A deep-learning approach for pattern recognition allows rapid and reproducible quantification of histological NASH parameters: Integration into the QuPath platform

2018 ◽  
Vol 68 ◽  
pp. S123 ◽  
Author(s):  
E. Rexhepaj ◽  
D. Nathalie ◽  
N. Benoit ◽  
H. Alison ◽  
B. Carole ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Learning Approach

scholarly journals Presensi Kelas Berbasis Pola Wajah, Senyum dan Wi-Fi Terdekat dengan Deep Learning

2021 ◽  
Vol 5 (1) ◽  
pp. 31-38
Author(s):  
Miftakhurrokhmat ◽  
Rian Adam Rajagede ◽  
Ridho Rahmadi
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Learning Approach ◽  
Time Data ◽  
Location Data ◽  
Academic System ◽  
Facial Pattern ◽  
Testing Accuracy ◽  
Data Location ◽  
Facial Images

Students' attendance in class is often mandatory in education and becomes a benchmark for assessing students. Sometimes there are still fraudulent practices by students to achieve minimum attendance. From the administrative perspective, a paper-based presence system is potentially wasteful and extends the administrative stage because it requires manual recapitulation. This study aims to design a class attendance application based on facial pattern recognition, smile, and closest Wi-Fi. The method used in this research is a deep learning approach with CNN based architecture, FaceNet, to recognize faces. In addition to facial images, the system will also validate the attendance with location and time data. Location data is obtained from matching SSID from the database, and time data is taken when the user sends attendance data through API. This attendance system consists of three applications: web, mobile, and services installed on a mini-computer, which are integrated to sending attendance data to the academic system automatically. As confirmation, students are required to smile selfies to strengthen the validity of their presence. The testing model's accuracy results are 92.6%, while for live testing accuracy the model obtained 66.7%.  

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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