In this article, we propose a Bayesian Adaptive bridge regression for ordinal model. We developed a new hierarchical model for ordinal regression in the Bayesian adaptive bridge. We consider a fully Bayesian approach that yields a new algorithm with tractable full conditional posteriors. All of the results in real data and simulation application indicate that our method is effective and performs very good compared to other methods. We can also observe that the estimator parameters in our proposed method, compared with other methods, are very close to the true parameter values.

Two well-known fluorescent molecules: fluorescein sodium salt (FSS) and 2,7-dichloro fluorescein (DCF) were tried to prove the efficiency, trustability and repeatability of ISNAG fluorimeter by using discrete and continuous flow injection analysis modes.A linear range of 0.002-1 mmol/L for FSS and 0.003-0.7 mmol/L was for DCF, with LOD 0.0018 mmol/L and 0.002 mmol/L for FSS and DCF respectively, were obtained for discrete mode of analysis. While the continuous mode gave a linear range of 0.002-0.7 mmol/L and 0.003-0.5 mmol/L for FSS and DCF respectively, the LOD were 0.0016mmol/L and 0.0018 mmol/L for FSS and DCF respectively. The results were compared with classical method at variable λ_ex for both fluorescent molecules at 95

Merging biometrics with cryptography has become more familiar and a great scientific field was born for researchers. Biometrics adds distinctive property to the security systems, due biometrics is unique and individual features for every person. In this study, a new method is presented for ciphering data based on fingerprint features. This research is done by addressing plaintext message based on positions of extracted minutiae from fingerprint into a generated random text file regardless the size of data. The proposed method can be explained in three scenarios. In the first scenario the message was used inside random text directly at positions of minutiae in the second scenario the message was encrypted with a choosen word before ciphering

Let M be an R-module, where R is a commutative ring with unity. A submodule N of M is called e-small (denoted by N e  M) if N + K = M, where K e  M implies K = M. We give many properties related with this type of submodules.

In this paper, we prove that our proposed localization algorithm named Improved
Accuracy Distribution localization for wireless sensor networks (IADLoc) [1] is the
best when it is compared with the other localization algorithms by introducing many
cases of studies. The IADLoc is used to minimize the error rate of localization
without any additional cost and minimum energy consumption and also
decentralized implementation. The IADLoc is a range free and also range based
localization algorithm that uses both type of antenna (directional and omnidirectional)
it allows sensors to determine their location based on the region of
intersection (ROI) when the beacon nodes send the information to the sink node and
the la

In this paper, we prove that our proposed localization algorithm named Improved
Accuracy Distribution localization for wireless sensor networks (IADLoc) [1] is the
best when it is compared with the other localization algorithms by introducing many
cases of studies. The IADLoc is used to minimize the error rate of localization
without any additional cost and minimum energy consumption and also
decentralized implementation. The IADLoc is a range free and also range based
localization algorithm that uses both type of antenna (directional and omnidirectional)
it allows sensors to determine their location based on the region of
intersection (ROI) when the beacon nodes send the information to the sink node and
the la

Let R be associative; ring; with an identity and let D be unitary left R- module; . In this work we present semiannihilator; supplement submodule as a generalization of R-a- supplement submodule, Let U and V be submodules of an R-module D if D=U+V and whenever Y≤ V and D=U+Y, then annY≪R;. We also introduce the the concept of semiannihilator -supplemented ;modules and semiannihilator weak; supplemented modules, and we give some basic properties of this conseptes

Let R be associative; ring; with an identity and let D be unitary left R- module; . In this work we present semiannihilator; supplement submodule as a generalization of R-a- supplement submodule, Let U and V be submodules of an R-module D if D=U+V and whenever Y≤ V and D=U+Y, then annY≪R;. We also introduce the the concept of semiannihilator -supplemented ;modules and semiannihilator weak; supplemented modules, and we give some basic properties of this conseptes.

Malware represents one of the dangerous threats to computer security. Dynamic analysis has difficulties in detecting unknown malware. This paper developed an integrated multi – layer detection approach to provide more accuracy in detecting malware. User interface integrated with Virus Total was designed as a first layer which represented a warning system for malware infection, Malware data base within malware samples as a second layer, Cuckoo as a third layer, Bull guard as a fourth layer and IDA pro as a fifth layer. The results showed that the use of fifth layers was better than the use of a single detector without merging. For example, the efficiency of the proposed approach is 100% compared with 18% and 63% of Virus Total and Bel

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB