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Learning technology standards development - planning for an improved process and product
No full textThis paper presents a framework for improving the legitimacy of learning technology standards by focussing on a better process and product. It is suggested that there is a need for a change in the standardisation paradigm, moving from monolithic to more modular standards
Proceedings of the 4th International Conference on Advances in Communication Technology and Computer Engineering (ICACTCE’24) Transforming Industries: Harnessing the Power of Artificial Intelligence and the Internet of Things, Volume 2
No full textThis proceedings book offers a refined and comprehensive exploration of cutting-edge advancements in communication networks, computational intelligence, and smart applications, seamlessly blending theoretical insights with practical solutions. Each paper outlines objectives, challenges, proposed solutions, and key findings, enabling swift comprehension of complex topics. By adopting a problem-solving approach and including case studies, the book effectively demonstrates the application of advanced techniques in domains such as industry, healthcare, and smart cities. Addressing the demands of an evolving digital landscape, it highlights emerging technologies like artificial intelligence (AI), the Internet of Things (IoT), and autonomous systems, ensuring its relevance to both current challenges and future innovations. Covering a wide spectrum of topics, including network security, AI applications, IoT ecosystems, and smart technologies, the book serves as a comprehensive resource for understanding the innovations shaping the future of communication and computing. Targeted at graduate students, researchers, professors, and industry professionals, it functions as both an educational tool and a reference guide for those seeking to remain at the forefront of technological advancements. Featuring state-of-the-art research contributions, the book introduces new techniques, algorithms, and solutions to real-world challenges, complemented by structured insights into objectives, problems, and results. Practical applications are brought to life through successful case studies in key areas like smart cities and healthcare, illustrating the tangible impact of these innovations. With contributions reviewed by a distinguished editorial team of leading researchers, engineers, and academics, the book ensures credibility, academic rigor, and relevance. By blending theoretical depth, practical utility, and expert validation, this proceedings book is an indispensable resource for navigating the rapidly evolving fields of computing and communication technologies, equipping readers with the knowledge and tools to excel in an increasingly digital and interconnected world
Enhancing disease clustering through symptom-based analysis and large language model interpretations
No full textHumans face various diseases that are mainly caused by environmental conditions and living habits. These diseases exhibit several symptoms and can share a relationship based on their symptoms. The identification and interpretation of these groups of symptom-based diseases can aid in developing treatment plans for a new outbreak of disease. This research explores the intersection of machine learning and healthcare, specifically focusing on the enhancement of disease classification through symptom-based cluster analysis. By leveraging unsupervised machine learning algorithms, patterns and relationships within diverse symptom datasets were identified, revealing novel associations and subtypes in disease manifestation. The integration of a Large Language Model (LLM), specifically OpenAI's Generative Pretrained Transformer(GPT), played a pivotal role in interpreting and communicating the complex outputs of the machine learning process. The results indicated a significant improvement in defining distinct clusters based on the relationship between diseases and symptoms, with GPT-4o providing simplified explanations that bridge the gap between machine-generated insights and healthcare professional's understanding. The study's findings offer a more profound understanding of the distinctive features characterising the different clusters of diseases generated by the machine learning models. The healthcare field produces extensive and varied data, which machine learning algorithms can leverage to detect new illnesses and optimize treatment plans 1. Deep learning (DL), when trained on high-quality data, has significantly advanced clinical diagnostics and facilitated disease clustering 2. One example is symptom-based clustering, which can enhance diagnostic accuracy and support personalized patient care 3. Diseases with overlapping symptoms pose significant challenges for accurate clinical diagnosis, a problem that can be mitigated through coordinated care and collaboration between multidisciplinary teams 4. Traditionally, physical exams or laboratory tests are used to identify diseases. This process can be complicated and sometimes inaccurate, as many diseases share similar symptoms 5. ML-enabled techniques help to discover new disease subtypes and understand the diversity of the patient population by uncovering hidden patterns within complex data sets 6. Symptom-based cluster analysis is an effective technique for providing precise and targeted medical information 7. However, interpreting these complex models poses a unique challenge. Watson 8 argued that while clustering algorithms efficiently reveal connections, converting these clusters and patterns into meaningful medical insights is difficult
AI-Powered Pedagogy and Curriculum Design: Practical Insights for Educators
No full textAI-Powered Pedagogy and Curriculum Design offers practical insights and guidance on the effective integration of AI tools into teaching practices and curriculum design. While numerous claims exist as to the validity and authenticity of the applications of AI in schools, too little attention has been paid to empirical research conducted with and by teachers in real-world classrooms. This book synthesises diverse viewpoints from teacher educators across disciplines and levels toward a comprehensive, context-specific understanding of the challenges and best practices for responsibly leveraging Generative AI to enhance outcomes in classrooms. Contributors further shed light on how Generative AI can align with standards, assessment practices, and teacher training programs in different settings. Firsthand classroom experiences and experimental approaches of educators in the United Kingdom and Europe will provide current and aspiring teachers with insights into the intersection between AI and teacher empowerment, student participation, ethical implications, and socially just approaches
Conclusion Building Skills, Ethics and Human Autonomy within a Future of AI-Enhanced Learning
No full textIn developing this special collection, it has become clear that strategically integrating Generative AI into classroom practice accompanied by focused, robust professional development, is essential. These findings align with insights from The Shape of the Future report, which underscores the importance of educators becoming 'AI ready' by developing a deeper understanding of the distinctions between human intelligence and AI (Luckin, 2024). Equally important is incorporating the perspective and experiences of key stakeholders, including students and teachers, who will provide a balance in shaping and guiding these initiatives for outcomes that are both equitable and impactful. The integration of new professional development initiatives emphasise a move that upholds sound pedagogical principles and reinforces the capacity of educators as active agents of change in their choice to leverage Generative AI. This redistribution of agency is reinforced by Sharples (2023), who asserts that Generative AI should function within clear ethical boundaries, respecting human agency and the essential role of educators. Key aspects include empowering users with control over their data, ensuring transparency, and maintaining trust within educational settings. This approach promotes a balanced dynamic where educators retain autonomy in shaping AI-driven learning experiences, allowing AI to enhance rather than diminish their role
Data from Dressing without Direction: Designing Adaptive Fashion that Looks and Feels Like Everyday Clothing
No full textRisking it all: authentic leadership in crisis
No full textThe COVID-19 pandemic has revealed the vulnerability of organisations, institutions, and society, underscoring the need for new leadership approaches to develop them. This chapter examines authentic leadership, highlighting the complexity and problematic nature of the construct. Although scholars have acknowledged its significance and linked it to positive organisational outcomes, the construct has not progressed beyond its current conceptualisation. Consequently, the chapter explores the rise of authentic leadership and its limitations, accentuating the lack of a clear path for developing the construct. To contribute to the literature on authentic leadership theory, the chapter proposes the authentic leadership plumb line and discusses its role in facilitating its resurgence within organisations. The chapter concludes that authentic leadership should be embraced during crises as it enables the alignment of the leader and follower interaction
Pixels to pathogens: a deep learning approach to plant pathology detection
No full textIt is known that accurately identifying, early and timely treatment and elimination of the plant diseases is essential for crop protection and healthy crop growth. In traditional or conventional methods, identification and classification were done by testing in laboratories or through visual inspection by farmers. Now going through the testing in labs is very time consuming, while the visual inspection requires enough experience and knowledge. To solve this problem, our study proposes a robust plant pathogen detection method based on a Deep Learning approach on a large dataset containing about 38 categories of different species like Maize, Potatoes, Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases like rust , molds, blight (late and early). This crop disease detection model leverages the power of the EfficientNetB3 architecture, a state-of-art convolutional neural network(CNN). The main backbone is served by EfficientNetB3and then it is fine-tuned using different hyperparameters and other regularization techniques like weight decay, dropout method and optimizers like RAdam,to enhance the overall accuracy coupled with dynamic learning rate adjustment. In the testing set of the dataset, the proposed model shows encouraging accuracy of about 99.25%, high precision of about 97.35%. A thorough evaluation of the model’s functionality is given by the help of training and validation line chart and loss chart that gives the in-depth information on the prediction. And then we implemented the detection model in our mobile application whose interface screen shots are given below. In the application the image can be taken by camera or fed from folders and it will detect the type of disease
Mass Observation, Counterculture and the 'Art of Living'
No full textMass Observation was the most ambitious and controversial investigation into cultural life in Britain in the twentieth century. Buoyed by a democratic spirit yet riven by eclectic intellectual allegiances, the project, in its inception, revelled in contradictions, many of which have endured in its legacy. This paper revisits the early countercultural aspirations of Mass Observation in order to reflect on the significance of these contradictions for the fate of popular writing. It is argued that the tensions between art, philosophy and science, as articulated in the inaugural statements of Mass Observation, are illuminated by the anti-elitist agenda of the founders. Building on these insights, the paper revisits controversies in the use of Mass Observation data for research and calls upon the findings from a recent recreation of Mass Observation Diary Day (12 May 2024) to argue that Mass Observation's 'science of ourselves' be reconsidered as creative cultural production and a contribution to the 'art of living'
Harnessing the Power of Gaming to Influence Policies Addressing Climate Change
No full textIn this paper, the authors present the findings of an empirical case study examining the efficacy of the Games Realising Effective & Affective Transformation (GREAT) Case Study design process. The process is underpinned by an established Mixed Methodological Research (MMR) framework for eliciting the preferences of gamers and determining their priorities in climate change policies. Funded by the Horizon Europe programme, the GREAT Project examines the impact and affordances of games for social engagement. The project explores the innovative potential of games as new forms of dialogue between citizens and policy stakeholders. The games are used as tools for players to express their preferences and actively shape policy issues. We present the first case study on this approach, which is one of ten to be undertaken with various partners over the next two years to test and validate the methodology, investigate its potential, and present findings. In partnership with the popular PC & Console game Smite, by the Hi Rez, game development studio. The study involved stakeholders’ participation in the co-creation of research questions, designed to influence the prioritisation of future climate policies. The activity was embedded the Smite game playing community via the Playmob platform in January 2024 and engaged over four thousand active players with a completed response rate of 58 %. Quantitative analysis of the data collected during this period will be presented by the authors. In summary, the engagement in and completion rates of the activity were high, validating the initial GREAT project approach. The methodological approach and the substantive data sets produced are of interest to any organisation considering engaging diverse groups active in gaming communities in the political process, including NGOs and policymakers. The project and methodology applied is at the core of this paper