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https://www.readbyqxmd.com/read/28328518/application-of-lms-based-nn-structure-for-power-quality-enhancement-in-a-distribution-network-under-abnormal-conditions
#1
Rahul Kumar Agarwal, Ikhlaq Hussain, Bhim Singh
This paper proposes an application of a least mean-square (LMS)-based neural network (NN) structure for the power quality improvement of a three-phase power distribution network under abnormal conditions. It uses a single-layer neuron structure for the control in a distribution static compensator (DSTATCOM) to attenuate the harmonics such as noise, bias, notches, dc offset, and distortion, injected in the grid current due to connection of several nonlinear loads. This admittance LMS-based NN structure has a simple architecture which reduces the computational complexity and burden which makes it easy to implement...
March 16, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28328517/convolution-in-convolution-for-network-in-network
#2
Yanwei Pang, Manli Sun, Xiaoheng Jiang, Xuelong Li
Network in network (NiN) is an effective instance and an important extension of deep convolutional neural network consisting of alternating convolutional layers and pooling layers. Instead of using a linear filter for convolution, NiN utilizes shallow multilayer perceptron (MLP), a nonlinear function, to replace the linear filter. Because of the powerfulness of MLP and 1 x 1 convolutions in spatial domain, NiN has stronger ability of feature representation and hence results in better recognition performance...
March 16, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28328516/anrad-a-neuromorphic-anomaly-detection-framework-for-massive-concurrent-data-streams
#3
Qiuwen Chen, Ryan Luley, Qing Wu, Morgan Bishop, Richard W Linderman, Qinru Qiu
The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic...
March 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28327449/integrated-local-binary-pattern-texture-features-for-classification-of-breast-tissue-imaged-by-optical-coherence-microscopy
#4
Sunhua Wan, Hsiang-Chieh Lee, Xiaolei Huang, Ting Xu, Tao Xu, Xianxu Zeng, Zhan Zhang, Yuri Sheikine, James L Connolly, James G Fujimoto, Chao Zhou
This paper proposes a texture analysis technique that can effectively classify different types of human breast tissue imaged by Optical Coherence Microscopy (OCM). OCM is an emerging imaging modality for rapid tissue screening and has the potential to provide high resolution microscopic images that approach those of histology. OCM images, acquired without tissue staining, however, pose unique challenges to image analysis and pattern classification. We examined multiple types of texture features and found Local Binary Pattern (LBP) features to perform better in classifying tissues imaged by OCM...
March 8, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28326656/skin-cancer-diagnosed-by-using-artificial-intelligence-on-clinical-images
#5
Isaäc van der Waal
In a recent Research Letter in Nature an automated classification of a few selected skin lesions has been published, using a deep convolutional neural network (CNN) (Esteva et al, 2017). Convolutional neural network is an important innovation in the field of computer vision. A popular use is for image processing, e.g. applied in face recognition. In the reported study CNN has been applied to a dataset of almost 130,000 clinical images, including some 3,000 dermoscopic images. This article is protected by copyright...
March 22, 2017: Oral Diseases
https://www.readbyqxmd.com/read/28325448/diagnosis-of-autism-through-eeg-processed-by-advanced-computational-algorithms-a-pilot-study
#6
Enzo Grossi, Chiara Olivieri, Massimo Buscema
BACKGROUND: Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28325441/a-review-of-fuzzy-cognitive-maps-in-medicine-taxonomy-methods-and-applications
#7
REVIEW
Abdollah Amirkhani, Elpiniki I Papageorgiou, Akram Mohseni, Mohammad R Mosavi
BACKGROUND AND OBJECTIVE: A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28324924/automatic-tissue-characterization-of-air-trapping-in-chest-radiographs-using-deep-neural-networks
#8
Awais Mansoor, Geovanny Perez, Gustavo Nino, Marius George Linguraru
Significant progress has been made in recent years for computer-aided diagnosis of abnormal pulmonary textures from computed tomography (CT) images. Similar initiatives in chest radiographs (CXR), the common modality for pulmonary diagnosis, are much less developed. CXR are fast, cost effective and low-radiation solution to diagnosis over CT. However, the subtlety of textures in CXR makes them hard to discern even by trained eye. We explore the performance of deep learning abnormal tissue characterization from CXR...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324707/chemical-structure-based-predictive-model-for-the-oxidation-of-trace-organic-contaminants-by-sulfate-radical
#9
Tiantian Ye, Zongsu Wei, Richard Spinney, Chong-Jian Tang, Shuang Luo, Ruiyang Xiao, Dionysios D Dionysiou
Second-order rate constants [Formula: see text] for the reaction of sulfate radical anion (SO4(•-)) with trace organic contaminants (TrOCs) are of scientific and practical importance for assessing their environmental fate and removal efficiency in water treatment systems. Here, we developed a chemical structure-based model for predicting [Formula: see text] using 32 molecular fragment descriptors, as this type of model provides a quick estimate at low computational cost. The model was constructed using the multiple linear regression (MLR) and artificial neural network (ANN) methods...
March 6, 2017: Water Research
https://www.readbyqxmd.com/read/28323894/a-generalized-phase-resetting-method-for-phase-locked-modes-prediction
#10
Sorinel A Oprisan, Dave I Austin
We derived analytically and checked numerically a set of novel conditions for the existence and the stability of phase-locked modes in a biologically relevant master-slave neural network with a dynamic feedback loop. Since neural oscillators even in the three-neuron network investigated here receive multiple inputs per cycle, we generalized the concept of phase resetting to accommodate multiple inputs per cycle. We proved that the phase resetting produced by two or more stimuli per cycle can be recursively computed from the traditional, single stimulus, phase resetting...
2017: PloS One
https://www.readbyqxmd.com/read/28322914/computer-aided-prediction-of-extent-of-motor-recovery-following-constraint-induced-movement-therapy-in-chronic-stroke
#11
Sarah Hulbert George, Mohammad Hossein Rafiei, Lynne Gauthier, Alexandra Borstad, John A Buford, Hojjat Adeli
Constraint-induced movement therapy (CI therapy) is a well-researched intervention for treatment of upper limb function. Overall, CI therapy yields clinically meaningful improvements in speed of task completion and greatly increases use of the more affected upper extremity for daily activities. However, individual improvements vary widely. It has been suggested that intrinsic feedback from somatosensation may influence motor recovery from CI therapy. To test this hypothesis, an enhanced probabilistic neural network (EPNN) prognostic computational model was developed to identify which baseline characteristics predict extent of motor recovery, as measured by the Wolf Motor Function Test (WMFT)...
March 17, 2017: Behavioural Brain Research
https://www.readbyqxmd.com/read/28321787/a-mathematical-theory-of-shape-and-neuro-fuzzy-methodology-based-diagnostic-analysis-a-comparative-study-on-early-detection-and-treatment-planning-of-brain-cancer
#12
Subrata Kar, D Dutta Majumder
BACKGROUND: Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg-Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy...
March 20, 2017: International Journal of Clinical Oncology
https://www.readbyqxmd.com/read/28321440/neuronify-an-educational-simulator-for-neural-circuits
#13
Svenn-Arne Dragly, Milad Hobbi Mobarhan, Andreas Våvang Solbrå, Simen Tennøe, Anders Hafreager, Anders Malthe-Sørenssen, Marianne Fyhn, Torkel Hafting, Gaute T Einevoll
Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker)...
March 2017: ENeuro
https://www.readbyqxmd.com/read/28321249/mexican-hat-wavelet-kernel-elm-for-multiclass-classification
#14
Jie Wang, Yi-Fan Song, Tian-Lei Ma
Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28320846/working-memory-load-strengthens-reward-prediction-errors
#15
Anne G E Collins, Brittany Ciullo, Michael J Frank, David Badre
Reinforcement learning in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we asked how working memory and incremental reinforcement learning processes interact to guide human learning. Working memory load was manipulated by varying the number of stimuli to be learned across blocks...
March 20, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28320670/bci-use-and-its-relation-to-adaptation-in-cortical-networks
#16
Kaitlyn Casimo, Kurt E Weaver, Jeremiah Wander, Jeffrey G Ojemann
Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In this review, we discuss how BCI designs often take advantage of the brain's motor system infrastructure as sources of command signals. We highlight a growing body of literature examining how this approach leads to changes in activity across cortex, including beyond motor regions, as a result of learning the new skill of BCI control...
March 13, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28319275/mrf-ann-a-machine-learning-approach-for-automated-er-scoring-of-breast-cancer-immunohistochemical-images
#17
T Mungle, S Tewary, D K DAS, I Arun, B Basak, S Agarwal, R Ahmed, S Chatterjee, C Chakraborty
Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation-maximization optimization is employed to segment the ER cells...
March 20, 2017: Journal of Microscopy
https://www.readbyqxmd.com/read/28306716/localization-and-diagnosis-framework-for-pediatric-cataracts-based-on-slit-lamp-images-using-deep-features-of-a-convolutional-neural-network
#18
Xiyang Liu, Jiewei Jiang, Kai Zhang, Erping Long, Jiangtao Cui, Mingmin Zhu, Yingying An, Jia Zhang, Zhenzhen Liu, Zhuoling Lin, Xiaoyan Li, Jingjing Chen, Qianzhong Cao, Jing Li, Xiaohang Wu, Dongni Wang, Haotian Lin
Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets...
2017: PloS One
https://www.readbyqxmd.com/read/28298703/a-tutorial-on-the-free-energy-framework-for-modelling-perception-and-learning
#19
Rafal Bogacz
This paper provides an easy to follow tutorial on the free-energy framework for modelling perception developed by Friston, which extends the predictive coding model of Rao and Ballard. These models assume that the sensory cortex infers the most likely values of attributes or features of sensory stimuli from the noisy inputs encoding the stimuli. Remarkably, these models describe how this inference could be implemented in a network of very simple computational elements, suggesting that this inference could be performed by biological networks of neurons...
February 2017: Journal of Mathematical Psychology
https://www.readbyqxmd.com/read/28298702/fixed-versus-mixed-rsa-%C3%A2-explaining-visual-representations-by-fixed-and-mixed-feature-sets-from-shallow-and-deep-computational-models
#20
Seyed-Mahdi Khaligh-Razavi, Linda Henriksson, Kendrick Kay, Nikolaus Kriegeskorte
Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set...
February 2017: Journal of Mathematical Psychology
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