"Artificial lung" is a device that simulates breathing process of occupants in a room. This allows you to safely test, e.g., the impact of HVAC systems on the spread of pathogens. The paper describes the con...
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Network optimization and continued availability depend on a number of capabilities that are part of network management. The Maintenance, operating and also offering a safeguarded interaction network is very complicate...
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'The cloud' points out to servers, the databases and software that run on those servers. The Internet has always been constructed of infrastructure, servers and clients. Clients send their requests to servers,...
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This paper presents the performance analysis and the most significant design decisions taken to improve the runtime performance of a quality-diversity volume rendering framework, focusing on using parallel and distrib...
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ISBN:
(数字)9798350364293
ISBN:
(纸本)9798350364309
This paper presents the performance analysis and the most significant design decisions taken to improve the runtime performance of a quality-diversity volume rendering framework, focusing on using parallel and distributed computing to optimize specific stages within the framework pipeline. After considering the benefits and trade-offs of several aspects like minimization of execution time, portability and hardware independence, we developed a hybrid CPU-GPU parallel computing model that increased the overall efficiency. Using this model, we achieved significant improvement in runtime speed for critical components of the framework compared to the sequential implementation. Additionally, this model can be applied in a wide range of scenarios, where the performance improvements provided by GPU processing can be complemented by CPU parallelization, with better runtime results.
This paper presents a blockchain-based platform for managing and issuing academic certificates that features security, transparency, non-repudiation and traceability. Relying on the security provided by Ethereum block...
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ISBN:
(数字)9798350364293
ISBN:
(纸本)9798350364309
This paper presents a blockchain-based platform for managing and issuing academic certificates that features security, transparency, non-repudiation and traceability. Relying on the security provided by Ethereum blockchain and using asymmetrical cryptography in certificate generation, combined with certificate revocation mechanisms, the proposed platform offers a reliable, decentralized and secure solution that can be used by academic institutions to streamline the management of academic certificates and reduce bureaucracy.
This paper presents an algorithmic approach for segmenting news broadcasts produced by television stations. The proposed approach uses advanced signal processing techniques to extract and classify audio segments from ...
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ISBN:
(数字)9798350364293
ISBN:
(纸本)9798350364309
This paper presents an algorithmic approach for segmenting news broadcasts produced by television stations. The proposed approach uses advanced signal processing techniques to extract and classify audio segments from Romanian TV news broadcasts. By using derivative of 1-dimensional Gaussian filter and binary classification models, this paper aims to automatically identify and classify news segments into categories such as studio news, field news, commercials and music breaks. The primary objective of this paper is to develop a segmentation methodology optimized for reduced computational memory and quick processing. By leveraging efficient computational techniques, the news segmentation can be performed in a timely manner without compromising accuracy. This optimization is crucial for real-time or near-real-time applications within TV networks, facilitating tasks such as content monitoring and analysis.
This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by dee...
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Recent advancements in machine learning (ML) have significantly impacted the medical field, particularly in diagnosing and treating breast cancer. This study models the time from diagnosis to treatment for breast canc...
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ISBN:
(数字)9798331532147
ISBN:
(纸本)9798331532154
Recent advancements in machine learning (ML) have significantly impacted the medical field, particularly in diagnosing and treating breast cancer. This study models the time from diagnosis to treatment for breast cancer patients, focusing on delay factors and ML-based solutions. The goal is to develop precise ML models that predict treatment delays, thereby improving the quality and efficiency of medical services. We investigated socio-economic disparities affecting treatment access and developed models to predict these delays. The predictive models used include K-Nearest Neighbors, Decision Trees, Linear Regression, and Boosting Algorithms such as AdaBoost and Gradient Boosting Regressor. By accurately predicting the interval from diagnosis to the first treatment, our work aims to promote equity in healthcare, ensuring timely treatment for all patients. Our findings highlight the potential of ML in optimizing treatment timelines and resource allocation, contributing to improved patient outcomes and advancing the medical system.
Recent advancements in artificial intelligence have led to significant results in various domains, including image classification, natural language processing, and mastering complex games. However, current deep neural...
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ISBN:
(数字)9798350364293
ISBN:
(纸本)9798350364309
Recent advancements in artificial intelligence have led to significant results in various domains, including image classification, natural language processing, and mastering complex games. However, current deep neural networks seem to process information differently from humans. Neuro-symbolic methods may offer a promising solution to address this concern. This paper proposes a preliminary cognitive architecture focused on neural cell assemblies, which can combine the adaptability of neural approaches with the explicit reasoning capabilities of symbolic systems. It presents a case study on learning to count, and highlights mechanisms for learning, generalization, and adaptation based on predictive errors.
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