درخت حوزه‌های تخصصی

Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine learning algorithms. In this paper, we have reviewed algorithms for automatic cyberbullying detection in Arabic of machine learning, and after comparing the highest accuracy of these classifications we will propose the techniques Ridge Regression (RR) and Logistic Regression (LR), which achieved the highest accuracy between the various techniques applied in the automatic cyberbullying detection in English and between the techniques that was used in the sentiment analysis in Arabic text, The purpose of this work is applying these techniques for detecting cyberbullying in Arabic.

Online reviews have become essential aspect in E-commerce platforms due to its role for assisting customers’ buying choices. Furthermore, the most helpful reviews that have some attributes are support customers buying decision; therefore, there is needs for investigating what are the attributes that increase the Review Helpfulness (RH). This research paper proposed novel model called inclusive review helpfulnessmodel (IRHM) can be used to detect the most attributes affecting the RH and build classifier that can predict RH based on these attributes. IRHM is implemented on Amazon.com using collection of reviews from different categories. The results show that IRHM can detect the most important attributes and classify the reviews as helpful or not with accuracy of 94%, precision of 0.20 and had excellent area under curve close to 0.94.

In recent privacy has emerged as one of the major concerns of deep learning, since it requires huge amount of personal data. Medical Image Analysis is one of the prominent areas where sensitive data are shared to a third party service provider. In this paper, a secure deep learning scheme called Metamorphosed Learning (MpLe) is proposed to protect the privacy of images in medical image analysis. An augmented convolutional layer and image morphing are two main components of MpLe scheme. Data providers morph the images without privacy information using image morphing component. The human unrecognizable image is then delivered to the service providers who then apply deep learning algorithms on morphed data using augmented convolution layer without any performance penalty. MpLe provides sturdy security and privacy with optimal computational overhead. The proposed scheme is experimented using VGG-16 network on CIFAR dataset. The performance of MpLe is compared with similar works such as GAZELLE and MiniONN and found that the MpLe attracts very less computational and data transmission overhead. MpLe is also analyzed for various adversarial attack and realized that the success rate is as low as . The efficiency of the proposed scheme is proved through experimental and performance analysis.

Logistics has long been recognized as the main effective tool for generating competitive advantages in trading enterprises, and therefore there is an acute problem in finding strategic guidelines for improving logistics activities through the lens of organizational and economic support of logistic activity of a trading enterprise. The article compares the main reference models for the analysis of logistics activities and found the most appropriate for use - SCOR model, which identified the main indicators for evaluating the performance of logistics activities. An algorithm for determining the level of efficiency and performance of logistic activity have been constructed. According to the results of the study, the level of organizational and economic support, perormance and efficiency of logistic activity of trade enterprises of Ukraine of the sample population have been determined.

The introduction of cloud computing techniques revolutionized the current of information processing and storing. Cloud computing as a competitive edge provides easy and automated access to the vast ocean of resources through standard network mechanisms to businesses and organizations. Due to the vast diversity of service providers and their respective variety of available services with different qualities, top managements often face difficulty for choosing the best available option. So, considering the growing significance of the mentioned issue, this study aims to identify and rank contributing factors in selection of cloud service providers. In that attempt, this research approaches its goal by going through three major phases. Firstly, in phase one, prior studies are reviewed for extracting related elements of selection. Secondly, by employing Fuzzy Delphi method and obtaining results by interviewing experts in this field such as IT managers and technicians, this study tries to finalize the list of contributing factors. Lastly, by utilizing Fuzzy best-worst multi-criteria decision-making method, which is one of the most recent techniques employed to statistically rank variables, this research introduces a list of vital factors for cloud service selection. Based on the findings of this study, there are five major categories involved in the selection process which are: performance, security, data management, personal data protection and environmental-organizational. The finalized result of ranking shows that, performance related factors such as accessibility, response time and capacity are the first priority. The runner-up is security with reliability and governance. Environmental-organizational variables lands in the third place by considering rental and network costs.

In recent years, brain tumors become the leading cause of death in the world. Detection and rapid classification of this tumor are very important and may indicate the likely diagnosis and treatment strategy. In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). The scope of this research is the use of gray level of co-occurrence matrix (GLCM) features images and the original images as inputs to CNNs. Two GLCM features images are used (contrast and energy image). Our experiments show that the original image with energy image as input has better distinguishing features than other input combinations; accuracy can achieve average of 96.5% which is higher than accuracy in state-of-the-art classifiers.