Journal articles: 'Training set analysi'

1

Jumaniyazov, Anvarbek B. "PEDAGOGICAL ANALYSIS OF TRAINING IN PHYSICAL EDUCATION AND SPORTS MANAGEMENT." CURRENT RESEARCH JOURNAL OF PEDAGOGICS 03, no. 05 (May 1, 2022): 1–10. http://dx.doi.org/10.37547/pedagogics-crjp-03-05-01.

Full text

Abstract:

Over the past three years, a number of positive steps have been taken in our country to modernize higher education, develop the social sphere and the economy based on advanced educational technologies and innovative scientific developments. In the Address of the President to the Oliy Majlis of January 24, 2020, a wide range of tasks were set for various ministries and departments. “... As we aim to turn Uzbekistan into a developed country, we can achieve this only through rapid reforms, education and innovation. To do this, first of all, we need to nurture a new generation of knowledgeable and qualified personnel who will emerge as enterprising reformers, think strategically.

APA, Harvard, Vancouver, ISO, and other styles

2

Kara, Gökhan, Ozan Hikmet Arıcan, and Olgay Okşaş. "Analysis of the Effect of Electronic Chart Display and Information System Simulation Technologies in Maritime Education." Marine Technology Society Journal 54, no. 3 (May 1, 2020): 43–57. http://dx.doi.org/10.4031/mtsj.54.3.6.

Full text

Abstract:

AbstractAn accident that may occur during maritime transport has substantially tangible, intangible, and environmental consequences. Approximately 85% of accidents at sea depend on human and communication factors. Therefore, it is expected to prevent adverse events in advance and to determine the procedures to be followed with simulator trainings before navigation at sea. National and international agreements set a standard for seafarers' education programs. The use of the simulator is recommended according to the Standards of Training, Certification and Watchkeeping for Seafarers (STCW) for maritime training, which is in accordance with international standards. These training programs should be designed to improve seafarers' ability in order to make accurate decisions, think quickly, and find solutions. Developments in the field of technology have enabled a wide range of simulation applications in electronic devices. The studies have shown that the professional knowledge of students has been increased with simulator-based education in many professions. This article presents a comparison that was made between simulation training and theoretical education. Two hundred surveys were conducted for 100 maritime students who received Electronic Chart Display and Information System (ECDIS) simulation training. It is aimed to measure the effect of ECDIS computer simulators on educational efficiency and suggest the use of simulators for the training of maritime students.

APA, Harvard, Vancouver, ISO, and other styles

3

Wang, Jianzhong. "Mathematical analysis on out-of-sample extensions." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 05 (September 2018): 1850042. http://dx.doi.org/10.1142/s021969131850042x.

Full text

Abstract:

Let [Formula: see text] be a data set in [Formula: see text], where [Formula: see text] is the training set and [Formula: see text] is the test one. Many unsupervised learning algorithms based on kernel methods have been developed to provide dimensionality reduction (DR) embedding for a given training set [Formula: see text] ([Formula: see text]) that maps the high-dimensional data [Formula: see text] to its low-dimensional feature representation [Formula: see text]. However, these algorithms do not straightforwardly produce DR of the test set [Formula: see text]. An out-of-sample extension method provides DR of [Formula: see text] using an extension of the existent embedding [Formula: see text], instead of re-computing the DR embedding for the whole set [Formula: see text]. Among various out-of-sample DR extension methods, those based on Nyström approximation are very attractive. Many papers have developed such out-of-extension algorithms and shown their validity by numerical experiments. However, the mathematical theory for the DR extension still need further consideration. Utilizing the reproducing kernel Hilbert space (RKHS) theory, this paper develops a preliminary mathematical analysis on the out-of-sample DR extension operators. It treats an out-of-sample DR extension operator as an extension of the identity on the RKHS defined on [Formula: see text]. Then the Nyström-type DR extension turns out to be an orthogonal projection. In the paper, we also present the conditions for the exact DR extension and give the estimate for the error of the extension.

APA, Harvard, Vancouver, ISO, and other styles

4

Pattnaik, Saumendra, and Binod Kumar Pattanayak. "Empirical analysis of software quality prediction using a TRAINBFG algorithm." International Journal of Engineering & Technology 7, no. 2.6 (March 11, 2018): 259. http://dx.doi.org/10.14419/ijet.v7i2.6.10780.

Full text

Abstract:

Software quality plays a major role in software fault proneness. That’s why prediction of software quality is essential for measuring the anticipated faults present in the software. In this paper we have proposed a Neuro-Fuzzy model for prediction of probable values for a predefined set of software characteristics by virtue of using a rule base. In course of it, we have used several training algorithms among which TRAINBFG algorithm is observed to be the best one for the purpose. There are various training algorithm available in MATLAB for training the neural network input data set. The prediction using fuzzy logic and neural network provides better result in comparison with only neural network. We find out from our implementation that TRAINBFG algorithm can provide better predicted value as compared to other algorithm in MATLAB. We have validated this result using the tools like SPSS and MATLAB.

APA, Harvard, Vancouver, ISO, and other styles

5

KING, FOO SHOU, P. SARATCHANDRAN, and N. SUNDARARAJAN. "ANALYSIS OF TRAINING SET PARALLELISM FOR BACKPROPAGATION NEURAL NETWORKS." International Journal of Neural Systems 06, no. 01 (March 1995): 61–78. http://dx.doi.org/10.1142/s0129065795000068.

Full text

Abstract:

Training set parallelism and network based parallelism are two popular paradigms for parallelizing a feedforward (artificial) neural network. Training set parallelism is particularly suited to feedforward neural networks with backpropagation learning where the size of the training set is large in relation to the size of the network. This paper analyzes training set parallelism for feedforward neural networks when implemented on a transputer array configured in a pipelined ring topology. Theoretical expressions for the time per epoch (iteration) and optimal size of a processor network are derived when the training set is equally distributed among the processing nodes. These show that the speed up is a function of the number of patterns per processor, communication overhead per epoch and the total number of processors in the topology. Further analysis of how to optimally distribute the training set on a given processor network when the number of patterns in the training set is not an integer multiple of the number of processors, is also carried out. It is shown that optimal allocation of patterns in such cases is a mixed integer programming problem. Using this analysis it is found that equal distribution of training patterns among the processors is not the optimal way to allocate the patterns even when the training set is an integer multiple of the number of processors. Extension of the analysis to processor networks comprising processors of different speeds is also carried out. Experimental results from a T805 transputer array are presented to verify all the theoretical results.

APA, Harvard, Vancouver, ISO, and other styles

6

Aliyuda, Kachalla, and John Howell. "Machine-learning algorithm for estimating oil-recovery factor using a combination of engineering and stratigraphic dependent parameters." Interpretation 7, no. 3 (August 1, 2019): SE151—SE159. http://dx.doi.org/10.1190/int-2018-0211.1.

Full text

Abstract:

The methods used to estimate recovery factor change through the life cycle of a field. During appraisal, prior to development when there are no production data, we typically rely on analog fields and empirical methods. Given the absence of a perfect analog, these methods are typically associated with a wide range of uncertainty. During plateau, recovery factors are typically associated with simulation and dynamic modeling, whereas in later field life, once the field drops off the plateau, a decline curve analysis is also used. The use of different methods during different stages of the field life leads to uncertainty and potential inconsistencies in recovery estimates. A wide range of interacting, partially related, reservoir and production variables controls the production and recovery factor. Machine learning allows more complex multivariate analysis that can be used to investigate the roles of these variables using a training data set and then to ultimately predict future performance in fields. To investigate this approach, we used a data set consisting of producing reservoirs all of which are at plateau or in decline to train a series of machine-learning algorithms that can potentially predict the recovery factor with minimal percentage error. The database for this study consists of categorical and numerical properties for 93 reservoirs from the Norwegian Continental Shelf. Of these, 75 are from the Norwegian Sea, the Norwegian North Sea, and the Barents Sea, whereas the remaining 18 reservoirs are from the Viking Graben in the UK sector of the North Sea. The data set was divided into training and testing sets: The training set comprised approximately 80% of the total data, and the remaining 20% was the testing set. Linear regression models and a support vector machine (SVM) models were trained with all parameters in the data set (30 parameters); then with the 16 most influential parameters in the data set, the performance of these models was compared from results of fivefold crossvalidation. SVM training using a combination of 16 geologic/engineering parameters models with Gaussian kernel function has a root-mean-square error of 0.12, mean square error of 0.01, and [Formula: see text]-squared of 0.76. This model was tested on 18 reservoirs from the testing set; the test results are very similar to crossvalidation results during models training phase, suggesting that this method can potentially be used to predict the future recovery factor.

APA, Harvard, Vancouver, ISO, and other styles

7

Cohen, Albert, Wolfgang Dahmen, Ronald DeVore, and James Nichols. "Reduced Basis Greedy Selection Using Random Training Sets." ESAIM: Mathematical Modelling and Numerical Analysis 54, no. 5 (July 16, 2020): 1509–24. http://dx.doi.org/10.1051/m2an/2020004.

Full text

Abstract:

Reduced bases have been introduced for the approximation of parametrized PDEs in applications where many online queries are required. Their numerical efficiency for such problems has been theoretically confirmed in Binev et al. (SIAM J. Math. Anal. 43 (2011) 1457–1472) and DeVore et al. (Constructive Approximation 37 (2013) 455–466), where it is shown that the reduced basis space Vn of dimension n, constructed by a certain greedy strategy, has approximation error similar to that of the optimal space associated to the Kolmogorov n-width of the solution manifold. The greedy construction of the reduced basis space is performed in an offline stage which requires at each step a maximization of the current error over the parameter space. For the purpose of numerical computation, this maximization is performed over a finite training set obtained through a discretization of the parameter domain. To guarantee a final approximation error ε for the space generated by the greedy algorithm requires in principle that the snapshots associated to this training set constitute an approximation net for the solution manifold with accuracy of order ε. Hence, the size of the training set is the ε covering number for M and this covering number typically behaves like exp(Cε−1/s) for some C > 0 when the solution manifold has n-width decay O(n−s). Thus, the shear size of the training set prohibits implementation of the algorithm when ε is small. The main result of this paper shows that, if one is willing to accept results which hold with high probability, rather than with certainty, then for a large class of relevant problems one may replace the fine discretization by a random training set of size polynomial in ε−1. Our proof of this fact is established by using inverse inequalities for polynomials in high dimensions.

APA, Harvard, Vancouver, ISO, and other styles

8

Embros, Grzegorz. "Audyt zachowań jako narzędzie systemu zarządzania bezpieczeństwem i higieną pracy." Studia Ecologiae et Bioethicae 7, no. 1 (June 30, 2009): 165–79. http://dx.doi.org/10.21697/seb.2009.7.1.11.

Full text

Abstract:

In the article there were presented general assumptions concerning employees’ hazardous behaviour modification programme with use of a behavioural audit. The author depicted a method of the audit conduct underlining the meaning of a dialogue with an employee for proper identification of reasons for hazardous behaviours. Therefore, a crucial role of auditors’ selection and training was emphasised for they deliver data for further analysis. The quality of data obtained in the behaviour and work conditions observation process is vital for the programme success. Consequently, periodic trainings for behavioural auditors need to be carried out to deepen their knowledge and improve the methods of auditing.

APA, Harvard, Vancouver, ISO, and other styles

9

Núñez, Sergio, Daniel Borrajo, and Carlos Linares López. "Performance Analysis of Planning Portfolios." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (August 20, 2021): 65–71. http://dx.doi.org/10.1609/socs.v3i1.18238.

Full text

Abstract:

In recent years the concept of sequential portfolio has become an important topic to improve the performance of modern problem solvers, such as SAT engines or planners. The PbP planner and more recently Fast Downward Stone Soup are successful approaches in Automated Planning that follow this trend. However, neither a theoretical analysis nor formal definitions about sequential portfolios have been described. In this paper, we focus on studying how to evaluate the performance of planners defining a baseline for a set of problems. We present a general method based on Mixed-Integer Programming to define the baseline for a training data set. In addition to prior work, we also introduce a short empirical analysis of the utility of training problems to configure sequential portfolios.

APA, Harvard, Vancouver, ISO, and other styles

10

Kozma, Gábor Viktor. "Actor Training as a Method of Directors. Training in Context of the Odin Teatret’s Creative Work and Higher Education." Studia Universitatis Babeş-Bolyai Dramatica 67, no. 2 (December 13, 2022): 29–45. http://dx.doi.org/10.24193/subbdrama.2022.2.02.

Full text

Abstract:

"In my recent research, I am interested in investigating the logic of actor training practices in different approaches, such as the training of the Odin Teatret, the Suzuki Method, or the Viewpoints technique, trying to compare them and expose the training's common logic. The present paper focuses on the analysis of training at the Odin Teatret and tries to employ a deconstructive analytical technique to analyze this training according to a straightforward set of standards: (1) From where? – the context and history of the training practicing company (2) What? – a comparison of several concepts of training (3) When? – the scheduling of training inside the companies (4) For what? – the training's objectives and the ideal actor image they create (5) How? – the training tools. To set the stage for my article, I will first define the term “training” as I use it, then I will examine Odin Teatret’s methods, and lastly, based on all of these, I will summarize the notion of training as a directing method from an Eastern European perspective. Keywords: Actor’s training, Odin Teatret, education, Eugenio Barba."

APA, Harvard, Vancouver, ISO, and other styles

11

Leischow, Scott J., J. Taylor Hays, Thomas J. Glynn, Katherine E. Kemper, Janet Okamoto, and Richard D. Hurt. "Global Bridges: Development and Analysis of a Tobacco Treatment Network." Journal of Smoking Cessation 11, no. 2 (March 23, 2016): 90–98. http://dx.doi.org/10.1017/jsc.2016.1.

Full text

Abstract:

The Framework Convention on Tobacco Control (FCTC) set standards for global tobacco control, including the implementation of evidence-based tobacco dependence treatment. However, efforts to implement tobacco treatment programmes globally have been few. In order to expand tobacco treatment expertise and programmes, a new network called Global Bridges (GB) was established. This network provided training in tobacco treatment and opportunities to share best practices on implementation of tobacco dependence treatment and training programmes. In this analysis of the GB network, we found that 75% of the network members attended trainings, 60% disseminated knowledge gained through GB training, and network centralization was high (0.85). These results demonstrate initial success in network implementation, and create a foundation for expanded focus on tobacco treatment globally.

APA, Harvard, Vancouver, ISO, and other styles

12

Pei, Yuanzhao, and Linlin Ye. "Cluster analysis of MNIST data set." Journal of Physics: Conference Series 2181, no. 1 (January 1, 2022): 012035. http://dx.doi.org/10.1088/1742-6596/2181/1/012035.

Full text

Abstract:

Abstract The full name of MNIST is Mixed National Institute of Standards and Technology database. This is a very large database of handwritten digits. The data set is divided into two parts: the first part is 60,000 training set images, the second part is 10,000 test set images, each image is composed of 28×28 pixels, and the value of each pixel between 0 and 1, based on this data set, not only can a judgment method for handwritten numbers be designed, but also the accuracy of the recognition algorithm can be compared through the test set. At present, machine learning experts around the world use different learning methods to test the data set. At present, the mainstream methods with higher accuracy are: support vector machines, convolutional neural networks, and naive Bayes classification. This article tries to use the clustering method to train the MNIST data set. First, the image data of the training set is converted into 60000×785 rows of two-dimensional matrix data, one of which is the real value of the image, and then through K-means the algorithm divides the data into 10 categories, uses the T-SNE algorithm to reduce the dimensionality, and visualizes by randomly selecting 1000 training data. It is found that the clustering effect of the numbers 0, 6, and 8 is the best, while the number 1 is completely invisible, the accuracy of K-means algorithm clustering is 83.33%. In order to further explore the test effects of different clustering methods on the data set, other clustering methods are introduced: MiniBatchKMeans algorithm. When the cluster is set to 10, the clustering of data 6 and 8 in MiniBatchKMeans the effect is good. At this time, the accuracy of the MiniBatchKMeans algorithm clustering is 87.67%. By comparing the test results of other algorithms, it is found that the MiniBatchKMeans algorithm is better than the K-means algorithms and the clustering result of is better than some supervised learning methods.

APA, Harvard, Vancouver, ISO, and other styles

13

Kapsung Kim, 주현준, and Yong Kim. "An Analysis of the effectiveness of Training Programs for Labor-management Relationship." SECONDARY EDUCATION RESEARCH 61, no. 2 (June 2013): 389–412. http://dx.doi.org/10.25152/ser.2013.61.2.389.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Park, Eun-Young, and Okin Lee. "Meta-Analysis of Safety Skill Training Program for Individuals with Developmental Disabilities." Special Education Research 19, no. 1 (February 29, 2020): 115–37. http://dx.doi.org/10.18541/ser.2020.02.19.1.115.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Bettiza, Martaleli. "An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method." E3S Web of Conferences 324 (2021): 05002. http://dx.doi.org/10.1051/e3sconf/202132405002.

Full text

Abstract:

Weather factors in the archipelago have an important role in sea transportation. Weather factors, especially wind speed and wave height, become the determinants of sailing permits besides transportation’s availability, routes, and fuel. Wind speed is also a potential source of renewable energy in the archipelago. Accurate wind speed forecasting is very useful for marine transportation and development of wind power technology. One of the methods in the artificial neural network field, Elman Recurrent Neural Network (ERNN), is used in this study to forecast wind speed. Wind speed data in 2019 from measurements at the Badan Meteorolog Klimatologi dan Geofisika (BMKG) at Hang Nadim Batam station were used in the training and testing process. The forecasting results showed an accuracy rate of 88.28% on training data and 71.38% on test data. The wide data range with the randomness and uncertainty of wind speed is the cause of low accuracy. The data set is divided into the training set and the testing set in several ratio schemas. The division of this data set considered to have contributed to the MAPE value. The observation data and data division carried out in different seasons, with varying types of wind cycles. Therefore, the forecasting results obtained in the training process are 17% better than the testing data.

APA, Harvard, Vancouver, ISO, and other styles

16

Bassett, Neil, David Rapetti, Keith Tauscher, Jack O. Burns, and Joshua J. Hibbard. "Ensuring Robustness in Training-set-based Global 21 cm Cosmology Analysis." Astrophysical Journal 908, no. 2 (February 24, 2021): 189. http://dx.doi.org/10.3847/1538-4357/abdb29.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

WOLFE, BRIAN L., LINDA M. LEMURA, and PHILLIP J. COLE. "QUANTITATIVE ANALYSIS OF SINGLE- VS. MULTIPLE-SET PROGRAMS IN RESISTANCE TRAINING." Journal of Strength and Conditioning Research 18, no. 1 (February 2004): 35–47. http://dx.doi.org/10.1519/00124278-200402000-00005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Wolfe, Brian L., Linda M. LeMura, and Phillip J. Cole. "Quantitative Analysis of Single- vs. Multiple-Set Programs in Resistance Training." Journal of Strength and Conditioning Research 18, no. 1 (2004): 35. http://dx.doi.org/10.1519/1533-4287(2004)018<0035:qaosvm>2.0.co;2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Novák, Drahomír, and David Lehký. "ANN inverse analysis based on stochastic small-sample training set simulation." Engineering Applications of Artificial Intelligence 19, no. 7 (October 2006): 731–40. http://dx.doi.org/10.1016/j.engappai.2006.05.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Petti, Samantha, and Sean R. Eddy. "Constructing benchmark test sets for biological sequence analysis using independent set algorithms." PLOS Computational Biology 18, no. 3 (March 7, 2022): e1009492. http://dx.doi.org/10.1371/journal.pcbi.1009492.

Full text

Abstract:

Biological sequence families contain many sequences that are very similar to each other because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.

APA, Harvard, Vancouver, ISO, and other styles

21

Stawicki, Piotr, and Ivan Volosyak. "cVEP Training Data Validation—Towards Optimal Training Set Composition from Multi-Day Data." Brain Sciences 12, no. 2 (February 8, 2022): 234. http://dx.doi.org/10.3390/brainsci12020234.

Full text

Abstract:

This paper investigates the effects of the repetitive block-wise training process on the classification accuracy for a code-modulated visual evoked potentials (cVEP)-based brain–computer interface (BCI). The cVEP-based BCIs are popular thanks to their autocorrelation feature. The cVEP-based stimuli are generated by a specific code pattern, usually the m-sequence, which is phase-shifted between the individual targets. Typically, the cVEP classification requires a subject-specific template (individually created from the user’s own pre-recorded EEG responses to the same stimulus target), which is compared to the incoming electroencephalography (EEG) data, using the correlation algorithms. The amount of the collected user training data determines the accuracy of the system. In this offline study, previously recorded EEG data collected during an online experiment with 10 participants from multiple sessions were used. A template matching target identification, with similar models as the task-related component analysis (TRCA), was used for target classification. The spatial filter was generated by the canonical correlation analysis (CCA). When comparing the training models from one session with the same session’s data (intra-session) and the model from one session with the data from the other session (inter-session), the accuracies were (94.84%, 94.53%) and (76.67%, 77.34%) for intra-sessions and inter-sessions, respectively. In order to investigate the most reliable configuration for accurate classification, the training data blocks from different sessions (days) were compared interchangeably. In the best training set composition, the participants achieved an average accuracy of 82.66% for models based only on two training blocks from two different sessions. Similarly, at least five blocks were necessary for the average accuracy to exceed 90%. The presented method can further improve cVEP-based BCI performance by reusing previously recorded training data.

APA, Harvard, Vancouver, ISO, and other styles

22

KIM, Won-Ouk. "Analysis of Emergency Evacuation for Training ship HANNARA using SEA-Pro & FDS." JOURNAL OF FISHRIES AND MARINE SCIENCES EDUCATION 33, no. 4 (August 31, 2021): 875–83. http://dx.doi.org/10.13000/jfmse.2021.8.33.4.875.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Raškinis, A. J. "Boolean Discriminant Functions in Symbolic Learning with Subclasses." Nonlinear Analysis: Modelling and Control 6, no. 2 (December 5, 2001): 71–86. http://dx.doi.org/10.15388/na.2001.6.1.15216.

Full text

Abstract:

Finding methods to increase the complexity of the Boolean discriminant functions and to stay within the limits of tractability set by combinatorics is an important task in the field of symbolic machine learning. The original formalism based on meta-features is introduced. Meta-features are predicates that describe relations between the features of the investigated objects and the subclasses (clusters inside classes) of the training set. The formalism facilitates finding Boolean discriminant functions of three variables. These are more complecated than simple conjunctions if the partition of the original training set into subclasses is given. The structure of meta-feature predicates is close to the structure of statements used by domain experts to describe their knowledge. Consequently, the formalism can be applied in hybrid learning systems, which incorporate information obtained from domain experts.

APA, Harvard, Vancouver, ISO, and other styles

24

WEI, QUANLING, and HONG YAN. "A DATA ENVELOPMENT ANALYSIS (DEA) EVALUATION METHOD BASED ON SAMPLE DECISION MAKING UNITS." International Journal of Information Technology & Decision Making 09, no. 04 (July 2010): 601–24. http://dx.doi.org/10.1142/s021962201000397x.

Full text

Abstract:

Most of evaluation methods on large number of candidates are based a single criterion. To bring the multiple attribute evaluation method Data Envelopment Analysis (DEA) into evaluating large number of elements, it needs to set up the performance standards and an evaluation procedure by the DEA model. In this paper, we first determine a set of "standard" candidates, called in decision making units (DMUs) in the DEA terminology. This standard set is called "training set". We then establish the evaluation procedure based on this "training set" for measuring a large number of DMUs. We first investigate the efficiency evaluation of a new DMU along with the original definition based on the sum formed production possibility set which is formed by the n DMUs in the training set and the new DMU. We then identify the intersection form of the production possibility set formed only by the n DMUs from the training set. And show that the new DMU evaluation is simply to check if the DMU satisfies a set of linear inequalities. The intersection formed production possibility set formed by the n DMUs from the training set is fixed for evaluating any new DMU. Therefore, it provides an efficient and effective method for dealing with a large amount of data. The method can be regarded as a complementary approach for data mining.

APA, Harvard, Vancouver, ISO, and other styles

25

Gomera, William, and George Oreku. "Mobile Technology in Training Micro Businesses." International Journal of ICT Research in Africa and the Middle East 5, no. 2 (July 2016): 14–24. http://dx.doi.org/10.4018/ijictrame.2016070102.

Full text

Abstract:

This paper establishes users' requirements and develops architectural design for mobile training platform between Micro Finance Institutions (MFIs) and Micro Businesses (MBs). Users' requirements and architectural design for mobile training platform will set a base for the development of application mobile technology in training MBs. This finding responds to the existing challenges facing trainings Micro business vendors which are learning coverage and ubiquitouness, caused by physical contact as model of delivering. Subsequently this study create base for development of mobile training application for MFIs and MBs. The study was conducted through literature review, in-depth interview, focused groups, observation and tasks analysis. User requirement and architecture design was established and presented to match specifications, characteristics and working environment of both MBs and MFIs.

APA, Harvard, Vancouver, ISO, and other styles

26

Pistoia, Jenny, Nadia Pinardi, Paolo Oddo, Matthew Collins, Gerasimos Korres, and Yann Drillet. "Development of super-ensemble techniques for ocean analyses: the Mediterranean Sea case." Natural Hazards and Earth System Sciences 16, no. 8 (August 9, 2016): 1807–19. http://dx.doi.org/10.5194/nhess-16-1807-2016.

Full text

Abstract:

Abstract. A super-ensemble methodology is proposed to improve the quality of short-term ocean analyses for sea surface temperature (SST) in the Mediterranean Sea. The methodology consists of a multiple linear regression technique applied to a multi-physics multi-model super-ensemble (MMSE) data set. This is a collection of different operational forecasting analyses together with ad hoc simulations, created by modifying selected numerical model parameterizations. A new linear regression algorithm based on empirical orthogonal function filtering techniques is shown to be efficient in preventing overfitting problems, although the best performance is achieved when a simple spatial filter is applied after the linear regression. Our results show that the MMSE methodology improves the ocean analysis SST estimates with respect to the best ensemble member (BEM) and that the performance is dependent on the selection of an unbiased operator and the length of training. The quality of the MMSE data set has the largest impact on the MMSE analysis root mean square error (RMSE) evaluated with respect to observed satellite SST. The MMSE analysis estimates are also affected by training period length, with the longest period leading to the smoothest estimates. Finally, lower RMSE analysis estimates result from the following: a 15-day training period, an overconfident MMSE data set (a subset with the higher-quality ensemble members) and the least-squares algorithm being filtered a posteriori.

APA, Harvard, Vancouver, ISO, and other styles

27

Yang, Xue, Han-Qi Yu, Shi-Ming Sun, and Wei Chen. "Using correspondence analysis to select training set for multi-modal information data." Cluster Computing 21, no. 1 (June 1, 2017): 893–905. http://dx.doi.org/10.1007/s10586-017-0945-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Zuo, Wan Li, Zhi Yan Wang, Ning Ma, and Hong Liang. "Study on Consistency Analysis in Text Categorization." Applied Mechanics and Materials 539 (July 2014): 181–84. http://dx.doi.org/10.4028/www.scientific.net/amm.539.181.

Full text

Abstract:

Accurate classification of text is a basic premise of extracting various types of information on the Web efficiently and utilizing the network resources properly. In this paper, a brand new text classification method was proposed. Consistency analysis method is a type of iterative algorithm, which mainly trains different classifiers (weak classifier) by aiming at the same training set, and then these classifiers will be gathered for testing the consistency degrees of various classification methods for the same text, thus to manifest the knowledge of each type of classifier. It main determines the weight of each sample according to the fact is the classification of each sample is accurate in each training set, as well as the accuracy of the last overall classification, and then sends the new data set whose weight has been modified to the subordinate classifier for training. In the end, the classifier gained in the training will be integrated as the final decision classifier. The classifier with consistency analysis can eliminate some unnecessary training data characteristics and place the key words on key training data. According to the experimental result, the average accuracy of this method is 91.0%, while the average recall rate is 88.1%.

APA, Harvard, Vancouver, ISO, and other styles

29

Oderov, Artur, Maksym Kuznetsov, Serhii Romanchuk, Dmytro Pohrebniak, Svitlana Indyka, and Natalia Bielikova. "Analysis of the level of physical fitness of cadets of the Military College of Sergeants of the National Academy of Land Forces in Lviv at the primary stage." Sport i Turystyka. Środkowoeuropejskie Czasopismo Naukowe 5, no. 1 (2022): 93–102. http://dx.doi.org/10.16926/sit.2022.01.05.

Full text

Abstract:

Introduction: Providing troops with modern military equipment, technical means of transportation has greatly facilitated the implementation of many elements of military activities by servicemen, but at the same time contributed to some negative impact on their body and psyche, increased the need for general and special physical training [2, 3]. The need for integrated use of physical education and sports is well known and scientifically sound. The system of physical training of troops as a component of professional activity ensures the functioning of personnel in accordance with the requirements of physical fitness. Thus, the topical issue is to improve the content of physical training of cadets of military colleges of sergeants, taking into account the categories of servicemen.Purpose: Determining and improving the dynamics of general and special physical fitness of cadets, taking into account the categories of servicemen.Results: A study of the level of physical fitness of cadets of the military college of sergeants showed that during the training of cadets in the first year indicators of general and special physical fitness have positive changes, but no significant difference was found (p > 0.05).

APA, Harvard, Vancouver, ISO, and other styles

30

Postalcıoğlu, Seda. "Performance Analysis of Different Optimizers for Deep Learning-Based Image Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 02 (June 14, 2019): 2051003. http://dx.doi.org/10.1142/s0218001420510039.

Full text

Abstract:

Deep learning refers to Convolutional Neural Network (CNN). CNN is used for image recognition for this study. The dataset is named Fruits-360 and it is obtained from the Kaggle dataset. Seventy percent of the pictures are selected as training data and the rest of the images are used for testing. In this study, an image size is [Formula: see text]. Training is realized using Stochastic Gradient Descent with Momentum (sgdm), Adaptive Moment Estimation (adam) and Root Mean Square Propogation (rmsprop) techniques. The threshold value is determined as 98% for the training. When the accuracy reaches more than 98%, training is stopped. Calculation of the final validation accuracy is done using trained network. In this study, more than 98% of the predicted labels match the true labels of the validation set. Accuracies are calculated using test data for sgdm, adam and rmsprop techniques. The results are 98.08%, 98.85%, 98.88%, respectively. It is clear that fruits are recognized with good accuracy.

APA, Harvard, Vancouver, ISO, and other styles

31

Myburgh, Gerhard, and Adriaan van Niekerk. "Impact of Training Set Size on Object-Based Land Cover Classification." International Journal of Applied Geospatial Research 5, no. 3 (July 2014): 49–67. http://dx.doi.org/10.4018/ijagr.2014070104.

Full text

Abstract:

Supervised classifiers are commonly employed in remote sensing to extract land cover information, but various factors affect their accuracy. The number of available training samples, in particular, is known to have a significant impact on classification accuracies. Obtaining a sufficient number of samples is, however, not always practical. The support vector machine (SVM) is a supervised classifier known to perform well with limited training samples and has been compared favourably to other classifiers for various problems in pixel-based land cover classification. Very little research on training-sample size and classifier performance has been done in a geographical object-based image analysis (GEOBIA) environment. This paper compares the performance of SVM, nearest neighbour (NN) and maximum likelihood (ML) classifiers in a GEOBIA environment, with a focus on the influence of training-set size. Training-set sizes ranging from 4-20 per land cover class were tested. Classification tree analysis (CTA) was used for feature selection. The results indicate that the performance of all the classifiers improved significantly as the size of the training set increased. The ML classifier performed poorly when few (<10 per class) training samples were used and the NN classifier performed poorly compared to SVM throughout the experiment. SVM was the superior classifier for all training-set sizes although ML achieved competitive results for sets of 12 or more training areas per class.

APA, Harvard, Vancouver, ISO, and other styles

32

Chong, De Wei, Kenny, and Abel Yang. "Photometric Redshift Analysis using Supervised Learning Algorithms and Deep Learning." EPJ Web of Conferences 206 (2019): 09006. http://dx.doi.org/10.1051/epjconf/201920609006.

Full text

Abstract:

We present a catalogue of galaxy photometric redshifts for the Sloan Digital Sky Survey (SDSS) Data Release 12. We use various supervised learning algorithms to calculate redshifts using photometric attributes on a spectroscopic training set. Two training sets are analysed in this paper. The first training set consists of 995,498 galaxies with redshifts up to z ≈ 0.8. On the first training set, we achieve a cost function of 0.00501 and a root mean squared error value of 0.0707 using the XGBoost algorithm. We achieved an outlier rate of 2.1% and 86.81%, 95.83%, 97.90% of our data points lie within one, two, and three standard deviation of the mean respectively. The second training set consists of 163,140 galaxies with redshifts up to z ≈ 0.2 and is merged with the Galaxy Zoo 2 full catalog. We also experimented on convolutional neural networks to predict five morphological features (Smooth, Features/Disk, Star, Edge-on, Spiral). We achieve a root mean squared error of 0.117 when validated against an unseen dataset with over 200 epochs. Morphological features from the Galaxy Zoo, trained with photometric features are found to consistently improve the accuracy of photometric redshifts.

APA, Harvard, Vancouver, ISO, and other styles

33

Rock, Elana Esterson, Michael S. Rosenberg, and Deborah T. Carran. "Variables Affecting the Reintegration Rate of Students with Serious Emotional Disturbance." Exceptional Children 61, no. 3 (December 1994): 254–68. http://dx.doi.org/10.1177/001440299506100305.

Full text

Abstract:

This study examined educational program and teacher variables to identify factors that predict the reintegration of students with serious emotional disturbance (SED) into less restrictive placements. Data on program demographics, reintegration orientation, teacher reintegration training, and teacher attitudes toward reintegration were collected from 162 special education teachers and 31 administrators in restrictive placements for K-12 students with SED. This information was compared to the reintegration rates of students in those schools through the use of a hierarchical set regression analysis. Results indicated that reintegration orientation, demographic characteristics of restrictive SED programs, and particular experiences/training of special educators predict the reintegration of students with SED into less restrictive programs.

APA, Harvard, Vancouver, ISO, and other styles

34

Sarychev, Alexander, and Lyudmila Sarycheva. "GMDH-BASED OPTIMAL SET FEATURES DETERMINATION IN DISCRIMINANT ANALYSIS." System technologies 6, no. 125 (December 27, 2019): 26–40. http://dx.doi.org/10.34185/1562-9945-6-125-2019-03.

Full text

Abstract:

The task of searching optimum on complexity discriminant function is considered. Criteria of quality of the discriminant functions developed in the Group Method of Data Handling are described: the criterion based on a partition of observations on training and testing samples, and criterion of sliding examination. The tasks of this class belong to pattern recognition problems under the condition of structural uncertainty, which were considered by academician A.G. Ivakhnenko as long ago as 60–70-th of the last century as actual problems of an engineering cybernetics.

APA, Harvard, Vancouver, ISO, and other styles

35

Chen, Jie, Jing-Yun Wen, Zhan-Hong Chen, Qu Lin, Xiao-Kun Ma, Li Wei, Xing Li, et al. "Use of the mitotic kinase aurora-A activation to predict outcome for primary duodenal adenocarcinoma." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 4131. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.4131.

Full text

Abstract:

4131 Background: We and others had proved that hypoxia-inducible factor-1α (HIF-1α) and transcriptionally upregulated Aurora-A were required for disease progression in several tumors. Methods: We addressed the clinicopathologic value of HIF-1α and Aurora-a in primary duodenal adenocarcinoma (PDA). Aurora-a and HIF-1α expression were semi-quantitative evaluated by immunohistochemistry in 140 PDA. Among which, 76 patients from one institute acted as training set, and 64 cases from another two institutes were used as testing set to validate the prognostic effect of Aurora-a and HIF-1α. Results: We found that Aurora-a was high or sufficient expressed in tumor zone, whereas low-expressed in the normal adjacent epithelia. Moreover, Aurora-a high expression, classified by training set ROC analysis-generated cutoff score, predicted an inferior overall survival both in testing set and training set. Multivariate Cox regression confirmed that Aurora-a was indeed an independent prognostic factor (Table). Contrary to previous studies, we did not detect any correlation between Aurora-a and HIF-1α in PDA. Additionally, survival analysis showed that HIF-1α level was not correlated with patient outcome (p = 0.466). Conclusions: Activation of Aurora-A, an independent negative prognostic biomarker, might be used to identify particular patients for more selective therapy. [Table: see text]

APA, Harvard, Vancouver, ISO, and other styles

36

Zhang, L., L. Ning, X. Zhang, Z. Q. Pan, X. Wang, F. Xu, and Z. Z. Guan. "Preliminary analysis of biomarkers in plasma by SELDI to predict the response to EGFR tyrosine kinase inhibitors (TKIs) in NSCLC patients." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 7189. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.7189.

Full text

Abstract:

7189 Background: The identification of NSCLC patients who are most likely to respond to EGFR tyrosine kinase inhibitors (TKIs) have been investigated intensively. Although screenings for EGFR mutation and gene copy number are promising, these tests are not yet widely available. New predictor markers are urgently needed. The objective of this study was to identify proteomic markers in plasma to predict benefits for patients treated with EGFR TKIs. Methods: Proteomic spectra derived from plasma samples from EGFR TKIs-responsive patients and non-responsive patients were generated by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). These proteomic spectra (WCX2 chips, Ciphergen Biosystems, Inc.) were then analyzed by comparing protein profiles in different response groups (PR Vs PD, training set). Another group of patients treaded with EGFR TKIs will be serving as testing set to validate the result of training set. Results: Totally, fifty-four advanced NSCLC patients were included in this study. All patients were treated with single agent of gefitinib or erlotinib. Twenty-nine patients were included in training set of this study. All were suitable for response evaluation. Ten patients (34.5%) were PR, 8 (27.6%) were SD, and 11 (37.9%) were progressive disease (PD). Total six significant protein peaks were significant (m/z 4288, 4595, 9191, 9349, 9397, and 9563) between PR group and PD group (table). Another twenty-five patients were included for testing set. Analyzing of testing set is still going on. Table shows PR and PD patients’ plasma comparison on WCX2 chips. Conclusions: This preliminary “training” set of spectra that uses SELDI-TOF MS technology found that six protein peaks seemed to be very important biomarkers to predict the response to gefitinib. Prospective tests to confirm these proteins and peptides will be present at this meeting. These results are promising for identifying new biomarkers of EGFR TKIs with SELDI. [Table: see text] No significant financial relationships to disclose.

APA, Harvard, Vancouver, ISO, and other styles

37

Bruni, Renato, and Gianpiero Bianchi. "Effective Classification Using a Small Training Set Based on Discretization and Statistical Analysis." IEEE Transactions on Knowledge and Data Engineering 27, no. 9 (September 1, 2015): 2349–61. http://dx.doi.org/10.1109/tkde.2015.2416727.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Marković, Milan Ž., Milan M. Milosavljević, and Branko D. Kovačević. "Quadratic classifier with sliding training data set in robust recursive AR speech analysis." Speech Communication 37, no. 3-4 (July 2002): 283–302. http://dx.doi.org/10.1016/s0167-6393(01)00019-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Bae, Han Suk. "A Phenomenological Analysis on the Experiences of Using English for MICE Purposes: Suggestions for ESP Training in Tourism." Studies in English Education 27, no. 3 (September 30, 2022): 441–65. http://dx.doi.org/10.22275/see.27.3.09.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

M. Daoud, Daoud, and Samir Abou El-Seoud. "Building a Sentiment Analysis System Using Automatically Generated Training Dataset." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 06 (May 28, 2020): 48. http://dx.doi.org/10.3991/ijoe.v16i06.13623.

Full text

Abstract:

In this paper, we describe a methodology to develop a large training set for sentiment analysis automatically. We extract Arabic tweets and then annotates them for negativeness and positiveness sentiment without human intervention. These annotated tweets are used as a training data set to build our experimental sentiment analysis by using Naive Bayes algorithm and TF-IDF enhancement. The large size of training data for a highly inflected language is necessary to compensate for the sparseness nature of such languages. We present our techniques and explain our experimental system. We use 200 thousand annotated tweets to train our system. The evaluation shows that our sentiment analysis system has high precision and accuracy measures compared to existing ones.

APA, Harvard, Vancouver, ISO, and other styles

41

Daura-Oller, Elias, Maria Cabré, Miguel A. Montero, José L. Paternáin, and Antoni Romeu. "A First-Stage Approximation to Identify New Imprinted Genes through Sequence Analysis of Its Coding Regions." Comparative and Functional Genomics 2009 (2009): 1–7. http://dx.doi.org/10.1155/2009/549387.

Full text

Abstract:

In the present study, a positive training set of 30 known human imprinted gene coding regions are compared with a set of 72 randomly sampled human nonimprinted gene coding regions (negative training set) to identify genomic features common to human imprinted genes. The most important feature of the present work is its ability to use multivariate analysis to look at variation, at coding region DNA level, among imprinted and non-imprinted genes. There is a force affecting genomic parameters that appears through the use of the appropriate multivariate methods (principle components analysis (PCA) and quadratic discriminant analysis (QDA) to analyse quantitative genomic data. We show that variables, such as CG content, [bp]% CpG islands, [bp]% Large Tandem Repeats, and [bp]% Simple Repeats, are able to distinguish coding regions of human imprinted genes.

APA, Harvard, Vancouver, ISO, and other styles

42

Benedik, Ľudovít. "Psycho-Physiological Aspects in Karate Sports Preparation." Sport i Turystyka. Środkowoeuropejskie Czasopismo Naukowe 3, no. 2 (2020): 79–90. http://dx.doi.org/10.16926/sit.2020.03.14.

Full text

Abstract:

In the study, we tried to point out, on the basis of comparison conclusions of several research works, how much the psycho-physiological aspects contribute to influencing the conception of sport preparation in terms of the philosophical context of preparation in traditional martial arts. We elucidated the perception of the difference between martial arts and combat sports and emphasized the psychological dimension of training in sports karate in relation to physical training. At the same time, we were looking for relationships that would give us an answer as to which of these aspects are more effective in terms of sports performance. In conclusion, on the basis of a comprehensive content analysis of the findings, we draw attention to the fact that only the mutual correlation of both selected aspects can achieve the desired results in the training of karate practitioners.

APA, Harvard, Vancouver, ISO, and other styles

43

Fletcher, Roger, and Gaetano Zanghirati. "Binary separation and training support vector machines." Acta Numerica 19 (May 2010): 121–58. http://dx.doi.org/10.1017/s0962492910000024.

Full text

Abstract:

We introduce basic ideas of binary separation by a linear hyperplane, which is a technique exploited in the support vector machine (SVM) concept. This is a decision-making tool for pattern recognition and related problems. We describe a fundamental standard problem (SP) and show how this is used in most existing research to develop a dual-based algorithm for its solution. This algorithm is shown to be deficient in certain aspects, and we develop a new primal-based SQP-like algorithm, which has some interesting features. Most practical SVM problems are not adequately handled by a linear hyperplane. We describe the nonlinear SVM technique, which enables a nonlinear separating surface to be computed, and we propose a new primal algorithm based on the use of low-rank Cholesky factors.It may be, however, that exact separation is not desirable due to the presence of uncertain or mislabelled data. Dealing with this situation is the main challenge in developing suitable algorithms. Existing dual-based algorithms use the idea of L1 penalties, which has merit. We suggest how penalties can be incorporated into a primal-based algorithm. Another aspect of practical SVM problems is often the huge size of the data set, which poses severe challenges both for software package development and for control of ill-conditioning. We illustrate some of these issues with numerical experiments on a range of problems.

APA, Harvard, Vancouver, ISO, and other styles

44

Müller, Evamaria, Alena Strukava, Isabelle Scholl, Martin Härter, Ndeye Thiab Diouf, France Légaré, and Angela Buchholz. "Strategies to evaluate healthcare provider trainings in shared decision-making (SDM): a systematic review of evaluation studies." BMJ Open 9, no. 6 (June 2019): e026488. http://dx.doi.org/10.1136/bmjopen-2018-026488.

Full text

Abstract:

Design and objectivesWe performed a systematic review of studies evaluating healthcare provider (HCP) trainings in shared decision-making (SDM) to analyse their evaluation strategies.Setting and participantsHCP trainings in SDM from all healthcare settings.MethodsWe searched scientific databases (Medline, PsycInfo, CINAHL), performed reference and citation tracking, contacted experts in the field and scanned the Canadian inventory of SDM training programmes for healthcare professionals. We included articles reporting data of summative evaluations of HCP trainings in SDM. Two reviewers screened records, assessed full-text articles, performed data extraction and assessed study quality with the integrated quality criteria for review of multiple study designs (ICROMS) tool. Analysis of evaluation strategies included data source use, use of unpublished or published measures and coverage of Kirkpatrick’s evaluation levels. An evaluation framework based on Kirkpatrick’s evaluation levels and the Quadruple Aim framework was used to categorise identified evaluation outcomes.ResultsOut of 7234 records, we included 41 articles reporting on 30 studies: cluster-randomised (n=8) and randomised (n=9) controlled trials, controlled (n=1) and non-controlled (n=7) before-after studies, mixed-methods (n=1), qualitative (n=1) and post-test (n=3) studies. Most studies were conducted in the USA (n=9), Germany (n=8) or Canada (n=7) and evaluated physician trainings (n=25). Eleven articles met ICROMS quality criteria. Almost all studies (n=27) employed HCP-reported outcomes for training evaluation and most (n=19) additionally used patient-reported (n=12), observer-rated (n=10), standardised patient-reported (n=2) outcomes or training process and healthcare data (n=10). Most studies employed a mix of unpublished and published measures (n=17) and covered two (n=12) or three (n=10) Kirkpatrick’s levels. Identified evaluation outcomes covered all categories of the proposed framework.ConclusionsStrategies to evaluate HCP trainings in SDM varied largely. The proposed evaluation framework maybe useful to structure future evaluation studies, but international agreement on a core set of outcomes is needed to improve evidence.PROSPERO registration numberCRD42016041623.

APA, Harvard, Vancouver, ISO, and other styles

45

Turki, Turki, Zhi Wei, and Jason T. L. Wang. "A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction." Journal of Bioinformatics and Computational Biology 16, no. 03 (June 2018): 1840014. http://dx.doi.org/10.1142/s0219720018400140.

Full text

Abstract:

Transfer learning (TL) algorithms aim to improve the prediction performance in a target task (e.g. the prediction of cisplatin sensitivity in triple-negative breast cancer patients) via transferring knowledge from auxiliary data of a related task (e.g. the prediction of docetaxel sensitivity in breast cancer patients), where the distribution and even the feature space of the data pertaining to the tasks can be different. In real-world applications, we sometimes have a limited training set in a target task while we have auxiliary data from a related task. To obtain a better prediction performance in the target task, supervised learning requires a sufficiently large training set in the target task to perform well in predicting future test examples of the target task. In this paper, we propose a TL approach for cancer drug sensitivity prediction, where our approach combines three techniques. First, we shift the representation of a subset of examples from auxiliary data of a related task to a representation closer to a target training set of a target task. Second, we align the shifted representation of the selected examples of the auxiliary data to the target training set to obtain examples with representation aligned to the target training set. Third, we train machine learning algorithms using both the target training set and the aligned examples. We evaluate the performance of our approach against baseline approaches using the Area Under the receiver operating characteristic (ROC) Curve (AUC) on real clinical trial datasets pertaining to multiple myeloma, nonsmall cell lung cancer, triple-negative breast cancer, and breast cancer. Experimental results show that our approach is better than the baseline approaches in terms of performance and statistical significance.

APA, Harvard, Vancouver, ISO, and other styles

46

Shi, Dawei, Chao Li, Zhu Zhu, Runqing Lv, Shengjie Chen, and Yunfeng Zhu. "The Prediction Algorithm and Characteristics Analysis of Kuroshio Sea Surface Temperature Anomalies." Advances in Meteorology 2022 (April 27, 2022): 1–8. http://dx.doi.org/10.1155/2022/7236527.

Full text

Abstract:

Based on 130 climate signal indexes provided by National Climate Center of China, this paper established a decision tree diagnostic prediction model for Spring Kuroshio Sea Surface Temperature (SST) from 1961 to 2015 (65 years) by using Chi-Squared Automatic Interaction Detector (CHAID) algorithm in data mining and obtained five rule sets to determine whether Spring Kuroshio SST is high or not. Considering the data of the 44 years from 1961 to 2004 as the training set of the model and the other years as the test set, the training accuracy of the model can reach to 95.45% and the test accuracy can reach to 81.82%. Three types of Spring Kuroshio SST are different in intensity and distribution. The results show that the prediction model of Spring Kuroshio SST based on CHAID algorithm has a high prediction accuracy, with the reasonable and effective model and the well-thought-out decision rules. Moreover, based on the results of decision classification, the SST anomalies correspond to different distribution characteristics of summer daily precipitation anomalies in eastern China, which can provide a new idea and method for climate prediction of regional summer precipitation.

APA, Harvard, Vancouver, ISO, and other styles

47

YAMIN, Muhammad Mudassar, and Basel KATT. "Cyber Security Skill Set Analysis for Common Curricula Development." International Journal of Information Security and Cybercrime 8, no. 1 (June 28, 2019): 59–64. http://dx.doi.org/10.19107/ijisc.2019.01.08.

Full text

Abstract:

The field of cyber security is getting diversified day by day, with new specialist responsibilities and roles at different levels of competence being required by the industry. The competencies can be mapped with required skills set in multiple cyber security certification programs. However, different certification programs use different curricula and terminology, which makes the offerings overlap in some aspect and be distinct in others. This makes it hard for new institutes and cyber ranges to decide upon their training offerings. The aim of this study is to identify commonalities in skill set requirement for multiple cyber security roles like penetration tester, security operation center analysts, digital forensic and incident responders and information security managers. The identified commonalities will be used for development of a standard common curricula to set skill set requirement for the achievement of specific competence levels in a specific cyber security field.

APA, Harvard, Vancouver, ISO, and other styles

48

Mandal, Satyendra Nath, Pritam Ghosh, Nanigopal Shit, Dilip Kumar Hajra, and Santanu Banik. "A Framework for Selection of Training Algorithm of Neuro-Statistic Model for Prediction of Pig Breeds in India." Vietnam Journal of Computer Science 08, no. 01 (September 26, 2020): 153–75. http://dx.doi.org/10.1142/s2196888821500068.

Full text

Abstract:

Various training algorithms are used in artificial neural networks for updating the weights during training the network. But, the selection of the appropriate training algorithm is dependent on the input–output mapping of dataset for which the network is constructed. In this paper, a framework has been proposed consisting of five modules to select the optimal training algorithm for predicting pig breeds from their images. The individual pig images from five pig-breeds have been captured using inbuilt camera of mobile phone and the contour of pig has been segmented from each captured image by HUE-based segmentation algorithm. In Statistical Parameter and Color Component retrieval module, parameters like entropy, standard deviation, variance, mean, median, and mode and color properties like hue, saturation, value (HSV) extracted from the content of each segmented image. Values of all extracted parameters have been transferred into Training Algorithm Selection Module. In this module, a fitting neural network with different numbers of hidden neurons has been executed by feeding all extracted values from pig images for mapping their breeds. Ten training algorithms have been applied on the same extracted dataset separately for five epochs each keeping other network parameters constants. The mean square error (MSE) and correlation coefficient ([Formula: see text]) for the validation set have been calculated after adjustment of weights and biases in each connection of the neurons. One training algorithm among 10 and its suitable number of hidden neurons has been selected based on comparative analysis for getting lower MSE and higher [Formula: see text] in the validation set. Then, the fitting network with selected training algorithm has been run on the same extracted datasets until the stopping condition is reached. Then the test set images are fed into the network and the network output has been categorized to class which has been assigned to each breed of pig in Breed Prediction Module. The proposed framework has been able to predict breeds with 96.00% accuracy, achieved by the trial with 50 images of the test set. It may be concluded that the Neuro Statistic Neural Network model may be used for breed prediction of pigs by using images of individual pigs.

APA, Harvard, Vancouver, ISO, and other styles

49

Zhao, Yaning, Li Wang, Nannan Zhang, Xiangwei Huang, Lunke Yang, and Wenbiao Yang. "Co-Training Semi-Supervised Learning for Fine-Grained Air Quality Analysis." Atmosphere 14, no. 1 (January 9, 2023): 143. http://dx.doi.org/10.3390/atmos14010143.

Full text

Abstract:

Due to the limited number of air quality monitoring stations, the data collected are limited. Using supervised learning for air quality fine-grained analysis, that is used to predict the air quality index (AQI) of the locations without air quality monitoring stations, may lead to overfitting in that the models have superior performance on the training set but perform poorly on the validation and testing set. In order to avoid this problem in supervised learning, the most effective solution is to increase the amount of data, but in this study, this is not realistic. Fortunately, semi-supervised learning can obtain knowledge from unlabeled samples, thus solving the problem caused by insufficient training samples. Therefore, a co-training semi-supervised learning method combining the K-nearest neighbors (KNN) algorithm and deep neural network (DNN) is proposed, named KNN-DNN, which makes full use of unlabeled samples to improve the model performance for fine-grained air quality analysis. Temperature, humidity, the concentrations of pollutants and source type are used as input variables, and the KNN algorithm and DNN model are used as learners. For each learner, the labeled data are used as the initial training set to model the relationship between the input variables and the AQI. In the iterative process, by labeling the unlabeled samples, a pseudo-sample with the highest confidence is selected to expand the training set. The proposed model is evaluated on a real dataset collected by monitoring stations from 1 February to 30 April 2018 over a region between 118° E–118°53′ E and 39°45′ N–39°89′ N. Practical application shows that the proposed model has a significant effect on the fine-grained analysis of air quality. The coefficient of determination between the predicted value and the true value is 0.97, which is better than other models.

APA, Harvard, Vancouver, ISO, and other styles

50

Park, Min Jun, and Mauricio D. Sacchi. "Automatic velocity analysis using convolutional neural network and transfer learning." GEOPHYSICS 85, no. 1 (November 22, 2019): V33—V43. http://dx.doi.org/10.1190/geo2018-0870.1.

Full text

Abstract:

Velocity analysis can be a time-consuming task when performed manually. Methods have been proposed to automate the process of velocity analysis, which, however, typically requires significant manual effort. We have developed a convolutional neural network (CNN) to estimate stacking velocities directly from the semblance. Our CNN model uses two images as one input data for training. One is an entire semblance (guide image), and the other is a small patch (target image) extracted from the semblance at a specific time step. Labels for each input data set are the root mean square velocities. We generate the training data set using synthetic data. After training the CNN model with synthetic data, we test the trained model with another synthetic data that were not used in the training step. The results indicate that the model can predict a consistent velocity model. We also noticed that when the input data are extremely different from those used for the training, the CNN model will hardly pick the correct velocities. In this case, we adopt transfer learning to update the trained model (base model) with a small portion of the target data to improve the accuracy of the predicted velocity model. A marine data set from the Gulf of Mexico is used for validating our new model. The updated model performed a reasonable velocity analysis in seconds.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Training set analysi'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles