A brain tumor is a tumor in the brain that has grown out of control, which is a dangerous condition for the human body. For later prognosis and treatment planning, the accurate segmentation and categorization of cancers are crucial. Radiologists must use an automated approach to identify brain tumors, since it is an error-prone and time-consuming operation. This work proposes conditional deep learning for brain tumor segmentation, residual network-based classification, and overall survival prediction using structural multimodal magnetic resonance images (MRI). First, we propose conditional random field and convolution network-based segmentation, which identifies non-overlapped patches. These patches need minimal time to identify the tumor. If they overlap, the errors increase. The second part of this paper proposes residual network-based feature mapping with XG-Boost-based learning. In the second part, the main emphasis is on feature mapping in nonlinear space with residual features, since residual features reduce the chances of loss information, and nonlinear space mapping provides efficient tumor information. Features mapping learned by XG-Boost improves the structural-based learning and increases the accuracy class-wise. The experiment uses two datasets: one for two classes (cancer and non-cancer) and the other for three classes (meningioma, glioma, pituitary). The performance on both improves significantly compared to another existing approach. The main objective of this research work is to improve segmentation and its impact on classification performance parameters. It improves by conditional random field and residual network. As a result, two-class accuracy improves by 3.4% and three-class accuracy improves by 2.3%. It is enhanced with a small convolution network. So, we conclude in fewer resources, and better segmentation improves the results of brain tumor classification.

Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets.

Content-based image retrieval (CBIR) is a recent method used to retrieve different types of images from repositories. The traditional content-based medical image retrieval (CBMIR) methods commonly used low-level image representation features extracted from color, texture, and shape image descriptors. Since most of these CBMIR systems depend mainly on the extracted features, the methods used in the feature extraction phase are more important. Features extraction methods, which generate inaccurate features, lead to very poor performance retrieval because of semantic gap widening. Hence, there is high demand for independent domain knowledge features extraction methods, which have automatic learning capabilities from input images. Pre-trained deep convolution neural networks (CNNs), the recent generation of deep learning neural networks, could be used to extract expressive and accurate features. The main advantage of these pre-trained CNNs models is the pre-training process for huge image data of thousands of different classes, and their knowledge after the training process could easily be transferred. There are many successful models of pre-trained CNNs models used in the area of medical image retrieval, image classification, and object recognition. This study utilizes two of the most known pre-trained CNNs models; ResNet18 and SqueezeNet for the offline feature extraction stage. Additionally, the highly accurate features extracted from medical images are used for the CBMIR method of medical image retrieval. This study uses two popular medical image datasets; Kvasir and PH2 to show that the proposed methods have good retrieval results. The retrieval performance evaluation measures of our proposed method have average precision of 97.75% and 83.33% for Kvasir and PH2 medical images respectively, and outperform some of the state-of-the-art methods in this field of study because these pre-trained CNNs have well trained layers among a huge number of image types. Finally, intensive statistical analysis shows that the proposed ResNet18-based retrieval method has the best performance for enhancing both recall and precision measures for both medical images.

Software-Defined Networking (SDN) is a trending architecture that separates controller and forwarding planes. This improves network agility and efficiency. The proliferation of the Internet of Things devices has increased traffic flow volume and its heterogeneity in contemporary networks. Since SDN is a flow-driven network, it requires the corresponding rule for each flow in the flowtable. However, the traffic heterogeneity complicates the rules update operation due to varied quality of service requirements and en-route behavior. Some flows are delay-sensitive while others are long-lived with a propensity to consume network buffers, thereby inflicting congestion and delays on the network. The delay-sensitive flows must be routed through a path with minimal delay, while congestion-susceptible flows are guided along a route with adequate capacity. Although several efforts were introduced over the years to efficiently route flows based on different QoS parameters, the current path selection techniques consider either link or switch operation during decisions. Incorporating composite path metrics with flow classification during path selection decisions has not been adequately considered. This paper proposes a technique based on composite metrics with flow classification to differentiate congestion-prone flows and reroute them along appropriate paths to avoid congestion and loss. The technique is integrated into the SDN controller to guide the selection of paths suitable to each traffic class. Compared to other works, the proposed approach improved the path load ratio by 25%, throughput by 35.6%, and packet delivery ratio by 31.7%.

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