Results for 'Data-driven innovation'

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  1. The virtuous smart city: Bridging the gap between ethical principles and practices of data-driven innovation.Viivi Lähteenoja & Kimmo Karhu - 2023 - Data and Policy 5 (E15).
    For smart cities, data-driven innovation promises societal benefits and increased well-being for residents and visitors. At the same time, the deployment of data-driven innovation poses significant ethical challenges. Although cities and other public-sector actors have increasingly adopted ethical principles, employing them in practice remains challenging. In this commentary, we use a virtue-based approach that bridges the gap between abstract principles and the daily work of practitioners who engage in and with data-driven (...) processes. Inspired by Aristotle, we describe practices of data-driven innovation in a smart city applying the concepts of virtue and phronêsis, meaning good judgment of and sensitivity to ethical issues. We use a dialogic case-study approach to study two cases of data-driven innovation in the city of Helsinki. We then describe as an illustration of how our approach can help bridge the gap between concrete practices of data-driven innovation and high-level principles. Overall, we advance a theoretically grounded, virtue-based approach, which is practice oriented and linked to the daily work of data scientists and other practitioners of data-driven innovation. Further, this approach helps understand the need for and importance of individual application of phronêsis, which is particularly important in public-sector organizations that can experience gaps between principle and practice. This importance is further intensified in cases of data-driven innovation in which, by definition, novel and unknown contexts are explored. (shrink)
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  2. AI-Driven Innovations in Agriculture: Transforming Farming Practices and Outcomes.Jehad M. Altayeb, Hassam Eleyan, Nida D. Wishah, Abed Elilah Elmahmoum, Ahmed J. Khalil, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Applied Research (Ijaar) 8 (9):1-6.
    Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource (...)
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  3. A conceptual framework for data-driven sustainable finance in green energy transition.Omotayo Bukola Adeoye, Ani Emmanuel Chigozie, Ninduwesuor-Ehiobu Nwakamma, Jose Montero Danny, Favour Oluwadamilare Usman & Kehinde Andrew Olu-Lawal - 2024 - World Journal of Advanced Research and Reviews 21 (2):1791–1801.
    As the world grapples with the urgent need for sustainable development, the transition towards green energy stands as a critical imperative. Financing this transition poses significant challenges, requiring innovative approaches that align financial objectives with environmental sustainability goals. This review presents a conceptual framework for leveraging data-driven techniques in sustainable finance to facilitate the transition towards green energy. The proposed framework integrates principles of sustainable finance with advanced data analytics to enhance decision-making processes across the financial ecosystem. (...)
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  4. Smart Prototyping: From Data-Driven Mass-Customization to Community-Enabled Co-Production.Sina Mostafavi, Bahar Bagheri, Ding Wen Bao & Asma Mehan - 2024 - In Mitra Kanaani (ed.), The Routledge Companion to Smart Design Thinking in Architecture & Urbanism for a Sustainable, Living Planet. London: Routledge. pp. 633-642.
    Materialization practices in the architecture and building industry have evolved with the advancement of manufacturing and information technologies. This evolution is evident across various design and production phases, with a pronounced impact on prototyping. Advances in design and fabrication tools have empowered prototypes, integral in any production cycle, to furnish a growing array of information and feedback for designers and manufacturers. In this context, prototypes have transformed from merely showcasing data-driven building solutions to presenting socio-environmentally conscious systems. (...) in prototyping connects the initial design and construction stages to the operational phase, creating a seamless transition throughout the project lifecycle. This chapter provides a range of definitions and prototypical case studies for smart prototyping by identifying practiced approaches in integrated design to production workflows. This chapter introduces three paradigms for smart prototyping: Digital prototyping focuses on data-driven design for mass customization, phygital prototyping involves mixed-reality-enabled design and assembly, and thirdly collaborative prototyping explores human-machine hybrid intelligence and co-production in architectural and urban contexts. The chosen case studies in this chapter and how they are categorized aim to provide a comprehensive overview of smart prototyping, covering projects conducted in both research and practice. This chapter concludes with potential future trends and the role of emerging and evolving mediums of prototyping for smart design and construction. (shrink)
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  5. AI-Driven Organizational Change: Transforming Structures and Processes in the Modern Workplace.Mohammed Elkahlout, Mohammed B. Karaja, Abeer A. Elsharif, Ibtesam M. Dheir, Basem S. Abunasser & Samy S. Abu-Naser - 2024 - Information Journal of Academic Information Systems Research (Ijaisr) 8 (8):38-45.
    Abstract: Artificial Intelligence (AI) is revolutionizing organizational dynamics by reshaping both structures and processes. This paper explores how AI-driven innovations are transforming organizational frameworks, from hierarchical adjustments to decentralized decision-making models. It examines the impact of AI on various processes, including workflow automation, data analysis, and enhanced decision support systems. Through case studies and empirical research, the paper highlights the benefits of AI in improving efficiency, driving innovation, and fostering agility within organizations. Additionally, it addresses the challenges (...)
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  6.  20
    Deep Learning - Driven Data Leakage Detection for Secure Cloud Computing.Yoheswari S. - 2024 - International Journal of Engineering Innovations and Management Strategies 5 (1):1-4.
    Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short- Term Memory (LSTM) (...)
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  7.  31
    Deep Learning - Driven Data Leakage Detection for Secure Cloud Computing.Yoheswari S. - 2025 - International Journal of Engineering Innovations and Management Strategies 1 (1):1-4.
    Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short-Term Memory (LSTM) and (...)
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  8.  72
    Innovative Approaches in Cardiovascular Disease Prediction Through Machine Learning Optimization.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-359.
    Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, (...)
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  9. OPTIMIZING DATA SCIENCE WORKFLOWS IN CLOUD COMPUTING.Tummalachervu Chaitanya Kanth - 2024 - Journal of Science Technology and Research (JSTAR) 4 (1):71-76.
    This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes of data, (...)
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  10. Modeling Semantic Emotion Space Using a 3D Hypercube-Projection: An Innovative Analytical Approach for the Psychology of Emotions.Radek Trnka, Alek Lačev, Karel Balcar, Martin Kuška & Peter Tavel - 2016 - Frontiers in Psychology 7.
    The widely accepted two-dimensional circumplex model of emotions posits that most instances of human emotional experience can be understood within the two general dimensions of valence and activation. Currently, this model is facing some criticism, because complex emotions in particular are hard to define within only these two general dimensions. The present theory-driven study introduces an innovative analytical approach working in a way other than the conventional, two-dimensional paradigm. The main goal was to map and project semantic emotion space (...)
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  11. Talent Flow Network, the Life Cycle of Firms, and Their Innovations.Bo Sun, Ao Ruan, Biyu Peng & Wenzhu Lu - 2022 - Frontiers in Psychology 13:788515.
    This paper explores how talent flow network and the firm life cycle affect the innovative performances of firms. We first established an interorganizational talent flow network with the occupational mobility data available from the public resumes on LinkedIn China. Thereafter, this information was combined with the financial data of China’s listed companies to develop a unique dataset for the time period between 2000 and 2015. The empirical results indicate the following: (1) The breadth and depth of firms’ embedding (...)
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  12. The Role of AI in Enhancing Business Decision-Making: Innovations and Implications.Faten Y. A. Abu Samara, Aya Helmi Abu Taha, Nawal Maher Massa, Tanseen N. Abu Jamie, Fadi E. S. Harara, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Pedagogical Research (IJAPR) 8 (9):8-15.
    Abstract: Artificial Intelligence (AI) has rapidly advanced, offering significant potential to transform business decision-making. This paper delves into how AI can be harnessed to enhance strategic decision-making within business contexts. It investigates the integration of AI-driven analytics, predictive modeling, and automation, emphasizing their role in improving decision accuracy and operational efficiency. By examining current applications and case studies, the paper underscores the opportunities AI offers, including improved data insights, risk management, and personalized customer experiences. It also addresses the (...)
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  13. From Past to Present: A study of AI-driven gamification in heritage education.Sepehr Vaez Afshar, Sarvin Eshaghi, Mahyar Hadighi & Guzden Varinlioglu - 2024 - 42Nd Conference on Education and Research in Computer Aided Architectural Design in Europe: Data-Driven Intelligence 2:249-258.
    The use of Artificial Intelligence (AI) in educational gamification marks a significant advancement, transforming traditional learning methods by offering interactive, adaptive, and personalized content. This approach makes historical content more relatable and promotes active learning and exploration. This research presents an innovative approach to heritage education, combining AI and gamification, explicitly targeting the Silk Roads. It represents a significant progression in a series of research, transitioning from basic 2D textual interactions to a 3D environment using photogrammetry, combining historical authenticity and (...)
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  14. Artificial Intelligence and Organizational Evolution: Reshaping Workflows in the Modern Era.Ahmed S. Sabah, Ahmed A. Hamouda, Yasmeen Emad Helles, Sami M. Okasha, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Pedagogical Research (IJAPR) 8 (9):16-19.
    Abstract: Artificial Intelligence (AI) is transforming organizational dynamics by reshaping both structures and processes. This paper examines how AI-driven innovations are redefining organizational frameworks, ranging from shifts in hierarchical models to the adoption of decentralized decision-making. It explores AI's impact on key processes, including workflow automation, data analysis, and decision support systems. Through case studies and empirical research, the paper illustrates the advantages of AI in enhancing efficiency, driving innovation, and fostering agility within organizations. Additionally, it addresses (...)
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  15. Values for a Post-Pandemic Future.Matthew James Dennis, Georgy Ishmaev, Steven Umbrello & Jeroen van den Hoven (eds.) - 2022 - Cham: Springer.
    This Open Access book shows how value sensitive design (VSD), responsible innovation, and comprehensive engineering can guide the rapid development of technological responses to the COVID-19 crisis. Responding to the ethical challenges of data-driven technologies and other tools requires thinking about values in the context of a pandemic as well as in a post-COVID world. Instilling values must be prioritized from the beginning, not only in the emergency response to the pandemic, but in how to proceed with (...)
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  16. AI in HRM: Revolutionizing Recruitment, Performance Management, and Employee Engagement.Mostafa El-Ghoul, Mohammed M. Almassri, Mohammed F. El-Habibi, Mohanad H. Al-Qadi, Alaa Abou Eloun, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Applied Research (Ijaar) 8 (9):16-23.
    Artificial Intelligence (AI) is rapidly transforming Human Resource Management (HRM) by enhancing the efficiency and effectiveness of key functions such as recruitment, performance management, and employee engagement. This paper explores the integration of AI technologies in HRM, focusing on their potential to revolutionize these critical areas. In recruitment, AI-driven tools streamline candidate sourcing, screening, and selection processes, leading to more accurate and unbiased hiring decisions. Performance management is similarly transformed, with AI enabling continuous, data-driven feedback and personalized (...)
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  17. Predicting Life Expectancy in Diverse Countries Using Neural Networks: Insights and Implications.Alaa Mohammed Dawoud & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):45-54.
    Life expectancy prediction, a pivotal facet of public health and policy formulation, has witnessed remarkable advancements owing to the integration of neural network models and comprehensive datasets. In this research, we present an innovative approach to forecasting life expectancy in diverse countries. Leveraging a neural network architecture, our model was trained on a dataset comprising 22 distinct features, acquired from Kaggle, and encompassing key health indicators, socioeconomic metrics, and cultural attributes. The model demonstrated exceptional predictive accuracy, attaining an impressive 99.27% (...)
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  18.  13
    Data-Driven Health Monitoring: Visual and Analytical Solutions for Improved Care.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):640-655.
    This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote (...)
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  19. Going in, moral, circles: A data-driven exploration of moral circle predictors and prediction models.Hyemin Han & Marja Graham - manuscript
    Moral circles help define the boundaries of one’s moral consideration. One’s moral circle may provide insight into how one perceives or treats other entities. A data-driven model exploration was conducted to explore predictors and prediction models. Candidate predictors were built upon past research using moral foundations and political orientation. Moreover, we also employed additional moral psychological indicators, i.e., moral reasoning, moral identity, and empathy, based on prior research in moral development and education. We used model exploration methods, i.e., (...)
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  20.  86
    Data-Driven HR Strategies: AI Applications in Workforce Agility and Decision Support.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):520-530.
    By embracing AI-driven HR analytics, organizations can anticipate market shifts, prepare their workforce for future challenges, and stay ahead of the competition. This study outlines the essential components of AI-driven HR analytics, demonstrates its impact on workforce agility, and concludes with potential future enhancements to further optimize HR functions. Key words: Predictive Workforce Analytics, Talent Optimization, Machine Learning in.
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  21. Five Ethical Challenges for Data-Driven Policing.Jeremy Davis, Duncan Purves, Juan Gilbert & Schuyler Sturm - 2022 - AI and Ethics 2:185-198.
    This paper synthesizes scholarship from several academic disciplines to identify and analyze five major ethical challenges facing data-driven policing. Because the term “data-driven policing” emcompasses a broad swath of technologies, we first outline several data-driven policing initiatives currently in use in the United States. We then lay out the five ethical challenges. Certain of these challenges have received considerable attention already, while others have been largely overlooked. In many cases, the challenges have been articulated (...)
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  22. From Galton’s Pride to Du Bois’s Pursuit: The Formats of Data-Driven Inequality.Colin Koopman - 2024 - Theory, Culture and Society 41 (1):59-78.
    Data increasingly drive our lives. Often presented as a new trajectory, the deep immersion of our lives in data has a history that is well over a century old. By revisiting the work of early pioneers of what would today be called data science, we can bring into view both assumptions that fund our data-driven moment as well as alternative relations to data. I here excavate insights by contrasting a seemingly unlikely pair of early (...)
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  23.  91
    Who's Anthropocene?: a data driven look at the prospects for collaboration between natural science, social science, and the humanities.Carlos Santana, K. Petrozzo & Timothy Perkins - 2024 - Digital Scholarship in the Humanities 39 (2):723-735.
    Although the idea of the Anthropocene originated in the earth sciences, there have been increasing calls for questions about the Anthropocene to be addressed by pan-disciplinary groups of researchers from across the natural sciences, social sciences, and humanities. We use data analysis techniques from corpus linguistics to examine academic texts about the Anthropocene from these disciplinary families. We read the data to suggest that barriers to a broadly interdisciplinary study of the Anthropocene are high, but we are also (...)
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  24. What Isn’t Obvious about ‘obvious’: A Data-driven Approach to Philosophy of Logic.Moti Mizrahi - 2019 - In Andrew Aberdein & Matthew Inglis (eds.), Advances in Experimental Philosophy of Logic and Mathematics. London: Bloomsbury Academic. pp. 201-224.
    It is often said that ‘every logical truth is obvious’ (Quine 1970: 82), that the ‘axioms and rules of logic are true in an obvious way’ (Murawski 2014: 87), or that ‘logic is a theory of the obvious’ (Sher 1999: 207). In this chapter, I set out to test empirically how the idea that logic is obvious is reflected in the scholarly work of logicians and philosophers of logic. My approach is data-driven. That is to say, I propose (...)
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  25. Urban scale digital twins in data-driven society: Challenging digital universalism in urban planning decision-making.Marianna Charitonidou - 2022 - International Journal of Architectural Computing 19:1-16.
    The article examines the impact of the virtual public sphere on how urban spaces are experienced and conceived in our data-driven society. It places particular emphasis on urban scale digital twins, which are virtual replicas of cities that are used to simulate environments and develop scenarios in response to policy problems. The article also investigates the shift from the technical to the socio-technical perspective within the field of smart cities. Despite the aspirations of urban scale digital twins to (...)
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  26. Beyond categorical definitions of life: a data-driven approach to assessing lifeness.Christophe Malaterre & Jean-François Chartier - 2019 - Synthese 198 (5):4543-4572.
    The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can contribute to (...)
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  27. Bourdieu's Theory of Economic Practice and Organisational Modelling.John Tredinnick-Rowe - 2023 - Cambridge: Cambridge Scholars Publishing.
    This book is unique because it is the first single-author monograph which applies Bourdieu’s theory to management studies. It takes a theory-driven approach to develop models to describe service innovation. This will give the reader a full understanding of the variety of different theoretical concepts that Bourdieu created and used and how they can be applied to the study of management and innovation. Moreover, it is also the only book that links Bourdieu’s theory to his methodological approach, (...)
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  28. Transforming Human Resource Management: The Impact of Artificial Intelligence on Recruitment and Beyond.Hazem A. S. Alrakhawi, Randa Elqassas, Mohammed M. Elsobeihi, Basel Habil, Basem S. Abunasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (8):1-5.
    Abstract: The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) is fundamentally transforming how organizations approach recruitment, performance management, and employee engagement. This paper explores the multifaceted impact of AI on HR practices, highlighting its role in enhancing efficiency, reducing bias, and driving strategic decision-making. Through an in-depth analysis of AI-driven recruitment tools, performance management systems, and personalized employee engagement strategies, this study examines both the opportunities and challenges associated with AI in HRM. Ethical considerations, including (...) privacy, algorithmic bias, and the potential displacement of human jobs, are critically discussed to provide a balanced perspective on the adoption of AI technologies in the HR domain. By presenting case studies of organizations successfully leveraging AI, this paper offers insights into the future trajectory of HRM in an increasingly AI-driven world. Ultimately, the findings underscore the need for HR professionals to adapt and innovate in response to the growing influence of AI, ensuring a harmonious balance between technological advancement and the human element in the workplace. (shrink)
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  29. A framework of AI-Powered Engineering Technology to aid Altair Data Intelligence Start-up Benefits; speeding up Data-Driven Solution.Md Majidul Haque Bhuiyan - manuscript
    Today, software instruments support all parts of engineering work, from design to creation. Many engineering processes call for tedious routine appointments and torments with manual handoffs and data storehouses. AI designers train profound brain networks and incorporate them into software structures.
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  30.  73
    Efficient Data Center Management: Advanced SLA-Driven Load Balancing Solutions.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):368-376.
    In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm optimization, (...)
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  31. Globalization and Transformation : State, Ideas, and Economic Policy in Bangladesh.A. S. M. Mostafizur Rahman - 2024 - Dissertation, Heidelberg University
    Understanding the policymaking process in an emerging economy in the global south, such as Bangladesh, holds significant importance. The country's remarkable socio-economic development, once the most impoverished in the region, has been facilitated by post-globalization economic transformation. While the literature on institutional change has predominantly focused on states in industrialist countries, this dissertation presents an innovative theoretical approach. It deeply explores primary case materials to illustrate how the state engages in policy evolution in a developing country's gradual shift from the (...)
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  32.  62
    Information and Communications Technology in Romania - Comparative Analysis with the EU, Social Impact, Challenges and Opportunities, Future Directions.Nicolae Sfetcu - 2024 - Bucharest, Romania: MultiMedia Publishing.
    The modern global technological landscape is shaped by rapid advances and interconnectivity, leading to a complex ecosystem of innovation, competition and collaboration. Significant developments are being seen in artificial intelligence, telecommunications, biotechnology and energy technologies. Digitalization is redefining industries such as healthcare, transport and finance, while cross-border data flows and 5G infrastructure are accelerating global connectivity. Key players such as the United States, China and Japan are investing heavily in research and development, pushing the capabilities of AI and (...)
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  33. Enabling the Nonhypothesis-Driven Approach: On Data Minimalization, Bias, and the Integration of Data Science in Medical Research and Practice.C. W. Safarlou, M. van Smeden, R. Vermeulen & K. R. Jongsma - 2023 - American Journal of Bioethics 23 (9):72-76.
    Cho and Martinez-Martin provide a wide-ranging analysis of what they label “digital simulacra”—which are in essence data-driven AI-based simulation models such as digital twins or models used for i...
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  34. The Use of Artificial Intelligence (AI) in Qualitative Research for Theory Development.Prokopis A. Christou - 2023 - The Qualitative Report 28 (9):2739-2755.
    Theory development is an important component of academic research since it can lead to the acquisition of new knowledge, the development of a field of study, and the formation of theoretical foundations to explain various phenomena. The contribution of qualitative researchers to theory development and advancement remains significant and highly valued, especially in an era of various epochal shifts and technological innovation in the form of Artificial Intelligence (AI). Even so, the academic community has not yet fully explored the (...)
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  35.  64
    Optimizing Data Center Operations with Enhanced SLA-Driven Load Balancing".S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):368-376.
    The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. Extensive simulations are conducted using synthetic and real-world datasets to evaluate the performance of the proposed algorithm. The results demonstrate that the optimized load balancing approach outperforms traditional algorithms in terms of SLA compliance, resource utilization, and energy efficiency. The findings suggest that the integration of optimization techniques into load balancing algorithms can significantly enhance (...)
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  36.  48
    AI-Driven Deduplication for Scalable Data Management in Hybrid Cloud Infrastructure.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):587-597.
    The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based (...)
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  37.  79
    ENHANCED SLA-DRIVEN LOAD BALANCING ALGORITHMS FOR DATA CENTER OPTIMIZATION USING ADVANCED OPTIMIZATION TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):369-376.
    In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm optimization, (...)
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  38.  38
    Innovative Deduplication Strategies for Cost-Effective Data Management in Hybrid Cloud Models.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):625-635.
    This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% (...)
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  39. Disaster Data Centre—An Innovative Educational Tool for Disaster Reduction through Education in Schools.Lekkas Efthymis - 2014 - Journal of Power and Energy Engineering 2:25-40.
    During the last decades, mankind has suffered from devastation caused by natural disasters and technological accidents of increased frequency and children are among the most vulnerable population group, especially those attending school during times of disaster. The importance of education in promoting and enabling disaster risk reduction has already been identified by researchers. In this paper “Disaster Date Center (DDC)” is presented, a new, powerful and innovative tool for the study of and education on disasters. One noteworthy application of DDC (...)
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  40. “Who Should I Trust with My Data?” Ethical and Legal Challenges for Innovation in New Decentralized Data Management Technologies.Haleh Asgarinia, Andrés Chomczyk Penedo, Beatriz Esteves & Dave Lewis - 2023 - Information (Switzerland) 14 (7):1-17.
    News about personal data breaches or data abusive practices, such as Cambridge Analytica, has questioned the trustworthiness of certain actors in the control of personal data. Innovations in the field of personal information management systems to address this issue have regained traction in recent years, also coinciding with the emergence of new decentralized technologies. However, only with ethically and legally responsible developments will the mistakes of the past be avoided. This contribution explores how current data management (...)
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  41. Data and the Good?Daniel Susser - 2022 - Surveillance and Society 20 (3):297-301.
    Surveillance studies scholars and privacy scholars have each developed sophisticated, important critiques of the existing data-driven order. But too few scholars in either tradition have put forward alternative substantive conceptions of a good digital society. This, I argue, is a crucial omission. Unless we construct new “sociotechnical imaginaries,” new understandings of the goals and aspirations digital technologies should aim to achieve, the most surveillance studies and privacy scholars can hope to accomplish is a less unjust version of the (...)
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  42. Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS.Stefano Canali - 2016 - Big Data and Society 3 (2).
    Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show (...)
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  43. (1 other version)Disruptive Innovation and Moral Uncertainty.Philip J. Nickel - forthcoming - NanoEthics: Studies in New and Emerging Technologies.
    This paper develops a philosophical account of moral disruption. According to Robert Baker (2013), moral disruption is a process in which technological innovations undermine established moral norms without clearly leading to a new set of norms. Here I analyze this process in terms of moral uncertainty, formulating a philosophical account with two variants. On the Harm Account, such uncertainty is always harmful because it blocks our knowledge of our own and others’ moral obligations. On the Qualified Harm Account, there is (...)
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  44. Big Data Analytics in Project Management: A Key to Success.Tareq Obaid & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (7):1-8.
    This review delves into the influence of big data analytics on project management effectiveness and project success rates. By examining applications, accomplishments, hindrances, and emerging developments in the context of big data analytics and project management, this review provides insights into its transformative potential. Results indicate that big data analytics fosters improved project performance, more robust risk management, and heightened adaptability. However, challenges related to data quality, privacy, and project manager training remain to be addressed. This (...)
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  45. Innovation management and effectiveness of educational research in tertiary institutions in Cross River State, Nigeria.Bassey Asuquo Bassey & Valentine Joseph Owan - 2018 - EPRA International Journal of Research and Development (IJRD) 3 (13):11-17.
    This study investigated innovation management and effectiveness of educational research in tertiary institutions in Cross River State. One research question and one null hypothesis were formulated to direct the study. The study adopted factorial research design. Census technique was adopted by the researcher in selecting the entire population of 80 participants from four (4) tertiary institutions in Cross River State. “Innovation Management Questionnaire (IMQ)” and “Effectiveness of Educational Research Rating Scale (EERRS) were used as instruments for data (...)
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  46. AI-Driven Human Resource Analytics for Enhancing Workforce Agility and Strategic Decision-Making.S. M. Padmavathi - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):530-540.
    In today’s rapidly evolving business landscape, organizations must continuously adapt to stay competitive. AI-driven human resource (HR) analytics has emerged as a strategic tool to enhance workforce agility and inform decision-making processes. By leveraging advanced algorithms, machine learning models, and predictive analytics, HR departments can transform vast data sets into actionable insights, driving talent management, employee engagement, and overall organizational efficiency. AI’s ability to analyze patterns, forecast trends, and offer data-driven recommendations empowers HR professionals to make (...)
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  47. Responsible innovation across societal sectors: a practice perspective on Quadruple Helix collaboration.Johannes Starkbaum & Vincent Blok - 2024 - Journal of Responsible Innovation 1 (1):1.
    To address societal challenges, research and innovation approaches, involving a wide range of actors, are increasingly promoted by policy communities. This paper explores the practice of Quadruple Helix collaborations for responsible innovation and how these implement the theoretical ambition of including actors from different societal sectors in innovation, including actors from the fields of arts, media and civil society, which is conceptualized as the Fourth Helix in this concept. Referring to cross-sector collaboration literature and based on an (...)
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  48. Annotating affective neuroscience data with the Emotion Ontology.Janna Hastings, Werner Ceusters, Kevin Mulligan & Barry Smith - 2012 - In Janna Hastings, Werner Ceusters, Kevin Mulligan & Barry Smith (eds.), Third International Conference on Biomedical Ontology. ICBO. pp. 1-5.
    The Emotion Ontology is an ontology covering all aspects of emotional and affective mental functioning. It is being developed following the principles of the OBO Foundry and Ontological Realism. This means that in compiling the ontology, we emphasize the importance of the nature of the entities in reality that the ontology is describing. One of the ways in which realism-based ontologies are being successfully used within biomedical science is in the annotation of scientific research results in publicly available databases. Such (...)
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  49. The mindsponge and BMF analytics for innovative thinking in social sciences and humanities.Quan-Hoang Vuong, Minh-Hoang Nguyen & Viet-Phuong La (eds.) - 2022 - Berlin, Germany: De Gruyter.
    Academia is a competitive environment. Early Career Researchers (ECRs) are limited in experience and resources and especially need achievements to secure and expand their careers. To help with these issues, this book offers a new approach for conducting research using the combination of mindsponge innovative thinking and Bayesian analytics. This is not just another analytics book. 1. A new perspective on psychological processes: Mindsponge is a novel approach for examining the human mind’s information processing mechanism. This conceptual framework is used (...)
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  50. Innovating with confidence: embedding AI governance and fairness in a financial services risk management framework.Luciano Floridi, Michelle Seng Ah Lee & Alexander Denev - 2020 - Berkeley Technology Law Journal 34.
    An increasing number of financial services (FS) companies are adopting solutions driven by artificial intelligence (AI) to gain operational efficiencies, derive strategic insights, and improve customer engagement. However, the rate of adoption has been low, in part due to the apprehension around its complexity and self-learning capability, which makes auditability a challenge in a highly regulated industry. There is limited literature on how FS companies can implement the governance and controls specific to AI-driven solutions. AI auditing cannot be (...)
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