Diyar Qader Zeebaree¹, Adnan Mohsin Abdulazeez², Dilovan Asaad Zebari^3,*, Habibollah Haron⁴, Haza Nuzly Abdull Hamed⁴

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3363-3382, 2021, DOI:10.32604/cmc.2021.013314

Abstract Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed. This paper proposes a multi-level… More >

Muhammad Attique Khan¹, Nazar Hussain¹, Abdul Majid¹, Majed Alhaisoni², Syed Ahmad Chan Bukhari³, Seifedine Kadry⁴, Yunyoung Nam^5,*, Yu-Dong Zhang⁶

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2923-2938, 2021, DOI:10.32604/cmc.2021.013191

Abstract In medical imaging, computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis. In response to the coronavirus 2019 (COVID-19) pandemic, new testing procedures, medical treatments, and vaccines are being developed rapidly. One potential diagnostic tool is a reverse-transcription polymerase chain reaction (RT-PCR). RT-PCR, typically a time-consuming process, was less sensitive to COVID-19 recognition in the disease’s early stages. Here we introduce an optimized deep learning (DL) scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography (CT) scans. In the proposed method, contrast enhancement is used to… More >

H. Asha Gnana Priya¹, J. Anitha¹, Daniela Elena Popescu², Anju Asokan¹, D. Jude Hemanth¹, Le Hoang Son^3,4,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2771-2786, 2021, DOI:10.32604/cmc.2021.012907

Abstract Diabetic Retinopathy (DR) is an eye disease that mainly affects people with diabetes. People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage. Once the vision is lost, it cannot be regained but can be prevented from causing any further damage. Early diagnosis of DR is required for preventing vision loss, for which a trained ophthalmologist is required. The clinical practice is time-consuming and is not much successful in identifying DR at early stages. Hence, Computer-Aided Diagnosis (CAD) system is a suitable alternative for screening and grading… More >

Mazin Abed Mohammed¹, Karrar Hameed Abdulkareem², Begonya Garcia-Zapirain³, Salama A. Mostafa⁴, Mashael S. Maashi⁵, Alaa S. Al-Waisy¹, Mohammed Ahmed Subhi⁶, Ammar Awad Mutlag⁷, Dac-Nhuong Le^8,9,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3289-3310, 2021, DOI:10.32604/cmc.2021.012874

Abstract The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor… More >

Feng Yuan, Xiao Shao^*

Journal on Big Data, Vol.2, No.4, pp. 167-176, 2020, DOI:10.32604/jbd.2020.015357

Abstract Traditional image quality assessment methods use the hand-crafted features to predict the image quality score, which cannot perform well in many scenes. Since deep learning promotes the development of many computer vision tasks, many IQA methods start to utilize the deep convolutional neural networks (CNN) for IQA task. In this paper, a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution, which consists of two tasks: A distortion recognition task and a quality regression task. For the first task, image distortion type is obtained by the fully connected layer. For… More >

M. A. Rahim Khan^*, Manoj Kumar Jain

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1359-1373, 2020, DOI:10.32604/iasc.2020.013849

Abstract Repacked mobile applications and obfuscation attacks constitute a significant threat to the Android technological ecosystem. A novel method using the Constant Key Point Selection and Limited Binary Pattern Feature (CKPS: LBP) extraction-based Hashing has been proposed to identify repacked Android applications in previous works. Although the approach was efficient in detecting the repacked Android apps, it was not suitable for detecting obfuscation attacks. Additionally, the time complexity needed improvement. This paper presents an optimization technique using Scalable Bivariant Feature Transformation extract optimum feature-points extraction, and the Harris method applied for optimized image hashing. The experiments produced better results than the… More >

Fahd N. Al-Wesabi^1,*, Huda G. Iskandar², Mohammad Alamgeer³, Mokhtar M. Ghilan²

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1267-1283, 2020, DOI:10.32604/iasc.2020.013782

Abstract In this paper, a robust approach INLPETWA (an Intelligent Natural Language Processing and English Text Watermarking Approach) is proposed to tampering detection of English text by integrating zero text watermarking and hidden Markov model as a soft computing and natural language processing techniques. In the INLPETWA approach, embedding and detecting the watermark key logically conducted without altering the plain text. Second-gram and word mechanism of hidden Markov model is used as a natural text analysis technique to extracts English text features and use them as a watermark key and embed them logically and validates them during detection process to detect… More >

Lingyun Xiang^1,2,3, Guoqing Guo², Qian Li⁴, Chengzhang Zhu^5,*, Jiuren Chen⁶, Haoliang Ma²

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1375-1390, 2020, DOI:10.32604/iasc.2020.013382

Abstract Current works on spam detection in product reviews tend to ignore the temporal relevance among reviews in the user or product entity, resulting in poor detection performance. To address this issue, the present paper proposes a spam detection method that jointly learns comprehensive temporal features from both behavioral and text features in user and product entities. We first extract the behavioral features of a single review, then employ a convolutional neural network (CNN) to learn the text features of this review. We next combine the behavioral features with the text features of each review and train a Long-Short-Term Memory (LSTM)… More >

Ujala Yasmeen¹, Jamal Hussain Shah¹, Muhammad Attique Khan², Ghulam Jillani Ansari¹, Saeed ur Rehman¹, Muhammad Sharif¹, Seifedine Kadry³, Yunyoung Nam^4,*

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775

Abstract Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image. (b) Subsequently, the standard maximal… More >

Haifeng Song¹, Weiwei Yang^1,*, Haiyan Yuan², Harold Bufford³

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1441-1458, 2020, DOI:10.32604/iasc.2020.011988

Abstract There are two main problems that lead to unsatisfactory classification performance for hyperspectral remote sensing images (HSIs). One issue is that the HSI data used for training in deep learning is insufficient, therefore a deeper network is unfavorable for spatial-spectral feature extraction. The other problem is that as the depth of a deep neural network increases, the network becomes more prone to overfitting. To address these problems, a dual-channel 3D-Multiscale DenseNet (3DMSS) is proposed to boost the discriminative capability for HSI classification. The proposed model has several distinct advantages. First, the model consists of dual channels that can extract both… More >