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Drawing --- Literature --- beeldverhalen --- Walker, Mort --- Flippie Flink [Fictieve Figuur] --- United States of America

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This book presents sixteen essays exploring the work of two of 17th-century Amsterdam's most ambitious painters, Govert Flinck and Ferdinand Bol. Museum curators, academic art historians, and conservation scientists from six different countries come together to investigate form, content, and context from a variety of perspectives. Eric Jan Slujter examines how changing patterns of patronage contributed to both artists' stylistic evolution. Hilbert Lootsma traces the rise and fall of their critical fortunes from their own time until today. Ann Jensen Adams situates their work in the shifting market for portraiture. Jasper Hillegers explores the origins of Flinck's career in the Leeuwarden studio of Lambert Jacobsz. Other authors present contextual and technical analyses of individual paintings. Portrait identities are revealed, painterly tricks uncovered, and both artists are shown to be influential teachers and members of an intellectual community in which art and theatre were closely linked. Many of these essays originated at an international conference held in preparation for the exhibition, Govert Flinck and Ferdinand Bol. Together, they shed new light on the methods and motivations of two artists who began as Rembrandt's acolytes but soon became his rivals.

Bol, Ferdinand --- Flinck, Govaert --- Rembrandt --- Painting, Dutch --- Bol, Ferdinand, --- Flinck, Govaert, --- Criticism and interpretation --- Painting, Dutch - 17th century --- Bol, Ferdinand, - 1616-1680 - Criticism and interpretation --- Flinck, Govaert, - 1615-1660 - Criticism and interpretation --- Flinck, Govert, --- Flink, Govaert, --- Flink, Govert, --- Criticism and interpretation. --- Bol, Ferdinand, - 1616-1680 --- Flinck, Govaert, - 1615-1660

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Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields.

fintech --- financial technology --- blockchain --- deep learning --- regtech --- environment --- social sciences --- machine learning --- learning analytics --- student field forecasting --- imbalanced datasets --- explainable machine learning --- intelligent tutoring system --- adversarial machine learning --- transfer learning --- cognitive bias --- stock market --- behavioural finance --- investor’s profile --- Teheran Stock Exchange --- unsupervised learning --- clustering --- big data frameworks --- fault tolerance --- stream processing systems --- distributed frameworks --- Spark --- Hadoop --- Storm --- Samza --- Flink --- comparative analysis --- a survey --- data science --- educational data mining --- supervised learning --- secondary education --- academic performance --- text-to-SQL --- natural language processing --- database --- machine translation --- medical image segmentation --- convolutional neural networks --- SE block --- U-net --- DeepLabV3plus --- cyber-security --- medical services --- cyber-attacks --- data communication --- distributed ledger --- identity management --- RAFT --- HL7 --- electronic health record --- Hyperledger Composer --- cybersecurity --- password security --- browser security --- social media --- ANOVA --- SPSS --- internet of things --- cloud computing --- computational models --- metaheuristics --- phishing detection --- website phishing

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• Reference Manager
• EndNote
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Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields.

fintech --- financial technology --- blockchain --- deep learning --- regtech --- environment --- social sciences --- machine learning --- learning analytics --- student field forecasting --- imbalanced datasets --- explainable machine learning --- intelligent tutoring system --- adversarial machine learning --- transfer learning --- cognitive bias --- stock market --- behavioural finance --- investor’s profile --- Teheran Stock Exchange --- unsupervised learning --- clustering --- big data frameworks --- fault tolerance --- stream processing systems --- distributed frameworks --- Spark --- Hadoop --- Storm --- Samza --- Flink --- comparative analysis --- a survey --- data science --- educational data mining --- supervised learning --- secondary education --- academic performance --- text-to-SQL --- natural language processing --- database --- machine translation --- medical image segmentation --- convolutional neural networks --- SE block --- U-net --- DeepLabV3plus --- cyber-security --- medical services --- cyber-attacks --- data communication --- distributed ledger --- identity management --- RAFT --- HL7 --- electronic health record --- Hyperledger Composer --- cybersecurity --- password security --- browser security --- social media --- ANOVA --- SPSS --- internet of things --- cloud computing --- computational models --- metaheuristics --- phishing detection --- website phishing