A Statistical Classification of the Benign and Malignant Neoplasm using Ensemble Learning and Classification Algorithms

2021 ◽  
Author(s):  
Tirth Kiranbhai Vyas
Keyword(s):  
Ensemble Learning ◽  
Malignant Neoplasm ◽  
Classification Algorithms ◽  
Statistical Classification

scholarly journals The classification of algorithms for Privacy Management in Ubiquitous Environments

2020 ◽  
Author(s):  
Valderi R. Q. Leithardt ◽  
Carlos O. Rolim ◽  
Anubis G. M. Rossetto ◽  
Guilherme A. Borges ◽  
Jorge Sá Silva ◽  
...  
Keyword(s):  
Data Management ◽  
Classification Algorithms ◽  
Privacy Management ◽  
Statistical Classification ◽  
Exchange Information

Control and data management in ubiquitous environments is not a trivial activity owing to the heterogeneity of the users, applications and devices, required to exchange information. However, various problems have been found in the literature with regard to privacy information and related to the data used in ubiquitous environments. This paper offers a solution by means of statistical classification algorithms that can be used for control and privacy management. On the basis of the algorithms used in the tests, it proved to be possible to control and manage information by providing definitions of the variables and parameters for users, devices, and ubiquitous environments.

Ensemble Learning for Facial Expression Recognition

2020 ◽  
pp. 31-41
Author(s):  
admin admin ◽  
◽  
◽  
Monika Gupta
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Ensemble Learning ◽  
Facial Expression Recognition ◽  
Computing Time ◽  
Classification Algorithms ◽  
Expression Recognition ◽  
Neural Network Algorithm

Facial expressions are the translation of the emotions such as anger, sadness, happiness, disgust felt by a person. Facial expression recognition, classification of expressions which has application in various industries such as hospitality, medical to name a few. There are various datasets available for facial expression recognition, we used FER 2013 dataset to build a classification algorithm. This algorithm classifies the emotions into seven categories namely, angry, disgust, happy, sad, fear, surprise and neutral. In traditional convolutional neural network algorithm the computing time is very large, ensemble learning significantly reduced the computing time and offered a promising accuracy. Features of images were extracted using the convolutional neural network, further these features were implemented using XGBoost and Random Forest to build classification algorithms and an accuracy of 77% and 74% was obtained. This was comparable to the accuracy obtained by traditional convolutional neural network which was 75% also with very less computing time.

Die Wirksamkeit eines Frühinterventionsprogramms für Jugendliche mit Computerspiel- und Internetabhängigkeit: Mittelfristige Effekte der PROTECT+ Studie

2020 ◽  
Vol 48 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Carolin Szász-Janocha ◽  
Eva Vonderlin ◽  
Katajun Lindenberg
Keyword(s):  
Internet Use ◽  
Health Problems ◽  
Reliable Change Index ◽  
Gaming Disorder ◽  
Statistical Classification ◽  
Reliable Change ◽  
Classification Of Diseases ◽  
International Statistical Classification

Zusammenfassung. Fragestellung: Das junge Störungsbild der Computerspiel- und Internetabhängigkeit hat in den vergangenen Jahren in der Forschung zunehmend an Aufmerksamkeit gewonnen. Durch die Aufnahme der „Gaming Disorder“ in die ICD-11 (International Statistical Classification of Diseases and Related Health Problems) wurde die Notwendigkeit von evidenzbasierten und wirksamen Interventionen avanciert. PROTECT+ ist ein kognitiv-verhaltenstherapeutisches Gruppentherapieprogramm für Jugendliche mit Symptomen der Computerspiel- und Internetabhängigkeit. Die vorliegende Studie zielt auf die Evaluation der mittelfristigen Effekte nach 4 Monaten ab. Methodik: N = 54 Patientinnen und Patienten im Alter von 9 bis 19 Jahren (M = 13.48; SD = 1.72) nahmen an der Frühinterventionsstudie zwischen April 2016 und Dezember 2017 in Heidelberg teil. Die Symptomschwere wurde zu Beginn, zum Abschluss der Gruppentherapie sowie nach 4 Monaten anhand von standardisierten Diagnostikinstrumenten erfasst. Ergebnisse: Mehrebenenanalysen zeigten eine signifikante Reduktion der Symptomschwere anhand der Computerspielabhängigkeitsskala (CSAS) nach 4 Monaten. Im Selbstbeurteilungsbogen zeigte sich ein kleiner Effekt (d = 0.35), im Elternurteil ein mittlerer Effekt (d = 0.77). Der Reliable Change Index, der anhand der Compulsive Internet Use Scale (CIUS) berechnet wurde, deutete auf eine starke Heterogenität im individuellen Symptomverlauf hin. Die Patientinnen und Patienten bewerteten das Programm zu beiden Follow-Up-Messzeitpunkten mit einer hohen Zufriedenheit. Schlussfolgerungen: Die vorliegende Arbeit stellt international eine der wenigen Studien dar, die eine Reduktion der Symptome von Computerspiel- und Internetabhängigkeit im Jugendalter über 4 Monate belegen konnte.

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.

scholarly journals A study of statistical classification of aphasia: multivariate analysis of SLTA.

1984 ◽  
Vol 4 (2) ◽  
pp. 620-628 ◽  
Author(s):  
Hiroichi Tasaki ◽  
Shunzo Watanabe ◽  
Kei Hojo ◽  
Kazue Chishima ◽  
Hirobumi Metoki
Keyword(s):  
Multivariate Analysis ◽  
Statistical Classification

Analysing Temporal Effects on Classification of SAR and Optical Images

2021 ◽  
Author(s):  
Ahmet Batuhan Polat ◽  
Ozgun Akcay ◽  
Fusun Balik Sanli
Keyword(s):  
Rural Areas ◽  
Classification Accuracy ◽  
Winter Season ◽  
Support Vector ◽  
Classification Algorithms ◽  
Sar Images ◽  
Optical Images ◽  
Object Based ◽  
Training Samples

<p>Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy.  As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.</p>

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