Visualization is an important component of modern computing. By animating the course of an algorithm’s temporal execution, many key features can be elucidated. We have developed a general framework, termed Call-Graph...
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Visualization is an important component of modern computing. By animating the course of an algorithm’s temporal execution, many key features can be elucidated. We have developed a general framework, termed Call-Graph Caching (CGC), for automating the construction of many complex ai algorithms. By incorporating visualization into CGC interpreters, principled animations can be automatically displayed as ai computations unfold. (1) Systems that support the automatic animation of ai algorithms must address these three design issues: (2) How to represent ai data structures in a general, uniform way that leads to perspicuous animation and efficient redisplay. (3) How to coordinate the succession of graphical events. (4) How to partition ai graphs to provide for separate, uncluttered displays. CGC provides a natural and effective solution to all these concerns. (5) We describe the CGC method, including detailed examples, and motivate why CGC works well for animation. We discuss the CACHE system, our CGC environment for ai algorithm animation. We demonstrate the animation of several ai algorithms – RETE match, linear unification, arc consistency, chart parsing, and truth maintenance – all of which have been implemented in CACHE. Finally, we discuss the application of these methods to interactive interfaces for intelligent systems, using molecular genetics as an example domain.
Under the rapid economic development trend, exploring the resource optimization strategy of cultural and creative enterprises for sustainable socio-economic development is highly relevant. This study applies the recom...
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Under the rapid economic development trend, exploring the resource optimization strategy of cultural and creative enterprises for sustainable socio-economic development is highly relevant. This study applies the recommendation system to decision-making and resource optimization of entrepreneurial projects for the current situation of complexs entrepreneurial enterprises in the cultural and creative industry (CCI). The neural network algorithm (NNA) is adopted to model project features, user's behavioral and content features. Finally, a recommendation and resource optimization model based on NNA is constructed for CCI-related entrepreneurial projects, and the model is evaluated and analyzed. The results demonstrate that with the increase in the training period, the model's recognition accuracy reaches 81.64%. Besides, the prediction error of the recommender system is minimized when the word vector length is 200, and the number of implied features is 200. Therefore, the entrepreneurial project recommendation and resource optimization model can significantly improve the recognition accuracy and reduce prediction errors, providing experimental references and contributing to the subsequent sustainable development of social economy and entrepreneurial resource optimization.
A special issue of the journal Dose-Response entitled "State of the Art CT and Image Quality, Radiation and Contrast Dose" is proposed. Technological improvements on CT scanners have the potentiality to redu...
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A special issue of the journal Dose-Response entitled "State of the Art CT and Image Quality, Radiation and Contrast Dose" is proposed. Technological improvements on CT scanners have the potentiality to reduce the issues related to ionizing radiation administration, opening new insights toward innovative applications also thanks to the contamination of other research fields like artificial intelligence algorithms and additive manufacturing technologies. In order to approach these new research directions, a multidisciplinary team becomes needed, overcoming the clinical and radiological point of view and enriching the workflow with different contributes. The real weight of these afferents on patient's management remains to be assessed and characterized. The main topics will be related to innovative CT applications able to improve patient management and treatment assessment and reduce patients risks due to radiation exposure and iodinated contrast injection.
Background: Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus. The global burden is immense with a worldwide prevalence of 8.5%. Recent advancements in artificial intelligence (ai)...
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Background: Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus. The global burden is immense with a worldwide prevalence of 8.5%. Recent advancements in artificial intelligence (ai) have demonstrated the potential to transform the landscape of ophthalmology with earlier detection and management of DR. Objective: This study seeks to provide an update and evaluate the accuracy and current diagnostic ability of ai in detecting DR versus ophthalmologists. Additionally, this review will highlight the potential of ai integration to enhance DR screening, management, and disease progression. Methods: A systematic review of the current landscape of ai's role in DR will be undertaken, guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta -Analysis) model. Relevant peer -reviewed papers published in English will be identified by searching 4 international databases: PubMed, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials. Eligible studies will include randomized controlled trials, observational studies, and cohort studies published on or after 2022 that evaluate ai's performance in retinal imaging detection of DR in diverse adult populations. Studies that focus on specific comorbid conditions, nonimage-based applications of ai, or those lacking a direct comparison group or clear methodology will be excluded. Selected papers will be independently assessed for bias by 2 review authors (JS and DM) using the Quality Assessment of Diagnostic Accuracy Studies tool for systematic reviews. Upon systematic review completion, if it is determined that there are sufficient data, a meta -analysis will be performed. Data synthesis will use a quantitative model. Statistical software such as RevMan and STATA will be used to produce a random -effects meta -regression model to pool data from selected studies. Results: Using selected search queries across multiple databases, we accumulated 3494 studies regarding
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