Showing papers in "Egyptian Informatics Journal in 2021"

TL;DR: The goal was to counter high-class imbalance so that the model can accurately predict underrepresented classes in multi-class image datasets and achieve an accuracy score of over 93% with class weight, SMOTE and focal loss with deep convolutional neural networks from scratch.

TL;DR: A review is presented to identify the health organizations’ privacy and security concerns and to examine solutions that could address the various concerns that have been identified and the IT security incidents that have taken place in healthcare settings.

TL;DR: A comprehensive survey on evolution, prevention and mitigation of Ransomware in IoT context is provided and is expected to be useful for researchers and practitioners who are involved in developing solutions for IoT security.

TL;DR: It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO and the overall results proved the ability of LPB in improving the initial population and converging towards the global optima.

TL;DR: The proposed approach outperforms recent works in multi-modal emotion recognition field and achieves recognition accuracies 96.13%, and 96.79% for valence, and arousal classes respectively.

TL;DR: This paper proposes new similarity measures for PFSs based on two parameters t and p, where t identifies the level of uncertainty and p is the L p norm, and provides an application of a proposed similarity measure for medical diagnosis.

TL;DR: The ability of this work to enhance LEACH while prolonging the lifetime and improving the performance of WSN is clarified, especially in the setup phase where CH is selected randomly.

TL;DR: The result shows that the rate of packet loss has been reduced when compared with a one-hop way of key management solution, and the proposed work enhances the performance of the network by lowering the energy, computational overheads, and delay.

TL;DR: Comparing with the recent WSNs authentication schemes, the AAA-WSN scheme cannot only achieve strong security services such as user anonymity and mutual authentication, but also performs the perfect forward secrecy feature with high level of efficiency.

TL;DR: This research studied seven annotation schemes and their impact on the task of NER using five different classifiers and investigated the ability of recognizing consecutive entities and provided an analysis of the running-time complexity.

TL;DR: The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications.

TL;DR: A hybrid method for detecting denial of service attacks is developed and incorporate this information in routing decisions so that nodes that are part of a botnet can be quickly identified and excluded from the network.

TL;DR: This work introduced an unsupervised method to generate large-scale synthetic training data based on confusion function to increase the amount of training set and introduced a supervised NMT model for AGEC called SCUT AGEC, which becomes the first supervised AGEC system based on the convolutional sequence-to-sequence learning to outperforms the current state-of-the-art neural AGEC models.

TL;DR: This research proposes an architecture that builds semantic recommendations with the aid of virtual agents based on user requirements and preferences, assisting academia in seeking appropriate courses in a real-world setting.

TL;DR: An indoor navigation support system based on the Building Information Models (BIM) paradigm using the ifcOWL ontology, which translates the IFC schemas into Ontology Web Language (OWL).

TL;DR: Experimental evaluations show that the GA- based offloading algorithm outperforms both of round robin offloading and the PSO-based offloading algorithms, and effectively improves mobile applications response time.

TL;DR: This work tries to bridge the literature gap by proposing an extended set of linguistic rules for concept-feature pair extraction along with enhanced set ontological relations and results depict the effectiveness of the proposed system in terms of improved efficiency.

TL;DR: The proposed hierarchical multi-label Arabic text classification model with a machine learning approach outperforms all models considered in the experiments in terms of the computational cost, and shows a significant improvement compared with the state-of-the-art model.

TL;DR: Experimental results show the performance of the proposed method has better speed to process application for security of data transmission systems, with performance to authenticate approximately 5 ms from the client side and server side.

TL;DR: Deterministic optimization techniques have been analysed that are efficiently employed for machine learning applications and reported neural networks techniques based on Back Propagation Neural Network, Recurrent Neural Networks, RNNs Algorithm and Levenberg-Marquardt with several hybridized classification algorithms based on optimization techniques are indicated.

TL;DR: The proposed model addresses well-known security threats to the reputation trust model system, and is shown to deal with all possible potential attack threats by specifying the identity of users and tracking activities undertaken by them in order to easily track unauthorized consumers or attackers and to provide proof of any kind of data leakage.

TL;DR: In this article, a new deep learning-based model for personality prediction and classification using both data and classifier level fusion is proposed, which gets benefit from, transfer learning in natural language processing through leading pre-trained language models.

TL;DR: The proposed DTGCN model can effectively use the grammatical information of Chinese text to mine the hidden relationship in statements, it can improve the accuracy of ChineseText sentiment classification and provides a new research method for social network public opinion identification.

TL;DR: A generalization of chaotic systems using two-dimensional affine transformations with six introduced parameters to achieve scaling, reflection, rotation, translation and/or shearing so that the location of the strange attractor in space can be controlled without changing its chaotic dynamics.

TL;DR: Results prove that the proposed scheme will allow an increase in system performance of nearly 10% under all load conditions, and validate the effectiveness of proposition CSP2P, which is integrated into the implementation of the AODV routing protocol.

TL;DR: An iris based cancelable biometric cryptosystem to securely store the healthcare data of patients on the smart card and bind the secret encryption key with the cancelable iris template of the patient impeccably without the need to store it securely.

TL;DR: The proposed work suggests the mutual authentication-based protocol, helping in the handshake between two nodes, which involves the various subsections that include registering the user, token generation, sending and receiving messages through multiple algorithms.

TL;DR: An SDN-based monitoring algorithm to detect the performance anomaly and identify the bottleneck of the distributed application in the cloud data center using the support vector machine algorithm is proposed.

TL;DR: A security maturity model that classifies the organizations into five levels, which helps the organizations bridging the cybersecurity gaps and helps the cybersecurity auditors create a comprehensive plan for measuring the security level of the organization.

TL;DR: The most expansive autism ontology up to date is created (AutimsOnt) through utilizing the Protege ontological framework that can serve as the foundation to support a wide range of applications ranging from decision support systems for practicing physicians to text annotation processes that would allow for the creation of an interdisciplinary research platform where investigators can easily share and retrieve scientific findings.