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

Computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan software competence center hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** 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 effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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Exploiting Unlabeled Data for Neural Grammatical Error Detection

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Journal of Computer Science & Technology 2017年第4期32卷 758-767页

作者： Zhuo-Ran Liu Yang Liu School of software Beihang University Beijing 100191 China State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Jiangsu Collaborative Innovation center for Language competence Xuzhou 221009 China

Identifying and correcting grammatical errors in the text written by non-native writers have received increasing attention in recent years. Although a number of annotated corpora have been established to facilitate data-driven grammatical error detection and correction approaches, they are still limited in terms of quantity and coverage because human annotation is labor-intensive, time-consuming, and expensive. In this work, we propose to utilize unlabeled data to train neural network based grammatical error detection models. The basic idea is to cast error detection as a binary classification problem and derive positive and negative training examples from unlabeled data. We introduce an attention-based neural network to capture long-distance dependencies that influence the word being detected. Experiments show that the proposed approach significantly outperforms SVM and convolutional networks with fixed-size context window.

关键词： unlabeled data grammatical error detection neural network

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3D-Printed PLA Filaments Reinforced with Nanofibrillated Cellulose

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Journal of Renewable Materials 2020年第7期8卷 759-772页

作者： Matea Perić Robert Putz Christian Paulik Wood K Plus-competence center for Wood Composites&Wood Chemistry Kompetenzzentrum Holz GmbHLinzA-4040Austria Institute for Chemical Technology of Organic Materials Johannes Kepler University LinzLinz4040Austria

In the current study poly(lactic acid)PLA composites with a 3 wt%and 5 wt%of nanofibrillated cellulose(NFC)were produced by 3D-printing *** enzymatic pretreatment coupled with mechanical fibrillation in a twin screw extruder was used to produce high consistency *** electron microscopy(SEM)equipped with Fibermetric software,FASEP fiber length distribution analysis,Furrier transform infrared spectroscopy(FT-IR),thermogravimetric analysis(TGA),tensile tests,impact tests and differential scanning calorimetry were used to characterize NFC and PLA/NFC *** results of the fiber length and width measurements together with the results of the SEM analysis showed that enzymatic hydrolysis coupled with a twin screw extrusion could effectively reduce the diameter and length of cellulose *** produced NFC consisted of microand nanosized fibers entangled in a characteristic *** on the FT-IR analysis,no new bonds were formed during the enzymatic hydrolysis or fibrillation *** TGA analysis confirmed that produced NFC can be used in hightemperature extrusion processing without NFC *** the PLA/NFC composites preparation the NFC agglomerates were formed,which negatively influenced PLA/NFC composites impact *** slightly improved tensile strength and elastic modulus were reported for all composites when compared to the neat *** elongation at break was not affected by the NFC *** significant differences in thermal stability were detectable among composites nor in comparation with the neat ***,the crystallinity degree of the composite containing 5 wt%NFC was increased in respect to the neat PLA.

关键词： Nanofibrillated cellulose enzymatic hydrolysis twin screw extruder poly(lactic acid) 3D-printing

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A study on key technologies of unmanned driving

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CAAI Transactions on Intelligence Technology 2016年第1期1卷 4-13页

作者： Xinyu Zhang Hongbo Gao Mu Guo Guopeng Li Yuchao Liu Deyi Li Department of Computer Science and Technology Tsinghua University Beijing 100084 China Information Technology center Tsinghua University Beijing 100084 China State Key Laboratory of software Development Environment Beihang University Beijing 100191 China The Institute of Electronic System Engineering Beijing 100039 China

Although the development of machine intelligence is far from simulating all the cognitive competence of our brains, still it is absolutely possible to peel the driving activity from people's cognitive activities and then make the machine finish some low-level, complicated and lasting driving cognition by simulating our brains. The goal of driving is to replace drivers and free them from boring driving activities. Based on some studies on unmanned driving, this paper summarizes and analyzes the background, significance, research status and key technology of unmanned driving and the research group also introduces some research on brain cognition of driving and sensor placement of intelligent vehicles, which offers more meaningful reference to push the study of unmanned driving.

关键词：机器智能车辆驾驶人工智能发展现状

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Numerical microstructure prediction for an aluminium casting and its experimental validation

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China Foundry 2011年第3期8卷 331-336页

作者： Unterreiter Guenter Ludwig Andreas Wu Menghuai Materials center Leoben Forschung GmbH Leoben Austria Chair for Simulation and Modelling of Metallurgical Processes Department of Metallurgy University of Leoben Leoben Austria

Virtual manufacturing based on through-process modelling becomes an evolving research area which aims at integrating diverse simulation tools to realize computer-aided design, analysis, prototyping and manufacturing. Numerical prediction of the as-cast microstructure is an initial and critical step in the whole through-process modelling chain for engineering components. A commercial software package with the capability of calculating important microstructure features for aluminium alloys is used to simulate a G-AlSi7MgCu0.5 laboratory casting. The simulated microstructure, namely grain size, secondary dendrite arm spacing and diverse phase fractions are verified experimentally. Correspondence and discrepancies are reported and discussed.

关键词：数字微观结构预言扔的铝通过过程建模

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The relationship between professional autonomy and moral distress among nurses working in children's units and pediatric intensive care wards

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International Journal of Nursing Sciences 2017年第2期4卷 117-121页

作者： Zahra Sarkoohijabalbarezi Arash Ghodousi Elham Davaridolatabadi Faculty of Nursing and Midwifery Isfahan(Khorasgan)BranchIslamic Azad UniversityIsfahanIran Forensic Medicine Research center Isfahan(Khorasgan)BranchIslamic Azad UniversityIsfahanIran

Background: Nurses serve as the primary source of care for minor patients in intensive care units. Even though they support both patients and their relatives, these nurses may experience moral distress from their profession. While managing their daily relationships with their patients, nurses must also be able to control their actions to feel that they are from a social unit and feel their competence in association with ***: This study aimed to investigate the relationship between professional autonomy and moral distress among nurses working in children's units and pediatric intensive care wards. Methods: This descriptive/comparative cross-sectional study was conducted in 2015 using 120 nurses as subjects. Subjects were selected using the census method. The research tools used to gain measurable data were the Pankratznursing questionnaire(PNQ) and Corley'sMoral distress scale (MDS). In order to analyze the collected data, descriptive statistic tests such as the relative frequency distribution, mean, standard deviation and the Pearson correlation test, T-test, ANOVA, and regression were used. The SPSSv.20 software was also used to analyze the data obtained. Results: The relationship between professional autonomy and moral distress revealed that there was a significant positive relationship between professional autonomy and moral distress in the intensity (r=0.39;P<0.001) and the iteration (r=0.41;P<0.001). In addition, professional autonomy predicted 18% of changes in intensity of moral distress in total(MR = 0.42, R2 = 0.18) and also professional au-tonomy predicted 25% of iteration in moral distress in total(MR = 0.507, R2= 0.25). Conclusions: The results of this study revealed that there was a direct positive relationship between professional autonomy and moral distress.

关键词： Nurses Moral distress Professional autonomy

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