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Trending Papers in artificial intelligence

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309
From Paper: Intelligent comprehensive evaluation system using artificial intelligence for environmental evaluation
Published: Jan 2021
From Paper: Intelligent comprehensive evaluation system using artificial intelligence for environmental evaluation
Published: Jan 2021
Environmental evaluation plays a significant role in the development of the culture and economy in modern times. When the environmental condition becomes degraded, the complete evaluation of ecological analysis is highly necessary to evaluate the environment. Hence, the development of a scientific assessment system of environmental assessment is highly significant in the development of the culture and economy. In this research, the present state of environmental evaluation is discussed, and the evaluation system is integrated through a computational process. Secondly, an environmental evaluation system is presented to establish the model in the evaluation process. Further, in this research, Advanced Artificial Intelligence Framework (AIF) for environmental development and protection has been proposed to improve the culture and economy in modern times. Studies reports indicate clear connections between the criteria of environmental assessment, the protection and development of the environment. The practical application of study findings involves establishing specific proposals for reducing pollution and improving the protection of the environment in China.
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ALYSSA ENG
3
From Paper: A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures
Authors: Yan Qin, Stefan Adams, Chau Yuen
Published: Jan 2021
From Paper: A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures
Authors: Yan Qin, Stefan Adams, Chau Yuen
Published: Jan 2021
Accurate and reliable state of charge (SoC) estimation becomes increasingly important to provide a stable and efficient environment for Lithium-ion batteries (LiBs) powered devices. Most data-driven SoC models are built for a fixed ambient temperature, which neglect the high sensitivity of LiBs to temperature and may cause severe prediction errors. Nevertheless, a systematic evaluation of the impact of temperature on SoC estimation and ways for a prompt adjustment of the estimation model to new temperatures using limited data have been hardly discussed. To solve these challenges, a novel SoC estimation method is proposed by exploiting temporal dynamics of measurements and transferring consistent estimation ability among different temperatures. First, temporal dynamics, which is presented by correlations between the past fluctuation and the future motion, is extracted using canonical variate analysis. Next, two models, including a reference SoC estimation model and an estimation ability monitoring model, are developed with temporal dynamics. The monitoring model provides a path to quantitatively evaluate the influences of temperature on SoC estimation ability. After that, once the inability of the reference SoC estimation model is detected, consistent temporal dynamics between temperatures are selected for transfer learning. Finally, the efficacy of the proposed method is verified through a benchmark. Our proposed method not only reduces prediction errors at fixed temperatures (e.g., reduced by 24.35% at -20{\deg}C, 49.82% at 25{\deg}C) but also improves prediction accuracies at new temperatures.
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khush deoja
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Authors: Nikica Zaninovic, Zev Rosenwaks
Published: Nov 2020
Authors: Nikica Zaninovic, Zev Rosenwaks
Published: Nov 2020
Embryo evaluation and selection embody the aggregate manifestation of the entire in vitro fertilization (IVF) process. It aims to choose the “best” embryos from the larger cohort of fertilized oocytes, the majority of which will be determined to be not viable either as a result of abnormal development or due to chromosomal imbalances. Indeed, it is generally acknowledged that even after embryo selection based on morphology, time-lapse microscopic photography, or embryo biopsy with preimplantation genetic testing, implantation rates in the human are difficult to predict. Our pursuit of enhancing embryo evaluation and selection, as well as increasing live birth rates, will require the adoption of novel technologies. Recently, several artificial intelligence (AI)-based methods have emerged as objective, standardized, and efficient tools for evaluating human embryos. Moreover, AI-based methods can be implemented for other clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the capability to analyze “big” data, the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments. In this chapter, we present an overview of existing AI technologies in reproductive medicine and envision their potential future applications in the field.
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ALYSSA ENG
4
Authors: Michael Tran Duong, Andreas M. Rauschecker, Suyash Mohan
Published: Nov 2020
Authors: Michael Tran Duong, Andreas M. Rauschecker, Suyash Mohan
Published: Nov 2020
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ALYSSA ENG
4
Published: May 2021
Published: May 2021
Sepsis is one of the leading causes of death in Intensive Care Units (ICU). The strategy for treating sepsis involves the infusion of intravenous (IV)…
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ALYSSA ENG
4
Authors: Naira Kaieski, Cristiano André Costa, Rodrigo Rosa Righi, Priscila Schmidt Lora, Björn Eskofier
Published: Nov 2020
Authors: Naira Kaieski, Cristiano André Costa, Rodrigo Rosa Righi, Priscila Schmidt Lora, Björn Eskofier
Published: Nov 2020
In a hospital environment, patients are monitored continuously by electronic devices and health professionals. Therefore, a large amount of data is collected and stored in electronic health records systems for each patient. Among such data, vital signs are one of the most common and relevant types of information monitored to assess a patient’s health status. Artificial intelligence techniques can be used to analyze and learn useful standards from clinical datasets to provide better evidence to support the decisions of health professionals and thus help to improve patient health outcomes in hospitals. This systematic literature review aims to provide an updated computational perspective of how artificial intelligence has been applied to analyze the vital signs of adult hospitalized patients and the outcomes obtained. To this end, we reviewed 2899 scientific articles published between 2008 and 2018 and selected 78 articles that met our inclusion criteria to answer the research questions. Moreover, we used the information found in the reviewed articles to propose a taxonomy and identified the main concerns, challenges, and opportunities in this field. Our findings demonstrate that many researchers are exploring the use of artificial intelligence methods in tasks related to improving the health outcomes of hospitalized patients in distinct units. Additionally, although vital signs are significant predictors of clinical deterioration, they are not analyzed in isolation to predict or identify a clinical outcome. Our taxonomy and discussion contribute to the achievement of a significant degree of coverage regarding the aspects related to using machine learning to improve health outcomes in hospital environments, while highlighting gaps in the literature for future research.
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ALYSSA ENG
3
Published: Mar 2021
Published: Mar 2021
The proliferation of digital technologies has received considerable attention in the business landscape. Artificial intelligence (AI) is proclaimed as…
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ALYSSA ENG
3
Authors: Thikra Dawood, Emad Elwakil, Hector Mayol Novoa, José Fernando Gárate Delgado
Published: Dec 2020
Authors: Thikra Dawood, Emad Elwakil, Hector Mayol Novoa, José Fernando Gárate Delgado
Published: Dec 2020
Water pipes deterioration modeling has been a prevalent research topic in the last two decades due to high water break incidents and contamination rates. Failure processes are de facto very intricate to be diagnosed since there is a time lag between the failure incidence and consequences. Artificial intelligence (A.I.) techniques have gained much momentum during the last two decades, specifically for the deterioration modeling and assessment of water distribution networks. However, a comprehensive critical review on water infrastructure modeling via artificial intelligence and machine learning techniques is missing in the literature. This paper aims to bridge the gap in the body of knowledge and address the aforementioned limitations. The intellectual contributions of this paper are twofold. First, a comprehensive literature review method is presented through sequential steps that systematize and synthesize the literature in a scientific way. The state-of-the-art of AI-based deterioration modeling for urban water systems is revealed along with models' methodologies, contributions, drawbacks, comparisons, and critiques. Second, future research directions and challenges are recommended to assist the construction automation research community in setting a vibrant agenda for the upcoming years.
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ALYSSA ENG
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