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.

Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm

With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.

Multi-Purpose Road Network Modeling and Traffic Organization by Using Spatial Information Systems

The first and most important step in designing a public transportation system is to determine the best way to move the fleet, which, in addition to providing the maximum demand for passengers, can be appropriate in terms of economic parameters and operating costs for the operating organization. The subject of this study is to determine the best route for starting the BRT line based on the eight objectives of the subset of the users’ needs, route of the fleet movement and its traffic conditions and the conditions of the operating organization, between the Fixed Origin and destination. The Analytic Network Process has been used to prioritize the intended goals and determine the contribution of each to advance the issue. Replacing a hierarchical structure with a network structure in order to model the complex communication between decision elements is the advantage of the Analytic Network Process compared to the conventional methods of multi-criteria evaluation. Then, urban roads are categorized according to their role in relation to each of the problem parameters, and finally, the selected route is identified using the Arc GIS10.5 specialized software. The reached path (between the Qods terminal and the Khorram terminal) has been able to cover a population of 40,000 people, equivalent to 20 percent of the population of the study area. Considering a large number of parameters affecting in this problem, has created comprehensive result from the understanding and application of factors affecting the process of the problem, as opposed to most existing studies that use up to two or three goals.

Research and application of GIS information system of Pipeline Network of Oil Field Water

Pipeline network of oil fi eld water is established on the basis of GIS geographic information and SCADA platform, and monitorthe running state of water supply system in oil fi eld mining area in real time through data acquisition, instrument measurement and control,wireless network, water quantity, water pressure and other on-line monitoring equipment. The water supply system is formed, and the informationof mass water supply system is analyzed and processed in a timely manner. The system manages the entire production, management and serviceprocesses of the water supply system in a dynamic manner in order to ensure water supply safety management, and realize the modernization,intelligence and information mode operation of the water supply system in the mining area.

Spatial analysis and evaluation of a coal deposit by coupling AHP & GIS techniques

Nowadays,the evaluation of coal deposits becomes crucial,due to many uncontrollable factors,which affect the energy sector.A comparative evaluation of coal deposits is essential for their hierarchical classification regarding their sustainable exploitation,when compared to other coal deposits or competitive fuels,which may be used as alternative solutions for electricity generation.In this paper,a method for spatial analysis and evaluation of a lignite deposit is proposed,by creating four spatial key indicators via GIS analysis,which are then aggregated by applying a weighted linear combination.The analytical hierarchy process is applied to estimate the relative weights of the indicators,in order to perform a weighted cartographic overlay.Through the synthesis of the indicators,an overall,total spatial quality indicator is calculated.The weighted analysis was shown to be more effective compared to the unweighted one,because it can provide more reliable results regarding the exploitation of the examined lignite deposit.The implementation of GIS-based analytical hierarchy process in spatial analysis and evaluation of lignite deposits,in terms of sustainable exploitation,demonstrates that this method can be extensively applied for evaluating the economic potential of mineral deposits.

Timely Updates in MEC-Assisted Status Update Systems: Joint Task Generation and Computation Offloading Scheme

Fresh status updates are vital to the efficient operation of network monitoring and real-time control applications. In this paper, we consider a mobile edge computing(MEC)-assisted status update system, where smart devices extract valuable status updates from sensed data to achieve timely awareness of the surroundings by exploiting computational resources at the device and edge server. To quantify the freshness of status updates obtained by executing computation tasks, we employ the concept of age of information(Ao I) to characterize the timeliness of status updates. To cope with the limited energy at devices, we investigate a joint task generation and computation offloading scheme under a given energy budget for minimizing the age of obtained status updates. The age minimization problem is modeled as a constrained Markov decision process(CMDP). To obtain the optimal policy, we derive the structural properties of the optimal deterministic policy and propose a lightweight structure-based status update algorithm in the case of known channel statistics. Moreover, we consider a more realistic scenario without prior knowledge of channel statistics, and propose a Q-learning-based status update algorithm to make online decisions. Simulation results show that the performance of our proposed algorithms is competitive when compared with existing schemes.

The Use of Fourier Descriptors for the Classification and Analysis of Peripheral Blood Smears Image

The article discusses the use of Fourier descriptors for the analysis and classification of blood cells. A model describing the contour boundaries in the form of two-dimensional numerical sequence Fourier descriptors. The influence of the shape and orientation of the figures on the parameters of the Fourier descriptors. Explore ways to ensure the invariance of the Fourier descriptors with respect to geometric transformations. A model of the graphical representation of the Fourier descriptors of computer graphics tools. A method of forming a space of informative features based on Fourier descriptors for the neural network, classifying the contours of borders image segments.

Enhancement of Sentiment Analysis Using Clause and Discourse Connectives

The sentiment of a text depends on the clausal structure of the sentence and the connectives’discourse arguments.In this work,the clause boundary,discourse argument,and syntactic and semantic information of the sentence are used to assign the text’s sentiment.The clause boundaries identify the span of the text,and the discourse connectives identify the arguments.Since the lexicon-based analysis of traditional sentiment analysis gives the wrong sentiment of the sentence,a deeper-level semantic analysis is required for the correct analysis of sentiments.Hence,in this study,explicit connectives in Malayalam are considered to identify the discourse arguments.A supervised method,conditional random fields,is used to identify the clause boundary and discourse arguments.For the study,1,000 sentiment sentences from Malayalam documents were analyzed.Experimental results show that the discourse structure integration considerably improves sentiment analysis performance from the baseline system.

Groundwater Exploration Using Multi Criteria Decision Analysis and Analytic Hierarchy Process in Federal Capital Territory, Abuja, Central Nigeria

There exists a growing demand for potable water resources to fill the abysmally insufficient water needs for domestic and industrial especially in the Basement Complex terrains of Nigeria. This situation is attributable to its complex hydrogeologic character. The present challenge has worsened due to the non-incorporation of integrated methods in groundwater exploration campaigns. To effectively combat the challenge of unacceptable failure rates in drilled water well development, there is a need for innovative scientific principles and quantitative assessment of groundwater resources to enhance sustainable and proper utilisation of these resources. Hence, it is the objective of this research to exploit the potential application of remote sensing, Geographic Information System (GIS), and Multi-Criteria Decision Analysis (MCDA) techniques and freely open datasets in mapping groundwater potential zones. Seven thematic maps have been produced based on factors that are deemed to influence and deemed to have significant control on the occurrence and movement of groundwater. These factors are geology, lineament density, slope, drainage density, rainfall, land-use/land cover, and soil class. Analytic Hierarchy Process (AHP) was used to assign normalised weights to the thematic maps based on the various relative contributions to groundwater occurrence and movement. These thematic maps were then processed in a GIS environment using the Weighted Overlay tool which implements the MCDA. The resulting Groundwater Potential Zones (GPZ) of the area gave rise to Five classes viz: Very good, Good, Moderate, Poor and Very Poor representing 19%, 8%, 14%, 47% and 13% respectively. It is recommended that the GPZ map should be used as a reconnaissance tool for selecting prospective sites for detailed groundwater resource exploitation.

Indexing habitat suitability and human-elephant conflicts using GIS-MCDA in a human-dominated landscape

Concerns for biodiversity loss, wildlife conservation, and habitat destruction have dominated the policy agenda worldwide for decades. Unsustainable human-induced development and negative interaction between humans and wildlife have emerged as predominant issues globally. The present study deals with human and elephant conflicts (HEC) in the Polpitigama Divisional Secretariat, Sri Lanka, which is located in the Kahalla-Pallekele elephant corridor and connects Wilpattu and Kaudulla wildlife sanctuaries. The research objectives are identifying spatial patterns of elephant habitat suitability and probable risk zones for HEC. The elephant habitat suitability and HEC risk zones were identified on spatial and temporal scales using Geographic Information System integrating Multi-Criteria Decision Analysis. Different factors, including habitat suitability, distance to roads, distance to croplands, distance to forests and protected areas, settlements, and population density, were considered to determine HEC risk zones in the area. Topography, water, and vegetation criteria are considered when determining elephant habitat suitability. The results of the Analytic Hierarchy Process run the spatially explicit model. The results revealed that of the total area, 15.3% is very highly suitable for elephant habitats, while the least suitable areas contribute only 4%. About 33.8% of the area is moderately suitable for elephants. The risk map indicates that 23.7% of the total area is under very high risk for HEC, and the least risk areas only account for 5.4%. About 26.2% of the area falls under the moderate risk zone for HEC. Since the model considered three aspects of HEC, it will help policymakers in wildlife conservation to avoid and minimize the HEC.

2022年末安徽一次大气重污染过程特征及成因分析

综合利用多源数据,应用HYSPLIT4轨迹分析、颗粒物区域传输分析等方法对2022年末安徽一次大气重污染过程变化特征及成因进行分析。结果表明:污染前期,受弱冷空气影响,有利于污染物输送至安徽地区;污染期间,地面处于弱气压场,混合层厚度较低,存在持续性逆温,且地面风速小、相对湿度较高、基本无降水,不利于污染物的扩散和清除,受区域输送叠加本地污染排放累积共同影响,污染程度加剧;2023年1月2日地面以东北、偏东风为主,风速增大、相对湿度下降、混合层厚度抬升,扩散条件转好,污染程度减轻。在此次污染过程中,山东、江苏、河南对安徽累积的区域输送贡献率占比为48.5%,高于安徽本地污染的贡献率(21.4%)。

专利视角下蒙古文信息处理技术发展动态研究

以专利数据为基础,绘制蒙古文信息处理技术的发展图谱,分析该技术发展现状和趋势,为技术研发提供数据支撑。利用innojoy和incopat专利检索分析平台对中国蒙古文信息处理技术专利进行统计和对比分析。从不同维度,对蒙古文信息处理技术的专利数量、申请量、活跃度、区域分布、专利技术构成等方面进行了分析和讨论。经过几十年的技术储备、应用尝试等,蒙古文信息处理技术处于大幅增长的态势。在区域分布上,内蒙古具有天然的优势,其中内蒙古工业大学相对于其他创新主体表现突出。然而,有权专利数量和占比均表现不佳。蒙古文信息处理技术通常注重在研发阶段以合作开发的方式开展研究以及应用工作,而在专利权取得后,以转让方式进行专利价值释放或者落地实施的方式并不明显。苏依拉和仁庆道尔吉研发力优势突出,即专利申请总量以及活跃度均表现较好。从蒙古文信息处理专利技术构成看,自然语言处理技术专利最多,其次为信息检索技术,最后是识别模式的方法和装置。

基于集对分析理论和数字化平台的山岭隧道塌方风险评价

为更准确、更科学地完成山岭隧道塌方风险评价,提出一种基于层次分析法(AHP)-熵值法(Entropy)集对分析理论的山岭隧道塌方风险评价方法。首先,选取地质因素、设计因素、施工因素、管理因素4项因素作为塌方风险评价指标体系的准则层,选取12项因素作为指标层,运用层次分析法和熵值法分别计算2级指标权重;然后,引入集对分析理论确定指标联系度,并结合置信度准则对塌方风险的等级进行判定;接着,利用Grasshopper以及Python软件,对集对分析理论进行可视化程序开发,并将其与隧道-地质BIM交互模型进行耦合,建立基于AHP-Entropy集对分析模型的隧道塌方风险数字化分析平台;最后,结合重庆某隧道实际工程,验证隧道塌方风险评价的高效化、可视化效果。研究表明:基于集对分析理论的山岭隧道塌方风险评价数字化分析平台运行时间约为2 s,与传统的隧道塌方风险评价方法相比可以大大提高风险评价效率,同时利用AHP-Entropy法确定评价指标权重,提高了评价结果的可靠性。

全过程咨询模式下的信息共享影响因素研究

本文用SNA法(社会网络分析法)对全过程工程咨询模式下信息共享的影响因素进行研究。先在现有研究的基础上整理归纳出26个影响双方信息共享的因素,再通过问卷调查收集数据,借助SPSS软件将其归纳为环境、经济、风险、协同四类,然后通过Ucinet软件从宏观层面的网络密度、k-核指标分析和微观层面的度数中心度、中间中心度、接近中心度分析,最终确定了关键影响因素,对全过程工程咨询模式的推广与建筑业信息化发展有重要参考价值。

全程信息化管理在提高医院检验分析前阶段质量中的应用效果

目的分析在医院检验分析前阶段实施全程信息化管理,对提升检验质量的应用价值。方法选取2022年4月—2023年3月陕西航天医院检验科的21名医护人员进行常规管理模式,为对照组,2023年4—7月进行全程信息化管理模式,为观察组,其间涉及的医护人员为同一批,比较两组管理效果。结果观察组时效性、实用性、专业性、服务性等维度评分高于对照组,差异有统计学意义(P均<0.05)。观察组样本采样至送出时间、样本送出至抵达实验室时间均短于对照组,差异有统计学意义(P均<0.05)。两组抗凝样本凝固、标本丢失、样本容器错误、血培养污染、样本溶血检验前不良事件发生率比较,差异无统计学意义(P均>0.05)。观察组医护人员对管理的满意度为100.00%,高于对照组的80.95%,差异有统计学意义(P<0.05)。结论在提高医院检验分析前阶段,实施全程信息化管理对于提高医院的整体运营效率和患者满意度有着重要作用。

K-Medoids聚类算法的计算机信息处理技术研究

当前计算机信息处理技术在大规模数据集上存在计算效率低下、对噪声和异常值敏感等问题。为了解决这些问题,本文提出了一种改进的K-medoids聚类算法。该方法通过优化初始中心点的选择和更新策略,提高了算法的收敛速度和稳定性,并引入基于密度的聚类评价指标,提高了对噪声数据的鲁棒性。通过在真实和人工数据集上的实验验证,证明了本方法在提高聚类效果和处理大规模数据方面的有效性。

基于非下采样Bandelet变换的多聚焦图像融合

针对同一场景的多聚焦图像,提出了基于非下采样Bandelet变换(NSBT)的图像融合方法。首先求出源图像的平均图像,再利用该图像的小波系数计算出最优的几何流,进而构造出一组Bandelet基,然后将源图像通过这组基进行表示,对得到的系数按所对应的不同物理意义分别进行融合,最后将融合系数进行非下采样Bandelet逆变换得到融合图像。实验结果表明,与基于小波变换(WT)和非下采样小波变换(NSWT)的融合方法相比,本文所提方法取得了更好的视觉效果和量化指标。

算法时代用户何以信息偏食:驱动因素、生成过程及纠偏策略

文章以网络有关信息偏食的公开语料为数据源,采用归纳式内容分析法和MAXQDA质性分析软件进行编码分析,共识别出848个代码,归纳为驱动因素、群体分布、类型分布、影响结果和纠偏策略五大内容,就此构建和阐述信息偏食的生成过程逻辑。研究发现,信息偏食可分为主动型、被动型、社交型和焦虑型四种类型,且受到主观因素(自身特性、认知偏差、心理状态和选择性接触)和客观因素(算法操纵、信息路径固化和信息资本双重裹挟)的共同影响;具有促进反茧性、推动专业细化和技术具身性反思等正向作用,但负向影响更大,易造成信息孤岛、信息结构失衡,思维固化、同质信息疲劳,群体极化、共情偏离等问题。基于此,提出主体自觉——培育用户多元素养,平台自控——建立逆向推荐工程,法律规范——引导技术算法向善的纠偏策略。

基于自然语言处理与结构化算法的病历信息高精度抽取方法研究

目的:综合运用自然语言处理、结构化算法和知识图谱等技术,探索实现电子病历高精度信息抽取和结构化处理的方法。方法:通过构建命名实体识别模型、关系识别模型、同义词识别模型完成病历文本的句内信息抽取;提出了一种病历生成树算法,可以有效实现大段落病历文本分层结构的解析;同时利用知识图谱技术存储信息抽取与分层解析的构造模型,实现病历文本信息高精度抽取。结果:形成了一套融合深度学习算法与结构化解析算法的病历信息高精度抽取方法,其中实体识别模型准确率达95.74%,关系识别模型准确率达89.20%,最终生成具有清晰层次结构、可精确定位和抽取信息的结构化病历。结论:本文所探索的病历信息高精度抽取方法,将深度学习算法与结构化解析算法相融合,兼顾了病历文本的句内信息抽取与病历结构层次的解析,可以实现对病历数据的自动抽取、精准定位与高效管理,可以为临床医学研究奠定数据基础,也可以为其他疾病病历文本数据的挖掘提供方法学参考。

大数据时代下的计算机信息处理技术分析

随着大数据技术的飞速发展,大数据已成为改善人们生活质量的重要工具,同时也促进了计算机信息处理技术的进一步完善。因此,企业和个人都要有创新思维,以便利用这项先进的科学技术促进社会的可持续发展。随着科技的不断发展,大数据时代的到来为企业带来了前所未有的挑战,企业必须充分利用这种新兴技术,强化各项信息的归集工作,以期获得更好的竞争力,并保证信息的安全性和可靠性。