Deep Spatiality: Unsupervised Learning of Spatially-Enhanced Global and Local 3D Features by Deep Neural Network With Coupled Softmax

Drug repositioning adalah penggunaan senyawa obat yang sudah lolos uji sebelumnya untuk mengatasi penyakit baru selain penyakit awal obat tersebut ditujukan. Drug repositioning dapat dilakukan dengan memprediksi interaksi senyawa obat dengan protein penyakit yang bereaksi positif. Salah satu tantangan dalam prediksi interaksi senyawa dan protein adalah masalah ketidakseimbangan data. Deep semi-supervised learning dapat menjadi alternatif untuk menangani model prediksi dengan data yang tidak seimbang. Proses pre-training berbasis unsupervised learning pada deep semi-supervised learning dapat merepresentasikan input dari unlabeled data (data mayoritas) dengan baik dan mengoptimasi inisialisasi bobot pada classifier. Penelitian ini mengimplementasikan Deep Belief Network (DBN) sebagai pre-training dan Deep Neural Network (DNN) sebagai classifier. Data yang digunakan pada penelitian ini adalah dataset ion channel, GPCR, dan nuclear receptor yang bersumber dari pangkalan data KEGG BRITE, BRENDA, SuperTarget, dan DrugBank. Hasil penelitian ini menunjukkan pada dataset tersebut, pre-training berupa ekstraksi fitur memberikan efek optimasi dilihat dari peningkatan performa model DNN pada akurasi (3-4.5%), AUC (4.5%), precision (5.9-6%), dan F-measure (3.8%). AbstractDrug repositioning is the reuse of an existing drug to treat a new disease other than its original medical indication. Drug repositioning can be done by predicting the interaction of drug compounds with disease proteins that react positively. One of the challenges in predicting the interaction of compounds and proteins is imbalanced data. Deep semi-supervised learning can be an alternative to handle prediction models with imbalanced data. The unsupervised learning based pre-training process in deep semi-supervised learning can represent input from unlabeled data (majority data) properly and optimize initialization of weights on the classifier. This study implements the Deep Belief Network (DBN) as a pre-training with Deep Neural Network (DNN) as a classifier. The data used in this study are ion channel, GPCR, and nuclear receptor dataset sourced from KEGG BRITE, BRENDA, SuperTarget, and DrugBank databases. The results of this study indicate that pre-training as feature extraction had an optimization effect. This can be seen from DNN performance improvement in accuracy (3-4.5%), AUC (4.5%), precision (5.9-6%), and F-measure (3.8%).

Download Full-text

Fast Defocus Blur Detection Network via Global Search and Local Refinements

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421520224 ◽

2021 ◽

Author(s):

Xiaoli Sun ◽

Yang Hai ◽

Xiujun Zhang ◽

Chen Xu ◽

Min Li

Keyword(s):

Neural Network ◽

Image Processing ◽

Mobile Devices ◽

Mobile Phones ◽

Deep Neural Network ◽

State Of The Art ◽

Multi Scale ◽

Blur Detection ◽

Defocus Blur ◽

Global And Local

Defocus blur detection aims at separating regions on focus from out-of-focus for image processing. With today’s popularity of mobile phones with portrait mode, accurate defocus blur detection has received more and more attention. There are many challenges that we currently confront, such as blur boundaries of defocus regions, interference of messy backgrounds and identification of large flat regions. To address these issues, in this paper, we propose a new deep neural network with both global and local pathways for defocus blur detection. In global pathway, we locate the objects on focus by semantical search. In local pathway, we refine the predicted blur regions via multi-scale supervisions. In addition, the refined results in local pathway are fused with searching results in global pathway by a simple concatenation operation. The structure of our new network is developed in a feasible way and its function appears to be quite effective and efficient, which is suitable for the deployment on mobile devices. It takes about 0.2[Formula: see text]s per image on a regular personal laptop. Experiments on both CUHK dataset and our newly proposed Defocus400 dataset show that our model outperforms existing state-of-the-art methods.

Download Full-text

Unsupervised learning of a deep neural network for metal artifact correction using dual‐polarity readout gradients

Magnetic Resonance in Medicine ◽

10.1002/mrm.27917 ◽

2019 ◽

Vol 83 (1) ◽

pp. 124-138 ◽

Cited By ~ 1

Author(s):

Kinam Kwon ◽

Dongchan Kim ◽

Byungjai Kim ◽

HyunWook Park

Keyword(s):

Neural Network ◽

Unsupervised Learning ◽

Deep Neural Network ◽

Metal Artifact ◽

Artifact Correction ◽

Dual Polarity

Download Full-text

Deep Neural Network Self-training Based on Unsupervised Learning and Dropout

International Journal of Fuzzy Logic and Intelligent Systems ◽

10.5391/ijfis.2017.17.1.1 ◽

2017 ◽

Vol 17 (1) ◽

pp. 1-9 ◽

Cited By ~ 21

Author(s):

Hye-Woo Lee ◽

Noo-ri Kim ◽

Jee-Hyong Lee

Keyword(s):

Neural Network ◽

Unsupervised Learning ◽

Deep Neural Network

Download Full-text

Supporting Deep Neural Network Safety Analysis and Retraining Through Heatmap-Based Unsupervised Learning

IEEE Transactions on Reliability ◽

10.1109/tr.2021.3074750 ◽

2021 ◽

pp. 1-17

Author(s):

Hazem Fahmy ◽

Fabrizio Pastore ◽

Mojtaba Bagherzadeh ◽

Lionel Briand

Keyword(s):

Neural Network ◽

Unsupervised Learning ◽

Deep Neural Network ◽

Safety Analysis ◽

Network Safety

Download Full-text

Unsupervised learning of haptic material properties

10.1101/2021.02.25.432896 ◽

2021 ◽

Author(s):

Anna Metzger ◽

Matteo Toscani

Keyword(s):

Neural Network ◽

Spatial Structure ◽

Unsupervised Learning ◽

Material Properties ◽

Deep Neural Network ◽

Perceptual System ◽

Tactile Receptors ◽

Haptic Exploration ◽

Latent Space

AbstractWhen touching the surface of an object, its spatial structure translates into a vibration on the skin. The perceptual system evolved to translate this pattern into a representation that allows to distinguish between different materials. Here we show that perceptual haptic representation of materials emerges from efficient encoding of vibratory patterns elicited by the interaction with materials. We trained a deep neural network with unsupervised learning (Autoencoder) to reconstruct vibratory patterns elicited by human haptic exploration of different materials. The learned compressed representation (i.e. latent space) allows for classification of material categories (i.e. plastic, stone, wood, fabric, leather/wool, paper, and metal). More importantly, distances between these categories in the latent space resemble perceptual distances, suggesting a similar coding. We could further show, that the temporal tuning of the emergent latent dimensions is similar to properties of human tactile receptors.

Download Full-text

A Study on the Application and the Advancement of Deep Neural Network Algorithm

Journal of Physics Conference Series ◽

10.1088/1742-6596/2146/1/012001 ◽

2022 ◽

Vol 2146 (1) ◽

pp. 012001

Author(s):

Jiulin Song ◽

Yansheng Chen

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Learning Algorithm ◽

Real Life ◽

Research Area ◽

Network Algorithms ◽

Network Algorithm ◽

New Type ◽

Neural Network Algorithm ◽

Global And Local

Abstract Deep neural network is a new type of learning algorithm, which has both global and local aspects and performs well in pattern recognition and computational speed. In recent years, deep neural network algorithm has been widely used in scientific research and real life, but its complexity, parallelism and other characteristics lead it to be a very challenging and innovative research area. This study briefly introduces the basic principles and theoretical knowledge of deep neural network algorithms, and mainly discusses their applications and Advancement of feature extraction in the field.

Download Full-text