A combined deep learning method for internet car evaluation

Neural Computing and Applications - In recent years, the Internet has become a trend in the development of the global automotive industry. Numerous Internet companies have joined the automobile...     

A deep learning approach for the forensic evaluation of sexual assault

Despite the existence of patterns able to discriminate between consensual and non-consensual intercourse, the relevance of genital lesions in the corroboration of a legal rape complaint is currently under debate in many countries. The testimony of the physicians when assessing these lesions has been questioned in court due to several factors (e.g., a lack of comprehensive knowledge of lesions, wide spectrum of background area, among others). Therefore, it is relevant to provide automated tools to support the decision process in an objective manner. In this work, we evaluate the performance of state-of-the-art deep learning architectures for the forensic assessment of sexual assault. We propose a deep architecture and learning strategy to tackle the class imbalance on deep learning using ranking. The proposed methodologies achieved the best results when compared with handcrafted feature engineering and with other deep architectures.     

Player target tracking and detection in football game video using edge computing and deep learning

The purpose is to explore the player detection and motion tracking in football game video based on edge computing and deep learning (DL), thus improving the detection effect of player trajectory in different scenes. First, the basic technology of player target tracking and detection task is analyzed based on the Histograms of Oriented Gradients feature. Then, the neural network structure in DL is combined with the target tracking method to improve the miss detection problem of the Faster R-CNN (FRCN) algorithm in detecting small targets. Edge computing places massive computing nodes close to the terminal devices to meet the high computing and low latency requirements of DL on edge devices. After the occlusion problem in the football game is analyzed, the optimized algorithm is applied to the public dataset OTB2013 and the football game dataset containing 80 motion trajectories. After testing, the target tracking accuracy of the improved FRCN is 89.1%, the target tracking success rate is 64.5%, and the running frame rate is still about 25 fps. The high confidence of FRCN algorithm also avoids template pollution. In the ordinary scene, the FRCN algorithm basically does not lose the target. The area under curve value of the proposed FRCN algorithm decreases slightly in the scene where the target is occluded. The FRCN algorithm based on DL technology can achieve the target tracking of players in football game video and has strong robustness to the situation of players occlusion. The designed target detection algorithm is applied to the football game video, which can better analyze the technical characteristics of players, promote the development of football technology, bring different viewing experiences to the audience, drive the development of economic products derived from football games, and promote the dissemination and promotion of football.

     

Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning

The Journal of Supercomputing - The Internet of Things (IoT) is driving the digital revolution. AlSome palliative measures aremost all economic sectors are becoming “Smart” thanks to...     

Advances in deep learning for multimodal fusion and alignment

Multimedia Tools and Applications -     

An optimal deep learning framework for multi-type hemorrhagic lesions detection and quantification in head CT images for traumatic brain injury

Applied Intelligence - Traumatic Brain Injury (TBI) could lead to intracranial hemorrhage (ICH), which has now been identified as a major cause of death after trauma if it is not adequately...     

Applying learning behavioral Petri nets to the analysis of learning behavior in web-based learning environments

This paper proposes the learning behavioral Petri nets (LBPN) to model learning behavior in web-based environments. Fully useful records of learning behaviors must contain their expended time and corresponding contents. Therefore, the LBPN extends the colored tokens of colored Petri nets to identify learners and learning contents, and raises the time variable to represent diverse learning times for individual learners. To verify the viability of the LBPN, this paper also proposes a LBPN-based learning behavioral model to simulate a situation in which many learners participate in an e-learning course, and then to generate their behavioral patterns. The experimental results illustrated in this paper confirm that (1) the generated behavioral pattern based on the LBPN-based model is very close to actual data, (2) the time and cost spent to verify the effectiveness of an ITS is substantially reduced, (3) adequate testing data for estimating the performance and accuracy of an ITS is easily acquired, and (4) the LBPN-based model can be built to recommend appropriate learning contents and to accomplish adaptive learning.     

Representation learning in a deep network for license plate recognition

Multimedia Tools and Applications - The goal of license plate recognition (LPR) is to read the license plate characters. Due to image degradation, there are many difficulties in the way of...     

A novel deep learning framework for copy-moveforgery detection in images

Multimedia Tools and Applications - In this era of technology, digital images turn out to be ubiquitous in a contemporary society and they can be generated and manipulated by a wide variety of...     

Adoption value of deep learning and serological indicators in the screening of atrophic gastritis based on artificial intelligence

The Journal of Supercomputing - This work aimed to improve the early clinical diagnosis rate of atrophic gastritis (AG) and reduce the risk of disease deterioration or cancerization. Three hundred...     

深度学习最佳深度的确定

确定最佳深度可以降低运算成本,同时可以进一步提高精度。针对深度置信网络深度选择的问题,文章分析了通过设定阈值方法选择最佳深度的不足之处。从信息论的角度,验证了信息熵在每层玻尔兹曼机(RBM)训练达到稳态之后会达到收敛,以收敛之后的信息熵作为判断最佳层数的标准。通过手写数字识别的实验发现该方法可以作为最佳层数的判断标准。     

Unsupervised deep learning for real-time assessment of video streaming services

Multimedia Tools and Applications - Evaluating quality of experience in video streaming services requires a quality metric that works in real time and for a broad range of video types and network...     

A method for evaluation of learning components

Today, it is common to include machine learning components in software products. These components offer specific functionalities such as image recognition, time series analysis, and forecasting but may not satisfy the non-functional constraints of the software products. It is difficult to identify suitable learning algorithms for a particular task and software product because the non-functional requirements of the product affect algorithm suitability. A particular suitability evaluation may thus require the assessment of multiple criteria to analyse trade-offs between functional and non-functional requirements. For this purpose, we present a method for APPlication-Oriented Validation and Evaluation (APPrOVE). This method comprises four sequential steps that address the stated evaluation problem. The method provides a common ground for different stakeholders and enables a multi-expert and multi-criteria evaluation of machine learning algorithms prior to inclusion in software products. Essentially, the problem addressed in this article concerns how to choose the appropriate machine learning component for a particular software product.     

Application of deep learning and chaos theory for load forecasting in Greece

Neural Computing and Applications - In this paper, a novel combination of deep learning recurrent neural network and Lyapunov time is proposed to forecast the consumption of electricity load, in...     

Low precision matrix multiplication for efficient deep learning in NVIDIA Carmel processors

The Journal of Supercomputing - We introduce a high performance, multi-threaded realization of the gemm kernel for the ARMv8.2 architecture that operates with 16-bit (half precision)/queryKindly...     

Unsupervised deep learning system for local anomaly event detection in crowded scenes

Multimedia Tools and Applications - Anomaly detection in video surveillance is a significant research subject because of its immense use in real-time applications. These days, open spots like...     

A comprehensive survey of recent trends in deep learning for digital images augmentation

Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the existence of many images in datasets, different deep learning models will not be able to learn and produce accurate models. Unfortunately, several fields don't have access to large amounts of evidence, such as medical image processing. For example. The world is suffering from the lack of COVID-19 virus datasets, and there is no benchmark dataset from the beginning of 2020. This pandemic was the main motivation of this survey to deliver and discuss the current image data augmentation techniques which can be used to increase the number of images. In this paper, a survey of data augmentation for digital images in deep learning will be presented. The study begins and with the introduction section, which reflects the importance of data augmentation in general. The classical image data augmentation taxonomy and photometric transformation will be presented in the second section. The third section will illustrate the deep learning image data augmentation. Finally, the fourth section will survey the state of the art of using image data augmentation techniques in the different deep learning research and application.

     

Dual-source computed tomography image information under deep learning algorithm in evaluation of coronary artery lesion in children with Kawasaki disease

The Journal of Supercomputing - To explore the diagnostic value of deep learning algorithm in coronary arteries in children with Kawasaki disease, convolutional neural network (CNN) was applied in...