Basanta Kumar Swain¹, Mohammad Zubair Khan^2,*, Chiranji Lal Chowdhary³, Abdullah Alsaeedi⁴

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2337-2349, 2023, DOI:10.32604/csse.2023.036192

Abstract This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients (GFCC) for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network (DHO-ANN). The noisy crowdsourced cough datasets were collected from the public domain. This research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech corpora. The proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures, F1 score, confusion matrix, More >

Mingshuai Sheng¹, Jingbing Li^1,2,*, Uzair Aslam Bhatti^1,2,3, Jing Liu⁴, Mengxing Huang^1,5, Yen-Wei Chen⁶

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 293-309, 2023, DOI:10.32604/cmc.2023.036438

Abstract Medical images are used as a diagnostic tool, so protecting their confidentiality has long been a topic of study. From this, we propose a Resnet50-DCT-based zero watermarking algorithm for use with medical images. To begin, we use Resnet50, a pre-training network, to draw out the deep features of medical images. Then the deep features are transformed by DCT transform and the perceptual hash function is used to generate the feature vector. The original watermark is chaotic scrambled to get the encrypted watermark, and the watermark information is embedded into the original medical image by XOR… More >

Joo Woo¹, Jonghyun Kim¹, Jong Hwan Park^2,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1985-2003, 2023, DOI:10.32604/cmc.2023.033102

Abstract A fuzzy extractor can extract an almost uniform random string from a noisy source with enough entropy such as biometric data. To reproduce an identical key from repeated readings of biometric data, the fuzzy extractor generates a helper data and a random string from biometric data and uses the helper data to reproduce the random string from the second reading. In 2013, Fuller et al. proposed a computational fuzzy extractor based on the learning with errors problem. Their construction, however, can tolerate a sub-linear fraction of errors and has an inefficient decoding algorithm, which causes the reproducing time… More >

Yu Fan^1,6, Jingbing Li^1,2,*, Uzair Aslam Bhatti^1,2, Chunyan Shao¹, Cheng Gong¹, Jieren Cheng^3,5, Yenwei Chen⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1279-1302, 2023, DOI:10.32604/cmc.2023.031445

Abstract Medical images are a critical component of the diagnostic process for clinicians. Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis, they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images. Traditional watermark embedding algorithm embeds the watermark information into the medical image, which reduces the quality of the medical image and affects the physicians’ judgment of patient diagnosis. In addition, watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks… More >

Soonshin Seo¹, Junseok Oh², Eunsoo Cho², Hosung Park², Gyujin Kim², Ji-Hwan Kim^2,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3291-3303, 2022, DOI:10.32604/cmc.2022.026259

Abstract Acoustic scene classification (ASC) is a method of recognizing and classifying environments that employ acoustic signals. Various ASC approaches based on deep learning have been developed, with convolutional neural networks (CNNs) proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models. When using ASC systems in the real world, model complexity and device robustness are essential considerations. In this paper, we propose a two-pass mobile network for low-complexity classification of the acoustic scene, named TP-MobNet. With inverse residuals and linear bottlenecks, TP-MobNet is based on… More >

Reena Thomas^1,*, M. Sucharitha²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 879-889, 2022, DOI:10.32604/iasc.2022.024070

Abstract The major intention of this work is to introduce a novel hybrid image watermarking technique for RGB color images. This hybrid watermarking algorithm uses two transforms such as Contourlet and Gould transform. The Contourlet transform is used as first stage while the Gould transform is used as second stage. In the watermark embedding phase, the R, G and B channels are transformed using Contourlet transform. The bandpass directional sub band coefficients of Contourlet transformed image are then divided into sub-blocks. The sub-blocks are then transformed using Gould transform and the watermark information is embedded on More >

Ben Attia Selma^*, Ouerfelli Houssem Eddine, Salhi Salah

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 795-810, 2022, DOI:10.32604/iasc.2022.020435

Abstract This study addressed sufficient conditions for the robust monotonic convergence of repetitive discrete-time linear parameter varying systems, with the parameter variation rate bound. The learning law under consideration is an anticipatory iterative learning control. Of particular interest in this study is that the iterations can eliminate the influence of disturbances. Based on a simple quadratic performance function, a sufficient condition for the proposed learning algorithm is presented in terms of linear matrix inequality (LMI) by imposing a polytopic structure on the Lyapunov matrix. The set of LMIs to be determined considers the bounds on the More >

Abdollah Amirkhani^1,*, Amir Hossein Barshooi¹, Amir Ebrahimi²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 981-997, 2022, DOI:10.32604/cmc.2022.019001

Abstract The performance and accuracy of computer vision systems are affected by noise in different forms. Although numerous solutions and algorithms have been presented for dealing with every type of noise, a comprehensive technique that can cover all the diverse noises and mitigate their damaging effects on the performance and precision of various systems is still missing. In this paper, we have focused on the stability and robustness of one computer vision branch (i.e., visual object tracking). We have demonstrated that, without imposing a heavy computational load on a model or changing its algorithms, the drop in… More >

Suliman A. Alsuhibany^*, Hessah Abdulaziz Alhodathi

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 523-537, 2022, DOI:10.32604/iasc.2022.019913

Abstract The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been an interesting topic for several years. An Arabic CAPTCHA has recently been proposed to serve Arab users. Since there have been few scientific studies supporting a systematic design or tuning for users, this paper aims to analyze the Arabic text-based CAPTCHA at the parameter level by conducting an experimental study. Based on the results of this study, we propose an Arabic text-based CAPTCHA scheme with Fast Gradient Sign Method (FGSM) adversarial images. To evaluate the security of the proposed More >

Marwan Albahar^*, Mohammed Thanoon, Monaj Alzilai, Alaa Alrehily, Munirah Alfaar, Maimoona Algamdi, Norah Alassaf

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2181-2202, 2021, DOI:10.32604/cmc.2021.018260

Abstract Malicious Portable Document Format (PDF) files represent one of the largest threats in the computer security space. Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction. These approaches are time consuming, require substantial prior knowledge, and the list of features must be updated with each newly discovered vulnerability individually. In this study, we propose two models for PDF malware detection. The first model is a convolutional neural network (CNN) integrated into a standard deviation based regularization model to detect malicious PDF documents. The second model is a More >