Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Microbiome and Machine Learning, Volume II
Due to the success of Microbiome and Machine Learning, which collected research results and perspectives of researchers working in the field of machine learning (ML) applied to the analysis of microbiome data, we are launching the second volume to collate any new findings in the field to further our understanding and encourage the participation of experts worldwide in the discussion. The success of ML algorithms in the field is substantially due to their capacity to process high-dimensional data and deal with uncertainty and noise. However, to maximize the combinatory potential of these emerging fields (microbiome and ML), researchers have to deal with some aspects that are complex and inherently related to microbiome data. Microbiome data are convoluted, noisy and highly variable, and non-standard analytical methodologies are required to unlock their clinical and scientific potential. Therefore, although a wide range of statistical modelling and ML methods are available, their application is only sometimes optimal when dealing with microbiome data.
High-Throughput Field Phenotyping to Advance Precision Agriculture and Enhance Genetic Gain, Volume II
This Research Topic is part of the High-Throughput Field Phenotyping to Advance Precision Agriculture and Enhance Genetic Gain series. The discipline of “High Throughput Field Phenotyping” (HTFP) has gained momentum in the last decade. HTFP includes a wide range of disciplines such as plant science, agronomy, remote sensing, and genetics; as well as biochemistry, imaging, computation, agricultural engineering, and robotics. High throughput technologies have substantially increased our ability to monitor and quantify field experiments and breeding nurseries at multiple scales. HTFP technology can not only rapidly and cost-effectively replace tedious and subjective ratings in the field, but can also unlock the potential of new, latent phenotypes representing underlying biological function. These advances have also provided the ability to follow crop growth and development across seasons at high and previously inaccessible spatial and temporal resolutions. By combining these data with measurements of all environmental factors affecting plant growth and yield (“Envirotyping”), genotypic-specific reaction norms and phenotypic plasticity may be elucidated.
Bioinformatics and Biomedical Engineering
Zusammenfassung: This volume constitutes the proceedings of the 11th International Work-Conference on IWBBIO 2023, held in Gran Canaria, Spain, during July 15-17, 2022. The 54 full papers were carefully reviewed and selected from 148 submissions. They were organized in the following topical sections: Biomarker Identification, Biomedical Engineering, Biomedical Signal Analysis, E-Health.
Progress in Artificial Intelligence
The two-volume set LNAI 14115 and 14116 constitutes the refereed proceedings of the 22nd EPIA Conference on Progress in Artificial Intelligence, EPIA 2023, held in Faial Island, Azores, in September 2023. The 85 full papers presented in these proceedings were carefully reviewed and selected from 163 submissions. The papers have been organized in the following topical sections: ambient intelligence and affective environments; ethics and responsibility in artificial intelligence; general artificial intelligence; intelligent robotics; knowledge discovery and business intelligence; multi-agent Systems: theory and applications; natural language processing, text mining and applications; planning, scheduling and decision-making in AI; social simulation and modelling; artifical intelligence, generation and creativity; artificial intelligence and law; artificial intelligence in power and energy systems; artificial intelligence in medicine; artificial intelligence and IoT in agriculture; artificial intelligence in transportation systems; artificial intelligence in smart computing; artificial intelligence for industry and societies.
Advances in Artificial Intelligence, Computation, and Data Science
Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity—in both time and memory requirements—for machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed f...
Energy and Behaviour
Changes to energy behaviour - the role of people and organisations in energy production, use and efficiency - are critical to supporting a societal transition towards a low carbon and more sustainable future. However, which changes need to be made, by whom, and with what technologies are still very much under discussion. This book, developed by a diverse range of experts, presents an international and multi-faceted approach to the sociotechnical challenge of engaging people in energy systems and vice versa. By providing a multidisciplinary view of this field, it encourages critical thinking about core theories, quantitative and qualitative methodologies, and policy challenges. It concludes by addressing new areas where additional evidence is required for interventions and policy-making. It is designed to appeal to new entrants in the energy-efficiency and behaviour field, particularly those taking a quantitative approach to the topic. Concurrently, it recognizes ecological economist Herman Daly's insight: what really counts is often not countable.
Machine Learning, Optimization, and Data Science
This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.
