Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in prov...
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In Software engineering, context can be understood as the overall set of information used to characterize the situation of an entity. A software system is context-aware if it uses the context to provide relevant infor...
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To understand and characterize the diffusion trends of opposing viewpoints on Twitter (X), we applied two epidemiological models to six datasets related to COVID-19. We compared the results of the SIR (Susceptible, In...
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We say that a sequence a1 · · · a2t of integers is repetitive if ai = ai+t for every i ∈ {1, ..., t}. A walk in a graph G is a sequence v1 · · · vr of vertices of G in which vivi+1 ∈ E(...
Federated Learning (FL) has emerged as a promising solution to address challenges in traditional machine learning (ML) regarding data privacy and security. However, training federated models in resource-constrained en...
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ISBN:
(数字)9798331522742
ISBN:
(纸本)9798331522759
Federated Learning (FL) has emerged as a promising solution to address challenges in traditional machine learning (ML) regarding data privacy and security. However, training federated models in resource-constrained environments, such as IoT devices, presents challenges due to limited computational resources and complex data. This paper proposes data sampling techniques to optimize federated training in such environments, aiming to reduce training time while maintaining model quality. The study evaluates the impact of data sampling on federated model performance and compares it with traditional approaches. The methodology involves implementing random data selection in client datasets within the context of federated learning and conducting experiments across different configurations to analyze results. The findings provide insights for practical application in real-world scenarios with computational constraints.
The rise of artificial intelligence agents and information technology platforms governed by data-driven algorithms has had profound impacts on the way knowledge work is managed and carried out. Concerning the novel us...
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ISBN:
(数字)9798350349184
ISBN:
(纸本)9798350349191
The rise of artificial intelligence agents and information technology platforms governed by data-driven algorithms has had profound impacts on the way knowledge work is managed and carried out. Concerning the novel uses of digital work platforms, the arising tensions might suggest that the socio-technical design frameworks adopted by major tech firms are becoming less viable and in some cases ineffective. We propose a conceptual architecture to support a socio-technical framework for exploring Personal Knowledge Ecologies in the context of the digital knowledge economy. This framework aims to increase the degree of individual freedom for the interoperation of these knowledge information infrastructures.
In today’s complex global environments, it is highly challenging to achieve effective healthcare management. The critical challenges faced by healthcare management are being met by the revolution of m-health where mo...
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ISBN:
(数字)9798350387537
ISBN:
(纸本)9798350387544
In today’s complex global environments, it is highly challenging to achieve effective healthcare management. The critical challenges faced by healthcare management are being met by the revolution of m-health where mobile cloud computing plays a major role. The taxonomy of mobile computing comprises operational aspects, end-user issues, and service quality and mobility management. The use of smartphone technologies and applications has become a highly significant approach to improving healthcare management. Mobile health services such as mobile pathology, mobile neurosurgery, cancer treatment, and behavioral/psychological disorders are gaining significance where smartphone applications are being used. Portability, flexibility, and convenience are major characteristics of mobile computing that have helped patients and doctors to develop better relationships through coordination and communication. The relationship between smartphone technologies and applications and healthcare management can be understood in several broad aspects. This article aims to analyze the current and future implications of these technologies on healthcare and disease management systems.
This study proposed a novel extraction method of c-Fos protein regions in DAB(3,3'-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and 1×1 convo...
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With a growing demand for new technologies, concepts such as the Internet of Everything (IoE) - in which smart sensors (humans and machines) connect, communicate, and share information from the surrounding environment...
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This paper focuses on a performance enhancement of communication performance by compressing data stream. ASE coding is an effective lossless data compression method for data stream. The software implementation of the ...
This paper focuses on a performance enhancement of communication performance by compressing data stream. ASE coding is an effective lossless data compression method for data stream. The software implementation of the coding/decoding method inevitably meets a performance mismatch in memory and storage devices. In the compressor side, it is predictable to decide the size of an original data block and is available to process a flexible buffer memory. However, the decompressor is not able to predict the buffer size because the original data size is not obvious before the decompression. This causes a performance mismatch in the filesystem level. This paper proposes a novel method to address the performance mismatch by applying a notification mechanism of compression size from the compressor. This paper describes the mechanism focusing on the system call usage. Through experimental evaluations, we show the performance improvement of the decompression performance for handling data stream.
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