34 results on '"Zhang, Yudong"'
Search Results
2. Noise‐tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root.
- Author
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Xiao, Xiuchun, Jiang, Chengze, Mei, Qixiang, and Zhang, Yudong
- Subjects
SQUARE root ,ARTIFICIAL intelligence ,MATRICES (Mathematics) - Abstract
The solving of dynamic matrix square root (DMSR) problems is frequently encountered in many scientific and engineering fields. Although the original zeroing neural network is powerful for solving the DMSR, it cannot vanish the influence of the noise perturbations, and its constant‐coefficient design scheme cannot accelerate the convergence speed. Therefore, a noise‐tolerate and adaptive coefficient zeroing neural network (NTACZNN) is raised to enhance the robust noise immunity performance and accelerate the convergence speed simultaneously. Then, the global convergence and robustness of the proposed NTACZNN are theoretically analysed under an ideal environment and noise‐perturbed circumstances. Furthermore, some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution. Compared with some existing ZNNs, the proposed NTACZNN possesses advanced performance in terms of noise tolerance, solution accuracy, and convergence rate. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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3. Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges.
- Author
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Giacobbe, Daniele Roberto, Zhang, Yudong, and de la Fuente, José
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MACHINE learning ,ARTIFICIAL intelligence ,COMMUNICABLE diseases ,COVID-19 ,MEDICAL personnel ,EMERGING infectious diseases - Abstract
Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine and infectious diseases being not exempt from their rapid and exponential growth. Furthermore, the field of explainable AI and ML has gained particular relevance and is attracting increasing interest. Infectious diseases have already started to benefit from explainable AI/ML models. For example, they have been employed or proposed to better understand complex models aimed at improving the diagnosis and management of coronavirus disease 2019, in the field of antimicrobial resistance prediction and in quantum vaccine algorithms. Although some issues concerning the dichotomy between explainability and interpretability still require careful attention, an in-depth understanding of how complex AI/ML models arrive at their predictions or recommendations is becoming increasingly essential to properly face the growing challenges of infectious diseases in the present century. AI and ML are revolutionizing human activities in various fields, and infectious diseases are not exempt from their rapid and exponential growth. Despite some notable challenges, explainable AI/ML could provide insights into the decision-making process, making the outcomes of models more transparent. Improved transparency can help to build trust among healthcare professionals, policymakers, and the general public in leveraging AI/ML-based systems to face the growing challenges of infectious diseases in the present century. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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4. Deep learning in crowd counting: A survey
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Deng, Lijia, Zhou, Qinghua, Wang, Shuihua, Gorriz Sáez, Juan Manuel, and Zhang, Yudong
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Artificial intelligence ,Image processing ,Computer vision ,Image Analysis - Abstract
Counting high-density objects quickly and accurately is a popular area of research. Crowd counting has significant social and economic value and is a major focus in artificial intelligence. Despite many advancements in this field, many of them are not widely known, especially in terms of research data. The authors proposed a three-tier standardised dataset taxonomy (TSDT). The Taxonomy divides datasets into small-scale, large-scale and hyper-scale, according to different application scenarios. This theory can help researchers make more efficient use of datasets and improve the performance of AI algorithms in specific fields. Additionally, the authors proposed a new evaluation index for the clarity of the dataset: average pixel occupied by each object (APO). This new evaluation index is more suitable for evaluating the clarity of the dataset in the object counting task than the image resolution. Moreover, the authors classified the crowd counting methods from a data-driven perspective: multi-scale networks, single-column networks, multi-column networks, multi-task networks, attention networks and weak-supervised networks and introduced the classic crowd counting methods of each class. The authors classified the existing 36 datasets according to the theory of three-tier standardised dataset taxonomy and discussed and evaluated these datasets. The authors evaluated the performance of more than 100 methods in the past five years on different levels of popular datasets. Recently, progress in research on small-scale datasets has slowed down. There are few new datasets and algorithms on small-scale datasets. The studies focused on large or hyper-scale datasets appear to be reaching a saturation point. The combined use of multiple approaches began to be a major research direction. The authors discussed the theoretical and practical challenges of crowd counting from the perspective of data, algorithms and computing resources. The field of crowd counting is moving towards combining multiple methods and requires fresh, targeted datasets. Despite advancements, the field still faces challenges such as handling real-world scenarios and processing large crowds in real-time. Researchers are exploring transfer learning to overcome the limitations of small datasets. The development of effective algorithms for crowd counting remains a challenging and important task in computer vision and AI, with many opportunities for future research., BHF, AA/18/3/34220, Hope Foundation for Cancer Research, RM60G0680, GCRF, P202PF11, Sino‐UK Industrial Fund, RP202G0289, LIAS, P202ED10, P202RE969, Data Science Enhancement Fund, P202RE237, Sino‐UK Education Fund, OP202006, Fight for Sight, 24NN201, Royal Society International Exchanges Cost Share Award, RP202G0230, MRC, MC_PC_17171, BBSRC, RM32G0178B8
- Published
- 2023
5. SSRNet: A Deep Learning Network via Spatial‐Based Super‐resolution Reconstruction for Cell Counting and Segmentation.
- Author
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Deng, Lijia, Zhou, Qinghua, Wang, Shuihua, and Zhang, Yudong
- Abstract
Cell counting and segmentation are critical tasks in biology and medicine. The traditional methods for cell counting are labor‐intensive, time‐consuming, and prone to human errors. Recently, deep learning‐based cell counting methods have become a trend, including point‐based counting methods, such as cell detection and cell density prediction, and non‐point‐based counting, such as cell number regression prediction. However, the point‐based counting method heavily relies on well‐annotated datasets, which are scarce and difficult to obtain. On the other hand, nonpoint‐based counting is less interpretable. The task of cell counting by dividing it into two subtasks is approached: cell number prediction and cell distribution prediction. To accomplish this, a deep learning network for spatial‐based super‐resolution reconstruction (SSRNet) is proposed that predicts the cell count and segments the cell distribution contour. To effectively train the model, an optimized multitask loss function (OM loss) is proposed that coordinates the training of multiple tasks. In SSRNet, a spatial‐based super‐resolution fast upsampling module (SSR‐upsampling) is proposed for feature map enhancement and one‐step upsampling, which can enlarge the deep feature map by 32 times without blurring and achieves fine‐grained detail and fast processing. SSRNet uses an optimized encoder network. Compared with the classic U‐Net, SSRNet's running memory read and write consumption is only 1/10 of that of U‐Net, and the total number of multiply and add calculations is 1/20 of that of U‐Net. Compared with the traditional sampling method, SSR‐upsampling can complete the upsampling of the entire decoder stage at one time, reducing the complexity of the network and achieving better performance. Experiments demonstrate that the method achieves state‐of‐the‐art performance in cell counting and segmentation tasks. The method achieves nonpoint‐based counting, eliminating the need for exact position annotation of each cell in the image during training. As a result, it has demonstrated excellent performance on cell counting and segmentation tasks. The code is public on GitHub (https://github.com/Roin626/SSRnet). [ABSTRACT FROM AUTHOR]
- Published
- 2023
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6. DLSANet: Facial expression recognition with double‐code LBP‐layer spatial‐attention network.
- Author
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Guo, Xing, Lu, Siyuan, Wang, Shuihua, Lu, Zhihai, and Zhang, Yudong
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FACIAL expression ,FEATURE extraction ,CONVOLUTIONAL neural networks ,PATTERN recognition systems ,ARTIFICIAL intelligence - Abstract
Facial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double‐code LBP‐layer spatial‐attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double‐code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross‐validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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7. Deep Learning for Medical Image-Based Cancer Diagnosis.
- Author
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Jiang, Xiaoyan, Hu, Zuojin, Wang, Shuihua, and Zhang, Yudong
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TUMOR diagnosis ,DEEP learning ,DIGITAL image processing ,X-rays ,MAGNETIC resonance imaging ,POSITRON emission tomography computed tomography ,DIAGNOSTIC imaging ,COMPUTER-aided diagnosis ,COMPUTED tomography ,ARTIFICIAL neural networks ,PREDICTION models - Abstract
Simple Summary: Deep learning has succeeded greatly in medical image-based cancer diagnosis. To help readers better understand the current research status and ideas, this article provides a detailed overview of the working mechanisms and use cases of commonly used radiological imaging and histopathology, the basic architecture of deep learning, classical pretrained models, common methods to overcome overfitting, and the application of deep learning in medical image-based cancer diagnosis. Finally, the data, label, model, and radiomics were discussed specifically and the current challenges and future research hotspots were discussed and analyzed. (1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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8. Fighting against COVID‐19: Innovations and applications.
- Author
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Zhang, Yudong
- Subjects
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DEEP learning , *COVID-19 , *ARTIFICIAL intelligence , *CLINICAL decision support systems , *CONVOLUTIONAL neural networks , *COVID-19 pandemic - Abstract
Fighting against COVID-19: Innovations and applications Abbreviations AI artificial intelligence CNN convolutional neural network CT computerized tomography CXR chest x-ray radiograph DL deep learning DNN deep neural network DSS decision support system ML machine learning COVID-19 has triggered over 6.90 million deaths worldwide until the cutoff date of April 23, 2023. Furthermore, chest CT can be used to evaluate the response to treatment in COVID-19 patients.[33] Repeat chest CT scans can show whether the lung damage is improving or stabilizing, which can help healthcare professionals to adjust the treatment plan as needed.[34] In this virtual special issue, many CT-based AI models are reported. COLI-Net: deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images. [Extracted from the article]
- Published
- 2023
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9. Research on application of artificial intelligence in movie industry
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Du, Wenda, Han, Qing, Zhang, Yudong, CLUE+, Art and Culture, History, Antiquity, and Zhang, Yudong
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Artificial intelligence ,Engineering ,business.industry ,Audio-video language ,Film distribution ,Film industry ,Data science ,GeneralLiterature_MISCELLANEOUS ,and Infrastructure ,Movie ,Movie recommendation system ,SDG 9 - Industry, Innovation, and Infrastructure ,Innovation ,business ,SDG 9 - Industry ,ComputingMilieux_MISCELLANEOUS - Abstract
The development of artificial intelligence has given birth to a series of innovations in the film industry. This article systematically summarizes these innovations into three parts; movie recommendation, movie distribution and audiovisual language creation. In the first part, the paper lists a series of movie recommendation systems based on AI technology. These systems can help users quickly find the movie resources they are interested in among the massive movie resources. In the second part, the article lists a series of cases of film production using AI, including analyzing film projects, estimating film box office, analyzing film reviews, and writing abstracts. In the third part, the article lists several cases about audio-visual language using AI systemsi1/4Eincluding classifying films and the aesthetic feature, analyzing image features through AI, changing the appearance and age of actors, and creating visual characters.
- Published
- 2021
10. Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis.
- Author
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Liu, Zhe, Wang, Shuzhe, Zhang, Yudong, Feng, Yichen, Liu, Jiajia, and Zhu, Hengde
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FOOD safety ,BIBLIOMETRICS ,ARTIFICIAL intelligence ,ARTIFICIAL foods ,FOOD science ,COMPUTERS in agriculture - Abstract
Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles. [ABSTRACT FROM AUTHOR]
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- 2023
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11. Medical Big Data and Artificial Intelligence for Healthcare.
- Author
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Zhang, Yudong, Hong, Jin, and Chen, Shuwen
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ARTIFICIAL intelligence ,DEEP learning ,BIG data ,MEDICAL terminology ,MACHINE learning ,MEDICAL care - Abstract
The complexity of AI-related theories may impede the applications of AI to MBD healthcare Deep learning (DL) is currently the most popular AI category. There are roughly six main categories of common AI methods [[21]]: deep learning, machine learning, neural networks, computer vision, robotics, and natural language processing, as shown in Figure 2. Big data have altered the way we manage, explore, evaluate, analyze, and leverage data across many different industries [[1]]. However, most MBD research thus far still focuses on either (i) traditional AI methods [[38]] due to the complexity of AI categories and sub-categories; or (ii) small-sized healthcare datasets involving the expensive collection of healthcare-related samples [[39]]. [Extracted from the article]
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- 2023
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12. Informetric analysis of researches on application of artificial intelligence in COVID-19 prevention and control
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Liu, Zhuozhu, Chen, Sijing, Han, Qing, Zhang, Yudong, Zhang, Yudong, Art and Culture, History, Antiquity, and CLUE+
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Coronavirus disease 2019 (COVID-19) ,SDG 3 - Good Health and Well-being ,Computer science ,business.industry ,Artificial Intelligence ,COVID-19 ,Artificial intelligence ,Control (linguistics) ,business ,Informetric analysis - Abstract
The COVID-19 (2019 novel Coronavirus) is the most widespread pandemic infectious disease encountered in human history. Its economic losses and the number of countries involved rank first in the history of human viruses. Since the outbreak of the COVID-19 pandemic around the world, AI has made a great contribution to the prevention and control of the COVID-19 pandemic. In this paper, researches on the application of artificial intelligence in COVID-19 pandemic prevention and control were analyzed by informetric method. 432 papers indexed in Thomson Reuters's Web of Science were studied by the perspectives of categories of researches, high frequency keywords, authors, institutions, journals and countries, and we get conclusions as follows: The analysis of keywords cooccurence shows application of machine learning and deep learning in COVID-19 pandemic diagnosis and prediction. The journal that received the most cites was (Radiology) and the journal that published the most papers was (Journal of Medical Internet Research). USA, India and China have the largest number of published articles. USA, China and UK are most influential countries. We also analyzed the review literature on the application of AI in COVID-19 pandemic prevention and control in the Web of Science, and found that these papers specifically can be divided into the following three categories: The first is the application of AI in clinical diagnosis and treatment, the second is the application of AI in the development of anti-epidemic drugs, and the third is the role of AI in the epidemiological research of COVID-19 and the social governance of pandemic prevention and control.
- Published
- 2021
13. An intelligent sv-neutrosophic parameterized MCDM approach to risk evaluation based on complex fuzzy hypersoft set for real estate investments.
- Author
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Zhang, Huilong, Zhang, Yudong, Rahman, Atiqe Ur, and Saeed, Muhammad
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REAL estate investment ,MULTIPLE criteria decision making ,RISK assessment ,FUZZY sets ,RESIDENTIAL real estate ,ARTIFICIAL intelligence ,SOFT sets - Abstract
Purpose: In this article, the elementary notions and aggregation operations of single-valued neutrosophic parameterized complex fuzzy hypersoft set (sv-NPCFHSS) are characterized initially. Then by using matrix version of sv-NPCFHSS, a decision-support system is constructed for the evaluation of real estate residential projects by observing various risk factors. Design/methodology/approach: Two approaches are utilized in this research: set-theoretic approach and algorithmic approach. The first approach is used to investigate the notions of sv-NPCFHSS and its some aggregations whereas the second approach is used to propose an algorithm for designing its decision-support system by using the aggregation operations like reduced fuzzy matrix, decision matrix, etc. of sv-NPCFHSS. The adopted algorithm is validated in real estate scenario for the selection of residential project by observing various risk factors to avoid any expected investment loss. Findings: The proposed approach is more flexible and reliable as it copes with the shortcomings of literature on sv-neutrosophic set, sv-neutrosophic soft set and other fuzzy soft set-like structures by considering hypersoft setting, complex setting and neutrosophic setting collectively. Research limitations/implications: It has limitations for complex intuitionistic fuzzy hypersoft set, complex neutrosophic hypersoft set and other complex neutrosophic hypersoft set-like models. Practical implications: The scope of this research may cover a wide range of applications in several fields of mathematical sciences like artificial intelligence, optimization, MCDM, theoretical computer science, soft computing, mathematical statistics etc. Originality/value: The proposed model bears the characteristics of most of the relevant existing fuzzy soft set-like models collectively and fulfills their limitations. [ABSTRACT FROM AUTHOR]
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- 2023
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14. Aerodynamic Intelligent Topology Design (AITD)-A Future Technology for Exploring the New Concept Configuration of Aircraft.
- Author
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Liao, Peng, Song, Wei, Du, Peng, Feng, Feng, and Zhang, Yudong
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TOPOLOGY ,ARTIFICIAL intelligence ,AERODYNAMICS of buildings - Abstract
With the increasing requirements for aerodynamic performance, aerodynamic configuration design of aircraft based on traditional design experience has gradually failed to meet the needs of the future. Therefore, the new concept aerodynamic shape design will be the development trend for future aircraft, but the current new concept aerodynamic shape design is still based on the designer's understanding of the existing flow physics. One novel technology that can be useful is topology design. Compared with traditional design, topology design not only has more undetermined parameters, but also its topology variables have a greater impact on the design goals. In this perspective, we propose the concept of Artificial Intelligent Topology Design (AITD) for aerodynamic configuration design based on topology design and artificial intelligence technology and discuss its potential in the application of the new concept of aerodynamic configuration design. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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15. Detection of Algorithmically Generated Domain Names Using SMOTE and Hybrid Neural Network
- Author
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Zhang Yankun, Zhang Yudong, Yangyang Lin, and Chen Yuzhong
- Subjects
Artificial neural network ,business.industry ,Computer science ,Botnet ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,computer.software_genre ,Convolutional neural network ,Hybrid neural network ,Sampling distribution ,Robustness (computer science) ,0202 electrical engineering, electronic engineering, information engineering ,Malware ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer ,Classifier (UML) - Abstract
Domain generation algorithms (DGA) provide methods that use specific parameters as random seeds to generate a large number of random domain names for preventing malicious domain name detection, which greatly increases the difficulty of detecting and defending botnets and malware. State-of-the-art models for detecting algorithmically generated domain names are generally based on the principle of analyzing the statistical characteristics of the domain name and building a classifier to locate the algorithmically generated ones. However, most current models have problems of requiring the manual construction of feature sets for classification, as they are sensitive to the imbalance of the sample distribution in the domain name dataset and are difficult to adapt to frequent changes of the domain-name algorithm. To address this issue, we propose a hybrid model that combines a convolutional neural network (CNN) and a bidirectional long-term memory network (BLSTM). First, to solve the problem of the number of domain names generated by DGAs being relatively small and the sample distribution being unbalanced, which consequently decreases detection accuracy, the borderline synthetic minority over-sampling technique is employed to optimize the sample balance of the domain name dataset. Second, a hybrid deep neural network that combines CNN and BLSTM is introduced to extract the semantic and context-dependency features from the domain names. The experimental results from different domain-name datasets demonstrate that the proposed model achieves significant improvement over state-of-the-art models with regard to precision and robustness.
- Published
- 2019
16. Deep learning in food category recognition.
- Author
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Zhang, Yudong, Deng, Lijia, Zhu, Hengde, Wang, Wei, Ren, Zeyu, Zhou, Qinghua, Lu, Siyuan, Sun, Shiting, Zhu, Ziquan, Gorriz, Juan Manuel, and Wang, Shuihua
- Subjects
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DEEP learning , *SUPERVISED learning , *MACHINE learning , *ARTIFICIAL intelligence , *DATA augmentation , *CONVOLUTIONAL neural networks - Abstract
• We analysed over 350 references from all well-famed databases. • We provided a comprehensive survey on deep learning in food category recognition. • This review encompassed current challenges & applications, strengths & limitations. • Fundamental deep learning rules and performance assessment methods were reviewed. • Convolutional neural networks, transfer and semi-supervised learning were reviewed. Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach's potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applications. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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17. A Fruit Sensing and Classification System by Fractional Fourier Entropy and Improved Hybrid Genetic Algorithm
- Author
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LU Zhihai, Zhang Yudong, Wang Shuihua, LU Siyuan, Lu Huimin, and Li Yujie
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Engineering ,business.industry ,Pattern recognition ,computer.software_genre ,Fractional Fourier transform ,symbols.namesake ,Fourier transform ,Multilayer perceptron ,symbols ,Entropy (information theory) ,Artificial intelligence ,Data mining ,business ,computer - Abstract
It remains a challenge to classify different categories of fruits because of the similarities of shape, color, and texture among them. We presented a novel approach in order to classify fruits accurately and efficiently based on computer vision techniques. We obtained the coefficients using fractional Fourier transform. The entropies extracted from the coefficients were fed into the classifier as the features. A multilayer perceptron optimized by an improved hybrid genetic algorithm was used as the classifier. The experiment results on 1653 fruit images demonstrated that the proposed method achieved an overall accuracy of 89.59%, which was superior to the state-of-the art approaches. Our method is effective in identifying fruit cagegories.
- Published
- 2017
18. Fast MPEG-2 to H.264 Transcoding for Intra Frame in Transform Domain
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Wang Shuihua, Zhang Yudong, Yang Guang, and Wei Geng
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Computer Networks and Communications ,Computer science ,business.industry ,computer.file_format ,Transcoding ,computer.software_genre ,Intra-frame ,Domain (software engineering) ,MPEG-2 ,Computer vision ,Artificial intelligence ,business ,computer ,Software - Published
- 2013
19. An Automatic Defocus Compensation Method for Human Eye Hartmann-Shark Wave-Front Aberrometer
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Rao Xunjun, Dai Yun, Bao Hua, Yu Xiang, Zhang Yudong, and Rao Changhui
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Wavefront ,Optics ,medicine.anatomical_structure ,business.industry ,Computer science ,medicine ,Computer vision ,Human eye ,Artificial intelligence ,business ,Atomic and Molecular Physics, and Optics ,Electronic, Optical and Magnetic Materials ,Compensation (engineering) - Published
- 2010
20. Special issue on intelligent software engineering.
- Author
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Gao, Honghao, Zhang, Yudong, and Hussain, Walayat
- Subjects
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SOFTWARE engineers , *WORKFLOW management systems , *GENERATIVE adversarial networks , *ARTIFICIAL intelligence , *CONVOLUTIONAL neural networks - Abstract
It relies on the synergy between artificial intelligence (AI) and software engineering, improving both software productivity and quality, for example, providing coding suggestions for better productivity or locating defects to avoid poor quality. Intelligent software engineering is an emerging topic that has attracted great attention in both academia and industry. The first paper, titled 'Syntax-based metamorphic relation prediction via the bagging framework' by Li et al., proposes a multi-dimensional program structure-based metamorphic relation prediction approach, which is composed of feature extraction and prediction model building. [Extracted from the article]
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- 2022
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21. A Privacy-Preserving Intelligent Medical Diagnosis System Based on Oblivious Keyword Search.
- Author
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Lin, Zhaowen, Xiao, Xinglin, Sun, Yi, Zhang, Yudong, and Ma, Yan
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MEDICAL technology ,ARTIFICIAL intelligence ,CRYPTOSYSTEMS ,DATA security ,INFORMATION sharing - Abstract
One of the concerns people have is how to get the diagnosis online without privacy being jeopardized. In this paper, we propose a privacy-preserving intelligent medical diagnosis system (IMDS), which can efficiently solve the problem. In IMDS, users submit their health examination parameters to the server in a protected form; this submitting process is based on Paillier cryptosystem and will not reveal any information about their data. And then the server retrieves the most likely disease (or multiple diseases) from the database and returns it to the users. In the above search process, we use the oblivious keyword search (OKS) as a basic framework, which makes the server maintain the computational ability but cannot learn any personal information over the data of users. Besides, this paper also provides a preprocessing method for data stored in the server, to make our protocol more efficient. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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22. Multi-resolution rigid image registration using bacterial multiple colony chemotaxis
- Author
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Zhang Yudong and Wu Lenan
- Subjects
Fitness function ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image registration ,Pattern recognition ,Mutual information ,Biology ,Quantitative Biology::Cell Behavior ,Image (mathematics) ,Maxima and minima ,Wavelet ,Rate of convergence ,Computer vision ,Artificial intelligence ,business ,Interpolation - Abstract
Keyword: rigid image registration, mutual information, bacteria chemotaxis, bacteria multiple colony chemotaxis Abstract A novel approach for image registration based on mutual information is presented in this paper. Firstly, normalized mutual information is introduced as the fitness function to supersede the traditional mutual information. Meanwhile, the extrema in fitness function resulted from interpolation is analyzed and the solution is presented as adding a weighted parameter into the variables set. Secondly, search model is improved. The 2 nd generation wavelet is utilized to decompose the original image into multi-resolution sub-images to search optimal solution in different precision layer, which harmonizes the contradiction between search precision and convergence rate. Finally, we introduce a novel optimization means—bacterial multiple colony chemotaxis. Experiments demonstrate the legitimacy and efficiency of our betterment.
- Published
- 2008
23. The Theoretical Analysis and Simulation Research of Segmented Telescope Image Systems
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张雨东 Zhang Yudong, 邱琪 Qiu Qi, 廖周 Liao Zhou, and 刘超 Liu Chao
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Telescope ,Physics ,law ,business.industry ,Computer vision ,Artificial intelligence ,business ,Atomic and Molecular Physics, and Optics ,law.invention ,Image (mathematics) - Published
- 2014
24. Adaptive optics optical coherence tomography for retina imaging
- Author
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Guohua Shi Guohua Shi, Yun Dai Yun Dai, Ling Wang Ling Wang, Zhihua Ding Zhihua Ding, Xuejun Rao Xuejun Rao, and Yudong Zhang Yudong Zhang
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Physics ,Retina ,genetic structures ,medicine.diagnostic_test ,business.industry ,Resolution (electron density) ,Lateral resolution ,Frame rate ,eye diseases ,Atomic and Molecular Physics, and Optics ,Electronic, Optical and Magnetic Materials ,Optics ,medicine.anatomical_structure ,Optical coherence tomography ,medicine ,Computer vision ,sense organs ,Time domain ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Adaptive optics ,Closed loop - Abstract
When optical coherence tomography (OCT) is used for human retina imaging, its transverse resolution is limited by the aberrations of human eyes. To overcome this disadvantage, a high resolution imaging system for living human retina, which consists of a time domain OCT system and a 37-elements adaptive optics (AO) system, has been developed. The AO closed loop rate is 20 frames per second, and the OCT has a 6.7-μm axial resolution. In this paper, this system is introduced and the high resolution imaging results for retina are presented.
- Published
- 2008
25. Human Eye Neural Contrast Sensitivity Function Measurement with Wave-Front Technology
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赵豪欣 Zhao Haoxin, 张雨东 Zhang Yudong, 戴云 Dai Yun, and 周逸峰 Zhou Yifeng
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Physics ,Wavefront ,business.industry ,media_common.quotation_subject ,Atomic and Molecular Physics, and Optics ,Electronic, Optical and Magnetic Materials ,medicine.anatomical_structure ,medicine ,Contrast (vision) ,Computer vision ,Human eye ,Artificial intelligence ,business ,media_common - Published
- 2012
26. Artificial Intelligence and Its Applications 2014.
- Author
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Zhang, Yudong, Balochian, Saeed, Agarwal, Praveen, Bhatnagar, Vishal, and Housheya, Orwa Jaber
- Subjects
- *
ARTIFICIAL intelligence , *INFORMATION technology , *PARTICLE swarm optimization , *GENETIC algorithms , *SOFTWARE frameworks - Published
- 2016
- Full Text
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27. A note on the weight of inverse complexity in improved hybrid genetic algorithm.
- Author
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Lu, Siyuan, Wang, Shuihua, and Zhang, Yudong
- Subjects
ALGORITHMS ,ARTIFICIAL intelligence ,DIAGNOSIS ,ARTIFICIAL neural networks - Abstract
The article present the authors' comment on the study "Intelligent medical disease diagnosis using improved hybrid genetic algorithm-multilayer perception network," by F. Ahmad and colleagues, published in a 2013 issue of the "Journal of Medical Systems." Topics discussed include the utilization of machine learning and computer vision in the development of the diagnosis system, corrected form to equation two, and possible applications of the results.
- Published
- 2016
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28. A Change Detection with Machine Learning Approach for Medical Image Analysis
- Author
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Mazzei, Mauro, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Su, Ruidan, editor, Zhang, Yudong, editor, Liu, Han, editor, and F Frangi, Alejandro, editor
- Published
- 2023
- Full Text
- View/download PDF
29. Artificial intelligence for visually impaired.
- Author
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Wang, Jiaji, Wang, Shuihua, and Zhang, Yudong
- Subjects
- *
ARTIFICIAL intelligence , *PEOPLE with visual disabilities , *VISION disorders , *DEEP learning , *EYE diseases , *DIGITAL technology - Abstract
• This paper summarizes 181 studies on artificial intelligence used to help the visually impaired. • DL can help diagnose eye diseases, including cataracts, diabetic retinopathy, and glaucoma. • DL can help build assisted reading and obstacle avoidance systems. The eyes are an essential tool for human observation and perception of the world, helping people to perform their tasks. Visual impairment causes many inconveniences in the lives of visually impaired people. Therefore, it is necessary to focus on the needs of the visually impaired community. Researchers work from different angles to help visually impaired people live normal lives. The advent of the digital age has profoundly changed the lives of the visually impaired community, making life more convenient. Deep learning, as a promising technology, is also expected to improve the lives of visually impaired people. It is increasingly being used in the diagnosis of eye diseases and the development of visual aids. The earlier accurate diagnosis of the eye disease by the doctor, the sooner the patient can receive the appropriate treatment and the better chances of a cure. This paper summarises recent research on the development of artificial intelligence-based eye disease diagnosis and visual aids. The research is divided according to the purpose of the study into deep learning methods applied in diagnosing eye diseases and smart devices to help visually impaired people in their daily lives. Finally, a summary is given of the directions in which artificial intelligence may be able to assist the visually impaired in the future. In addition, this overview provides some knowledge about deep learning for beginners. We hope this paper will inspire future work on the subjects.. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
30. Online Matching Method of News Communication Innovative Teaching Mode Driven by Artificial Intelligence
- Author
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Qian, Jia, Wang, Li-li, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Fu, Weina, editor, Xu, Yuan, editor, Wang, Shui-Hua, editor, and Zhang, Yudong, editor
- Published
- 2021
- Full Text
- View/download PDF
31. Design of Advance Security Early Warning System for Network Data Based on Artificial Intelligence
- Author
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Wang, Ya-fei, He, Wei- na, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Fu, Weina, editor, Xu, Yuan, editor, Wang, Shui-Hua, editor, and Zhang, Yudong, editor
- Published
- 2021
- Full Text
- View/download PDF
32. Research on Network Information Security Risk Assessment Based on Artificial Intelligence
- Author
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Wang, Ya-fei, He, Wei-na, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Fu, Weina, editor, Xu, Yuan, editor, Wang, Shui-Hua, editor, and Zhang, Yudong, editor
- Published
- 2021
- Full Text
- View/download PDF
33. Research on Grid Planning Method of Distribution Network Based on Artificial Intelligence Technology
- Author
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Guan-hua, Fu, Da-xing, Chen, Yang, Sun, Jia, Xia, Fei-feng, Wang, Lian-huan, Zhu, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Fu, Weina, editor, Xu, Yuan, editor, Wang, Shui-Hua, editor, and Zhang, Yudong, editor
- Published
- 2021
- Full Text
- View/download PDF
34. A review of deep learning in dentistry.
- Author
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Huang, Chenxi, Wang, Jiaji, Wang, Shuihua, and Zhang, Yudong
- Subjects
- *
DEEP learning , *MACHINE learning , *ARTIFICIAL intelligence , *DENTISTRY , *IMAGE segmentation , *IMAGE recognition (Computer vision) - Abstract
• More than 245 references from top journals are analyzed. • A systematic theoretical analysis of deep learning in dentistry is provided. • Extensive works on deep learning systems of dentistry are well summarized. • Future challenges of deep learning methods in dentistry are analyzed. Oral diseases have a significant impact on human health, often going unnoticed in their early stages. Deep learning, a promising field in artificial intelligence, has shown remarkable success in various domains, especially dentistry. This paper aims to provide an overview of recent research on deep learning applications in dentistry, with a focus on dental imaging. Deep learning algorithms perform well in difficult tasks such as image segmentation and recognition, enabling accurate identification of oral conditions and abnormalities. Integration of deep learning with other oral health data offers a holistic understanding of the relationship between oral and systemic health. However, there are still many challenges that need to be addressed. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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