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学者姓名：李建强
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摘要 ：
Energy efficiency and security are critical components of Quality of Service (QoS) and remain a challenge in WSN-assisted IoT owing to its open and resource -limited nature. Despite intensive research on WSN-IoT, only a few have achieved significant levels of energy efficiency and load balancing on clustering nodes. This study proposes a novel approach for dynamic cluster -based WSN-IoT networks to enhance the network's resilience using data fusion techniques and eliminate illogical clustering. The Mean Value and Minimum Distance Method identifies the optimal cluster heads within the network by reducing data redundancy, resulting in improved quality of service, energy optimization, and enhanced lifetime. The proposed fused deep learning -based data mining method (RNN-LSTM) mitigates the data fitting and enhances the dynamic routing and balancing load at the WSN fusion center. The novel approach splits the network into layers, assigning sensor nodes to each layer, drastically reducing latency, data transfers, and the fusion center's overhead. Distinct experiments evaluated the suggested approach's efficacy by varying the hidden layer nodes and signaling intervals. The empirical verdicts exhibit that the presented routing algorithms surpass state-of-the-art conventional routing systems in energy depletion, average latency, signaling overhead, cumulative throughput, and route heterogeneity.
关键词 ：
RNN-LSTM RNN-LSTM Data fusion Data fusion Inclusive innovation Inclusive innovation Internet of things Internet of things Multi-hop clustering Multi-hop clustering Energy balanced Energy balanced
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| GB/T 7714 | Mahmood, Tariq , Li, Jianqiang , Saba, Tanzila et al. Energy optimized data fusion approach for scalable wireless sensor network using deep learning-based scheme [J]. | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 224 . |
| MLA | Mahmood, Tariq et al. "Energy optimized data fusion approach for scalable wireless sensor network using deep learning-based scheme" . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 224 (2024) . |
| APA | Mahmood, Tariq , Li, Jianqiang , Saba, Tanzila , Rehman, Amjad , Ali, Saqib . Energy optimized data fusion approach for scalable wireless sensor network using deep learning-based scheme . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 224 . |
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摘要 ：
Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements, which are essential in improving learning experiences and overall outcomes. Educational data mining (EDM) is a thriving research field that employs data mining and machine learning methods to extract valuable insights from educational databases, primarily focused on predicting students' academic performance. This study proposes a novel federated learning (FL) standard that ensures the confidentiality of the dataset and allows for the prediction of student grades, categorized into four levels: low, good, average, and drop. Optimized features are incorporated into the training process to enhance model precision. This study evaluates the optimized dataset using five machine learning (ML) algorithms, namely support vector machine (SVM), decision tree, Naive Bayes, K-nearest neighbors, and the proposed federated learning model. The models' performance is assessed regarding accuracy, precision, recall, and F1-score, followed by a comprehensive comparative analysis. The results reveal that FL and SVM outperform the alternative models, demonstrating superior predictive performance for student grade classification. This study showcases the potential of federated learning in effectively utilizing educational data from various institutes while maintaining data privacy, contributing to educational data mining and machine learning advancements for student performance prediction.
关键词 ：
Federal learning Federal learning Machine learning Machine learning Learning outcome Learning outcome Inclusive innovation Inclusive innovation EDM EDM SVM SVM Prediction Prediction
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| GB/T 7714 | Farooq, Umer , Naseem, Shahid , Mahmood, Tariq et al. Transforming educational insights: strategic integration of federated learning for enhanced prediction of student learning outcomes [J]. | JOURNAL OF SUPERCOMPUTING , 2024 , 80 (11) : 16334-16367 . |
| MLA | Farooq, Umer et al. "Transforming educational insights: strategic integration of federated learning for enhanced prediction of student learning outcomes" . | JOURNAL OF SUPERCOMPUTING 80 . 11 (2024) : 16334-16367 . |
| APA | Farooq, Umer , Naseem, Shahid , Mahmood, Tariq , Li, Jianqiang , Rehman, Amjad , Saba, Tanzila et al. Transforming educational insights: strategic integration of federated learning for enhanced prediction of student learning outcomes . | JOURNAL OF SUPERCOMPUTING , 2024 , 80 (11) , 16334-16367 . |
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| GB/T 7714 | Jabbar, Ayesha , Naseem, Shahid , Li, Jianqiang et al. Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas (vol 17, 135, 2024) [J]. | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS , 2024 , 17 (1) . |
| MLA | Jabbar, Ayesha et al. "Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas (vol 17, 135, 2024)" . | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 17 . 1 (2024) . |
| APA | Jabbar, Ayesha , Naseem, Shahid , Li, Jianqiang , Mahmood, Tariq , Jabbar, Muhammad Kashif , Rehman, Amjad et al. Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas (vol 17, 135, 2024) . | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS , 2024 , 17 (1) . |
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摘要 ：
Non-invasive fundus images can be used to diagnose various fundus diseases, such as high myopia (HM). Existing deep learning-based research mainly relies on data to drive the model to learn key features. However, the data related to HM is limited (especially for young children), making it difficult for deep networks to accurately focus on key features. Hence, we propose a prior knowledge-guided deep learning network for pediatric HM detection. It comprises four modules: (1) Prior Feature-Based Channel Fusion: This module extracts key features (brightness, edges, texture) from fundus images using image processing methods to obtain corresponding single-channel slices. Through channel-level feature fusion, these slices are used to construct multiple sets of feature-enhanced datasets. (2) Global Fundus Feature Extraction: It uses residual blocks to build the backbone, and builds a U-shaped attention component based on the U-shaped network. This module extracts the global and context information of the original fundus image to obtain a global feature map. (3) Knowledge-Guided Attention Generation: The residual structure is employed to further extract the hidden features of the feature-enhanced data, thereby obtaining local key feature maps. (4) Pediatric HM Classification: By combining local key feature maps (obtained in module 3) with global feature maps (obtained in module 2) through spatial attention mechanism, the deep network is guided to complete the classification task of pediatric HM. Extensive experiments on real-world datasets demonstrate the effectiveness of our method (accuracy is 0.921, F1 score is 0.903).
关键词 ：
deep learning deep learning knowledge guidance knowledge guidance fundus images fundus images pediatric high myopia diagnosis pediatric high myopia diagnosis
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| GB/T 7714 | Cheng, Wenxiu , Li, Jianqiang , Xu, Xi et al. Attention to Key Fundus Features: A Prior Knowledge-Guided Deep Learning Network for Pediatric High Myopia Detection [J]. | 2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024 , 2024 : 2113-2118 . |
| MLA | Cheng, Wenxiu et al. "Attention to Key Fundus Features: A Prior Knowledge-Guided Deep Learning Network for Pediatric High Myopia Detection" . | 2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024 (2024) : 2113-2118 . |
| APA | Cheng, Wenxiu , Li, Jianqiang , Xu, Xi , Peng, Haoran , Zhao, Linna , Liu, Suqin et al. Attention to Key Fundus Features: A Prior Knowledge-Guided Deep Learning Network for Pediatric High Myopia Detection . | 2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024 , 2024 , 2113-2118 . |
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摘要 ：
Sign language video translation, which converts sign language information into textual expressions, play a vital role in breaking down the language communication barrier between deaf and healthy people. Existing translation methods are mainly focus on the single and pure background. However, the background in real-world environments is always complex, and these methods are difficult to achieve effective recognition results. To address this issue, we have exploratively constructed a real-world complex background sign language dataset (CBSL), containing sign language videos captured in various authentic environments (e.g., different backgrounds and lighting conditions). Based on this, we propose a progressive sign language translation model to effectively separate sign language users from the background and reduce environmental interference, thus significantly improving the generalization ability. Our proposed method significantly outperforms various comparative methods across all performance metrics on the CBSL dataset. Furthermore, on the publicly available Chinese Sign Language Continuous Recognition dataset(CSL), our method performs comparably to the current state-of-the-art (SOTA).
关键词 ：
Complex Background Complex Background Real-world Environment Real-world Environment Sign Language Video Translation Sign Language Video Translation Deep Learning Deep Learning
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| GB/T 7714 | Zou, Jingchen , Li, Jianqiang , Xu, Xi et al. Progressive Sign Language Video Translation Model for Real-World Complex Background Environments [J]. | 2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024 , 2024 : 519-524 . |
| MLA | Zou, Jingchen et al. "Progressive Sign Language Video Translation Model for Real-World Complex Background Environments" . | 2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024 (2024) : 519-524 . |
| APA | Zou, Jingchen , Li, Jianqiang , Xu, Xi , Huang, Yuning , Tang, Jing , Song, Changwei et al. Progressive Sign Language Video Translation Model for Real-World Complex Background Environments . | 2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024 , 2024 , 519-524 . |
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摘要 ：
Wireless Body Area Networks (WBANs) are a cost-effective, low-power technology that has been advancing due to the need to improve their performance. The predominant challenges encompass packet delivery ratio (PDR), packet loss ratio (PLR), and end-to-end (E2D) delay. Advances in wireless technology emphasize the urgency to surmount these issues. Channel congestion and collisions, increased latency, unfairness in access, high energy consumption, performance heterogeneity, QoS degradation, interference and reliability are some of the particular concerns largely caused by CW that influence WBAN performance. Researchers and developers strive to create adaptive CW techniques and protocols that dynamically modify CWsize in response to traffic loads, network conditions, and the particular needs of WBAN applications in order to maximise WBAN performance. In response, we propose GFuCWO-a genetic fuzzy logic technique-for optimizing contention windows in IEEE802.15.6 WBANs. This study introduces three distinct algorithms to accomplish this. We evaluate the efficiency of the GFuCWO technique against the ABEB and Improved-CSMA/CA algorithms. The approach is implemented in Castalia OMNeT++. The study calculates PDR, PLR, and E2D delay using experimental data from four sensor nodes under different traffic conditions. The GFuCWO technique demonstrated superior performance in PDR, PLR, and E2D, with average enhancements of 4%-11% and 3%-13%, respectively, with a 95% confidence interval, indicating its potential for community benefit and medical performance improvements.
关键词 ：
CW optimisation CW optimisation ABEB ABEB IEEE-802.15.6 IEEE-802.15.6 WBANs WBANs Genetic-algorithm Genetic-algorithm Fuzzy logic controller Fuzzy logic controller
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| GB/T 7714 | Qureshi, Imran Ali , Bhatti, Kabeer Ahmed , Li, Jianqiang et al. GFuCWO: A genetic fuzzy logic technique to optimize contention window of IEEE-802.15.6 WBAN [J]. | AIN SHAMS ENGINEERING JOURNAL , 2024 , 15 (5) . |
| MLA | Qureshi, Imran Ali et al. "GFuCWO: A genetic fuzzy logic technique to optimize contention window of IEEE-802.15.6 WBAN" . | AIN SHAMS ENGINEERING JOURNAL 15 . 5 (2024) . |
| APA | Qureshi, Imran Ali , Bhatti, Kabeer Ahmed , Li, Jianqiang , Atta-ur-Rahman , Mahmood, Tariq , Mukhtar, Muhammad et al. GFuCWO: A genetic fuzzy logic technique to optimize contention window of IEEE-802.15.6 WBAN . | AIN SHAMS ENGINEERING JOURNAL , 2024 , 15 (5) . |
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摘要 ：
Integrating lexical knowledge in Chinese named entity recognition (NER) has been proven effective. Among the existing methods, Flat-LAttice Transformer (FLAT) has achieved great success in both performance and efficiency. FLAT performs lexical enhancement for each sentence by constructing a flat lattice (i.e., a sequence of tokens including the characters in a sentence and the matched words in a lexicon) and calculating self-attention with a fully-connected structure. However, the different interactions between tokens, which can bring different aspects of semantic information for Chinese NER, cannot be well captured by self-attention with a fully-connected structure. In this paper, we propose a novel Multi-View Transformer (MVT) to effectively capture the different interactions between tokens. We first define four views to capture four different token interaction structures. We then construct a view-aware visible matrix for each view according to the corresponding structure and introduce a view-aware dot-product attention for each view to limit the attention scope by incorporating the corresponding visible matrix. Finally, we design three different MVT variants to fuse the multi-view features at different levels of the Transformer architecture. Experimental results conducted on four public Chinese NER datasets show the effectiveness of the proposed method. Specifically, on the most challenging dataset Weibo, which is in an informal text style, MVT outperforms FLAT in F1 score by 2.56%, and when combined with BERT, MVT outperforms FLAT in F1 score by 3.03%.
关键词 ：
Context modeling Context modeling Transformer Transformer Transformers Transformers Chinese NER Chinese NER Speech processing Speech processing Semantics Semantics lexicon-based Chinese NER lexicon-based Chinese NER Urban areas Urban areas Bridges Bridges Multi-view Multi-view Rivers Rivers multi-view Transformer multi-view Transformer
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| GB/T 7714 | Xiao, Yinlong , Ji, Zongcheng , Li, Jianqiang et al. MVT: Chinese NER Using Multi-View Transformer [J]. | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING , 2024 , 32 : 3656-3668 . |
| MLA | Xiao, Yinlong et al. "MVT: Chinese NER Using Multi-View Transformer" . | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 32 (2024) : 3656-3668 . |
| APA | Xiao, Yinlong , Ji, Zongcheng , Li, Jianqiang , Han, Mei . MVT: Chinese NER Using Multi-View Transformer . | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING , 2024 , 32 , 3656-3668 . |
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摘要 ：
Alzheimer's disease (AD) is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function, but the etiology of AD is still unclear. With the explosive growth of electronic health data, the application of artificial intelligence (Al) in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches, and improving the disease's diagnostic and prognostic outcome. This paper first briefly introduces Al technologies and applications in medicine, and then presents a comprehensive review of Al in AD. In simple, it includes etiology discovery based on genetic data, computer-aided diagnosis (CAD), computer-aided prognosis (CAP) of AD using multi-modality data (genetic, neuroimaging and linguistic data), and pharmacological or non-pharmacological approaches for treating AD. Later, some popular publicly available AD datasets are introduced, which are important for advancing Al technologies in AD analysis. Finally, core research challenges and future research directions are discussed.
关键词 ：
Linguistics Linguistics Neuroimaging Neuroimaging Medical services Medical services Neurosurgery Neurosurgery Solid modeling Solid modeling artificial intelligence artificial intelligence computer-aided prognosis computer-aided prognosis Alzheimer's disease Alzheimer's disease etiology discovery etiology discovery Machine learning Machine learning Genetics Genetics computer-aided diagnosis computer-aided diagnosis treatment treatment
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| GB/T 7714 | Zhao, Qing , Xu, Hanrui , Li, Jianqiang et al. The Application of Artificial Intelligence in Alzheimer's Research [J]. | TSINGHUA SCIENCE AND TECHNOLOGY , 2024 , 29 (1) : 13-33 . |
| MLA | Zhao, Qing et al. "The Application of Artificial Intelligence in Alzheimer's Research" . | TSINGHUA SCIENCE AND TECHNOLOGY 29 . 1 (2024) : 13-33 . |
| APA | Zhao, Qing , Xu, Hanrui , Li, Jianqiang , Rajput, Faheem Akhtar , Qiao, Liyan . The Application of Artificial Intelligence in Alzheimer's Research . | TSINGHUA SCIENCE AND TECHNOLOGY , 2024 , 29 (1) , 13-33 . |
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摘要 ：
At the end of 2019, the COVID-19 outbreak emerged abruptly. Chinese health authorities highlighted the role of CT scans, X-rays, and other computerized lung imaging in aiding COVID-19 diagnosis. This study aims to develop a computer-based system to assist healthcare professionals in diagnosing COVID-19 infections based on computerized imaging analysis. This approach aims to alleviate the workload of COVID-19 specialists, improving diagnostic and treatment efficiency and allowing specialists to focus on devising appropriate patient care plans promptly. The proposed method focuses on analyzing COVID-19 lesion characteristics within individual CT slices and their serial characteristics across CT sequences. This approach mirrors the diagnostic process of radiologists closely. To validate our model, we compiled a dataset from real medical diagnostic settings, minimizing the impact of lesion-like artifacts. We conducted a series of comparative and ablation experiments to evaluate the model's performance. Results indicate that our model outperforms the classic classification models and other commonly used models for COVID-19 diagnosis on our constructed dataset.
关键词 ：
COVID-19 automated diagnosis COVID-19 automated diagnosis Attention mechanism Attention mechanism Lung CT images Lung CT images Image sequence classification Image sequence classification
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| GB/T 7714 | Xu, Jingxiang , Li, Jianqiang , Li, Juan et al. Covid-IRLNet: A COVID-19 Diagnostic Model For Extracting CT Image Features and CT Sequence Features [J]. | 2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024 , 2024 : 2159-2164 . |
| MLA | Xu, Jingxiang et al. "Covid-IRLNet: A COVID-19 Diagnostic Model For Extracting CT Image Features and CT Sequence Features" . | 2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024 (2024) : 2159-2164 . |
| APA | Xu, Jingxiang , Li, Jianqiang , Li, Juan , Zhao, Linna , Ding, Shujie . Covid-IRLNet: A COVID-19 Diagnostic Model For Extracting CT Image Features and CT Sequence Features . | 2024 IEEE 48TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC 2024 , 2024 , 2159-2164 . |
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摘要 ：
本发明公开了一种基于空间时域卷积网络的PM10浓度精细化预测方法，本发明考虑了空气监测站点间的时空异质性，分析了对PM10的输送演化有重要影响的因素，利用因果卷积网络提取空间、时域依赖特征对PM10进行预测。本发明提出了可接入多源影响因素的时序预测框架，考虑空间站点间的时空异质性特征、空气污染物间的相互影响和气象因素对PM10的演化驱动作用，对PM10进行预测。本发明的建模方法克服了传统时序预测建模方法的弊端，并将多种模型优化方法应用于提升本发明的预测性能，为后续PM10浓度预测研究提供有效的指导框架。
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| GB/T 7714 | 刘希亮 , 张羽民 , 赵俊杰 et al. 一种基于空间时域卷积网络的PM10浓度精细化预测方法 : CN202310163385.2[P]. | 2023-02-24 . |
| MLA | 刘希亮 et al. "一种基于空间时域卷积网络的PM10浓度精细化预测方法" : CN202310163385.2. | 2023-02-24 . |
| APA | 刘希亮 , 张羽民 , 赵俊杰 , 高雨瑶 , 李建强 , 石宇良 . 一种基于空间时域卷积网络的PM10浓度精细化预测方法 : CN202310163385.2. | 2023-02-24 . |
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