Ice accumulation on power lines during an ice storm can result in permanent damage and widespread power outages if left unaddressed. This paper presents a literature survey on the resilience of power systems against i...
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To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was *** chaotic elite opposition-based l...
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To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was *** chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the *** inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local *** LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence *** addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.
Customer churn prediction is an important task in customer relationship management because it helps businesses know who is at risk of leaving and retain such at-risk *** and time-efficient churn prediction is essentia...
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Studies to solve the transmission expansion problem (TEP) for a power system can benefit from employing complex network or graph theory metrics to speed up analysis and lead to more realistic methodologies. Currently,...
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Currently, there is no standardized approach for evaluating the efficiency of planar wireless power transfer (WPT) systems across varying specifications and sizes, particularly under positional deviations. This paper ...
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Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. Howe...
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Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo
In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an ...
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In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware *** model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware *** model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware *** these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under *** outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final *** strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of *** efficacy of our proposed APIbased hybrid model is evident in its performance *** outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.
This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network ...
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This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.
The rapid evolution of smartphone technology and the diverse range of available models have made selecting a cost-effective mobile phone a complex decision for consumers. Although brand, internal memory, camera qualit...
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