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Journal articles on the topic 'DEEP LEARNING MODEL'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

Wang, Yipu, and Stuart Perrin. "Deep Chinese Teaching and Learning Model Based on Deep Learning." International Journal of Languages, Literature and Linguistics 10, no. 1 (2024): 32–35. http://dx.doi.org/10.18178/ijlll.2024.10.1.479.

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Deep learning is a more situational and reflective way of learning that integrates complex knowledge and skills into intuitive thinking. As a language that closely combines sound, form and meaning, Chinese teaching and learning from the perspective of deep learning can help break through the limitations of the current teaching model that only focuses on certain language knowledge or cultural behaviors. This paper combines deep learning with international Chinese education, creates deep Chinese teaching and learning model including “four stages and ten steps”, and carries out practical applicat

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Bhat Kannagi Rajkhowa, Puran. "Deep Learning Model to Revive Indian Manuscripts." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 1365–68. http://dx.doi.org/10.21275/sr23422084622.

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Karthick Chaganty, Siva. "Database Failure Prediction Based on Deep Learning Model." International Journal of Science and Research (IJSR) 10, no. 4 (2021): 83–86. https://doi.org/10.21275/sr21329110526.

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

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

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Torkunova, J. V., V. Y. Ilichev, V. Drach, F. L. Chubarov, and A. N. Paсukevich. "UTILIZING DEEP LEARNING TECHNOLOGIES TO FORM PRICING MODELS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 6/3, no. 147 (2024): 262–69. http://dx.doi.org/10.36871/ek.up.p.r.2024.06.03.032.

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The work is devoted to evaluating pricing models for calculating the profitability of individual financial instruments, for example, such as stocks, using a multilayer generative-adversarial artificial neural network (GAN) and developing its own model based on the analysis. A huge amount of specially selected data is supplied to the inputs of the neural network, changing over time (dynamic). To improve the objectivity of the model, this work does not implement the arbitration capabilities of the markets. This is how one can analyze and explain variations and errors in pricing, as well as ident

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Parvez, Shaik. "Deep Learning Model for Image Classification Using Convolutional Neural Network." International Journal of Science and Research (IJSR) 11, no. 8 (2022): 132–37. http://dx.doi.org/10.21275/sr22731164616.

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Taraban, Roman, and Philip H. Marshall. "Deep Learning and Competition in Psycholinguistic Research." East European Journal of Psycholinguistics (2017) 4, no. 2 (2017): 67–74. https://doi.org/10.5281/zenodo.1147694.

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<strong>Abstract.</strong> MacWhinney, Bates, and colleagues developed the Competition Model in the 1980s as an alternate to Chomskyan models that encapsulate syntax as a special-purpose module. The Competition Model adopted the functional perspective that language serves communicative goals and functions. In contrast to the premise that knowledge of language is innate, the Competition model asserts that language is learned and processed through general cognitive mechanisms that identify and weight phonological, morphological, syntactic, and semantic cues in the language experiences of th

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Happy, Nurina, Tomi Apra Santosa, Siti Fatimah Hiola, et al. "Deep Learning Model in Science Learning: Bibliometric Analysis." Science Education and Application Journal 7, no. 1 (2025): 22–30. https://doi.org/10.30736/seaj.v7i1.1160.

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The deep learning model is one of the learning models that can be applied in science learning. Research related to deep learning has grown very rapidly in recent years. Research on deep learning models has produced many theoretical and empirical findings. Many trends have emerged to highlight the complexity and dynamics of deep learning models in science learning. This study aims to discover the latest trends in deep learning model research in science learning. This study uses a bibliometric approach of analysis based on the Google Scholar database. Based on this study's title, abstract, and k

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Ganesh Viswanathan, Gaurav Samdani, Yawal Dixit, and Ranjith Gopalan. "Deep Learning." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 512–27. https://doi.org/10.30574/wjaets.2025.14.3.0149.

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Deep learning has revolutionized artificial intelligence by enabling machines to learn complex patterns from vast amounts of data. This white paper explores the fundamental principles of deep learning, including neural network architectures, training methodologies, and key advancements such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. We discuss applications across various domains, including computer vision, natural language processing, healthcare, and finance, highlighting real-world use cases and breakthroughs. Additionally, we examine th

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白家納, 白家納, та 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, № 1 (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

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Kumar, P. V. Punith, K. M. Kiran Raj, and Harish Kunder. "Monkeypox Disease Detection using Deep Learning Techniques." Indian Journal Of Science And Technology 18, no. 26 (2025): 2135–47. https://doi.org/10.17485/ijst/v18i26.833.

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Objectives: To strengthen diagnostic precision and slow the transmission of the illness by creating an AI-based system to speed up monkeypox detection via an improved VGG-19 Convolutional Neural Network (CNN) system. Method: In order to maximize performance, bottleneck layers were integrated into a CNN model centered on the VGG-19 design. An openly accessible dataset of monkeypox photos was used to train and evaluate the algorithm. Accuracy, F1 Score, Precision, and Recall criteria were used to assess performance. Findings: With 95% accuracy, the VGG-19-based model showed promise in monkeypox

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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 m

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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 (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 (2019): 213–20. http://dx.doi.org/10.5373/jardcs/v11i11/20193190.

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Gandhar, Abhishek, Prakhar Priyadarshi, Shashi Gandhar, S. B. Kumar, Arvind Rehalia, and Mohit Tiwari. "An Effective Deep Learning Model Design for Cyber Intrusion Prevention System." Indian Journal Of Science And Technology 18, no. 10 (2025): 811–15. https://doi.org/10.17485/ijst/v18i10.318.

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Objectives: The increasing frequency of cyber threats necessitates the advancement of Intrusion Prevention Systems (IPS). However, existing IPS models suffer from high false positive rates, inefficiencies in real-time detection, and suboptimal accuracy levels. Methods: This study presents a CNN-LSTM hybrid model optimized for real-time cyber intrusion detection. The CICIDS2018 dataset was utilized for training, incorporating feature selection, hyper-parameter tuning, and dropout-based regularization to improve efficiency and prevent over-fitting. Findings: The proposed system achieved an F1-sc

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M, Karthikeyan, Karunyanathan K, Ranjith Kannan U, Manoj Chowdary U, and Mr Mahadevan K. "Wheat Leaf Disease Prediction Using Deep Learning Algorithm with VGG16 Model." International Journal of Research Publication and Reviews 6, no. 4 (2025): 2820–23. https://doi.org/10.55248/gengpi.6.0425.1407.

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20

Hao, Xing, Guigang Zhang, and Shang Ma. "Deep Learning." International Journal of Semantic Computing 10, no. 03 (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 (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

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Akshay, R. Naik, A. V. Deorankar Prof., and P. B. Ambhore Dr. "Design and Analysis of Deep Neural Network for Rainfall Prediction System." Advancement of Computer Technology and its Applications 3, no. 2 (2020): 1–4. https://doi.org/10.5281/zenodo.3886888.

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<em>Rainfall prediction is useful for farmers and others to take decision for doing various activities. There are various methods are available for rainfall prediction like machine learning, artificial neural network. In this proposed system we are using deep neural network for rainfall prediction, as deep neural network gives better results than machine learning algorithms. This proposed method is based on classification technique which is supervised learning method in deep neural network, this classification technique we are using for predicting rainfall. As deep neural network are capable f

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Daurenbekov, Kuanysh, Ulzada Aitimova, Aigul Dauitbayeva, et al. "Noisy image enhancements using deep learning techniques." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 811–18. https://doi.org/10.11591/ijece.v14i1.pp811-818.

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This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object rec

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Nurmuhammet, Abdullayev. "DEEP REINFORCEMENT LEARNING ON STOCK DATA." Alatoo Academic Studies 23, no. 2 (2023): 505–18. http://dx.doi.org/10.17015/aas.2023.232.49.

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This study proposes using Deep Reinforcement Learning (DRL) for stock trading decisions and prediction. DRL is a machine learning technique that enables agents to learn optimal strategies by interacting with their environment. The proposed model surpasses traditional models and can make informed trading decisions in real-time. The study highlights the feasibility of applying DRL in financial markets and its advantages in strategic decision- making. The model's ability to learn from market dynamics makes it a promising approach for stock market forecasting. Overall, this paper provides valuable

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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 (2020): 388–95. http://dx.doi.org/10.5373/jardcs/v12i5/20201726.

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Santos Silva, Jose Vitor, Leonardo Matos Matos, Flavio Santos, et al. "Combining deep learning model compression techniques." IEEE Latin America Transactions 20, no. 3 (2022): 458–64. http://dx.doi.org/10.1109/tla.2022.9667144.

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Meshram, Anurag, Amol Patil, Saurabh Mulik, Aniruddh Pawar, and Prof Sheetal Nirve. "Text Generation Model Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 23–25. https://doi.org/10.22214/ijraset.2025.66190.

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Abstract: The rapid growth of artificial intelligence (AI) and natural language processing (NLP) has given rise to conversational AI solutions known as chatbots. This project aims to explore and develop a chatbot using the OpenAI API. The chatbot is designed to offer users an interactive, responsive, and context-aware conversation experience. This report presents a detailed overview of the development process, design considerations, implementation, evaluation, and potential improvements. Chatbots have gained prominence for improving online interactions and information retrieval. The chatbot's

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R. Swadi, Mazin, and Muayad S. Croock. "Deep Learning Model For Autism Diagnosing." International Journal of Computing and Digital Systems 15, no. 1 (2024): 1541–49. http://dx.doi.org/10.12785/ijcds/1501109.

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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 inform

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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 (2019): 143–48. http://dx.doi.org/10.18178/ijmlc.2019.9.2.778.

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Hung Nguyen, Manh. "Hybrid Deep Learning and Distrust Model for Fault Detection in IoT Networks." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 166–70. http://dx.doi.org/10.21275/sr241030132810.

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Ji Bangying Wu, Kan. "A Score Prediction Model based on Deep Learning for Teaching Quality Analysis." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 653–58. http://dx.doi.org/10.21275/sr23410130539.

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Wagle, Aumkar. "Deep Learning for Financial Time Series using Long Short-Term Memory Model." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1944–72. http://dx.doi.org/10.21275/sr24418141736.

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Senthamizhselvi, S., and A. Saravanan. "Cuckoo Search Algorithm with Deep Learning Driven Robust Visual Place Recognition Model." International Journal of Science and Research (IJSR) 12, no. 10 (2023): 1443–49. http://dx.doi.org/10.21275/sr231018143622.

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Luong Thanh Tra, Luong Thanh Tra, Nguyen Minh Nguyen Nguyen Minh Nguyen, Jongtae Lim, Hyungsik Shin, and Seongwon Cho. "Deep Learning Model on Embedded Board for Vehicle Detection and Vehicle Tracking." Korean Institute of Smart Media 14, no. 2 (2025): 43–52. https://doi.org/10.30693/smj.2025.14.2.43.

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d, d., d. d, d. d, and d. d. "Optimized Deep Learning Models Using Ensemble Learning for COVID-19 Detection on CT Scan Images." Korean Data Analysis Society 25, no. 6 (2023): 2027–39. http://dx.doi.org/10.37727/jkdas.2023.25.6.2027.

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Early identification of COVID-19 can facilitate the establishment of a swift medical response plan, thereby slowing the rapid dissemination of this deadly disease. Recent advancements in medical imaging technology, coupled with the successful application of deep learning to visual tasks, have driven numerous studies investigating early disease diagnosis through medical imaging. In particular, deep learning has been employed for COVID-19 diagnosis from CT scan images. This paper proposes an ensemble COVID detection model that integrates four models including GoogleNet, EfficientNet, Hybrid Effi

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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 (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 (CN

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Usha, MG, and BM Pradeep. "Deep fake video/image detection using deep learning." Global Journal of Engineering and Technology Advances 20, no. 2 (2024): 074–80. https://doi.org/10.5281/zenodo.14921434.

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With the widespread of deep fake technology, the potential to detect manipulated images has become an insistent concern. This study investigates the application of machine learning concept and its techniques, precisely CNNs (Convolutional-Neural-Networks) and LSTM (Long-Short-Term-Memory) networks to rectify deep fake images. CNNs are utilized for their strength in feature extraction from images capturing spatial hierarchies in data, while LSTMs are employed to understand the temporal dependencies that might exist in sequential frames of manipulated videos. The proposed theory combines these t

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Hogeweg, Laurens, Maarten Schermer, Sander Pieterse, Timo Roeke, and Wilfred Gerritsen. "Machine Learning Model for Identifying Dutch/Belgian Biodiversity." Biodiversity Information Science and Standards 3 (August 20, 2019): e39229. https://doi.org/10.3897/biss.3.39229.

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The potential of citizen scientists to contribute to information about occurrences of species and other biodiversity questions is large because of the ubiquitous presence of organisms and friendly nature of the subject. Online platforms that collect observations of species from the public have existed for several years now. They have seen a rapid growth recently, partly due to the widespread availability of mobile phones. These online platforms, and many scientific studies as well, suffer from a <em>taxonomic bias</em>: the effect that certain species groups are overrepresented in the data (Tr

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Smithu, B. S., D. R. Janardhana, C. P. Leela, and G. Pushpa. "Forest Fire Risk Assessment and Detection using Deep Learning Models." Indian Journal Of Science And Technology 17, no. 46 (2024): 4921–28. https://doi.org/10.17485/ijst/v17i46.2138.

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Background: There is a severe need to detect any kind of fire in a faster and accurate method, especially forest fires to stop huge losses to the human community and the environment losses. The main purpose of the proposal is to identify and evaluate the accuracy of the existing Artificial Intelligence (AI) methods for detecting fire and improve the methods to detect fire in real-world scenarios in faster and accurate methods. Methods: The proposal uses a dataset to train a model, and in addition uses a few test images from an existing database to test the models developed. We develop and test

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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 (2023): 155–66. http://dx.doi.org/10.17485/ijst/v16i3.1904.

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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 (card

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A., Geetha Devi, Aparna M., Mounika N., Pavan kalian U., and Meghna Nath R. "Hand Gesture Recognition using Deep Learning." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 455–59. https://doi.org/10.35940/ijeat.D6765.049420.

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Hearing impaired individuals use sign languages to communicate with others within the community. Because of the wide spread use of this language, hard-of-hearing individuals can easily understand it but it is not known by a lot of normal people. In this paper a hand gesture recognition system has been developed to overcome this problem, for those who don't recognize sign language to communicate simply with hard-of-hearing individuals. In this paper a computer vision-based system is designed to detect sign Language. Datasets used in this paper are binary images. These images are given to th

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Dr., Rekha Patil, Kumar Katrabad Vidya, Mahantappa, and Kumar Sunil. "Image Classification Using CNN Model Based on Deep Learning." Journal Of Scientific Research And Technology (JSRT) 1, no. 2 (2023): 60–71. https://doi.org/10.5281/zenodo.7965526.

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In this work, we will use a convolutional neural network to classify images. In the field of visual image analysis, CNNs (a subset of deep neural networks) are the norm. Multilayer perceptron is used to develop CNN; it is based on a hierarchical model that works on network construction and then delivers to a fully linked layer. All the neurons are linked together and their output is processed in this layer. Here, we demonstrate how our system can get the job done in challenging domains like computer vision by using a deep learning approach. Convolutional Neural Networks (CNNs) are a machine le

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Doke, Yash. "Deep fake Detection Through Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (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

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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 (2022): 114–40. http://dx.doi.org/10.4208/jml.220115.

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Nehal, Mohamed Ali, Mostafa Abd El Hamid Marwa, and Youssif Aliaa. "Sentiment Analysis for Movies Reviews Dataset Using Deep Learning Models." International Journal of Data Mining & Knowledge Management Process (IJDKP) 9, no. 2/3 (2019): 19–27. https://doi.org/10.5281/zenodo.3340668.

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Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN we

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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 neura

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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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Shin, Hyeon-Soo, Seung-Pil Lee, Yun-Su Ha, and Hwan-Seong Kim. "Designing container crane control learning model using deep learning." Journal of Advanced Marine Engineering and Technology 47, no. 6 (2023): 367–78. http://dx.doi.org/10.5916/jamet.2023.47.6.367.

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