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Journal articles on the topic 'DEEP LEARNING MODEL'

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Author: Grafiati

Published: 11 September 2023

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1

Wang, Yating, Siu Wun Cheung, Eric T. Chung, Yalchin Efendiev, and Min Wang. "Deep multiscale model learning." Journal of Computational Physics 406 (April 2020): 109071. http://dx.doi.org/10.1016/j.jcp.2019.109071.

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2

Xu, Zongben, and Jian Sun. "Model-driven deep-learning." National Science Review 5, no. 1 (August 25, 2017): 22–24. http://dx.doi.org/10.1093/nsr/nwx099.

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3

Shlezinger, Nir, and Yonina C. Eldar. "Model-Based Deep Learning." Foundations and Trends® in Signal Processing 17, no. 4 (2023): 291–416. http://dx.doi.org/10.1561/2000000113.

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4

Bakhtiari, Shahab. "Can Deep Learning Model Perceptual Learning?" Journal of Neuroscience 39, no. 2 (January 9, 2019): 194–96. http://dx.doi.org/10.1523/jneurosci.2209-18.2018.

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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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Evseenko, Alla, and Dmitrii Romannikov. "Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 1-2 (August 26, 2020): 7–25. http://dx.doi.org/10.17212/2307-6879-2020-1-2-7-25.

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Today, such a branch of science as «artificial intelligence» is booming in the world. Systems built on the basis of artificial intelligence methods have the ability to perform functions that are traditionally considered the prerogative of man. Artificial intelligence has a wide range of research areas. One such area is machine learning. This article discusses the algorithms of one of the approaches of machine learning – reinforcement learning (RL), according to which a lot of research and development has been carried out over the past seven years. Development and research on this approach is mainly carried out to solve problems in Atari 2600 games or in other similar ones. In this article, reinforcement training will be applied to one of the dynamic objects – an inverted pendulum. As a model of this object, we consider a model of an inverted pendulum on a cart taken from the Gym library, which contains many models that are used to test and analyze reinforcement learning algorithms. The article describes the implementation and study of two algorithms from this approach, Deep Q-learning and Double Deep Q-learning. As a result, training, testing and training time graphs for each algorithm are presented, on the basis of which it is concluded that it is desirable to use the Double Deep Q-learning algorithm, because the training time is approximately 2 minutes and provides the best control for the model of an inverted pendulum on a cart.

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白家納, 白家納, and 黃崇能 Pachara Opattrakarnkul. "以深度學習模式估測控制之駕駛輔助系統的研發." 理工研究國際期刊 12, no. 1 (April 2022): 015–24. http://dx.doi.org/10.53106/222344892022041201002.

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<p>Adaptive cruise control (ACC) systems are designed to provide longitudinal assistance to enhance safety and driving comfort by adjusting vehicle velocity to maintain a safe distance between the host vehicle and the preceding vehicle. Generally, using model predictive control (MPC) in ACC systems provides high responsiveness and lower discomfort by solving real-time constrained optimization problems but results in computational load. This paper presents an architecture of deep learning based on model predictive control in ACC systems to avoid real-time optimization problems required by MPC, which in turn, reduces computational load. The learning dataset is acquired from the simulation data of the input/output of the MPC controller. We designed the proposed deep learning controller using long short-term memory networks (LSTMs) and simulated it in MATLAB/Simulink using the vehicle’s characteristics from the advanced vehicle simulator (ADVISOR). Finally, the safety and driving comfort are compared with the PID-based control to demonstrate the performance of the proposed deep-learning architecture.</p> <p> </p>

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Hao, Xing, Guigang Zhang, and Shang Ma. "Deep Learning." International Journal of Semantic Computing 10, no. 03 (September 2016): 417–39. http://dx.doi.org/10.1142/s1793351x16500045.

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Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been applied to various fields. This paper provides an overview of deep learning in neural networks including popular architecture models and training algorithms.

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Djellali, Choukri, and Mehdi adda. "An Enhanced Deep Learning Model to Network Attack Detection, by using Parameter Tuning, Hidden Markov Model and Neural Network." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (March 1, 2021): 35–41. http://dx.doi.org/10.5383/juspn.15.01.005.

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In recent years, Deep Learning has become a critical success factor for Machine Learning. In the present study, we introduced a Deep Learning model to network attack detection, by using Hidden Markov Model and Artificial Neural Networks. We used a model aggregation technique to find a single consolidated Deep Learning model for better data fitting. The model selection technique is applied to optimize the bias-variance trade-off of the expected prediction. We demonstrate its ability to reduce the convergence, reach the optimal solution and obtain more cluttered decision boundaries. Experimental studies conducted on attack detection indicate that our proposed model outperformed existing Deep Learning models and gives an enhanced generalization.

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Bunrit, Supaporn, Thuttaphol Inkian, Nittaya Kerdprasop, and Kittisak Kerdprasop. "Text-Independent Speaker Identification Using Deep Learning Model of Convolution Neural Network." International Journal of Machine Learning and Computing 9, no. 2 (April 2019): 143–48. http://dx.doi.org/10.18178/ijmlc.2019.9.2.778.

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Siddanna, S. R., and Y. C. Kiran. "Two Stage Multi Modal Deep Learning Kannada Character Recognition Model Adaptive to Discriminative Patterns of Kannada Characters." Indian Journal Of Science And Technology 16, no. 3 (January 22, 2023): 155–66. http://dx.doi.org/10.17485/ijst/v16i3.1904.

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13

Zhihua Chen, Zhihua Chen, Xiaolin Ju Zhihua Chen, Haochen Wang Xiaolin Ju, and Xiang Chen Haochen Wang. "Hybrid Multiple Deep Learning Models to Boost Blocking Bug Prediction." 網際網路技術學刊 23, no. 5 (September 2022): 1099–107. http://dx.doi.org/10.53106/160792642022092305018.

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<p>A blocking bug (BB) is a severe bug that could prevent other bugs from being fixed in time and cost more effort to repair itself in software maintenance. Hence, early detection of BBs is essential to save time and labor costs. However, BBs only occupy a small proportion of all bugs during software life cycle, making it difficult for developers to identify these blocking relationships. This study proposes a novel blocking bug prediction approach based on the hybrid deep learning model, a combination of Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN). Our approach first extracts summaries and descriptions from bug reports to construct two classifiers, respectively. Second, our approach combines two classifiers into a hybrid model to predict the blocking relationship of each blocking bug pair. Finally, our approach produces a report of identified blocking bugs for developers. To investigate the effectiveness of proposed approach, we conducted an empirical study on bug reports of seven large-scale projects. The final experimental results illustrate that our approach can perform better than the recent state-of-the-art baselines. Precisely, the hybrid model can predict BB better with an average accuracy of 57.20%, and an improvement of 73.53% in terms of the F1-measure when compared to ELBlocker. Moreover, according to the bug report’s description, BB can be predicted well with an average accuracy of 49.16%.</p> <p> </p>

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Tamboli, Mohasin B., and Dr Nageswara Rao Moparthi. "Deep Learning Model for Intrusion Identification." Journal of Advanced Research in Dynamical and Control Systems 12, no. 5 (May 30, 2020): 388–95. http://dx.doi.org/10.5373/jardcs/v12i5/20201726.

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15

Santos Silva, Jose Vitor, Leonardo Matos Matos, Flavio Santos, Helisson Oliveira Magalhaes Cerqueira, Hendrik Macedo, Bruno Otavio Piedade Prado, Gilton Jose Ferreira da Silva, and Kalil Araujo Bispo. "Combining deep learning model compression techniques." IEEE Latin America Transactions 20, no. 3 (March 2022): 458–64. http://dx.doi.org/10.1109/tla.2022.9667144.

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Yang, Fan, and Yutai Rao. "Practice and Research of Blended Learning Model Guided by Deep Learning Model." Mathematical Problems in Engineering 2022 (May 26, 2022): 1–6. http://dx.doi.org/10.1155/2022/8915162.

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An innovative approach to education and teaching, with a deeper integration of teaching and learning through a deeper mix of learning and study was proposed. The new organisational format combines independent learning in the form of microlessons and flipped classrooms with communication and cooperation in forums. In the context of the rapid development of Internet + education, big data information technology, and the accelerated promotion of education informatization by the Ministry of Education, this paper studies how to use the blended learning model to achieve the deep integration of information technology and classroom teaching through the innovative form of “microlesson and flipped classroom,” so as to improve students’ independent learning ability. Taking the university course of dynamic web design as an example, this course aims to achieve the teaching objectives of this course by using a deep learning model to guide the deep integration of information technology and classroom in a blended learning mode.

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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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Lv, Qing, Suzhen Zhang, and Yuechun Wang. "Deep Learning Model of Image Classification Using Machine Learning." Advances in Multimedia 2022 (July 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/3351256.

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Not only were traditional artificial neural networks and machine learning difficult to meet the processing needs of massive images in feature extraction and model training but also they had low efficiency and low classification accuracy when they were applied to image classification. Therefore, this paper proposed a deep learning model of image classification, which aimed to provide foundation and support for image classification and recognition of large datasets. Firstly, based on the analysis of the basic theory of neural network, this paper expounded the different types of convolution neural network and the basic process of its application in image classification. Secondly, based on the existing convolution neural network model, the noise reduction and parameter adjustment were carried out in the feature extraction process, and an image classification depth learning model was proposed based on the improved convolution neural network structure. Finally, the structure of the deep learning model was optimized to improve the classification efficiency and accuracy of the model. In order to verify the effectiveness of the deep learning model proposed in this paper in image classification, the relationship between the accuracy of several common network models in image classification and the number of iterations was compared through experiments. The results showed that the model proposed in this paper was better than other models in classification accuracy. At the same time, the classification accuracy of the deep learning model before and after optimization was compared and analyzed by using the training set and test set. The results showed that the accuracy of image classification had been greatly improved after the model proposed in this paper had been optimized to a certain extent.

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Fang, Qiqing, Gen Liu, Yamin Hu, Yahui Hu, and Jingjing Wang. "A blended collaborative learning model aiming to deep learning." SHS Web of Conferences 140 (2022): 01017. http://dx.doi.org/10.1051/shsconf/202214001017.

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To solve the problem of mechanized blending and low-level cooperation in Blended Cooperative Learning, this paper proposes a Blended Cooperative Learning Model aiming to Deep Learning, which including the definition, key features and its framework. Through the application in the course of Radar Maintenance Engineering and Performance Parameters Measurement, our teaching practice shows that the students’ practical skills and scientific teamwork ability are significantly improved.

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Silpa, C., A. Vani, and K. Rama Naidu. "Deep Learning Based Channel Estimation for MIMO-OFDM System with Modified ResNet Model." Indian Journal Of Science And Technology 16, no. 2 (January 15, 2023): 97–108. http://dx.doi.org/10.17485/ijst/v16i2.2154.

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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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22

Doke, Yash. "Deep fake Detection Through Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 861–66. http://dx.doi.org/10.22214/ijraset.2023.51630.

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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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Choiriyati, Nur, Yandra Arkeman, and Wisnu Ananta Kusuma. "Deep learning model for metagenome fragment classification using spaced k-mers feature extraction." Jurnal Teknologi dan Sistem Komputer 8, no. 3 (May 25, 2020): 234–38. http://dx.doi.org/10.14710/jtsiskom.2020.13407.

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An open challenge in bioinformatics is the analysis of the sequenced metagenomes from the various environments. Several studies demonstrated bacteria classification at the genus level using k-mers as feature extraction where the highest value of k gives better accuracy but it is costly in terms of computational resources and computational time. Spaced k-mers method was used to extract the feature of the sequence using 111 1111 10001 where 1 was a match and 0 was the condition that could be a match or did not match. Currently, deep learning provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. In this research, two different deep learning architectures, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), trained to approach the taxonomic classification of metagenome data and spaced k-mers method for feature extraction. The result showed the DNN classifier reached 90.89 % and the CNN classifier reached 88.89 % accuracy at the genus level taxonomy.

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ST, Suganthi, Mohamed Uvaze Ahamed Ayoobkhan, Krishna Kumar V, Nebojsa Bacanin, Venkatachalam K, Hubálovský Štěpán, and Trojovský Pavel. "Deep learning model for deep fake face recognition and detection." PeerJ Computer Science 8 (February 22, 2022): e881. http://dx.doi.org/10.7717/peerj-cs.881.

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Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.

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Yang, Dong, and Jian Sun. "A Model-Driven Deep Dehazing Approach by Learning Deep Priors." IEEE Access 9 (2021): 108542–56. http://dx.doi.org/10.1109/access.2021.3101319.

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Noori, Amani Y., Dr Shaimaa H. Shaker, and Dr Raghad Abdulaali Azeez. "Semantic Segmentation of Urban Street Scenes Using Deep Learning." Webology 19, no. 1 (January 20, 2022): 2294–306. http://dx.doi.org/10.14704/web/v19i1/web19156.

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Scene classification is essential conception task used by robotics for understanding the environmental. The outdoor scene like urban street scene is composing of image with depth having greater variety than iconic object image. The semantic segmentation is an important task for autonomous driving and mobile robotics applications because it introduces enormous information need for safe navigation and complex reasoning. This paper introduces a model for classification all pixel’s image and predicates the right object that contains this pixel. This model adapts famous network image classification VGG16 with fully convolution network (FCN-8) and transfer learned representation by fine tuning for doing segmentation. Skip Architecture is added between layers to combine coarse, semantic, and local appearance information to generate accurate segmentation. This model is robust and efficiency because it efficient consumes low memory and faster inference time for testing and training on Camvid dataset. The output module is designed by using a special computer equipped by GPU memory NVIDIA GeForce RTX 2060 6G, and programmed by using python 3.7 programming language. The proposed system reached an accuracy 0.8804 and MIOU 73% on Camvid dataset.

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Lee, S., J. Banzon, K. Le, and D. Kim. "Estimating animal pose using deep learning: a trained deep learning model outperforms morphological analysis." EAI Endorsed Transactions on Bioengineering and Bioinformatics 1, no. 4 (April 22, 2022): 173951. http://dx.doi.org/10.4108/eai.22-4-2022.173951.

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Ghoniem, Rania M., Abeer D. Algarni, Basel Refky, and Ahmed A. Ewees. "Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis." Symmetry 13, no. 4 (April 10, 2021): 643. http://dx.doi.org/10.3390/sym13040643.

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Ovarian cancer (OC) is a common reason for mortality among women. Deep learning has recently proven better performance in predicting OC stages and subtypes. However, most of the state-of-the-art deep learning models employ single modality data, which may afford low-level performance due to insufficient representation of important OC characteristics. Furthermore, these deep learning models still lack to the optimization of the model construction, which requires high computational cost to train and deploy them. In this work, a hybrid evolutionary deep learning model, using multi-modal data, is proposed. The established multi-modal fusion framework amalgamates gene modality alongside with histopathological image modality. Based on the different states and forms of each modality, we set up deep feature extraction network, respectively. This includes a predictive antlion-optimized long-short-term-memory model to process gene longitudinal data. Another predictive antlion-optimized convolutional neural network model is included to process histopathology images. The topology of each customized feature network is automatically set by the antlion optimization algorithm to make it realize better performance. After that the output from the two improved networks is fused based upon weighted linear aggregation. The deep fused features are finally used to predict OC stage. A number of assessment indicators was used to compare the proposed model to other nine multi-modal fusion models constructed using distinct evolutionary algorithms. This was conducted using a benchmark for OC and two benchmarks for breast and lung cancers. The results reveal that the proposed model is more precise and accurate in diagnosing OC and the other cancers.

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P, Sanjeevi. "Social Distancing Detection with Deep Learning Model." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (April 30, 2021): 1683–85. http://dx.doi.org/10.22214/ijraset.2021.33996.

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Pyo, Jongcheol, Sanghun Park, Kyung-Hwa Cho, and Sang-Soo Baek. "Deep learning model in water-environment field." Journal of the Korean Society of Water and Wastewater 34, no. 6 (December 30, 2020): 481–93. http://dx.doi.org/10.11001/jksww.2020.34.6.481.

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Zhou, Xingchen, Ming Xu, Yiming Wu, and Ning Zheng. "Deep Model Poisoning Attack on Federated Learning." Future Internet 13, no. 3 (March 14, 2021): 73. http://dx.doi.org/10.3390/fi13030073.

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Federated learning is a novel distributed learning framework, which enables thousands of participants to collaboratively construct a deep learning model. In order to protect confidentiality of the training data, the shared information between server and participants are only limited to model parameters. However, this setting is vulnerable to model poisoning attack, since the participants have permission to modify the model parameters. In this paper, we perform systematic investigation for such threats in federated learning and propose a novel optimization-based model poisoning attack. Different from existing methods, we primarily focus on the effectiveness, persistence and stealth of attacks. Numerical experiments demonstrate that the proposed method can not only achieve high attack success rate, but it is also stealthy enough to bypass two existing defense methods.

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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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Annam, Sangeetha, and Anshu Singla. "Hyperspectral Image Classification Using Deep Learning Model." ECS Transactions 107, no. 1 (April 24, 2022): 6427–33. http://dx.doi.org/10.1149/10701.6427ecst.

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In today’s scenario, the classification process is required for numerous purposes, including planning of modern cities, agriculture production, and monitoring of environmental factors. So far, the optimal categorization level has remained unattainable. Manual feature-based approaches, unsupervised feature learning methods, and supervised feature learning methods are the three types of remote-sensing images classification methods available. Supervised deep learning methods have recently been widely used in a variety of remote sensing applications, including as object detection and land use scene classification. In contrast to traditional computer vision tasks that only use the spatial context, this article can improve hyperspectral image classification by utilizing both spatial context and spectral correlation. To achieve this, the benchmark hyperspectral dataset with CNN deep learning model for hyperspectral image classification is taken and analyzed for different learning rates for SGD optimizer. The accuracy percentage were maximum when the SGD optimizer were trained with maximum learning rate. As the learning rate decreases, so does the accuracy percentage.

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Zhao, Ming, Meng Li, Sheng-Lung Peng, and Jie Li. "A Novel Deep Learning Model Compression Algorithm." Electronics 11, no. 7 (March 28, 2022): 1066. http://dx.doi.org/10.3390/electronics11071066.

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In order to solve the problem of large model computing power consumption, this paper proposes a novel model compression algorithm. Firstly, this paper proposes an interpretable weight allocation method for the loss between a student network (a network model with poor performance), a teacher network (a network model with better performance) and real label. Then, different from the previous simple pruning and fine-tuning, this paper performs knowledge distillation on the pruned model, and quantifies the residual weights of the distilled model. The above operations can further reduce the model size and calculation cost while maintaining the model accuracy. The experimental results show that the weight allocation method proposed in this paper can allocate a relatively appropriate weight to the teacher network and real tags. On the cifar-10 dataset, the pruning method combining knowledge distillation and quantization can reduce the memory size of resnet32 network model from 3726 KB to 1842 KB, and the accuracy can be kept at 93.28%, higher than the original model. Compared with similar pruning algorithms, the model accuracy and operation speed are greatly improved.

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Sophiya, E., and S. Jothilakshmi. "Audio event detection using deep learning model." International Journal of Computer Aided Engineering and Technology 16, no. 3 (2022): 328. http://dx.doi.org/10.1504/ijcaet.2022.10046064.

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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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Sophiya, E., and S. Jothilakshmi. "Audio event detection using deep learning model." International Journal of Computer Aided Engineering and Technology 16, no. 3 (2022): 328. http://dx.doi.org/10.1504/ijcaet.2022.122149.

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Abdin, Osama, and Philip M. Kim. "Rapid protein model refinement by deep learning." Nature Computational Science 1, no. 7 (July 2021): 456–57. http://dx.doi.org/10.1038/s43588-021-00104-0.

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Ganapriya, K., N. Uma Maheswari, and R. Venkatesh. "Deep Learning Model for Epileptic Seizure Prediction." Journal of Medical Imaging and Health Informatics 11, no. 12 (December 1, 2021): 3199–208. http://dx.doi.org/10.1166/jmihi.2021.3916.

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Prediction of occurrence of a seizure would be of greater help to make necessary precaution for taking care of the patient. A Deep learning model, recurrent neural network (RNN), is designed for predicting the upcoming values in the EEG values. A deep data analysis is made to find the parameter that could best differentiate the normal values and seizure values. Next a recurrent neural network model is built for predicting the values earlier. Four different variants of recurrent neural networks are designed in terms of number of time stamps and the number of LSTM layers and the best model is identified. The best identified RNN model is used for predicting the values. The performance of the model is evaluated in terms of explained variance score and R2 score. The model founds to perform well number of elements in the test dataset is minimal and so this model can predict the seizure values only a few seconds earlier.

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E, Yugesh. "Deep Learning Model for Motion Video Processing." International Journal for Research in Applied Science and Engineering Technology 7, no. 3 (March 31, 2019): 2158–61. http://dx.doi.org/10.22214/ijraset.2019.3398.

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Shadeed, Ghada A., Mohammed A. Tawfeeq, and Sawsan M. Mahmoud. "Deep learning model for thorax diseases detection." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 1 (February 1, 2020): 441. http://dx.doi.org/10.12928/telkomnika.v18i1.12997.

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Bakhteev, O. Yu, and V. V. Strijov. "Deep Learning Model Selection of Suboptimal Complexity." Automation and Remote Control 79, no. 8 (August 2018): 1474–88. http://dx.doi.org/10.1134/s000511791808009x.

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Nigri, Andrea, Susanna Levantesi, Mario Marino, Salvatore Scognamiglio, and Francesca Perla. "A Deep Learning Integrated Lee–Carter Model." Risks 7, no. 1 (March 16, 2019): 33. http://dx.doi.org/10.3390/risks7010033.

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In the field of mortality, the Lee–Carter based approach can be considered the milestone to forecast mortality rates among stochastic models. We could define a “Lee–Carter model family” that embraces all developments of this model, including its first formulation (1992) that remains the benchmark for comparing the performance of future models. In the Lee–Carter model, the κ t parameter, describing the mortality trend over time, plays an important role about the future mortality behavior. The traditional ARIMA process usually used to model κ t shows evident limitations to describe the future mortality shape. Concerning forecasting phase, academics should approach a more plausible way in order to think a nonlinear shape of the projected mortality rates. Therefore, we propose an alternative approach the ARIMA processes based on a deep learning technique. More precisely, in order to catch the pattern of κ t series over time more accurately, we apply a Recurrent Neural Network with a Long Short-Term Memory architecture and integrate the Lee–Carter model to improve its predictive capacity. The proposed approach provides significant performance in terms of predictive accuracy and also allow for avoiding the time-chunks’ a priori selection. Indeed, it is a common practice among academics to delete the time in which the noise is overflowing or the data quality is insufficient. The strength of the Long Short-Term Memory network lies in its ability to treat this noise and adequately reproduce it into the forecasted trend, due to its own architecture enabling to take into account significant long-term patterns.

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Wu, Guoxing, Wenjie Lu, Guangwei Gao, Chunxia Zhao, and Jiayin Liu. "Regional deep learning model for visual tracking." Neurocomputing 175 (January 2016): 310–23. http://dx.doi.org/10.1016/j.neucom.2015.10.064.

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Lee, Miran, Jong Wook Kim, and Beakcheol Jang. "Chicken pox Prediction Using Deep Learning Model." Transactions of The Korean Institute of Electrical Engineers 69, no. 1 (January 31, 2020): 127–37. http://dx.doi.org/10.5370/kiee.2020.69.1.127.

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Araya-Polo, Mauricio, Stuart Farris, and Manuel Florez. "Deep learning-driven velocity model building workflow." Leading Edge 38, no. 11 (November 2019): 872a1–872a9. http://dx.doi.org/10.1190/tle38110872a1.1.

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Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

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Bakhteev, Oleg, and Vadim Strijov. "Deep Learning Model Selection of Suboptimal Complexity." Автоматика и телемеханика, no. 8 (2018): 129–47. http://dx.doi.org/10.31857/s000523100001252-1.

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He, Hengtao, Chao-Kai Wen, Shi Jin, and Geoffrey Ye Li. "Model-Driven Deep Learning for MIMO Detection." IEEE Transactions on Signal Processing 68 (2020): 1702–15. http://dx.doi.org/10.1109/tsp.2020.2976585.

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Matiisen, Tambet, Aqeel Labash, Daniel Majoral, Jaan Aru, and Raul Vicente. "Do Deep Reinforcement Learning Agents Model Intentions?" Stats 6, no. 1 (December 28, 2022): 50–66. http://dx.doi.org/10.3390/stats6010004.

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Inferring other agents’ mental states, such as their knowledge, beliefs and intentions, is thought to be essential for effective interactions with other agents. Recently, multi-agent systems trained via deep reinforcement learning have been shown to succeed in solving various tasks. Still, how each agent models or represents other agents in their environment remains unclear. In this work, we test whether deep reinforcement learning agents trained with the multi-agent deep deterministic policy gradient (MADDPG) algorithm explicitly represent other agents’ intentions (their specific aims or plans) during a task in which the agents have to coordinate the covering of different spots in a 2D environment. In particular, we tracked over time the performance of a linear decoder trained to predict the final targets of all agents from the hidden-layer activations of each agent’s neural network controller. We observed that the hidden layers of agents represented explicit information about other agents’ intentions, i.e., the target landmark the other agent ended up covering. We also performed a series of experiments in which some agents were replaced by others with fixed targets to test the levels of generalization of the trained agents. We noticed that during the training phase, the agents developed a preference for each landmark, which hindered generalization. To alleviate the above problem, we evaluated simple changes to the MADDPG training algorithm which lead to better generalization against unseen agents. Our method for confirming intention modeling in deep learning agents is simple to implement and can be used to improve the generalization of multi-agent systems in fields such as robotics, autonomous vehicles and smart cities.

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