Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio

Manual fruit picking is labor-intensive and can damage fruit. Fully mechanized picking is efficient, but it also risks fruit damage. Therefore, semi-automated tools are needed to improve bitter orange picking. This paper presents a smart manual picker designed to facilitate picking while predicting fruit maturity based on picking force as well as various chemical and physical parameters using machine learning (ML). The study methodology consists of five stages: (1) manufacturing the smart picker, (2) picking 50 bitter orange samples, (3) measuring the characteristics of the bitter oranges in the laboratory, (4) training different ML models, and (5) identifying the most accurate model for predicting fruit maturity. The results indicate that

The aim of this study to evaluate the effects of die holes diameter and speed of die on the performance of machine and feed pellet quality. Machine productivity (Kg.h-1), consumed power (kW), pellet durability (%) and pellet bulk density (g.cm-3) was studied. The study factors consisted of three diameter of die holes (3, 4, and 5 mm), and three speeds die (280, 300, and 320 rpm). Results showed with increasing of die holes diameter from 3 to 4 and to 5 mm give a significant increase in machine productivity, while consumed power, pellet durability and pellet bulk density a significant decreased. By increasing the die speed, from 280 to 300 then to 320 rpm, the machine productivity increased significantly, while consumed power, pellet durabil

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

Collapsing building structures during recent earthquakes, especially in Northern and Eastern Kurdistan, including the 2003 earthquake in Cewlig; the 2011 earthquake in Van; and the 2017 earthquake near Halabja province, has raised several concerns about the safety of pre-seismic code buildings and emergency facilities in Erbil city. The seismic vulnerability assessment of the hospital buildings as emergency facilities is one of the necessities which have a critical role in the recovery period following earthquakes. This research aims to study in detail and to extend the present knowledge about the seismic vulnerability of the Rizgary public hospital building in Erbil city, which was constructed before releasing the seism

מטרת המחקר הזה היא לבדוק את שלושה סיפורים קצרים מהקובץ הראשון של אהרון אפלפלד , שנחשב ההצהרה הרשמית על היותו סופר השואה הראשון בישראל , המשותף לשלושת הסיפורים הוא הנימה של הגיבורים היוצאים למסע פיזי ונפשי לגלות את אשר אירע להם , מגמתו של המחקר היא לבדוק את הצורה המיוחדת שבה אפיין אפלפלד את גיבוריו שמעידים על כך , כי השואה היא החוויה המרה והקשה בקרב היהודים והשלכותיה נשארה טמונה בקרבם גם אחרי המלחמה ואחרי שנים

Asthma is a chronic respiratory disease highly prevalent worldwide. Recent studies have suggested a role for microbiome-associated gut–lung axis in asthma development. In the current study, we investigated if Resveratrol (RES), a plant-based polyphenol, can attenuate ovalbumin (OVA)-induced murine allergic asthma, and if so, the role of microbiome in the gut–lung axis in this process. We found that RES attenuated allergic asthma with significant improvements in pulmonary functions in OVA-exposed mice when tested using plethysmography for frequency (F), mean volume (MV), specific airway resistance (sRaw), and delay time(dT). RES treatment also suppressed inflammatory cytokines in the lungs. RES modulated lung microbiota and cause