Predicting movie box-office revenues using deep neural networks

Neural Computing and Applications - In the film industry, the ability to predict a movie’s box-office revenues before its theatrical release can decrease its financial risk. However, accurate...     

Unrestricted deep metric learning using neural networks interaction

Pattern Analysis and Applications - In many machine learning applications and algorithms, the algorithm performance and accuracy are highly dependent on the metric used to measure the distance...     

Cell tracking using deep neural networks with multi-task learning

Cell tracking plays crucial role in biomedical and computer vision areas. As cells generally have frequent deformation activities and small sizes in microscope image, tracking the non-rigid and non-significant cells is quite difficult in practice. Traditional visual tracking methods have good performances on tracking rigid and significant visual objects, however, they are not suitable for cell tracking problem. In this paper, a novel cell tracking method is proposed by using Convolutional Neural Networks (CNNs) as well as multi-task learning (MTL) techniques. The CNNs learn robust cell features and MTL improves the generalization performance of the tracking. The proposed cell tracking method consists of a particle filter motion model, a multi-task learning observation model, and an optimized model update strategy. In the training procedure, the cell tracking is divided into an online tracking task and an accompanying classification task using the MTL technique. The observation model is trained by building a CNN to learn robust cell features. The tracking procedure is started by assigning the cell position in the first frame of a microscope image sequence. Then, the particle filter model is applied to produce a set of candidate bounding boxes in the subsequent frames. The trained observation model provides the confidence probabilities corresponding to all of the candidates and selects the candidate with the highest probability as the final prediction. Finally, an optimized model update strategy is proposed to enable the multi-task observation model for the variation of the tracked cell over the entire tracking procedure. The performance and robustness of the proposed method are analyzed by comparing with other commonly-used methods. Experimental results demonstrate that the proposed method has good performance to the cell tracking problem.     

基于改进遗传算法的深度神经网络优化研究

深度前馈神经网络在分类和回归问题上得到了很好的应用,但网络性能极大程度上受到其结构和超参数影响.为了获得高性能的神经网络,首先对遗传算法的选择策略进行改进,之后利用该改进遗传算法,采用二进制编码与实数编码的混合编码策略对深度前馈神经网络层数、每层节点量以及学习率和权重进行优化.改进的选择策略,在最优保存策略的基础上从父...     

Stem cell motion-tracking by using deep neural networks with multi-output

Neural Computing and Applications - The aim of automated stem cell motility analysis is reliable processing and evaluation of cell behaviors such as translocation, mitosis, death, and so on. Cell...     

Online clinical decision support system using optimal deep neural networks

The Internet of Health things (IoHT) has numerous applications in healthcare by integrating health monitoring things like sensors and medical devices for remotely observe patient’s records to provide smarter and intelligent medicare services. To avail best healthcare services to the users using the e-health applications, in this paper, we propose an IoT with cloud based clinical decision support system for the prediction and observance of Chronic Kidney Disease (CKD) with its level of severity. The proposed framework collects the patient data using the IoT devices attached to the user which will be stored in the cloud along with the related medical records from the UCI repository. Furthermore, we employ a Deep Neural Network (DNN) classifier for the prediction of CKD and its level of severity. A Particle Swarm Optimization (PSO) based feature selection method is also used to improve the performance of DNN classifier. The proposed model is validated by employing the benchmark CKD dataset. Different classifiers are employed to compare the performance of the proposed model under several classification measures. The proposed DNN classifier alone predicts CKD with an accuracy of 98.25% and is further enhanced to 99.25 by PSO-FS method. At the same time, the improved classification performance is verified with higher values of 98.03 specificity, 99.25 accuracy, 99.39 F-score and 98.40 kappa value respectively.     

A multi-class skin Cancer classification using deep convolutional neural networks

Skin Cancer accounts for one-third of all diagnosed cancers worldwide. The prevalence of skin cancers have been rising over the past decades. In recent years, use of dermoscopy has enhanced the diagnostic capability of skin cancer. The accurate diagnosis of skin cancer is challenging for dermatologists as multiple skin cancer types may appear similar in appearance. The dermatologists have an average accuracy of 62% to 80% in skin cancer diagnosis. The research community has been made significant progress in developing automated tools to assist dermatologists in decision making. In this work, we propose an automated computer-aided diagnosis system for multi-class skin (MCS) cancer classification with an exceptionally high accuracy. The proposed method outperformed both expert dermatologists and contemporary deep learning methods for MCS cancer classification. We performed fine-tuning over seven classes of HAM10000 dataset and conducted a comparative study to analyse the performance of five pre-trained convolutional neural networks (CNNs) and four ensemble models. The maximum accuracy of 93.20% for individual model amongst the set of models whereas maximum accuracy of 92.83% for ensemble model is reported in this paper. We propose use of ResNeXt101 for the MCS cancer classification owing to its optimized architecture and ability to gain higher accuracy.

     

一种改进的基于深度神经网络的偏微分方程求解方法

偏微分方程求解是计算流体力学等科学与工程领域中数值分析的计算核心。由于物理的多尺度特性和对离散网格质量的敏感性,传统的数值求解方法通常包含复杂的人机交互和昂贵的网格剖分开销,限制了其在许多实时模拟和优化设计问题上的应用效率。提出了一种改进的基于深度神经网络的偏微分方程求解方法TaylorPINN。该方法利用深度神经网络的万能逼近定理和泰勒公式的函数拟合能力,实现了无网格的数值求解过程。在Helmholtz、Klein-Gordon和Navier-Stokes方程上的数值实验结果表明,TaylorPINN能够很好地拟合计算域内时空点坐标与待求函数值之间的映射关系,并提供了准确的数值预测结果。与常用的基于物理信息神经网络方法相比,对于不同的数值问题,TaylorPINN将预测精度提升了3~20倍。     

Image interpolation using convolutional neural networks with deep recursive residual learning

Multimedia Tools and Applications - Recent developments of image super-resolution often utilize the deep convolutional neural network (CNN) and residual learning to relate the observed...     

Pedestrian detection with unsupervised multispectral feature learning using deep neural networks

Multispectral pedestrian detection is an important functionality in various computer vision applications such as robot sensing, security surveillance, and autonomous driving. In this paper, our motivation is to automatically adapt a generic pedestrian detector trained in a visible source domain to a new multispectral target domain without any manual annotation efforts. For this purpose, we present an auto-annotation framework to iteratively label pedestrian instances in visible and thermal channels by leveraging the complementary information of multispectral data. A distinct target is temporally tracked through image sequences to generate more confident labels. The predicted pedestrians in two individual channels are merged through a label fusion scheme to generate multispectral pedestrian annotations. The obtained annotations are then fed to a two-stream region proposal network (TS-RPN) to learn the multispectral features on both visible and thermal images for robust pedestrian detection. Experimental results on KAIST multispectral dataset show that our proposed unsupervised approach using auto-annotated training data can achieve performance comparable to state-of-the-art deep neural networks (DNNs) based pedestrian detectors trained using manual labels.     

Human activity recognition with smartphone sensors using deep learning neural networks

Human activities are inherently translation invariant and hierarchical. Human activity recognition (HAR), a field that has garnered a lot of attention in recent years due to its high demand in various application domains, makes use of time-series sensor data to infer activities. In this paper, a deep convolutional neural network (convnet) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics of activities and 1D time-series signals, at the same time providing a way to automatically and data-adaptively extract robust features from raw data. Experiments show that convnets indeed derive relevant and more complex features with every additional layer, although difference of feature complexity level decreases with every additional layer. A wider time span of temporal local correlation can be exploited (1 × 9–1 × 14) and a low pooling size (1 × 2–1 × 3) is shown to be beneficial. Convnets also achieved an almost perfect classification on moving activities, especially very similar ones which were previously perceived to be very difficult to classify. Lastly, convnets outperform other state-of-the-art data mining techniques in HAR for the benchmark dataset collected from 30 volunteer subjects, achieving an overall performance of 94.79% on the test set with raw sensor data, and 95.75% with additional information of temporal fast Fourier transform of the HAR data set.     

Fast and robust multiple ColorChecker detection using deep convolutional neural networks

ColorCheckers are reference standards that professional photographers and filmmakers use to ensure predictable results under every lighting condition. The objective of this work is to propose a new fast and robust method for automatic ColorChecker detection. The process is divided into two steps: (1) ColorCheckers localization and (2) ColorChecker patches recognition. For the ColorChecker localization, we trained a detection convolutional neural network using synthetic images. The synthetic images are created with the 3D models of the ColorChecker and different background images. The output of the neural networks are the bounding box of each possible ColorChecker candidates in the input image. Each bounding box defines a cropped image which is evaluated by a recognition system, and each image is canonized with regards to color and dimensions. Subsequently, all possible color patches are extracted and grouped with respect to the center's distance. Each group is evaluated as a candidate for a ColorChecker part, and its position in the scene is estimated. Finally, a cost function is applied to evaluate the accuracy of the estimation. The method is tested using real and synthetic images. The proposed method is fast, robust to overlaps and invariant to affine projections. The algorithm also performs well in case of multiple ColorCheckers detection.     

Detection of novel coronavirus from chest X-rays using deep convolutional neural networks

Multimedia Tools and Applications - With over 172 Million people infected with the novel coronavirus (COVID-19) globally and with the numbers increasing exponentially, the dire need of a fast...     

FoodRecNet: a comprehensively personalized food recommender system using deep neural networks

Knowledge and Information Systems - Today, the huge variety of foods and the existence of different food preferences among people have made it difficult to choose the right food according to...     

Detection of weather images by using spiking neural networks of deep learning models

Neural Computing and Applications - The transmission of weather information of a location at certain time intervals affects the living conditions of the people there directly or indirectly....     

Localization of the slab information in factory scenes using deep convolutional neural networks

This paper proposes a novel algorithm for localizing slab identification numbers (SINs) in factory scenes. Automatic identification of product information is important for the process management, and localization of SINs in complex scenes is a major challenge for the recognition. A previous rule-based localization algorithm for SINs requires lots of prior knowledge and heuristic tuning for parameters. In this paper, a deep convolutional neural network (DCNN) is employed to overcome these limitations, and accumulated confidence is proposed to utilize neighboring outputs of the DCNN in a scene. The localization error is remarkably reduced to 1.44% by the proposed algorithm compared to 4.59% in the previous work. The proposed data-driven method can be applied to construct other automatic identification systems with minimal manual handling.     

Classification of multiple motor imagery using deep convolutional neural networks and spatial filters

Brain–Computer Interfaces (BCI) are systems that translate brain activity patterns into commands for an interactive application, and some of them recognize patterns generated by motor imagery. Currently, these systems present performances and methodologies that still are not practical enough for realistic applications. Therefore, this paper proposes two methodologies for multiple motor imagery classification. Both methodologies use features extracted by a variant of Discriminative Filter Bank Common Spatial Pattern (DFBCSP) presented in this paper. The frequency bands selection in this variant is carried out by a novel iterative algorithm that selects the frequency band that attains the highest classification accuracy for specific binary classification. For each binary combination of classes, a frequency band is selected. The resulting samples are then set into a matrix which feeds one or many Convolutional Neural Networks previously optimized by using a Bayesian optimization. The first methodology applies a Convolutional Neural Network (CNN) for the classification of all classes and the second is a modular network composed of four expert CNNs. In this modular network, each expert CNN performs a binary classification, and a fully connected network analyzes their results. To validate both approaches two datasets were used, the BCI competition IV dataset 2a and another presented in this paper recorded from eight subjects by using the OpenBCI device. The experimental results demonstrated an improvement in the classification accuracy over many classic intelligent recognition methods, without a high computation time in order that they can be implemented in an online application.