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Artykuły w czasopismach na temat „Deep learning technology”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING TECHNOLOGY IN DISEASE DIAGNOSIS." NATURE AND SCIENCE 04, no. 05 (2020): 4–11. http://dx.doi.org/10.36719/2707-1146/05/4-11.

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The rapid development of deep learning technology provides new methods and ideas for assisting physicians in high-precision disease diagnosis. This article reviews the principles and features of deep learning models commonly used in medical disease diagnosis, namely convolutional neural networks, deep belief networks, restricted Boltzmann machines, and recurrent neural network models. Based on several typical diseases, the application of deep learning technology in the field of disease diagnosis is introduced; finally, the future development direction is proposed based on the limitations of cu

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Naylor, Amanda, and Janet Gibbs. "Deep Learning." International Journal of Mobile and Blended Learning 10, no. 1 (2018): 62–77. http://dx.doi.org/10.4018/ijmbl.2018010105.

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This article presents results from an international collaboration between college students and pre-service teachers in Norway and the UK. This research is part of a large, international project exploring and developing the interrelationship between mobile technology and teachers' perceptions of teaching and learning. Data was collected for this study through an on-line survey of 37 pre-service teachers followed by six semi-structured, in-depth interviews. The data analysis revealed the themes of collaboration, authenticity and professional learning through the use of mobile technology in the d

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Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING IN MEDICAL IMAGING." NATURE AND SCIENCE 03, no. 04 (2020): 7–13. http://dx.doi.org/10.36719/2707-1146/04/7-13.

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Medical imaging technology plays an important role in the detection, diagnosis and treatment of diseases. Due to the instability of human expert experience, machine learning technology is expected to assist researchers and physicians to improve the accuracy of imaging diagnosis and reduce the imbalance of medical resources. This article systematically summarizes some methods of deep learning technology, introduces the application research of deep learning technology in medical imaging, and discusses the limitations of deep learning technology in medical imaging. Key words: Artificial Intellige

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Hao, Xing, and Guigang Zhang. "Deep learning." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630018. http://dx.doi.org/10.1142/s2425038416300184.

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Artificial intelligence is one of the most beautiful dreams of mankind. Although computer technology has made considerable progress, so far, there is no computer showing intelligence like human beings. The emergence of deep learning gives people a glimmer of hope. So, what is learning deep? Why is it so important? How does it work? And what are the existing achievements and difficulties? This paper provides an overview of deep learning which will answer these questions.

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Baimakhanova, A. S., K. M. Berkimbayev, A. K. Zhumadillayeva, and E. T. Abdrashova. "Technology of using deep learning algorithms." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 89, no. 3 (2023): 35–45. http://dx.doi.org/10.47533/2023.1606-146x.30.

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Deep learning is a branch of machine learning (machine learning-ML). Deep learning methods utilize high-level model abstraction of nonlinear transformations in large databases. In other areas, the implementation of deep learning architectures has contributed significantly to the development of artificial intelligence. This paper presents recent research on newly applied deep learning algorithms. Convolutional Neural Networks are used in deep learning. Database Management System PostgreSQL object-relational database. The implementation resulted in achieving the set goals and objectives. The met

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Lee, Suk-Ho, and Dae-Ki Kang. "Deep Learning Technology and Image Sensing." Sensors 24, no. 16 (2024): 5130. http://dx.doi.org/10.3390/s24165130.

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Huu Huy, NGO, BUI Van Tung, LE Hung Linh, and NGUYEN Duy Minh. "Garbage classification using deep learning technology." Vinh University Journal of Science 52, no. 1A (2023): 35–44. http://dx.doi.org/10.56824/vujs.2022a048.

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Garbage classification has always been an important issue in environmental protection, resource recycling, and social livelihood. However, garbage classification takes a lot of time and effort. Moreover, garbage classification directly affects the health of workers. Currently, due to the development of artificial intelligence, advanced garbage classification robots are being used more and more in recycling factories. With the sufficient support of robots integrated with artificial intelligence technology, garbage will be more and more quickly processed and accurately classified. Therefore, thi

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Yuan, Bin. "Deep learning technology for face recognition." Thermal Science 29, no. 3 Part A (2025): 2007–14. https://doi.org/10.2298/tsci2503007y.

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In China, the rapid development of public transportation network construction has been accompanied by a high incidence of traffic accidents caused by sleep-deprived driving. The monitoring of drivers' sleep-deprived driving and the sending out of early warnings has been identified as a field of research with both important theoretical and practical value. This article proposes a fatigue detection algorithm based on facial recognition information fusion. The algorithm extracts facial feature information and head features from the driver's face and fuses them into facial recognition information

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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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Gaurav, Singh, Kumar Shubham, Vijayan Surya, Perumal Thinagaran, and Sathiyanarayanan Mithileysh. "CYBER BULLYING DETECTION USING MACHINE LEARNING AND DEEP LEARNING." International Journal For Technological Research In Engineering 9, no. 7 (2022): 11–17. https://doi.org/10.5281/zenodo.6392440.

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The use of information and technology to bully a person online is referred to as cyberbullying. Individuals use Information and Communication Technology (ICT) to ridicule, embarrass, taunt, defame, intimidate, and criticise a person without making a direct eye contact. With the rise of social media, bullies have created a “virtual playground” in Facebook, Instagram, WhatsApp, Twitter and YouTube by targeting specific set of individuals or groups. It is necessary to deploy models and mechanisms in place for bullying contents, where the content is automatically detected and resolved,

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Nagashree, K. T., Shristi, Firdaushi Sania, B. Patil Shweta, and Singh Shristi. "Deep-Fake Detection Using Deep Learning." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 1700–1706. https://doi.org/10.5281/zenodo.14808073.

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Deep-Fake Detection is a new technology which has caught extreme fashionability in the present generation. Deep-Fake has now held serious pitfalls over spreading misinformation to the world, destroying political faces and also blackmailing individualities to prize centrals. As this technology has taken over the internet in a veritably short span of time and also numerous readily apps are also available to execute Deep-Fake contents, and numerous of the individualities has made systems grounded on detecting the deepfake contents whether it’s fake or real. From the DL(deep learning) &ndash

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Xian Guo, Xian Guo, Keyu Chen Xian Guo, An Yang Keyu Chen, and Zhanhui Gang An Yang. "Research on Industrial IoT Security Based on Deep Learning." 網際網路技術學刊 24, no. 3 (2023): 727–44. http://dx.doi.org/10.53106/160792642023052403017.

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<p>With the rapid development of &quot;Internet +&quot; and the construction of a new generation of information infrastructure, the intrusion behaviors against the Industrial Internet of Things are increasingly common. How to ensure the security of the industrial Internet of Things is one of the current research hotspots. The modern technology trend has the hottest technologies of the Internet of Things (IoT). The application of IoT on the other hand improves work efficiency and brings convenience to people’s life; on the other hand, it makes the network face increasingly serious

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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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Fukuda, Atushi, and Masakazu Morimoto. "Detection of brick firing defects using image processing technology and deep learning." Journal of the Institute of Industrial Applications Engineers 13, no. 2 (2025): 82–88. https://doi.org/10.12792/jiiae.13.82.

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Yang, Beijia. "Deep Learning-Based Information Security." Applied and Computational Engineering 97, no. 1 (2024): 145–51. http://dx.doi.org/10.54254/2755-2721/97/20241358.

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Abstract. With the rapid development of network technology, information security is facing increasingly complex challenges. Deep learning technology, due to its strong capabilities in data processing and pattern recognition, has become a key technology to improve the detection efficiency and accuracy in the field of information security. This paper delves into the application of deep learning in various aspects such as malware detection, network intrusion identification, User and Entity Behavior Analytics (UEBA), privacy protection technology, model explainability, and network security vulnera

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Verma, Dr Neeta. "Assistive Vision Technology using Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 2695–704. http://dx.doi.org/10.22214/ijraset.2021.36815.

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One of the most important functions of the human visual system is automatic captioning. Caption generation is one of the more interesting and focused areas of AI, with numerous challenges to overcome. If there is an application that automatically captions the scenes in which a person is present and converts the caption into a clear message, people will benefit from it in a variety of ways. In this, we offer a deep learning model that detects things or features in images automatically, produces descriptions for the images, and transforms the descriptions to audio for louder readout. The model u

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Chaubey, Abhinav. "Facial Emotions Recognition Using Deep Learning Technology." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 1245–49. http://dx.doi.org/10.22214/ijraset.2021.38100.

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Abstract: Artificial Intelligent give us capability to detect emotions of human being. Due variation of individual expression it is difficult to find precisely. With AI we can mimics a human's capability like recognising someone with a restricted facial feature. this . paper, of mine indentify the face emotions by detecting areas of face like eyes, nose, lips, and forehead. By implementing two repressing methods like histogram and data augmentation we propos to extract characteristics of facial emotion. Here in this paper two dimensional architecture is used. First is used for inputting greysc

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Hu, Guangdong, Fengyuan Qian, Longgui Sha, and Zilong Wei. "Application of Deep Learning Technology in Glioma." Journal of Healthcare Engineering 2022 (February 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/8507773.

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A common and most basic brain tumor is glioma that is exceptionally dangerous to health of various patients. A glioma segmentation, which is primarily magnetic resonance imaging (MRI) oriented, is considered as one of common tools developed for doctors. These doctors use this system to examine, analyse, and diagnose appearance of the glioma’s outward for both patients, i.e., indoor and outdoor. In the literature, a widely utilized approach for the segmentation of glioma is the deep learning-oriented method. To cope with this issue, a segmentation of glioma approach, i.e., primarily on the conv

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Lai, Yucong, and Yifeng Wang. "Adversarial Attacks Technology in Deep Learning Models." Journal of Physics: Conference Series 1966, no. 1 (2021): 012007. http://dx.doi.org/10.1088/1742-6596/1966/1/012007.

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Thi Thu Hoa, NGUYEN, NGO Huu Huy, GIAP Manh Tuyen, NONG Van Duong, NGUYEN Thi Kieu Oanh, and NGUYEN Ba Bang. "FACIAL EMOTION RECOGNITION USING DEEP LEARNING TECHNOLOGY." Vinh University Journal of Science 53, no. 3A (2024): 23–31. http://dx.doi.org/10.56824/vujs.2024a048a.

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Recognizing emotions on human faces has always received attention and attraction from researchers. Along with the development of artificial intelligence, facial emotion recognition cameras are increasingly being introduced into various fields such as healthcare, education, and comerce. Thanks to the effective support of cameras integrated with artificial intelligence technology, the above fields are growing strongly. Therefore, this study will present an effective and simple facial emotion recognition model base on deep learning technology. Facial emotion recognition is performed automatically

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Cheng, Fuchao, Hong Zhang, Wenjie Fan, and Barry Harris. "Image Recognition Technology Based on Deep Learning." Wireless Personal Communications 102, no. 2 (2018): 1917–33. http://dx.doi.org/10.1007/s11277-018-5246-z.

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Abdu-al Kadhm, Salam Jabbar. "Contactless Palmprint Recognition using Deep Learning Technology." Academic Science Journal 2, no. 1 (2024): 361–70. http://dx.doi.org/10.24237/asj.02.01.678b.

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B, Thulasi Thanmai, Vani K., Dwaraka Srihith I., Venkat Sai I., and Shasikala I. "Revolutionizing Healthcare with Deep Learning." Recent Trends in Information Technology and its Application 6, no. 3 (2023): 16–30. https://doi.org/10.5281/zenodo.8138446.

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<em>Deep learning technology is becoming increasingly prevalent in the healthcare industry, which has the potential to revolutionise medical diagnosis, treatment, and patient care. Deep learning algorithms are capable of analysing immense quantities of healthcare data, such as patient records, medical images, and genomic information, to identify patterns and make highly accurate predictions. This technology is currently being used, among other things, to enhance diagnostic accuracy, personalise treatment plans, and predict patient outcomes.</em>

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Shukla, Dheeraj. "Deep Fake Face Detection Using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50976.

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Artificial Intelligence, deepfake technology, Generative Adversarial Networks GAN, Detection System, Detection Accuracy, User accessibility, Digital content verification. Abstract: In recent years, the rise of deepfake technology has raised significant concerns. regarding the authenticity of digital content. Deepfakes, which are synthetic media created using advanced artificial intelligence techniques, can mislead viewers and pose risks to personal privacy, public trust, and social discourse. The proposed system focuses on developing a Generative Adversarial Network (GAN)- based deepfake detec

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Jatinder, Kumar. "An Overview of Deep Learning Methods for Segmenting Thyroid Ultrasound Images." International Journal of Advanced Engineering and Nano Technology (IJAENT) 10, no. 12 (2023): 1–8. https://doi.org/10.35940/ijaent.A9759.12101223.

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<strong>Abstract: </strong>One of the various imaging modalities that is most frequently utilized in clinical practice is ultrasound (US). It is an emerging technology that has certain advantages along with disadvantages such as poor imaging quality and a lot of fluctuation. To aid in US diagnosis and/or to increase the objectivity and accuracy of such evaluation, effective automatic US image assessment techniques must be created from the perspective of image analysis. The most effective machine learning technology, notably in computer vision and general evaluation of images, has since been pr

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Okatani, Takayuki. "Deep Learning." Journal of the Institute of Image Information and Television Engineers 68, no. 6 (2014): 466–71. http://dx.doi.org/10.3169/itej.68.466.

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Usha MG and Pradeep BM. "Deep fake video/image detection using deep learning." Global Journal of Engineering and Technology Advances 20, no. 2 (2024): 074–80. http://dx.doi.org/10.30574/gjeta.2024.20.2.0148.

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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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An, Byounguk, JongChan Lee, JeSung Chi, and Wonhyung Park. "Malicious Packet Detection Technology Using Machine Learning and Deep Learning." Jouranl of Information and Security 21, no. 4 (2021): 109–15. http://dx.doi.org/10.33778/kcsa.2021.21.4.109.

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Surma, Jerzy. "Deep Learning in Military Applications." Safety & Defense 10, no. 1 (2024): 1–7. https://doi.org/10.37105/sd.214.

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The latest advancements in Artificial Intelligence, especially in Deep Learning technology, accelerate innovation and development in different application domains. The development of Deep Learning technology has profoundly impacted military development trends, leading to major changes in the forms and models of war. In this paper, we overview Deep Learning's history and architecture. Then, we review related work and extensively describe Deep Learning in two primary military applications: intelligence operations and autonomous platforms. Finally, we discuss related threats, opportunities, techn

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Gupta, Adhyayan. "Machine Learning and Deep Learning: A Comprehensive Overview." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1620–26. https://doi.org/10.22214/ijraset.2025.72470.

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Machine Learning (ML) and Deep Learning (DL) are two core areas of Artificial Intelligence (AI) that have significantly transformed technology and research. Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, te

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K, Mr Gopi. "Deep Fake Detection using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33196.

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Deep learning is an effective method that is broadly used across a wide range of areas, i.e., computer vision, machine vision, and natural language processing. Deepfakes is an application of this technology where the images and videos of someone are manipulated in such a way that it is difficult for human beings to tell the difference between them and their true selves. Deepfakes have been the subject of several studies recently, and a number of deep learning approaches have been proposed for their detection. Here, we provide an extensive survey on deepfake generation and recognition technique

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S., Gayathri, Santhiya S., Nowneesh T., Sanjana Shuruthy K., and Sakthi S. "Deep fake detection using deep learning techniques." Applied and Computational Engineering 2, no. 1 (2023): 1010–19. http://dx.doi.org/10.54254/2755-2721/2/20220655.

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Deep fake is the artificial manipulation and creation of data, primarily through photo-graphs or videos into the likeness of another person. This technology has a variety of ap-plications. Despite its uses, it can also influence society in a controversial way like de-faming a person, Political distress, etc. Many models had been proposed by different re-searchers which give an average accuracy of 90%. To improve the detection efficiency, this proposed paper uses 3 different deep learning techniques: Inception ResNetV2, Effi-cientNet, and VGG16. These proposed models are trained by the combinat

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Patil, Nilesh, Aaditya Gadiyar, Darshan Mehta, and Harsh Khatri. "MedicHub – Disease Detection Using Deep Learning." Mapana Journal of Sciences 22, Special Issue 2 (2023): 37–61. https://doi.org/10.12723/mjs.sp2.3.

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The integration of technology in healthcare is rapidly revolutionizing the sector and transforming the traditional modus operandi that used to be followed into a more efficient and accurate automated system. Machine Learning is a sophisticated technology used to analyze clinical symptoms to predict diseases and deliver accurate diagnoses based on strong evidence. The major advantage of using technology to assist in diagnosis is to understand more aboutunderlying illnesses that are often overlooked while searching for a more severe disease, or when the patient is not in imminent danger. This of

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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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Wada, Yoshitaka. "Construction of Surrogate Model using Deep Learning Technology." Seikei-Kakou 32, no. 3 (2020): 83–87. http://dx.doi.org/10.4325/seikeikakou.32.83.

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Ishii, Masato, and Atsushi Sato. "1. Deep Learning Technology for Small-scale Data." Journal of The Institute of Image Information and Television Engineers 74, no. 1 (2020): 25–29. http://dx.doi.org/10.3169/itej.74.25.

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Chen, Xize, Xiaoyu Qu, Yufeng Qian, and Yiyao Zhang. "Music Recognition Using Blockchain Technology and Deep Learning." Computational Intelligence and Neuroscience 2022 (August 8, 2022): 1–13. http://dx.doi.org/10.1155/2022/7025338.

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The purposes are to recognize and classify different music characteristics and strengthen the copyright protection system for original digital music in the big data era. Deep learning (DL) and blockchain technology are applied and researched herein. Based on CNN (Convolutional Neural Network), a music recognition method combined with hashing learning is proposed. The error generated when outputting the binary hash code is considered, and the semantic similarity of the hash code is ensured. Besides, the application of blockchain technology in the current intellectual property protection in orig

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Huo, Yingying. "Talent Management Recommendation Technology Based on Deep Learning." Mathematical Problems in Engineering 2022 (September 7, 2022): 1–7. http://dx.doi.org/10.1155/2022/7697192.

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Nowadays, with the vigorous development of information management technology, talent management has become a hot field that scholars pay attention to. The flow of talent between companies has become increasingly frequent. A large number of cooperative behaviors have produced a large number of cooperative results and subsequently brought a large amount of data on what to do. A huge network of collaborators has also been quietly formed, and how to mine valuable information from it has become a research hotspot, among which talent recommendation is one of the most important topics. Talent recomme

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Saeed, Fahman, Muhammad Hussain, and Hatim A. Aboalsamh. "Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet)." Mathematics 10, no. 8 (2022): 1285. http://dx.doi.org/10.3390/math10081285.

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Fingerprints are gaining in popularity, and fingerprint datasets are becoming increasingly large. They are often captured utilizing a variety of sensors embedded in smart devices such as mobile phones and personal computers. One of the primary issues with fingerprint recognition systems is their high processing complexity, which is exacerbated when they are gathered using several sensors. One way to address this issue is to categorize fingerprints in a database to condense the search space. Deep learning is effective in designing robust fingerprint classification methods. However, designing th

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Weng, C. H., and C. Y. Tan. "Applying deep learning to develop endoscopic imaging technology." Journal of Physics: Conference Series 2020, no. 1 (2021): 012026. http://dx.doi.org/10.1088/1742-6596/2020/1/012026.

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Chen, Chengjun, Chunlin Zhang, Tiannuo Wang, et al. "Monitoring of Assembly Process Using Deep Learning Technology." Sensors 20, no. 15 (2020): 4208. http://dx.doi.org/10.3390/s20154208.

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Monitoring the assembly process is a challenge in the manual assembly of mass customization production, in which the operator needs to change the assembly process according to different products. If an assembly error is not immediately detected during the assembly process of a product, it may lead to errors and loss of time and money in the subsequent assembly process, and will affect product quality. To monitor assembly process, this paper explored two methods: recognizing assembly action and recognizing parts from complicated assembled products. In assembly action recognition, an improved th

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Zhang, Changyun. "Deep Learning Based Multi-user Interference Cancellation Technology." Science Discovery 7, no. 6 (2019): 379. http://dx.doi.org/10.11648/j.sd.20190706.11.

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Gao, Wei, Yaojun Chen, Abdul Qudair Baig, and Yunqing Zhang. "Ontology geometry distance computation using deep learning technology." Journal of Intelligent & Fuzzy Systems 35, no. 4 (2018): 4517–24. http://dx.doi.org/10.3233/jifs-169770.

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chentian, Ma. "Application of Deep Learning in Vehicle Driverless Technology." Journal of Physics: Conference Series 1682 (November 2020): 012071. http://dx.doi.org/10.1088/1742-6596/1682/1/012071.

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Wang, Yu, Xiaojun Bai, Liuhua Di, and Shifeng Zhao. "UAV detection technology research based on deep learning." Journal of Physics: Conference Series 2872, no. 1 (2024): 012022. http://dx.doi.org/10.1088/1742-6596/2872/1/012022.

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Abstract In the process of UAV target recognition, the target is small, the feature is not obvious, and the recognition accuracy is low, a UAV target detection method called YOLOv8-UD is proposed based on YOLOv8. This method replaces downsampling layers with SPD convolutions to mitigate fine-grained feature loss issues. It employs a BiFPN structure to get information which is multi-scale, enhancing representation of global semantic message and Improve awareness of small goals. As a result, to target detection, it significantly enhances the correctness and effectiveness. Additionally, to addres

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He, Tao, and Xiaofeng Li. "Image quality recognition technology based on deep learning." Journal of Visual Communication and Image Representation 65 (December 2019): 102654. http://dx.doi.org/10.1016/j.jvcir.2019.102654.

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Yonetsuji, Taizan. "2-2 PaintsChainer, A Deep Learning Coloring Technology." Journal of The Institute of Image Information and Television Engineers 72, no. 5 (2018): 353–57. http://dx.doi.org/10.3169/itej.72.353.

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Engineering, Journal of Healthcare. "Retracted: Application of Deep Learning Technology in Glioma." Journal of Healthcare Engineering 2022 (December 28, 2022): 1. http://dx.doi.org/10.1155/2022/9764617.

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Zhao, Xuefeng, Yang Zhang, and Niannian Wang. "Bolt loosening angle detection technology using deep learning." Structural Control and Health Monitoring 26, no. 1 (2018): e2292. http://dx.doi.org/10.1002/stc.2292.

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Li, Jinbei, Yu Tian, Juan Chen, and Hao Wang. "Rock Crack Recognition Technology Based on Deep Learning." Sensors 23, no. 12 (2023): 5421. http://dx.doi.org/10.3390/s23125421.

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The changes in cracks on the surface of rock mass reflect the development of geological disasters, so cracks on the surface of rock mass are early signs of geological disasters such as landslides, collapses, and debris flows. To research geological disasters, it is crucial to swiftly and precisely gather crack information on the surface of rock masses. Drone videography surveys can effectively avoid the limitations of the terrain. This has become an essential method in disaster investigation. This manuscript proposes rock crack recognition technology based on deep learning. First, images of cr

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