Enhancing The Quality Of Service In Mobile Networks Based On Nemo Basic Support Protocol

To fulfil the need for an uninterrupted Internet access along with the move in mobile networks as an alternative to the end-host mobility, the IETF NEMO working group was created to extend basic end-host mobility support in Mobile IPv6 (MIPv6). This group standardizes NEMO Basic Support Protocol (NEMO BS) to support network mobility. However, the handover latency in NEMO BS is high and the nested tunnels’ problem in the nested NEMO networks is not considered in the main specification of this protocol. Issues affecting the provision of QoS guarantees during the handoff process in NEMO BS are the handover latency, the disruption time, and the handoff failure and the packet loss

Design of Nanofluid-Based Spring Water/Tap Water and Nanoparticles of Fe2O3/ZnO as a Coolant for the Engines

In this work, an experimental system was established to measure the heat transfer characteristics, including the heat transfer coefficient, overall heat transfer, Nusselt number, and thermal conductivity. The investigation focused on spring water and tap water-based nanofluids containing Fe2O3 and ZnO nanoparticles with particle sizes of 50 nm and 70 nm, respectively. The experiments were conducted inside an automobile engine, studying the effects of varying nanoparticle volume fractions at a constant temperature. Fe2O3 and ZnO concentration in the respective based fluids was verified between 0.02 % and 0.08 % v/v and 0.01 and 0.07 %, respectively.  The spring water is not so far used in the previous studies and is much more available in Kurdistan region. Reynolds numbers of nanofluids inside the engine were considered between 1000 to 8000 in a different range as that of the literature review. Reynolds analogy for heat and momentum has been employed in this study. It was observed that the thermo-physio-mechanical properties of nanofluids increased with increase in the concentration of nanoparticles and Reynolds number. However, the friction factor decreased with increasing Reynolds number but increased with an increasing volume concentration of nanoparticles. Generally, the results showed that the enhancement of the effective heat transfer of the nanofluids reached 46%, the overall heat transfer coefficient reached 39%, thermal conductivity reached 21.35% and Nusselt number reached to 38%. at 0.08% volume fraction of Fe2O3/spring water nanofluid. Based on all previous parameters estimated, the designed nanofluids in this study could be classified as a workable nanofluid in many industry application

An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

The widespread usage of social media has led to the increasing popularity of online advertisements, which have been accompanied by a disturbing spread of clickbait headlines. Clickbait dissatisfies users because the article content does not match their expectation. Detecting clickbait posts in online social networks is an important task to fight this issue. Clickbait posts use phrases that are mainly posted to attract a user’s attention in order to click onto a specific fake link/website. That means clickbait headlines utilize misleading titles, which could carry hidden important information from the target website. It is very difficult to recognize these clickbait headlines manually. Therefore, there is a need for an intelligent method to detect clickbait and fake advertisements on social networks. Several machine learning methods have been applied for this detection purpose. However, the obtained performance (accuracy) only reached 87% and still needs to be improved. In addition, most of the existing studies were conducted on English headlines and contents. Few studies focused specifically on detecting clickbait headlines in Arabic. Therefore, this study constructed the first Arabic clickbait headline news dataset and presents an improved multiple feature-based approach for detecting clickbait news on social networks in Arabic language. The proposed approach includes three main phases: data collection, data preparation, and machine learning model training and testing phases. The collected dataset included 54,893 Arabic news items from Twitter (after preprocessing). Among these news items, 23,981 were clickbait news (43.69%) and 30,912 were legitimate news (56.31%). This dataset was pre-processed and then the most important features were selected using the ANOVA F-test. Several machine learning (ML) methods were then applied with hyperparameter tuning methods to ensure finding the optimal settings. Finally, the ML models were evaluated, and the overall performance is reported in this paper. The experimental results show that the Support Vector Machine (SVM) with the top 10% of ANOVA F-test features (user-based features (UFs) and content-based features (CFs)) obtained the best performance and achieved 92.16% of detection accuracy

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning.

Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients' chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity

Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84

Chebyshev Polynomial-Based Fog Computing Scheme Supporting Pseudonym Revocation for 5G-Enabled Vehicular Networks

he privacy and security of the information exchanged between automobiles in 5G-enabled vehicular networks is at risk. Several academics have offered a solution to these problems in the form of an authentication technique that uses an elliptic curve or bilinear pair to sign messages and verify the signature. The problem is that these tasks are lengthy and difficult to execute effectively. Further, the needs for revoking a pseudonym in a vehicular network are not met by these approaches. Thus, this research offers a fog computing strategy for 5G-enabled automotive networks that is based on the Chebyshev polynomial and allows for the revocation of pseudonyms. Our solution eliminates the threat of an insider attack by making use of fog computing. In particular, the fog server does not renew the signature key when the validity period of a pseudonym-ID is about to end. In addition to meeting privacy and security requirements, our proposal is also resistant to a wide range of potential security breaches. Finally, the Chebyshev polynomial is used in our work to sign the message and verify the signature, resulting in a greater performance cost efficiency than would otherwise be possible if an elliptic curve or bilinear pair operation had been employed

Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84%

The effect of sodium benzoate and sodium 4-(phenylamino)benzenesulfonate on the corrosion behavior of low carbon steel

The effect of sodium benzoate (SB) and sodium 4-(phenylamino) benzenesulfonate (SPABS) on the corrosion behavior of low carbon steel has been investigated using gravimetric method in the temperature range of 30-80 degrees C, velocity range of 1.44-2.02 m s(-1) and concentration range of 6.94 x 10(-4) to 4.16 x 10(-3) mol dm(-3) SB and 3.69 x 10(-4) to 2.06 x 10(-3) mol dm(-3) SPABS. Optimization of temperature, fluid velocity, and inhibitors concentration has been made. The obtained results indicate that the inhibition efficiency (w(IE) %) at 1.56 m s(-1) is not in excess of 81.5% at 4.16 x 10(-3) mol dm(-3) SB and 84.4% at 2.06 x 10(-3) mol dm(-3) SPABS. The inhibitive performance of these compounds showed an improvement with increasing concentration up to critical values of SB and SPABS; beyond these concentrations no further effectiveness is observed. These inhibitors retard the anodic dissolution of low carbon steel by protective layer bonding on the metal surface. The adsorption of SB and SPABS on the low carbon steel surface was found to obey the Freundlich isotherm model. The FT-IR spectroscopy was used to analyze the surface adsorbed film

A new fast local laplacian completed local ternary count (FLL-CLTC) for facial image classification

Face recognition is one of the most interesting areas of research areas because of its importance in authentication and security. Differentiating between different facial images is not easy because of the similarities in facial features. Human faces can also be covered obscured by eyeglasses, facial expressions and hairstyles can also be changed causing difficulty in finding similar faces. Thus, the need for powerful image features has become a critical issue in the face recognition systems. Many texture features have been used in these systems, including Local Binary Pattern (LBP), Local Ternary Pattern (LTP), Completed Local Binary Pattern (CLBP), Completed Local Binary Count (CLBC) and Completed Local Ternary Pattern (CLTP). In this paper, a new texture descriptor, namely, Completed Local Ternary Count (CLTC), is proposed by adding a threshold value for the CLBC to overcome its sensitivity to noise drawback. The CLTC is also enhanced by adding the Fast-Local Laplacian filter during the pre-processing stage to increase the discriminative property of the proposed descriptor. The proposed Fast-Local Laplacian CLTC (FLL-CLTC) texture descriptor is evaluated for face recognition task using five different face image datasets. The experimental results of the FLL-CLTC showed that the proposed FLL-CLTC outperformed the CLBP and CLTP texture descriptors in term of recognition accuracy. The FLL-CLTC achieved 99.1%, 86.93%, 93.21%, 84.92% and 99.15% with JAFFE, YALE, Georgia Tech, Caltech and ORL face image datasets, respectively

Diagnosis of Histopathological Images to Distinguish Types of Malignant Lymphomas Using Hybrid Techniques Based on Fusion Features

Malignant lymphoma is one of the types of malignant tumors that can lead to death. The diagnostic method for identifying malignant lymphoma is a histopathological analysis of lymphoma tissue images. Because of the similar morphological characteristics of the lymphoma types, it is difficult for doctors and specialists to manually distinguish the types of lymphomas. Therefore, deep and automated learning techniques aim to solve this problem and help clinicians reconsider their diagnostic decisions. Because of the similarity of the morphological characteristics between lymphoma types, this study aimed to extract features using various algorithms and deep learning models and combine them together into feature vectors. Two datasets have been applied, each with two different systems for the reliable diagnosis of malignant lymphoma. The first system was a hybrid system between DenseNet-121 and ResNet-50 to extract deep features and reduce their dimensions by the principal component analysis (PCA) method, using the support vector machine (SVM) algorithm for classifying low-dimensional deep features. The second system was based on extracting the features using DenseNet-121 and ResNet-50 and combining them with the hand-crafted features extracted by gray level co-occurrence matrix (GLCM), fuzzy color histogram (FCH), discrete wavelet transform (DWT), and local binary pattern (LBP) algorithms and classifying them using a feed-forward neural network (FFNN) classifier. All systems achieved superior results in diagnosing the two datasets of malignant lymphomas. An FFNN classifier with features of ResNet-50 and hand-crafted features reached an accuracy of 99.5%, specificity of 100%, sensitivity of 99.33%, and AUC of 99.86% for the first dataset. In contrast, the same technique reached 100% for all measures to diagnose the second dataset