|
|
|
|
|
Yu Yao and Quan Qian
We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization t...
ver más
|
|
|
|
|
|
|
|
|
|
|
Reenu Mohandas, Mark Southern, Eoin O?Connell and Martin Hayes
Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedure...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ying-Hsun Lai, Shin-Yeh Chen, Wen-Chi Chou, Hua-Yang Hsu and Han-Chieh Chao
Federated learning trains a neural network model using the client?s data to maintain the benefits of centralized model training while maintaining their privacy. However, if the client data are not independently and identically distributed (non-IID) becau...
ver más
|
|
|
|
|
|
|
|
|
|
|
Abdul Rehman Khalid, Nsikak Owoh, Omair Uthmani, Moses Ashawa, Jude Osamor and John Adejoh
In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble met...
ver más
|
|
|
|
|
|
|
|
|
|
|
Danilo Pau, Andrea Pisani and Antonio Candelieri
In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zhehu Yuan, Yinqi Sun and Dennis Shasha
Database and data structure research can improve machine learning performance in many ways. One way is to design better algorithms on data structures. This paper combines the use of incremental computation as well as sequential and probabilistic filterin...
ver más
|
|
|
|
|
|
|
|
|
|
|
Guilherme Yukio Sakurai, Jessica Fernandes Lopes, Bruno Bogaz Zarpelão and Sylvio Barbon Junior
The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems. Change detection algorithms, ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Sijia Jia, Zhenkai Zhang, Qi Dang, Chen Song and Chao Yang
Compared with traditional wings equipped with conventional control surfaces, variable-camber morphing wings have become a hot research topic in the field of aviation due to their ability to maintain a smooth and continuous overall shape while ensuring ex...
ver más
|
|
|
|
|
|
|
|
|
|
|
Abishek Manikandaraja, Peter Aaby and Nikolaos Pitropakis
Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices. Because machine learning and artificial intelligence can detect malware better than traditional sig...
ver más
|
|
|
|
|
|
|
|
|
|
|
Viacheslav Moskalenko, Vyacheslav Kharchenko, Alona Moskalenko and Borys Kuzikov
Artificial intelligence systems are increasingly being used in industrial applications, security and military contexts, disaster response complexes, policing and justice practices, finance, and healthcare systems. However, disruptions to these systems ca...
ver más
|
|
|
|
|
|