Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

The ongoing research to improve the clinical outcome of titanium implants has resulted in the implementation of multiple approaches to deliver osteogenic growth factors accelerating and sustaining osseointegration. Here we show the presentation of human bone morphogenetic protein 7 (BMP-7) adsorbed to titanium discs coated with poly(ethyl acrylate) (PEA). We have previously shown that PEA promotes fibronectin organization into nanonetworks exposing integrin- and growth-factor-binding domains, allowing a synergistic interaction at the integrin/growth factor receptor level. Here, titanium discs were coated with PEA and fibronectin and then decorated with ng/mL doses of BMP-7. Human mesenchymal stem cells were used to investigate cellular resp

Shallow foundations are usually used for structures with light to moderate loads where the soil underneath can carry them. In some cases, soil strength and/or other properties are not adequate and require improvement using one of the ground improvement techniques. Stone column is one of the common improvement techniques in which a column of stone is installed vertically in clayey soils. Stone columns are usually used to increase soil strength and to accelerate soil consolidation by acting as vertical drains. Many researches have been done to estimate the behavior of the improved soil. However, none of them considered the effect of stone column geometry on the behavior of the circular footing. In this research, finite ele

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

This paper presents a comprehensive numerical analysis of the improvement in bearing capacity and settlement performance of hexagonal shallow footings with inclined skirts. Various numerical analyses were conducted using PLAXIS 3D to investigate the influence of skirt length-to-footing width (L/B) ratios and skirt inclination angles (θ) on hexagonal footings in loose sand. The models showed very good agreement with experimental data reported in previous studies, with an R² value of 0.996 and a maximum error of less than 4.31%. It was concluded that the inclusion of inclined skirts has a positive effect on bearing capacity, increasing it by up to approximately 2.97 times compared to non-inclined configurations, while significantly

—Fundus images play an essential role in ophthalmic diagnostics for the detection of many eye illnesses. The experiment begins with a thorough image preprocessing. technique, which includes clipping the circular borders, scaling the image, enhancing the contrast, removing noise, and augmenting the data. The new combined block applies to extracting distinctive deep feature representations, which help to detect the first shape of the edges of each lesion. It is namely the Attention Block and the Conv-Deconv UNET model. The attention block is subsequently implemented in order to augment the robustness and quality of feature depictions derived from a pair of DR images. The Dual Attention Block for the backbone, which is supplement

Business organizations have faced many challenges in recent times, most important of which is information technology, because it is widely spread and easy to use. Its use has led to an increase in the amount of data that business organizations deal with an unprecedented manner. The amount of data available through the internet is a problem that many parties seek to find solutions for. Why is it available there in this huge amount randomly? Many expectations have revealed that in 2017, there will be devices connected to the internet estimated at three times the population of the Earth, and in 2015 more than one and a half billion gigabytes of data was transferred every minute globally. Thus, the so-called data mining emerged as a

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

Purpose: to demonstrate the possibility of moving to electronic data exchange dimensions (regulatory requirements, technical requirements, human requirements, senior management support) to simplify the work procedures dimensions (modern procedures, clarity of procedures, short procedures, availability of information and means required. The simplicity of the models used because of its importance to keep abreast of recent developments in the service of municipal works through the application of electronic data interchange, which simplifies procedures and out of the routine in the performance of the work of municipal departments has developed. It was applied to Municipality (Hilla) so that the transformation