The usage of multivariate time series to identify diseases plays an important role in the medical field, as it can help medical staff to improve diagnose accuracy and reduce medical costs. Current research shows that deep Convolutional Neural Networks (CNN) can automatically capture features from raw data and Long Short-Term Memory (LSTM) networks can manage and learn temporal dependence between time series data such as physiological signals. In this work, we propose a deep learning framework called DeepCNN-LSTM by combining the CNN and LSTM to leverage their respective advantages for disease recognition, allowing itself to characterize complex temporal varieties with multiple autoencoded features. In particular, we use stationary wavelet transform together with median filter to preprocess low-frequency signal data, and introduce sliding window to segment physiological time series before model training for performance improvement on the training speed as well as the accuracy for recogn
Conclusion (~1,700 words).
All backed up by over 200 references (~6,500 words).
We must stop crediting the wrong people for inventions made by others.
Instead let s heed the recent call in the journal
Nature: Let 2020 be the year in which we value those who ensure that
science is self-correcting [SV20].
Like those who know me can testify, finding and citing original sources of scientific and technological innovations is important to me, whether they are mine or other people s [DL1][DL2][HIN][NASC1-9]. The present page is offered as a resource for computer scientists who share this inclination.
By grounding research in its true intellectual foundations and crediting the original inventors,
Cogniac provides Doosan Bobcat with AI Visual Operations Intelligence Platform Bobcat adopts Cogniac s AI machine vision platform to enhance the efficiency of the manufacturing kitting process
April 13, 2021 08:00 ET | Source: Cogniac Corporation Cogniac Corporation San Jose, California, UNITED STATES
SAN JOSE, Calif., April 13, 2021 (GLOBE NEWSWIRE) Cogniac Corporation (“Cogniac”), a San Jose, California-based provider of enterprise-class Artificial Intelligence (AI) image and video analysis, has partnered with Doosan Bobcat North America (“Bobcat”). Bobcat, a global compact equipment leader, is implementing Cogniac’s proprietary visual data processing platform within the manufacturing warehouse kitting inspection process. This move will increase operational efficiencies, boost productivity and reduce material handling times, all part of Bobcat’s commitment to quality and drive for innovation.
“We believe that the proposed network architecture could easily be implemented in a clinical setting, providing a simple, inexpensive and accurate screening tool for SSc.”
According to the University of Houston, early diagnosis of SSc is critical but often elusive. Studies have shown that organ involvement could occur far earlier than expected in the early phase of the disease, but early diagnosis and determining the extent of disease progression pose a significant challenge for physicians, resulting in delays in therapy and management.
In artificial intelligence, deep learning organises algorithms into layers – an artificial neural network – that can make its own decisions. To speed up the learning process, the new network was trained using the parameters of MobileNetV2, a mobile vision application, pre-trained on the ImageNet dataset with 1.4 million images.
How a Team from MIT is Using an AI Tool to Detect Melanoma
Thought LeadersLuis R. Soenksen Ph.D.Research Scientist and Venture Builder
Massachusetts Institute of Technology
AZoRobotics speaks with Luis R. Soenksen, entrepreneur and medical device expert, currently acting as MIT s first Venture Builder in Artificial Intelligence and Healthcare. Luis was part of a team that has developed a system that uses neural networks to spot ugly duckling pre-cancerous lesions on a patient s skin, helping to detect melanoma more efficiently.
Can you give our readers a summary of your recent research? What were the main aims of the research?