Search

The cultivation of creativity is closely related to language learning. How to design the creativity promotion mechanism of English teaching in the public environment is the challenge faced by English teachers. With the advent of the era of big data, English teachers can apply the latest research results to classroom teaching. For example, the rational use of social media helps students to learn and communicate in the language, cultivate students' creativity in learning English, and improves the quality of teaching. Corporate social media has become the most important way for corporate employees to record their lives, express opinions, share and communicate, and it is also one of the reliable and real-time sources of big data that reflects the true state of English learners. Real, accurate, and timely enterprise social media big data samples contain an enormous educational value, providing more possibilities for educational research. From the perspective of value, through sentiment analysis, topic mining, social network analysis, etc. on social media big data, learner portraits can be realized, thereby providing decision-making reference and support for stakeholders. This paper first builds a learning interest classification model based on TCNN-GRU deep learning, collects experimental data sets from an online English learner's social media platform and performs learning interest classification and labeling, and then uses the TCNN-GRU model to determine the user's learning interest tendency. On this basis, the concept of learning interest index is further proposed, and a neural network-based corporate social platform English learner portrait model is established. The experimental results show that, compared with the traditional machine learning model, convolutional neural network model, and recurrent neural network model, the TCNN-GRU model can obtain better results in learning interest classification. [ABSTRACT FROM AUTHOR]

The identification of nonlinear system is studied to establish an accurate model of coordination system. The original dynamic fuzzy neural network (DFNN) is first used to identify the nonlinear system for finding the existing problems. Aiming at the problems found, two improvements are made. For the problem of too many pre-set parameters in the original algorithm, the fuzzy completeness − is introduced to allocate the parameters, and the width of the membership function is modified. The simulation results reveal that the fuzzy neural network (FNN) model not improved produces seven fuzzy rules, and the root mean square error (RMSE) of training is 0.0261, while the improved FNN model produces six fuzzy rules, and the RMSE of training is 0.0161. The improved network has more advantages in performance. The model proposed provides some references for the application of FNN in dynamic nonlinear systems. [ABSTRACT FROM AUTHOR]

COVID-19 was declared a global pandemic by the World Health Organisation (WHO) on 11th March 2020. Many researchers have, in the past, attempted to predict a COVID outbreak and its effect. Some have regarded time-series variables as primary factors which can affect the onset of infectious diseases like influenza and severe acute respiratory syndrome (SARS). In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the COVID-19 outbreak to and throughout Malaysia, Morocco and Saudi Arabia. We have made use of certain effective deep learning (DL) models for this purpose. We assessed some specific major features for predicting the trend of the existing COVID-19 outbreak in these three countries. In this study, we also proposed a DL approach that includes recurrent neural network (RNN) and long short-term memory (LSTM) networks for predicting the probable numbers of COVID-19 cases. The LSTM models showed a 98.58% precision accuracy while the RNN models showed a 93.45% precision accuracy. Also, this study compared the number of coronavirus cases and the number of resulting deaths in Malaysia, Morocco and Saudi Arabia. Thereafter, we predicted the number of confirmed COVID-19 cases and deaths for a subsequent seven days. In this study, we presented their predictions using the data that was available up to December 3rd, 2020. [ABSTRACT FROM AUTHOR]

Recently, the number of Internet of Things (IoT)-connected devices has increased daily. Consequently, cybersecurity challenges have increased due to the natural diversity of the IoT, limited hardware resources, and limited security capabilities. Intrusion detection systems (IDSs) play a substantial role in securing IoT networks. Several researchers have focused on machine learning (ML) and deep learning (DL) to develop intrusion detection techniques. Although ML is good for classification, other methods perform better in feature transformation. However, at the level of accuracy, both learning techniques have their own certain compromises. Although IDSs based on ML and DL methods can achieve a high detection rate, the performance depends on the training dataset size. Incidentally, collecting a large amount of data is one of the main drawbacks that limits performance when training datasets are lacking, and such methods can fail to detect novel attacks. Few-shot learning (FSL) is an emerging approach that is employed in different domains because of its proven ability to learn from a few training samples. Although numerous studies have addressed the issues of IDSs and improved IDS performance, the literature on FSL-based IDSs is scarce. Therefore, an investigation is required to explore the performance of FSL in IoT IDSs. This work proposes an IoT intrusion detection model based on a convolutional neural network as a feature extractor and a prototypical network as an FSL classifier. The empirical results were analyzed and compared with those of recent intrusion detection approaches. The accuracy results reached 99.44%, which shows a promising direction for involving FSL in IoT IDSs. [ABSTRACT FROM AUTHOR]

In order to solve the challenges brought by multi-source and cross-domain scenarios to online music education, this paper designs an online music education system based on advanced artificial intelligence technology, which can provide personalized learning course resource recommendations for music online learners. The system includes four layers, consisting of user interface layer, application module layer, function module layer and data storage layer. At the application module level, this paper proposes a music recommendation algorithm based on a personalized multimodal network model. The recommendation algorithm performs music information retrieval (MIR) based on the similarity judgment of the contour of music pitch and the overall change, and constructs a multimodal network model based on the user's preference for resources to achieve personalized music recommendation. This paper crawls more than one million music score data from a well-known music platform database in China to establish a dataset to evaluate the performance of this method. The comparison results with three existing works show that the method proposed in this paper has good performance and can provide users with suitable music recommendations. The artificial intelligence technology-driven online music education mechanism proposed in this paper has good prospects. [ABSTRACT FROM AUTHOR]

• This study was commenced by realizing the scope of wide scale adoption of cloud computing in the Saudi healthcare organizations to fulfil the increasing demand of modernizing healthcare services by the means of new technology adoption such as adoption of IoT, • The result shows that users' attitude towards using technology, Data security, compliance and reliability of the cloud computing services are important determining factors in the adoption of cloud computing in Saudi healthcare organizations. • The distinction in the finding regarding Data privacy and Data control in Saudi healthcare organizational context is a clear manifestation of the fact that there is need for policy formation for specific data protection legislation in Saudi Arabia The population in Saudi Arabia is expected to reach 40 million by 2025. Consequently, healthcare information will become critical to manage. Despite the fact that adopting cloud computing in the Saudi healthcare organizations can facilitate cost reduction, capacity building, institutional interoperability, and get access to data analytics, the adoption rate is very low. Hence, a new model is proposed to adopt cloud computing in the Saudi healthcare organization. The novelty of this work comes from using a quantitative method to test users' attitudes, data security, data control, data privacy, compliance, and reliability influence on the cloud computing adoption intention in the context of Saudi Arabian healthcare organizations. Partial Least Squares (PLS) method based Structural Equation Modeling (SEM) was used for model development. About 160 respondents from the relevant health organizations participated. The result shows that the attitude towards using technology, data security, compliance, and reliability of the cloud computing services are important determining factors in the adoption of cloud computing in Saudi healthcare organizations. However, the distinction in the findings regarding Data privacy and Data control in the Saudi healthcare organizational context is a clear manifestation of the fact that there is a need for policy formation for data privacy, data control, and data protection legislation in Saudi Arabia. Therefore, raising awareness regarding the practice of data privacy and data control policies among IT managers is essential. Future study should use a more holistic and industry-specific framework such as the technology-organization-environment (TOE) framework to find new influencing factors from the domains of technological context, the organizational context, and the environmental context. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) are designed to provide connectivity among diverse miniature body sensors that support different Internet of Things (IoT) healthcare applications. Among diverse body sensors, WBANs exploit in-vivo sensor nodes that detect and collect the required biometric data of certain physiological change inside the human body, and transmits the sensed data utilizing wireless communication. However, sensing and wireless communication activities of in-vivo sensors produce heat and could result thermal damage to the human tissue if the sensing and communication continues for a long period. Furthermore, Quality of Service (QoS) provisioning for diverse traffic types is another striking requirement for WBANs. These pressing yet conflicting concerns trigger the design of ThMAC—a Thermal aware duty cycle MAC protocol for IoT healthcare. The protocol regulates the communication operation of a body sensor based on estimated temperature surrounding a tissue to maintain moderate temperature level in a body, also avoiding hotspot. Exploiting both contention-based and contention free channel access mechanisms, ThMAC introduces a superframe structure, where disjoint periods are allocated for diverse traffic types to achieve QoS provisioning. Moreover, ThMAC ensures a reliable and timely delivery of sporadically generated emergency data through an emergency data management mechanism. ThMAC performance is evaluated through computer simulations in terms of thermal rise, energy consumption as well as QoS metrics such as delay and reliability. The results show superior performance of ThMAC compared to that of IEEE 802.15.6. [ABSTRACT FROM AUTHOR]

The modelling and prediction of extreme temperature changes in enclosed compartments is a domain with applications ranging from residential fire alarms, industrial temperature sensors to search and rescue personnel safety systems. The spread of fire in enclosed compartments is a highly uncertain and nonlinear process. Hence, in safety-critical cases, any false negatives pose a serious threat to the safety of individuals such as firefighters that are engaged in rescue activities. This work aims to model the nonlinear fire spread behaviour as a temporal, deep learning-based regressive methodology. The objective is to efficiently identify abrupt and extreme temperature changes that often result in increases of 300+ °C. A major challenge in such time-series models is that of learning from historic time-series samples which are known to suffer from high noise levels, outliers and data imbalance. This work contributes on the development of a convolutional neural network (CNN)-long short-term memory (LSTM) methodology to handle temperature data originating from body-mounted and fixed sensors and develop a temperature increase warning mechanism. The main contribution exploits the contextualisation ability of CNN-LSTM to predict temperature changes in windows of 5–120 s. The model identifies the spatial temperature change patterns via a CNN encoder, which are then fed into an LSTM network. This regression mechanism is trained and validated against a set of unique fire spread conditions and involved live tests ranging from containers to residential and industrial units. The model's performance was evaluated with MAPE sensitivity analysis against data originating from body-mounted sensors and third-party NIST datasets. The outcome showed an error ranging from 0.89 to 2.05% to 5.46% and 6.23%, respectively. The model efficacy was also evaluated against a range of input–output temperature ranges from 5, 30 to 120-s windows and showed a FN rate of 2.15% for pre-alarm-to-normal and 3.11% for alarm-to-pre-alarm cases in body-mounted sensors and a higher FN rate of 5.14% reported for pre-alarm-to-normal case for the raised platfor sensor tests. [ABSTRACT FROM AUTHOR]

Detecting cyber intrusions in network traffic is a tough task for cybersecurity. Current methods struggle with the complexity of understanding patterns in network data. To solve this, we present the Hybrid Deep Learning Intrusion Detection Model (HD-IDM), a new way that combines GRU and LSTM classifiers. GRU is good at catching quick patterns, while LSTM handles long-term ones. HD-IDM blends these models using weighted averaging, boosting accuracy, especially with complex patterns. We tested HD-IDM on four datasets: CSE-CIC-IDS2017, CSE-CIC-IDS2018, NSL KDD, and CIC-DDoS2019. The HD-IDM classifier achieved remarkable performance metrics on all datasets. It attains an outstanding accuracy of 99.91%, showcasing its consistent precision across the dataset. With an impressive precision of 99.62%, it excels in accurately categorizing positive cases, crucial for minimizing false positives. Additionally, maintaining a high recall of 99.43%, it effectively identifies the majority of actual positive cases while minimizing false negatives. The F1-score of 99.52% emphasizes its robustness, making it the top choice for classification tasks requiring precision and reliability. It is particularly good at ROC and precision/recall curves, discriminating normal and harmful network activities. While HD-IDM is promising, it has limits. It needs labeled data and may struggle with new intrusion methods. Future work should find ways to handle unlabeled data and adapt to emerging threats. Also, making HD-IDM work faster for real-time use and dealing with scalability challenges is key for its broader use in changing network environments. [ABSTRACT FROM AUTHOR]

For the problem of class-imbalance in the operation monitoring data of wind turbine (WT) pitch connecting bolts, an improved Borderline-SMOTE oversampling method based on "two-step decision" with adaptive selection of synthetic instances (TSDAS-SMOTE) is proposed. Then, TSDAS-SMOTE is combined with XGBoost to construct a WT pitch connection bolt fault detection model. TSDAS-SMOTE generates new samples by "two-step decision making" to avoid the problem of class–class boundary blurring that Borderline-SMOTE tends to cause when oversampling. First, the nearest neighbor sample characteristics are perceived by the fault class samples in the first decision step. If the characteristics of this fault class sample are different from the characteristics of all its nearest neighbor samples, the fault class sample is identified as interference and filtered. Second, the faulty class samples in the boundary zone are extracted as synthetic instances to generate new samples adaptively. Finally, the normal class samples in the boundary zone are used to perceive the unqualified new generated samples in the boundary zone based on the minimum Euclidean distance characteristics, and these unqualified samples are eliminated. For the second step of decision making, since the first step decision removes some of the newly generated samples, the remaining fault class samples without interference samples and boundary zone samples are used as synthetic instances to continue adaptively generating new samples. Thus, a balanced data set with clear class–class boundary zone is obtained, which is then used to train a WT pitch connection bolt fault detection model based on the XGBoost algorithm. The experimental results show that compared with six popular oversampling methods such as Borderline-SMOTE, Cluster-SMOTE, K -means-SMOTE, etc., the fault detection model constructed by the proposed oversampling method is better than the compared fault detection models in terms of missed alarm rate (MAR) and false alarm rate (FAR). Therefore, it can well achieve the fault detection of large WT pitch connection bolts. [ABSTRACT FROM AUTHOR]

Abnormal detection of wind turbine converter (WT) is one of the key technologies to ensure long-term stable operation and safe power generation of WT. The number of normal samples in the SCADA data of WT converter operation is much larger than the number of abnormal samples. In order to solve the problem of low abnormal data and low recognition rate of WTs, we propose a sample enhancement method for WT abnormality detection based on an improved conditional Wasserstein generative adversarial network. Since the anomaly samples of WT converters are few and difficult to obtain, the CWGANGP oversampling method is constructed to increase the anomaly samples in the WT converter dataset. The method adds additional category labels to the inputs of the generative and discriminative models of the generative adversarial network, constrains the generative model to generate few types of anomalous samples, and enhances the generative model's ability to generate few types of anomalous samples, enabling data generation in a prescribed direction. The smooth continuous Wasserstein distance is used instead of JS divergence as a distance metric to measure the probability distribution of real and generated data in the conditional generative response network and reduce pattern collapse. The gradient constraint is added to the CWGANGP model to enhance the convergence of the WGAN model, so that the generative model can synthesize minority class anomalous samples more effectively and accurately under the condition of unbalanced sample data categories. The quality of anomalous sample generation is also improved. Finally, the anomaly detection is made on the actual operating variator dataset for the unbalanced dataset and the dataset after reaching Nash equilibrium. The experimental results show that the method used in this paper has lower MAR and FAR in WT converter anomaly detection compared with other oversampling data balance optimization methods such as SMOTE, RandomOverSampler, GAN, etc. The method can be well implemented for anomaly detection of large wind turbines and can be better applied in WT intelligent systems. [ABSTRACT FROM AUTHOR]

Alzheimer's disease has become a major concern in the healthcare domain as it is growing rapidly. Much research has been conducted to detect it from MRI images through various deep learning approaches.However, the problems of the availability of medical data and preserving the privacy of patients still exists. To mitigate this issue in Alzheimer's disease detection, we implement the federated approach, which is found to be more efficient, robust, and consistent compared with the conventional approach. For this, we need deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilize two publicly available datasets (OASIS and ADNI) and design various cases to evaluate the performance of the federated approach. The federated approach achieves better accuracy and sensitivity compared with the conventional approaches in most of the cases. Moreover, the robustness of the proposed approach is also found to be better than the conventional approach. In our federated approach, MobileNet, a low-cost transfer learning architecture, achieves the highest 95.24%, 81.94%, and 83.97% accuracy in the OASIS, ADNI, and merged (ADNI + OASIS) test sets, which is much higher than the achieved performance in the conventional approach. Furthermore, in the proposed approach, only the weights of the model are shared, which keeps the original MRI images in their respective hospital or institutions, preserving privacy in the healthcare domain. [ABSTRACT FROM AUTHOR]

This article presents an indirect neural-based finite-time integral sliding mode control algorithm for the reference trajectory tracking guidance of Mars entry vehicle under uncertainties. The proposed controller is developed as a combination of finite-time integral sliding mode controller and indirect neural identification. The finite-time integral sliding mode controller is designed by constructing a new type of finite-time integral sliding mode surface to prevent the singularity problem. Moreover, the neural network (NN) is combined with the finite-time integral sliding mode controller to identify the lumped uncertainty and attenuate the chattering phenomenon. Particularly, the concept of indirect neural identification is adopted and only a single adaptive parameter is required to be learned online. In this way, the proposed controller is not only strongly robust against aerodynamic and density uncertainties, but also computationally simple for onboard implementations. Stability argument indicates that the proposed controller can ensure the radial distance tracking error and its time differentiation regulate to the small residual sets around zero in finite time. Lastly, the effectiveness and excellent guidance performance of the proposed control algorithm are demonstrated through simulation studies on a Mars Science Laboratory-type (MSL-type) entry vehicle. [ABSTRACT FROM AUTHOR]

In this paper, a fractional neural integral sliding-mode controller based on the Caputo-Fabrizio derivative and Riemann–Liouville integral for a robot manipulator mounted on a free-floating satellite is proposed and developed. The mathematical model is obtained by using the Euler–Lagrange formalism and generalized through the Caputo-Fabrizio derivative. The conventional neural integral sliding-mode controller reported in the literature were also developed to make a comparative analysis between both controllers. The controllers are tuned using a swarm intelligence algorithm. External disturbances are also considered. Simulation results show the superiority and robustness of the proposed control system for trajectory tracking tasks through the root-mean-square error. Additionally, two additional reference trajectories were considered. In this sense, the numerical simulations were done by using the same orders and gains, showing the robustness of our fractional-order control strategy under different operating conditions. Further, the proposed controller uses the fractional derivative in the sense of Caputo-Fabrizio which has not been used enough in the area of robotics, so this is a start to analyse the contributions that arise when using said derivative in this robotic. [ABSTRACT FROM AUTHOR]

Deep learning is expanding and continues to evolve its capabilities toward more accuracy, speed, and cost-effectiveness. The core ingredients for getting its promising results are appropriate data, sufficient computational resources, and best use of a particular algorithm. The application of these algorithms in medical image analysis tasks has achieved outstanding results compared to classical machine learning approaches. Localizing the area-of-interest is a challenging task that has vital importance in computer aided diagnosis. Generally, radiologists interpret the radiographs based on their knowledge and experience. However, sometimes, they can overlook or misinterpret the findings due to various reasons, e.g., workload or judgmental error. This leads to the need for specialized AI tools that assist radiologists in highlighting abnormalities if exist. To develop a deep learning driven localizer, certain alternatives are available within architectures, datasets, performance metrics, and approaches. Informed decision for selection within the given alternative can lead to batter outcome within lesser resources. This paper lists the required components along-with explainable AI for developing an abnormality localizer for X-ray images in detail. Moreover, strong-supervised vs weak-supervised approaches have been majorly discussed in the light of limited annotated data availability. Likewise, other correlated challenges have been presented along-with recommendations based on a relevant literature review and similar studies. This review is helpful in streamlining the development of an AI based localizer for X-ray images while extendable for other radiological reports. [ABSTRACT FROM AUTHOR]

The effect of the COVID-19 pandemic on crude oil prices just faded; at this moment, the Russia–Ukraine war brought a new crisis. In this paper, a new application is developed that predicts the change in crude oil prices by incorporating these two global effects. Unlike most existing studies, this work uses a dataset that involves data collected over twenty-two years and contains seven different features, such as crude oil opening, closing, intraday highest value, and intraday lowest value. This work applies cross-validation to predict the crude oil prices by using machine learning algorithms (support vector machine, linear regression, and rain forest) and deep learning algorithms (long short-term memory and bidirectional long short-term memory). The results obtained by machine learning and deep learning algorithms are compared. Lastly, the high-performance estimation can be achieved in this work with the average mean absolute error value over 0.3786. [ABSTRACT FROM AUTHOR]

The finite-time tracking control issue for a class of nonlinear pure-feedback system with prescribed performance and unknown hysteresis is investigated in this work. To solve the Bouc-Wen hysteresis with unknown parameters and direction conditions, the Nussbaum function and auxiliary virtual control function are used. A finite-time performance function is applied in prescribed performance, which can make the tracking error is limited to a pre-given boundary in finite time. Moreover, the mean-value theorem is applied to solve the difficulty of pure-feedback form. Combined with backstepping technique, an adaptive tracking control scheme is designed to make sure that all the closed-loop signals are bounded and that the tracking error converges to pro-given boundary. Finally, a simulation example is presented to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In today's sophisticated global marketplace, supply chains are complex nonlinear systems in the presence of different types of uncertainties, including supply-demand and delivery uncertainties. Though up to now, some features of these systems are studied, there are still many aspects of these systems which need more attention. This necessitates more research studies on these systems. Hence, in this study, we propose a variable-order fractional supply chain network. The dynamic of the system is investigated using the Lyapunov exponent and bifurcation diagram. It is demonstrated that a minor change in the system's fractional-derivative may dramatically affect its behavior. Then, distributed consensus tracking of the multi-agent network is studied. To this end, a control technique based on the sliding concept and Chebyshev neural network estimator is offered. The system's stability is demonstrated using the Lyapunov stability theorem and Barbalat's lemma. Finally, through numerical results, the proposed controller's excellent performance for distributed consensus tracking of multi-agent supply chain network is demonstrated. [ABSTRACT FROM AUTHOR]

In this study, a novel heuristic computing technique is presented to solve bioinformatics problem for the corneal shape model of eye surgery using Morlet wavelet artificial neural network optimized by the global search schemes, i.e. genetic algorithm (GA), local search technique, i.e. sequential quadratic programming (SQP) and the hybrid of GA-SQP. To measure the performance of the design network configuration, different cases based on nonlinear second-order differential equations governing the corneal model have been solved effectively. The numerical procedure of Adams method is implemented for the comparison purpose of the presented outcomes of the stochastic solver, which shows the worth of the present scheme based on accuracy and convergence with negligible values of absolute error in the range 10 − 7 to 10 − 8 . Furthermore, statistical measures are presented based on "mean absolute error", "root mean square error" and "coefficient of Theil's inequality" which additionally endorsed consistently accurate performance of integrated intelligent computing framework for solving the corneal shape model. [ABSTRACT FROM AUTHOR]

In this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to estimate unknown nonlinear functions. Then, an event‐triggering mechanism based on the tracking error is introduced to avoid the over‐consumption of communication resources. Furthermore, command filters are employed to solve the problem of complexity explosion that exists in conventional backstepping control design, and the error compensation signals are designed to reduce the errors caused by the filters. Considering that the unknown control gain and input saturation exist in many practical applications, a Nussbaum‐type function is thus introduced into the controller design to address these challenging issues. Finally, stability of the closed‐loop system is strictly proven under a standard Lyapunov stability analysis framework. The effectiveness of the proposed control scheme is illustrated by a simulation example. [ABSTRACT FROM AUTHOR]

In this paper, an output‐based event‐triggered control problem of discrete‐time networked control systems (NCSs) subject to bilateral packet dropouts is investigated. In view of stochastic sequences of packet dropouts in the measurement channels (from sensors to controller) and the control channels (from controller to actuators), the NCS is converted into a closed‐loop stochastic parameter system. In the aid of a Lyapunov functional based on stochastic variables, the sufficient conditions on design of a dynamic output feedback control law and bilateral event‐triggering conditions are derived to guarantee the exponentially mean‐square stability for NCSs. Furthermore, an improved iterative algorithm is given to obtain the output‐based control law and event‐triggering parameters from a set of nonconvex inequalities. Finally, a numerical example and the corresponding simulation results are given to illustrate the validity and applicability of the developed techniques. [ABSTRACT FROM AUTHOR]

The aim of this study is to present the numerical solutions of the Liénard nonlinear model by designing the structure of the computational Gudermannian neural networks (GNNs) along with the global/local search efficiencies of genetic algorithms (GAs) and interior-point algorithm (IPA), i.e. GNNs–GAs–IPA. A merit function in terms of differential system and its boundary conditions is designed and optimization is performed by using the proposed computational procedures of GAs–IPA to solve the Liénard nonlinear differential system. Three different highly nonlinear examples based on the Liénard differential system have been tested to check the competence, exactness and proficiency of the proposed computational paradigm of GNNs–GAs–IPA. The statistical performances in terms of different operators have been provided to check the reliability, consistency and stability of the computational GNNs–GAs–IPA. The plots of the absolute error, performance measures, results comparison, convergence analysis based on different operators, histograms and boxplots are also illustrated. Moreover, statistical gauges using minimum, mean, maximum, semi-interquartile range, standard deviation and median are also provided to authenticate the optimal performance of the GNNs–GAs–IPA. [ABSTRACT FROM AUTHOR]

Stochastic robustness of discrete noises has already been proposed and studied in the previous work. Nevertheless, the significant phenomenon of delays is left in the basket both in the deterministic and the stochastic parts of the considered equation by the existing work. Stimulated by the above, this paper is devoted to studying the stochastic robustness issue of delayed discrete noises for delay differential equations, including the issues of robust stability and robust boundedness. [ABSTRACT FROM AUTHOR]

Image retrieval techniques are becoming famous due to the vast availability of multimedia data. The present image retrieval system performs excellently on labeled data. However, often, data labeling becomes costly and sometimes impossible. Therefore, self-supervised and unsupervised learning strategies are currently becoming illustrious. Most of the self/unsupervised strategies are sensitive to the number of classes and can not mix labeled data on availability. In this paper, we introduce AutoRet, a deep convolutional neural network (DCNN) based self-supervised image retrieval system. The system is trained on pairwise constraints. Therefore, it can work in self-supervision and can also be trained on a partially labeled dataset. The overall strategy includes a DCNN that extracts embeddings from multiple patches of images. Further, the embeddings are fused for quality information used for the image retrieval process. The method is benchmarked with three different datasets. From the overall benchmark, it is evident that the proposed method works better in a self-supervised manner. In addition, the evaluation exhibits the proposed method's performance to be highly convincing while a small portion of labeled data are mixed on availability. [ABSTRACT FROM AUTHOR]

This paper introduces a fractional-order financial risk system for the first time. Employing well-known tools and analyses such as bifurcations diagrams and spectral entropy, the dynamical behaviors of the system associated with fractional derivative are investigated. The impacts of the fractional derivative on the system's behavior and its dynamical feature are shown. Then, tracking control and stabilization of the systems are studied. As it is obvious, the existence of faults and failures in the process of control of financial and economic systems is undeniable — this issue necessitates applying proper control techniques for the systems. So as to achieve appropriate results in the control of fractional financial risk system, two finite-time fault-tolerant controllers are proposed, namely, finite-time active fault-tolerant control and finite-time passive fault-tolerant control. Not only do these techniques force the system to reach desired values in finite time, but also, the proposed techniques are robust against uncertainties, faults, and failures in actuators. Through finite-time observers, the effects of all uncertainties are taken to account in the active controller. Finally, numerical simulations of tracking control and stabilization are presented. For numerical simulations, the fractional financial risk system is considered to be in the presence of unknown disturbance as well as faults and failures in actuators. It is assumed that the system is in the presence of various types of actuator faults. Numerical results affirm the ability of the offered control techniques for pushing the states of the fractional-order risk system to the desired value in a short period of time. [ABSTRACT FROM AUTHOR]

Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full segmentation and quantification of the LV myocardium border is even more challenging because of different shapes and sizes of the myocardium border zone. The foremost purpose of this research is to design a precise automatic segmentation technique employing deep learning models for the myocardium border using cardiac magnetic resonance imaging (MRI). The ASPP module (Atrous Spatial Pyramid Pooling) was integrated with a proposed 2D-residual neural network for segmentation of the myocardium border using a cardiac MRI dataset. Further, the ensemble technique based on a majority voting ensemble method was used to blend the results of recent deep learning models on different set of hyperparameters. The proposed model produced an 85.43% dice score on validation samples and 98.23% on training samples and provided excellent performance compared to recent deep learning models. The myocardium border was successfully segmented across diverse subject slices with different shapes, sizes and contrast using the proposed deep learning ensemble models. The proposed model can be employed for automatic detection and segmentation of the myocardium border for precise quantification of reflow, myocardial infarction, myocarditis, and h cardiomyopathy (HCM) for clinical applications. [ABSTRACT FROM AUTHOR]

This study is concerned with dynamical investigation, electrical circuit realization, and emulation of a fractional three-echelon supply chain system. In the financial realm, long-term memory effects play important roles. On the other hand, most financial systems are uncertain with unknown nonlinear dynamics. However, most studies on nonlinear supply chains neither consider the fractional calculus nor take advantage of state-of-the-art emulation methods. These issues motivated the current study. A fractional-order chaotic three-echelon supply chain system is studied. At first, the system's dynamic is studied through Lyapunov exponent and bifurcation diagrams. It is shown that a slight deferent in some parameters of the system can dramatically change the behavior of the system. Then, a real-time analog circuit is designed and implemented to investigate the system's chaotic behavior. This way, the system's chaotic attractors are empirically demonstrated. Finally, emulation and interpolation of the fractional-order chaotic system using the Gaussian process have been studied, and its luminous results have been presented. [ABSTRACT FROM AUTHOR]

Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. [ABSTRACT FROM AUTHOR]

Due to the vital role of financial systems in today's sophisticated world, applying intelligent controllers through management strategies is of crucial importance. We propose to formulate the control problem of the macroeconomic system as an optimization problem and find optimal actions using a reinforcement learning algorithm. Using the Q-learning algorithm, the best optimal action for the system is obtained, and the behavior of the system is controlled. We illustrate that it is possible to control the nonlinear dynamics of the macroeconomic systems using restricted actuation. The highly effective performance of the proposed controller for uncertain systems is demonstrated. The simulation results evidently confirm that the proposed controller satisfies the expected performance. In addition, the numerical simulations clearly confirm that even when we confined the control actions, the proposed controller effectively finds optimal actions for the nonlinear macroeconomic system. [ABSTRACT FROM AUTHOR]

Over the last years, distributed consensus tracking control has received a lot of attention due to its benefits, such as low operational costs, high resilience, flexible scalability, and so on. However, control methods that do not consider faults in actuators and control agents are impractical in most systems. There is no research in the literature investigating the consensus tracking of supply chain networks subject to disturbances and faults in control input. Motivated by this, the current research studies the fault-tolerant, finite-time, and smooth consensus tracking problems for chaotic multi-agent supply chain networks subject to disturbances, uncertainties, and faults in actuators. The chaotic attractors of a supply chain network are shown, and its corresponding multi-agent system is presented. A new control technique is then proposed, which is suitable for distributed consensus tracking of nonlinear uncertain systems. In the proposed scheme, the effects of faults in control actuators and robustness against unknown time-varying disturbances are taken into account. The proposed technique also uses a finite-time super-twisting algorithm that avoids chattering in the system's response and control input. Lastly, the multi-agent system is considered in the presence of disturbances and actuator faults, and the proposed scheme's excellent performance is displayed through numerical simulations. [ABSTRACT FROM AUTHOR]

Brain connectivity-based methods are efficient and reliable for assessing the mental workload during high task demands as the human brain is functionally interconnected during any psychological task. On the other hand, the graph theory approach is a mathematical study that draws the pairwise relationships between objects. This paper covers the deployment of graph theory concepts on the brain connectivity methods to find the complex underlying behaviors of the brain in the simplest way. Furthermore, in this work, mental workload assessments on multimedia animations were performed using a brain connectivity approach based on partial directed coherence (PDC) with graph theory analysis. Electroencephalography (EEG) data were collected from 34 adult participants at baseline and during multimedia learning tasks. The results revealed that the EEG-based connectivity approach with graph theory offers more promising results than the traditional feature extraction techniques. The connectivity approach achieved an accuracy of 85.77% in comparison with the 78.50% accuracy achieved by the existing feature extraction techniques. It is concluded that the proposed PDC method with graph theory network analysis is a better solution for cognitive load assessment during any cognitive task. [ABSTRACT FROM AUTHOR]

There is a significant need for a computer-aided modeling, effective information analysis and ontology knowledge base models to support both special needs children and care providers. As this research work correlated to the symmetry scope, it proposes an innovative generic smart knowledge-based "School Care Coordination System" (SCCS), which is established on a novel holistic six-layered data management model. The development of the Smart-SCCS adopts a methodology of ontology engineering to transform the given theoretical unstructured special educational needs and disabilities (SEND) code of practice into a comprehensive knowledge representation and reasoning system. The intended purpose is to deliver a system that can coordinate and bring together education, health and social care services into a single application to meet the needs of children and young people (CYP) with SEND. Moreover, it enables coordination, integration and monitoring of education, health and social care activities between different actors (formal, informal and CYP in the education sector) involved in the school care process network to provide personalized care interventions based on a predefined care plan. The developed ontology knowledge-based model has been proven efficient and solved the enormous difficulties faced by schools and local authorities on a daily basis. It enabled the coordination of care and integration of information for CYP from different departments in health, social care and education. The developed model has received significant attention with great feedback from all the schools and the local authorities involved, showing its efficiency and robustness. [ABSTRACT FROM AUTHOR]

Efficient and Symmetry based precoding plays a key role in wireless communications. In order to improve the transmission performance of multi-user millimeter wave Multiple-Input Multiple-Output (MIMO) (MU-mmWave MIMO) systems, this paper proposes an analog precoding scheme for the receiver of mmWave MIMO with split sub-array hybrid analog and digital architecture. Then, we propose a hybrid analog and digital precoding algorithm based on channel reciprocity (APoCR) to maximize the spectral efficiency by utilizing the triple joint optimization problem, which can be divided into the analog and digital part. The analog combination vectors (ACVs) are obtained by the signal-to-interference-and-noise ratio (SINR) reception maximization of each downlink user and the analog precoding vectors (APVs) are obtained by the SINR reception maximization of each uplink antenna array. The digital precoder of the transmitter is designed after the analog part optimization to alleviate the interference between multiple data streams of the users. The simulation results show that the proposed precoding algorithm has a better sum rate, fast convergence, and improved SINR than the other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In order to increase the reliability, accuracy, and efficiency in the eHealth, Internet of Medical Things is playing a vital role. Current development in telemedicine and the Internet of Things have delivered efficient and low-cost medical devices. The Internet of Medical Things architectures being developed do not completely recognize the potential of Internet of Things. The Internet of Medical Things sensor devices have limited computation power; in case if a patient is using implanted medical devices, it is not easy to recharge or replace the devices immediately. Biosensors are small devices with limited energy if these devices do not wisely utilize the energy may drain sharply and devices become inactive. The current medical solutions place the bulk of data on cloud-based systems that ultimately creates a bottleneck. In this article, an energy-efficient fog-to-cloud Internet of Medical Things architecture is proposed to optimize energy consumption. In the proposed architecture, Bluetooth enabled biosensors are used, because Bluetooth technology is an energy efficient and also helps to enable the sleep and awake modes. The proposed fog-to-cloud Internet of Medical Things works in three different modes periodic, sleep–awake, and continue to optimize the energy consumption. The proposed technique enabled the sensing modes that gathers the patients' data efficiently based on their health conditions. The sensed data are transmitted to the relevant fog and cloud devices for further processing. The performance of fog-to-cloud Internet of Medical Things is evaluated through simulation; the results are compared with the results of existing techniques in terms of an end-to-end delay, throughput, and energy consumption. It is analyzed that the proposed technique reduces the energy consumption between 30% and 40%. [ABSTRACT FROM AUTHOR]