Execution trace analysis is particularly valuable in the context of object-oriented software comprehension for the maintenance tasks. It involves analyzing dynamic information and behaviors that are stored in the exec...
详细信息
According to the existing researches, a new Takagi-Sugeno fuzzy systems under the time-varying delay were constructed. To reaching a superior sampled-data stability, the approach of input delay with free-weighting mat...
详细信息
Non-negative matrix factorization (NMF) has been a popular data analysis tool and has been widely applied in computer vision. However, conventional NMF methods cannot adaptively learn grouping structure froma *** pape...
详细信息
Instance selection aims to improve the performance of a model by removing uninformative and noisy data. Two issues to consider during instance selection are: (1) the volume of data reduction, and (2) conservation of g...
详细信息
ISBN:
(纸本)9781665402095
Instance selection aims to improve the performance of a model by removing uninformative and noisy data. Two issues to consider during instance selection are: (1) the volume of data reduction, and (2) conservation of generalization performance. Since maximizing dataset reduction leads to poor performance, both issues conflict. Regarding this issue, a chaos-based evolutionary algorithm is proposed which aims to keep only the boundary instances by a combined fuzzy weighted average distance-based decision surface. The performance has been evaluated on real-world datasets by the 10-fold cross-validation method. Evaluations manifest the competitive performance for instance selection in terms of error rate, reduction rate, and Gmean.
The Internet of Spatial Things (IoST), continuously generates a large volume of geospatial data from a large number of connected smart devices. Cloud computing is inefficient to respond to IoST because of latency conc...
详细信息
In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), ther...
详细信息
In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.
The problem of sharing radio spectrum is analyzed using a multiple server queueing model which represents channels owned by primary users and allowed opportunistic access by a secondary user group. The primary users a...
详细信息
Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive *** study introduces a phishing email detection framework that combines Bidirectional Encode...
详细信息
Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive *** study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information ***’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing *** an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing *** approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.
Two families of the point-symmetric linear coupled consolidation models - differing in one boundary condition - are treated. The models are with space dimension one (oedometric), two (cylindrical) or three (spherical)...
详细信息
暂无评论