Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.

Synthesis of indirect work exchange networks considering both isothermal and adiabatic process together with exergy analysis

In this paper, an efficient methodology for synthesizing the indirect work exchange networks(WEN) considering isothermal process and adiabatic process respectively based on transshipment model is first proposed. In contrast with superstructure method, the transshipment model is easier to obtain the minimum utility consumption taken as the objective function and more convenient for us to attain the optimal network configuration for further minimizing the number of units. Different from division of temperature intervals in heat exchange networks,different pressure intervals are gained according to the maximum compression/expansion ratio in consideration of operating principles of indirect work exchangers and the characteristics of no pressure constraints for stream matches. The presented approach for WEN synthesis is a linear programming model applied to the isothermal process, but for indirect work exchange networks with adiabatic process, a nonlinear programming model needs establishing. Additionally, temperatures should be regarded as decision variables limited to the range between inlet and outlet temperatures in each sub-network. The constructed transshipment model can be solved first to get the minimum utility consumption and further to determine the minimum number of units by merging the adjacent pressure intervals on the basis of the proposed merging methods, which is proved to be effective through exergy analysis at the level of units structures. Finally, two cases are calculated to confirm it is dramatically feasible and effective that the optimal WEN configuration can be gained by the proposed method.

Mathematical Modeling in Social Network Analysis： Using TOPSIS to Find Node Influences in a Social Network

In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes？ Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers＇ inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature： the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers＇ inputs or the topology of the network.

STABILITY ANALYSIS OF ARTIFICIAL NEURAL NETWORKS UNDER RANDOM PERTURBATION

Based on the theory of stochastic differential equation,the stability of a kind ofcontinuous-time generalized Hopfield neural networks with white noise perturbation is studiedin the paper,and the related stability criteria and design requirements of neural networks areestablished.

Underwater multiple target tracking decision making based on an analytic network process

Underwater multi-target tracking logic and decision （UMTLD） has difficulty resolving multi-target tracking problems for underwater vehicles. Present methods assume factors in UMTLD are uncorrelated, when these are actually in a complex, interdependent relationship. To provide this, an index set of multi-target tracking decision characteristics and an analytic network process （ANP） model of the UMTLD method was -established. This method brings the index set of multi-target tracking decision into the ANP model, and the optimization multitarket tracking decision is achieved via computation of the resulting supermatrix. The rationality and robustness of decision results increase in simulations by 13% and 47% respectively with analytic hierarchy process （AHP）. These results indicate that the ANP method should be the preferred method when UMTLD factors are interdependent.

Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.

Using analytic network process to analyze problems for implementing turn-key construction projects in Taiwan

The turn-key construction project is implemented in Taiwan not by a single company but by a make-shift group of several companies. Hence,problems to coordinate the professional construction management (PCM) and the supervising architectural company often occur for the lack of long-term experience to work together. The various factors that affect the implementation of turn-key projects currently practiced in Taiwan are analyzed using the analytic network process (ANP). The objective is to study how the twelve key factors in the four layers of "Role assignment","Signing contract","Operational procedures" and "Losing capital investment" affect the progress of implementing the turn-key project in Taiwan. The results reveal that "Delay in payment" has the most negative influence with 15.62% weighing factor; "Latent risk" comes next with 11.14% weighing factor,and "Responsibility of construction company for project quality" is the third with 10.79% weighing factor.

XML-based Data Processing in Network Supported Collaborative Design

In the course of network supported collaborative design, the data processing plays a very vital role. Much effort has been spent in this area, and many kinds of approaches have been proposed. Based on the correlative materials, this paper presents extensible markup language （XML） based strategy for several important problems of data processing in network supported collaborative design, such as the representation of standard for the exchange of product model data （STEP） with XML in the product information expression and the management of XML documents using relational database. The paper gives a detailed exposition on how to clarify the mapping between XML structure and the relationship database structure and how XML-QL queries can be translated into structured query language （SQL） queries. Finally, the structure of data processing system based on XML is presented.

Numerical‐discrete‐scheme‐incorporated recurrent neural network for tasks in natural language processing

A variety of neural networks have been presented to deal with issues in deep learning in the last decades.Despite the prominent success achieved by the neural network,it still lacks theoretical guidance to design an efficient neural network model,and verifying the performance of a model needs excessive resources.Previous research studies have demonstrated that many existing models can be regarded as different numerical discretizations of differential equations.This connection sheds light on designing an effective recurrent neural network(RNN)by resorting to numerical analysis.Simple RNN is regarded as a discretisation of the forward Euler scheme.Considering the limited solution accuracy of the forward Euler methods,a Taylor‐type discrete scheme is presented with lower truncation error and a Taylor‐type RNN(T‐RNN)is designed with its guidance.Extensive experiments are conducted to evaluate its performance on statistical language models and emotion analysis tasks.The noticeable gains obtained by T‐RNN present its superiority and the feasibility of designing the neural network model using numerical methods.

Web Intelligence with Enhanced Sunflower Optimization Algorithm for Sentiment Analysis

Exponential increase in the quantity of user generated content in websites and social networks have resulted in the emergence of web intelligence approaches.Several natural language processing(NLP)tools are commonly used to examine the large quantity of data generated online.Particularly,sentiment analysis(SA)is an effective way of classifying the data into different classes of user opinions or sentiments.The latest advances in machine learning(ML)and deep learning(DL)approaches offer an intelligent way of analyzing sentiments.In this view,this study introduces a web intelligence with enhanced sunflower optimization based deep learning model for sentiment analysis(WIESFO-DLSA)technique.The major intention of the WIESFO-DLSA technique is to identify the expressions or sentiments that exist in the social networking data.The WIESFO-DLSA technique initially performs pre-processing and word2vec feature extraction processes to generate a meaningful set of features.At the same time,bidirectional long short term memory(BiLSTM)model is applied for classification of sentiments into different class labels.Moreover,an enhanced sunflower optimization(ESFO)algorithm is exploited to optimally adjust the hyperparameters of the BiLSTM model.A wide range of simulation analyses is performed to report the better outcomes of the WISFO-DLSA technique and the experimental outcomes ensured its promising performance under several measures.

基于DANP—模糊综合评价的危化品救援队伍应急能力评估

为量化评价我国危化品应急救援队伍能力,明确应急救援队伍建设中存在的不足,通过能力素质冰山模型研究影响因素,从应急准备、应急响应和总结评估3个方面建立评估指标体系,结合集成决策实验室分析法(DEMATEL)与网络层次分析法(ANP)确定指标权重,并和DEMATEL法的权重结果进行对比,运用模糊综合评价法建立全国危化品救援队伍应急能力评价模型。结果表明:应急准备能力评价为“较弱”,应急响应能力和总结评估能力评价为“一般”,队伍总体应急能力评价为“一般”,两种权重的评价结果一致。

The Selection of Dry Port Location by Analytic Network Process Model: A Case Study of Dosso-Niger

The aim of this paper is to select the best location for the construction of a dry port in Niger which is a land locked country (LLC). Niger is located in the Sahel and has a land area of 1,267,000 square kilometers [1], with the closest port being port of Cotonou in Benin. The transport corridor from Niamey to Cotonou is approximately 1036 km long [2]. It is estimated that this corridor carries about 40 percent of Niger’s overseas trade traffic [3]. In this work, the Analytic Network Process (ANP) model is used to determine the optimal location of the dry port, among three major cities: Niamey (capital city), Dosso and Gaya. From the application of this selection model, Dosso was selected as the best location for the location of the dry port, while Gaya and Niamey were placed second and third respectively. The results obtained in this work strongly confirm the decision of the government of Niger to construct a dry port in Dosso, a project that commenced in 2010 and is still in progress.

分享经济企业信任保障模式创新与决策研究——基于ANP模型的多案例比较分析

信任保障是驱动分享经济高质量发展的关键要素,对分享经济信任保障模式决策机制的深入研究具有重要的理论意义和实践价值。在将分享经济信任保障模式划分为企业主导型信任保障、第三方主导型信任保障、政府主导型信任保障和技术驱动型信任保障四种类型基础上,通过构建分享经济企业信任保障模式决策的ANP模型,并选取滴滴出行、小猪民宿和猪八戒网三个分享经济典型企业进行了算例应用,验证了模型的合理性与可行性。研究结果表明:(1)随着分享经济的持续快速发展,科学合理、符合实际、契合度高的分享经济信任保障对分享经济的持续健康发展十分重要。(2)分享经济企业信任保障模式选择受到行业发展、行业环境、平台因素、供方因素、需方因素等多方因素的影响。(3)政府主导型信任保障模式最适合发展速度快、与传统行业冲突大、恶性舆论事件频发的分享经济企业;第三方主导型信任保障模式最适合供需双方同时面临较大风险的分享经济企业;企业主导型信任保障模式最适合发展水平、技术水平和服务水平较高的分享经济企业;技术驱动型信任保障模式是重塑数字化背景下分享经济信任体系的重要方式,也是未来分享经济信任保障的最重要模式。就整体层面而言,政府对分享经济新业态加强包容审慎监管;就不同行业而言,政府要针对不同情况采取有针对性的治理措施;就未来发展而言,政府要进一步提升数字化和智能化治理水平。

A Sentimental Analysis System for Film Review based on Deep Learning

The paper will be introduced as sentimental analysis system of film criticism based on deep learning.Which contains four main processing sections.Compared with other systems,our sentimental analysis system based on deep learning has plenty of advantages,including simple structure,high accuracy,and rapid encoding speed.

基于ANP-可拓云模型的网络型航空公司综合竞争力评价

构建网络型航空公司综合竞争力评价指标体系并实施客观评价,是国内网络型航空公司明确自身定位、快速提升综合竞争力的重要手段。从创新发展能力、运营管理能力和品牌塑造能力3个维度入手建立综合竞争力评价体系,并采用网络分析法(ANP,analytic network process)-可拓云模型对全球20家典型网络型航空公司进行综合评价,得到各航空公司在全球航空运输市场上的相应地位以及各方面的优势与短板,以期为国内网络型航空公司实现高质量发展、进一步提升综合竞争力提供理论和实践参考。研究表明:中国三大网络型航空公司的创新发展能力和运营管理能力尚有不足,建议以枢纽网络和效率提升为重要抓手,着力提升优化。

Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

基于ANP与物元可拓模型的冻土区高速公路施工安全风险评价研究

冻土区高速公路施工难度大,施工过程受多种风险因素以及风险因素之间的联动效应影响,客观全面的施工安全风险评价需构建特定情境的风险评价指标体系,并考虑指标之间的相互作用,得出量化的风险数值。因此,研究提出网络分析法(ANP)与物元可拓模型相结合的风险评价模型。首先,构建总目标、4个一级指标和12个二级指标的三级冻土区高速公路施工安全风险评价指标体系;其次,建立指标相互关联的ANP网络结构模型,计算各级指标的综合权重;最后,根据物元可拓模型得出多级可拓评价结果。案例分析显示,该冻土区高速公路施工安全风险等级为3级(安全风险较大),与实际施工情况相符,进一步针对性地提出了加强施工安全的建议措施。

基于ANP-FCE的矿井风险防控系统研发

为加强煤矿安全风险防控能力,采用ANP-FCE相结合的方法构建矿井风险防控系统。首先,基于事故致因理论对矿井生产体系进行调研,并建立矿井风险防控指标体系;其次,运用ANP分析主要影响因素,并采用FCE建立矿井风险防控评价模型;最后,利用Java平台,依托JSP+SSM框架与MySQL数据库联合开发矿井风险防控系统,并在某煤矿试运行。结果表明:该系统可以确定矿井安全生产工作的重点,实现矿井生产数据实时显示,并具有重点作业监控和预警功能。系统的开发有利于企业各级领导及时掌握各种数据信息,提高企业自身的风险防控和预警能力,为煤矿企业的运营和发展提供安全保障。

基于ANP-BP方法的煤矿应急能力评价研究

合理选用评价方法对企业认清应急能力现状及提升应急能力水平具有重要意义。为开展煤矿应急能力评价,强化煤矿应急能力建设,通过对ANP网络层次分析法和BP神经网络各自优势进行分析并组合,以ANP计算出的结果作为BP神经网络的训练样本数据,提出了ANP-BP评价方法。对该方法在煤矿应急能力评价过程中的实现进行了研究,证明了该方法的可适用性和可操作性。

基于网络分析法(ANP)的水电工程风险分析及其应用

水电工程项目在开发建设过程中往往面临着来自技术、经济、自然和社会环境等诸多方面的风险和干扰,其风险因素又难以量化且他们之间相互关联、相互影响,本文针对此问题,提出了基于网络分析法(ANP)对水电工程风险进行分析,建立了风险因素多准则、多层次的ANP结构模型,并对模型的求解进行了详细的阐述。最后,以某水电工程为例,应用此方法求得风险因素总排序,结果表明ANP能够比较有效地处理多种风险因素之间复杂影响关系,从而发现了其主要关键的风险因素,为工程项目的风险控制和管理提供了重要的参考。