Search Results - "Sparse Autoencoder"

ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data by Almuqhim, Fahad, Saeed, Fahad

“… We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features…”
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Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed by Zhang, Yu-Dong, Zhang, Yin, Hou, Xiao-Xia, Chen, Hong, Wang, Shui-Hua

“… we developed a seven-layer deep neural network (DNN), which includes one input layer, four sparse autoencoder layers, one softmax layer, and one output layer…”
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Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass by Shao, Zhenfeng, Zhang, Linjing, Wang, Lei

“… However, field data are limited in remote and unmanaged areas. In addition, optical reflectance usually saturates at high-density biomass level and is subject to cloud contaminations…”
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Removal of EOG artifacts from EEG using a cascade of sparse autoencoder and recursive least squares adaptive filter by Yang, Banghua, Duan, Kaiwen, Zhang, Tao

“… (more than three channels) EEG recording is required. To address these limitations of existing methods, a method using a cascade of sparse autoencoder (SAE…”
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A 3D Sparse Autoencoder for Fully Automated Quality Control of Affine Registrations in Big Data Brain MRI Studies by Thadikemalla, Venkata Sainath Gupta, Focke, Niels K., Tummala, Sudhakar

“…This paper presents a fully automated pipeline using a sparse convolutional autoencoder for quality control (QC) of affine registrations in large-scale…”
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Alz-SAENet: A Deep Sparse Autoencoder based Model for Alzheimer’s Classification by Reddy, G Nagarjuna, Reddy, K Nagi

“… we built an advanced deep sparse autoencoder-based architecture, named Alz-SAENet for the identification of diseased from typical control subjects using MRI volumes…”
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Gait phase recognition of lower limb exoskeleton system based on the integrated network model by Zhang, Zaifang, Wang, Zhaoyang, Lei, Han, Gu, Wenquan

“…•This paper adopts sparse autoencoder into the model to extract key features of gait information and use bidirectional long short-term memory for learning more critical information from gait data…”
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A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes by Li, Hailong, Parikh, Nehal A., He, Lili

“…Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A…”
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Multimodal learning using convolution neural network and Sparse Autoencoder by Vu, Tien Duong, Yang, Hyung-Jeong, Nguyen, Van Quan, Oh, A-Ran, Kim, Mi-Sun

“…) have been the subject of extensive research. Deep learning has recently been a great interest in AD classification…”
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Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework by He, Lili, Li, Hailong, Holland, Scott K., Yuan, Weihong, Altaye, Mekibib, Parikh, Nehal A.

“…Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive…”
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An Optimized Hybrid Soil Texture Classification Model using Stacked Sparse Autoencoder by Prabavathi, R, Chelliah, Balika J

“… The dynamic subject of soil classification starts with the model and moves on to explain the classes and, at the end, apply the knowledge gained from the field…”
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Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics by Yousefi, Bardia, Akbari, Hamed, Maldague, Xavier P.V.

“… In this study for breast cancer screening 208 subjects participated and normal and abnormal (diagnosed by mammography or clinical diagnosis…”
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Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and Sparse Auto Encoder Method by K. Oommen, Deepthi, Arunnehru, J.

“… In light of research investigations, it is vital to consider age as one of the key criteria when choosing the subjects…”
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Decomposed Temporal Complexity Analysis of Neural Oscillations and Machine Learning Applied to Alzheimer’s Disease Diagnosis by Furutani, Naoki, Nariya, Yuta, Takahashi, Tetsuya, Noto, Sarah, Yang, Albert C., Hirosawa, Tetsu, Kameya, Masafumi, Minabe, Yoshio, Kikuchi, Mitsuru

“… We applied resting-state magnetoencephalography (MEG) to 17 AD patients and 21 healthy control subjects…”
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A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion by Yang, Jun, Ma, Zhengmin, Wang, Jin, Fu, Yunfa

“…Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled…”
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New Horizons in Web Search, Web Data Mining, and Web-Based Applications

“… It consists of ten peer-reviewed papers covering various subjects and applications related to web intelligence…”
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Health Monitoring of Movement Disorder Subject based on Diamond Stacked Sparse Autoencoder Ensemble Model by Tang, Likun, Ma, Jie, Li, Yongming

“… To solve this problem, a health monitoring of movement disorder subject based on diamond stacked sparse autoencoder ensemble model (DsaeEM…”
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Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data by Wang, Wenguang, Ren, Xin, Zhang, Yan, Li, Meng

“… satisfactory classification accuracy due to high similarity of certain classes. In this paper, a novel Pol-SAR lithology classification method based on a stacked sparse autoencoder (SSAE) is proposed…”
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Diagnosis of osteoporosis disease from bone X-ray images with stacked sparse autoencoder and SVM classifier by Nasser, Yassine, El Hassouni, Mohammed, Brahim, Abdelbasset, Toumi, Hechmi, Lespessailles, Eric, Jennane, Rachid

“…). However, the diagnosis of osteoporosis confronts two major challenges, the difficulty of distinguishing between osteoporosis and healthy subjects just from the visual inspection of bone X-ray images…”
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Detection of lumen and media-adventitia borders in IVUS images using sparse auto-encoder neural network by Shengran Su, Zhifan Gao, Heye Zhang, Qiang Lin, Hau, William Kongto, Shuo Li

“… using two sparse autoencoders and one softmax classifier. To smooth the lumen and MA borders detected by the ANN method, we used the active contour model…”
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