App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While t...App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior performance.This research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and satisfaction.We propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification accuracy.Comparative analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,respectively.Thesignificant contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews dataset.These advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.展开更多
In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance o...In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance of two advanced models,the Large Language Model(LLM)LLaMA model and NLP BERT model,in the context of airline review sentiment analysis.Through fine-tuning,domain adaptation,and the application of few-shot learning,the study addresses the subtleties of sentiment expressions in airline-related text data.Employing predictive modeling and comparative analysis,the research evaluates the effectiveness of Large Language Model Meta AI(LLaMA)and Bidirectional Encoder Representations from Transformers(BERT)in capturing sentiment intricacies.Fine-tuning,including domain adaptation,enhances the models'performance in sentiment classification tasks.Additionally,the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis.By conducting experiments on a diverse airline review dataset,the research quantifies the impact of fine-tuning,domain adaptation,and few-shot learning on model performance,providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content(UGC).This research contributes to refining sentiment analysis models,ultimately fostering improved customer satisfaction in the airline industry.展开更多
Purpose:This study provides a comprehensive bibliometric analysis of the environmental management control tools literature.It seeks to summarize this body of literature’s growth and identify the most influential auth...Purpose:This study provides a comprehensive bibliometric analysis of the environmental management control tools literature.It seeks to summarize this body of literature’s growth and identify the most influential authors,journals,and articles in this field.The main objective of this article is to determine which tools are most prominent in the literature.Methodology/approach:The study examined 541 articles published in 126 academically indexed journals in the Scopus database.The analyzed timeframe covers the period from 2011 to 2023.We used VOSviewer software for statistical calculations to map the collaborations among authors and journals and to develop a conceptual and intellectual map of the field.Results:Our findings show that the literature on environmental management control tools is flourishing.The authors who dominated this period are mainly Schaltegger,Sala,and Ulgiati.The Journal of Cleaner Production is the primary source of publications,with an astounding 241 documents.The United States attained the leading position in terms of publication with 86 documents,which explains its willingness to collaborate with other countries,followed by China and Australia with 70 and 66 papers,respectively.Finally,bibliometric analysis shows that“life cycle assessment”,“cost-benefit analysis”,and“sustainability reporting”are the most prominent tools in research on this topic.Originality/value:This article provides several starting points for researchers and practitioners investigating environmental management control tools.It contributes to broadening the field’s vision and then offers recommendations for future studies.展开更多
Sentiment analysis is a method to identify and understand the emotion in the text through NLP and text analysis. In the era of information technology, there is often a certain error between the comments on the movie w...Sentiment analysis is a method to identify and understand the emotion in the text through NLP and text analysis. In the era of information technology, there is often a certain error between the comments on the movie website and the actual score of the movie, and sentiment analysis technology provides a new way to solve this problem. In this paper, Python is used to obtain the movie review data from the Douban platform, and the model is constructed and trained by using naive Bayes and Bi-LSTM. According to the index, a better Bi-LSTM model is selected to classify the emotion of users’ movie reviews, and the classification results are scored according to the classification results, and compared with the real ratings on the website. According to the error of the final comparison results, the feasibility of this technology in the scoring direction of film reviews is being verified. By applying this technology, the phenomenon of film rating distortion in the information age can be prevented and the rights and interests of film and television works can be safeguarded.展开更多
This editorial discusses recent advancements and ongoing challenges in diabetic retinopathy,as reviewed by Morya et al in their comprehensive analysis.In their review,Morya et al discussed the pathophysiology of diabe...This editorial discusses recent advancements and ongoing challenges in diabetic retinopathy,as reviewed by Morya et al in their comprehensive analysis.In their review,Morya et al discussed the pathophysiology of diabetic retinopathy and explored novel treatment modalities.This editorial highlights the importance of these advancements and emphasizes the need for continued research and innovation for the enhanced management of diabetic retinopathy.It also reflects upon the implications of the authors’review findings for clinical practice and future research directions,underscoring the potential of emerging therapies for improving patient outcomes and providing a deeper understanding of disease mechanisms.展开更多
BACKGROUND Osteoporotic vertebral fracture(OVF)is one of the most common sequelae of osteoporosis.Differential diagnosis between OVF and malignant vertebral fracture(MVF)is a challenge in clinical practice.Findings on...BACKGROUND Osteoporotic vertebral fracture(OVF)is one of the most common sequelae of osteoporosis.Differential diagnosis between OVF and malignant vertebral fracture(MVF)is a challenge in clinical practice.Findings on computed tomography and magnetic resonance images(MRI)may help to differentiate between these two types of fracture.AIM To determine whether paravertebral soft tissue swelling is useful for differentiation between OVF and MVF.METHODS We retrospectively reviewed the MRI for 165 patients diagnosed with a vertebral fracture between May 2021 and July 2022.Three radiologists evaluated the vertebral segments and thickness of soft tissue swelling on sagittal MRI by consensus.The morphology of the soft tissue swelling was also evaluated.The statistical analyses were performed using theχ^(2) test and analysis of variance.RESULTS The study included 117 patients(153 vertebrae)with OVF and 48 patients(63 vertebrae)with MVF.Soft tissue swelling was observed beneath the anterior longitudinal ligament on sagittal MRI and rim-shaped in the paravertebral area on axial MRI in all 153 vertebrae with OVF(100%)and in 12(19%)of the 63 vertebra with MVF;the difference was statistically significant(P<0.001),95%CI:3.156–8.735.Soft tissue swelling beneath the anterior longitudinal ligament spanned significantly more vertebral segments in patients with OVF than in those with MVF(P<0.001),95%CI:0.932-1.546.The mean thickness of the soft tissue swelling was significantly greater for OVF than for MVF(5.62 mm±2.50 mm vs 3.88 mm±1.73 mm,P<0.05,95%CI:0.681–0.920).Post-contrast examination was performed in 13 patients;T1-weighted images confirmed OVF in 11 cases and MVF in 2 cases.Soft tissue swelling in OVF and MVF had a fusiform appearance or appeared as a thin line on sagittal MRI and was rim-shaped on axial MRI.The length and diameter of the soft tissue swelling in patients with OVF decreased during follow-up.CONCLUSION Paravertebral soft tissue swelling is helpful for differentiating between OVF and MVF.展开更多
Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in text.Computational linguistics aims t...Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in text.Computational linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural language.Sentiment is classified as a negative or positive assessment articulated through language.SA can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been reviewed.Deep learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise SA.With this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)technique.The major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review data.Primarily,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding process.For the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this work.At last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different datasets.The comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.展开更多
Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review ...Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.Design/methodology/approach： We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain＇s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.Findings： The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages： conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.Research limitations： The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.Practical implications： The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.Originality/value： Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.展开更多
With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after ...With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.展开更多
With the increasing usage of drugs to remedy different diseases,drug safety has become crucial over the past few years.Often medicine from several companies is offered for a single disease that involves the same/simil...With the increasing usage of drugs to remedy different diseases,drug safety has become crucial over the past few years.Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae.Such diversification is both helpful and danger-ous as such medicine proves to be more effective or shows side effects to different patients.Despite clinical trials,side effects are reported when the medicine is used by the mass public,of which several such experiences are shared on social media platforms.A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed.Sentiment analysis of drug reviews has a large poten-tial for providing valuable insights into these cases.Therefore,this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques.A dataset acquired from the‘Drugs.Com’contain-ing reviews of drug-related side effects and reactions,is used for experiments.A lexicon-based approach,Textblob is used to extract the positive,negative or neu-tral sentiment from the review text.Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory(CNN-LSTM)network.The CNN is used at thefirst level to extract the appropriate features while LSTM is used at the second level.Several well-known machine learning models including logistic regression,random for-est,decision tree,and AdaBoost are evaluated using term frequency-inverse docu-ment frequency(TF-IDF),a bag of words(BoW),feature union of(TF-IDF+BoW),and lexicon-based methods.Performance analysis with machine learning models,long short term memory and convolutional neural network models,and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy.We also performed a statistical sig-nificance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.展开更多
Nonlinear solution of reinforced concrete structures, particularly complete load-deflection response, requires tracing of the equilibrium path and proper treatment of the limit and bifurcation points. In this regard, ...Nonlinear solution of reinforced concrete structures, particularly complete load-deflection response, requires tracing of the equilibrium path and proper treatment of the limit and bifurcation points. In this regard, ordinary solution techniques lead to instability near the limit points and also have problems in case of snap-through and snap-back. Thus they fail to predict the complete load-displacement response. The arc-length method serves the purpose well in principle, received wide acceptance in finite element analysis, and has been used extensively. However modifications to the basic idea are vital to meet the particular needs of the analysis. This paper reviews some of the recent developments of the method in the last two decades, with particular emphasis on nonlinear finite element analysis of reinforced concrete structures.展开更多
基金supported by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:13-612-2024).
文摘App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior performance.This research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and satisfaction.We propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification accuracy.Comparative analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,respectively.Thesignificant contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews dataset.These advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.
文摘In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance of two advanced models,the Large Language Model(LLM)LLaMA model and NLP BERT model,in the context of airline review sentiment analysis.Through fine-tuning,domain adaptation,and the application of few-shot learning,the study addresses the subtleties of sentiment expressions in airline-related text data.Employing predictive modeling and comparative analysis,the research evaluates the effectiveness of Large Language Model Meta AI(LLaMA)and Bidirectional Encoder Representations from Transformers(BERT)in capturing sentiment intricacies.Fine-tuning,including domain adaptation,enhances the models'performance in sentiment classification tasks.Additionally,the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis.By conducting experiments on a diverse airline review dataset,the research quantifies the impact of fine-tuning,domain adaptation,and few-shot learning on model performance,providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content(UGC).This research contributes to refining sentiment analysis models,ultimately fostering improved customer satisfaction in the airline industry.
文摘Purpose:This study provides a comprehensive bibliometric analysis of the environmental management control tools literature.It seeks to summarize this body of literature’s growth and identify the most influential authors,journals,and articles in this field.The main objective of this article is to determine which tools are most prominent in the literature.Methodology/approach:The study examined 541 articles published in 126 academically indexed journals in the Scopus database.The analyzed timeframe covers the period from 2011 to 2023.We used VOSviewer software for statistical calculations to map the collaborations among authors and journals and to develop a conceptual and intellectual map of the field.Results:Our findings show that the literature on environmental management control tools is flourishing.The authors who dominated this period are mainly Schaltegger,Sala,and Ulgiati.The Journal of Cleaner Production is the primary source of publications,with an astounding 241 documents.The United States attained the leading position in terms of publication with 86 documents,which explains its willingness to collaborate with other countries,followed by China and Australia with 70 and 66 papers,respectively.Finally,bibliometric analysis shows that“life cycle assessment”,“cost-benefit analysis”,and“sustainability reporting”are the most prominent tools in research on this topic.Originality/value:This article provides several starting points for researchers and practitioners investigating environmental management control tools.It contributes to broadening the field’s vision and then offers recommendations for future studies.
文摘Sentiment analysis is a method to identify and understand the emotion in the text through NLP and text analysis. In the era of information technology, there is often a certain error between the comments on the movie website and the actual score of the movie, and sentiment analysis technology provides a new way to solve this problem. In this paper, Python is used to obtain the movie review data from the Douban platform, and the model is constructed and trained by using naive Bayes and Bi-LSTM. According to the index, a better Bi-LSTM model is selected to classify the emotion of users’ movie reviews, and the classification results are scored according to the classification results, and compared with the real ratings on the website. According to the error of the final comparison results, the feasibility of this technology in the scoring direction of film reviews is being verified. By applying this technology, the phenomenon of film rating distortion in the information age can be prevented and the rights and interests of film and television works can be safeguarded.
文摘This editorial discusses recent advancements and ongoing challenges in diabetic retinopathy,as reviewed by Morya et al in their comprehensive analysis.In their review,Morya et al discussed the pathophysiology of diabetic retinopathy and explored novel treatment modalities.This editorial highlights the importance of these advancements and emphasizes the need for continued research and innovation for the enhanced management of diabetic retinopathy.It also reflects upon the implications of the authors’review findings for clinical practice and future research directions,underscoring the potential of emerging therapies for improving patient outcomes and providing a deeper understanding of disease mechanisms.
基金approved by the Institutional Review Board of the Affiliated hospital of Qingdao University(No.QYFYWZLL29413/dated 5rd September 2023),and all patient requirements for informed consent were waived.
文摘BACKGROUND Osteoporotic vertebral fracture(OVF)is one of the most common sequelae of osteoporosis.Differential diagnosis between OVF and malignant vertebral fracture(MVF)is a challenge in clinical practice.Findings on computed tomography and magnetic resonance images(MRI)may help to differentiate between these two types of fracture.AIM To determine whether paravertebral soft tissue swelling is useful for differentiation between OVF and MVF.METHODS We retrospectively reviewed the MRI for 165 patients diagnosed with a vertebral fracture between May 2021 and July 2022.Three radiologists evaluated the vertebral segments and thickness of soft tissue swelling on sagittal MRI by consensus.The morphology of the soft tissue swelling was also evaluated.The statistical analyses were performed using theχ^(2) test and analysis of variance.RESULTS The study included 117 patients(153 vertebrae)with OVF and 48 patients(63 vertebrae)with MVF.Soft tissue swelling was observed beneath the anterior longitudinal ligament on sagittal MRI and rim-shaped in the paravertebral area on axial MRI in all 153 vertebrae with OVF(100%)and in 12(19%)of the 63 vertebra with MVF;the difference was statistically significant(P<0.001),95%CI:3.156–8.735.Soft tissue swelling beneath the anterior longitudinal ligament spanned significantly more vertebral segments in patients with OVF than in those with MVF(P<0.001),95%CI:0.932-1.546.The mean thickness of the soft tissue swelling was significantly greater for OVF than for MVF(5.62 mm±2.50 mm vs 3.88 mm±1.73 mm,P<0.05,95%CI:0.681–0.920).Post-contrast examination was performed in 13 patients;T1-weighted images confirmed OVF in 11 cases and MVF in 2 cases.Soft tissue swelling in OVF and MVF had a fusiform appearance or appeared as a thin line on sagittal MRI and was rim-shaped on axial MRI.The length and diameter of the soft tissue swelling in patients with OVF decreased during follow-up.CONCLUSION Paravertebral soft tissue swelling is helpful for differentiating between OVF and MVF.
基金Supporting Project Number(PNURSP2022R281),Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4320484DSR08).
文摘Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in text.Computational linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural language.Sentiment is classified as a negative or positive assessment articulated through language.SA can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been reviewed.Deep learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise SA.With this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)technique.The major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review data.Primarily,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding process.For the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this work.At last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different datasets.The comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.
文摘Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.Design/methodology/approach： We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain＇s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.Findings： The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages： conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.Research limitations： The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.Practical implications： The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.Originality/value： Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.
文摘With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.
文摘With the increasing usage of drugs to remedy different diseases,drug safety has become crucial over the past few years.Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae.Such diversification is both helpful and danger-ous as such medicine proves to be more effective or shows side effects to different patients.Despite clinical trials,side effects are reported when the medicine is used by the mass public,of which several such experiences are shared on social media platforms.A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed.Sentiment analysis of drug reviews has a large poten-tial for providing valuable insights into these cases.Therefore,this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques.A dataset acquired from the‘Drugs.Com’contain-ing reviews of drug-related side effects and reactions,is used for experiments.A lexicon-based approach,Textblob is used to extract the positive,negative or neu-tral sentiment from the review text.Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory(CNN-LSTM)network.The CNN is used at thefirst level to extract the appropriate features while LSTM is used at the second level.Several well-known machine learning models including logistic regression,random for-est,decision tree,and AdaBoost are evaluated using term frequency-inverse docu-ment frequency(TF-IDF),a bag of words(BoW),feature union of(TF-IDF+BoW),and lexicon-based methods.Performance analysis with machine learning models,long short term memory and convolutional neural network models,and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy.We also performed a statistical sig-nificance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.
文摘Nonlinear solution of reinforced concrete structures, particularly complete load-deflection response, requires tracing of the equilibrium path and proper treatment of the limit and bifurcation points. In this regard, ordinary solution techniques lead to instability near the limit points and also have problems in case of snap-through and snap-back. Thus they fail to predict the complete load-displacement response. The arc-length method serves the purpose well in principle, received wide acceptance in finite element analysis, and has been used extensively. However modifications to the basic idea are vital to meet the particular needs of the analysis. This paper reviews some of the recent developments of the method in the last two decades, with particular emphasis on nonlinear finite element analysis of reinforced concrete structures.
