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"Recognizing diseases with multivariate physiological signals by a Deep" by Jun Liao, Dandan Liu et al.

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 ....

Convolutional Neural Networks , Long Short Term Memory , Deepcnn Lstm , Disease Recognition , Multivariate Time Series , Hysiological Signal , நீண்டது குறுகிய கால நினைவு ,

Critique of 2018 Turing Award for Drs. Bengio & Hinton & LeCun

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, ....

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Cogniac provides Doosan Bobcat with AI Visual Operations Intelligence Platform

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 dr ....

United States , North Dakota , West Fargo , Adrienne Olson , Jordan Schmidt , Russ Honeyman , Chuck Myers , Cogniac Corporation , Cogniac Press Contact , Gateway Group , Bobcat Company , Clark Equipment Company , Convolutional Neural Networks , San Jose , Artificial Intelligence , Doosan Bobcat North America , Visual Operations Intelligence Platform , Quality Management , Doosan Bobcat North , Visual Operations Intelligence , Hyper Parameter Optimization , Bobcat North America , Clark Equipment , Press Contact , Manager Public Relations , Artificial Intelligence ,

Systemic sclerosis accurately screened on laptop

“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. ....

Metin Akay , Johns Dunn , Yasemin Akay , Convolutional Neural Networks Cnns , Biomedical Engineering Department , University Of Houston , Endowed Chair Professor , Convolutional Neural Networks , ஜான்ஸ் டன் , உயிர் மருத்துவ பொறியியல் துறை , பல்கலைக்கழகம் ஆஃப் ஹூஸ்டன் , வழங்கப்பட்டது நாற்காலி ப்ரொஃபெஸர் ,

How a Team from MIT is Using an AI Tool to Detect Melanoma

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? ....

Nuevo Leóx , Leadersluisr Soenksen , Luisr Soenksen , Joan Nugent , Joan Nugentapr , Johns Hopkins University , Monterrey Institute Of Technology , Massachusetts Institute Of Technology , Artificial Neural Networks , Showa Team , Innovation Initiative , Convolutional Neural Networks , Research Scientist , Venture Builder , Artificial Intelligence , Neural Networks , Mechanical Engineering , Monterrey Institute , Johns Hopkins , ஜோன் நூஜெஂட் , ஜான்ஸ் ஹாப்கின்ஸ் பல்கலைக்கழகம் , மோந்டேர்றெய் நிறுவனம் ஆஃப் தொழில்நுட்பம் , மாசசூசெட்ஸ் நிறுவனம் ஆஃப் தொழில்நுட்பம் , செயற்கை நரம்பியல் நெட்வொர்க்குகள் , கண்டுபிடிப்பு முயற்சி , ஆராய்ச்சி விஞ்ஞானி ,