The spread of dengue fever depends mainly on the availability of favourable breeding sites for its mosquito vectors around human dwellings. To investigate if the various factors influencing breeding habitats can be mapped from space, dengue indices, such as the container index, the house index and the Breteau index, were calculated from Ministry of Public health data collected three times annually in Phitsanulok, Thailand between 2009 and 2011. The most influential factors were found to be temperature, humidity, rainfall, population density, elevation and land cover. Models were worked out using parameters mostly derived from freely available satellite images and fuzzy logic software with parameter synchronisation and a predication algorithm based on data mining and the Decision Tree method. The models developed were found to be sufficiently flexible to accommodate additional parameters and sampling data that might improve prediction of favourable breeding hotspots. The algorithm applied can not only be used for the prediction of near real-time scenarios with respect to dengue, but can also be applied for monitoring other diseases influenced by environmental and climatic factors. The multi-criteria model presented is a cost-effective way of identifying outbreak hotspots and early warning systems lend themselves for development based on this strategy. The proposed approach demonstrates the successful utilisation of remotely sensed images to map mosquito breeding habitats.
Coronavirus disease (Covid-19) has been spreading all over the world and its diagnosis is attracting more research every moment. It is need of the hour to develop automated methods, which could detect this disease at its early stage, in a non-invasive way and within lesser time. Currently, medical specialists are analyzing Computed Tomography (CT), X-Ray, and Ultrasound (US) images or conducting Polymerase Chain Reaction (PCR) for its confirmation on manual basis. In Pakistan, CT scanners are available in most hospitals at district level, while X-Ray machines are available in all tehsil (large urban towns) level hospitals. Being widely used imaging modalities to analyze chest related diseases, produce large volume of medical data each moment clinical environments. Since automatic, time efficient and reliable methods for Covid-19 detection are required as alternate methods, therefore an automatic method of Covid-19 detection using Convolutional Neural Networks (CNN) has been proposed. Three publically available and a locally developed dataset, obtained from Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur (BVHB), Pakistan have been used. The proposed method achieved on average accuracy (96.68%), specificity (95.65%), and sensitivity (96.24%). Proposed model is trained on a large dataset and is being used at the Radiology Department, (BVHB), Pakistan.
Recognition of age-separated face images is a challenging and open research problem. In this paper we propose a facial asymmetry based matching-score space (MSS) approach for recognition of age-separated face images.Motivated by its discriminatory information, we evaluate facial asymmetry across small and large temporal variations and use asymmetric facial features to recognize age-separated face images. We extract three different facial features including holistic feature descriptors using Principal Component Analysis (PCA), local feature descriptors using Local Binary Patterns (LBP), and Densely Sampled Asymmetric Features (DSAF) to represent face images. Then we develop MSS to discriminate genuine and imposter classes using support vector machine (SVM) as a classifier. Experimental results on three widely used face aging databases, the FERET, MORPH and FG-NET, show that proposed approach has superior performance compared to some existing state-of-the-art approaches.
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