A complete public catalogue of verified journal articles, 2026 IEEE papers, earlier conference work, books and book chapters, with plain-language descriptions of the contribution.
Featured chapter: Deep LearningThis paper presents a domain-focused conversational AI system designed to support interactive engagement with Bhagavad Gita source material. Its public contribution is the exploration of culturally grounded dialogue, multi-turn interaction and digital access to philosophical literature through a web-based research interface.
The paper describes a camera-based sports-analysis workflow that reviews a football player’s posture at the key moment of a curve shot. Multiple viewpoints support comparison of body alignment and movement, producing structured, explainable feedback for coaching and performance-review contexts without requiring wearable motion sensors.
This paper investigates a field-oriented method for estimating beach-sand grain size from ordinary smartphone images. It combines visual measurements with predictive analysis to support faster and more accessible sediment assessment, with potential value for coastal monitoring where specialised laboratory equipment is not immediately available.
The research presents a multi-view video framework for reviewing basketball free-throw technique. It identifies the shot-release moment and evaluates body alignment from synchronised camera views, allowing players and coaches to receive structured observations about movement and shooting posture using standard video equipment.
This paper presents a multi-view cricket-analysis workflow that locates the bat-ball contact event and reviews the player’s movement around that moment. The system converts ordinary video into structured observations about joint alignment and shot technique, supporting more repeatable coaching and sports-performance analysis.
This study examines how a person’s wider health history—including associated diseases, symptoms, surgical history and demographic factors—can add context to Parkinson’s disease research. Its public value is a broader, patient-history view of risk and symptom patterns rather than an assessment based only on the best-known motor signs.
The paper maps a set of co-occurring diseases and symptoms reported with Parkinson’s disease to an Ayurvedic Tridosha framework, then studies how the resulting patterns vary with factors such as age and body-mass index. It is best positioned as an exploratory bridge between traditional-health classification and quantitative analysis.
This research combines Dosha-derived symptom scores with general health attributes and compares machine-learning models on the Fox Insight dataset. The contribution is a proof-of-concept showing how a traditional classification framework can be translated into variables for computational screening research.
The paper presents a research prototype that estimates glucose-related optical changes at the fingertip and sends readings to a mobile interface through an IoT workflow. It demonstrates the integration of sensing, display and remote access in a small pilot study.
This work discusses an IoT-enabled health-monitoring approach for continuously observing parameters such as heartbeat and temperature and making the information available for remote review. Its central theme is extending basic monitoring beyond the immediate clinical setting, especially where continuous access to clinicians is limited.
This review organises Parkinson’s disease research around its history, possible etiological factors, and the range of motor and non-motor symptoms. It highlights why early assessment benefits from looking beyond tremor alone and provides a structured foundation for later symptom-based and data-driven studies.
The article reviews how deep learning can identify patterns in complex neurological data and support research into neuronal disorders. It also explains practical barriers, including computational cost, the need for large datasets and the difficulty of translating promising models into dependable healthcare use.
This review surveys smartphone, wearable and mobile-cloud systems used to observe Parkinsonian features such as tremor, gait and freezing episodes. It shows how portable applications may support repeated home monitoring while also revealing the need for stronger validation and consistent evaluation across devices.
The paper explores interactive computer and mobile games as engaging additions to oral-motor and rehabilitation activities for children with cerebral palsy. Its contribution is the idea of using purposeful game tasks to encourage participation and repetition alongside therapist-led care.
AirOrb converts live weather and air-quality data into a compact visual display that can be understood at a glance. The paper focuses on accessible environmental awareness through a combination of text information and changing light patterns.
This work demonstrates touch-free drone interaction using coordinated two-hand gestures. The research explores whether natural movement can offer a more intuitive command interface while retaining the explicit safety checks required for flight control.
The study investigates whether smoking history is associated with differences in the pattern or severity of motor and non-motor symptoms reported by people with Parkinson’s disease. It contributes to a broader health-history view of disease research.
The paper presents a connected monitoring concept for identifying common streetlight faults and reporting them remotely. The public story is faster fault visibility, more targeted maintenance and improved oversight of distributed lighting infrastructure.
This work combines live asset observation with predictive analysis so that changes in condition or activity can be reviewed earlier. It is suited to a consultancy narrative about visibility, exception detection and proactive operational planning.
The paper studies how a family history of neurological conditions may relate to Parkinson’s disease patterns in a research population. It extends the author’s health-history programme by treating family history as contextual evidence that can be analysed alongside symptoms and demographics.
This research explores game-based rehabilitation activities designed to make repetitive therapeutic tasks more engaging and measurable. It connects human-centred interaction design with the practical needs of sustained participation in guided therapy.
The study examines whether prior surgical procedures are statistically associated with Parkinson’s disease occurrence or progression in the analysed records. It demonstrates a broader method of treating surgical history as a potentially useful research variable.
This paper describes an automated system intended to sanitise multiple books within a controlled cycle, supporting libraries and shared-book environments. The website should emphasise batch handling, operator workflow and repeatable processing rather than disclosing the mechanism.
The prototype links spoken commands from a mobile interface to the movement of a small robotic vehicle. It serves as an accessible demonstration of human–machine interaction, remote command handling and embedded control.
This work considers how sensor measurements can make Parkinsonian motor features such as tremor or movement changes more objective and trackable. It supports the longer-term goal of repeatable, data-assisted assessment outside a single visual examination.
The paper explores machine-learning analysis of Parkinson’s-related data as an aid to earlier identification of patterns that merit clinical attention. The public contribution is the translation of research variables into a repeatable computational screening workflow.
This work presents an active orthotic concept intended to support ankle and foot movement during gait. It brings together mechanical assistance, sensing and rehabilitation-oriented design to investigate more responsive mobility support.
The paper reviews how computational analysis of biological and clinical data can support healthcare research, including pattern discovery, sequence analysis and data-informed decision making. It establishes an early connection between the author’s computing and biomedical research interests.
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Artificial Intelligence in e-Health Framework, Volume 1
This chapter connects deep-learning concepts with practical questions in healthcare: how can medical images, health records and monitoring data become more useful for research and informed decision-making?
The discussion also highlights what responsible adoption requires: privacy protection, well-labelled datasets and understandable models. It offers a starting point for students, researchers and interdisciplinary teams exploring AI-enabled healthcare—not a claim of clinical validation.
Probir Mondal, Pratyay Banerjee & Dr. Vinayak Majhi. Chapter 3 in Artificial Intelligence in e-Health Framework, Volume 1, Academic Press, 2025, pp. 63–90. ISBN 9780443138164. DOI: 10.1016/B978-0-443-13816-4.00003-6.
A broad introduction to the principles and uses of biomedical instruments across diagnostic, monitoring and therapeutic settings. Publisher material highlights medical imaging, laboratory analysis, life-support and stimulation equipment, together with patient-safety considerations—making the book suitable for students and early-career practitioners who need an application-oriented foundation.
This chapter explains why magnetic-resonance imaging is central to quantitative brain-tumour research and reviews how machine-learning methods can support the classification and interpretation of imaging features. The website story should focus on MRI as a rich, non-invasive source of information for radiomics research, while retaining the need for clinical expertise and validation.
The chapter surveys how artificial intelligence and machine learning can help organise and analyse biological data for areas such as disease research, DNA sequencing, protein-structure work and data-informed drug development. It is a concise bridge between foundational bioinformatics tasks and intelligent computational tools.
The chapter reviews content-based image retrieval as a way to locate clinically or visually similar medical images using image features rather than text labels alone. It discusses its relevance across modalities such as MRI, CT, PET and ultrasound, where organised retrieval can support research, education and image-based decision support.
This chapter reviews technologies and machine-learning approaches used to identify and classify Parkinsonian tremor. It connects measurable movement characteristics with computational analysis, giving readers a structured view of how tremor data may support neurological research and future decision-support systems.
The chapter reviews Ayurvedic perspectives relevant to Alzheimer’s disease, including preventive practices, diet, lifestyle and plant-based traditions discussed in the wider phytomedicine literature. It is appropriate for a scholarly page about traditional-knowledge research, provided the language clearly separates historical or review-based discussion from established clinical treatment evidence.