100 children worldwide. Early diagnosis is crucial for effective treatment and intervention. Artificial Intelligence (AI) has emerged as a revolutionary tool in ASD research, enhancing early detection mechanisms and improving symptom management. ML, supervised, unsupervised, semi-supervised, and reinforcement learning algorithms are used for image recognition, language understanding, and speech processing. This paper aims to emphasize the role of AI in early autism diagnosis and to identify and evaluate various AI-based algorithms used for accurate autism detection. Autism diagnosis is challenging due to the lack of standard medical tests. AI interventions, such as natural language processing, machine learning and deep learning. Can improve early diagnosis by identifying complex patterns and detecting subtle symptoms. However, issues such as algorithm transparency and data privacy must be addressed. With continued research and ethical AI technology, these algorithms can transform early ASD diagnosis, enabling timely intervention and improved outcomes.
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    A comprehensive survey on federated learning for privacy preservation in digital healthcare applications
    (Springer, 2026-01-11) Kanauzia, Rohit; Singh, Mridula et al.
    Internet of Medical Things (IoMT) is a relatively new service that has the possible to revolutionize healthcare by connecting previously analog technologies digitally. Consequently, numerous healthcare applications based on IoMT are utilized in the course of daily life. Despite the abundance of machine learning (ML) techniques aimed at improving healthcare data management, none of them have been able to guarantee the data's complete privacy and security. The precise nature of the clinical data makes ML application difficult and yields unsatisfactory results. A new paradigm in ML called federated learning (FL) has arisen as a means to discover untapped potential in digital healthcare uses that protect patients' and clients' privacy without compromising their data. This survey comprehensively reviews over 105 peer-reviewed publications (2018–2025) sourced from IEEE, Elsevier, Springer, ACM, and MDPI digital libraries. It classifies existing FL approaches for digital healthcare based on architecture, communication efficiency, privacy preservation, and application domain. Survey highlights key findings, comparative analyses with conventional FL-based systems, and lessons learned from prior studies. Finally, open challenges such as scalability, energy efficiency, model heterogeneity, and secure aggregation are deliberated, along with future research directions to enable trustworthy FL-based IoMT healthcare ecosystems.