@inproceedings{barzinji_machine_2021, address = {Zhuhai, China}, title = {A {Machine} {Learning} {Approach} to {Predict} the {Trend} of {Obesity} {Prevalence} at a {Global} {Level}}, isbn = {9781728176819}, url = {https://ieeexplore.ieee.org/document/9581579/}, doi = {10.1109/BCD51206.2021.9581579}, urldate = {2023-04-26}, booktitle = {2021 {IEEE}/{ACIS} 6th {International} {Conference} on {Big} {Data}, {Cloud} {Computing}, and {Data} {Science} ({BCD})}, publisher = {IEEE}, author = {Barzinji, Ala Othman and Ma, Chaoying and Du, Wencai and Ma, Jixin}, month = sep, year = {2021}, pages = {25--30}, } @incollection{lee_noise_2023, address = {Cham}, title = {Noise {Detection} and {Classification} in {Chagasic} {ECG} {Signals} {Based} on {One}-{Dimensional} {Convolutional} {Neural} {Networks}}, volume = {1055}, isbn = {9783031121265 9783031121272}, url = {https://link.springer.com/10.1007/978-3-031-12127-2_8}, language = {en}, urldate = {2023-04-26}, booktitle = {Computer and {Information} {Science}}, publisher = {Springer International Publishing}, author = {Caldas, Weslley Lioba and Do Vale Madeiro, João Paulo and Pedrosa, Roberto Coury and Gomes, João Paulo Pordeus and Du, Wencai and Marques, João Alexandre Lobo}, editor = {Lee, Roger}, year = {2023}, doi = {10.1007/978-3-031-12127-2_8}, pages = {117--129}, } @inproceedings{du_exploring_2023, title = {Exploring the {Current} {Landscape} and {Future} {Directions} of {Information} {Technology} in {Dance} {Education}}, url = {https://ieeexplore.ieee.org/document/10224030}, doi = {10.1109/SNPD-Winter57765.2023.10224030}, abstract = {Dance education has undergone significant changes with the integration of information technology. Traditional dance pedagogy is now complemented by innovative digital software tools and applications. This work surveys the diverse applications of information technology in dance education at college or university level and the impact it has on teaching and learning processes. We discuss the integration of technology in various aspects of dance education, including skill development, choreography, performance analysis, VR/AR, online virtual learning, and collaborative learning. Additionally, the benefits and challenges associated with the use of information technology are also examined and the future research directions for research and practice in this field are proposed.}, urldate = {2024-02-27}, booktitle = {2023 26th {ACIS} {International} {Winter} {Conference} on {Software} {Engineering}, {Artificial} {Intelligence}, {Networking} and {Parallel}/{Distributed} {Computing} ({SNPD}-{Winter})}, author = {Du, Wencai and Chen, Jiao and Xu, Simon}, month = jul, year = {2023}, keywords = {College and University Education, Dance Education, Education, Federated learning, Humanities, Information Technology, Interactive, Surveys, Technical requirements, Training, Visualization, applications}, pages = {120--126}, } @incollection{lee_malaria_2023, address = {Cham}, title = {Malaria {Blood} {Smears} {Object} {Detection} {Based} on {Convolutional} {DCGAN} and {CNN} {Deep} {Learning} {Architectures}}, volume = {1055}, isbn = {9783031121265 9783031121272}, url = {https://link.springer.com/10.1007/978-3-031-12127-2_14}, language = {en}, urldate = {2023-04-26}, booktitle = {Computer and {Information} {Science}}, publisher = {Springer International Publishing}, author = {Gois, Francisco Nauber Bernardo and Marques, João Alexandre Lobo and De Oliveira Dantas, Allberson Bruno and Santos, Márcio Costa and Neto, José Valdir Santiago and De Macêdo, José Antônio Fernandes and Du, Wencai and Li, Ye}, editor = {Lee, Roger}, year = {2023}, doi = {10.1007/978-3-031-12127-2_14}, pages = {197--212}, } @inproceedings{han_research_2021, address = {Zhuhai, China}, title = {Research on {Quality} {Control} of {Intelligent} {Injection} {Molding} {System} {Based} on {Process} {Monitoring}}, isbn = {9781665402729}, url = {https://ieeexplore.ieee.org/document/9743218/}, doi = {10.1109/CSII54342.2021.00013}, urldate = {2023-04-26}, booktitle = {2021 8th {International} {Conference} on {Computational} {Science}/{Intelligence} and {Applied} {Informatics} ({CSII})}, publisher = {IEEE}, author = {Han, Wang and Shasha, Lv and Shaobin, Li and Wencai, Du and Qinggu, Li and Bo, Wang}, month = sep, year = {2021}, pages = {1--6}, } @incollection{hao_dcrc_2023, address = {Cham}, series = {Studies in {Computational} {Intelligence}}, title = {A {DCRC} {Model} for {Text} {Classification}}, isbn = {978-3-031-12127-2}, url = {https://doi.org/10.1007/978-3-031-12127-2_6}, abstract = {Traditional text classification models have some drawbacks, such as the inability of the model to focus on important parts of the text contextual information in text processing. To solve this problem, we fuse the long and short-term memory network BiGRU with a convolutional neural network to receive text sequence input to reduce the dimensionality of the input sequence and to reduce the loss of text features based on the length and context dependency of the input text sequence. Considering the extraction of important features of the text, we choose the long and short-term memory network BiLSTM to capture the main features of the text and thus reduce the loss of features. Finally, we propose a BiGRU-CNN-BiLSTM model (DCRC model) based on CNN, GRU and LSTM, which is trained and validated on the THUCNews and Toutiao News datasets. The model outperformed the traditional model in terms of accuracy, recall and F1 score after experimental comparison.}, language = {en}, urldate = {2023-04-11}, booktitle = {Computer and {Information} {Science}}, publisher = {Springer International Publishing}, author = {Hao, Zhaoquan and Jin, Jiangyong and Liang, Shengbin and Cheng, Suying and Shen, Yanqing}, editor = {Lee, Roger}, year = {2023}, doi = {10.1007/978-3-031-12127-2_6}, keywords = {BiGRU, BiLSTM, CNN, Text classification}, pages = {85--99}, } @article{he_data_2021, title = {Data {Augmentation} for {Deep} {Neural} {Networks} {Model} in {EEG} {Classification} {Task}: {A} {Review}}, volume = {15}, issn = {1662-5161}, shorttitle = {Data {Augmentation} for {Deep} {Neural} {Networks} {Model} in {EEG} {Classification} {Task}}, url = {https://www.frontiersin.org/articles/10.3389/fnhum.2021.765525/full}, doi = {10.3389/fnhum.2021.765525}, abstract = {Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.}, urldate = {2022-04-28}, journal = {Frontiers in Human Neuroscience}, author = {He, Chao and Liu, Jialu and Zhu, Yuesheng and Du, Wencai}, month = dec, year = {2021}, note = {4 citations (Crossref) [2022-09-21]}, pages = {765525}, } @article{li_exploratory_2022, title = {Exploratory {Research} on {Key} {Technology} of {Human}-{Computer} {Interactive} 2.5-{Minute} {Fast} {Digital} {Early} {Warning} for {Mild} {Cognitive} {Impairment}}, volume = {2022}, issn = {1687-5273, 1687-5265}, url = {https://www.hindawi.com/journals/cin/2022/2495330/}, doi = {10.1155/2022/2495330}, abstract = {Objective. As the preclinical stage of Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) is characterized by hidden onset, which is difficult to detect early. Traditional neuropsychological scales are main tools used for assessing MCI. However, due to its strong subjectivity and the influence of many factors such as subjects’ educational background, language and hearing ability, and time cost, its accuracy as the standard of early screening is low. Therefore, the purpose of this paper is to propose a new key technology of fast digital early warning for MCI based on eye movement objective data analysis. Methodology. Firstly, four exploratory indexes (test durations, correlation degree, lengths of gaze trajectory, and drift rate) of MCI early warning are determined based on the relevant literature research and semistructured expert interview; secondly, the eye movement state is captured based on the eye tracker to realize the data extraction of four exploratory indexes. On this basis, the human-computer interactive 2.5-minute fast digital early warning paradigm for MCI is designed; thirdly, the rationality of the four early warning indexes proposed in this paper and their early warning effectiveness on MCI are verified. Results. Through the small sample test of human-computer interactive 2.5 fast digital early warning paradigm for MCI conducted by 32 elderly people aged 70–90 in a medical institution in Hangzhou, the two indexes of “correlation degree” and “drift rate” with statistical differences are selected. The experiment results show that AUC of this MCI early warning paradigm is 0.824. Conclusion. The key technology of human-computer interactive 2.5 fast digital early warning for MCI proposed in this paper overcomes the limitations of the existing MCI early warning tools, such as low objectification level, high dependence on professional doctors, long test time, requiring high educational level, and so on. The experiment results show that the early warning technology, as a new generation of objective and effective digital early warning tool, can realize 2.5-minute fast and high-precision preliminary screening and early warning for MCI in the elderly.}, language = {en}, urldate = {2023-04-26}, journal = {Computational Intelligence and Neuroscience}, author = {Li, Nan and Yang, Xiaotong and Du, Wencai and Ogihara, Atsushi and Zhou, Siyu and Ma, Xiaowen and Wang, Yujia and Li, Shuwu and Li, Kai}, editor = {Gong, Daqing}, month = mar, year = {2022}, pages = {1--15}, } @incollection{thomas_tourist_2022, title = {Tourist {Sentiment} {Mining} {Based} on {Deep} {Learning}}, volume = {8}, isbn = {9781839692666 9781839692673}, url = {https://www.intechopen.com/chapters/77605}, abstract = {Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection.}, language = {en}, urldate = {2023-04-26}, booktitle = {Artificial {Intelligence}}, publisher = {IntechOpen}, author = {Li, Weijun and Yang, Qun and Du, Wencai}, editor = {Thomas, Ciza}, month = mar, year = {2022}, doi = {10.5772/intechopen.98836}, } @article{li_lsda-apf_2023, title = {{LSDA}-{APF}: {A} {Local} {Obstacle} {Avoidance} {Algorithm} for {Unmanned} {Surface} {Vehicles} {Based} on {5G} {Communication} {Environment}}, volume = {138}, issn = {1526-1492, 1526-1506}, shorttitle = {{LSDA}-{APF}}, url = {https://www.techscience.com/CMES/v138n1/54251}, doi = {10.32604/cmes.2023.029367}, abstract = {In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account.}, language = {en}, number = {1}, urldate = {2024-01-13}, journal = {Computer Modeling in Engineering \& Sciences}, author = {Li, Xiaoli and Jiao, Tongtong and Ma, Jinfeng and Duan, Dongxing and Liang, Shengbin}, year = {2023}, note = {Publisher: Tech Science Press}, pages = {595--617}, } @article{li_model_2023, title = {A model of integrating convolution and {BiGRU} dual-channel mechanism for {Chinese} medical text classifications}, volume = {18}, issn = {1932-6203}, url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282824}, doi = {10.1371/journal.pone.0282824}, abstract = {Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65\%, 8.94\% and 11.62\% respectively compared with the baseline models in average, which proves that our model has better performance and robustness.}, language = {en}, number = {3}, urldate = {2023-04-11}, journal = {PLOS ONE}, author = {Li, Xiaoli and Zhang, Yuying and Jin, Jiangyong and Sun, Fuqi and Li, Na and Liang, Shengbin}, month = mar, year = {2023}, note = {Publisher: Public Library of Science}, keywords = {Convolution, Deep learning, Machine learning, Memory recall, Neural networks, Recurrent neural networks, Semantics, Syntax}, pages = {e0282824}, } @article{liang_improved_2021, title = {An {Improved} {Double} {Channel} {Long} {Short}-{Term} {Memory} {Model} for {Medical} {Text} {Classification}}, volume = {2021}, issn = {2040-2309, 2040-2295}, url = {https://www.hindawi.com/journals/jhe/2021/6664893/}, doi = {10.1155/2021/6664893}, abstract = {There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model.}, language = {en}, urldate = {2022-04-28}, journal = {Journal of Healthcare Engineering}, author = {Liang, Shengbin and Chen, Xinan and Ma, Jixin and Du, Wencai and Ma, Huawei}, editor = {Li, Xingwang}, month = feb, year = {2021}, note = {1 citations (Crossref) [2022-09-21]}, pages = {1--8}, } @article{liang_improved_2021, title = {An improved ant colony optimization algorithm based on context for tourism route planning}, volume = {16}, issn = {1932-6203}, url = {https://dx.plos.org/10.1371/journal.pone.0257317}, doi = {10.1371/journal.pone.0257317}, abstract = {To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people’s choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5\% and the convergence speed is increased by 21.2\% compared with the basic algorithm, which proves that the improved algorithm is notably effective.}, language = {en}, number = {9}, urldate = {2022-04-28}, journal = {PLOS ONE}, author = {Liang, Shengbin and Jiao, Tongtong and Du, Wencai and Qu, Shenming}, editor = {Oliva, Diego}, month = sep, year = {2021}, note = {3 citations (Crossref) [2022-09-21]}, pages = {e0257317}, } @article{liang_multi-channel_2023, title = {A {Multi}-{Channel} {Text} {Sentiment} {Analysis} {Model} {Integrating} {Pre}-training {Mechanism}}, volume = {52}, copyright = {Copyright (c) 2023 Information Technology and Control}, issn = {2335-884X}, url = {https://itc.ktu.lt/index.php/ITC/article/view/31803}, doi = {10.5755/j01.itc.52.2.31803}, abstract = {The number of tourist attractions reviews, travel notes and other texts has grown exponentially in the Internet age. Effectively mining users’ potential opinions and emotions on tourist attractions, and helping to provide users with better recommendation services, which is of great practical significance. This paper proposes a multi-channel neural network model called Pre-BiLSTM combined with a pre-training mechanism. The model uses a combination of coarse and fine- granularity strategies to extract the features of text information such as reviews and travel notes to improve the performance of text sentiment analysis. First, we construct three channels and use the improved BERT and skip-gram methods with negative sampling to vectorize the word-level and vocabulary-level text, respectively, so as to obtain more abundant textual information. Second, we use the pre-training mechanism of BERT to generate deep bidirectional language representation relationships. Third, the vectors of the three channels are input into the BiLSTM network in parallel to extract global and local features. Finally, the model fuses the text features of the three channels and classifies them using SoftMax classifier. Furthermore, numerical experiments are conducted to demonstrate that Pre-BiLSTM outperforms the baselines by 6.27\%, 12.83\% and 18.12\% in average in terms of accuracy, precision and F1-score.}, language = {en}, number = {2}, urldate = {2024-01-13}, journal = {Information Technology and Control}, author = {Liang, Shengbin and Jin, Jiangyong and Du, Wencai and Qu, Shenming}, month = jul, year = {2023}, note = {Number: 2}, keywords = {Pre-training mechanism}, pages = {263--275}, } @article{liang_improved_2023, title = {An {Improved} {Dual}-{Channel} {Deep} {Q}-{Network} {Model} for {Tourism} {Recommendation}}, issn = {2167-6461, 2167-647X}, url = {https://www.liebertpub.com/doi/10.1089/big.2021.0353}, doi = {10.1089/big.2021.0353}, language = {en}, urldate = {2023-04-26}, journal = {Big Data}, author = {Liang, Shengbin and Jin, Jiangyong and Ren, Jia and Du, Wencai and Qu, Shenming}, month = mar, year = {2023}, keywords = {context-aware, deep reinforcement learning, dual-channel, tourism recommendation}, pages = {big.2021.0353}, } @article{liang_medical_2023, title = {A medical text classification approach with {ZEN} and capsule network}, issn = {1573-0484}, url = {https://doi.org/10.1007/s11227-023-05612-6}, doi = {10.1007/s11227-023-05612-6}, abstract = {Text classification is an important topic in natural language processing, with the development of social network, many question-and-answer pairs regarding health-care and medicine flood social platforms. It is of great social value to mine and classify medical text and provide targeted medical services for patients. The existing algorithms of text classification can deal with simple semantic text, especially in the field of Chinese medical text, the text structure is complex and includes a large number of medical nomenclature and professional terms, which are difficult for patients to understand. We propose a Chinese medical text classification model using a BERT-based Chinese text encoder by N-gram representations (ZEN) and capsule network, which represent feature uses the ZEN model and extract the features by capsule network, we also design a N-gram medical dictionary to enhance medical text representation and feature extraction. The experimental results show that the precision, recall and F1-score of our model are improved by 10.25\%, 11.13\% and 12.29\%, respectively, compared with the baseline models in average, which proves that our model has better performance.}, language = {en}, urldate = {2024-01-13}, journal = {The Journal of Supercomputing}, author = {Liang, Shengbin and Sun, Fuqi and Sun, Haoran and Chen, Tingting and Du, Wencai}, month = sep, year = {2023}, keywords = {Capsule network, Medical text classification, Text mining, ZEN model}, } @article{lin_deep_2022, title = {A {Deep} {Neural} {Network} {Based} on {Circular} {Representation} for {Target} {Detection}}, volume = {2022}, issn = {1687-7268, 1687-725X}, url = {https://www.hindawi.com/journals/js/2022/4437446/}, doi = {10.1155/2022/4437446}, abstract = {Convolutional neural network (CNN) model based on deep learning has excellent performance for target detection. However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet’s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.}, language = {en}, urldate = {2022-04-28}, journal = {Journal of Sensors}, author = {Lin, Cong and Chen, Zhoujian and Huang, Yiquan and Jiang, Haoyu and Du, Wencai and Chen, Qiong}, editor = {Khan, Waliullah}, month = apr, year = {2022}, note = {0 citations (Crossref) [2022-09-21]}, pages = {1--10}, } @inproceedings{ma_model_2022, title = {A {Model} of {Integrating} {Bert} and {BiGRU}+ {Attention} {Dual}-channel {Mechanism} for {Investor} {Sentiment} {Analysis} of {Stock} {Price} {Forecast}}, url = {https://ieeexplore.ieee.org/document/10051779}, doi = {10.1109/SNPD54884.2022.10051779}, abstract = {Investor sentiment and emotions have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors for stock price prediction using machine learning. Existing prediction models mostly depend on the analysis of trading data and company profit. few prediction theories have been built based on individual investors' sentiments. The fundamental reason is the difficulty to measure individual investors' sentiment.}, urldate = {2024-01-14}, booktitle = {2022 {IEEE}/{ACIS} 23rd {International} {Conference} on {Software} {Engineering}, {Artificial} {Intelligence}, {Networking} and {Parallel}/{Distributed} {Computing} ({SNPD})}, author = {Ma, Huawei and Ma, Jixin and Liang, Shengbin and Du, Wencai}, month = dec, year = {2022}, note = {ISSN: 2693-8421}, pages = {126--131}, } @inproceedings{ma_model_2022, address = {Taichung, Taiwan}, title = {A {Model} of {Integrating} {Bert} and {BiGRU}+ {Attention} {Dual}-channel {Mechanism} for {Investor} {Sentiment} {Analysis} of {Stock} {Price} {Forecast}}, isbn = {9798350310412}, url = {https://ieeexplore.ieee.org/document/10051779/}, doi = {10.1109/SNPD54884.2022.10051779}, urldate = {2023-04-26}, booktitle = {2022 {IEEE}/{ACIS} 23rd {International} {Conference} on {Software} {Engineering}, {Artificial} {Intelligence}, {Networking} and {Parallel}/{Distributed} {Computing} ({SNPD})}, publisher = {IEEE}, author = {Ma, Huawei and Ma, Jixin and Liang, Shengbin and Du, Wencai}, month = dec, year = {2022}, pages = {126--131}, } @inproceedings{ma_comprehensive_2021, title = {A {Comprehensive} {Review} of {Investor} {Sentiment} {Analysis} in {Stock} {Price} {Forecasting}}, doi = {10.1109/ICISFall51598.2021.9627470}, abstract = {Sentiment analysis technologies have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors. The objective of this paper is to evaluate the current state of the art and synthesize the published literature related to the financial sentiment analysis, especially in investor sentiment for prediction of stock price. Starting from this overview the paper provides answers to the questions about how and to what extent research on investor sentiment analysis and stock price trend forecasting in the financial markets has developed and which tools are used for these purposes remains largely unexplored. This paper represents the comprehensive literature-based study on the fields of the investors sentiment analytics and machine learning applied to analyzing the sentiment of investors and its influencing stock market and predicting stock price.}, booktitle = {2021 {IEEE}/{ACIS} 20th {International} {Fall} {Conference} on {Computer} and {Information} {Science} ({ICIS} {Fall})}, author = {Ma, Huawen and Ma, Jixin and Wang, Han and Li, Pengsheng and Du, Wencai}, month = oct, year = {2021}, keywords = {Analytical models, Information science, Investor sentiment analysis, Machine learning, Market research, Predictive models, Reliability, Sentiment analysis, Social media, Stock price trend forecasting, Tools}, pages = {264--268}, } @article{ma_foreword_2023, title = {Foreword}, volume = {1055}, issn = {1860-949X}, language = {English}, journal = {Studies in Computational Intelligence}, author = {Ma, J. and Du, W. and Lu, W.}, year = {2023}, note = {ISBN: 9783031121265}, pages = {v--vii}, } @article{ma_message_2022, title = {Message from {Program} {Chairs}}, doi = {10.1109/ICIS54925.2022.9882414}, language = {English}, journal = {Proceedings - 2022 IEEE/ACIS 22nd International Conference on Computer and Information Science, ICIS 2022}, author = {Ma, J. and Du, W. and Lu, W.}, year = {2022}, note = {ISBN: 9781665494632}, pages = {X}, } @inproceedings{ma_quality_2021, address = {Zhuhai, China}, title = {Quality {Detection} {Method} for {Controller} {Process} {Based} on {Mask} {R}-{CNN} {Network} {Model}}, isbn = {9781665402729}, url = {https://ieeexplore.ieee.org/document/9742715/}, doi = {10.1109/CSII54342.2021.00012}, urldate = {2023-04-26}, booktitle = {2021 8th {International} {Conference} on {Computational} {Science}/{Intelligence} and {Applied} {Informatics} ({CSII})}, publisher = {IEEE}, author = {Ma, Xinlei and Chen, Yanyu and Li, Shaobin and Du, Wencai and Tan, Zehan and Li, Qinggu and Ma, Yaqi and Deng, Haiyan}, month = sep, year = {2021}, pages = {18--22}, } @incollection{wang_key_2022, address = {Cham}, series = {Studies in {Computational} {Intelligence}}, title = {Key {Issues} for {Digital} {Factory} {Designing} and {Planning}: {A} {Survey}}, isbn = {978-3-030-92317-4}, shorttitle = {Key {Issues} for {Digital} {Factory} {Designing} and {Planning}}, url = {https://doi.org/10.1007/978-3-030-92317-4_2}, abstract = {Digital Factory (DF) planning is the key of intelligent factory construction, where intelligent production technologies of big data analysis, cloud computing, blockchain, Internet of Things, artificial intelligence, 5G, Time Sensitive Network (TSN), Digital Twin (DT), additive manufacturing are included. By applying the modern techniques, DF performs great advantages on the aspects of product lifecycle management, enterprise resource planning, operation management, supply chain management, real-time database construction, advanced process control, as well as the new technologies of distributed control system and fieldbus control system. This article delivers a review of key issues of DF top-level design and planning from the aspects of networking, precision, automation and digitalization. Solutions are explored based on 5G, TSN and DT advanced technologies, literately and practically. Additionally, the article describes the method and application of efficient big data comprehensive solution. Therefore, this study contributes valuable decision-making support for DF applications.}, language = {en}, urldate = {2023-04-11}, booktitle = {Software {Engineering}, {Artificial} {Intelligence}, {Networking} and {Parallel}/{Distributed} {Computing}}, publisher = {Springer International Publishing}, author = {Wang, Han and Du, Wencai and Li, Shaobin}, editor = {Lee, Roger}, year = {2022}, doi = {10.1007/978-3-030-92317-4_2}, keywords = {5G, Big data comprehensive solution, Digital Factory, Digital twin, Time Sensitive Network}, pages = {18--29}, } @techreport{wang_fast_2021, type = {preprint}, title = {A {Fast} {Lightweight} {Based} {Deep} {Fusion} {Learning} for {Detecting} {Macula} {Fovea} {Using} {Ultra}-{Widefield} {Fundus} {Images}}, url = {https://www.preprints.org/manuscript/202108.0469/v2}, abstract = {Macula fovea detection is a crucial prerequisite towards screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither accuracy nor effectiveness of the diagnose process could be guaranteed. In this project, we proposed a deep learning approach on ultra-widefield fundus (UWF) images for macula fovea detection. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. Methods based on U-shape network (Unet) and Fully Convolutional Networks (FCN) are implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived\ from the spatial relationship between macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. Results are measured by calculating the Euclidean distance between proposed approaches and the accurate grounded standard, which is detected by Ultra-widefield swept-source optical coherence tomograph (UWF-OCT) approach. Through a comparation of proposed methods, we conclude that, deep learning approach of Unet outperformed other methods on macula fovea detection tasks, by which outcomes obtained are comparable to grounded standard method.}, urldate = {2023-04-26}, institution = {MATHEMATICS \& COMPUTER SCIENCE}, author = {Wang, Han and Yang, Jie and Wu, Yaoyang and Du, Wencai and Fong, Simon and Duan, Yutong and Yao, Xiaoping and Zhou, Xiaoshu and Li, Qingqian and Lin, Chen and Liu, Jiang and Huang, Lina and Wu, Feng}, month = sep, year = {2021}, doi = {10.20944/preprints202108.0469.v2}, } @article{wu_learning_2021, title = {Learning dynamics of kernel-based deep neural networks in manifolds}, volume = {64}, issn = {1674-733X, 1869-1919}, url = {https://link.springer.com/10.1007/s11432-020-3022-3}, doi = {10.1007/s11432-020-3022-3}, language = {en}, number = {11}, urldate = {2023-04-26}, journal = {Science China Information Sciences}, author = {Wu, Wei and Jing, Xiaoyuan and Du, Wencai and Chen, Guoliang}, month = nov, year = {2021}, pages = {212103}, } @incollection{yao_hierarchical_2023, address = {Cham}, series = {Studies in {Computational} {Intelligence}}, title = {Hierarchical {Medical} {Classification} {Based} on {DLCF}}, isbn = {978-3-031-12127-2}, url = {https://doi.org/10.1007/978-3-031-12127-2_7}, abstract = {Medical classification is affected by many factors, and the traditional medical classification is usually restricted by factors such as too long text, numerous categories and so on. In order to solve these problems, this paper uses word vector and word vector to mine the text deeply, considering the problem of scattered key features of medical text, introducing long-term and short-term memory network to effectively retain the features of historical information in long text sequence, and using the structure of CNN to extract local features of text, through attention mechanism to obtain key features, considering the problems of many diseases, by using hierarchical classification. To stratify the disease. Combined with the above ideas, a deep DLCF model suitable for long text and multi-classification is designed. This model has obvious advantages in CMDD and other datasets. Compared with the baseline models, this model is superior to the baseline model in accuracy, recall and other indicators.}, language = {en}, urldate = {2023-04-11}, booktitle = {Computer and {Information} {Science}}, publisher = {Springer International Publishing}, author = {Yao, Mingyuan and Sun, Haoran and Liang, Shengbin and Shen, Yanqing and Yukie, Niki}, editor = {Lee, Roger}, year = {2023}, doi = {10.1007/978-3-031-12127-2_7}, keywords = {Dual channel, Hierarchical classification, LSTM-CNN, Medical classification, RF}, pages = {101--115}, } @article{yin_power_2021, title = {Power {Allocation} for {5G} {Mobile} {Multiuser} {Cooperative} {Networks}}, volume = {2021}, issn = {2042-3195, 0197-6729}, url = {https://www.hindawi.com/journals/jat/2021/3882100/}, doi = {10.1155/2021/3882100}, abstract = {With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2\% and 66.6\%, respectively.}, language = {en}, urldate = {2022-04-28}, journal = {Journal of Advanced Transportation}, author = {Yin, Fagen and Du, Wencai}, editor = {Tsai, Sang-Bing}, month = dec, year = {2021}, note = {1 citations (Crossref) [2022-09-21]}, pages = {1--7}, } @book{_java_2023, title = {Java面向对象程序设计}, isbn = {978-7-302-63675-5}, url = {http://www.tup.tsinghua.edu.cn/Wap/tsxqy.aspx?id=09722601}, abstract = {结合实用案例讲解Java语法、面向对象程序设计技术和核心API。 全书共10章,内容涵盖Java概述、Java语法基础、面向对象基础、面向对象高级技术、 Java API、异常处理机制、Java I/O流、多线程、Java GUI编程和Java网络编程等知识要点。案例丰富,以JDK 17和IntelliJ IDEA等流行的开发环境为依托,力求让读者通过案例 掌握Java编程技术。另一个特色是在阐释专业内容的同时自然融入思政元素, 具有鲜明的时代性和引领性。 可作为普通高等院校计算机、软件工程、人工智能等专业“面向对象程序设计”“Java程序设计”课程的教材,也适 合编程爱好者自学和培训使用。}, urldate = {2024-01-14}, publisher = {清华大学出版社}, author = {{梁胜彬}}, month = oct, year = {2023}, }