scholarly journals The Perceptual Categorization of Enshi Mandarin Regional Varieties

2015 ◽  
Vol 3 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Qingyang Yan
Keyword(s):  
Response Bias ◽  
Positive Response ◽  
Social Background ◽  
Classification Performance ◽  
Classification Task ◽  
Perceptual Categorization ◽  
Rating Task ◽  
Dialect Classification ◽  
The Relationship

The current study used a hand-drawn map task, a dialect difference rating task, and a dialect classification task to explore the relationship between participants’ ideologies about dialect differences and their classification of authentic talkers from six regional varieties in Enshi Prefecture, China. The talkers frequently mistaken for each other in the dialect classification task were those who came from counties that were perceived to have similar dialects in the hand-drawn map task and the dialect difference rating task. Participants showed a positive response bias for the Enshi dialect in classifying talkers, corresponding to the dialect difference ratings that Enshi was rated as least different. Thus participants’ classification of real talkers was largely consistent with their ideologies about differences among “imagined” dialects. Participants’ ideologies about dialect differences were shaped by their home county, and their classification performance was affected by their home county and the talker’s social background.

Sentiment classification of Chinese cosmetic reviews based on integration of collocations and concepts

2019 ◽  
Vol 38 (1) ◽  
pp. 155-169
Author(s):  
Chihli Hung ◽  
You-Xin Cao
Keyword(s):  
Design Methodology ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Support Vector ◽  
Research Approach ◽  
Content Type ◽  
Novel Approach ◽  
The Relationship ◽  
Modelling Approach

Purpose This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a WOM document contains sentiments. Chinese collocations consist of the main sentiments of WOM. This paper reduces the complexity of the document dimensionality and makes an improvement for sentiment classification. Design/methodology/approach This paper builds two contextual lexicons for feature words and sentiment words, respectively. Based on these contextual lexicons, this paper uses the techniques of associated rules and mutual information to build possible Chinese collocation sets. This paper applies preference vector modelling as the vector representation approach to catch the relationship between Chinese collocations and their associated concepts. Findings This paper compares the proposed preference vector models with benchmarks, using three classification techniques (i.e. support vector machine, J48 decision tree and multilayer perceptron). According to the experimental results, the proposed models outperform all benchmarks evaluated by the criterion of accuracy. Originality/value This paper focuses on Chinese collocations and proposes a novel research approach for sentiment classification. The Chinese collocations used in this paper are adaptable to the content and domains. Finally, this paper integrates collocations with the preference vector modelling approach, which not only achieves a better sentiment classification performance for Chinese WOM documents but also avoids the curse of dimensionality.

scholarly journals Independent Multisensory Integration and Crossmodal Attention Processing: Evidence from Audiovisual Gender Congruency Tasks

2020 ◽  
Author(s):  
Guochun Yang ◽  
Di Fu ◽  
Li Zhenghan ◽  
Haiyan Wu ◽  
Honghui Xu ◽  
...  
Keyword(s):  
Reaction Time ◽  
Multisensory Integration ◽  
Congruency Effect ◽  
The Other ◽  
Classification Task ◽  
Rating Task ◽  
Crossmodal Attention ◽  
Inverse Effectiveness ◽  
The Relationship ◽  
Speeded Classification

Multisensory integration and crossmodal attention are two of the basic mechanisms in processing multisensory inputs, and they are usually mixed. Whether these two processes are dependent or independent remains controversial. To examine the relationship between multisensory integration and crossmodal attention, we adopted modified multilevel audiovisual gender judgment paradigms and evaluated the congruency effects in reaction time (RT) and the inverse effectiveness (IE) effects. If they were dependent, the occurrence of one effect would be accompanied with that of the other. Using both morphed faces and voices, we first performed a speeded classification task, in which participants were either asked to attend to faces (experiment 1a) or attend to voices (experiment 1b); then, we performed an unspeeded rating task with faces as the targets (experiment 2). We observed both a congruency effect in RT and an IE effect in experiment 1a, a congruency effect in RT alone in experiment 1b, and an IE effect alone in experiment 2. These results indicate that the two processes are independent of each other.

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

DONG SON BRONZE DRUM - THE WORK OF THE LAC VIET PEOPLE

2018 ◽  
Author(s):  
Nguyen Van Hao
Keyword(s):  
Classification Criteria ◽  
Way Of Life ◽  
Tribal Identity ◽  
The Relationship

Bronze drums are widely distributed, broader than the range of a nation. Therefore, the identification of each kind of drum is a basic subject, should be concerned. In determining the tribal identity of the drum, the classification of drum is the key stage, the relationship between the objective of the classification and classification criteria is the relation as shape and shadow, if there is no right criteria then the result of division will be difficult to reach the desired goal. Likewise, the criterion of the pattern on the bronze drum brought to the affirmation is the Dong Son bronze drum of the Lac Viet people. And the parallel is the affirmation of the culture, way of life, residence of the nation created the drum.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Pituitary MRI characteristics in 297 acromegaly patients based on T2-weighted sequences

2015 ◽  
Vol 22 (2) ◽  
pp. 169-177 ◽  
Author(s):  
Iulia Potorac ◽  
Patrick Petrossians ◽  
Adrian F Daly ◽  
Franck Schillo ◽  
Claude Ben Slama ◽  
...  
Keyword(s):  
Large Population ◽  
Optic Chiasm ◽  
Research Priority ◽  
Maximal Diameter ◽  
Magnetic Resonance Imaging Mri ◽  
Multimodal Management ◽  
Mri Characteristics ◽  
Weighted Sequences ◽  
The Relationship

Responses of GH-secreting adenomas to multimodal management of acromegaly vary widely between patients. Understanding the behavioral patterns of GH-secreting adenomas by identifying factors predictive of their evolution is a research priority. The aim of this study was to clarify the relationship between the T2-weighted adenoma signal on diagnostic magnetic resonance imaging (MRI) in acromegaly and clinical and biological features at diagnosis. An international, multicenter, retrospective analysis was performed using a large population of 297 acromegalic patients recently diagnosed with available diagnostic MRI evaluations. The study was conducted at ten endocrine tertiary referral centers. Clinical and biochemical characteristics, and MRI signal findings were evaluated. T2-hypointense adenomas represented 52.9% of the series, were smaller than their T2-hyperintense and isointense counterparts (P<0.0001), were associated with higher IGF1 levels (P=0.0001), invaded the cavernous sinus less frequently (P=0.0002), and rarely caused optic chiasm compression (P<0.0001). Acromegalic men tended to be younger at diagnosis than women (P=0.067) and presented higher IGF1 values (P=0.01). Although in total, adenomas had a predominantly inferior extension in 45.8% of cases, in men this was more frequent (P<0.0001), whereas in women optic chiasm compression of macroadenomas occurred more often (P=0.0067). Most adenomas (45.1%) measured between 11 and 20 mm in maximal diameter and bigger adenomas were diagnosed at younger ages (P=0.0001). The T2-weighted signal differentiates GH-secreting adenomas into subgroups with particular behaviors. This raises the question of whether the T2-weighted signal could represent a factor in the classification of acromegalic patients in future studies.

scholarly journals POS1456-HPR THE SERUM IRON LEVEL MIGHT BE USEFUL IN DETERMINING THE SEVERITY OF MALNUTRITION IN PATIENTS WITH RHEUMATOID ARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1012.2-1012
Author(s):  
Y. Matsumoto ◽  
Y. Sugioka ◽  
M. Tada ◽  
T. Okano ◽  
K. Mamoto ◽  
...  
Keyword(s):  
Grip Strength ◽  
Serum Iron ◽  
Roc Analysis ◽  
Severe Malnutrition ◽  
Inflammatory Condition ◽  
Research Support ◽  
Chugai Pharmaceutical ◽  
The Relationship ◽  
Nutritional Indicator

Background:The Global Leadership Initiative on Malnutrition (GLIM) criteria, the first international criteria for diagnosis of malnutrition, was released in 2018 [1]. Patients with rheumatoid arthritis (RA) are thought to be prone to malnutrition due to decreased food intake and increased muscle catabolism caused by chronic inflammation or pain. However, there has been no report to assess the nutritional status of RA patients in accordance with the GLIM criteria. In addition, commonly used blood nutrient indicators such as albumin might not be appropriate as nutritional indicators for RA because these values are affected by inflammation.Objectives:This study aims to examine the rates of malnutrition in RA patients according to GLIM criteria, and the relationship between blood nutrient indicators and the severity of malnutrition.Methods:In this study, we conducted a cross-sectional survey of 135 female RA patients in 2020. According to the GLIM criteria, patients were considered to be malnourished if patients had one of the following phenotypic: (1) low body mass index, (2) non-volitional weight loss, (3) reduced muscle mass, and one of the following etiologic: (1) reduced food intake or assimilation, (2) disease burden/inflammatory condition. Reduced muscle mass was evaluated by measuring calf circumference, and inflammatory condition was evaluated by Disease Activity Score (DAS) 28. In accordance with the GLIM criteria, the severity of malnutrition was judged as three levels: no problem, moderate, and severe malnutrition. Albumin, transthyretin, transferrin, retinol binding protein, zinc, iron, ceruloplasmin, and total cholesterol were assessed as blood nutrition indicators. Also grip strength was assessed. We compared each nutritional indicator among the three groups according to the severity of malnutrition using age-adjusted analysis of covariance, and examined the relationship between each nutritional indicator and the severity of malnutrition using receiver operating characteristic (ROC) analysis.Results:In RA patients, 20% were classified as severe malnutrition, and 40% were moderate or more. Serum iron levels were significantly lower in the severe malnutrition group compared to the no problem group (p = 0.001). In ROC analysis, serum iron, zinc, albumin, and grip strength (area under curve; AUC; 0.680, 0.696, 0.636, 0.790, respectively) were significant parameters for classification of moderate and severe malnutrition. Serum iron and grip strength (AUC for respective parameters were 0.741, 0.747) were significant parameters for classification of severe malnutrition.Conclusion:Evaluation based on the GLIM criteria showed that about 40% of RA patients were under moderate or severe malnutrition. It was suggested that serum iron and grip strength might be useful to predict the severity of malnutrition.References:[1]Cederholm T, Jensen GL, Correia MITD, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition – A consensus report from the global clinical nutrition community. Clinical Nutrition 2019; 38: 1-9.Acknowledgements:We thank to Tomoko Nakatsuka, and the Center for Drug & Food Clinical Evaluation, Osaka City University Hospital, for management and collection of the study data. We also thank to study participants.Disclosure of Interests:Yoshinari Matsumoto Grant/research support from: Yamada Research Grant, Yuko Sugioka: None declared, Masahiro Tada: None declared, Tadasi Okano Speakers bureau: AbbVie, Asahikasei, Astellas Pharma Inc, Ayumi Pharmaceutical, Bristol-Myers Squibb, Chugai Pharmaceutical, Daiich Sankyo, Eisai, Janssen, Lilly, Mitsubishi Tanabe Pharma Corporation, Novartis Pharma, Ono Pharmaceutical, Pfizer, Sanofi, Takeda Pharmaceutical, Teijin Pharma and UCB, Grant/research support from: AbbVie, Eisai, Mitsubishi Tanabe Pharma Corporation and Nipponkayaku, Kenji Mamoto: None declared, Kentaro Inui Speakers bureau: Daiichi Sankyo Co. Ltd., Mitsubishi Tanabe Pharma, Janssen Pharmaceutical K.K., Astellas Pharma Inc., Takeda Pharmaceutical Co. Ltd., Ono Pharmaceutical Co. Ltd., Abbvie GK, Pfizer Inc., Eisai Co., Ltd., Chugai Pharmaceutical Co., Ltd, Grant/research support from: anssen Pharmaceutical K.K., Astellas Pharma Inc., Sanofi K.K., Abbvie GK, Takeda Pharmaceutical Co. Ltd., QOL RD Co. Ltd., Mitsubishi Tanabe Pharma, Ono Pharmaceutical Co. Ltd., Eisai Co., Ltd., Daiki Habu: None declared, Tatsuya Koike Speakers bureau: AbbVie, Astellas Pharma Inc, Bristol-Myers Squibb, Chugai Pharmaceutical, Eisai, Janssen, Lilly, Mitsubishi Tanabe Pharma Corporation, MSD, Ono Pharmaceutical, Pfizer, Roche, Takeda Pharmaceutical, Teijin Pharma, and UCB, Grant/research support from: AbbVie, Astellas Pharma Inc, Bristol-Myers Squibb, Chugai Pharmaceutical, Eisai, Janssen, Lilly, Mitsubishi Tanabe Pharma Corporation, MSD, Ono Pharmaceutical, Pfizer, Roche, Takeda Pharmaceutical, Teijin Pharma, and UCB

scholarly journals Formation of an Export-Oriented Agricultural Economy and Regional Open Innovations

2021 ◽  
Vol 7 (1) ◽  
pp. 32
Author(s):  
Victor L. Shabanov ◽  
Marianna Ya Vasilchenko ◽  
Elena A. Derunova ◽  
Andrey P. Potapov
Keyword(s):  
Agricultural Economy ◽  
Institutional Management ◽  
Agricultural Exports ◽  
Modeling Methodology ◽  
Fixed Assets ◽  
Analysis Application ◽  
The Impact ◽  
The Relationship

The aim of the work is to find relevant indicators for assessing the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports using tools for modeling the impact of innovation and investment development on increasing production and export potential in the context of the formation of an export-oriented agricultural economy. The modeling methodology and the proposed estimating and forecasting tools for diagnosing and monitoring the state of sectoral and regional innovative agricultural systems are used to analyze the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports based on the construction of the classification of Russian regions by factors that aggregate these features to diagnose incongruence problems and to improve institutional management in regional innovative export-oriented agrosystems. Based on the results of the factor analysis application, an underestimated role of indicators of investment in agriculture, the intensity and efficiency of agricultural production, were established. Based on the results of the cluster analysis, the established five groups of regions were identified, with significant differences in the level of investment in agriculture, the volume of production of the main types of agricultural products, and the export and exported food. The research results are of practical value for use in improving institutional management when planning reforms and transformations of regional innovative agrosystems.

scholarly journals Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Kun Zeng ◽  
Yibin Xu ◽  
Ge Lin ◽  
Likeng Liang ◽  
Tianyong Hao
Keyword(s):  
Clinical Trial ◽  
Ensemble Learning ◽  
Metric Learning ◽  
Classification Performance ◽  
Ensemble Model ◽  
Automated Classification ◽  
Eligibility Criteria ◽  
Data Imbalance ◽  
The Impact

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

A Functional Reading of the Epicurean Classification of Desires

Apeiron ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Maximilian Robitzsch
Keyword(s):  
Human Nature ◽  
Practical Significance ◽  
Textual Evidence ◽  
The Relationship

Abstract This paper examines the classification of desires that the Epicureans offer in their writings. It surveys the extant textual evidence for the classification and discusses the relationship between natural and necessary, natural and unnecessary, and unnatural and unnecessary desires. It argues that while the practical significance of the Epicurean classification is clear, which desires fall into which class is not. The paper suggests the reason for this may be that the Epicureans acknowledge some variability in their concept of human nature, arguing for a functional reading of the Epicurean classification of desires.

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