Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different

Intrusion Detection Systems (IDS) is the main defense mechanism deployed by the current networks to prevent cyber threats. Recurrent Neural Network (RNN) are also a novel IDS structure that replaces the conventional training and testing mechanism. The strategy encodes network traffic data as biological sequences using amino acid codons in such a fashion that the RNN is capable of effectively analyzing temporal and sequence data patterns. RNN architecture design adopts embedding layers to handle codon representations and Long Short-Term Memory (LSTM) layers to perform sequential data learning, which is followed by a fully connected network to perform classification functions, which preserve high feature extraction and classification

Chronic myelogenous leukemia (CML) is a myeloproliferative neoplasm arises from Bcr-Abl gene translocation (called Ph chromosome) in hematopoietic stem cells (HSCs). This genetic abnormality results in constitutive activation of tyrosine kinase and subsequent uncontrol growth and multiplication of granulocytes. The cornerstone in treatment of CML are tyrosine kinase inhibitors, of which imatinib is the most effectively used. JAK2V617F mutation is an acquired single nucleotide polymorphism (SNP) occurs in JAK2 gene and is associated with many hematological malignancy other than CML. It was thought that the two genetic abnormalities (Bcr-Abl and JAK2V617F) occur mutually; however, growing body of evidences suggested the reverse. This study a

Community structures are fundamental in understanding the structure and functionality of complex networks. Different optimization algorithms, including both single-objective and multi-objective approaches, have been employed to address the challenge of community detection. Recently, multi-objective evolutionary algorithms (MOEAs) have attracted many researchers to identify communities in static networks. Many algorithms have been proposed to find a solution that achieves a trade-off between exploring new areas of the solution space and improving the quality of existing solutions. In this trade-off is crucial; whereas exploitation improves existing solutions, it may fail to find better solutions from insufficiently explored regions o

Forty eight isolates (41.02%) were obtained from 117 wound and burn samples. The isolates that showed high resistance for both antibiotic was two only that represent 4,1% from all isolates. The result of PCR product electrophoresis was referred that the gene is VIM gene. Lactose and raffinose showed double increasing in diameter of inhibition zone of imipenem with 1% that mean showed highest susceptibility that decreased with the concentration increasing, the same result were with meropenem. But no effect were detected on meropenem inhibition zone diameter. Mannose have no effect on the resistance in 1%, 3% and 7%. Results showed that only three case that increase the expression of gene, they were lactose at 1% concentration that increased

To study and understand the mechanism of living systems, and how it works, it is quite important to investigate it at molecular level (like genomic, proteomic) as well as the methodologies, and how to apply and imply it on different branch of sciences and how can use it in developing medical diagnosis, treatments, drugs, and increased it in the future. Additionally it can also be applied in forensic techniques, food production and agriculture, as well as genetic profiling. This can be well understand by interfering and combinations of all branches of life sciences such as chemistry, physics, biotechnology, genetic evolution, and minimize the gap between them, this

This study focused on the improvement of the quality of gasoline and enhancing its octane number by the reduction of n-paraffins using zeolite 5A. This study was made using batch and continuous mode. The parameters which affected the n-paraffin removal efficiency for each mode were studied. Temperature (30 and 40 ˚C) and mixing time up to 120 min for different amounts of zeolite ranging (10-60 g) were investigated in a batch mode. A maximum removal efficiency of 64% was obtained using 60 g of zeolite at 30 ˚C after a mixing time 120 min. The effect of feed flow rate (0.3-0.8 l/hr) and bed height (10-20 cm) were also studied in a continuous mode. The equilibrium isotherm study was made using different amounts of zeolite (2-20 g) and the