/library/oar/handle/123456789/519 The Faculty of Engineering is located at the University's main campus and offers tuition and supervision to about 477 students at both undergraduate and postgraduate levels while conducting research in all fields covered by its departments. Tue, 04 Nov 2025 15:40:25 GMT 2025-11-04T15:40:25Z /library/oar/handle/123456789/140754 Title: Advances in emerging technologies and computing innovations Authors: Ghonge, Mangesh M.; Liu, Haipeng; Khan, Mudassir; Tran, Tien Anh Abstract: International Conference on Emerging Technologies and Computing Innovations (ICETCI 2025) covers discussions from different stakeholders, researchers, practitioners, and industries across the globe in the recent technology trends and their impact on human life. This book discusses a microcosm of the conference experience bringing together multi-disciplinary research, real-world applications, and tomorrow’s aspirations. As all papers are double-blind peer-reviewed and of high quality, the proceedings are a unique publication dealing with a variety of topics such as artificial intelligence, machine learning, blockchain, quantum computing, Internet of Things (IoT), cybersecurity, data science, edge computing, and sustainable technology solutions. No contributions reflect the leading-edge breakthroughs, imaginative practices, and influential examples that are abundantly illustrative of the reader as if the reader is viewing the technical realm through a telescope into the future, full of contexts that would change how we connect and work. With the ushering in of a new era, additional technologies will power world progress, transforming businesses, economies, and societies. ICETCI-2025 is therefore important since digitization changes are happening across the world at a rapid pace. Also, this book is a codification of this partnership, through novel thinking and transformative research that will help solve some of the biggest challenges of today’s time. The depth of the topics signals the multifaceted reality of the topic and also highlights how innovation can span those boundaries and lead to transformative change. Apart from academic rigor, there are also practical aspects and future directions discussed in the proceedings. Whether it’s exploring discussions on the role of AI in healthcare or education, or others on the emerging potential of blockchain within supply chain management, contributions here have practical lessons for researchers, practitioners, and policymakers. The volume stands as a curatorial jeopardy that connects theory and praxis to engender outcomes and yield in the global tech ecology Wed, 01 Jan 2025 00:00:00 GMT /library/oar/handle/123456789/140754 2025-01-01T00:00:00Z /library/oar/handle/123456789/140745 Title: Decoding diagnosis : AI explainability for enhanced skin cancer detection Authors: Sandamal, Nipun; Cristina, Stefania; Camilleri, Kenneth P Abstract: Skin cancer is one of the most common cancers worldwide and is primarily diagnosed through visual examination. With the availability of large amounts of dermoscopic data, recent advancements in artificial intelligence (AI) have achieved remarkable accuracy in skin cancer classification. However, due to the black-box nature of deep learning models, dermatologists often struggle to understand the underlying decision-making process, limiting the transparency and interpretability of AI-driven diagnoses. In this work, we investigate advancements in Prototypical Part Networks (ProtoPNet) to skin cancer detection by applying the Pixel-Grounded Prototypical Part Network (PIXPNET), designed to address the challenge of pixel-space mapping in prototype projection. The PIXPNET architecture was trained and evaluated to assess its generalizability. Our results show that PIXPNET significantly outperforms ProtoPNet for skin cancer detection in a multi-class classification setting. Additionally, we analyze the learned prototypes to assess their relevance to input images, demonstrating improved interpretability compared to its counterpart, ProtoPNet. Wed, 01 Jan 2025 00:00:00 GMT /library/oar/handle/123456789/140745 2025-01-01T00:00:00Z /library/oar/handle/123456789/140744 Title: Remote monitoring of vital signs Authors: Cristina, Stefania; Počta, Peter; Zgank, Andrej; Camilleri, Kenneth P; Colantonio, Sara; Lambrinos, Lambros Abstract: This chapter aims to explore state-of-the-art vision- and audio-based methods for vital sign monitoring, as applied to ambient assisted living for older adults and people with special needs. We review different vision- and audio-based monitoring techniques, identify their advantages and limitations, explore emerging trends and open challenges, and draw recommendations for future directions. This work will serve as a starting point for beginners who are looking to gain an entry point into the area, as well as a guide to practitioners who are interested in learning more about recent developments of vision- and audio-based methods for active assisted living in general, and remote monitoring of vital signs in particular. Wed, 01 Jan 2025 00:00:00 GMT /library/oar/handle/123456789/140744 2025-01-01T00:00:00Z /library/oar/handle/123456789/140735 Title: Activities of daily living (ADL) and behavior recognition Authors: Aleksic, Slavisa; Despotovic, Vladimir; Cristina, Stefania Abstract: This chapter addresses the state of research in human activity recognition (HAR) for active assisted living (AAL) applications. We provide a comprehensive review of the ongoing research efforts and identify future trends in this area, especially regarding the activities of daily living (ADL) and behavior recognition. The focus of this work is on privacy-preserving methods and technologies that use audio and video modalities for HAR, as well as combining them with various sensors and wearables in a multimodal setup. Wed, 01 Jan 2025 00:00:00 GMT /library/oar/handle/123456789/140735 2025-01-01T00:00:00Z