As Maritime Autonomous Surface Ships (MASSs) increasingly become part of global maritime operations, the reliability and security of their object detection systems have become a major concern. These systems, which pla...
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In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mo...
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In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user ***, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.
The healthcare sector holds valuable and sensitive *** amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast *** to their nature,software-defined networks(SDNs)are widely use...
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The healthcare sector holds valuable and sensitive *** amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast *** to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and *** this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe *** attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human *** can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or *** this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various *** propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS *** then evaluate the accuracy and performance of the proposed TBDC *** technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.
Even though there exist many research efforts trying to develop forecasting models based on machine learning (ML) or statistical techniques, feature selection is not employed in a large majority of the studies. To fil...
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Piezoelectric accelerometers excel in vibration *** the emerging trend of fully organic electronic microsystems,polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signa...
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Piezoelectric accelerometers excel in vibration *** the emerging trend of fully organic electronic microsystems,polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signals,such as vocal vibrations in wearable speaking assistants for those with speaking ***,high-performance polymeric piezoelectric accelerometers suitable for such applications are *** organic compounds such as PVDF have inferior properties to their inorganic counterparts such as ***,most existing polymeric piezoelectric accelerometers have very unbalanced performance *** often sacrifice resonance frequency and bandwidth for a flat-band sensitivity comparable to those of PZT-based accelerometers,leading to increased noise density and limited application *** this study,a new polymeric piezoelectric accelerometer design to overcome the material limitations of PVDF is *** new design aims to simultaneously achieve high sensitivity,broad bandwidth,and low *** samples were manufactured and characterized,demonstrating an average sensitivity of 29.45 pC/g within a±10 g input range,a 5%flat band of 160 Hz,and an in-band noise density of 1.4μg/√*** results surpass those of many PZT-based piezoelectric accelerometers,showing the feasibility of achieving comprehensively high performance in polymeric piezoelectric accelerometers to increase their potential in novel applications such as organic microsystems.
This paper proposes a novel fault location method for overhead feeders,which is based on the direct load flow *** method is developed in the phase domain to effectively deal with unbalanced network conditions,while it...
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This paper proposes a novel fault location method for overhead feeders,which is based on the direct load flow *** method is developed in the phase domain to effectively deal with unbalanced network conditions,while it can also handle any type of distributed generation(DG)units without requiring equivalent *** utilizing the line series parameters and synchronized or unsynchronized voltage and current phasor measurements taken from the sources,the method reliably identifies the most probable faulty *** the aid of an index,the exact faulty section among the multiple candidates is *** simulation studies for the IEEE 123-bus test feeder demonstrate that the proposed method accu-rately estimates the fault position under numerous short-circuit conditions with varying prefault system loading conditions,fault resistances,and measurement *** proposed method is promising for practical applications due to the limited number of required measurement devices as well as the short computation time.
Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the pre...
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Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident *** learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and *** prior success,there is a need for an efficient system recognizing ASL in diverse road *** address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient *** framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved *** and factor contribution analyses identify influential *** on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB *** proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.
As renewable energy is becoming the major re-source in future power grids,the weather and climate can have a higher impact on grid *** expansion planning(TEP)has the potential to reinforce the power trans-fer capabili...
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As renewable energy is becoming the major re-source in future power grids,the weather and climate can have a higher impact on grid *** expansion planning(TEP)has the potential to reinforce the power trans-fer capability of a transmission network for climate-impacted power *** this paper,we propose a systematic TEP proce-dure for renewable-energy-dominated power grids considering climate impact(CI).Particularly,this paper develops an im-proved model for TEP considering climate impact(TEP-CI)and evaluates the reliability of power grid with the obtained transmission investment ***,we create climate-impact-ed spatio-temporal future power grid data to facilitate the study of TEP-CI,which include the future climate-dependent re-newable power generation as well as the dynamic line rating profiles of the Texas 123-bus backbone transmission(TX-123BT)***,the TEP-CI model is proposed,which considers the variation in renewable power generation and dy-namic line rating,and the investment plan for future TX-123BT system is ***,a customized security-con-strained unit commitment(SCUC)is presented specifically for climate-impacted power *** reliability of future power grid in various investment scenarios is analyzed based on the daily operation conditions from SCUC *** whole procedure presented in this paper enables numerical studies on power grid planning considering climate *** can also serve as a benchmark for other studies of the TEP-CI model and its performance evaluation.
Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can...
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Neonatal medical data holds critical information within the healthcare industry, and it is important to analyze this data effectively. Machine learning algorithms offer powerful tools for extracting meaningful insight...
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