درخت حوزه‌های تخصصی

The steadily growing dependency over network environment introduces risk over information flow. The continuous use of various applications makes it necessary to sustain a level of security to establish safe and secure communication amongst the organizations and other networks that is under the threat of intrusions. The detection of Intrusion is the major research problem faced in the area of information security, the objective is to scrutinize threats or intrusions to secure information in the network Intrusion detection system (IDS) is one of the key to conquer against unfamiliar intrusions where intruders continuously modify their pattern and methodologies. In this paper authors introduces Intrusion detection system (IDS) framework that is deployed over KDD Cup99 dataset by using machine learning algorithms as Support Vector Machine (SVM), Naïve Bayes and Random Forest for the purpose of improving the precision, accuracy and recall value to compute the best suited algorithm.

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.

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.

The accuracy of brain tumor detection and segmentation are greatly affected by tumors’ location, shape, and image properties. In some situations, brain tumor detection and segmentation processes are greatly complicated and far from being completely resolved. The accuracy of the segmentation process significantly influences the diagnosis process, such as abnormal tissue detection, disease classification, and assessment. However, medical images, in particular, the Magnetic Resonance Imaging (MRI), often include undesirable artefacts such as noise, density inhomogeneity, and partial volume effects. Although many segmentation methods have been proposed, the accuracy of the segmentation results can be further improved. Subsequently, this study attempts to provide very important properties about the size, initial location and shape of tumors known as Region of Interest (RoI) to kick-start the segmentation process. The MRI consists of a sequence of images (MRI slices) of a particular person and not one image. Our method chooses the best image among them based on the tumor size, initial location and shape to avoid the partial volume effects. The selected algorithms to test our method are Active Contour and Otsu Thresholding algorithms. Several experiments are conducted in this research using the BRATS standard dataset that consist of 100 samples. These experiments comprised of MRI slices of 65 patients. The proposed method is evaluated by the similarity coefficient as a standard measure using Dice, Jaccard, and BF scores. The results revealed that the Active Contour algorithm has higher segmentation accuracy when tested across the three different similarity coefficients. Moreover, the achieved results of the two algorithms verify the ability of the proposed method to choose the best RoIs of the MRI samples.

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.