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2022 ◽  
Vol 2161 (1) ◽  
pp. 012074
Author(s):  
Hemavati ◽  
V Susheela Devi ◽  
R Aparna

Abstract Nowadays, multi-label classification can be considered as one of the important challenges for classification problem. In this case instances are assigned more than one class label. Ensemble learning is a process of supervised learning where several classifiers are trained to get a better solution for a given problem. Feature reduction can be used to improve the classification accuracy by considering the class label information with principal Component Analysis (PCA). In this paper, stacked ensemble learning method with augmented class information PCA (CA PCA) is proposed for classification of multi-label data (SEMML). In the initial step, the dimensionality reduction step is applied, then the number of classifiers have to be chosen to apply on the original training dataset, then the stacking method is applied to it. By observing the results of experiments conducted are showing our proposed method is working better as compared to the existing methods.


2021 ◽  
Vol 31 (14) ◽  
Author(s):  
Rakesh Kumar ◽  
Anuj Kumar Sharma

This article is concerned with the diffusion of a sport in a region, and the innovation diffusion model comprising of population classes, viz. nonadopters class, information class and adopters class. A qualitative analysis is carried out to assess the global asymptotic stability of the interior equilibrium for null delay. It has also been proved that the parameter [Formula: see text] (age gaps among sportspersons) in the intra-specific competition between the new players and the senior players can even destabilize the otherwise globally stable interior equilibrium state and the coexistence of all the populations is possible through periodic solutions due to Hopf bifurcation. With the help of normal form theory and center manifold arguments, the stability of bifurcating periodic orbits is determined. Numerical simulations have been executed in support of the analytical findings.


2021 ◽  
Vol 9 (3) ◽  
pp. 800-806
Author(s):  
Hacer Kömürcü ◽  

This research aims to determine the relationship between the computer use related self-efficacy perceptions and academic success of conservatory students in distance education during the COVID-19 pandemic. The sample group of the study consists of 130 students who received distance education at Zonguldak Bülent Ecevit University State Conservatory during the COVID-19 pandemic. The quantitative data of the study were obtained via the "Computer Self-Efficacy Perception Scale" developed by Aşkar and Umay, and the academic success scores were obtained through correspondence with the conservatory administration. The demographic characteristics of the participants including gender, branch, age, and class information in the sample group were collected through a form prepared by the researcher. SPSS 21.0 program was used in the analysis of the research data. The data was analysed using a t-test, ANOVA and correlation and regression analyses. According to the results of the research, there is a positive, significant, and moderate relationship between conservatory students' computer self-efficacy perceptions and their academic success scores in distance education. The current study revealed that conservatory students' computer self-efficacy perceptions are a predictor of the academic success scores in distance education and can explain 30.2% of the academic success score. Gender, branch, age, and class variables do not have a significant effect on academic success scores and computer self-efficacy perceptions.


2021 ◽  
Vol 15 ◽  
Author(s):  
Zhaoliang Zheng ◽  
Xuan Dong ◽  
Jian Yao ◽  
Leyuan Zhou ◽  
Yang Ding ◽  
...  

We propose a new model to identify epilepsy EEG signals. Some existing intelligent recognition technologies require that the training set and test set have the same distribution when recognizing EEG signals, some only consider reducing the marginal distribution distance of the data while ignoring the intra-class information of data, and some lack of interpretability. To address these deficiencies, we construct a TSK transfer learning fuzzy system (TSK-TL) based on the easy-to-interpret TSK fuzzy system the transfer learning method. The proposed model is interpretable. By using the information contained in the source domain and target domains more effectively, the requirements for data distribution are further relaxed. It realizes the identification of epilepsy EEG signals in data drift scene. The experimental results show that compared with the existing algorithms, TSK-TL has better performance in EEG recognition of epilepsy.


Author(s):  
Han Zhao ◽  
Xu Yang ◽  
Zhenru Wang ◽  
Erkun Yang ◽  
Cheng Deng

By contrasting positive-negative counterparts, graph contrastive learning has become a prominent technique for unsupervised graph representation learning. However, existing methods fail to consider the class information and will introduce false-negative samples in the random negative sampling, causing poor performance. To this end, we propose a graph debiased contrastive learning framework, which can jointly perform representation learning and clustering. Specifically, representations can be optimized by aligning with clustered class information, and simultaneously, the optimized representations can promote clustering, leading to more powerful representations and clustering results. More importantly, we randomly select negative samples from the clusters which are different from the positive sample's cluster. In this way, as the supervisory signals, the clustering results can be utilized to effectively decrease the false-negative samples. Extensive experiments on five datasets demonstrate that our method achieves new state-of-the-art results on graph clustering and classification tasks.


Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 956
Author(s):  
Hao Li ◽  
Yuanshu Zhang ◽  
Yong Ma ◽  
Xiaoguang Mei ◽  
Shan Zeng ◽  
...  

The representation-based algorithm has raised a great interest in hyperspectral image (HSI) classification. l1-minimization-based sparse representation (SR) attempts to select a few atoms and cannot fully reflect within-class information, while l2-minimization-based collaborative representation (CR) tries to use all of the atoms leading to mixed-class information. Considering the above problems, we propose the pairwise elastic net representation-based classification (PENRC) method. PENRC combines the l1-norm and l2-norm penalties and introduces a new penalty term, including a similar matrix between dictionary atoms. This similar matrix enables the automatic grouping selection of highly correlated data to estimate more robust weight coefficients for better classification performance. To reduce computation cost and further improve classification accuracy, we use part of the atoms as a local adaptive dictionary rather than the entire training atoms. Furthermore, we consider the neighbor information of each pixel and propose a joint pairwise elastic net representation-based classification (J-PENRC) method. Experimental results on chosen hyperspectral data sets confirm that our proposed algorithms outperform the other state-of-the-art algorithms.


2021 ◽  
Author(s):  
Nicholas J Sexton ◽  
Bradley C Love

One reason the mammalian visual system is viewed as hierarchical, such that successive stages of processing contain ever higher-level information, is because of functional correspondences with deep convolutional neural networks (DCNNs). However, these correspondences between brain and model activity involve shared, not task-relevant, variance. We propose a stricter test of correspondence: If a DCNN layer corresponds to a brain region, then replacing model activity with brain activity should successfully drive the DCNN's object recognition decision. Using this approach on three datasets, we found all regions along the ventral visual stream best corresponded with later model layers, indicating all stages of processing contained higher-level information about object category. Time course analyses suggest long-range recurrent connections transmit object class information from late to early visual areas.


2021 ◽  
Vol 4 (1) ◽  
pp. 140-144
Author(s):  
Muya Syaroh Iwanda lubis ◽  
Cut Alma Nuraflah ◽  
Azizah Hanum

Communication has an important role in every family especially. Imagine what happens when in a family far from the word communication, of course misunderstandings will continue to occur. Because there is no good communication between each other. Family communication exists to provide order and help overcome stress levels in children, especially in doing online learning at home after the Covid-19 pandemic. Where the child also feels that his freedom in learning is limited without any friends in class. Information provided if this online learning period will be permanent. This is certainly homework for parents in dealing with stress on children. The Technology Readiness Level (TKT) in this study is expected that parents can receive suggestions from the author to make alternative learning at home by bringing some of their friends, teaching parents to use video call technology in the learning process with the child's teacher and school friends, if all this time learn only to use WA (whatsapp) only. This research was conducted in Si Rotan Village, to be precise Dusun I and Dusun II, because in these areas there are more than other hamlets. From the research conducted it shows that the role of family communication in overcoming the stress of online learning can be done well by parents.


Author(s):  
D. A. Uvarov

Bio-pharmaceutics is one of the most science-intensive industries. Annually a lot of money is spent on applied research aimed at development and commercialization of new medications. Many pharmaceutical companies try to have in their product line or pipeline drugs on the basis of monoclonal antibodies, i.e. a class of biotechnological preparations that are used to combat oncologic and autoimmune diseases and are based on target therapy principle. Because of the high interest in bio-pharmaceutical industry on the part of businessmen, state and science any advanced data dealing with the situation inside the market can be useful for shaping the adequate picture of the present day condition and for making managerial decisions on state and private level. The article provides information about global sales of preparations based on monoclonal antibodies. Apart from sales in terms of money the author calculates the natural volume of products being sold based on price analysis of products. The article gives a list of preparations registered on EU and US markets rated by their sales. By analyzing preparation prices corrected to dosage it was possible to find the most expensive and the cheapest medications in their class. Information concerning the natural volume of drug being sold can help understand the scale of preparation production.


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