R. Bala Krishnan¹, M. M. Anishin Raj², N. Rajesh Kumar¹, B. Karthikeyan³, G. Manikandan^3,*, N. R. Raajan⁴

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 221-235, 2022, DOI:10.32604/iasc.2022.021775 - 26 October 2021

Abstract In recent years information hiding has got much attention as it acts as an alternate option for secured communication. The Secret content would get imbedded with the image using various possible image embodiment techniques, in which the Least Significant Bit (LSB) substitution is one of the preferred content embodiment strategy; however, asserting the quality and the originality of the content embedded image (stego) is yet a grievous concern in the field of Information Security. In this article, a proficient Scrambling Based Haar Wavelet Transform (SBHWT) approach has been sought to ensure the novelty of the… More >

Hesham Alhumyani^1,*, Ibrahim Alrube¹, Sameer Alsharif¹, Ashraf Afifi¹, Chokri Ben Amar¹, Hala S. El-Sayed², Osama S. Faragallah³

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4657-4673, 2022, DOI:10.32604/cmc.2022.020338 - 11 October 2021

Abstract The rapid development in the information technology field has introduced digital watermark technologies as a solution to prevent unauthorized copying and redistribution of data. This article introduces a self-embedded image verification and integrity scheme. The images are firstly split into dedicated segments of the same block sizes. Then, different Analytic Beta-Wavelet (ABW) orthogonal filters are utilized for embedding a self-segment watermark for image segment using a predefined method. ABW orthogonal filter coefficients are estimated to improve image reconstruction under different block sizes. We conduct a comparative study comparing the watermarked images using three kinds of More >

S. Dilip Kumar^1,*, T. V. Sivasubramonia Pillai²

Computer Systems Science and Engineering, Vol.41, No.1, pp. 19-33, 2022, DOI:10.32604/csse.2022.019366 - 08 October 2021

Abstract Fuel is a very important factor and has considerable influence on the air quality in the environment, which is the heart of the world. The increase of vehicles in lived-in areas results in greater emission of carbon particles in the environment. Adulterated fuel causes more contaminated particles to mix with breathing air and becomes the main source of dangerous pollution. Adulteration is the mixing of foreign substances in fuel, which damages vehicles and causes more health problems in living beings such as humans, birds, aquatic life, and even water resources by emitting high levels of… More >

R. Kingsy Grace^1,*, R. Manimegalai²

Computer Systems Science and Engineering, Vol.40, No.2, pp. 593-606, 2022, DOI:10.32604/csse.2022.019240 - 09 September 2021

Abstract The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind power. Wind power is the clean, free and conservative renewable energy. It is necessary to predict the wind speed, to implement wind power generation. This paper proposes a new model, named WT-GWO-BPNN, by integrating Wavelet Transform (WT), Back Propagation Neural Network (BPNN) and Grey Wolf Optimization (GWO). The wavelet transform is adopted to decompose the original time series data (wind speed) into approximation and detailed band. GWO – BPNN is applied to predict the wind speed. GWO is… More >

Simy Mary Kurian¹, Sujitha Juliet Devaraj^1,*, Vinodh P. Vijayan²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2093-2109, 2021, DOI:10.32604/cmc.2021.018090 - 21 July 2021

Abstract Magnetic resonance imaging (MRI) is an essential tool for detecting brain tumours. However, identification of brain tumours in the early stages is a very complex task since MRI images are susceptible to noise and other environmental obstructions. In order to overcome these problems, a Gamma MAP denoised Strömberg wavelet segmentation based on a maximum entropy classifier (GMDSWS-MEC) model is developed for efficient tumour detection with high accuracy and low time consumption. The GMDSWS-MEC model performs three steps, namely pre-processing, segmentation, and classification. Within the GMDSWS-MEC model, the Gamma MAP filter performs the pre-processing task and… More >

Marriam Nawaz¹, Zahid Mehmood^2,*, Tahira Nazir¹, Momina Masood¹, Usman Tariq³, Asmaa Mahdi Munshi⁴, Awais Mehmood¹, Muhammad Rashid⁵

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1927-1944, 2021, DOI:10.32604/cmc.2021.018052 - 21 July 2021

Abstract Copy-move forgery is the most common type of digital image manipulation, in which the content from the same image is used to forge it. Such manipulations are performed to hide the desired information. Therefore, forgery detection methods are required to identify forged areas. We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern (LTrP) features to detect the single and multiple copy-move attacks from the images. The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations. It also… More >

Sidra Naseem¹, Kashif Javed¹, Muhammad Jawad Khan¹, Saddaf Rubab², Muhammad Attique Khan³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 471-486, 2021, DOI:10.32604/cmc.2021.018239 - 04 June 2021

Abstract Electroencephalography is a common clinical procedure to record brain signals generated by human activity. EEGs are useful in Brain controlled interfaces and other intelligent Neuroscience applications, but manual analysis of these brainwaves is complicated and time-consuming even for the experts of neuroscience. Various EEG analysis and classification techniques have been proposed to address this problem however, the conventional classification methods require identification and learning of specific EEG characteristics beforehand. Deep learning models can learn features from data without having in depth knowledge of data and prior feature identification. One of the great implementations of deep… More >

Mohit Kumar Sharma, Arun Kumar^*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1391-1406, 2021, DOI:10.32604/cmc.2021.017666 - 04 June 2021

Abstract Non-orthogonal multiple access (NOMA) is gaining considerable attention due to its features, such as low out-of-band radiation, signal detection capability, high spectrum gain, fast data rate, and massive D2D connectivity. It may be considered for 5G networks. However, the high peak-to-average power ratio (PAPR) is viewed as a significant disadvantage of a NOMA waveform, and it weakens the quality of signals and the throughput of the scheme. In this article, we introduce a modified NOMA system by employing a block of wavelet transform, an alternative to FFT (Fast Fourier transform). The modified system combines the More >

Imam Yuadi^1,*, Myrtati D. Artaria², Sakina³, A. Taufiq Asyhari⁴

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3979-3995, 2021, DOI:10.32604/cmc.2021.015417 - 06 May 2021

Abstract The size, shape, and physical characteristics of the human skull are distinct when considering individual humans. In physical anthropology, the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner. For example, labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections. Given the multiple issues associated with the manual identification of skulls, we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features, Gabor features, fractal features, discrete wavelet… More >

Ajla Kulaglic^1,*, Burak Berk Ustundag²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3577-3593, 2021, DOI:10.32604/cmc.2021.014768 - 06 May 2021

Abstract Machine Learning (ML) algorithms have been widely used for financial time series prediction and trading through bots. In this work, we propose a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improves the prediction of next day closing prices. In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs. An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence. The performance of the proposed model is evaluated using… More >