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

Hepatitis is one of the diseases that has become more developed in recent years in terms of the high number of infections. Hepatitis causes inflammation that destroys liver cells, and it occurs as a result of viruses, bacteria, blood transfusions, and others. There are five types of hepatitis viruses, which are (A, B, C, D, E) according to their severity. The disease varies by type. Accurate and early diagnosis is the best way to prevent disease, as it allows infected people to take preventive steps so that they do not transmit the difference to other people, and diagnosis using artificial intelligence gives an accurate and rapid diagnostic result. Where the analytical method of the data relied on the radial basis network to diagnose the

The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the  classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an

Plane cubics curves may be classified up to isomorphism or projective equivalence. In this paper, the inequivalent elliptic cubic curves which are non-singular plane cubic curves have been classified projectively over the finite field of order nineteen, and determined if they are complete or incomplete as arcs of degree three. Also, the maximum size of a complete elliptic curve that can be constructed from each incomplete elliptic curve are given.

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

Astronomy image is regarded main source of information to discover outer space, therefore to know the basic contain for galaxy (Milky way), it was classified using Variable Precision Rough Sets technique to determine the different region within galaxy according different color in the image. From classified image we can determined the percentage for each class and then what is the percentage mean. In this technique a good classified image result and faster time required to done the classification process.

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Mixed ligand metal complexes are synthesized from oxalic acid with Schiff base, and the Schiff base was obtained from trimethoprim and acetylacetone. The synthesized complexes were of the type [M(L1)(L2)], where the metal, M, is Ni(II), Cu(II), Cr(III), and Zn(II), L1 corresponds to the trimethoprim ((Z)-4-((4-amino-5-(3,4,5- trimethoxybenzyl)pyrimidine-2-yl)imino)pentane-2-one) as the first ligand and L2 represent the oxalate anion (𝐶􀬶𝑂􀬶 􀬶􀬿) as a second ligand. Characterization of the prepared compounds was performed by elemental analysis, molar conductivity, magnetic measurements, 1H-NMR, 13C-NMR, FT-IR, and Ultraviolet-visible (UV-Vis) spectral studies. The recorded infrared data is reinforced with density functional th

Mixed ligand metal complexes are synthesized from oxalic acid with Schiff base, and the Schiff base was obtained from trimethoprim and acetylacetone. The synthesized complexes were of the type [M(L1)(L2)], where the metal, M, is Ni(II), Cu(II), Cr(III), and Zn(II), L1 corresponds to the trimethoprim ((Z)-4-((4-amino-5-(3,4,5-trimethoxybenzyl)pyrimidine-2-yl)imino)pentane-2-one) as the first ligand and L2 represent the oxalate anion ( ) as a second ligand. Characterization of the prepared compounds was performed by elemental analysis, molar conductivity, magnetic measurements, 1H-NMR, 13C-NMR, FT-IR, and Ultraviolet-visible (UV-Vis) spectral studies. The recorded infrared data is reinforced with density functional theory (DFT) calcul

The new media scene reveals that the unprecedented overlap of a number of technical, economic, and political factors has made the new media a very complicated issue; and the focus of specialized and public debates about its impact on traditional means of communication and forms of social media and social relations. Then, the same scene discloses the reality of the relationship between the new and the traditional. These are the axes that will be will be discussed in this study.