The growing adoption of electric vehicles (EVs) necessitates advancements in motor performance to overcome range limitations caused by the lower energy density of batteries compared to fossil fuels. This study aims to...
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The global health crisis caused by the COVID-19 pandemic has brought new challenges to speaker identification systems, particularly due to the acoustic alterations caused by the widespread use of face masks. Aiming to...
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Freezing of gait (FoG) refers to sudden, relatively brief episodes of gait arrest in Parkinson’s disease, known to manifest in the advanced stages of the condition. Events of freezing are associated with tumbles, tra...
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Recent progress made in the prediction,characterisation,and mitigation of multipactor discharge is reviewed for single‐and two‐surface ***,an overview of basic concepts including secondary electron emission,electron...
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Recent progress made in the prediction,characterisation,and mitigation of multipactor discharge is reviewed for single‐and two‐surface ***,an overview of basic concepts including secondary electron emission,electron kinetics under the force law,multipactor susceptibility,and saturation mechanisms is provided,followed by a discus-sion on multipactor mitigation *** strategies are categorised into two broad areas–mitigation by engineered devices and engineered radio frequency(rf)*** approach is useful in different *** advances in multipactor physics and engineering during the past decade,such as novel multipactor prediction methods,un-derstanding space charge effects,schemes for controlling multipacting particle trajec-tories,frequency domain analysis,high frequency effects,and impact on rf signal quality are *** addition to vacuum electron multipaction,multipactor‐induced ioni-zation breakdown is also reviewed,and the recent advances are summarised.
Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often requ...
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Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Lan...
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Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.
作者：
A.E.M.EljialyMohammed Yousuf UddinSultan AhmadDepartment of Information Systems
College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science
College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia
Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks i...
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Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.
The disappearance of Indigenous languages results in a decrease in cultural diversity, hence making the preservation of these languages extremely important. Conventional methods of documentation are lengthy, and the p...
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Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitoni...
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Penetration of harmonics from downstream low voltage networks and resonance phenomenon has caused the level of harmonics to increase in transmission networks. Compensation for these harmonics at high voltage levels is...
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