Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

Diabetes imposes a substantial public health burden; according to the International Diabetes Federation, there were about 3.4 million diabetes related deaths worldwide in 2024, and in Iraq, the Federation reports that one in nine adults lives with diabetes in 2024, with 14,683 adult deaths attributable to diabetes and a total diabetes related health expenditure of 2,078 million United States dollars. The dataset analyzed in this study contains 1,000 records collected in 2020 from two Iraqi teaching hospitals and includes multiple clinical and laboratory measurements with three outcome classes, namely Non diabetic, Pre diabetic, and Diabetic, with a low prevalence of the Pre diabetic class and an imbalanced overall class distribution; the da

High vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, and constraints for routing protocols in vehicular ad hoc networks (VANETs). The routing must avoid forwarding packets through segments with low network density and high scale of network disconnections that may result in packet loss, delays, and increased communication overhead in route recovery. Therefore, both traffic and segment status must be considered. This paper presents real-time intersection-based segment aware routing (RTISAR), an intersection-based segment aware algorithm for geographic routing in VANETs. This routing algorithm provides an optimal route for forwarding the data packets toward their destination

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

In this study new derivatives of Schiff bases 5-8, 1, 3-oxazepine 9-16 and tetrazoles 17-19 have been synthesized from the new starting material 1 which has synthesized the reaction of one mole of dichloro acetic acid and two moles of thiophenol, the esters 2-3 were synthesized from the reaction of compound 1 with methanol or ethanol respectively in the presence of H2SO4 as catalyst then 2, 2-dithiophenylaceto Hydrazide 4 were synthesized from the reaction of 2 or 3 with hydrazine hydrate 80%, Schiff bases 5-8 were synthesized from the reaction of 4 with appropriate aldehyde or ketone. Treatment of Schiff bases with maleic and phathalic anhydride in dry benzene to give 1, 3-oxazepen derivatives 9-16 and with sodium azide in tetrahydrofuran

A specific, sensitive and simple method was used for the determination of: vitamin B9 (Folic acid) in pure and pharmaceutical formulations using continuous flow injection analysis. The method is based on formation of ion pair compound between folic acid and ammonium molybdate in an aqueous medium to obtain a gray precipitate complex, using homemade; Ayah-6SX1-ST-2D solar cell CFI Analyzer. Optimum parameters was studied to increase the sensitivity for developed method. The linear range for the calibration graph was 0.01-0.6 mMol.L-1 of vitamin B9 and LOD was 131.994 ng/sample with correlation coefficient ( r ) of 0.9810, RSD% was lower than 0.1%, (n=9) for the determination of vitamin B9 at concentration (0.07and 0.5) mMol.L-1 respectiv