图 书 馆开课单位--约瑟夫·斯特凡学院
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31Human stefin B as a model protein to study protein folding and aggregation[以人类Stefin B作为模型蛋白来研究蛋白折叠和聚集]
Eva Žerovnik(约瑟夫·斯特凡学院) Folding and aggregation mechanisms are in common to most proteins. Proteins differ whether they are folded or intrinsically unfolded/disordered, howev...
热度：50
Eva Žerovnik(约瑟夫·斯特凡学院) Folding and aggregation mechanisms are in common to most proteins. Proteins differ whether they are folded or intrinsically unfolded/disordered, howev...
热度：50
32Structural properties of magnetic nanoparticle[磁性纳米粒子的结构性质]
Darko Makovec(约瑟夫·斯特凡学院) The basic magnetic properties of nanoparticles have been intensively studied and the influence of the small (nano) size on the magnetic properties is ...
热度：63
Darko Makovec(约瑟夫·斯特凡学院) The basic magnetic properties of nanoparticles have been intensively studied and the influence of the small (nano) size on the magnetic properties is ...
热度：63
33Human stefin B as a model protein to study protein folding and aggregation[人类Stefin B作为模型蛋白来研究蛋白折叠和聚集]
Eva Žerovnik(约瑟夫·斯特凡学院) Folding and aggregation mechanisms are in common to most proteins. Proteins differ whether they are folded or intrinsically unfolded/disordered, howev...
热度：27
Eva Žerovnik(约瑟夫·斯特凡学院) Folding and aggregation mechanisms are in common to most proteins. Proteins differ whether they are folded or intrinsically unfolded/disordered, howev...
热度：27
34Siemens: Big Data in industrial applications[西门子：工业应用中的大数据]
Gerhard Kress(约瑟夫·斯特凡学院) Siemens does not only stand for innovative products, infrastructure components and automation in manufacturing, energy, cities and healthcare. Siemens...
热度：35
Gerhard Kress(约瑟夫·斯特凡学院) Siemens does not only stand for innovative products, infrastructure components and automation in manufacturing, energy, cities and healthcare. Siemens...
热度：35
3536Machine learning for environmental and life sciences[环境与生命科学的机器学习]
Sašo Džeroski(约瑟夫·斯特凡学院) Increasingly often, we need to learn predictive models from big or complex data, which may comprise many examples and many input/output dimensions. Wh...
热度：23
Sašo Džeroski(约瑟夫·斯特凡学院) Increasingly often, we need to learn predictive models from big or complex data, which may comprise many examples and many input/output dimensions. Wh...
热度：23
37Data Science, Machine Learning and Big Data: Current trends[数据科学，机器学习和大数据：当前趋势]
Nada Lavrač(约瑟夫·斯特凡学院) Machine Learning is one of the most flourishing areas of computer science. Traditionally, machine learning was concerned with the discovery of models,...
热度：19
Nada Lavrač(约瑟夫·斯特凡学院) Machine Learning is one of the most flourishing areas of computer science. Traditionally, machine learning was concerned with the discovery of models,...
热度：19
38AI Applications in Manufacturing[人工智能在制造业中的应用]
Bogdan Filipič(约瑟夫·斯特凡学院) Artificial Intelligence (AI) is a key component of Industry 4.0 needed for dealing with excessive amounts of data and enabling autonomy and flexibilit...
热度：36
Bogdan Filipič(约瑟夫·斯特凡学院) Artificial Intelligence (AI) is a key component of Industry 4.0 needed for dealing with excessive amounts of data and enabling autonomy and flexibilit...
热度：36
39Exploring the hubness-related properties of oceanographic sensor data[探索海洋传感器数据的与中心度相关的属性]
Nenad Tomašev(约瑟夫·斯特凡学院) In this paper we examine how the high dimensionality of oceanographic sensor data impacts the potential use of nearest-neighbor machine learning metho...
热度：16
Nenad Tomašev(约瑟夫·斯特凡学院) In this paper we examine how the high dimensionality of oceanographic sensor data impacts the potential use of nearest-neighbor machine learning metho...
热度：16
40Forecasting sales based on card transactions data[基于信用卡交易数据的销售预测]
Alexandra Moraru(约瑟夫·斯特凡学院) Smart cities are an important topic in today’s research problems, with high impact in many domains from economy to transportation, health and li...
热度：40
Alexandra Moraru(约瑟夫·斯特凡学院) Smart cities are an important topic in today’s research problems, with high impact in many domains from economy to transportation, health and li...
热度：40