Multi-label deep learning models for continuous monitoring of road infrastructures
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A multi-class, multi-label deep learning model for the monitoring of road infrastructures is presented in this paper. The employed detection methodology can identify animals, debris, road defects, fire, fog, flooded areas and humans. All these categories are strongly related to the efficient movement of vehicles through a transportation network. Possible detections indicate roadway disruptions of various types. Therefore, they should be detected as fast as possible. Experimental results indicate that the proposed scheme presents high detection results and, thus, can be used in any motorway monitoring process.The use of the deep learning approach in the textile industry for the purpose of defect detection has become an increasing trend in the past 20 years. The majority of publications have investigated a specific problem in this field. Furthermore, many of published reviews or survey articles preferred to investigate papers from a more general perspective. Compared with published review publications, this study is the first up-to-date study that investigates the implementation of deep learning approaches for the detection of fabric defects from 2003 to the present. As the main objective of this study is to review deep learning-based fabric defect detection, the publications regarding fabric defect detection by using deep learning are examined. The methods, database, performance rates, comparisons, and architecture type of these works were compared with each other. The most widely used deep learning architectures customized deep convolutional neural networks, long short-term memory, generative adversarial networks, and autoencoders. Besides the use of the most used deep learning algorithms, the advantages and disadvantages of these approaches have also been expressed.
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It is a complex and systematic project to design cultivating scheme. The cultivating quality talent is much affected by rationality of the cultivating scheme. By analyzing the thought of how BP neural network works, a new model for planning cultivating scheme based on multi-feedback is presented. By this model multiple feedback information should be considered while planning cultivating scheme. Experiments were performed by this model on cultivating scheme planning on industrial design and the new cultivating scheme was obtained.
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The Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI) has been heavily supporting Machine Learning and Deep Learning research from its foundation in 2012. We have asked six leading ICRI-CI Deep Learning researchers to address the challenge of Why & When Deep Learning works, with the goal of looking inside Deep Learning, providing insights on how deep networks function, and uncovering key observations on their expressiveness, limitations, and potential. The output of this challenge resulted in five papers that address different facets of deep learning. These different facets include a high-level understating of why and when deep networks work (and do not work), the impact of geometry on the expressiveness of deep networks, and making deep networks interpretable.
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Abstract It is widely suggested that feedback on assignments is useful to students’ learning, however, little research has examined how this feedback may be provided in large classes or the actual effects of such a scheme. We designed and implemented a voluntary ‘earlybird scheme’ that provided detailed feedback to undergraduate Business students on a first draft of a literature review. We then evaluated the effect of this scheme on students’ final marks for the assignment, the scheme’s usage, and students’ perceptions of the scheme. We found that although the usage of the scheme was quite low, it increased the learning of the students who did use it. The most common reason for not using the scheme was a lack of time and, therefore, given the beneficial nature of the scheme overall, we suggest that future implementations include greater structure or a complementary time‐management workshop. Keywords: assessmentfeedbackliterature reviews
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The various hurdles in machine learning are beaten by deep learning techniques and then the deep learning has gradually become preeminent in artificial intelligence. Deep learning uses neural networks to kindle decisions like humans. Deep learning flourished as an energetic approach and clarity marked its success in various domains. The study includes some dominant deep learning algorithms such as convolution neural network, fully convolutional network, autoencoder, and deep belief network to analyze the medical image and to detect and diagnose of cancer at an early stage. As early as the detection of cancer than to treat the disease is uncomplicated. Early diagnosis was particularly relevant for some cancers such as breast, skin, colon, and rectum, which prohibit the chance to grow and spread. Deep learning contributes to enhanced performance and better prediction in detection of cancer with medical images. The paper presents the study of a few deep learning software frameworks such as tensor flow, theano, caffe, torch, and keras. Tensor Flow provides excellent functionality for deep learning. Keras is a high-level neural network API that operates above on tensor flow or theano. The survey winds up by presenting several future avenues and open challenges that should be addressed by the researcher in the future.
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Abstract: Deep fake is a rapidly growing concern in society, and it has become a significant challenge to detect such manipulated media. Deep fake detection involves identifying whether a media file is authentic or generated using deep learning algorithms. In this project, we propose a deep learning-based approach for detecting deep fakes in videos. We use the Deep fake Detection Challenge dataset, which consists of real and Deep fake videos, to train and evaluate our deep learning model. We employ a Convolutional Neural Network (CNN) architecture for our implementation, which has shown great potential in previous studies. We pre-process the dataset using several techniques such as resizing, normalization, and data augmentation to enhance the quality of the input data. Our proposed model achieves high detection accuracy of 97.5% on the Deep fake Detection Challenge dataset, demonstrating the effectiveness of the proposed approach for deep fake detection. Our approach has the potential to be used in real-world scenarios to detect deep fakes, helping to mitigate the risks posed by deep fakes to individuals and society. The proposed methodology can also be extended to detect in other types of media, such as images and audio, providing a comprehensive solution for deep fake detection.
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In this paper, we put forward a novel human computer cooperation system the scheme producing and evaluation system for the hall for workshop of metasynthetic engineering (HWME).The proposed system can produce schemes automatically and evaluate schemes effectively whose functions are implemented by the cooperation of human and computers. The system mainly consists of three parts: the scheme framework model, the scheme forming model and the scheme evaluation model. Firstly, the scheme framework model is adopted for group experts to produce a scheme framework to write scheme contents; secondly, the scheme forming model is applied for group experts to write scheme contents and then the computer combine the similar contents to form optional schemes; finally, the scheme evaluation model is introduced to evaluate the optional schemes to obtain the optimal scheme. Experimental results demonstrate that the proposed system is feasible and effective for experts in HWME to solve complex problem which provides a good tool for experts to make decisions and schemes.
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