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Split-n-Swap: A New Modification of the Twofish Block Cipher Algorithm

Computers, Materials & Continua 2023年第1期74卷 1723-1734页

作者： Awny Sayed Maha Mahrous enas elgeldawi Computer Science Department Faculty of ScienceMinia UniversityMinia61519Egypt Faculty of Computing and Information Technology Information Technology DepartmentKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationMinia UniversityMinia61519Egypt

Securing digital data from unauthorized access throughout its entire lifecycle has been always a critical concern.A robust data security system should protect the information assets of any organization against cybercriminal *** Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid *** when it comes to security,it is not the preferred cryptographic algorithm to use compared to other algorithms that have shown better *** applications and social platforms have adopted other symmetric key block cipher cryptographic algorithms such as the Advanced Encryption Standard(AES)algorithm to construct their main security *** this paper,a new modification for the original Twofish algorithm is proposed to strengthen its security and to take advantage of its fast *** new algorithm has been named Split-n-Swap(SnS).Performance analysis of the new modification algorithm has been performed using different measurement *** experimental results show that the complexity of the SnS algorithm exceeds that of the original Twofish algorithm while maintaining reasonable values for encryption and decryption times as well as memory utilization.A detailed analysis is given with the strength and limitation aspects of the proposed algorithm.

关键词： Twofish advanced encryption standard(AES) cryptography symmetric key block cipher

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Ensemble Machine Learning to Enhance Q8 Protein Secondary Structure Prediction

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Computers, Materials & Continua 2022年第11期73卷 3951-3967页

作者： Moheb R.Girgis Rofida M.Gamal enas elgeldawi Computer Science Department Faculty of ScienceMinia University61519MiniaEgypt

Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine,biotechnology and *** secondary structure prediction(PSSP)has a significant role in the prediction of protein tertiary structure,as it bridges the gap between the protein primary sequences and tertiary structure *** secondary structures are classified into two categories:3-state category and 8-state *** the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems,*** 8 classes of secondary structures reveal more precise structural information for a variety of applications than the 3 classes of secondary structures,however,Q8 prediction has been found to be very challenging,that is why all previous work done in PSSP have focused on Q3 *** this paper,we develop an ensemble Machine Learning(ML)approach for Q8 PSSP to explore the performance of ensemble learning algorithms compared to that of individual ML algorithms in Q8 *** ensemble members considered for constructing the ensemble models are well known classifiers,namely SVM(Support Vector Machines),KNN(K-Nearest Neighbor),DT(Decision Tree),RF(Random Forest),and NB(Naïve Bayes),with two feature extraction techniques,namely LDA(Linear Discriminate Analysis)and PCA(Principal Component Analysis).Experiments have been conducted for evaluating the performance of single models and ensemble models,with PCA and LDA,in Q8 *** novelty of this paper lies in the introduction of ensemble learning in Q8 PSSP *** experimental results confirmed that ensemble ML models are more accurate than individual ML *** also indicated that features extracted by LDA are more effective than those extracted by PCA.

关键词： Protein secondary structure prediction(PSSP) Q3 prediction Q8 prediction ensemble machine leaning boosting bagging

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