During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

This paper describes a research effort that aims of developing solar models for housing suitable for the Arabian region since the Arabian Peninsula is excelled with very high levels of solar radiation.
The current paper is focused on achieving energy efficiency through utilizing solar energy and conserving energy. This task can be accomplished by implementation the major elements related to energy efficiency in housing design , such as embark on an optimum photovoltaic system orientation to maximize seize solar energy and produce solar electricity. All the precautions were taken to minimizing the consumption of solar energy for providing the suitable air-condition to the inhibitor of the solar house in addition to use of energy effici

Tin oxide was deposited by using vacuum thermal method on silicon wafer engraved by Computer Numerical Controlled (CNC) Machine. The inscription was engraved by diamond-made brine. Deep 0.05 mm in the form of concentric squares. Electrical results in the dark were shown high value of forward current and the high value of the detection factor from 6.42 before engraving to 10.41 after engraving. (I-V) characters in illumination with powers (50, 100, 150, 200, 250) mW/cm2 show Improved properties of the detector, Especially at power (150, 200, 250) mW/cm2. Response improved in rise time from 2.4 μs to 0.72 μs and time of inactivity improved 515.2 μs to 44.2 μs. Sensitivity angle increased at zone from 40o to 65o.

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

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

Has been studied both processes Almetzaz and extortion of a substance Alklanda Maysan different amounts of Alcaúlan Guy 70% alcohol solution using the method when the wavelength

Suicidal ideation is one of the most severe mental health issues faced by people all over the world. There are various risk factors involved that can lead to suicide. The most common & critical risk factors among them are depression, anxiety, social isolation and hopelessness. Early detection of these risk factors can help in preventing or reducing the number of suicides. Online social networking platforms like Twitter, Redditt and Facebook are becoming a new way for the people to express themselves freely without worrying about social stigma. This paper presents a methodology and experimentation using social media as a tool to analyse the suicidal ideation in a better way, thus helping in preventing the chances of being the victim o

     The research involved a rapid, automated and highly accurate developed CFIA/MZ technique for estimation of phenylephrine hydrochloride (PHE) in pure, dosage forms and biological sample. This method is based on oxidative coupling reaction of 2,4-dinitrophenylhydrazine (DNPH) with PHE in existence of sodium periodate as oxidizing agent in alkaline medium to form a red colored product at ʎ_max )520 nm (. A flow rate of 4.3 mL.min^-1 using distilled water as a carrier, the method of FIA proved to be as a sensitive and economic analytical tool for estimation of PHE.

Within the concentration range of 5-300 μg.mL^-1, a calibration curve was rectilinear, where the detection limit was 3.252 μg.mL