Purpose: Demanding lifestyle characterized by extended working hours, shift work schedules as well as excessive use of mobile gadgets leads to the disruption of the circadian and homeostatic factors affecting the...
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Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbi...
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A research on developing a system that integrates clean production and waste water treatment for biogas production in tofu small industry has been conducted. In this research, tofu waste water was turned into biogas u...
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Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL custom...
Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, 'transform case', 'tokenize', 'token filters by length', 'n-gram', 'stemming' were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.
Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology p...
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The choice of contraceptive tools is not an easy thing because the risks or effects will give impact on the body that never using it previously. in the other side, there is no contraception always suit for everybody b...
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Cloud data centers (DCs) can be aptly regarded as the epicenter of today's business and economy; which support seamless data processing, analysis, and storage. However, various studies advocate that the existing D...
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ISBN:
(数字)9781728109602
ISBN:
(纸本)9781728109619
Cloud data centers (DCs) can be aptly regarded as the epicenter of today's business and economy; which support seamless data processing, analysis, and storage. However, various studies advocate that the existing DCs are often underutilized. To be precise, almost 30% of the installed DCs in the United States are comatose. In addition to this, the existing DC architecture leads to extensive energy utilization, which severely hampers the environment and places a severe risk on the power sector. Thus, it is highly essential to reduce DC's energy utilization through efficient resource consolidation approaches. In this work, we investigate the joint impact of resource consolidation and load balancing on cutting down the energy utilization indices of the cloud DCs. In this vein, we formulate a multi-objective optimization problem (MOOP) for container placement across heterogeneous infrastructure, primarily with the intent to minimize the overall energy consumption and balance the load amongst the operating hosts. However, due to the hardness of the underlying problem and its infeasibility to furnish optimal solutions in polynomial time, we designed an online solution based on the incremental exploration of the solution space to map containers on the available array of hosts such that the objectives mentioned above can be attained. Finally, we evaluated the performance of the proposed algorithm in contrast to an existing algorithm on real-time workload traces obtained from PlanetLab. The obtained results confirm the superior performance of the proposed algorithm relative state-of-the-art.
Epidemiological surveillance of Tuberculosis (TB) requires a strong integration of different health services, programs and levels of care. The deepening and broadening of data management techniques must be constantly ...
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Epidemiological surveillance of Tuberculosis (TB) requires a strong integration of different health services, programs and levels of care. The deepening and broadening of data management techniques must be constantly carried out to increase the integrality of healthcare. Otherwise, knowledge extraction and clinical and administrative decision-making processes are significantly hampered, directly affecting the management and quality of health services. Thus, this work aims to establish a computerized decision support system capable of collecting, integrating and sharing TB health data in Brazilian Unified Public Health System. Also, it will allow the monitoring of infected patients and the visualization of consolidated information of regular TB and its resistant variants for health professionals and managers. The data will be made available from heterogeneous, disconnected and unstructured sources by combining traditional web services, Semantic Web resources and security algorithms. A solid knowledge base applied to epidemiological surveillance, health information governance and clinical support will be enabled to integrate the multiple areas of TB patients care, as well as to support the creation of more accurate operational and diagnostics models.
Call Detail Record (CDR) datasets provide enough information about personal interactions of cell phone service customers to enable building detailed social networks. We take one such dataset and create a realistic soc...
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