No abstract

Mobile applications create their own security and privacy models through permission-based models. Some applications may request extra permissions that they do not need but may use for suspicious activities. The aim of this study is to identify those spare permissions requested and use this information in the security and privacy approach, which uses static and code analysis together and applies them to the existing datasets; then the results are compared and accuracy level is determined. Classification is made with an accuracy rate of 91.95%.

Kidney tumors represent a type of cancer that people of advanced age are more likely to develop. For this reason, it is important to exercise caution and provide diagnostic tests in the later stages of life. Medical imaging and deep learning methods are becoming increasingly attractive in this sense. Developing deep learning models to help physicians identify tumors with successful segmentation is of great importance. However, not many successful systems exist for soft tissue organs, such as the kidneys and the prostate, of which segmentation is relatively difficult. In such cases where segmentation is difficult, V-Net-based models are mostly used. This paper proposes a new hybrid model using the superior features of existing V-Net models. The model represents a more successful system with improvements in the encoder and decoder phases not previously applied. We believe that this new hybrid V-Net model could help the majority of physicians, particularly those focused on kidney and kidney tumor segmentation. The proposed model showed better performance in segmentation than existing imaging models and can be easily integrated into all systems due to its flexible structure and applicability. The hybrid V-Net model exhibited average Dice coefficients of 97.7% and 86.5% for kidney and tumor segmentation, respectively, and, therefore, could be used as a reliable method for soft tissue organ segmentation.

Abstract:The estimation of hourly electricity load consumption is highly important for planning short-term supply-demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when using fuzzy logic techniques, have more accurate load estimations when datasets include high uncertainty. However, as the knowledge base-which is defined by expert insights and decisions-gets larger, the load forecasting performance decreases. This study handles the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods. The proposed models have been optimized by using ant colony optimization and genetic algorithm (GA) techniques. The training and testing processes of the proposed systems were performed on historical hourly load consumption and temperature data collected between 2011 and 2014. The results show that the proposed models can sufficiently improve the performance of hourly short-term load forecasting. The mean absolute percentage error (MAPE) of the monthly minimum in the forecasting model, in terms of the forecasting accuracy, is 3.9% (February 2014). The results show that the proposed methods make it possible to work with large-scale rule bases in a more flexible estimation environment.

This paper presents a correction methodology for Long Short Term Memory (LSTM) based speech recognition. A strategy that validates with a reference database was developed for LSTM. It is conceptually simple but requires a large keyword database to match test templates. The correction method is based on the “most matching method” that is finding the word in which the system output is closest among the “Referenced Template Database”. Each LSTM model recognition output was corrected with the proposed new concept. Thus, system recognition performance was improved by correcting faulty outputs. The effectiveness, efficiency, and contribution of this approach to system performance were demonstrated by experiments. Tests carried out using different speech-text datasets and LSTM models yielded an average performance increase of 2.25%. With some advanced models, this ratio rises to 3.84%.