This study was conducted to detect C.sakazakii PIF and raw milk. Two hundred samples of PIF were taken from the infected hospital infants who used this type of milk and from the local markets in addition to 16 sample of raw milk were collected. The study is the first to report the isolation of C. sakazakii and Enterobacter spp. from raw milk in Iraq. The distribution of C.sakazakii and Enterobacter spp. among the presumptive isolates using Vitek-GN2 system gave 1/16(6.25%) isolates of C.sakazakii and 4/16 (25%) isolates of Enterobacter spp. Enterobacter spp. isolates include (E.cloacae ssp. cloacae and E.cloacae ssp. dissolvens, E.hormaechei, and E.ludwigii) that isolate from raw milk Differences in between percentages of each isolate perse

The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com

Given the importance of possessing the digital competence (DC) required by the technological age, whether for teachers or students and even communities and governments, educational institutions in most countries have sought to benefit from modern technologies brought about by the technological revolution in developing learning and teaching and using modern technologies in providing educational services to learners. Since university students will have the doors to work opened in all fields, the research aims to know their level of DC in artificial intelligence (AI) applications and systems utilizing machine learning (ML) techniques. The descriptive approach was used, as the research community consisted of students from the University

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m

The present study is to investigate the possibility of using wastes in the form of scrap iron (ZVI) and/ or aluminum ZVAI for the detention and immobilization of the chromium ions in simulated wastewater. Different batch equilibrium parameters such as contact time (0-250) min, sorbent dose (2-8 g ZVI/100 mL and 0.2-1 g ZVAI/100 mL), initial pH (3-6), initial pollutant concentration of 50 mg/L, and speed of agitation (0-250) rpm were investigated. Maximum contaminant removal efficiency corresponding to (96 %) at 250 min contact time, 1g ZVAI/ 6g ZVI sorbent mass ratio, pH 5.5, pollutant concentration of 50 mg/L initially, and 250 rpm agitation speed were obtained.

The best isotherm model for the batch single Cr(III) uptake by ZVI