The statistically inspired modification of the partial least squares (SIMPLS) is the most commonly used algorithm to solve a partial least squares regression problem when the number of explanatory variables (p) is larger than the sample size (n). Nonetheless, in the presence of irregular points (outliers), this method is no longer efficient. Therefore, the robust iteratively reweighted SIMPLS (RWSIMPLS), which is an improvement of the SIMPLS algorithm, is put forward to remedy this problem. However, the RWSIMPLS is still not very efficient with regard to its parameter estimations and outlier diagnostics. It also suffers from long computational times. This paper proposes a new robust SIMPLS that incorporates a new weight function constructed from nu-Support Vector Regression in its establishment. We call this method the robust iteratively reweighted SIMPLS based on nu-Support Vector Regression, denoted as SVR-RWSIMPLS. To avoid misclassification of observations, a new diagnostic plot is proposed to classify observations into regular observations, vertical outliers, good (GLPs) and bad leverage points (BLPs). The numerical results clearly indicate that the SVR-RWSIMPLS is more efficient, more robust and has less computational running times than the RWSIMPLS when multiple leverage points and vertical outliers exist. The proposed diagnostic plot is also very successful in classifying observations into correct groups.
Nowadays, a lot of images and documents are saved on data sets and cloud servers such as certificates, personal images, and passports. These images and documents are utilized in several applications to serve residents living in smart cities. Image similarity is considered as one of the applications of smart cities. The major challenges faced in the field of image management are searching and retrieving images. This is because searching based on image content requires a long time. In this paper, the researchers present a secure scheme to retrieve images in smart cities to identify wanted criminals by using the Gray Level Cooccurrence Matrix. The proposed scheme extracts only five features of the query image which are contrast, homogeneity, entropy, energy, and dissimilarity. This work consists of six phases which are registration, authentication, face detection, features extraction, image similarity, and image retrieval. The current study runs on a database of 810 images which was borrowed from face94 to measure the performance of image retrieval. The results of the experiment showed that the average
The wide use of vehicular ad hoc networks (VANETs) in the last decade hasled many researchers to find efficient and reliable methods to obtain the desired benefits and offer services, such as healthcare and traffic management. However, VANETs suffer from security issues represented by authentication and data integrity. In thispaper, we propose a robust mutual authentication scheme based on elliptic curve cryptography (ECC), cryptography hash function, and a pseudonym. The proposed work was twofold in focus: first, on healthcare in emergency cars which use VANETs, and second, on overcoming security issues, such as resisting familiar attacks (e.g. insider attacks and reply attacks). Because of the serious situation generated by the worldwide outbreak of the Covid-19 epidemic, we also found this research valuable in supporting global efforts to combat the rapid spread of this virus, by finding the safest and fastest routes to epidemic treatment centres for medical staff, assistance teams in medical operations, fumigation control, and all work teams associated with disease control. This research attempts to contribute by proposing a special signal used to define epidemic teams. The best route, fast route can be chosen by using VANETs infrastructure. This scheme also deals with metric security features, such as key management, data integrity, and data privacy. In the communication and computation cost, we noticed that our proposed scheme achieved good results compared with the related works.
Support Vector Regression (SVR) is gaining in popularity in the detection of outliers and classification problems in high-dimensional data (HDD) as this technique does not require the data to be of full rank. In real application, most of the data are of high dimensional. Classification of high-dimensional data is needed in applied sciences, in particular, as it is important to discriminate cancerous cells from non-cancerous cells. It is also imperative that outliers are identified before constructing a model on the relationship between the dependent and independent variables to avoid misleading interpretations about the fitting of a model. The standard SVR and the μ-ε-SVR are able to detect outliers; however, they are computationally expensive. The fixed parameters support vector regression (FP-ε-SVR) was put forward to remedy this issue. However, the FP-ε-SVR using ε-SVR is not very successful in identifying outliers. In this article, we propose an alternative method to detect outliers i.e. by employing nu-SVR. The merit of our proposed method is confirmed by three real examples and the Monte Carlo simulation. The results show that our proposed nu-SVR method is very successful in identifying outliers under a variety of situations, and with less computational running time.
As a hopeful computing paradigm, cloud services are obtainable to end users based on pay-as-you-go service. Security is represented one of the vital issues for the extended adoption of cloud computing, with the object of accessing several cloud service providers, applications, and services by using anonymity features to authenticate the user. We present a good authentication scheme based on quick response (QR) code and smart card. Furthermore, our proposed scheme has several crucial merits such as key management, mutual authentication, one-time password, user anonymity, freely chosen password, secure password changes, and revocation by using QR code. The security of proposed scheme depends on crypto-hash function, QR-code validation, and smart card. Moreover, we view that our proposed scheme can resist numerous malicious attacks and are more appropriate for practical applications than other previous works. The proposed scheme has proved as a strong mutual authentication based on burrows-abadi-needham (BAN) logic and security analysis. Furthermore, our proposed scheme has good results compared with related work.
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