Trust-based secure clustering in WSN-based intelligent transportation systems

Increasing the number of vehicles on roads leads to congestion and safety problems. Wireless Sensor Network (WSN) is a promising technology providing Intelligent Transportation Systems (ITS) to address these problems. Usually, WSN-based applications, including ITS ones, incur high communication overhead to support efficient connectivity and communication activities. In the ITS environment, clustering would help in addressing the high communication overhead problem. In this paper, we introduce a bio-inspired and trust-based cluster head selection approach for WSN adopted in ITS applications. A trust model is designed and used to compute a trust level for each node and the Bat Optimization Algorithm (BOA) is used to select the cluster heads based on three parameters: residual energy, trust value and the number of neighbors. The simulation results showed that our proposed model is energy efficient (i.e., its power consumption is more efficient than many well-known clustering algorithm such as LEACH, SEP, and DEEC under homogeneous and heterogeneous networks). In addition, the results demonstrated that our proposed model achieved longer network lifetime, i.e., nodes are kept alive longer than what LEACH, SEP and DEEC can achieve. Moreover, the the proposed model showed that the average trust value of selected Cluster Head (CH) is high under different percentage (30% and 50%) of malicious nodes

Personal identification based on mobile-based keystroke dynamics

This paper is addressing the personal identification problem by using mobile-based keystroke dynamics of touch mobile phone. The proposed approach consists of two main phases, namely feature selection and classification. The most important features are selected using Genetic Algorithm (GA). Moreover, Bagging classifier used the selected features to identify persons by matching the features of the unknown person with the labeled features. The outputs of all Bagging classifiers are fused to determine the final decision. In this experiment, a keystroke dynamics database for touch mobile phones is used. The database, which consists of four sets of features, is collected from 51 individuals and consists of 985 samples collected from males and females with different ages. The results of the proposed model conclude that the third subset of features achieved the best accuracy while the second subset achieved the worst accuracy. Moreover, the fusion of all classifiers of all ensembles will improve the accuracy and achieved results better than the individual classifiers and individual ensembles

Reliable Machine Learning Model for IIoT Botnet Detection

Due to the growing number of Internet of Things (IoT) devices, network attacks like denial of service (DoS) and floods are rising for security and reliability issues. As a result of these attacks, IoT devices suffer from denial of service and network disruption. Researchers have implemented different techniques to identify attacks aimed at vulnerable Internet of Things (IoT) devices. In this study, we propose a novel features selection algorithm FGOA-kNN based on a hybrid filter and wrapper selection approaches to select the most relevant features. The novel approach integrated with clustering rank the features and then applies the Grasshopper algorithm (GOA) to minimize the top-ranked features. Moreover, a proposed algorithm, IHHO, selects and adapts the neural network’s hyper parameters to detect botnets efficiently. The proposed Harris Hawks algorithm is enhanced with three improvements to improve the global search process for optimal solutions. To tackle the problem of population diversity, a chaotic map function is utilized for initialization. The escape energy of hawks is updated with a new nonlinear formula to avoid the local minima and better balance between exploration and exploitation. Furthermore, the exploitation phase of HHO is enhanced using a new elite operator ROBL. The proposed model combines unsupervised, clustering, and supervised approaches to detect intrusion behaviors. The N-BaIoT dataset is utilized to validate the proposed model. Many recent techniques were used to assess and compare the proposed model’s performance. The result demonstrates that the proposed model is better than other variations at detecting multiclass botnet attacks

Mapping Agricultural Soil in Greenhouse Using an Autonomous Low-Cost Robot and Precise Monitoring

Our work is focused on developing an autonomous robot to monitor greenhouses and large fields. This system is designed to operate autonomously to extract useful information from the plants based on precise GPS localization. The proposed robot is based on an RGB camera for plant detection and a multispectral camera for extracting the different special bands for processing, and an embedded architecture integrating a Nvidia Jetson Nano, which allows us to perform the required processing. Our system uses a multi-sensor fusion to manage two parts of the algorithm. Therefore, the proposed algorithm was partitioned on the CPU-GPU embedded architecture. This allows us to process each image in 1.94 s in a sequential implementation on the embedded architecture. The approach followed in our implementation is based on a Hardware/Software Co-Design study to propose an optimal implementation. The experiments were conducted on a tomato farm, and the system showed that we can process different images in real time. The parallel implementation allows to process each image in 36 ms allowing us to satisfy the real-time constraints based on 5 images/s. On a laptop, we have a total processing time of 604 ms for the sequential implementation and 9 ms for the parallel processing. In this context, we obtained an acceleration factor of 66 for the laptop and 54 for the embedded architecture. The energy consumption evaluation showed that the prototyped system consumes a power between 4 W and 8 W. For this raison, in our case, we opted a low-cost embedded architecture based on Nvidia Jetson Nano

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps, for the miss-valued attributes, we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes. However, in order to ensure the model optimality, we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers. The preprocessing step led to 161 out of 279 attributes (features). Thereafter, a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms. In short, our study comprises three main blocks: (1) sensing data and preprocessing; (2) feature queuing, selection, and extraction; and (3) the predictive model. Our proposed method improves classification performance in terms of accuracy, F1 measure, recall, and precision when compared to state-of-the-art techniques. It achieves 98.5% accuracy for binary class mode and 98.9% accuracy for categorized class mode

Secure and Robust Fragile Watermarking Scheme for Medical Images

Over the past decade advances in computer-based communication and health services, the need for image security becomes urgent to address the requirements of both safety and non-safety in medical applications. This paper proposes a new fragile watermarking based scheme for image authentication and self-recovery for medical applications. The proposed scheme locates image tampering as well as recovers the original image. A host image is broken into 4×4 blocks and Singular Value Decomposition (SVD) is applied by inserting the traces of block wise SVD into the Least Significant Bit (LSB) of the image pixels to figure out the transformation in the original image. Two authentication bits namely block authentication and self-recovery bits were used to survive the vector quantization attack. The insertion of self-recovery bits is determined with Arnold transformation, which recovers the original image even after a high tampering rate. SVD-based watermarking information improves the image authentication and provides a way to detect different attacked area. The proposed scheme is tested against different types of attacks such are text removal attack, text insertion attack, and copy and paste attack

Synergic Deep Learning For Smart Health Diagnosis Of Covid-19 For Connected Living And Smart Cities

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods

Resilience and quality of work life among academic faculties in Gulf Countries (Qatar, Saudi Arabia and Oman)

يهدف البحث الحالي إلى الكشف عن العلاقة بين الصمود النفسي وجودة حياة العمل لدى عينة من أعضاء هيئة التدريس العاملين بالجامعات الخليجية في دول؛ قطر، والسعودية، وعمان. كما يهدف إلى استقصاء الفروق بين عينة الدراسة في الصمود النفسي وجودة حياة العمل في ضوء بعض المتغيرات الديموغرافية (النوع، والتخصص العلمي)، وإمكانية التنبؤ بجودة حياة العمل من خلال الصمود النفسي. وتكونت عينة البحث من عدد 110 أعضاء من هيئة التدريس الجامعي بدول الخليج كالتالي: قطر 34، السعودية 39، عمان 37. طبق الباحث مقياس الصمود النفسي (Smith et al., 2008) ومقياس جودة حياة العمل (Swamy, Nanjundeswaraswamy, Rashmi, 2015) بعد تعريبيهما والتحقق من صدقهما وثباتهما في البيئة العربية. وتشير نتائج الدراسة الأولية إلى وجود علاقة ارتباطية إيجابية دالة إحصائيًّا، وموجبة، بين الصمود النفسي وأبعاد جودة حياة العمل، كدرجة كلية، لدى عينة الدراسة. كما كشفت نتائج البحث عن وجود فروق بين عينة الدراسة في النوع والتخصص العلمي. كما أمكن التنبؤ ببعض أبعاد جودة حياة العمل من خلال الصمود النفسي.The current research aims to explore the relationship between resilience and quality of work life in a sample of academic faculties in gulf universities (Qatar, Saudi Arabia and Oman). The research aims also to test differences among the sample in resilience and quality of work life in the light of some demographic variables (gender and scientific specialty), in addition to investigating the ability of resilience in predicting quality of work life. Sample of the research included 110 (34 Qataris, 39 Saudis and 37 Omanis). Resilience scale (Smith et al., 2008) and quality of work life (Swamy, Nanjundeswaraswamy, Rashmi, 2015) were administrated after being translated and validated on the Arab settings. Preliminary results show a statistically significant positive relationship between resilience and quality of work life, and difference attributed to gender and academic specialty. Resilience predicted some dimensions of quality of work life