The increasing use of computing devices and applications in human daily life triggers the need for natural human-computer interaction. Emotion Recognition using multiple features using a semi-serial fusion method is proposed. The study analyses the impact of the feature combinations in the enhancement of the recognition enhancement. The paper presents the use of the multi-view learning principle to a fusion of different features for one emotion expression-based recognition. The results prove that a planned method is operative. The proposed combination method outperforms the use of one type of features and the concatenated way in recognition accuracy, improvement of execution time, and stability.
Rotating machines are widely used in today’s world. As these machines perform the biggest tasks in industries, faults are naturally observed on their components. For most rotating machines such as wind turbine, bearing is one of critical components. To reduce failure rate and increase working life of rotating machinery it is important to detect and diagnose early faults in this most vulnerable part. In the recent past, technologies based on computational intelligence, including machine learning (ML) and deep learning (DL), have been efficiently used for detection and diagnosis of bearing faults. However, DL algorithms are being increasingly favoured day by day because of their advantages of automatically extracting features from training data. Despite this, in DL, adding neural layers reduces the training accuracy and the vanishing gradient problem arises. DL algorithms based on convolutional neural networks (CNN) such as DenseNet have proved to be quite efficient in solving this kind of problem. In this paper, a transfer learning consisting of fine-tuning DenseNet-121 top layers is proposed to make this classifier more robust and efficient. Then, a new intelligent model inspired by DenseNet-121 is designed and used for detecting and diagnosing bearing faults. Continuous wavelet transform is applied to enhance the dataset. Experimental results obtained from analyses employing the Case Western Reserve University (CWRU) bearing dataset show that the proposed model has higher diagnostic performance, with 98% average accuracy and less complexity.
The development of prediction supports is a critical step in information systems engineering in this era defined by the knowledge economy, the hub of which is big data. Currently, the lack of a predictive model, whether qualitative or quantitative, depending on a company's areas of intervention can handicap or weaken its competitive capacities, endangering its survival. In terms of quantitative prediction, depending on the efficacy criteria, a variety of methods and/or tools are available. The multiple linear regression method is one of the methods used for this purpose. A linear regression model is a regression model of an explained variable on one or more explanatory variables in which the function that links the explanatory variables to the explained variable has linear parameters. The purpose of this work is to demonstrate how to use multiple linear regressions, which is one aspect of decisional mathematics. The use of multiple linear regressions on random data, which can be replaced by real data collected by or from organizations, provides decision makers with reliable data knowledge. As a result, machine learning methods can provide decision makers with relevant and trustworthy data. The main goal of this article is therefore to define the objective function on which the influencing factors for its optimization will be defined using the linear regression method.
This paper deals with the implementation of Algorithms and tools for the security of academic data protection in the Democratic Republic of Congo. It consists principally in implementing two algorithms and two distinct tools to secure data and in this particular case, academic data of higher and university education in the Democratic Republic of Congo. The design of algorithms meets the approach that any researcher in data encryption must use during the development of a computer system. Briefly, these algorithms are steps to follow to encrypt information in any programming language. These algorithms are based on symmetric and asymmetric encryptions, the first one uses Christopher Hill's algorithm, which uses texts in the form of matrices before they are encrypted and RSA as one of the asymmetric algorithms, which uses the prime numbers that we have encoded on more than 512 bits. As for tools, we have developed them in php which is only a programming language taken as an example because it is impossible to use all of them. The tools implemented are based on the algorithms of Caesar, Christopher Hill and RSA showing how the encryption operations are carried out thanks to graphical interfaces. They are only tools for pedagogical reasons to help students and other researchers learn how to use developed algorithms. We have not developed them for pleasure but rather used them in any information system, which would prevent and limit unauthorized access to computer systems. They will not be used only for the management of academic fees but for any other information system, which explains and shows the complexity of the tools developed. We have not been able to solve the problems of versions for the developed prototype, because if there is a new version later, some functions may be obsolete, which would constitute the limitation of these tools. This work targets primarily the Ministry of Higher Education and Universities, which will make these results their own and implement them in order to solve the problem of intrusions, unauthorized access to developers and researchers who will use tools already made instead of thinking about their development. We are trying to demonstrate the steps and the methodology that allowed us to reach our results, in the following lines.
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