The robust learning model

In this study, we explore reseach methodologi, industry and the aspect of uncertainty. We make an analysis uncertainty for the topic, approach, model formulation and make comparison of uncertainty reseach area. The most used approach and method on uncertainty are Mixed Integer Linear Programing, mixed integer nonlinear Programing, Robust Fuzzy Stochastic Programming, and Improved kriging-assisted robust optimization method. Customer demand, total cost, product returns are the most widely researched aspects.

11p longtimenosee07 29-03-2024 2 2   Download

In this paper, the authors propose to use the Sparse Principal Component Analysis (PCA) method to denoise adversarial images. With the experimental results, the authors have demonstrated that the Robust sparse PCA method is effective in selecting and classifying key features of the image to remove unwanted noise present in the input image.

9p vigojek 02-02-2024 1 1   Download

Ebook "Java for bioinformatics and biomedical applications" represents an important development, giving the reader an opportunity to discover how the use of open and reusable Java code can solve large bioinformatics problems in a software engineered and robust way. This has lead to an explosion of interest in the subject, and a similar explosion in tools and data resources for researchers to learn and use in their work.

353p tachieuhoa 28-01-2024 8 5   Download

Continued part 1, part 2 of ebook "Automated machine learning: Methods, systems, challenges" provides readers with contents including: AutoML systems; AutoML challenges; automatic model selection and hyperparameter optimization in WEKA; efficient and robust automated machinelearning; a tree-based pipeline optimization toolfor automating machine learning; the automatic statistician;...

136p tieulangtran 28-09-2023 1 1   Download

The Dempster-Shafer (DS) theory of evidence is frequently used to combine multiple supervised machine learning models into a robust fusion-based model. However, using the DS theory to create a fusion model from multiple one-class classifications (OCCs) for network anomaly detection is a challenging task.

16p vimulcahy 18-09-2023 5 4   Download

This paper presents the Smartcall - ITS’s systems submitted to the Vietnamese Language and Speech Processing, Speaker Verification (SV) task. The challenge consists of two tasks focusing on the development of SV models with limited data and testing the robustness of SV systems.

5p viberkshire 09-08-2023 8 5   Download

The article summarizes the results of the successful attack rate using the proposed indicators according to the research through the experimental process conducted by the authors to verify the robustness of the deep learning model in general.

8p viwolverine 07-07-2023 3 3   Download

In recent years with the explosion of research in artificial intelligence, deep learning models based on convolutional neural networks (CNNs) are one of the promising architectures for practical applications thanks to their reasonably good achievable accuracy. However, CNNs characterized by convolutional layers often have a large number of parameters and computational workload, leading to large energy consumption for training and network inference.

10p vikissinger 03-03-2022 11 2   Download

Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data.

20p viarchimedes 26-01-2022 15 0   Download

This research study examined the role of the Robust Learning Model in achieving student-learning outcomes, regardless of the mode of delivery, e.g., online and on-campus courses. Results from this archival data analysis show that students in undergraduate level courses provided similar and high responses for items that measured learning outcomes whether they learned in the traditional classroom setting or online courses.

8p mudbound 06-12-2021 7 1   Download

Automatic Lane Detection is a prerequisite in the development of self-driving cars - a new trend that will surely prevail in the near future. Lane identification has two levels including determining purely by image processing (1) and determining by machine learning (2). Processing with machine learning will produce more accurate results under a variety of environmental conditions than simply processing images.

9p huyetthienthan 23-11-2021 11 2   Download

A significant increase of intrusion events over the years imposes a challenge on the robust intrusion detection system. In a computer system, execution traces of its programs can be audited as sequences of system calls and provide a rich and expressive source of data to identify anomalous activities. This paper presents a deep learning model, which combines multi-channel CNN and bidirectional LSTM (BiLSTM) models, to detect abnormal executions in host-based intrusion detection systems.

13p trinhthamhodang9 10-12-2020 11 2   Download

Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more accurate models, the importance of data preprocessing draws less attention.

10p vioklahoma2711 19-11-2020 8 0   Download

A proper design of the architecture of Artificial Neural Network (ANN) models can provide a robust tool in water resources modelling and forecasting. The performance of different neural networks in groundwater level forecasting was examined in order to identify an optimal ANN model for groundwater level forecast. The Devasugur nala watershed was selected for the study, located at northern part of Raichur district Karnataka and comes under middle Krishna river basin.

10p nguaconbaynhay6 23-06-2020 38 1   Download

Over the decades, personality factors (attitude, self-efficacy, anxiety and computer experience) have pervaded the underpinning determinants of behavioural intentions to accept and use emerging technologies, chiefly in purviews where integration is into the working processes that may be pro traditional. The chasm in the literature has been how these technology personality factors extensively relate within and among themselves in a definite model exclusive to these factors, and their overall variance explained in usage intentions.

26p kequaidan1 05-11-2019 33 1   Download

A renewed focus should be on human aspects and change behaviour in the uptake of e-learning. Thus, the overriding purpose of the study was to provide a diagnostic insight into how different factors come into play in the context of best practices of e-learning. The research aimed to help build a robust approach to the phenomenon. A dominant quantitative and less dominant qualitative method using survey approach was adopted.

26p kequaidan1 05-11-2019 51 4   Download

Conventional classroom instruction had already been transformed in to electronic mode of teaching and learning. Use of mobile technology is evolving in global and local context, as in Pakistan. Gaining insights from Media Richness Theory, the study intends to examine how m-learning pedagogy, opens up avenues for students’ learning and enhances their educational performance, endorsed by facilitation discourse and flexibility. In this cross-sectional study, data was collected from students in Private Universities in Lahore Pakistan.

44p kequaidan1 05-11-2019 39 2   Download

The analysis gave rise to a robust and parsimonious model of social network usage behavior that confirmed the proposed research hypotheses. The findings demonstrated that the extended TAM model is suitable for explaining the acceptance of web-based teaching tools as well as the validity of microblogging networks in combination with traditional classes.

17p guestgreat 06-05-2019 30 0   Download

We present an algorithm for pronounanaphora (in English) that uses Expectation Maximization (EM) to learn virtually all of its parameters in an unsupervised fashion. While EM frequently fails to find good models for the tasks to which it is set, in this case it works quite well. We have compared it to several systems available on the web (all we have found so far). Our program significantly outperforms all of them. The algorithm is fast and robust, and has been made publically available for downloading....

9p bunthai_1 06-05-2013 43 2   Download

The last years have seen a boost of work devoted to the development of machine learning based coreference resolution systems (Soon et al., 2001; Ng & Cardie, 2002; Kehler et al., 2004, inter alia). Similarly, many researchers have explored techniques for robust, broad coverage semantic parsing in terms of semantic role labeling (Gildea & Jurafsky, 2002; Carreras & M` rquez, 2005, SRL a henceforth). This paper explores whether coreference resolution can benefit from SRL, more specifically, which phenomena are affected by such information. ...

4p bunthai_1 06-05-2013 45 1   Download