Speech recognition plays an important role in a variety of applications for mobile communication. User communication devices for contact necessitate a broad vocabulary recognition scheme, greater precision, and a real...
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ISBN:
(纸本)9789811922817;9789811922800
Speech recognition plays an important role in a variety of applications for mobile communication. User communication devices for contact necessitate a broad vocabulary recognition scheme, greater precision, and a real-time, low-power schema. The power consumption and memory bandwidth of miniaturized battery-controlled devices are important. People's handheld devices often demand more effort, due to the speech challenge. As a result, a valuable technology based on the Stochastic binary cat swarm optimization algorithm (SBCSO) is proposed in this research study to transform the non-audible murmur to normal voice. From the input murmured speech signal, the characteristics such as spectral skewness, spectral centroid, pitch chroma, and Taylor-Amplitude Modulation Spectrum are extracted and trained in the Deep Convolutional Neural Network (DCNN) classifier. The proposed stochastic binary cat swarm optimization algorithm is used to train DCNN classifier for speech recognition. To boost the results in metric analysis, the stochastic gradient descent algorithm and a binary cat swarm optimization algorithm (BCSOA) are combined. In order to boost the experimental results in metric analysis, the stochastic gradient descent algorithm and BCSOA are combined in this research paper. The proposed algorithm performance is validated in terms of true positive rate, false positive rate and classification accuracy, and it showed better improved in speech recognition.
Microarray technology is beneficial in terms of diagnosing various diseases, including cancer. Despite all DNA microarray benefits, the high number of genes versus the low number of samples has always been a crucial c...
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Microarray technology is beneficial in terms of diagnosing various diseases, including cancer. Despite all DNA microarray benefits, the high number of genes versus the low number of samples has always been a crucial challenge for this technology. Accordingly, we need new optimizationalgorithms to select optimal genes for faster disease diagnosis. In this article, a new version of the binarycatoptimizationalgorithm, named SBCSO, for gene selection in DNA microarray expression cancer data is presented. The main contributions in this paper are listed as follows: First, the opposition-based learning (OBL) mechanism is employed to improve the proposed algorithm's population members' diversity. Second, a time-varying V-shaped transfer function is employed to balance the two phases of exploration and extraction in the proposed algorithm. Third, the MR and lambda parameters in the proposed algorithm are adapted over time, and finally, single-objective and multi-objective approaches are proposed to solve the gene selection problems. The 15 datasets pertinent to microarray data of various cancer types are employed to compare the proposed method with other well-known binaryoptimizationalgorithms. The experiments' results indicate that the proposed algorithm has a better capability to select the optimal genes for a faster disease diagnosis.
This research paper investigates the relevance of the catswarmoptimization (CSO) algorithm to cryptanalysis. It is a relatively new evolutionary metaheuristic technique to solve those problems which belong to NP-har...
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ISBN:
(纸本)9781538623473
This research paper investigates the relevance of the catswarmoptimization (CSO) algorithm to cryptanalysis. It is a relatively new evolutionary metaheuristic technique to solve those problems which belong to NP-hard class based on the swarm intelligence of felids, commonly known as cats. Cryptanalysis is an active field of experimental research as it can consolidate or depreciate the viability of modern ciphers. To the extent of our knowledge, there is no evidence that the CSO has ever been applied to the cryptanalysis problem. Experimental outcomes demonstrate that the binarycatswarmoptimization (BCSO) algorithm is efficient for the cryptanalysis of the Data Encryption Standard (DES) using chosen plaintext attack. Further, it produces optimal keys with higher fitness as compared to Particle swarmoptimization. This paper shows that BCSO can be successfully utilized for the cryptanalysis of block ciphers with promising outcomes.
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