To achieve safe security to transfer data from the sender to receiver, cryptography is one way that is used for such purposes. However, to increase the level of data security, DNA as a new term was introduced to cryptography. The DNA can be easily used to store and transfer the data, and it becomes an effective procedure for such aims and used to implement the computation. A new cryptography system is proposed, consisting of two phases: the encryption phase and the decryption phase. The encryption phase includes six steps, starting by converting plaintext to their equivalent ASCII values and converting them to binary values. After that, the binary values are converted to DNA characters and then converted to their equivalent complementary DN

Due to the vast using of digital images and the fast evolution in computer science and especially the using of images in the social network.This lead to focus on securing these images and protect it against attackers, many techniques are proposed to achieve this goal. In this paper we proposed a new chaotic method to enhance AES (Advanced Encryption Standards) by eliminating Mix-Columns transformation to reduce time consuming and using palmprint biometric and Lorenz chaotic system to enhance authentication and security of the image, by using chaotic system that adds more sensitivity to the encryption system and authentication for the system.

The data communication has been growing in present day. Therefore, the data encryption became very essential in secured data transmission and storage and protecting data contents from intruder and unauthorized persons. In this paper, a fast technique for text encryption depending on genetic algorithm is presented. The encryption approach is achieved by the genetic operators Crossover and mutation. The encryption proposal technique based on dividing the plain text characters into pairs, and applying the crossover operation between them, followed by the mutation operation to get the encrypted text. The experimental results show that the proposal provides an important improvement in encryption rate with comparatively high-speed Process

The technological development in the field of information and communication has been accompanied by the emergence of security challenges related to the transmission of information. Encryption is a good solution. An encryption process is one of the traditional methods to protect the plain text, by converting it into inarticulate form. Encryption implemented can be occurred by using some substitute techniques, shifting techniques, or mathematical operations. This paper proposed a method with two branches to encrypt text. The first branch is a new mathematical model to create and exchange keys, the proposed key exchange method is the development of Diffie-Hellman. It is a new mathematical operations model to exchange keys based on prime num

Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization

As s widely use of exchanging private information in various communication applications, the issue to secure it became top urgent. In this research, a new approach to encrypt text message based on genetic algorithm operators has been proposed. The proposed approach follows a new algorithm of generating 8 bit chromosome to encrypt plain text after selecting randomly crossover point. The resulted child code is flipped by one bit using mutation operation. Two simulations are conducted to evaluate the performance of the proposed approach including execution time of encryption/decryption and throughput computations. Simulations results prove the robustness of the proposed approach to produce better performance for all evaluation metrics with res

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences

Motifs template is the input for many bioinformatics systems such codons finding, transcription, transaction, sequential pattern miner, and bioinformatics databases analysis. The size of motifs arranged from one base up to several Mega bases, therefore, the typing errors increase according to the size of motifs. In addition, when the structures motifs are submitted to bioinformatics systems, the specifications of motifs components are required, i.e. the simple motifs, gaps, and the lower bound and upper bound of each gap. The motifs can be of DNA, RNA, or Protein. In this research, a motif parser and visualization module is designed depending on a proposed a context free grammar, CFG, and colors human recognition system. GFC describes the m

   Molecular barcoding was widely recognized as a powerful tool for the identification of organisms during the past decade; the aim of this study is to use the molecular approach to identify the diatoms by using the environmental DNA. The diatom specimens were taken from Tigris River. The environmental DNA(e DNA) extraction and analysis of sequences using the Next Generation Sequencing (NGS) method showed the highest percentage of epipelic diatom genera including Achnanthidium minutissimum (Kützing) Czarnecki, 1994 (21.1%), Cocconeis placentula Ehrenberg, 1838 (21.3%) and Nitzschia palea (Kützing) W. Smith, 1856 (16.3%).

   Five species of diatoms: Achnanthidiu