Journal articles: 'Model-Based Deep Learning'

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Wu, Chong. "A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology." International Journal of Machine Learning and Computing 11, no. 3 (May 2021): 182–87. http://dx.doi.org/10.18178/ijmlc.2021.11.3.1033.

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Srinivas, Dr Kalyanapu, and Reddy Dr.B.R.S. "Deep Learning based CNN Optimization Model for MR Braing Image Segmentation." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (November 20, 2019): 213–20. http://dx.doi.org/10.5373/jardcs/v11i11/20193190.

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Lee, A.-Hyun, Hyeongho Bae, Young-Ky Kim, and Chong-kwon Kim. "Deep Reinforcement Learning based MCS Decision Model." Journal of KIISE 49, no. 8 (August 31, 2022): 663–68. http://dx.doi.org/10.5626/jok.2022.49.8.663.

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Mohammed, Amal Ahmed Hasan, and Jiazhou Chen. "Cleanup Sketched Drawings: Deep Learning-Based Model." Applied Bionics and Biomechanics 2022 (May 6, 2022): 1–17. http://dx.doi.org/10.1155/2022/2238077.

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Rough drawings provide artists with a simple and efficient way to express shapes and ideas. Artists frequently use sketches to highlight their envisioned curves, using several groups’ raw strokes. These rough sketches need enhancement to remove some subtle impurities and completely simplify curves over the sketched images. This research paper proposes using a fully convolutional network (FCNN) model to simplify rough raster drawings using deep learning. As input, the FCNN takes a sketch image of any size and automatically generates a high-quality simplified sketch image as output. Our model intuitively addresses the shortcomings in the rough sketch image, such as noises and unwanted background, as well as the low resolution of the rough sketch image. The FCNN model is trained by three raster image datasets, which are publicly available online. This paper demonstrates the efficiency and effectiveness of using deep learning in cleaning and improving the roughly drawn image in an automatic way. For evaluating the results, the mean squared error (MSE) metric was used. From experimental results, it was observed that an enhanced FCNN model reported better accuracy, reducing the prediction error by 0.08 percent for simplifying the rough sketch compared to the existing methods.

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Jing, Jing. "Deep Learning-Based Music Quality Analysis Model." Applied Bionics and Biomechanics 2022 (June 13, 2022): 1–6. http://dx.doi.org/10.1155/2022/6213115.

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In order to build an efficient and effective deep much quality recognition model, a decision fusion method by leveraging the advantages of shallow learning and deep learning is formulated. In the literature, shallow learning is a traditional music-related quality recognition method, that is, artificial statistical feature extraction and recognition are designed. Meanwhile, our deep learning module leverages the so-called PCANET network to implement the feature extraction process, and subsequently takes the spectrogram describing the music-related information as the network input. First, a variety of task classifications for the music signal problem are divided. Afterward, the optimization and adoption of deep learning in the two major problems of music feature extraction and sequence modeling are introduced. Finally, a music application is presented to illustrate the practical application of deep learning in music quality evaluation. The shallow learning features and deep learning features are seamlessly combined into the SVM model for music quality modeling, based on which differential voting mechanisms are leveraged to realize the fusion of decision-making layers. Extensive experimental results have shown that the music quality recognition rate by this method can be significantly improved on our own compiled library and the Berlin database. Besides, it exhibits obvious advantages compared with the competitors.

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Fang, Lidong, Pei Ge, Lei Zhang, Weinan E. null, and Huan Lei. "DeePN$^2$: A Deep Learning-Based Non-Newtonian Hydrodynamic Model." Journal of Machine Learning 1, no. 1 (June 2022): 114–40. http://dx.doi.org/10.4208/jml.220115.

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Yuan, Zhen, and Jinfeng Liu. "A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning." Journal of Electrical and Computer Engineering 2022 (June 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/7608794.

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Trash classification is an effective measure to protect the ecological environment and improve resource utilization. With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. Firstly, the upper stream divides the input garbage image into category MPP (metal, paper, and plastic class) or category CGT (cardboard, glass, and trash class). Then, the lower stream predicts the exact class of trash according to the results of the upper stream. The proposed hybrid deep learning model achieves the best result with 98.5 % than that of the state-of-the-art approaches. Through the verification of CAM (class activation map), the proposed model can reasonably use the features of the image for classification, which explains the reason for the superior performance of this model.

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Ding, Shifei, Lili Guo, and Yanlu Hou. "Extreme learning machine with kernel model based on deep learning." Neural Computing and Applications 28, no. 8 (January 12, 2016): 1975–84. http://dx.doi.org/10.1007/s00521-015-2170-y.

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Dai, Xiaofeng, and Weidong Zhu. "Intelligent Financial Auditing Model Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (August 28, 2022): 1–5. http://dx.doi.org/10.1155/2022/8282854.

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The entire auditing process is complicated and tedious and requires a lot of human resources. Therefore, the intelligent development of auditing is the general trend. In order to improve the audit quality, this paper establishes an intelligent financial audit model that can predict the audit opinion of the consolidated financial statements. This paper proposes an audit opinion prediction model based on the fusion of deep belief neural network (DBN) and long-short term memory (LSTM). First, an indicator system is established for audit opinions, and multiple financial parameters are used to describe possible audit opinions. On this basis, a DBN network is designed to complete deep feature extraction and used for LSTM training. According to the prediction model obtained by training, the subsequent audit opinion can be scientifically predicted. In the experiment, the method in this paper is tested based on financial audit related data sets and compared with the prediction results of traditional multilayer perceptron (MLP), convolutional neural network (CNN), and LSTM models. The results verify the validity and reliability of the model in this paper.

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Sun, Chongxin, Bo Chen, Youjun Bu, Surong Zhang, Desheng Zhang, and Bingbing Jiang. "Lightweight Traffic Classification Model Based on Deep Learning." Wireless Communications and Mobile Computing 2022 (October 10, 2022): 1–16. http://dx.doi.org/10.1155/2022/3539919.

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The development of mobile computing and the Internet of Things (IoT) has led to a surge in traffic volume, which creates a heavy burden for efficient network management. The network management requires high computational overheads to make traffic classification, which is even worse when in edge networks; existing approaches sacrifice the efficiency to obtain high-precision classification results, which are no longer suitable for limited resources edge network scenario. Given the problem, existing traffic classification generally has huge parameters and especially computational complexity. We propose a lightweight traffic classification model based on the Mobilenetv3 and improve it for an ingenious balance between performance and lightweight. Firstly, we adjust the model scale, width, and resolution to substantially reduce the number of model parameters and computations. Secondly, we embed precise spatial information on the attention mechanism to enhance the traffic flow-level feature extraction capability. Thirdly, we use the lightweight multiscale feature fusion to obtain the multiscale flow-level features of traffic. Experiments show that our model has excellent classification accuracy and operational efficiency. The accuracy of the traffic classification model designed in our work has reached more than 99.82%, and the parameter and computation amount are significantly reduced to 0.26 M and 5.26 M. In addition, the simulation experiments on Raspberry Pi prove the proposed model can realize real-time classification capability in the edge network.

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Huang, Helin, Zhenyi Ge, Hairui Wang, Jing Wu, Chunqiang Hu, Nan Li, Xiaomei Wu, and Cuizhen Pan. "Segmentation of Echocardiography Based on Deep Learning Model." Electronics 11, no. 11 (May 27, 2022): 1714. http://dx.doi.org/10.3390/electronics11111714.

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In order to achieve the classification of mitral regurgitation, a deep learning network VDS-UNET was designed to automatically segment the critical regions of echocardiography with three sections of apical two-chamber, apical three-chamber, and apical four-chamber. First, an expert-labeled dataset of 153 echocardiographic videos and 2183 images from 49 subjects was constructed. Then, the convolution layer in the VGG16 network was used to replace the contraction path in the original UNet network to extract image features, and depth supervision was added to the expansion path to achieve the segmentation of LA, LV, and MV. The results showed that the Dice coefficients of LA, LV, and MV were 0.935, 0.915, and 0.757, respectively. The proposed deep learning network can achieve simultaneous and accurate segmentation of LA, LV, and MV in multi-section echocardiography, laying a foundation for quantitative measurement of clinical parameters related to mitral regurgitation.

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Hong, Ki Hyeon, and Byung Mun Lee. "A Deep Learning-Based Password Security Evaluation Model." Applied Sciences 12, no. 5 (February 25, 2022): 2404. http://dx.doi.org/10.3390/app12052404.

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It is very important to consider whether a password has been leaked, because security can no longer be guaranteed for passwords exposed to attackers. However, most existing password security evaluation methods do not consider the leakage of the password. Even if leakage is considered, a process of collecting, storing, and verifying a huge number of leaked passwords is required, which is not practical in low-performance devices such as IoT devices. Therefore, we propose another approach in this paper using a deep learning model. A password list was made for the proposed model by randomly extracting 133,447 words from a total of seven dictionaries, including Wikipedia and Korean-language dictionaries. After that, a deep learning model was created by using the three pieces of feature data that were extracted from the password list, as well as a label for the leakage. After creating an evaluation model in a lightweight file, it can be stored in a low-performance device and is suitable to predict and evaluate the security strength of a password in a device. To check the performance of the model, an accuracy evaluation experiment was conducted to predict the possibility of leakage. As a result, a prediction accuracy of 95.74% was verified for the proposed model.

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Zhao, Yuting. "Research on Management Model Based on Deep Learning." Complexity 2021 (August 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/9997662.

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In this study, the focus was on the development of management models and future prediction for the cost and risk by using an improved deep learning (DL) algorithm. Management model can be defined as the management activities that are interlinked and organized inside organization of institutions. Different opportunities and different organizations are offered by different management models. Proper management models lead to strategies and decisions help to success organization. Deep neural network (DNN) is proposed to make good prediction for organization for increasing the cost and reduce risk in companies and institutions. The error of prediction is updated according to variable hidden layers and nodes within iteration. Improved DNN is used and modify weights that have an effect on the features extracted in advance to increase the accuracy and precisions are used. The proposed method is based on dynamic hidden layers with backpropagation and feedforward. Absolute mean relative error (AMRE) and variance (R2) are used for evaluation in term of accuracy. The training system is used with three available datasets from big company, health issue, and industry. Gained result proves the worth of the proposed system and is suitable for predicting complex data and reducing the risk as possible.

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Surendran, R., Osamah Ibrahim Khalaf, and Carlos Andres Tavera Romero. "Deep Learning Based Intelligent Industrial Fault Diagnosis Model." Computers, Materials & Continua 70, no. 3 (2022): 6323–38. http://dx.doi.org/10.32604/cmc.2022.021716.

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Matsubara, Takashi. "Bayesian deep learning: A model-based interpretable approach." Nonlinear Theory and Its Applications, IEICE 11, no. 1 (2020): 16–35. http://dx.doi.org/10.1587/nolta.11.16.

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Li, Yue, Xutao Wang, and Pengjian Xu. "Chinese Text Classification Model Based on Deep Learning." Future Internet 10, no. 11 (November 20, 2018): 113. http://dx.doi.org/10.3390/fi10110113.

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Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts.

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Chen, Hongyi. "Leukemic cell detection model based on deep learning." Journal of Physics: Conference Series 1634 (September 2020): 012046. http://dx.doi.org/10.1088/1742-6596/1634/1/012046.

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Kim, Kyoungtae, and Kisung Seo. "Deep Learning Based Prediction Model for Easterly Wind." Transactions of The Korean Institute of Electrical Engineers 68, no. 12 (December 31, 2019): 1607–11. http://dx.doi.org/10.5370/kiee.2019.68.12.1607.

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Su, Chang, and Deling Huang. "Hybrid Recommender System based on Deep Learning Model." International Journal of Performability Engineering 16, no. 1 (2020): 118. http://dx.doi.org/10.23940/ijpe.20.01.p13.118129.

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Kumar, Saurabh. "Deep learning based affective computing." Journal of Enterprise Information Management 34, no. 5 (October 18, 2021): 1551–75. http://dx.doi.org/10.1108/jeim-12-2020-0536.

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PurposeDecision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments.Design/methodology/approachThe present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer.FindingsThe result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making.Originality/valueThe study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.

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Ullah, Sami, Muhammad Ramzan Talib, Toqir A. Rana, Muhammad Kashif Hanif, and Muhammad Awais. "Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification." Computers, Materials & Continua 72, no. 2 (2022): 2323–39. http://dx.doi.org/10.32604/cmc.2022.025543.

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Yang, Xi, Zhihan Zhou, and Yu Xiao. "Research on Students’ Adaptive Learning System Based on Deep Learning Model." Scientific Programming 2021 (December 16, 2021): 1–13. http://dx.doi.org/10.1155/2021/6593438.

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With the rapid development of deep learning in recent years, recommendation algorithm combined with deep learning model has become an important direction in the field of recommendation in the future. Personalized learning resource recommendation is the main way to realize students’ adaptation to the learning system. Based on the in-depth learning mode, students’ online learning action data are obtained, and further learning analysis technology is used to construct students’ special learning mode and provide suitable learning resources. The traditional method of introducing learning resources mainly stays at the level of examination questions. What ignores the essence of students’ learning is the learning of knowledge points. Students’ learning process is affected by “before” and “after” learning behavior, which has the characteristics of time. Among them, bidirectional length cyclic neural network is good at considering the “front” and “back” states of recommended nodes when recommending prediction results. For the above situation, this paper proposes a recommendation method of students’ learning resources based on bidirectional long-term and short-term memory cyclic neural network. Firstly, recommend the second examination according to the knowledge points, predict the scores of important steps including the accuracy of the recommended examination of the target students and the knowledge points of the recommended examination, and finally cooperate with the above two prediction results to judge whether the examination questions are finally recommended. Through the comparative experiment with the traditional recommendation algorithm, it is found that the student adaptive learning system based on the deep learning model proposed in this paper has better stability and interpretability in the recommendation results.

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Wang, Qiang, and Hongfeng Wang. "Deep Learning Model-Based Machine Learning for Chinese and Japanese Translation." Wireless Communications and Mobile Computing 2022 (March 9, 2022): 1–8. http://dx.doi.org/10.1155/2022/8762125.

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This paper takes deep learning as the background of researcher design, combines the relevant cutting-edge research results in recent years, addresses the linguistic characteristics of Japanese and the problems faced by completing Japanese machine translation system, and determines the neural network structure of encoding-decoding for Japanese translation based on the characteristics of high similarity between Japanese and Chinese and after referring to the neural network architecture of English translation, and the basic structure and the corresponding improvement of the hidden layer unit calculation are carried out. The training model is optimized and an integrated Japanese machine translation system is implemented. Finally, the translation models of Japanese and Chinese intertranslation and Japanese and Chinese intertranslation are tested, and the optimal model fusion achieves a BLEU value of 39.52.

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Ding, Hui, Yajun Chen, and Linling Wang. "College English Online Teaching Model Based on Deep Learning." Security and Communication Networks 2021 (December 21, 2021): 1–11. http://dx.doi.org/10.1155/2021/8919320.

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In today’s era, online teaching plays an important part in the college English teaching. Deep learning, famous for its ability of imitating the learning process of human brains and obtaining the internal essential features or rules of voice, videos, images, and other data, can be applied to assist and improve the college English online teaching which involves a wide use of those data. Based on the combination of the multilayer neural network model and the k-means clustering algorithm, this paper designs a kind of deep learning method that can be used to assist and improve the college English online teaching. Experiments were designed to test the reliability of this deep learning method. The results show that the optimization algorithm designed in this paper, which can adjust the learning rate, will improve the common probability gradient descent algorithm. Besides, it is proved that the deep learning’s efficiency of the CNN model is significantly higher than that of the MLP model. With the help of this deep learning method, it becomes feasible to apply the technologies related to the artificial intelligence to help teachers deeply analyze and diagnose students’ English learning behavior, replace the teachers in part to answer students’ questions in time, and automatically grade assignments in the process of the college English online teaching. Surveys and exams were then conducted to evaluate the effect of the application of the college English online teaching model based on deep learning on the students’ learning cognition and their academic performance. The results show that the college English online teaching model based on deep learning can stimulate students’ learning motivation and improve their academic performance.

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Kim, Seung-Hwan, Chang-Bae Moon, Ki-Hyeob Kwon, and Dong-Seong Kim. "Design of the Image-Based Deep Learning Model Using a Pre-Training Deep Learning Network." Journal of Korean Institute of Communications and Information Sciences 47, no. 4 (April 30, 2022): 615–24. http://dx.doi.org/10.7840/kics.2022.47.4.615.

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Han, Xiuying. "Investigation on Deep Learning Model of College English Based on Multimodal Learning Method." Computational Intelligence and Neuroscience 2022 (October 7, 2022): 1–10. http://dx.doi.org/10.1155/2022/7001392.

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Deep learning refers to active learning that allows students to perceive, experience, understand, and apply knowledge. Deep learning focuses on the mastery of knowledge and skills and more on the cultivation of higher-order thinking skills such as awareness, problem-solving, and knowledge transfer. In order to improve the quality of English classroom teaching in today’s colleges and universities and cultivate high-level applied foreign language talents, this paper constructs a multimodal teaching model based on deep learning theory and discusses how to apply the model to college English teaching practice in order to promote the realization of students’ deep learning, improve the effectiveness of English learning, and provide a reference for the teaching reform of college English courses.

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Wang, Zhendong, Yaodi Liu, Daojing He, and Sammy Chan. "Intrusion detection methods based on integrated deep learning model." Computers & Security 103 (April 2021): 102177. http://dx.doi.org/10.1016/j.cose.2021.102177.

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Tan, Ke, and DeLiang Wang. "Towards Model Compression for Deep Learning Based Speech Enhancement." IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021): 1785–94. http://dx.doi.org/10.1109/taslp.2021.3082282.

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Junzuo, Li, and Long Qiang. "An Automatic Parking Model Based on Deep Reinforcement Learning." Journal of Physics: Conference Series 1883, no. 1 (April 1, 2021): 012111. http://dx.doi.org/10.1088/1742-6596/1883/1/012111.

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Qiu, Gang, Changjun Song, Liping Jiang, and Yanli Guo. "Multi-view hybrid recommendation model based on deep learning." Intelligent Data Analysis 26, no. 4 (July 11, 2022): 977–92. http://dx.doi.org/10.3233/ida-215988.

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With the rapid development of technologies such as cloud computing, big data, and the Internet of Things, the scale of data continues to grow. The recommendation system has become one of the important intelligent software to help users make decisions. The recommendation model based on user rating data is widely studied and applied, but the data sparsity problem and the cold start problem seriously affect the recommendation quality. In this paper, Multi-view Hybrid Recommendation Model (MHRM) based on deep learning is proposed. First, we use WLDA (an improved Latent Dirichlet Allocation method) to extract the vector representation of user review text, and then apply LSTM to contextual semantic level user review sentiment analysis. At the same time, the emotion fusion method based on user score embedding is proposed. The problems such as deviations between the user score and actual interest preference, and unbalanced distribution of the score level are solved. This paper has been tested on Amazon product data and compared with various classic recommendation algorithms, using Mean Absolute Error (MAE), hit rate and standardized discount cumulative return for performance evaluation. The experimental results show that the prediction of the MHRM proposed in this paper on the 7 recommendation data and the TopN recommendation index have been significantly improved.

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Zhang, Lilei, Wenwen Wang, Yujie Dong, and Longliang Wu. "Vegetation Coverage Monitoring Model Design Based on Deep Learning." Scientific Programming 2022 (July 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/4818985.

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Based on the medium-resolution Landsat TM and OLI satellite images in the study area, the deep learning ENVINet-5 model is adopted for vegetation coverage monitoring. By referring to the fusion image and Google Earth high-resolution satellite image, the training samples and verification samples are manually labeled, and the labels of four types of ground objects (desert, water body, cultivated land, and construction land) are made. Through the ENVI deep learning binary classification model, the labeled training samples are trained, and a large number of samples of desert, water, and cultivated land are extracted and transformed into corresponding label images. Then, a large number of training sample labels extracted from the model are combined with the manually made construction land sample labels and both of them are used as the training samples of the ENVI deep learning multiclassification model. According to the classification process of the deep learning model (creating label image, initializing training model, and training model and model classification), through the adjustment of various parameters, the four types of ground objects in the study area are finally classified. Finally, the classification results that meet the accuracy requirements are statistically analyzed. It is proved that the model classification results can meet the use requirements.

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Cheng, Xinquan, and Wenlong Su. "Recommendation Model of Tourist Attractions Based on Deep Learning." Mathematical Problems in Engineering 2022 (August 28, 2022): 1–7. http://dx.doi.org/10.1155/2022/9080818.

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In order to solve the problem of tourism information overload caused by the rapid development of tourism and the Internet era, the author proposes a tourist attraction recommendation model based on deep learning. Convolutional Neural Network (CNN) is used to extract the sentiment of text comments, the Pearson similarity formula is used to calculate similar user groups, and the mean absolute error (MAE) is used to evaluate the resulting error. Compare with traditional collaborative filtering methods. Experimental results show that: the MAE value is smaller than the MAE value of the collaborative filtering method, indicating that considering tourists’ behavioral information, contextual information, and emotional factors in comments can effectively improve the accuracy of recommendation, as the data volume of the test set increased from 250 to 2000; although there was an increase in the MAE value, the overall trend showed a downward trend, indicating that the quality of the model can be more fully verified when the data volume is large. The model proposed by the author can effectively reduce the prediction error and improve the efficiency of tourist attractions recommendation.

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Sivaraju, S. S. "An Insight into Deep Learning based Cryptojacking Detection Model." Journal of Trends in Computer Science and Smart Technology 4, no. 3 (September 21, 2022): 175–84. http://dx.doi.org/10.36548/jtcsst.2022.3.006.

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To autonomously identify cyber threats is a non-trivial research topic. One area where this is most apparent is in the evolution of evasive cyber assaults, which are becoming better at masking their existence and obscuring their attack methods (for example, file-less malware). Particularly stealthy Advanced Persistent Threats may hide out in the system for a long time without being spotted. This study presents a novel method, dubbed CapJack, for identifying illicit bitcoin mining activity in a web browser by using cutting-edge CapsNet technology. Thus far, it is aware that deep learning framework CapsNet is pertained to the problem of detecting malware effectively using a heuristic based on system behaviour. Even more, in multitasking situations when several apps are all active at the same time, it is possible to identify fraudulent miners with greater efficiency.

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Zheng, Xiaoxia. "Higher Education Course Evaluation Based on Deep Learning Model." Wireless Communications and Mobile Computing 2022 (October 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/8929437.

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Higher education has completed a revolutionary transition stage from traditional elite education to mass quality education, and the improvement of higher education quality has increasingly become the focus of social attention. Courses are the most basic unit of university teaching, and the quality of courses directly affects the quality of personnel training. The expansion of higher education has resulted in insufficient teaching resources and a decline in the quality of courses. Improving quality is the eternal theme of higher education reform. How to formulate scientific curriculum quality standards and objectively evaluate curriculum quality is the most concerned issue of the society. Therefore, in-depth analysis of the factors that affect the quality of the curriculum and the establishment of the curriculum quality evaluation system and method has certain practical significance for improving the quality of the curriculum. This work combines the evaluation of higher education courses with deep learning models and proposes a network MSACNN for quality evaluation of higher education courses. This work proposes to use an attention-based multiscale network to explicitly learn the relationship between the qualities of various higher education courses. By using parallel networks with different convolution kernels, it combines different scale features at the same spatial location to better learn the relationship between the quality features of higher education courses. This work conducts sufficient experiments on the higher education curriculum quality dataset, and the experimental results demonstrate that the multiscale network based on the attention mechanism exhibits superior performance. MSACNN surpasses other machine learning methods in both precision and recall metrics.

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Albarrak, Khalied, Yonis Gulzar, Yasir Hamid, Abid Mehmood, and Arjumand Bano Soomro. "A Deep Learning-Based Model for Date Fruit Classification." Sustainability 14, no. 10 (May 23, 2022): 6339. http://dx.doi.org/10.3390/su14106339.

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A total of 8.46 million tons of date fruit are produced annually around the world. The date fruit is considered a high-valued confectionery and fruit crop. The hot arid zones of Southwest Asia, North Africa, and the Middle East are the major producers of date fruit. The production of dates in 1961 was 1.8 million tons, which increased to 2.8 million tons in 1985. In 2001, the production of dates was recorded at 5.4 million tons, whereas recently it has reached 8.46 million tons. A common problem found in the industry is the absence of an autonomous system for the classification of date fruit, resulting in reliance on only the manual expertise, often involving hard work, expense, and bias. Recently, Machine Learning (ML) techniques have been employed in such areas of agriculture and fruit farming and have brought great convenience to human life. An automated system based on ML can carry out the fruit classification and sorting tasks that were previously handled by human experts. In various fields, CNNs (convolutional neural networks) have achieved impressive results in image classification. Considering the success of CNNs and transfer learning in other image classification problems, this research also employs a similar approach and proposes an efficient date classification model. In this research, a dataset of eight different classes of date fruit has been created to train the proposed model. Different preprocessing techniques have been applied in the proposed model, such as image augmentation, decayed learning rate, model checkpointing, and hybrid weight adjustment to increase the accuracy rate. The results show that the proposed model based on MobileNetV2 architecture has achieved 99% accuracy. The proposed model has also been compared with other existing models such as AlexNet, VGG16, InceptionV3, ResNet, and MobileNetV2. The results prove that the proposed model performs better than all other models in terms of accuracy.

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Kumar, Suneel, Manoj Kumar Singh, and Manoj Kumar Mishra. "Improve Content-based Image Retrieval using Deep learning model." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012028. http://dx.doi.org/10.1088/1742-6596/2327/1/012028.

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Abstract The complexity of multimedia has expanded dramatically as a result of recent technology breakthroughs, and retrieval of similar multimedia material remains an ongoing research topic. Content-based image retrieval (CBIR) systems search huge databases for pictures that are related to the query image (QI). Existing CBIR algorithms extract just a subset of feature sets, limiting retrieval efficacy. The sorting of photos with a high degree of visual similarity is a necessary step in any image retrieval technique. Because a single feature is not resilient to image datasets modifications, feature combining, also known as feature fusion, is employed in CBIR to increase performance. This work describes a CBIR system in which combining DarkNet-19 and DarkNet-53 information to retrieve images. Experiments on the Wang (Corel 1K) database reveal a considerable improvement in precision over state-of-the-art classic techniques as well as Deep Convolutional Neural Network(DCNN).

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Aslam, Nida, Irfan Ullah Khan, Teaf I. Albahussain, Nouf F. Almousa, Mizna O. Alolayan, Sara A. Almousa, and Modhi E. Alwhebi. "MEDeep: A Deep Learning Based Model for Memotion Analysis." Mathematical Modelling of Engineering Problems 9, no. 2 (April 28, 2022): 533–38. http://dx.doi.org/10.18280/mmep.090232.

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K I, Ravikumar. "Memristor-Based Deep Learning Classification Model for Object Detection." ECS Transactions 107, no. 1 (April 24, 2022): 277–85. http://dx.doi.org/10.1149/10701.0277ecst.

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The memristor-based neural network takes full use of the benefits of memristive devices, such as their low power consumption, high integration density, and great network recognition capacity, and therefore can be efficiently used for binary image classification in AI based applications. Before implementing the memristor-based memory at circuit level, the performance need to be analyzed. In this work, a nine-layer neuromorphic network is designed and is used to classify binary images. Using the MNIST dataset, the performance of architecture is validated and the impact of device yield and resistance fluctuations under various neuron configurations on network performance are studied. The implementation of restive random access memory (memristor based memory) is carried out using the memtorch in python 3.7 scripting language and the simulation was carried out using MemTorch, an open-source framework for customized large-scale memristive DL applications. The findings indicate that the nine-layer network has an accuracy of about 98 percent in digit recognition.

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Kriti, Mohd Anul Haq, Urvashi Garg, Mohd Abdul Rahim Khan, and V. Rajinikanth. "Fusion-Based Deep Learning Model for Hyperspectral Images Classification." Computers, Materials & Continua 72, no. 1 (2022): 939–57. http://dx.doi.org/10.32604/cmc.2022.023169.

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Thakur, Akshay, and Souvik Chakraborty. "A deep learning based surrogate model for stochastic simulators." Probabilistic Engineering Mechanics 68 (April 2022): 103248. http://dx.doi.org/10.1016/j.probengmech.2022.103248.

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Li, Haiquan, Qinghui Zhang, and Xiaoqian Chen. "Deep Learning-Based Surrogate Model for Flight Load Analysis." Computer Modeling in Engineering & Sciences 128, no. 2 (2021): 605–21. http://dx.doi.org/10.32604/cmes.2021.015747.

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Geng, Huantong, and Tianlei Wang. "Spatiotemporal Model Based on Deep Learning for ENSO Forecasts." Atmosphere 12, no. 7 (June 23, 2021): 810. http://dx.doi.org/10.3390/atmos12070810.

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El Niño and Southern Oscillation (ENSO) is closely related to a series of regional extreme climates, so robust long-term forecasting is of great significance for reducing economic losses caused by natural disasters. Here, we regard ENSO prediction as an unsupervised spatiotemporal prediction problem, and design a deep learning model called Dense Convolution-Long Short-Term Memory (DC-LSTM). For a more sufficient training model, we will also add historical simulation data to the training set. The experimental results show that DC-LSTM is more suitable for the prediction of a large region and a single factor. During the 1994–2010 verification period, the all-season correlation skill of the Nino3.4 index of the DC-LSTM is higher than that of the current dynamic model and regression neural network, and it can provide effective forecasts for lead times of up to 20 months. Therefore, DC-LSTM can be used as a powerful tool for predicting ENSO events.

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Hoefling, Holger, Tobias Sing, Imtiaz Hossain, Julie Boisclair, Arno Doelemeyer, Thierry Flandre, Alessandro Piaia, et al. "HistoNet: A Deep Learning-Based Model of Normal Histology." Toxicologic Pathology 49, no. 4 (March 3, 2021): 784–97. http://dx.doi.org/10.1177/0192623321993425.

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We introduce HistoNet, a deep neural network trained on normal tissue. On 1690 slides with rat tissue samples from 6 preclinical toxicology studies, tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From these annotated regions, we sampled small 224 × 224 pixels images (patches) at 6 different levels of magnification. Using 4 studies as training set and 2 studies as test set, we trained VGG-16, ResNet-50, and Inception-v3 networks separately at each magnification level. Among these model architectures, Inception-v3 and ResNet-50 outperformed VGG-16. Inception-v3 identified the tissue from query images, with an accuracy up to 83.4%. Most misclassifications occurred between histologically similar tissues. Investigation of the features learned by the model (embedding layer) using Uniform Manifold Approximation and Projection revealed not only coherent clusters associated with the individual tissues but also subclusters corresponding to histologically meaningful structures that had not been annotated or trained for. This suggests that the histological representation learned by HistoNet could be useful as the basis of other machine learning algorithms and data mining. Finally, we found that models trained on rat tissues can be used on non-human primate and minipig tissues with minimal retraining.

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Naik, K. Jairam, Bhushan Thakre, and Ishu Rani. "IoT Based Vehicular Accident Detection Using Deep Learning Model." International Journal of Autonomous and Adaptive Communications Systems 18, no. 1 (2025): 1. http://dx.doi.org/10.1504/ijaacs.2025.10046127.

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KIKUCHI, Ryota, Masahiro MOMOI, and Shunji KOTSUKI. "Deep Learning based Emulator for Rainfall-Runoff-Inundation Model." Proceedings of Mechanical Engineering Congress, Japan 2021 (2021): J051–02. http://dx.doi.org/10.1299/jsmemecj.2021.j051-02.

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Sanayha, Manassakan, and Peerapon Vateekul. "Model-based deep reinforcement learning for wind energy bidding." International Journal of Electrical Power & Energy Systems 136 (March 2022): 107625. http://dx.doi.org/10.1016/j.ijepes.2021.107625.

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P. Narmadha, R., N. Sengottaiyan, and R. J. Kavitha. "Deep Transfer Learning Based Rice Plant Disease Detection Model." Intelligent Automation & Soft Computing 31, no. 2 (2022): 1257–71. http://dx.doi.org/10.32604/iasc.2022.020679.

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Mokhtar, Mai, Salma Doma, and Hala Abdel-Galil. "AUTOMATIC QUESTION GENERATION MODEL BASED ON DEEP LEARNING APPROACH." International Journal of Intelligent Computing and Information Sciences 21, no. 2 (July 31, 2021): 110–23. http://dx.doi.org/10.21608/ijicis.2021.80280.1102.

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Kang, Ji-Won, Byung-Seo Park, Jin-Kyum Kim, Dong-Wook Kim, and Young-Ho Seo. "Deep-learning-based hologram generation using a generative model." Applied Optics 60, no. 24 (August 19, 2021): 7391. http://dx.doi.org/10.1364/ao.427262.

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Khan, Imran Ullah, Sitara Afzal, and Jong Weon Lee. "Human Activity Recognition via Hybrid Deep Learning Based Model." Sensors 22, no. 1 (January 1, 2022): 323. http://dx.doi.org/10.3390/s22010323.

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In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications.

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Journal articles on the topic 'Model-Based Deep Learning'

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