Concordance Between the AmpFℓSTR®MiniFiler™ and AmpFℓSTR®Identifiler®PCR Amplification Kits in the Kuwaiti Population

The AmpFℓSTR® MiniFiler™ polymerase chain reaction amplification kit, developed and supplied by Applied Biosystems, complements the AmpFℓSTR® Identifiler® polymerase chain reaction amplification kit (Applied Biosystems, Warrington, U.K.) by improving the success rate when profiling DNA that is degraded or contains inhibitors. Before applying the MiniFiler™ kit to casework, the profiles from 200 unrelated Kuwaitis were compared to Identifiler® profiles. Concordance was observed for 99.875% (1598 of 1600) of the compared STR loci. The two discordant profiles displayed allelic dropout: one at the D13S317 locus due to nonamplification of allele 10 in the MiniFiler™ profile, and one at the D18S51 locus due to nonamplification of allele 18 in the Identifiler® profile

Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification

There are several statistical descriptors for feature extraction from texture images. Local binary pattern is one of the most popular descriptors for revealing the underlying structure of a texture. Recently several variants of local binary descriptors have been proposed. The completed local binary pattern is an efficient version that can provide discriminant features and consequently provide a high classification rate. It finely characterizes a texture by fusing three histograms of features. Fusing histograms is applied by jointing the histograms and it increases the feature number significantly; therefore, in this paper, a weighted constraint feature selection approach is proposed to select a very small number of features without any degradation in classification accuracy. It significantly enhances the classification rate by using a very low number of informative features. The proposed feature selection approach is a filter-based feature selection. It employed a weighted constraint score for each feature. After ranking the features, a threshold estimation method is proposed to select the most discriminant features. For a better comparison, a wide range of different datasets is used as a benchmark to assess the compared methods. Implementations on Outex, UIUC, CUReT, MeasTex, Brodatz, Virus, Coral Reef, and ORL face datasets indicate that the proposed method can provide high classification accuracy without any learning step just by selecting a few features of the descriptor

A novel fuzzy trust-based secure routing scheme in flying ad hoc networks

Today, many studies assess vulnerabilities, threats, and attacks in flying ad hoc networks (FANETs) to provide solutions for countermeasures. Protecting FANETs against attackers and coordinating connections are challenging. The purpose of this study is to increase and maintain communication security. In this paper, a fuzzy trust-based secure routing scheme (FTSR) is presented in FANETs. FTSR utilizes two trust assessment mechanisms, namely local trust and path trust. Local trust strategy is a distributed process for finding reliable neighboring nodes and isolating hostile nodes on the network. In this regard, only reliable nodes are allowed to contribute to the path discovery procedure. This lowers the risk of forming fake paths in FANETs. Path trust strategy is responsible for identifying hostile nodes that are not identified in the local trust process. This strategy shows a general view of the trust status of the desired path. To design this mechanism, the source node runs a fuzzy system to select the safest path between source and the destination. Finally, network simulator 2 (NS2) implements FTSR, and the results such as malicious detection rate, packet delivery ratio, packet loss, accuracy, and delay are obtained from the simulation process. These results indicate that FTSR presents better performance compared to TOPCM, MNRiRIP, and MNDA. However, FTSR takes more time to find paths compared to TOPCM