A lightweight and secure online/offline cross-domain authentication scheme for VANET systems in Industrial IoT.

In heterogeneous wireless networks, the industrial Internet of Things (IIoT) is an essential contributor to increasing productivity and effectiveness. However, in various domains, such as industrial wireless scenarios, small cell domains, and vehicular networks, an efficient and stable authentication algorithm is required (VANET). Specifically, IoT vehicles deal with vast amounts of data transmitted between VANET entities in different domains in such a large-scale environment.

SELAMAT: A New Secure and Lightweight Multi-Factor Authentication Scheme for Cross-Platform Industrial IoT Systems.

The development of the industrial Internet of Things (IIoT) promotes the integration of the cross-platform systems in fog computing, which enable users to obtain access to multiple application located in different geographical locations. Fog users at the network's edge communicate with many fog servers in different fogs and newly joined servers that they had never contacted before. This communication complexity brings enormous security challenges and potential vulnerability to malicious threats.

Wavelet Analysis of Resultant Velocity Belonging to Genuine and Forged Signatures.

This study presents a wavelet analysis of resultant velocity features belonging to genuine and forged groups of signature sample. Signatures of individuals were initially classified based on visual human perceptions of their relative sizes, complexities, and legibilities of the genuine counterparts. Then, the resultant velocity was extracted and modeled through wavelet analysis from each sample.

Online handwritten signature verification using neural network classifier based on principal component analysis.

One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates.

Estimating body related soft biometric traits in video frames.

Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information.

Analysis of the effects and relationship of perceived handwritten signature's size, graphical complexity, and legibility with dynamic parameters for forged and genuine samples.

This article presents an analysis of handwritten signature dynamics belonging to two authentication groups, namely genuine and forged signature samples. Genuine signatures are initially classified based on their relative size, graphical complexity, and legibility as perceived by human examiners. A pool of dynamic features is then extracted for each signature sample in the two groups.

Assessment of predictive models for chlorophyll-a concentration of a tropical lake.

Background: This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae.