Faculty Publications

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Scholarly Publications by Integral Academia

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    Study of an Innovative Approach to IoT Based Human Activity Recognition
    (BP International, 2025) Motashim Rasool , Rizwan Akhtar , Uvais Ahmad
    Recognizing human activities is vital for numerous contemporary applications rooted in the Internet of Things (IoT) framework, spanning from the creation of intelligent video surveillance setups to the advancement of robotic assistants for the elderly. Recently, there has been significant exploration into machine learning algorithms to enhance the recognition of human activities. Despite these research endeavors, there remains a notable dearth of studies focusing on efficiently recognizing complex human activities, particularly those involving transitions, and no research has been conducted to assess the impact of noise in training data on algorithm performance. This paper addresses these gaps by presenting an innovative activity recognition system centered on a neural classifier with memory capabilities, designed to optimize the classification of both transitional and non-transitional human activities. Utilizing unobtrusive IoT devices such as accelerometers and gyroscopes integrated into widely-used smartphones, the system effectively identifies human activities [1,2]. The key feature of the proposed system lies in leveraging a neural network augmented with short-term memory to retain information about preceding activities' characteristics. Experimental validation demonstrates the reliability and accuracy of the proposed system compared to state-of-the-art classifiers, highlighting its robustness in handling noisy data. Human Activity Recognition (HAR) is essential for various modern applications within the Internet of Things (IoT) framework, from developing intelligent video surveillance systems to enhancing robotic assistants for the elderly. Despite significant advancements in machine learning algorithms for HAR, there is a notable lack of research on effectively recognizing complex human activities, particularly those involving transitions, and assessing the impact of noise in training data on algorithm performance. This paper addresses these gaps by presenting an innovative activity recognition system centered on a neural classifier with memory capabilities. Designed to optimize the classification of both transitional and non-transitional human activities, the system employs unobtrusive IoT devices such as accelerometers and gyroscopes integrated into widely used smartphones [1,3]. A key feature of the proposed system is the utilization of a neural network augmented with short-term memory to retain information about preceding activities' characteristics. Experimental validation demonstrates the system's reliability and accuracy compared to state-of-the-art classifiers, emphasizing its robustness in handling noisy data.
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    Nanotechnology innovation: AI-enhanced polymer drug delivery systems
    (Walter de Gruyter GmbH, 2025) Usama Ahmad; Wan Nurhidayah Wan Hanaffi, Khan Aejaz Ahmed
    The confluence of polymer nanotechnology with artificial intelligence (AI) is revolutionizing drug development and targeted drug delivery, ensuring unprecedented precision in therapeutics. By coupling data analytics with AI-driven polymeric nanocarrier systems, the technology surpasses the constraints of traditional drug delivery, including bioavailability and patient-specificity, by virtue of superior design, encapsulation efficiency, and controlled release mechanisms for the drug. Predictive modeling and machine learning algorithms aid AI in optimizing drug–polymer interaction and optimizing the physicochemical characteristics of polymer nanocarriers so that therapeutics are released accurately and controllably at target sites to reduce side effects. AI’s capability to forecast the interaction of disease-specific, biocompati ble, and responsive polymers for patient profiles advances the principle of precision medicine. Improved encapsulation efficiency and optimized kinetics by AI-driven processes maximize the direct delivery of therapeutics to the target site with optimal doses for enhanced bioavailability and therapeutic efficacy. The amalgamation of AI with polymeric nanotechnology thus enables drug discovery in the rapid prototype and optimization of nanocarrier systems to deliver precise, individualized drug delivery with high accuracy to diverse therapeutic domains. This chapter discusses the development potential of AI-fortified polymer-based drug delivery systems, their ability to maximize the efficacy and selectivity of treatment regimens. With AI-driven innovation, polymer nanotechnology has the ability to improve patient treatment through highly personalized and potent medical options, beginning a new era of tailored medicine and new paths of drug innovation.