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 cybercr...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 activities.The Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid convergence.But when it comes to security,it is not the preferred cryptographic algorithm to use compared to other algorithms that have shown better security.Many 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 wall.In this paper,a new modification for the original Twofish algorithm is proposed to strengthen its security and to take advantage of its fast convergence.The new algorithm has been named Split-n-Swap(SnS).Performance analysis of the new modification algorithm has been performed using different measurement metrics.The 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.展开更多
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 more.Protein secondary structure ...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 more.Protein 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 prediction.Protein secondary structures are classified into two categories:3-state category and 8-state category.Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems,respectively.The 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 prediction.In 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 PSSP.The 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 PSSP.The novelty of this paper lies in the introduction of ensemble learning in Q8 PSSP problem.The experimental results confirmed that ensemble ML models are more accurate than individual ML models.They also indicated that features extracted by LDA are more effective than those extracted by PCA.展开更多
文摘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 activities.The Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid convergence.But when it comes to security,it is not the preferred cryptographic algorithm to use compared to other algorithms that have shown better security.Many 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 wall.In this paper,a new modification for the original Twofish algorithm is proposed to strengthen its security and to take advantage of its fast convergence.The new algorithm has been named Split-n-Swap(SnS).Performance analysis of the new modification algorithm has been performed using different measurement metrics.The 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.
文摘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 more.Protein 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 prediction.Protein secondary structures are classified into two categories:3-state category and 8-state category.Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems,respectively.The 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 prediction.In 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 PSSP.The 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 PSSP.The novelty of this paper lies in the introduction of ensemble learning in Q8 PSSP problem.The experimental results confirmed that ensemble ML models are more accurate than individual ML models.They also indicated that features extracted by LDA are more effective than those extracted by PCA.
