Corona virus sickness has become a big public health issue in 2019. Because of its contact-transparent characteristics, it is rapidly spreading. The use of a face mask is among the most efficient methods for preventing the transmission of the Covid-19 virus. Wearing the face mask alone can cut the chance of catching the virus by over 70\%. Consequently, World Health Organization (WHO) advised wearing masks in crowded places as precautionary measures. Because of the incorrect use of facial masks, illnesses have spread rapidly in some locations. To solve this challenge, we needed a reliable mask monitoring system. Numerous government entities are attempting to make wearing a face mask mandatory; this process can be facilitated by using face mask detection software based on AI and image processing techniques. For face detection, helmet detection, and mask detection, the approaches mentioned in the article utilize Machine learning, Deep learning, and many other approaches. It will be simple to distinguish between persons having masks and those who are not having masks using all of these ways. The effectiveness of mask detectors must be improved immediately. In this article, we will explain the techniques for face mask detection with a literature review and drawbacks for each technique.
Cephalexin and its derivatives are commonly utilized in the pharmaceutical and medicinal industry due to their biological and pharmaceutical activities, including anti-microbial, anti-cancer, anti-bacterial, and herbicidal activities as well as possessing high palatability and being useful for skin and joint infections. Interestingly, some organic drugs, including cephalexin, which exhibit toxicological and pharmacological properties, can be administered in forms of metal complexes. Many researchers have synthesized organic ligands derived from cephalexin in forms of Schiff bases and azo compounds which exhibited higher biological and medicinal properties when compared to cephalexin alone. One of the important features that make Schiff base
... Show MoreCollapsible soil has a metastable structure that experiences a large reduction in volume or collapse when wetting. The characteristics of collapsible soil contribute to different problems for infrastructures constructed on its such as cracks and excessive settlement found in buildings, railways channels, bridges, and roads. This paper aims to provide an art review on collapse soil behavior all over the world, type of collapse soil, identification of collapse potential, and factors that affect collapsibility soil. As urban grow in several parts of the world, the collapsible soil will have more get to the water. As a result, there will be an increase in the number of wetting collapse problems, so it's very important to com
... Show MoreThe study evaluated the recovery performance of any home herbs group as first aid in some emergency cases. The study involved 10 items of herbs (chamomile, black pepper, cloves, cinnamon, and anise) which used in acute cases such as cold, colic, inflammation of the intestine and tonsillitis, and (hibiscus, catnip, dried lime, cress seed, and ginger) were used for chronic cases such as hypertension, cardiovascular disease, and arthritis. The results showed the herbs had healing power and efficiency in the primary remedy for the above-mentioned diseases. The side effects, the contradictions, and the overdoses of these substances were discussed. It was concluded that home herbs could be used for treatment in emergency cases until moder
... Show MoreExplainable Artificial Intelligence (XAI) techniques enable transparency and trust in automated visual inspection systems by making black-box machine learning models understandable. While XAI has been widely applied, prior reviews have not addressed the specific demands of industrial and medical inspection tasks. This paper reviews studies applying XAI techniques to visual inspection across industrial and medical domains. A systematic search was conducted in IEEE Xplore, Scopus, PubMed, arXiv, and Web of Science for studies published between 2014 and 2025, with inclusion criteria requiring the application of XAI in inspection tasks using public or domain-specific datasets. From an initial pool of studies, 75 were included and categorized in
... Show MorePsidium guajava, belonging to the Myrtaceae family, thrives in tropical and subtropical regions worldwide. This important tropical fruit finds widespread cultivation in countries like India, Indonesia, Syria, Pakistan, Bangladesh, and South America. Throughout its various parts, including fruits, leaves, and barks, guava boasts a rich reservoir of bioactive compounds that have been traditionally utilized as folkloric herbal medicines, offering numerous therapeutic applications. Within guava, an extensive array of Various compounds with antioxidative properties and phytochemical constituents are present, including essential oils, polysaccharides, minerals, vitamins, enzymes, triterpenoids, alkaloids, steroids, glycosides, tannins, fl
... Show MoreAl-Dalmaj marsh and the near surrounding area is a very promising area for energy resources, tourism, agricultural and industrial activities. Over the past century, the Al-Dalmaje marsh and near surroundings area endrous from a number of changes. The current study highlights the spatial and temporal changes detection in land cover for Al-Dalmaj marsh and near surroundings area using different analyses methods the supervised maximum likelihood classification method, the Normalized Difference Vegetation Index (NDVI), Geographic Information Systems(GIS), and Remote Sensing (RS). Techniques spectral indices were used in this study to determine the change of wetlands and drylands area and of other land classes, th
... Show MoreAbstract Software-Defined Networking (commonly referred to as SDN) is a newer paradigm that develops the concept of a software-driven network by separating data and control planes. It can handle the traditional network problems. However, this excellent architecture is subjected to various security threats. One of these issues is the distributed denial of service (DDoS) attack, which is difficult to contain in this kind of software-based network. Several security solutions have been proposed recently to secure SDN against DDoS attacks. This paper aims to analyze and discuss machine learning-based systems for SDN security networks from DDoS attack. The results have indicated that the algorithms for machine learning can be used to detect DDoS
... Show MoreThe major objective of this study is to establish a network of Ground Control Points-GCPs which can use it as a reference for any engineering project. Total Station (type: Nikon Nivo 5.C), Optical Level and Garmin Navigator GPS were used to perform traversing. Traversing measurement was achieved by using nine points covered the selected area irregularly. Near Civil Engineering Department at Baghdad University Al-jadiriya, an attempt has been made to assess the accuracy of GPS by comparing the data obtained from the Total Station. The average error of this method is 3.326 m with the highest coefficient of determination (R2) is 0.077 m observed in Northing. While in
Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff
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