Novel deep learning architectures for marine and aquaculture applications

Alzayat Saleh's research was in the area of artificial intelligence and machine learning to autonomously recognise fish and their morphological features from digital images. Here he created new deep learning architectures that solved various computer vision problems specific to the marine and aquaculture context. He found that these techniques can facilitate aquaculture management and environmental protection. Fisheries and conservation agencies can use his results for better monitoring strategies and sustainable fishing practices

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Technology strategy and the inward transfer of foreign technology in the UK machine tool industry

The International competitiveness of machine tool manufacturing companies in the UK is in long term relative decline. This is evident in diminishing UK shares of world production and exports, Increasing Import penetration and the higher technical sophistication of Imports over exports. Executives in the Industry tend to explain declining performance by referring to exogenous factors beyond their control, such as adverse currency movements, weak demand and conservatism among users in the domestic market. Rising imports are often explained away as the inevitable consequence of growing specialisation and internationalisation. These claims are not without foundation but they are at a high level of generalisation and do not shed light on the managerial problems of adapting to unprecedented levels of foreign competition and technological change. Most policy prescriptions for restoring competitiveness in the 1980's have highlighted awareness of the international dimension and the contribution of technology in overall strategy development. One strategic option finding increasing interest among executives in machine tool manufacturing companies and receiving substantial encouragement from the UK Government, is that of supplementing indigenous technological capability by increasing the "inward" transfer of foreign technology. This dissertation examines the sourcing of appropriate machine tool technology from overseas via foreign direct Investment, joint ventures and licensing arrangements. The approach is multidisciplinary and focusses on the strategic management of technology at the level of Individual business units, giving due consideration to existing patterns of foreign ownership and collaboration. Particular emphasis is placed on understanding how foreign technology emerges as a strategic option, the conditions under which it is assimilated and the relative merits of the three modes of Inward technology transfer. The research shows that providing a critical mass of Indigenous skills and capital expenditure can be maintained, the inward transfer of foreign technology offers considerable potential for achieving and sustaining a future level of technological capability comparable with that of International best practice. To facilitate effective exploitation of these opportunities, however, the priorities are threefold: firstly, executives must pay greater attention to competitor analysis and monitoring technological developments worldwide; secondly, many companies should use foreign technology to reposition themselves in existing segments and/or redirect their strategies towards growth segments; and finally, there is an urgent need for management/organisatlonal development in machine tool companies to create a balanced Internal environment which Is more receptive to the potential "total" benefits embodied in both Internally generated and foreign technolog