Singular value decomposition (SVD) based image authentication has widespread applications for digital media protection including forensic and medical domains due to the high stability and robustness of singular values...
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In this paper, a cluster based association management for heterogeneous users has been addressed. The aim is to explore frame aggregation for the high throughput (HT) users such as 802.11n type, avoiding the drawback ...
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Many researchers have preferred non-invasive techniques for recognizing the exact type of physiological abnormality in the vocal tract by training machine learning algorithms with feature descriptors extracted from th...
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Many researchers have preferred non-invasive techniques for recognizing the exact type of physiological abnormality in the vocal tract by training machine learning algorithms with feature descriptors extracted from the voice signal. However, until now, most techniques have been limited to classifying whether a voice is normal or abnormal. It is crucial that the trained Artificial Intelligence (AI) be able to identify the exact pathology associated with voice for implementation in a realistic environment. Another issue is the need to suppress the ambient noise that could be mixed up with the spectra of the voice. Current work proposes a robust, less time-consuming and non-invasive technique for the identification of pathology associated with a laryngeal voice signal. More specifically, a two-stage signal filtering approach that encompasses a score-based geometric approach and a glottal inverse filtering method is applied to the input voice signal. The aim here is to estimate the noise spectra, to regenerate a clean signal and finally to deliver a completely fundamental glottal flow-derived signal. For the next stage, clean glottal derivative signals are used in the formation of a novel fused-scalogram which is currently referred to as the "Combinatorial Transformative Scalogram (CTS)." The CTS is a time-frequency domain plot which is a combination of two time-frequency scalograms. There is a thorough investigation of the performance of the two individual scalograms as well as that of the CTS *** classification metrics are used to investigate performance, which are: sensitivity, mean accuracy, error, precision, false positive rate, specificity, Cohen’s kappa, Matthews Correlation Coefficient, and F1 score. Implementation of the VOice ICar fEDerico II (VOICED) standard database provided the highest mean accuracy of 94.12% with a sensitivity of 93.85% and a specificity of 97.96% against other existing techniques. The current method performed well despite the d
The flow shop scheduling problem is important for the manufacturing *** flow shop scheduling can bring great benefits to the ***,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learni...
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The flow shop scheduling problem is important for the manufacturing *** flow shop scheduling can bring great benefits to the ***,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted *** work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned ***,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are *** to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local *** of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during ***,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed *** experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random *** verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving *** Friedman test is executed on the results by five *** is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.
Cloud Computing is a rapidly growing emerging technology in the IT environment. Internet-based computing provides services like sharing resources e.g. network, storage, applications and software through the Internet. ...
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In the global marketplace, agriculture plays an important role. However, diseases produced in plants mostly affect the financial system. Pest occurrence and climatic changes are the leading trouble that the banana pla...
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The explosion of the novel phenomenon of the combination of computer vision and Natural language processing is playing a vital role in converting the ordinary world into a more technological pool. Natural language pro...
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Developing effective strategies to address significant variations in segmented iris image quality is crucial for accurate iris recognition from remotely acquired facial or eye images. These variations are closely link...
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Because of the growing number of cars and the inadequate parking infrastructure, urban parking management has become more and more crucial. To predict parking spot availability in IoT-enabled intelligent systems, this...
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In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human beh...
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