Electronic Health Record (EHR) systems are used as an efficient and effective method of exchanging patients’ health information with doctors and other key stakeholders in the health sector to obtain improved patient treatment decisions and diagnoses. As a result, questions regarding the security of sensitive user data are highlighted. To encourage people to move their sensitive health records to cloud networks, a secure authentication and access control mechanism that protects users’ data should be established. Furthermore, authentication and access control schemes are essential in the protection of health data, as numerous responsibilities exist to ensure security and privacy in a network. So, the main goal of our s

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

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we

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

Background: The potential use of zinc oxide and other metal oxide nanoparticles in biomedical are gaining interest in the scientific and medical communities, largely due to the physical and chemical properties of these nanomaterials. The present work revealed the effect of zinc oxide nanoparticles (ZnONPs) on the total salivary peroxidase enzyme activity of human saliva in comparison to de-ionized water. Materials and methods: Forty eight unstimulated saliva samples were collected from dental students/University of Baghdad 18-22 years. Then measure the total salivary peroxidase activity first without any addition to human saliva as a control, second with dilution the saliva with de-ionized water, and third with zinc oxide nanoparticles in c

We have studied the effect of applying an external magnetic field on the characteristics of iron oxide (IO) nanoparticles (NPs) synthesized by pulsed laser ablation in dimethylformamide (DMF). The NPs synthesized with and without applying of magnetic field were characterized by Fourier transformation infrared spectroscopy (FT-IR), UV–Vis absorption, scanning electron microscope (SEM), atomic force microscope (AFM), and X-ray diffraction (XRD). SEM results confirmed that the particle size was decreased after applying magnetic field.

The effect of metal nanoparticles on the anaerobic digestion of sludge and the sludge bacterial community are still not well-understood, and both improvements and inhibitions have been reported. This study investigated the impact of 2, 10, and 30 mg/g TS silver and copper oxide nanoparticles (AgNPs and CuONPs) on the mesophilic anaerobic digestion of sludge and the bacterial community structure. The reactors were monitored for changes in tCOD, sCOD, TS, VS, biogas generation, and cell viability. Also, the relative abundance and taxonomic distribution of the bacterial communities were analyzed at the phylum and genus levels, including the genera involved in anaerobic digestion. Both AgNPs and CuONPs exhibited some inhibition on anaer

AlO-doped ZnO nanocrystalline thin films from with nano crystallite size in the range (19-15 nm) were fabricated by pulsed laser deposition technique. The reduction of crystallite size by increasing of doping ratio shift the bandgap to IR region the optical band gap decreases in a consistent manner, from 3.21to 2.1 eV by increasing AlO doping ratio from 0 to 7wt% but then returns to grow up to 3.21 eV by a further increase the doping ratio. The bandgap increment obtained for 9% AlO dopant concentration can be clarified in terms of the Burstein–Moss effect whereas the aluminum donor atom increased the carrier's concentration which in turn shifts the Fermi level and widened the bandgap (blue-shift). The engineering of the bandgap by low