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| 1 | Knowledge Graph Completion Based on GCN of Multi-Information Fusion and High-Dimensional Structure Analysis Weight显示文摘Knowledge graph completion(KGC)can solve the problem of data sparsity in the knowledge graph.A large number of models for the KGC task have been proposed in recent years.However,the underutilisation of the structure information around nodes is one of the main problems of the previous KGC model,which leads to relatively single encoding information.To this end,a new KGC model that encodes and decodes the feature information is proposed.First,we adopt the subgraph sampling method to extract node structure.Moreover,the graph convolutional network(GCN)introduced the channel attention convolution encode node structure features and represent them in matrix form to fully mine the node feature information.Eventually,the high-dimensional structure analysis weight decodes the encoded matrix embeddings and then constructs the scoring function.The experimental results show that the model performs well on the datasets used. | NIU Haoran HE Haitao FENG Jianzhou NIE Junlan ZHANG Yangsen REN Jiadong | 2022 | Chinese Journal of Electronics2022，31，2： | 3 |
| 2 | Clinical analysis of CHD2 gene mutations in pediatric patients with epilepsy显示文摘Importance:CHD2 is a member of the chromodomain helicase DNA-binding(CHD)family of proteins,which have important roles in the regulation of gene expression.Dysregulation of this protein may lead to various disorders.Objective:To delineate the genotypes and phenotypes of CHD2-related epilepsy.Methods:We analyzed the medical history,magnetic resonance imaging findings,and video-electroencephalogram recordings of 17 patients withCHD2 mutations in the Neurology Department of Beijing Children’s Hospital from June 2016 to June 2021.Results:Age at seizure onset ranged from 6 months to 10 years;the median age at onset was 4 years.Generalized tonic-clonic,myoclonic,eyelid myoclonic,atonic,atypical absence,myoclonic-atonic,and spasm seizures were observed.Ten of the 17 patients had multiple types of seizures.One patient exhibited photosensitivity epilepsy and one patient exhibited grid image-induced visual reflex epilepsy.Developmental disability was present in 14 patients,while autism features were present in five patients.Sixteen patients hadde novo mutations ofCHD2;one patient had an inherited variant.Eleven mutations were novel.One patient had two mutations;that patient exhibited development delay and refractory epilepsy.Seizures were controlled in eight patients,improved in seven patients,and resistant to treatment in two patients.Interpretation:Phenotype severity in patients withCHD2 variants ranged from drug-responsive seizures to severe epileptic encephalopathy.Most patients exhibited developmental disorders. | Weixing Feng Fang Fang Xiaohui Wang Chunhong Chen Junlan Lu Jie Deng | 2022 | Pediatric Investigation2022，6，2： | 2 |
| 3 | Molecular therapyof colorectal cancer:Progress and future directions显示文摘 | Wenhao Weng Junlan Feng Huanlong Qin | 2015 | Internation- al Journal of Cancer2015，136，3： | 1 |
| 4 | Molecular therapy of colorectal cancer : Progress and future directions 显示文摘 | Wenhao Weng Junlan Feng Huanlong Qin | 2015 | International Journal of Cancer2015，136，3： | 1 |
| 5 | Effects of damping, friction, gravity, and flexibility on the dynamic performance of a deployable mechanism with clearance显示文摘 | LI Junlan YAN Shaoze FENG Guo | 2013 | Journal of Mechanical Engineering Science2013，227，8： | 1 |
| 6 | Automatic Satisfaction Analysis in Call Centers Considering Global Features of Emotion and Duration显示文摘Analysis of customers' satisfaction provides a guarantee to improve the service quality in call centers.In this paper,a novel satisfaction recognition framework is introduced to analyze the customers' satisfaction.In natural conversations,the interaction between a customer and its agent take place more than once.One of the difficulties insatisfaction analysis at call centers is that not all conversation turns exhibit customer satisfaction or dissatisfaction. To solve this problem,an intelligent system is proposed that utilizes acoustic features to recognize customers' emotion and utilizes the global features of emotion and duration to analyze the satisfaction. Experiments on real-call data show that the proposed system offers a significantly higher accuracy in analyzing the satisfaction than the baseline system. The average F value is improved to 0. 701 from 0. 664. | Jing Liu Chaomin Wang Yingnan Zhang Pengyu Cong Liqiang Xu Zhijie Ren Jin Hu Xiang Xie Junlan Feng Jingming Kuang | 2018 | Journal of Beijing Institute of Technology2018，27，1： | 1 |
| 7 | Speech and Language Processing over theWeb显示文摘 | Gilbert M Feng Junlan | 2008 | IEEE Signal Processing Magazine2008，18，5： | 1 |
| 8 | Network Meets ChatGPT:Intent Autonomous Management,Control and Operation显示文摘Telecommunication has undergone significant transformations due to the continuous advancements in internet technology,mobile devices,competitive pricing,and changing customer preferences.Specifically,the most recent iteration of OpenAI’s large language model chat generative pre-trained transformer(ChatGPT)has the potential to propel innovation and bolster operational performance in the telecommunications sector.Nowadays,the exploration of network resource management,control,and operation is still in the initial stage.In this paper,we propose a novel network artificial intelligence architecture named language model for network traffic(NetLM),a large language model based on a transformer designed to understand sequence structures in the network packet data and capture their underlying dynamics.The continual convergence of knowledge space and artificial intelligence(AI)technologies constitutes the core of intelligent network management and control.Multi-modal representation learning is used to unify the multi-modal information of network indicator data,traffic data,and text data into the same feature space.Furthermore,a NetLM-based control policy generation framework is proposed to refine intent incrementally through different abstraction levels.Finally,some potential cases are provided that NetLM can benefit the telecom industry. | Jingyu Wang Lei Zhang Yiran Yang Zirui Zhuang Qi Qi Haifeng Sun Lu Lu Junlan Feng Jianxin Liao | 2023 | Journal of Communications and Information Networks2023，8，3： | 0 |
| 9 | Deep Learning for Medication Recommendation:A Systematic Survey显示文摘Making medication prescriptions in response to the patient's diagnosis is a challenging task.The number of pharmaceutical companies,their inventory of medicines,and the recommended dosage confront a doctor with the well-known problem of information and cognitive overload.To assist a medical practitioner in making informed decisions regarding a medical prescription to a patient,researchers have exploited electronic health records(EHRs)in automatically recommending medication.In recent years,medication recommendation using EHRs has been a salient research direction,which has attracted researchers to apply various deep learning(DL)models to the EHRs of patients in recommending prescriptions.Yet,in the absence of a holistic survey article,it needs a lot of effort and time to study these publications in order to understand the current state of research and identify the best-performing models along with the trends and challenges.To fill this research gap,this survey reports on state-of-the-art DL-based medication recommendation methods.It reviews the classification of DL-based medication recommendation(MR)models,compares their performance,and the unavoidable issues they face.It reports on the most common datasets and metrics used in evaluating MR models.The findings of this study have implications for researchers interested in MR models. | Zafar Ali Yi Huang Irfan Ullah Junlan Feng Chao Deng Nimbeshaho Thierry Asad Khan Asim Ullah Jan Xiaoli Shen Wu Ruil Guilin Qi | 2023 | Data Intelligence2023，5，2： | 0 |
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