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Go to Editorial ManagerNonlinear stream ciphers have become a viable alternative to traditional cryptosystems in response to the growing need for secure communication. These ciphers generate a keystream via feedback mechanisms and nonlinear functions, which are then utilized for encryption. Geffe generator system is one of the most keystream generators. Also, these systems have many benefits, like being fast, flexible, and able to create unpredictable and non-repeating keystreams, these systems are susceptible to cryptanalysis attacks, which have the potential to compromise their security. This paper presents the first study of applying chicken swarm optimization (CSO) algorithm in the field of cryptanalysis based on cipher only attack. The standard CSO algorithm and an adaptive multi points CSO (AMPCSO) algorithm are proposed to cryptanalysis nonlinear stream cipher based on Geffe keystream generator. Firstly, the traditional CSO is used to reveal the secret initial values of the Geffe generator. Secondly, an adaptive multi points chicken swarm optimization (AMPCSO) has been proposed to enhance the traditional CSO algorithm to attack Geffe generator systems. The AMPCSO is a new idea to advance the CSO search abilities and improve the foraging behavior of hens and chicks by allowing hens to be influenced by other individuals within the same or different groups and affected by the best individual in the population and enable chicks to learn from four reference points rather than learn from their respective mothers only. Lastly, a new criterion is used to estimate the value of fitness by utilizing a multi-objective fitness function (MOFF), which is grounded on Pareto dominance. The experimental results showed that the CSO and AMPCSO are very effective tools in terms of accuracy, information required, and CPU times when applied to the analysis of nonlinear stream cipher. The AMPCSO required a few characters from ciphertext to attack systems with total LFSRs length up to 59 bits with an appropriate CPU time.
Breast cancer is one of the most critical diseases suffered by many people around the world, making it the most common medical risk they will face. This disease is considered the leading cause of death around the world, and early detection is difficult. In the field of healthcare, where early diagnosis based on machine learning (ML) helps save patients’ lives from the risks of diseases, better-performing diagnostic procedures are crucial. ML models have been used to improve the effectiveness of early diagnosis. In this paper, we proposed a new feature selection method that combines two filter methods, Pearson correlation and mutual information (PC-MI), to analyse the correlation amongst features and then select important features before passing them to a classification model. Our method is capable of early breast cancer prediction and depends on a soft voting classifier that combines a certain set of ML models (decision tree, logistic regression and support vector machine) to produce one model that carries the strengths of the models that have been combined, yielding the best prediction accuracy. Our work is evaluated by using the Wisconsin Diagnostic Breast Cancer datasets. The proposed methodology outperforms previous work, achieving 99.3% accuracy, an F1 score of 0.9922, a recall of 0.9846, a precision of 1 and an AUC of 0.9923. Furthermore, the accuracy of 10-fold cross-validation is 98.2%.
The corrosion of metallic structures buried in soil or submerged in water which became a problem of worldwide significance and causes most of the deterioration in petroleum industry can be controlled by cathodic protection (CP).CP is a popular technique used to minimize the corrosion of metals in a variety of large structures. To prevent corrosion, voltage between the protection metal and the auxiliary anode has to be controlled on a desired level. In this study two types of controllers will be used to set a pipeline potential at required protection level. The first one is a conventional Proportional-Integral-Derivative (PID) controller and the second are intelligent controllers (fuzzy and neural controllers).The results were simulated and implemented using MATLAB R 2010a program which offers predefined functions to develop PID, fuzzy and neural control systems.