Breast cancer must be addressed by a multidisciplinary team aiming at the patients comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.

The goal of this study is to see how cold plasma affects rabbit bone tissue infected with osteoporosis. The search is divided into three categories: control, infected, and treated. The rabbits were subjected to cold plasma for five minutes in a room with a microwave plasma voltage of “175 V” and a gas flow of “2.” A histopathological photograph of infected bone cells is obtained to demonstrate the influence of plasma on infected bone cells, as well as the extent of destruction and effect of plasma therapy before and after exposure. The findings of the search show that plasma has a clear impact on Ca and vitamin D levels. In the cold plasma, the levels of osteocalcin and alkali phosphates (ALP) respond as well. Image processing techniques (second-order gray level matrix) with textural elements are employed as an extra proof. The outcome gives good treatment indicators, and the image processing result corresponds to the biological result.

This research explores how e-health systems’ features (information quality, quality of the system, usability perceived, and perceived usefulness) contribute to improving medical personnel performance in medical centers, patient care, and physician-patient interactions in Jordan. The objective is to evaluate a single integrated model consisting of the technology acceptance model. This study used the logical research method and approach. A collection of data from 212 medical personnel working in 19 healthcare facilities throughout Jordan were gathered. To analyze the data collected and test the hypotheses of the research, a partially square/structural equation modeling method has been employed. The study found that the health information system (HIS) information quality has a direct and indirect beneficial effect on the performance of the staff, beneficial effects on patient care alone, and only favorable, indirect effects on the doctor-patient relationship. On the contrary, system quality was shown to influence directly and indirectly and to have a direct and indirect beneficial effect both on the connection between doctors and patients. Remember that the HIS has accessibility, speed, and mistake detection and avoids error issues. These shortcomings are suggested to be rectified in conjunction with improved user perception towards easy usage and utilization of the system.

The aim of this study is to demonstrate the effect of particle size on semiconductor properties; artificial intelligence is being used for the research methods. As a result, we picked cadmium sulfide (CdS), which is a unique semiconductor material that is employed in a broad variety of current applications. Given that CdS has distinct electrical and optical characteristics, it may be employed in the production of solar cells, for example. Solar cells, as is also well known, have become an essential source of energy in the world. Within the visible range (500-700 nm), we create one layer of bulk CdS and one layer of nano-CdS air bulk CdS air and air nano-CdS air. We used a number of instrumentation methods to investigate the naked CdS nanoparticles, including XRD, SEM-EDX, UV-Vis spectroscopy, TEM, XPS, and PL spectroscopy, among others. The results show that for bulk CdS at normal incidence, the transmittance is , and for nano-CdS with particle size 3 nm, the transmittance is , with transverse-electric (S-polarized) and transverse-magnetic (P-polarized) transmittances of and , respectively.

The rapid development and deployment of mRNA vaccines during the COVID-19 pandemic marked a pivotal moment in medical science, showcasing the technologys potential beyond its initial applications. As researchers delve deeper into the versatility of mRNA platforms, the prospect of broadening their use to address a myriad of health challenges is becoming increasingly plausible. This exploration is not merely a theoretical pursuit; it encompasses a wide spectrum of infectious diseases and personalized medicine, including oncology and chronic infections such as HIV and tuberculosis. The inherent adaptability of mRNA technology, characterized by its quick customization in response to emerging pathogens, positions it as a front-line defense in future pandemics and a powerful tool for customized cancer immunotherapy. As such, the trajectory of mRNA vaccine development holds promising implications for transforming therapeutic strategies across various medical domains.

Pneumonia is a common lung disease that is the leading cause of death worldwide. It primarily affects children, accounting for 18% of all deaths in children under the age of five, the elderly, and patients with other diseases. There is a variety of imaging diagnosis techniques available today. While many of them are becoming more accurate, chest radiographs are still the most common method for detecting pulmonary infections due to cost and speed. A convolutional neural network (CNN) model has been developed to classify chest X‐rays in JPEG format into normal, bacterial pneumonia, and viral pneumonia. The model was trained using data from an open Kaggle database. The data augmentation technique was used to improve the model’s performance. A web application built with NextJS and hosted on AWS has also been designed. The model that was optimized using the data augmentation technique had slightly better precision than the original model. This model was used to create a web application that can process an image and provide a prediction to the user. A classification model was developed that generates a prediction with 78 percent accuracy. The precision of this calculation could be improved by increasing the epoch, among other subjects. With the help of artificial intelligence, this research study was aimed at demonstrating to the general public that deep‐learning models can be created to assist health professionals in the early detection of pneumonia.