An intelligent prediction model of epidemic characters based on multi-feature

The epidemic characters of Omicron(e.g.large-scale transmission)are significantly different from the initial variants of COVID-19.The data generated by large-scale transmission is important to predict the trend of epidemic characters.However,the re-sults of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission.In consequence,these inaccurate results have negative impacts on the process of the manufacturing and the service industry,for example,the production of masks and the recovery of the tourism industry.The authors have studied the epidemic characters in two ways,that is,investigation and prediction.First,a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters.Second,theβ-SEIDR model is established,where the population is classified as Susceptible,Exposed,Infected,Dead andβ-Recovered persons,to intelligently predict the epidemic characters of COVID-19.Note thatβ-Recovered persons denote that the Recovered persons may become Sus-ceptible persons with probabilityβ.The simulation results show that the model can accurately predict the epidemic characters.

Unlocking the Potential:A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks

As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.

Automatic Persian Text Summarization Using Linguistic Features from Text Structure Analysis

With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents,which must be done without losing important features and information.This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure.The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text,which improves the sentence feature selection process and leads to the generation of unambiguous,concise,consistent,and coherent summaries.The paper also presents the results of the evaluation of the proposed method based on precision and recall criteria.It is shown that the method produces summaries consisting of chains of sentences with the aforementioned characteristics from the original text.

A comprehensive review of existing corpora and methods for creating annotated corpora for event extraction tasks

Purpose:The purpose of this study is to serve as a comprehensive review of the existing annotated corpora.This review study aims to provide information on the existing annotated corpora for event extraction,which are limited but essential for training and improving the existing event extraction algorithms.In addition to the primary goal of this study,it provides guidelines for preparing an annotated corpus and suggests suitable tools for the annotation task.Design/methodology/approach:This study employs an analytical approach to examine available corpus that is suitable for event extraction tasks.It offers an in-depth analysis of existing event extraction corpora and provides systematic guidelines for researchers to develop accurate,high-quality corpora.This ensures the reliability of the created corpus and its suitability for training machine learning algorithms.Findings:Our exploration reveals a scarcity of annotated corpora for event extraction tasks.In particular,the English corpora are mainly focused on the biomedical and general domains.Despite the issue of annotated corpora scarcity,there are several high-quality corpora available and widely used as benchmark datasets.However,access to some of these corpora might be limited owing to closed-access policies or discontinued maintenance after being initially released,rendering them inaccessible owing to broken links.Therefore,this study documents the available corpora for event extraction tasks.Research limitations:Our study focuses only on well-known corpora available in English and Chinese.Nevertheless,this study places a strong emphasis on the English corpora due to its status as a global lingua franca,making it widely understood compared to other languages.Practical implications:We genuinely believe that this study provides valuable knowledge that can serve as a guiding framework for preparing and accurately annotating events from text corpora.It provides comprehensive guidelines for researchers to improve the quality of corpus annotations,especially for event extraction tasks across various domains.Originality/value:This study comprehensively compiled information on the existing annotated corpora for event extraction tasks and provided preparation guidelines.

Smart Approaches to Efficient Text Mining for Categorizing Sexual Reproductive Health Short Messages into Key Themes

To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.

Research on Feature Extraction of Composite Pseudocode Phase Modulation-Carrier Frequency Modulation Signal Based on PWD Transform

The identification features of composite pseudocode phase modulation and carry frequency modulation signal include pseudocode and modulation frequency. In this paper,PWD is used to extract these features. First,the feature of pseudocode is extracted using the amplitude output of PWD and the correlation filter technology. Then the feature of frequency modulation is extracted by way of PWD analysis on the signal processed by anti-phase operation according to the extracted feature of pseudo code,i.e. position information of changed abruptly point of phase. The simulation result shows that both the features of frequency modulation and phase change position caused by the pseudocode phase modulation can be extracted effectively for SNR=3 dB.

A Survey of Wi-Fi Sensing Techniques with Channel State Information

A review of signal processing algorithms employing Wi-Fi signals for positioning and recognition of human activities is presented.The principles of how channel state information(CSI)is used and how the Wi-Fi sensing systems operate are reviewed.It provides a brief introduction to the algorithms that perform signal processing,feature extraction and recognitions,including location,activity recognition,physiological signal detection and personal identification.Challenges and future trends of Wi-Fi sensing are also discussed in the end.

Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

In the realm of Multi-Label Text Classification(MLTC),the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches.Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content,often overlooking intrinsic textual cues such as label statistical features.In contrast,these endogenous insights naturally align with the classification task.In our paper,to complement this focus on intrinsic knowledge,we introduce a novel Gate-Attention mechanism.This mechanism adeptly integrates statistical features from the text itself into the semantic fabric,enhancing the model’s capacity to understand and represent the data.Additionally,to address the intricate task of mining label correlations,we propose a Dual-end enhancement mechanism.This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation.We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets.These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism.Our final model unequivocally outperforms the baseline model,attesting to its robustness.These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models.

基于脑电图的帕金森轻度认知障碍功能网络特征分析

帕金森氏轻度认知障碍(PDMCI)是帕金森氏症患者痴呆的先兆,这对使用神经评分量表和医生经验等传统方法进行准确诊断提出了挑战。利用26名PDMCI患者和23名正常人的脑电信号,基于定向传递函数构建了Delta、Theta、Alpha、Beta和Gamma频段的脑功能网络。引入了一种新颖的图论特征——效率密度来捕获网络密度和传输效率。研究结果揭示了独特的连接模式,Delta和Theta波段的连接更紧密,而Alpha、Beta和Gamma波段的连接更稀疏。帕金森病(PD)患者与对照组之间的Theta、Alpha、Beta和Gamma频带存在显著差异(p<0.05)。因此,脑功能网络可以有效反映PD脑功能异常状态,效率密度特征可以反映PD脑功能异常活动的特征量。

利用BERT和覆盖率机制改进的HiNT文本检索模型

为有效提升文本语义检索的准确度,本文针对当前文本检索模型衡量查询和文档的相关性时不能很好地解决文本歧义和一词多义等问题,提出一种基于改进的分层神经匹配模型(hierarchical neural matching model,HiNT)。该模型先对文档的各个段提取关键主题词,然后用基于变换器的双向编码器(bidirectional encoder representations from transformers,BERT)模型将其编码为多个稠密的语义向量,再利用引入覆盖率机制的局部匹配层进行处理,使模型可以根据文档的局部段级别粒度和全局文档级别粒度进行相关性计算,提高检索的准确率。本文提出的模型在MS MARCO和webtext2019zh数据集上与多个检索模型进行对比,取得了最优结果,验证了本文提出模型的有效性。

基于多级语义对齐的图像-文本匹配算法

图像中的区域特征更关注于图像中的前景信息,背景信息往往被忽略,如何有效的联合局部特征和全局特征还没有得到充分地研究。为解决上述问题,加强全局概念和局部概念之间的关联得到更准确的视觉特征,提出一种基于多级语义对齐的图像-文本匹配算法。提取局部图像特征,得到图像中的细粒度信息;提取全局图像特征,将环境信息引入到网络的学习中,从而得到不同的视觉关系层次,为联合的视觉特征提供更多的信息;将全局-局部图像特征进行联合,将联合后的视觉特征和文本特征进行全局-局部对齐得到更加精准的相似度表示。通过大量的实验和分析表明:所提算法在2个公共数据集上具有有效性。

面向医学影像报告生成的门归一化编解码网络

医学影像报告的自动生成可以减轻医生的工作强度,减少误诊或漏诊的情况发生。由于医学影像的独特性,通常病灶比较小,与正常区域灰度差异难以分辨,导致文本生成时关键词的缺失,报告不够准确。对此提出一种面向医学影像报告生成的门归一化编解码网络,通过门控通道变换单元优化视觉特征提取,加强特征间的差异,自动筛选关键特征;提出门归一化算法,沿通道维度整合上下文信息,在浅层网络激活、深层网络抑制通道间神经元活性,过滤无效特征,使文本和视觉语义充分交互,提高报告生成质量。在2种广泛使用的基准数据集IU X-Ray和MIMIC-CXR上的试验结果表明,模型能够取得先进的性能,生成的影像报告也具有更好的视觉语义一致性。

基于双分支特征融合的电力设备缺陷文本挖掘方法

针对电力设备缺陷文本信息的知识挖掘与分析任务中存在缺陷文本特征信息提取不足、缺陷文本分类精度不够的问题,提出一种基于BERT(bidirectional encoder representations from transformers)的双分支特征融合的电力设备缺陷文本分类模型。首先,对缺陷文本数据进行预处理,删除异常缺陷文本,并归纳了电力设备缺陷文本特点;然后,采用BERT模型作为文本编码器,将文本转化为向量后分别输入至BiLSTMAttention(attention-based bidirectional long short-term memory)模块和多分支CNN(multi-scale convolutional neural network,MCNN)模块,提取缺陷文本语义信息特征和局部关键信息特征;最后,将所提取出的语义特征和多维关键特征向量进行融合,并通过Softmax层实现对缺陷文本分类。与基准模型BERT-BiLSTMAttention相比,其准确率、召回率及F1值分别提高了2.76%、3.58%和4.39%,表明所建模型在缺陷文本分类任务中性能的优越性。

基于用户兴趣模型的大学生就业信息推荐方法

为提高推荐就业信息与大学生偏好就业信息的匹配程度,文章将个体就业需求作为前提条件,设计一种基于用户兴趣模型的大学生就业信息推荐方法。首先,利用兴趣模型中的关联规则,对高校提供的就业信息中兴趣特征点进行匹配;其次,在既定的分类规则下,根据就业文本信息的内容对其进行类别划分;最后,根据用户浏览高校就业信息、在就业招聘界面的停留时间等,针对大学生偏好进行计算。对比实验结果表明:本文中设计的推荐方法应用效果良好,按照规范使用该方法进行大学生就业信息推荐,能够增加推荐就业信息与大学生偏好就业信息的匹配程度,为大学生提供更加优质的就业服务,提高大学生就业质量。

A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.

基于用户偏好评分值修正的深度神经网络推荐模型

针对工业产业链上下游产品选购中用户对产品评分习惯差异较大的问题,结合用户评分习惯提出修正算法,构建一种基于用户偏好评分值修正的深度神经网络推荐模型(UPDNN)。该方法首先通过历史数据对各用户评分偏好进行学习,设计特有的满意度投影函数将用户评分投影至满意度空间进行修正,然后在满意度空间中通过深度神经网络进行推荐模型训练和待测产品满意度预测,最终给出用户的Top-k推荐产品表,实现产品推荐。实验结果表明,UPDNN较经典推荐算法在Movielens数据集上的推荐结果更贴合用户喜好,验证了所提方法的有效性。

Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies

Digitalization has changed the way of information processing, and newtechniques of legal data processing are evolving. Text mining helps to analyze andsearch different court cases available in the form of digital text documents toextract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reducedtime. The rapid development of judicial ontologies seems to deliver interestingproblem solving to legal knowledge formalization. Mining context informationthrough ontologies from corpora is a challenging and interesting field. Thisresearch paper presents a three tier contextual text mining framework throughontologies for judicial corpora. This framework comprises on the judicial corpus,text mining processing resources and ontologies for mining contextual text fromcorpora to make text and data mining more reliable and fast. A top-down ontologyconstruction approach has been adopted in this paper. The judicial corpus hasbeen selected with a sufficient dataset to process and evaluate the results.The experimental results and evaluations show significant improvements incomparison with the available techniques.

A Rule Based System for Speech Language Context Understanding

Speech or Natural language contents are major tools of communication. This research paper presents a natural language processing based automated system for understanding speech language text. A new rule based model has been presented for analyzing the natural languages and extracting the relative meanings from the given text. User writes the natural language text in simple English in a few paragraphs and the designed system has a sound ability of analyzing the given script by the user. After composite analysis and extraction of associated information, the designed system gives particular meanings to an assortment of speech language text on the basis of its context. The designed system uses standard speech language rules that are clearly defined for all speech languages as English, Urdu, Chinese, Arabic, French, etc. The designed system provides a quick and reliable way to comprehend speech language context and generate respective meanings.

预训练语言模型的应用综述

近年来,预训练语言模型发展迅速,将自然语言处理推到了一个全新的发展阶段。文中的综述旨在帮助研究人员了解强大的预训练语言模型在何处以及如何应用于自然语言处理。具体来讲,首先简要回顾了典型的预训练模型,包括单语言预训练模型、多语言预训练模型以及中文预训练模型;然后讨论了这些预训练模型对5个不同的自然语言处理任务的贡献,即信息提取、情感分析、问答系统、文本摘要和机器翻译;最后讨论了预训练模型的应用所面临的一些挑战。

鱼病实时检测系统的研制与试验

为实现集约化水产养殖中的鱼类因病毒细菌等感染体表病症的快速、准确识别,帮助养殖户快速了解养殖池内的鱼病危害程度和分布情况,基于改进的YOLOv5结合嵌入式技术设计一套鱼病的快速检测系统。使用改进过的YOLOv5神经网络模型生成鱼病的候选框,实现对鱼病的快速定级分类。检测系统根据候选框的数据对鱼病进行计数、分类,鱼病危害分类按正常、轻度、重度划分,结合患病鱼数形成对鱼病危害程度定量化测评的体系,最后引入GPRS模块获取检测点的位置信息,在软件端形成鱼病的热力图。模型测试结果表明:改进后的YOLOv5模型检测精准率为99.75%,召回率为93.21%,测试模型mAP50、mAP50:95对比原YOLOv5模型在帧数轻微下降3.22帧的情况下AP达到99.38%、88.09%,表明其拥有出色性能,改进后模型内存下降至13.6 MB。改进后YOLOv5模型体积更小,性能优越稳定强,适宜部署在鱼病检测嵌入式系统中。系统整体测试结果表明:系统能够实时的检测鱼病的发生,检测时系统能按正常,轻度,重度划分鱼病,并将鱼病的情况结合定位系统形成可视化的热力图像。