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DNA splice junction classification is a crucial job in computational biology. The challenge is to predict the junction type (IE, EI, or N) from a given DNA sequence. Predicting junction type is crucial for understanding gene expression patterns, disease causes, splicing regulation, and gene structure. The location of the regions where exons are joined, and introns are removed during RNA splicing is very difficult to determine because no universal rule guides this process. This study presents a two-layer hybrid approach inspired by ensemble learning to overcome this challenge. The first layer applies the grey wolf optimizer (GWO) for feature selection. GWO's exploration ability allows it to efficiently search a vast feature space, while its exploitation ability refines promising areas, thus leading to a more reliable feature selection. The selected features are then fed into the second layer, which employs a classification model trained on the retrieved features. Using cross-validation, the proposed method divides the DNA splice junction dataset into training and test sets, allowing for a thorough examination of the classifier's generalization ability. The ensemble model is trained on various partitions of the training set and tested on the remaining held-out fold. This process is performed for each fold, comprehensively evaluating the classifier's performance. We tested our method using the StatLog DNA dataset. Compared to various machine learning models for DNA splice junction prediction, the proposed GWO+SVM ensemble method achieved an accuracy of 96%. This finding suggests that the proposed ensemble hybrid approach is promising for DNA splice junction classification. The implementation code for the proposed approach is available at https://github.com/EFHamouda/DNA-splice-junction-prediction. [ABSTRACT FROM AUTHOR]

Cancellable biometrics is the solution for the trade-off between two concepts: Biometrics for Security and Security for Biometrics. The cancelable template is stored in the authentication system's database rather than the original biometric data. In case of the database is compromised, it is easy for the template to be canceled and regenerated from the same biometric data. Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters (cancelable key). Therefore, the cancelable key must be secret to be used in the system authentication process as a second authentication factor in conjunction with the biometric data. The main contribution of this paper is to tackle the risks of stolen/lost/shared cancelable keys by using biometric trait (in different feature domains) as the only authentication factor, in addition to achieving good performance with high security. The standard Generative Adversarial Network (GAN) is proposed as an encryption tool that needs the cancelable key during the training phase, and the testing phase depends only on the biometric trait. Additionally, random projection transformation is employed to increase the proposed system's security and performance. The proposed transformation system is tested using the standard ORL face database, and the experiments are done by applying different features domains. Moreover, a security analysis for the proposed transformation system is presented. [ABSTRACT FROM AUTHOR]

Cancellable biometrics enabled us to develop robust authentication systems by replacing the storage of the original biometric template with another secured version. A technique called biometric salting uses a parameter (key) and an invertible function to transform the human biometrics features into a secured format that can be protected and stored securely in a biometric database system. The salting key plays a main role in the success of this transformation, which makes it robust or vulnerable to many security attacks. One of the main challenges that faces biometrics' researchers currently is how to design and protect such a salting key considering two basic measures: security and recognition accuracy. In this article, we propose unimodal‐Bio‐GAN, a reliable keyless biometric salting technique based on standard generative adversarial network (GAN). In unimodal‐Bio‐GAN, a random permuted version of the human biometric data is implicitly considered as a salting key and required only during the enrolment stage, which increases the system reliability to overcome different security attacks. The experimental results of unimodal‐Bio‐GAN using the CASIA Iris‐V3‐Internal database outperform the previous methods and its security efficiency is analysed using different attack types. [ABSTRACT FROM AUTHOR]

The kidney exchange programs bring new insights in the field of organ transplantation. They make the previously not allowed surgery of incompatible patient-donor pairs easier to be performed on a large scale. Mathematically, the kidney exchange is an optimization problem for the number of possible exchanges among the incompatible pairs in a given pool. Also, the optimization modeling should consider the expected quality-adjusted life of transplant candidates and the shortage of computational and operational hospital resources. In this article, we introduce a bio-inspired stochastic-based Ant Lion Optimization, ALO, algorithm to the kidney exchange space to maximize the number of feasible cycles and chains among the pool pairs. Ant Lion Optimizer-based program achieves comparable kidney exchange results to the deterministic-based approaches like integer programming. Also, ALO outperforms other stochastic-based methods such as Genetic Algorithm in terms of the efficient usage of computational resources and the quantity of resulting exchanges. Ant Lion Optimization algorithm can be adopted easily for on-line exchanges and the integration of weights for hard-to-match patients, which will improve the future decisions of kidney exchange programs. A reference implementation for ALO algorithm for kidney exchanges is written in MATLAB and is GPL licensed. It is available as free open-source software from: . [ABSTRACT FROM AUTHOR]

Several cancellable biometrics (CBs) techniques have been proposed to protect biometric data and maintain users' privacy. Although such techniques can withstand brute‐force and/or pre‐image attacks, they are vulnerable to correlation attacks. In this study, the authors propose a novel correlation attack‐resistant CBs scheme that is based on a convolution operation and a bidirectional associative memory (BAM) neural network. The proposed scheme utilises BAM to bind biometric templates to random bit‐strings in a secure and efficient manner. These random bit‐strings are then employed to derive cancellable templates from the true templates linked to them via BAM weights, which are safely stored with the generated cancellable template in the system database. In this study, linear convolution is adopted as the cancellable transformation process. The result of convolving the original biometric template with the transformation key is binarised according to a predefined threshold to thwart blind de‐convolution. The security of the proposed scheme against different attacks is analysed and experiments on the CASIA‐IrisV3‐Interval dataset illustrate the efficacy of the proposed scheme. [ABSTRACT FROM AUTHOR]