Due to its high prevalence and incidence, diabetes is considered significant public health. Since has no known cure, early diagnosis plays a vital role in effectively managing the disease. Feature scaling step pre-processing data before building model using machine learning. The datasets used for training learning often contain unpredictable values that may have varying scales. This can result inequalities comparing these values. techniques address challenges by adjusting promoting easy fair...

COVID-19 has killed more than 5 million individuals worldwide within a short time. It is caused by SARS-CoV-2 which continuously mutates and produces transmissible new different strains. therefore of great significance to diagnose early curb its spread reduce the death rate. Owing pandemic, traditional diagnostic methods such as reverse-transcription polymerase chain reaction (RT-PCR) are ineffective for diagnosis. Medical imaging among most effective techniques respiratory disorders...

The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients early possible order control spread of virus, improve efficacy medical treatment, and, result, lower mortality rate. adopted method detecting COVID-19 reverse-transcription polymerase chain reaction (RT-PCR), process affected scarcity RT-PCR kits well its complexities. Medical imaging using machine learning and deep has...

The global significance of fluoride and nitrate contamination in coastal areas cannot be overstated, as these contaminants pose critical environmental public health challenges across the world. Water quality is an essential component sustaining health. This integrated study aimed to assess indexical spatial water quality, potential sources, risks associated with groundwater resources Al-Hassa, Saudi Arabia. Groundwater samples were tested using standard methods. physiochemical results...

Robotic bariatric surgery (RBS) uses robotics in weight loss operations. The robotic system has been tremendous the field of (BS) owing to its overwhelming advantages. As a result, surgeons perform RBS by controlling arms equipped with surgical equipment from console. In this study, our focus is evaluate BS dataset and techniques such as Roux-en-Y gastric bypass (RYGB), laparoscopic vertical sleeve gastrectomy (LGSV), adjustable band (AGB), mini (MGB), single anastomosis duodenal (SADI-S),...

Pneumatization of turbinates, also known as concha bullosa (CB), is associated with nasal septal deviation and sinonasal pathologies. This study aims to evaluate the performance deep learning models in detecting CB coronal cone-beam computed tomography (CBCT) images. Standardized images were obtained from 203 CBCT scans (83 119 without CB) radiology archives a dental teaching hospital. These underwent preprocessing through hybridized contrast enhancement (CE) method using discrete wavelet...

In this research, we evaluate the effectiveness of different MTD techniques on transformer-based cyber anomaly detection models trained KDD Cup'99 Dataset, a publicly available dataset commonly used for evaluating intrusion systems. We explore trade-offs between security and performance when using investigate how can be combined with other cybersecurity to improve overall system. their standard metrics such as accuracy FI score, well measures robustness against adversarial attacks. Our...

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that caused the outbreak of disease 2019 (COVID-19). COVID-19 has led to millions deaths and economic losses globally. Vaccination most practical solution, but finding epitopes (antigenic peptide regions) in SARS-CoV-2 proteome challenging, costly, time-consuming. Here, we proposed deep learning method based on standalone Recurrent Neural networks predict from proteins easily. We optimised...

We present a study on Explainable AI-based prediction of power conversion efficiency (PCE) organic solar cells, conducted dataset 566 small-molecule cell materials samples with varying donor and acceptor species combinations. This research uncovers an interesting phenomenon, the first its kind to be reported, PCE quantization, where values increase in steps feature values. Our findings have significant implications for development efficient as they provide better understanding factors that...

Abstract Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here the first time deep learning framework based on Deconvolutional Neural Networks (DCNN) Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used predict Mtb Multiepitope vaccine...

This study aimed at employing three data-driven models, namely the Hammerstein-Weiner (HW) model, support vector machine (SVM), and feedforward back propagation neural network (FFBPNN) traditional multi-linear regression, as well two non-linear ensemble techniques viz: HW-ensemble FFBPNN-ensemble, were employed to predict chemical oxygen demand (COD eff ).For prediction of COD , types data used, first one being environmental from new Nicosia waste water treatment plant conductivity (Cond inf...

Clustered regularly interspaced short palindromic repeat (CRISPR) technology is the most important tool in gene editing, it can be used to target any using guide RNA and Cas enzyme, one limitation of CRISPR systems low (gRNA) activity, therefore highly predict its gRNA activity. The activity determined by measuring score for frequency insertion or deletion (indel). In this work, CNN was optimized changing convolution layer depth filter kernel size determine how well model will perform, also,...

Numerical electronic health records often come with numerous features and outliers. These are usually indicators of medical diseases. To prevent poor model performance associated the curse dimensionality, these must be reduced only most important ones retained. Also, outliers may result in generalization a machine learning model. This study investigates impact dimensionality reduction on models for disease diagnosis. After fitting datasets to models, was evaluated using evaluation metrics....

Background Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic NSD has not yet been studied. Objective This research aimed to develop evaluate a real-time model that can detect probable using cone beam computed tomography (CBCT) images. Methods Coronal section images were...

Tuberculosis (TB) is a lung disease that tops the world mortality rate. The widely employed method of diagnosis X-ray images which gives pictorial information lungs. In era Internet Things (IoT), Artificial Intelligence (AI) using deep learning among most efficient methods in detecting lung-related diseases, and classifying related images. To trust achieved decision, this study ResNet-50 was used to classify TB normal patients images, also, Gradient-weighted Class Activation Mapping...

Smart tourism is a developing industry, and numerous nations are planning to establish smart cities in which technology employed make life easier link nearly everything. Many researchers have created object detectors; however, there demand for lightweight versions that can fit into smartphones other edge devices. The goal of this research demonstrate the notion employing mobile application detect statues efficiently on applications, also improve performance models by Gaussian Smoothing...

Accidents have contributed a lot to the loss of lives motorists and serious damage vehicles around globe. Potholes are major cause these accidents. It is very important build model that will help in recognizing potholes on vehicles. Several object detection models based deep learning computer vision were developed detect potholes. develop lightweight with high accuracy speed. In this study, we employed Mask RCNN ResNet-50 MobileNetv1 as backbone improve detection, also compared performance...

It is essential to use highly antigenic epitope areas, since the development of peptide vaccines heavily relies on precise design regions that can elicit a strong immune response. Choosing experimentally for production SARS-CoV-2 vaccine be time-consuming, costly, and labor-intensive. Scientists have created in silico prediction techniques based machine learning find these regions, cut down number candidate epitopes might tested experiments, and, as result, lessen time-consuming process...