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学者姓名：李建强
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Abstract ：
Hyperspectral image (HSI) classification attempts to classify each pixel, which is an important means of obtaining land-cover knowledge. HSIs are cubic data with spectral-spatial knowledge and can generally be considered as sequential data alongside spectral dimension. Unlike convolutional neural networks (CNNs), which mainly focus on local relationship models in images, transformers have been shown to be a powerful structure for qualifying sequence data. However, it lacks the excellent ability of CNNs in establishing local relationships in images and cannot perform good generalization in the case of insufficient data. In addition, the gradient disappearance problem hinders the convergence stability of deep learning networks as the layers get deeper. To address these problems, we propose a Cascaded Convolution-based Transformer with Densely Connected Mechanism (CDCformer) for HSI classification. First, we propose a cascaded convolution feature tokenization to extract spectral-spatial information, which will introduce some inductive bias properties of CNN into the transformer. In addition, we design a simple and effective densely connected transformer to enhance feature propagation and transfer memorable information from shallow to deep layers. It efficiently improves the performance of the transformer and extracts more discriminative spectral-spatial features from the HSI. Extensive experimental evaluation of three public hyperspectral datasets shows that CDCformer achieves competitive classification results.
Keyword ：
Data mining Data mining convolutional neural networks (CNNs) convolutional neural networks (CNNs) Convolution Convolution transformer transformer Hyperspectral imaging Hyperspectral imaging Transformers Transformers Tokenization Tokenization Convolutional neural networks Convolutional neural networks hyperspectral image (HSI) classification hyperspectral image (HSI) classification Feature extraction Feature extraction Cascaded convolution feature tokenization (CCFT) Cascaded convolution feature tokenization (CCFT) densely connected mechanism (DCM) densely connected mechanism (DCM) deep learning deep learning
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| GB/T 7714 | Zu, Baokai , Li, Yafang , Li, Jianqiang et al. Cascaded Convolution-Based Transformer With Densely Connected Mechanism for Spectral-Spatial Hyperspectral Image Classification [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
| MLA | Zu, Baokai et al. "Cascaded Convolution-Based Transformer With Densely Connected Mechanism for Spectral-Spatial Hyperspectral Image Classification" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61 (2023) . |
| APA | Zu, Baokai , Li, Yafang , Li, Jianqiang , He, Ziping , Wang, Hongyuan , Wu, Panpan . Cascaded Convolution-Based Transformer With Densely Connected Mechanism for Spectral-Spatial Hyperspectral Image Classification . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
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Abstract ：
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.
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| GB/T 7714 | Iqbal, Saeed , Qureshi, Adnan N. , Li, Jianqiang et al. On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks [J]. | ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING , 2023 , 30 (5) : 3173-3233 . |
| MLA | Iqbal, Saeed et al. "On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks" . | ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 30 . 5 (2023) : 3173-3233 . |
| APA | Iqbal, Saeed , Qureshi, Adnan N. , Li, Jianqiang , Mahmood, Tariq . On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks . | ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING , 2023 , 30 (5) , 3173-3233 . |
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Abstract ：
This paper explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment. Smart cities leverage technology and data to improve the quality of life for citizens and enhance the efficiency of urban services. IoT and machine learning have emerged as key technologies for enabling smart city solutions that rely on large-scale data collection, analysis, and decision-making. This paper presents an overview of smart cities' various applications and discusses the challenges associated with implementing IoT and machine learning in urban environments. The paper also compares different case studies of successful smart city implementations utilizing IoT and machine learning technologies. The findings suggest that these technologies have the potential to transform urban environments and enable the creation of more livable, sustainable, and efficient cities. However, significant challenges remain regarding data privacy, security, and ethical considerations, which must be addressed to realize the full potential of smart cities.
Keyword ：
Data acquisition technology Data acquisition technology Wireless and mobile networking Wireless and mobile networking Deep learning Deep learning Internet of Things (IoT) Internet of Things (IoT) Applications of smart cities Applications of smart cities Smart cities Smart cities
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| GB/T 7714 | Ullah, Amin , Anwar, Syed Myhammad , Li, Jianqiang et al. Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment [J]. | COMPLEX & INTELLIGENT SYSTEMS , 2023 , 10 (1) : 1607-1637 . |
| MLA | Ullah, Amin et al. "Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment" . | COMPLEX & INTELLIGENT SYSTEMS 10 . 1 (2023) : 1607-1637 . |
| APA | Ullah, Amin , Anwar, Syed Myhammad , Li, Jianqiang , Nadeem, Lubna , Mahmood, Tariq , Rehman, Amjad et al. Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment . | COMPLEX & INTELLIGENT SYSTEMS , 2023 , 10 (1) , 1607-1637 . |
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Abstract ：
The effective classification of pollen is a critical method to the prevention of pollen allergy. The conventional pollen classification primarily relies on manual handling under the microscope, which does not only require a lot of manpower but also has low classicization accuracy on results. In this paper, the optical microscope was used to scan the slides and the pollen image dataset was made for classification. We found from the pollen dataset that most of the images have clear contours, but there were also many pollen images with the following problems. One is that the pollen was covered by impurities like dust and pebbles, and the other is that the pollen images stain unevenly and the pollen image was blurred and the number of pollens was unbalanced. This paper proposes a Dual-Channel Attention method, we call it DCANet, which combines the advantages of channel attention and channel self-attention to improve the classification accuracy. The results of classification activation map analysis showed that DCANet paid more attention to pollen images.
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| GB/T 7714 | Li, Yanan , Li, Jianqiang , Pei, Yan et al. A Dual-channel Attention Model for Optical Microscope Pollen Classification [J]. | 2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22) , 2022 : 1118-1123 . |
| MLA | Li, Yanan et al. "A Dual-channel Attention Model for Optical Microscope Pollen Classification" . | 2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22) (2022) : 1118-1123 . |
| APA | Li, Yanan , Li, Jianqiang , Pei, Yan , Wang, Jin . A Dual-channel Attention Model for Optical Microscope Pollen Classification . | 2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22) , 2022 , 1118-1123 . |
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Abstract ：
Simple Summary Breast cancer is leading cancer increases the death rate in women. Early diagnosis of breast cancer in women can save their lives. The current study proposed a novel scheme to detect architectural distortion from mammogram images to predict breast cancer using a deep learning approach. Results are evaluated on a public and a private dataset which may help to improve the diagnostic ability of breast cancer of radiologists and doctors in daily clinical routines. Furthermore, the proposed method achieved maximum accuracy as compared with previous approaches. This study can be interesting and valuable in the healthcare predictive modeling domain and will add a real contribution to society. Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI's detection, training deep learning, and machine learning networks to classify AD's ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies.
Keyword ：
image processing image processing depth-wise convolutional neural network depth-wise convolutional neural network mammography mammography architectural distortion architectural distortion breast cancer breast cancer
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| GB/T 7714 | Rehman, Khalil ur , Li, Jianqiang , Pei, Yan et al. Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network [J]. | BIOLOGY-BASEL , 2022 , 11 (1) . |
| MLA | Rehman, Khalil ur et al. "Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network" . | BIOLOGY-BASEL 11 . 1 (2022) . |
| APA | Rehman, Khalil ur , Li, Jianqiang , Pei, Yan , Yasin, Anaa , Ali, Saqib , Saeed, Yousaf . Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network . | BIOLOGY-BASEL , 2022 , 11 (1) . |
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Abstract ：
As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases.
Keyword ：
monitoring and early warning monitoring and early warning public opinion analysis public opinion analysis COVID-19 COVID-19 forecasting of influenza-like illnesses forecasting of influenza-like illnesses
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| GB/T 7714 | Wei, Yu-Chih , Ou, Yan-Ling , Li, Jianqiang et al. Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion [J]. | SUSTAINABILITY , 2022 , 14 (5) . |
| MLA | Wei, Yu-Chih et al. "Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion" . | SUSTAINABILITY 14 . 5 (2022) . |
| APA | Wei, Yu-Chih , Ou, Yan-Ling , Li, Jianqiang , Wu, Wei-Chen . Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion . | SUSTAINABILITY , 2022 , 14 (5) . |
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Abstract ：
Deep learning is an obvious method for the detection of disease, analyzing medical images and many researchers have looked into it. However, the performance of deep learning algorithms is frequently influenced by hyperparameter selection, the question of which combination of hyperparameters are best emerges. To address this challenge, we proposed a novel algorithm for Adaptive Hyperparameter Tuning (AHT) that automates the selection of optimal hyperparameters for Convolutional Neural Network (CNN) training. All of the optimal hyperparameters for the CNN models were instantaneously selected and allocated using a novel proposed algorithm Adaptive Hyperparameter Tuning (AHT). Using AHT, enables CNN models to be highly autonomous to choose optimal hyperparameters for classifying medical images into various classifications. The CNN model (Deep-Hist) categorizes medical images into basic classes: malignant and benign, with an accuracy of 95.71%. The most dominant CNN models such as ResNet, DenseNet, and MobileNetV2 are all compared to the already proposed CNN model (Deep-Hist). Plausible classification results were obtained using large, publicly available clinical datasets such as BreakHis, BraTS, NIH-Xray and COVID-19 X-ray. Medical practitioners and clinicians can utilize the CNN model to corroborate their first malignant and benign classification assessment. The recommended Adaptive high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist's aid tool.
Keyword ：
hyperparameter hyperparameter convolutional neural network convolutional neural network adaptive hyperparameter tuning adaptive hyperparameter tuning optimization optimization random search random search grid search grid search
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| GB/T 7714 | Iqbal, Saeed , Qureshi, Adnan N. , Ullah, Amin et al. Improving the Robustness and Quality of Biomedical CNN Models through Adaptive Hyperparameter Tuning [J]. | APPLIED SCIENCES-BASEL , 2022 , 12 (22) . |
| MLA | Iqbal, Saeed et al. "Improving the Robustness and Quality of Biomedical CNN Models through Adaptive Hyperparameter Tuning" . | APPLIED SCIENCES-BASEL 12 . 22 (2022) . |
| APA | Iqbal, Saeed , Qureshi, Adnan N. , Ullah, Amin , Li, Jianqiang , Mahmood, Tariq . Improving the Robustness and Quality of Biomedical CNN Models through Adaptive Hyperparameter Tuning . | APPLIED SCIENCES-BASEL , 2022 , 12 (22) . |
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Abstract ：
The goal of biomedical relation extraction is to obtain structured information from electronic medical records by identifying relations among clinical entities. By integrating the advantages of unsupervised and semi-supervised learning, the distant supervision approach has achieved significant success for a relation extraction task without a large amount of labeled corpora. However, in many cases, the recognized entities from the Chinese clinical text are not defined in semantic knowledge base, which limits the application of distant supervision for biomedical relation extraction. This work proposes a Knowledge Guided Distance Supervision (KGDS) model for handling the biomedical relation extraction task in Chinese electronic medical records. To handle the unknown entities, entity-type alignment (instead of entity alignment in traditional distant supervision) is employed for extracting coarse-grained relations. Then, by learning the relation embeddings both from semantic knowledge base and electronic medical record dataset as knowledge-enhanced features, this work presents a knowledge-enhanced bootstrapping learning process for fine-grained relation disambiguation. The empirical experiments on the real-world dataset of electronic medical records illustrate that our KGDS model achieves the best performance comparing to other state-of-the-art models, thereby advancing the field of biomedical relation extraction from Chinese electronic medical records.
Keyword ：
Distant supervision Distant supervision Relation embedding Relation embedding Entity-type alignment Entity-type alignment Biomedical relation extraction Biomedical relation extraction
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| GB/T 7714 | Zhao, Qing , Xu, Dezhong , Li, Jianqiang et al. Knowledge guided distance supervision for biomedical relation extraction in Chinese electronic medical records [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2022 , 204 . |
| MLA | Zhao, Qing et al. "Knowledge guided distance supervision for biomedical relation extraction in Chinese electronic medical records" . | EXPERT SYSTEMS WITH APPLICATIONS 204 (2022) . |
| APA | Zhao, Qing , Xu, Dezhong , Li, Jianqiang , Zhao, Linna , Rajput, Faheem Akhtar . Knowledge guided distance supervision for biomedical relation extraction in Chinese electronic medical records . | EXPERT SYSTEMS WITH APPLICATIONS , 2022 , 204 . |
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Abstract ：
Breast cancer is the 2nd leading cancer of death among women around the world. In Asia and Africa due to low income, the mortality rates are very high as compared to Europe and America. Initially, image interpretation is manually conducted by the radiologist and physicians that requires expertise; thus, the computer-aided diagnostic is necessary to enhance the accuracy of cancer diagnostics in mammograms at early stages. To overcome human error computer-aided system was developed based on machine learning and deep learning algorithm to process medical images with efficient accuracy for the diagnosis of cancer and assist the physician for better decisions making. This research aims to present the state-of-the-art machine learning techniques for the detection of breast cancer, and critically analysis of the current literature in this area to identify the research gap. There are many studies presented in the literature to achieve similar goals. The main difference between these studies and this review is that this paper is more focused on those modalities that can figure out breast composition, mass, density, calcification, and architectural distortion. This study includes a summary of 110 papers, pointing out which techniques are applied for image preprocessing and classification, which method is implemented for the detection of breast density, mass, and calcification from mammogram images. Furthermore, we critically analyzed the performance measuring parameters for the evaluation of results and the datasets that have been used for experiments. Another focus in this review is to assess the modalities and features that can be helpful for the assessment of grading in mammogram images.
Keyword ：
Cancer prediction Cancer prediction Classification Classification Machine learning Machine learning Image preprocessing Image preprocessing Mammogram grading Mammogram grading Breast cancer Breast cancer
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| GB/T 7714 | Rehman, Khalil Ur , Li, Jianqiang , Pei, Yan et al. A review on machine learning techniques for the assessment of image grading in breast mammogram [J]. | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2022 , 13 (9) : 2609-2635 . |
| MLA | Rehman, Khalil Ur et al. "A review on machine learning techniques for the assessment of image grading in breast mammogram" . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 13 . 9 (2022) : 2609-2635 . |
| APA | Rehman, Khalil Ur , Li, Jianqiang , Pei, Yan , Yasin, Anaa . A review on machine learning techniques for the assessment of image grading in breast mammogram . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2022 , 13 (9) , 2609-2635 . |
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Abstract ：
Purpose: Computed tomography (CT) has the advantages of being low cost and noninvasive and is a primary diagnostic method for brain diseases. However, it is a challenge for junior radiologists to diagnose CT images accurately and comprehensively. It is necessary to build a system that can help doctors diagnose and provide an explanation of the predictions. Despite the success of deep learning algorithms in the field of medical image analysis, the task of brain disease classification still faces challenges: Researchers lack attention to complex manual labeling requirements and the incompleteness of prediction explanations. More importantly, most studies only measure the performance of the algorithm, but do not measure the effectiveness of the algorithm in the actual diagnosis of doctors. Methods: In this paper, we propose a model called DrCT2 that can detect brain diseases without using image-level labels and provide a more comprehensive explanation at both the slice and sequence levels. This model achieves reliable performance by imitating human expert reading habits: targeted scaling of primary images from the full slice scans and observation of suspicious lesions for diagnosis. We evaluated our model on two open-access data sets: CQ500 and the RSNA Intracranial Hemorrhage Detection Challenge. In addition, we defined three tasks to comprehensively evaluate model interpretability by measuring whether the algorithm can select key images with lesions. To verify the algorithm from the perspective of practical application, three junior radiologists were invited to participate in the experiments, comparing the effects before and after human-computer cooperation in different aspects. Results: The method achieved F1-scores of 0.9370 on CQ500 and 0.8700 on the RSNA data set. The results show that our model has good interpretability under the premise of good performance. Human radiologist evaluation experiments have proven that our model can effectively improve the accuracy of the diagnosis and improve efficiency. Conclusions: We proposed a model that can simultaneously detect multiple brain diseases. The report generated by the model can assist doctors in avoiding missed diagnoses, and it has good clinical application value.
Keyword ：
human-Al interaction human-Al interaction interpretability interpretability attention mechanism attention mechanism medical image classification medical image classification
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| GB/T 7714 | Wang, Ruiqian , Fu, Guanghui , Li, Jianqiang et al. Diagnosis after zooming in: A multilabel classification model by imitating doctor reading habits to diagnose brain diseases [J]. | MEDICAL PHYSICS , 2022 , 49 (11) : 7054-7070 . |
| MLA | Wang, Ruiqian et al. "Diagnosis after zooming in: A multilabel classification model by imitating doctor reading habits to diagnose brain diseases" . | MEDICAL PHYSICS 49 . 11 (2022) : 7054-7070 . |
| APA | Wang, Ruiqian , Fu, Guanghui , Li, Jianqiang , Pei, Yan . Diagnosis after zooming in: A multilabel classification model by imitating doctor reading habits to diagnose brain diseases . | MEDICAL PHYSICS , 2022 , 49 (11) , 7054-7070 . |
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