Human skin disease, the most infectious dermatological ailment globally, is initially diagnosed by sight. Some clinical screening and dermoscopic analysis of skin biopsies and scrapings for accurate classification are...
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Human skin disease, the most infectious dermatological ailment globally, is initially diagnosed by sight. Some clinical screening and dermoscopic analysis of skin biopsies and scrapings for accurate classification are medically compulsory. Classification of skin diseases using medical images is more challenging because of the complex formation and variant colors of the disease and data security concerns. Both the Convolution Neural Network (CNN) for classification and a federated learning approach for data privacy preservation show significant performance in the realm of medical imaging fields. In this paper, a custom image dataset was prepared with four classes of skin disease, a CNN model was suggested and compared with several benchmark CNN algorithms, and an experiment was carried out to ensure data privacy using a federated learning approach. An image augmentation strategy was followed to enlarge the dataset and make the model more general. The proposed model achieved a precision of 86%, 43%, and 60%, and a recall of 67%, 60%, and 60% for acne, eczema, and psoriasis. In the federated learning approach, after distributing the dataset among 1000, 1500, 2000, and 2500 clients, the model showed an average accuracy of 81.21%, 86.57%, 91.15%, and 94.15%. The CNN-based skin disease classification merged with the federated learning approach is a breathtaking concept to classify human skin diseases while ensuring data security.
Since human skin illnesses are among the most common dermatological conditions worldwide, visual observations are usually the first step in the diagnosis process. Accurate classification of skin biopsies and scrapings...
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To design low-energy buildings, mathematical optimization can be an effective method for the minimization of energy consumption. Due to the nonlinear thermal operation of buildings, simulation-based optimizations have...
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To design low-energy buildings, mathematical optimization can be an effective method for the minimization of energy consumption. Due to the nonlinear thermal operation of buildings, simulation-based optimizations have been used in recent studies. In the building industry, achieving optimum solutions with acceptable calculation cost is an important factor. Therefore, in this paper, an Improved Crow Search Algorithm (ICSA) coupled with the EnergyPlus simulation tool is used to optimize an office building in four cities of Australia. Based on the achievements more than 11.8% of energy consumption can be decreased by employing energy-saving actions. The proposed algorithm was compared to some benchmark algorithms and it has resulted that ICSA gives more proper solutions with lower calculation time.
Keystroke dynamics allows to authenticate individuals through their way of typing their password or a free text on a keyboard. In general, in biometrics, a novel algorithm is validated through a comparison to the stat...
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Keystroke dynamics allows to authenticate individuals through their way of typing their password or a free text on a keyboard. In general, in biometrics, a novel algorithm is validated through a comparison to the state of the art one's using some datasets in an offline way. Several benchmark datasets for keystroke dynamics have been proposed in the literature. They differ in many ways and their intrinsic properties influence the performance of the algorithms under evaluation. In this work, we (a) provide a literature review on existing benchmark datasets of keystroke dynamics;(b) present several criteria and tests in order to characterize them;(c) and apply these criteria on these available public benchmark datasets. The review analysis shows a great disparity in the acquisition protocol, the population involved, the complexity of the passwords, or the expected performance (there is a relative difference of 76% between the EER on the worst and best performing datasets with the same authentication method). (C) 2015 Elsevier Ltd. All rights reserved.
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