Faculty Publications

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Scholarly Publications by Integral Academia

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    Ai Technologies For Pollution Detection and Remediation Efforts
    (Book Rivers, 2025) Aqeel Ahmad Khan, Uzma
    The escalating challenges of environmental pollution demand innovative and sustainable solutions beyond traditional monitoring and remediation methods. Artificial Intelligence (AI) technologies have emerged as transformative tools in detecting, predicting, and mitigating pollution across air, water, and soil ecosystems. By leveraging advanced algorithms, machine learning models, and sensor-based data integration, AI enables real time monitoring, precise source identification, and predictive analytics for environmental risks. Moreover, AI-powered robotics and autonomous systems are increasingly being deployed in pollution remediation efforts, such as waste segregation, water purification, and soil restoration. This paper critically examines the role of AI in environmental protection, highlighting its applications in pollution detection and remediation. It also explores case studies, opportunities, and ethical challenges associated with deploying AI- driven solutions. The findings suggest that while AI offers significant potential to revolutionize environmental management, its effective implementation requires robust infrastructure, interdisciplinary collaboration, and responsible governance frameworks.
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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.
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    The Future of Farming: How AI Enhances Crop Yield and Sustainability
    (Agri Magazine (Online International E-Magazine), 2025) Muchapothula Shiva Prasad, Mamta J. Patange, Dr. Shubhangi J. Dhage, Shabbeer Ahmad, Aman Kumar
    This article explores how Artificial Intelligence (AI) is transforming agriculture by improving crop yields, optimizing resource use, and enhancing sustainability. It highlights AI applications such as robotic automation, drones, machine learning, precision farming, predictive analytics, and genetic innovations. The article further discusses real-world success stories in AI adoption, identifies major challenges including technological gaps, social barriers, high costs, and data privacy concerns, and proposes solutions through policy reforms, training, and infrastructure development. The future of AI-powered farming includes smart seeds, advanced robots, blockchain-based supply chains, and climate-resilient decision systems.
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    Machine Learning: navigating promises and pitfalls of AI
    (Shipra Publication New Delhi, 2025) Dhriti Tiwari, Sangeeta Suman
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    Research on Coding and Decoding Scheme for 5G Terminal Protocol Conformance Test Based on TTCN-3
    (Springer, Singapore, 2023) Asif Khan, Sharmin Ansar; Cao Jingyao, Amit Yadav
    Protocol conformance testing is an indispensable part of the marketization of commercial terminals. This paper elaborates and studies the architecture, test model, and design scheme of the 5G terminal protocol conformance testing system and proposes a 5G terminal protocol based on TTCN-3 Conformance test codec solution. The outcome of research shows that this solution has a positive role in promoting the industrialization of 5G.
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    Mask Wearing Detection System for Epidemic Control Based on STM32
    (Springer, Singapore, 2023) Luoli, Amit Yadav, Asif Khan, Naushad Varish, Priyanka Singh & Hiren Kumar Thakkar
    This paper designs an epidemic prevention and control mask wearing detection system based on STM32, which is used to monitor the situation of people wearing masks. Tiny-YOLO detection algorithm is adopted in the system, combined with image recognition technology, and two kinds of image data with and without masks are used for network training. Then, the trained model can be used to carry out real-time automatic supervision on the wearing of masks in the surveillance video. When the wrong wearing or not wearing masks are detected, the buzzer will send an alarm, so as to effectively monitor the wearing of masks and remind relevant personnel to wear masks correctly.
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    Detection of Rheumatoid Arthritis Using CNN by Transfer Learning
    (Springer, Singapore, 2024) Afroj Alam, Muhammad Kalamuddin Ahamad, K. O. Mohammed Aarif , Taushif Anwar
    Rheumatoid arthritis (RA) represents a long-term autoimmune condition marked by joint inflammation, resulting in discomfort and distortions of the joint structure. Early and accurate detection of RA plays a pivotal role in managing the disease and preventing irreversible joint damage. In this study, we propose a novel approach for detecting Rheumatoid Arthritis through the utilization of Convolutional Neural Networks (CNN) employing transfer learning. Our methodology leverages pre-trained CNN architectures to extract hierarchical features from medical images, particularly X-ray images of affected joints. Transfer learning enables the model to capitalize on the knowledge learned from large and diverse datasets, enhancing its ability to discriminate between healthy and RA-affected joints even with limited labeled data. Fine-tuning of the pre-trained model is performed to adapt the network to the specific characteristics of RA-related features. We present a comprehensive evaluation of our approach using a dataset consisting of X-ray images from both RA patients and healthy individuals. The experimental results showcase the effectiveness of our CNN-based transfer learning method in accurately detecting RA. The outcomes of this research have significant implications for early diagnosis and management of RA. By harnessing the power of deep learning and transfer learning, our approach contributes to the development of a non-invasive and efficient tool for RA detection, which can aid healthcare practitioners in making informed decisions and improving patient outcomes.