Book and Book Chapters

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    Assessment of how users perceive the usage of biometric technology applications
    (IntechOpen, 2022-07-27) Habibu, Taban; Luhanga, Edith; Sam, Anael
    Biometrics applications are progressively widespread as a means of authenticating end-users owing to the extensive range of benefits over traditional authentication (token-base-authentication). However, the transaction involves taking into account the perceptions and responses of end-users. If end-users are fearful, hesitant about these biometric technology-applications, misuse and implementation-complications can surely overshadow. The goal of this study is to sightsee the user’s-motivation, understanding, consciousness and acceptance towards utilization of biometric technology-applications. A 300-person survey was conducted to evaluate public-opinion on the use and adoption of biometrics. Stratified sample technique was used to administer the surveys. The results presented that perceived ease-of-use, user-motivation and attitude are more important-factors when deciding whether to accept new technology-applications. Although many end-users have become more familiar with biometric technology-applications (e.g., Fingerprints or facial-recognition), many individuals still have a negative-perception of the technology. Concerns regarding confidentiality and security i.e., storing and protecting personal-identification data, the fear of intruding into a person’s daily-life and disclosing personal-information remain a major problem. Some end-users claim that despite the potential resilience to biometrics, designers must mentally and psychologically prepare the general public for the new use of biometric technology. This will make it possible to transform negative user-perceptions into a positive-experience. Thus, this study can help end-users and companies understand and make the right decisions to promote the use of biometric-applications and services. The study is expected to be an important research-discovery that will greatly contribute to Uganda’s digital-economy.
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    The effect of human-computer interaction on new applications by exploring the use case of ChatGPT in healthcare services. In modern technology in healthcare and medical education: Blockchain, IoT, AR, and VR
    (IGI Global, 2024) Mijwil, Maad M.; Naji, Aseel Shakir; Doshi, Ruchi; Hiran, Kamal Kant; Bala, Indu; Ali, Guma
    Human-Computer interaction (HCI) is a domain that focuses on growing the interaction between humans and computer systems by designing and developing user interfaces that are efficient and delightful to use. In this chapter, the authors focus on the importance of deep human-computer interaction on new applications with an emphasis on using ChatGPT applications in the health services domain. This chapter provides full details on the importance of executing ChatGPT in various health-related scenarios while highlighting the importance of HCI to enhance user interactions in personalized medical advice in a ChatGPT application. This chapter concludes that the capabilities of ChatGPT and artificial intelligence applications can revolutionize the healthcare industry by enhancing the accessibility and effectiveness of new media communications between the user and applications while creating innovative resolutions to improve healthcare services.
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    Sensing of type 2 diabetes patients based on Internet of Things solutions: An Extensive survey
    (IGI Global, 2024) Mijwil, Maad M.; Bala, Indu; Ali, Guma; Aljanabi, Mohammad; Abotaleb, Mostafa; Doshi, Ruchi; Hiran, Kamal Kant; El-Kenawy, El-Sayed M.
    Internet of things solutions have brought about a significant revolution in the development of healthcare by providing remote monitoring capabilities and providing doctors with reports on patients in real-time, which leads to developing the care of patients with type 2 diabetes and enhancing their health condition. Through several sensors, IoT devices can collect patients' health data, such as glucose level, blood pressure, heart rate, and physical activity, so that healthcare workers can assess patients' health status and disease development within the body. These devices contribute to saving patients' lives by providing continuous monitoring of vital signs and disease management by physicians and healthcare workers. In this context, this article contributes to reviewing the development of IoT solutions in providing information and mechanisms adopted in monitoring patients with type 2 diabetes, data security issues, privacy concerns, and interoperability.
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    Transforming patient care machine learning and biomedical data for adaptive and real-time healthcare solutions
    (Springer, 2026-02-22) Kumaran, S.; Murugan, P. Sundara Bala; Sathish, A.; Geetha, D. Mohana; Venkatesan, T.; Ali, Guma
    Patient treatment is undergoing a revolution that is the fusion of ML and biomedical information, which facilitates adaptive and real-time healthcare. The existing healthcare systems exist in the form of non-responsive to dynamic patient status static models, which lead to diagnostic and therapeutic delays. This paper presents a novel hybrid DL algorithm that uses CNNs and LSTM networks to process real-time biomedical signals to provide individualized monitoring and early anomaly detection. The CNN element determines the spatial characteristics in biomedical signals, and LSTM module distinguishes the temporal dependencies to provide accurate predictions of trends. The new model is trained and self-updates in real-time using the principles of reinforcement learning and optimizes its pre-dictive quality as it gains new experience. Dataset based real-world assessments indicate high accuracy in anomaly detection, lower response times, and improved patient outcomes. This enhances the early detection of diseases, simplification of treatments, and reduction in healthcare costs by integrating ML-based predic-tive analytics with real-time observations, where the possibilities of intelligent, data-driven, personalized patient care.