Results 71 to 80 of about 24,371,806 (309)

Combining value stream mapping and process mining in production: a systematic literature review

Discover Applied Sciences
Value stream mapping is a widely used and proven method in lean production for gaining transparency into the current state of a production process.
Tim Teriete, Klaus Erlach, Thomas Bauernhansl, Wil M. P. van der Aalst   +3 more
doaj   +1 more source

On Evaluation of Data Stream Clustering Algorithms: A Survey

IEEE Access
Data stream mining is a research area that has grown enormously in recent years. The main challenge is extracting knowledge in real-time from a possibly unbounded data stream.
Christian Nordahl, Veselka Boeva, Hakan Grahn, Marie Persson-Netz   +3 more
doaj   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

IEEE Access, 2020
Traditional pattern mining is designed to handle binary database that assume all items in the database have same importance, there is a limitation to recognize accurate information from real-world databases using traditional method. To solve this problem,
Hyoju Nam, Unil Yun, Bay Vo, Tin Truong, Zhi-Hong Deng, Eunchul Yoon   +5 more
doaj   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Process mining online assessment data [PDF]

, 2009
Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can
Romero, C., Barnes, T., Ventura, S., Vasilyeva, E., Aalst, van der, W.M.P., Trcka, N., Pechenizkiy, M., De Bra, P.M.E., Desmarais, M.   +8 more
core  

Realization of Resource Service Based on Web Data Stream Mining

Gong-kuang zidonghua, 2010
In order to improve utilization rate and optimize website, the paper established a Web data stream mining system and introduced the system's frame structure.
WANG Chun-xia
doaj  

PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting

The Scientific World Journal, 2013
Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining.
Kaikuo Xu, Yexi Jiang, Mingjie Tang, Changan Yuan, Changjie Tang   +4 more
doaj   +1 more source

Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches

AIChE Journal, EarlyView.
Abstract Despite the growing use of ML in chemical engineering, the catalytic conversion of sulfur dioxide (SO2) to sulfur trioxide (SO3) remains underexplored from a data‐driven modeling perspective. This study evaluates an integrated workflow for literature‐derived SO2 oxidation data, combining data curation, preprocessing assessment, machine ...
Farough Agin, Jules Thibault, Clémence Fauteux‐Lefebvre   +2 more
wiley   +1 more source

Mining Frequent Item Sets Data Streams using "ÉclatAlgorithm" [PDF]

, 2014
Frequent pattern mining is the process of mining data in a set of items or some patterns from a largedatabase. The resulted frequent set data supports the minimum support threshold.
Richhariya, Pankaj; Computer Science Department BITS, Bhopal, India, Khambra, Geetanjli; Computer Science Department BITS, Bhopal, India   +1 more
core   +1 more source