Log in

Relevant bibliographies by topics / Deep learning based / Journal articles

Journal articles on the topic 'Deep learning based'

To see the other types of publications on this topic, follow the link: Deep learning based.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Deep learning based.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Jaiswal, Tarun, and Sushma Jaiswal. "Deep Learning Based Pain Treatment." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 193–211. http://dx.doi.org/10.31142/ijtsrd23639.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Jiang, Zhengfen, Boyi Li, Tho N. H. T. Tran, Jiehui Jiang, Xin Liu, and Dean Ta. "Fluo-Fluo translation based on deep learning." Chinese Optics Letters 20, no. 3 (2022): 031701. http://dx.doi.org/10.3788/col202220.031701.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Daehyeon Bae, Daehyeon Bae, Jongbae Hwang Daehyeon Bae, and Jaecheol Ha Jongbae Hwang. "Deep Learning-based Attacks on Masked AES Implementation." 網際網路技術學刊 23, no. 4 (July 2022): 897–902. http://dx.doi.org/10.53106/160792642022072304024.

Full text

Abstract:

<p>To ensure the confidentiality of the message, the AES (Advanced Encryption Standard) block cipher algorithm can be widely used. Furthermore, an implementation of masked AES is often used to resist side-channel attacks. To recover secret keys embedded in cryptographic devices with masked AES, we present some side-channel attacks based on deep learning models in profiling and non-profiling scenarios. The proposed method which applies the mask value profiling technique represents new approaches for extracting the secret key. To defeat the masked AES implementation, deep learning models such as multi-layer perceptron and convolutional neural networks are developed. In a non-profiling scenario, we adopt the DDLA (Differential Deep Learning Analysis) to extract sensitive information such as the secret key. The main idea of our method is that it is possible to adopt a new binary labeling method to conduct the DDLA based on the HW (Hamming Weight) model. We show several experiments using real power traces measured from the ChipWhisperer platform in profiling attacks and the ASCAD dataset in non-profiling attacks respectively. Whether we target naïve or masked AES implementation, the experimental results show the predominant key recovery accuracy.</p> <p> </p>

APA, Harvard, Vancouver, ISO, and other styles

4

AL-Oudat, Mohammad, Mohammad Azzeh, Hazem Qattous, Ahmad Altamimi, and Saleh Alomari. "Image Segmentation based Deep Learning for Biliary Tree Diagnosis." Webology 19, no. 1 (January 20, 2022): 1834–49. http://dx.doi.org/10.14704/web/v19i1/web19123.

Full text

Abstract:

Dilation of biliary tree can be an indicator of several diseases such as stones, tumors, benign strictures, and some cases cancer. This dilation can be due to many reasons such as gallstones, inflammation of the bile ducts, trauma, injury, severe liver damage. Automatic measurement of the biliary tree in magnetic resonance images (MRI) is helpful to assist hepatobiliary surgeons for minimally invasive surgery. In this paper, we proposed a model to segment biliary tree MRI images using a Fully Convolutional Neural (FCN) network. Based on the extracted area, seven features that include Entropy, standard deviation, RMS, kurtosis, skewness, Energy and maximum are computed. A database of images from King Hussein Medical Center (KHMC) is used in this work, containing 800 MRI images; 400 cases with normal biliary tree; and 400 images with dilated biliary tree labeled by surgeons. Once the features are extracted, four classifiers (Multi-Layer perceptron neural network, support vector machine, k-NN and decision tree) are applied to predict the status of patient in terms of biliary tree (normal or dilated). All classifiers show high accuracy in terms of Area Under Curve except support vector machine. The contributions of this work include introducing a fully convolutional network for biliary tree segmentation, additionally scientifically correlate the extracted features with the status of biliary tree (normal or dilated) that have not been previously investigated in the literature from MRI images for biliary tree status determinations.

APA, Harvard, Vancouver, ISO, and other styles

5

Wang, Weihao, Xing Zhao, Zhixiang Jiang, and Ya Wen. "Deep learning-based scattering removal of light field imaging." Chinese Optics Letters 20, no. 4 (2022): 041101. http://dx.doi.org/10.3788/col202220.041101.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Jimmington, Anjana. "A Baseline Based Deep Learning Approach of Live Tweets." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 829–33. http://dx.doi.org/10.31142/ijtsrd23918.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Debnath, Tanmoy, and Suvvari Sai Dileep. "A Deep-Learning based Approach for Automatic Lyric Generation." International Journal of Science and Research (IJSR) 11, no. 11 (November 5, 2022): 382–86. http://dx.doi.org/10.21275/sr221104005352.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Gadri, Said. "Efficient Arabic Handwritten Character Recognition based on Machine Learning and Deep Learning Approaches." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 9–17. http://dx.doi.org/10.5373/jardcs/v12sp7/20202076.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Liu, Qingzhong, Zhaoxian Zhou, Sarbagya Ratna Shakya, Prathyusha Uduthalapally, Mengyu Qiao, and Andrew H. Sung. "Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms." International Journal of Machine Learning and Computing 8, no. 2 (April 2018): 121–26. http://dx.doi.org/10.18178/ijmlc.2018.8.2.674.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Pan, Wei, Jide Li, and Xiaoqiang Li. "Portfolio Learning Based on Deep Learning." Future Internet 12, no. 11 (November 18, 2020): 202. http://dx.doi.org/10.3390/fi12110202.

Full text

Abstract:

Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China’s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market’s leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.

APA, Harvard, Vancouver, ISO, and other styles

11

Zhu, Miaomiao, Shengrong Gong, Zhenjiang Qian, Seiichi Serikawa, and Lifeng Zhang. "Person Re-identification on Mobile Devices Based on Deep Learning." Journal of the Institute of Industrial Applications Engineers 9, no. 1 (January 25, 2021): 26–32. http://dx.doi.org/10.12792/jiiae.9.26.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Ping Zhang, Ping Zhang, Xuchong Liu Ping Zhang, Wenjun Li Xuchong Liu, and Xiaofeng Yu Wenjun Li. "Pharmaceutical Cold Chain Management Based on Blockchain and Deep Learning." 網際網路技術學刊 22, no. 7 (December 2021): 1531–42. http://dx.doi.org/10.53106/160792642021122207007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Sen, Bandita, and V. Vedanarayanan. "Efficient Classification of Breast Lesion based on Deep Learning Technique." Bonfring International Journal of Advances in Image Processing 6, no. 1 (February 29, 2016): 01–06. http://dx.doi.org/10.9756/bijaip.10446.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

K., Yogeswara Rao. "Deep Learning-based Aspect-Level Sentiment Analysis of User-Generated Content." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1457–65. http://dx.doi.org/10.5373/jardcs/v12sp4/20201624.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Kumar, Bhavesh Shri, Naren J, Vithya G, and Prahathish K. "A Novel Architecture based on Deep Learning for Scene Image Recognition." International Journal of Psychosocial Rehabilitation 23, no. 1 (February 20, 2019): 400–404. http://dx.doi.org/10.37200/ijpr/v23i1/pr190251.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Arsa, Dewa Made Sri, Jonghoon Lee, OkJae Won, and Hyongsuk Kim. "Deep Learning for Weeds’ Growth Point Detection based on U-Net." Korean Institute of Smart Media 11, no. 7 (August 31, 2022): 94–103. http://dx.doi.org/10.30693/smj.2022.11.7.94.

Full text

Abstract:

Weeds bring disadvantages to crops since they can damage them, and a clean treatment with less pollution and contamination should be developed. Artificial intelligence gives new hope to agriculture to achieve smart farming. This study delivers an automated weeds growth point detection using deep learning. This study proposes a combination of semantic graphics for generating data annotation and U-Net with pre-trained deep learning as a backbone for locating the growth point of the weeds on the given field scene. The dataset was collected from an actual field. We measured the intersection over union, f1-score, precision, and recall to evaluate our method. Moreover, Mobilenet V2 was chosen as the backbone and compared with Resnet 34. The results showed that the proposed method was accurate enough to detect the growth point and handle the brightness variation. The best performance was achieved by Mobilenet V2 as a backbone with IoU 96.81%, precision 97.77%, recall 98.97%, and f1-score 97.30%.

APA, Harvard, Vancouver, ISO, and other styles

18

Hyunhee Park, Hyunhee Park. "Edge Based Lightweight Authentication Architecture Using Deep Learning for Vehicular Networks." 網際網路技術學刊 23, no. 1 (January 2022): 195–202. http://dx.doi.org/10.53106/160792642022012301020.

Full text

Abstract:

<p>When vehicles are connected to the Internet through vehicle-to-everything (V2X) systems, they are exposed to diverse attacks and threats through the network connections. Vehicle-hacking attacks in the road can significantly affect driver safety. However, it is difficult to detect hacking attacks because vehicles not only have high mobility and unreliable link conditions, but they also use broadcast-based wireless communication. To this end, V2X systems need a simple but a powerful authentication procedure on the road. Therefore, this paper proposes an edge based lightweight authentication architecture using a deep learning algorithm for road safety applications in vehicle networks. The proposed lightweight authentication architecture enables vehicles that are physically separated to form a vehicular cloud in which vehicle-to-vehicle communications can be secured. In addition, an edge-based cloud data center performs deep learning algorithms to detect car hacking attempts, and then delivers the detection results to a vehicular cloud. Extensive simulations demonstrate that the proposed authentication architecture significantly enhanced the security level. The proposed authentication architecture has 94.51 to 99.8% F1-score results depending on the number of vehicles in the intrusion detection system using control area network traffic.</p> <p> </p>

APA, Harvard, Vancouver, ISO, and other styles

19

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Minaee, Shervin, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. "Deep Learning--based Text Classification." ACM Computing Surveys 54, no. 3 (June 2021): 1–40. http://dx.doi.org/10.1145/3439726.

Full text

Abstract:

Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and we discuss future research directions.

APA, Harvard, Vancouver, ISO, and other styles

21

Vivekanand, Bade Ashwini, and Suresh Kumar M. "Deep Learning Based Tomato PLDD." International Journal of Engineering Trends and Technology 70, no. 7 (July 31, 2022): 414–21. http://dx.doi.org/10.14445/22315381/ijett-v70i7p243.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Chen, Yuwei, and Jianyu He. "Deep Learning-Based Emotion Detection." Journal of Computer and Communications 10, no. 02 (2022): 57–71. http://dx.doi.org/10.4236/jcc.2022.102005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

24

Seo, Myoungchan, YoungJin Suh, and Kyuman Jeong. "Deep Learning-based Video Summarization." International Journal on Advanced Science, Engineering and Information Technology 11, no. 6 (December 12, 2021): 2488. http://dx.doi.org/10.18517/ijaseit.11.6.12888.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Mohammadkarimi, Mostafa, Mehrtash Mehrabi, Masoud Ardakani, and Yindi Jing. "Deep Learning-Based Sphere Decoding." IEEE Transactions on Wireless Communications 18, no. 9 (September 2019): 4368–78. http://dx.doi.org/10.1109/twc.2019.2924220.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Soltani, Mehran, Vahid Pourahmadi, Ali Mirzaei, and Hamid Sheikhzadeh. "Deep Learning-Based Channel Estimation." IEEE Communications Letters 23, no. 4 (April 2019): 652–55. http://dx.doi.org/10.1109/lcomm.2019.2898944.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Zhang, Wei, Ting Yao, Shiai Zhu, and Abdulmotaleb El Saddik. "Deep Learning–Based Multimedia Analytics." ACM Transactions on Multimedia Computing, Communications, and Applications 15, no. 1s (February 23, 2019): 1–26. http://dx.doi.org/10.1145/3279952.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Kishida, Masako, Masaki Ogura, Yuichi Yoshida, and Tadashi Wadayama. "Deep Learning-Based Average Consensus." IEEE Access 8 (2020): 142404–12. http://dx.doi.org/10.1109/access.2020.3014148.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Yang, Zhenwei, and Xiangdong Zhang. "Entanglement-based quantum deep learning." New Journal of Physics 22, no. 3 (March 25, 2020): 033041. http://dx.doi.org/10.1088/1367-2630/ab7598.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Fan, Jicong, and Tommy Chow. "Deep learning based matrix completion." Neurocomputing 266 (November 2017): 540–49. http://dx.doi.org/10.1016/j.neucom.2017.05.074.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Liu, Dong, Yue Li, Jianping Lin, Houqiang Li, and Feng Wu. "Deep Learning-Based Video Coding." ACM Computing Surveys 53, no. 1 (May 29, 2020): 1–35. http://dx.doi.org/10.1145/3368405.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Zhang, Shuai, Lina Yao, Aixin Sun, and Yi Tay. "Deep Learning Based Recommender System." ACM Computing Surveys 52, no. 1 (February 28, 2019): 1–38. http://dx.doi.org/10.1145/3285029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Moshovos, Andreas, Jorge Albericio, Patrick Judd, Alberto Delmas Lascorz, Sayeh Sharify, Tayler Hetherington, Tor Aamodt, and Natalie Enright Jerger. "Value-Based Deep-Learning Acceleration." IEEE Micro 38, no. 1 (January 2018): 41–55. http://dx.doi.org/10.1109/mm.2018.112130309.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Goi, Hiroaki, Koshi Komuro, and Takanori Nomura. "Deep-learning-based binary hologram." Applied Optics 59, no. 23 (August 10, 2020): 7103. http://dx.doi.org/10.1364/ao.393500.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Cheon, Woohyun, and Kihyo Jung. "Development of Vector-based and Deep Learning-based OWAS Assessment Systems for Assessing Working Postures." Journal of the Ergonomics Society of Korea 40, no. 2 (April 30, 2021): 75–87. http://dx.doi.org/10.5143/jesk.2021.40.2.75.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Sivasankaran, Deepika, Sai Seena P, Rajesh R, and Madheswari Kanmani. "Sketch Based Image Retrieval using Deep Learning Based Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 79–86. http://dx.doi.org/10.35940/ijeat.e2622.0610521.

Full text

Abstract:

Sketch based image retrieval (SBIR) is a sub-domain of Content Based Image Retrieval(CBIR) where the user provides a drawing as an input to obtain i.e retrieve images relevant to the drawing given. The main challenge in SBIR is the subjectivity of the drawings drawn by the user as it entirely relies on the user's ability to express information in hand-drawn form. Since many of the SBIR models created aim at using singular input sketch and retrieving photos based on the given single sketch input, our project aims to enable detection and extraction of multiple sketches given together as a single input sketch image. The features are extracted from individual sketches obtained using deep learning architectures such as VGG16 , and classified to its type based on supervised machine learning using Support Vector Machines. Based on the class obtained, photos are retrieved from the database using an opencv library, CVLib , which finds the objects present in a photo image. From the number of components obtained in each photo, a ranking function is performed to rank the retrieved photos, which are then displayed to the user starting from the highest order of ranking up to the least. The system consisting of VGG16 and SVM provides 89% accuracy

APA, Harvard, Vancouver, ISO, and other styles

37

Zhou, Jie, Feng Ran, Guang Li, Jun Peng, Kun Li, and Zheng Wang. "Classroom Learning Status Assessment Based on Deep Learning." Mathematical Problems in Engineering 2022 (April 16, 2022): 1–9. http://dx.doi.org/10.1155/2022/7049458.

Full text

Abstract:

Student classroom behavior performance is an important part of classroom teaching evaluation, and conducting student classroom behavior recognition is important for classroom teaching evaluation. The article proposes a deep learning-based student classroom behavior recognition method, which extracts the key information of the human skeleton from student behavior images and combines a 10-layer deep convolutional neural network (CNN-10) to recognize students’ classroom behavior. To verify the effectiveness of this method, the paper conducts a comparison experiment on the student classroom behavior dataset using CNN-10 and the student classroom behavior recognition method. The experimental results show that the student classroom behavior recognition method can effectively exclude the interference of irrelevant information such as students’ physique, dress, and classroom background, highlight the key effective information, and have higher recognition accuracy and generalization ability. Using the human skeleton and a deep learning-based student classroom behavior detection approach to identify students’ typical classroom behaviors might improve intelligent classroom teaching by reflecting students’ learning status in a timely and effective manner.

APA, Harvard, Vancouver, ISO, and other styles

38

Li, Ying, Jianglei Di, Li Ren, and Jianlin Zhao. "Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy." Chinese Optics Letters 19, no. 5 (2021): 051701. http://dx.doi.org/10.3788/col202119.051701.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Chih-Ta Yen, Chih-Ta Yen, and 陳冠宇 Chih-Ta Yen. "A Deep Learning-Based Person Search System for Real-World Camera Images." 網際網路技術學刊 23, no. 4 (July 2022): 839–51. http://dx.doi.org/10.53106/160792642022072304018.

Full text

Abstract:

<p>A person search system was developed to identify the query person from images captured by cameras at four scenes in the study. This study analyzed three network architectures called Model Basic, Model One, and Model Two. To verify the validity of the model design, the models in the public data set and in the recorded system data set were compared to determine whether the results of the proposed model exhibited consistent performance between the camera images from the public data set and the recorded, unprocessed system data set. The detected pedestrian images then underwent distance matching relative to query person images by using the online instance matching (OIM) loss function. Based on Model Basic, Model One and Model Two were designed to further improve accuracy by incorporating different convolutional neural networks. In CUHK-SYSU data set, the testing results of Model Basic, Model One and Model Two achieved the accuracies of 72.38%, 75.96% and 75.32%, respectively. The testing results of Model Basic, Model One, and Model Two with the system data set achieved accuracies of 63.745%, 68.80%, and 69.33%, respectively.</p> <p> </p>

APA, Harvard, Vancouver, ISO, and other styles

40

Qiang Chen, Qiang Chen, and Linzhou Li Qiang Chen. "A Deep Learning Based Equalization Scheme for Bandwidthcompressed Non-orthogonal Multicarrier Communication." 網際網路技術學刊 22, no. 5 (September 2021): 999–1007. http://dx.doi.org/10.53106/160792642021092205006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Kanagala, Hari Krishna, and Dr V. V. Jayarama Krishnaiah. "Enhanced Mechanism for Classification of Glaucoma Images Using Deep Learning based CNN." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10 (October 31, 2019): 111–18. http://dx.doi.org/10.5373/jardcs/v11i10/20193013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Lin, Tianying, Ang Liu, Xiaopei Zhang, He Li, Liping Wang, Hailong Han, Ze Chen, Xiaoping Liu, and Haibin Lü. "Analyzing OAM mode purity in optical fibers with CNN-based deep learning." Chinese Optics Letters 17, no. 10 (2019): 100603. http://dx.doi.org/10.3788/col201917.100603.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Minghai Yuan, Minghai Yuan, Chenxi Zhang Minghai Yuan, Kaiwen Zhou Chenxi Zhang, and Fengque Pei Kaiwen Zhou. "Real-time Allocation of Shared Parking Spaces Based on Deep Reinforcement Learning." 網際網路技術學刊 24, no. 1 (January 2023): 035–43. http://dx.doi.org/10.53106/160792642023012401004.

Full text

Abstract:

<p>Aiming at the parking space heterogeneity problem in shared parking space management, a multi-objective optimization model for parking space allocation is constructed with the optimization objectives of reducing the average walking distance of users and improving the utilization rate of parking spaces, a real-time allocation method for shared parking spaces based on deep reinforcement learning is proposed, which includes a state space for heterogeneous regions, an action space based on policy selection and a reward function with variable coefficients. To accurately evaluate the model performance, dynamic programming is used to derive the theoretical optimal values. Simulation results show that the improved algorithm not only improves the training success rate, but also increases the Agent performance by at least 12.63% and maintains the advantage for different sizes of parking demand, reducing the user walking distance by 53.58% and improving the parking utilization by 6.67% on average, and keeping the response time less than 0.2 seconds.</p> <p> </p>

APA, Harvard, Vancouver, ISO, and other styles

44

S., Kayalvizhi, Kasthuri Bha J.K., and Ferents Koni Jiavana K. "Deep Learning based Micro Organism Recognition and Disease Prediction on Plants and Animals." NeuroQuantology 20, no. 5 (May 18, 2022): 741–46. http://dx.doi.org/10.14704/nq.2022.20.5.nq22231.

Full text

Abstract:

While deep learning has received a great deal of interest recently, it is still not utilized to its full potential in microbiology. To overcome the limitations of Microscopy that is operated by humans, the deep-learning-based methods of image analysis of these Microorganisms have been proposed for a variety of purposes, including bacteria, viruses, parasites, and fungi. Also, Deep-learning-based systems are expected to be at the forefront of detecting and investigating microorganisms. So, here we are proposing a deep learning model that can classify and detect the type of organism using a CNN-based transfer learning algorithm. A prediction of disease spread is also possible once an organism is detected. Through modernization, we came to know that micro-organisms were useful to plants and animals but at another side of the coin they are also harmful to them, so their study is vitally important but they haven’t gone to the next phase from testing under the microscope, so we proposed this model.

APA, Harvard, Vancouver, ISO, and other styles

45

Jeya, I. Jasmine Selvakumari, Dinesh Valluru, and A. Sherin. "Deep Learning based Mobilenet with Deep Belief Network for Lung Cancer Diagnosis in IOT and Cloud Enabled Environment." Indian Journal Of Science And Technology 15, no. 42 (November 12, 2022): 2219–29. http://dx.doi.org/10.17485/ijst/v15i42.1435.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Lorente, Maria Paz Sesmero, Elena Magán Lopez, Laura Alvarez Florez, Agapito Ledezma Espino, José Antonio Iglesias Martínez, and Araceli Sanchis de Miguel. "Explaining Deep Learning-Based Driver Models." Applied Sciences 11, no. 8 (April 7, 2021): 3321. http://dx.doi.org/10.3390/app11083321.

Full text

Abstract:

Different systems based on Artificial Intelligence (AI) techniques are currently used in relevant areas such as healthcare, cybersecurity, natural language processing, and self-driving cars. However, many of these systems are developed with “black box” AI, which makes it difficult to explain how they work. For this reason, explainability and interpretability are key factors that need to be taken into consideration in the development of AI systems in critical areas. In addition, different contexts produce different explainability needs which must be met. Against this background, Explainable Artificial Intelligence (XAI) appears to be able to address and solve this situation. In the field of automated driving, XAI is particularly needed because the level of automation is constantly increasing according to the development of AI techniques. For this reason, the field of XAI in the context of automated driving is of particular interest. In this paper, we propose the use of an explainable intelligence technique in the understanding of some of the tasks involved in the development of advanced driver-assistance systems (ADAS). Since ADAS assist drivers in driving functions, it is essential to know the reason for the decisions taken. In addition, trusted AI is the cornerstone of the confidence needed in this research area. Thus, due to the complexity and the different variables that are part of the decision-making process, this paper focuses on two specific tasks in this area: the detection of emotions and the distractions of drivers. The results obtained are promising and show the capacity of the explainable artificial techniques in the different tasks of the proposed environments.

APA, Harvard, Vancouver, ISO, and other styles

47

Albak, Lubab H., Raid Rafi Omar Al-Nima, and Arwa Hamid Salih. "Palm print verification based deep learning." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 3 (June 1, 2021): 851. http://dx.doi.org/10.12928/telkomnika.v19i3.16573.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Taşkıran, Murat, and Sibel Çimen Yetiş. "Deep learning based tobacco products classification." International Journal of Computing Science and Mathematics 13, no. 2 (2021): 167. http://dx.doi.org/10.1504/ijcsm.2021.114193.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Karthika, J., H. Mohammed Imtiaz, M. Deepakdharsan, B. Akash, and U. Adimulam. "Deep Learning Based Multiple Object Detection." Journal of Physics: Conference Series 1916, no. 1 (May 1, 2021): 012225. http://dx.doi.org/10.1088/1742-6596/1916/1/012225.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Choi, Jun Ho, Tae Young Han, Seung Hyun Lee, and Byung Cheol Song. "Deep learning-based small object detection." Journal of the Institute of Electronics and Information Engineers 55, no. 7 (July 31, 2018): 57–66. http://dx.doi.org/10.5573/ieie.2018.55.7.57.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!