Background and motivation: Over the last two decades, particularly in the Middle East, Red Palm Weevils (RPW, Rhynchophorus ferruginous) have proved to be the most destructive pest of palm trees across the globe. Problem: The RPW has caused considerable damage to various palm species. The early identification of the RPW is a challenging task for good date production since the identification will prevent palm trees from being affected by the RPW. This is one of the reasons why the use of advanced technology will help in the prevention of the spread of the RPW on palm trees. Many researchers have worked on finding an accurate technique for the identification, localization and classification of the RPW pest. This study aimed to develop a model that can use a deep-learning approach to identify and discriminate between the RPW and other insects living in palm tree habitats using a deep-learning technique. Researchers had not applied deep learning to the classification of red palm weevils previously. Methods: In this study, a region-based convolutional neural network (R-CNN) algorithm was used to detect the location of the RPW in an image by building bounding boxes around the image. A CNN algorithm was applied in order to extract the features to enclose with the bounding boxes—the selection target. In addition, these features were passed through the classification and regression layers to determine the presence of the RPW with a high degree of accuracy and to locate its coordinates. Results: As a result of the developed model, the RPW can be quickly detected with a high accuracy of 100% in infested palm trees at an early stage. In the Al-Qassim region, which has thousands of farms, the model sets the path for deploying an efficient, low-cost RPW detection and classification technology for palm trees.
Healthcare is a basic human need in any civilization. In modern sense of geopolitics, a welfare state has to ensure easy access of public to basic amenities which include medical facilities. However, the areas, off mainland, usually stay deprived of quality medical services. This is generally the case in third world countries, including Pakistan. Here, due to lack of education and poverty, the suburban or countryside population remains ignorant of available qualified doctors. Everybody does not afford travelling to the nearest main city hospital. Those who afford sometimes experience extreme frustration at absence of their desired medic. In order to assist the deprived, the authors have developed a mobile-based appointment system for remote patients. This application not only helps in requesting appointments with doctors, but lets the android mobile user search the list of quality medics around. The details related to the use and effectiveness of this application have been discussed in the main chapter.
Our retrospective study analyzed outcomes after combined full-thickness penetrating keratoplasty and retinal detachment repair for complex anterior and posterior segment abnormality. Although postoperative visual function is limited, the surgery had great anatomical outcomes; all of the patients were happy, and they would repeat surgery if presented with the choice again.
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