Function approximation has been found in many applications. The radial basis function (RBF) network is one approach which shown a great promise this sort of problems because its faster learning capacity. A traditional RBF takes Gaussian functions as and adopts the least-squares criterion objective function, However, it still suffers from two major problems. First, difficult to use approximate constant values. If nearly values some intervals, will be inefficient approximating these Second,...

The paper proposes an innovative deep convolutional neural network (DCNN) combined with texture map for detecting cancerous regions and marking the ROI in a single model automatically. proposed DCNN contains two collaborative branches, namely upper branch to perform oral cancer detection, lower semantic segmentation marking. With extracts regions, makes more precision. To make features regular, images from input image. A sliding window is then applied compute standard deviation values of...

Identifying abdominal organs is one of the essential steps in visualizing organ structure to assist teaching, clinical training, diagnosis, and medical image retrieval. However, due partial volume effects, gray-level similarities adjacent organs, contrast media affect, relatively high variations position shape, automatically identifying has always been a challenging task. To conquer these difficulties, this paper proposes combining multimodule contextual neural network spatial fuzzy rules...

Background Diagnosing underlying causes of nonneurogenic male lower urinary tract symptoms associated with bladder outlet obstruction (BOO) is challenging. Video-urodynamic studies (VUDS) and pressure-flow (PFS) are both invasive diagnostic methods for BOO. VUDS can more precisely differentiate etiologies BOO, such as benign prostatic obstruction, primary neck dysfunctional voiding, potentially outperforming PFS. Objective These examinations’ nature highlights the need developing noninvasive...

This paper presents a deep learning method of segmenting lungs in chest X-Ray image using Encoder-Decoder Convolutional Network on the JSRT (Japanese Society Radiological Technology) lung nodule dataset. The result segmentation has proven efficient enough to be applicable real world medical environments bring ease determining area occupied by and some other diagnosis.

Wi-Fi sensing for gesture recognition systems is a fascinating and challenging research topic. We propose multitask sign language framework called Wi-SignFi, which accounts gestures in the real world associated with various objects, actions, or scenes. The proposed comprises convolutional neural network (CNN) K-nearest neighbor (KNN) module. It evaluated on public SignFi dataset achieves 98.91%, 86.67%, 99.99% average accuracies 276/150 activities, five users, two environments, respectively....

This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, neutral state. The proposed utilizes regularized discriminant analysis-based boosting algorithm (RDAB) with Gabor features to recognize the expressions. Entropy criterion is applied select feature which subset of informative nonredundant features. RDAB uses RDA as learner in algorithm. combines strengths linear analysis (LDA) quadratic (QDA). It...

Lung segmentation of chest X-ray (CXR) images is a fundamental step in many diagnostic applications. Most lung field methods reduce the image size to speed up subsequent processing time. Then, low-resolution result upsampled original high-resolution image. Nevertheless, boundaries become blurred after downsampling and upsampling steps. It necessary alleviate during upsampling. In this paper, we incorporate with superpixel resizing framework achieve goal. The upsamples results based on...

In this paper, a liver diseases diagnosis based on Gabor wavelets and support vector machine (SVM) classifier is proposed. The scheme includes two steps: features extraction classification. derived from are obtained the regions of interest (ROIs) among normal abnormal CT images. classification step, SVM used to discriminate fiver diseases. Finally receiver operating characteristic (ROC) curve employed evaluate performance system. Three kinds identified including cyst, hepatoma cavernous...

We propose a classifier based on the support vector machine (SVM) for automatic classification in liver disease. The SVM, stemming from statistical learning theory, involves state-of-the-art learning. is part of computer-aided diagnosis (CADx), which assists radiologists accurately diagnosing formulate discriminating between cysts, hepatoma, cavernous hemangioma, and normal tissue as supervised problem, apply SVM to classifying diseases using gray level co-occurrence matrix features...

In this paper, a novel BP-CMAC neural network classifier for the classification of liver diseases is proposed. The takes advantages back-propagation (BP) and CMAC networks. It utilities to simplify input space forwards BP as inputs. Therefore, it can reduce memory allocation network, speed up learning process. used construct disease diagnosis system testing cyst, hepatoma, cavernous hemagioma. overall distinction rate about 87% even though symptoms hepatoma hemagioma are very similar.

In this paper, a liver disease diagnosis based on Gabor filters is proposed. Three kinds of diseases are identified: cyst, hepatoma and cavernous hemangioma. The scheme includes two steps: features extraction classification. derived from obtained the ROIs among normal abnormal CT images. classification step SVM classifier used to discriminate different types. Finally receiver operating characteristic curve employed evaluate performance system. effectiveness proposed method demonstrated...

FMS-like tyrosine kinase 3 (FLT3) is an attractive target for acute myeloid leukemia. This work provides a mechanism behind the severe and minor drug resistance experienced by PKC412 sorafenib, respectively, in response to G697R mutation.

Sign language is an important way for deaf people to understand and communicate with others. Many researchers use Wi-Fi signals recognize hand finger gestures in a non-invasive manner. However, usually contain signal interference, background noise, mixed multipath noise. In this study, Channel State Information (CSI) preprocessed by singular value decomposition (SVD) obtain the essential signals. includes positional relationship of space changes actions over time. We propose novel...

Detection of human activity with computer vision has become a concern lately. So many visionary approaches and algorithms. In such as Raw Footage, TVL-1 Optical Flow Image, Rectangle Ellipsoid Fitting, Area Distribution Around Centroid, Pose Estimation. And algorithmic CNN (Convolutional Neural Network), Random decision forest (RDF), 3D-CNN LSTM, R-CNN, Multi stream-deep CNN. The algorithm approach pose estimation is considered to have resistance sudden changes in the environment movement....

This paper proposes a new two-stream neural network which combines the traditional background modeling method with deep learning to detect moving objects. The input for is original image and its corresponding foreground image, while output bounding boxes of objects in image. Traditional CNN methods cannot distinguish from static objects, but this successfully solves problem.

This paper explores the performance of natural language processing in financial sentiment classification. We collected people's views on U.S. stocks from Stocktwits website. The messages this website reflect investors' stock. These are classified into positive or negative sentiments using a BERT-based model. Investor can be further analyzed to help more investors, businesses organizations make effective decisions. experimental results show that pre-trained BERT model has been fine-tuned...

This paper describes a method for automatic abdominal organ recognition from series of CT image slices, that is based on shape analysis, contextual constraint, and between-slice relationship. A neural network applied to segment each slice into disconnected regions. For region, its features are calculated, along with spatial relationships respect spine. Then, according the knowledge anatomy, these constructed form fuzzy rules used recognition. In process, obtained overlapping between adjacent...

In this paper, we propose an algorithm to detect captions from news videos. The method only detects excluding other miscellaneous types of text. makes use the fact that text remains in many consecutive frames reduce number processing frames. caption beginning frame is detected first, then a candidate strip defined. Moreover, difference between computed, and information transformed frequency domain by discrete cosine transform. Frequency analysis used define region, twelve wavelet features...

In this paper, we propose an algorithm to detect captions from news videos. The method only detects excluding other miscellaneous types of text. makes use the fact that text remains in many consecutive frames reduce number processing frames. caption beginning frame is detected firs, then a candidate region defined. Twelve wavelet features are extracted and considered as input classifier blocks. Experimental results show proposed approach can fast robustly video.