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natural language processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

Computers, Materials & Continua 2023年第2期74卷 4435-4451页

作者： Radwa Marzouk Eatedal Alabdulkreem Mohamed KNour Mesfer Al Duhayyim Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with natural language processing(NLP)model,seem to be a promising future of smart *** plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so *** an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be *** aim of the image captioning process is to generate a caption for *** next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically *** this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the *** current study develops anatural language processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input *** attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding ***,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)*** model represents the input data appropriately by inserting it into a predefined length ***,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the *** application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models *** proposed NLPODL-IICS model was experimentally validated with the help of two benchmark *** comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

关键词： natural language processing information retrieval image captioning deep learning metaheuristics

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natural language processing with Optimal Deep Learning Based Fake News Classification

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Computers, Materials & Continua 2022年第11期73卷 3529-3544页

作者： Sara AAlthubiti Fayadh Alenezi Romany F.Mansour Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster *** most of the times,the intention of fake news is to misinform the people and make manipulated societal *** spread of low-quality news in social networking sites has a negative influence upon people as well as the *** order to overcome the ever-increasing dissemination of fake news,automated detection models are developed using Artificial Intelligence(AI)and Machine Learning(ML)*** latest advancements in Deep Learning(DL)models and complex natural language processing(NLP)tasks make the former,a significant solution to achieve Fake News Detection(FND).In this background,the current study focuses on design and development of natural language processing with Sea Turtle Foraging Optimizationbased Deep Learning Technique for Fake News Detection and Classification(STODL-FNDC)*** aim of the proposed STODL-FNDC model is to discriminate fake news from legitimate news in an effectual *** the proposed STODL-FNDC model,the input data primarily undergoes pre-processing and Glove-based word ***,STODL-FNDC model employs Deep Belief Network(DBN)approach for detection as well as classification of fake ***,STO algorithm is utilized after adjusting the hyperparameters involved in DBN model,in an optimal *** novelty of the study lies in the design of STO algorithm with DBN model for *** order to improve the detection performance of STODL-FNDC technique,a series of simulations was carried out on benchmark *** experimental outcomes established the better performance of STODL-FNDC approach over other methods with a maximum accuracy of 95.50%.

关键词： natural language processing text mining fake news detection deep belief network machine learning evolutionary algorithm

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natural language processing(NLP)in management research:A literature review

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Journal of Management Analytics 2020年第2期7卷 139-172页

作者： Yue Kang Zhao Cai Chee-Wee Tan Qian Huang Hefu Liu School of Management University of Science and Technology of ChinaHefeiPeople’s Republic of China Nottingham University Business School China University of Nottingham Ningbo ChinaNingboPeople’s Republic of China cDepartment of Digitalization Copenhagen Business SchoolCopenhagenDenmark

natural language processing(NLP)is gaining momentum in management research for its ability to automatically analyze and comprehend human ***,despite its extensive application in management research,there is neither a comprehensive review of extant literature on such applications,nor is there a detailed walkthrough on how it can be employed as an analytical *** this end,we review articles in the UT Dallas List of 24 Leading Business Journals that employ NLP as their focal analytical technique to elucidate how textual data can be harnessed for advancing management theories across multiple *** describe the available toolkits and procedural steps for employing NLP as an analytical technique as well as its advantages and *** so doing,we highlight the managerial and technological challenges associated with the application of NLP in management research in order to guide future inquires.

关键词： natural language processing literature review sentiment analysis machine learning

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Literature classification and its applications in condensed matter physics and materials science by natural language processing

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Chinese Physics B 2024年第5期33卷 117-123页

作者：吴思远朱天念涂思佳肖睿娟袁洁吴泉生李泓翁红明 Institute of Physics Chinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100190China College of Materials Science and Optoelectronic Technology University of Chinese Academy of SciencesBeijing 100049China Condensed Matter Physics Data Center of Chinese Academy of Sciences Beijing 100190China

The exponential growth of literature is constraining researchers’access to comprehensive information in related *** natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled *** this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human *** apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials *** trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this ***,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

关键词： natural language processing text mining materials science

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Pre-trained models for natural language processing: A survey

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Science China(Technological Sciences) 2020年第10期63卷 1872-1897页

作者： QIU XiPeng SUN TianXiang XU YiGe SHAO YunFan DAI Ning HUANG XuanJing School of Computer Science Fudan UniversityShanghai 200433China Shanghai Key Laboratory of Intelligent Information processing Shanghai 200433China

Recently, the emergence of pre-trained models(PTMs) has brought natural language processing(NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy from four different perspectives. Next,we describe how to adapt the knowledge of PTMs to downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.

关键词： deep learning neural network natural language processing pre-trained model distributed representation word embedding self-supervised learning language modelling

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Eliciting Requirements from Stakeholders’ Responses Using natural language processing

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Computer Modeling in Engineering & Sciences 2021年第4期127卷 99-116页

作者： Mohammed Lafi Bilal Hawashin Shadi AlZu’bi Department of Software Engineering Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan Department of Computer Information Systems Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan

Most software systems have different stakeholders with a variety of *** process of collecting requirements from a large number of stakeholders is vital but *** propose an efficient,automatic approach to collecting requirements from different stakeholders’responses to a specific *** use natural language processing techniques to get the stakeholder response that represents most other stakeholders’*** study improves existing practices in three ways:Firstly,it reduces the human effort needed to collect the requirements;secondly,it reduces the time required to carry out this task with a large number of stakeholders;thirdly,it underlines the importance of using of data mining techniques in various software engineering *** approach uses tokenization,stop word removal,and word lemmatization to create a list of frequently accruing *** then creates a similarity matrix to calculate the score value for each response and selects the answer with the highest *** experiments show that using this approach significantly reduces the time and effort needed to collect requirements and does so with a sufficient degree of accuracy.

关键词： Software requirements requirements elicitation natural language processing

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

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1415-1424页

作者： Mei Liu Wendi Luo Zangtai Cai Xiujuan Du Jiliang Zhang Shuai Li School of Information Science and Engineering Lanzhou UniversityLanzhouChina The State Key Laboratory of Tibetan Intelligent Information processing and Application Qinghai Normal UniversityXiningChina Department of Electronic and Electrical Engineering The University of SheffieldSheffieldUK

A variety of neural networks have been presented to deal with issues in deep learning in the last *** 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 *** research studies have demonstrated that many existing models can be regarded as different numerical discretizations of differential *** connection sheds light on designing an effective recurrent neural network(RNN)by resorting to numerical *** RNN is regarded as a discretisation of the forward Euler *** 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 *** experiments are conducted to evaluate its performance on statistical language models and emotion analysis *** noticeable gains obtained by T‐RNN present its superiority and the feasibility of designing the neural network model using numerical methods.

关键词： deep learning natural language processing neural network text analysis

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Identifying disaster-related tweets and their semantic,spatial and temporal context using deep learning,natural language processing and spatial analysis:a case study of Hurricane Irma

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International Journal of Digital Earth 2019年第11期12卷 1205-1229页

作者： Muhammed Ali Sit Caglar Koylu Ibrahim Demir Department of Computer Science University of IowaIowa CityIAUSA Department of Geographical and Sustainability Sciences University of IowaIowa CityIAUSA Department of Civil and Environmental Engineering University of IowaIowa CityIAUSA

We introduce an analytical framework for analyzing tweets to(1)identify and categorize fine-grained details about a disaster such as affected individuals,damaged infrastructure and disrupted services;(2)distinguish impact areas and time periods,and relative prominence of each category of disaster-related information across space and *** first identify disaster-related tweets by generating a human-labeled training dataset and experimenting a series of deep learning and machine learning methods for a binary classification of *** employ LSTM(Long Short-Term Memory)networks for the classification task because LSTM networks outperform other methods by considering the whole text structure using long-term semantic word and feature ***,we employ an unsupervised multi-label classification of tweets using Latent Dirichlet Allocation(LDA),and identify latent categories of tweets such as affected individuals and disrupted ***,we employ spatiallyadaptive kernel smoothing and density-based spatial clustering to identify the relative prominence and impact areas for each information category,*** Hurricane Irma as a case study,we analyze over 500 million keyword-based and geo-located collection of tweets before,during and after the *** results highlight potential areas with high density of affected individuals and infrastructure damage throughout the temporal progression of the disaster.

关键词： Social sensing Twitter deep learning natural language processing spatial analysis hurricane

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Paradigm Shift in natural language processing

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Machine Intelligence Research 2022年第3期19卷 169-183页

作者： Tian-Xiang Sun Xiang-Yang Liu Xi-Peng Qiu Xuan-Jing Huang School of Computer Science Fudan UniversityShanghai 200438China Shanghai Key Laboratory of Intelligent Information processing Fudan UniversityShanghai 200438China

In the era of deep learning, modeling for most natural language processing (NLP) tasks has converged into several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as POS-tagging, named entity recognition (NER), and chunking, and adopt the classification paradigm to solve tasks like sentiment analysis. With the rapid progress of pre-trained language models, recent years have witnessed a rising trend of paradigm shift, which is solving one NLP task in a new paradigm by reformulating the task. The paradigm shift has achieved great success on many tasks and is becoming a promising way to improve model performance. Moreover, some of these paradigms have shown great potential to unify a large number of NLP tasks, making it possible to build a single model to handle diverse tasks. In this paper, we review such phenomenon of paradigm shifts in recent years, highlighting several paradigms that have the potential to solve different NLP tasks.

关键词： natural language processing pre-trained language models deep learning sequence-to-sequence paradigm shift

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Deep Learning with natural language processing Enabled Sentimental Analysis on Sarcasm Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2553-2567页

作者： Abdul Rahaman Wahab Sait Mohamad Khairi Ishak Department of Documents and Archive Center of Documents and Administrative CommunicationKing Faisal UniversityAl HofufAl-Ahsa31982Saudi Arabia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia

Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social *** existence of sarcasm in tex-tual data is a major challenge in the efficiency of the *** works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of *** the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted *** this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm *** proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input ***,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and ***,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and ***,the transformation of feature vectors takes place using the N-gram feature vector ***,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of *** verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

关键词： Sentiment analysis sarcasm detection deep learning natural language processing n-grams hyperparameter tuning

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