Yanlei Xu¹, Yuting Zhai¹, Bin Zhao¹, Yubin Jiao², ShuoLin Kong¹, Yang Zhou^1,*, Zongmei Gao³

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 669-682, 2021, DOI:10.32604/iasc.2021.015225

Abstract For improving the accuracy of weed recognition under complex field conditions, a weed recognition method using depthwise separable convolutional neural network based on deep transfer learning was proposed in this study. To improve the model classification accuracy, the Xception model was refined by using model transferring and fine-tuning. Specifically, the weight parameters trained by ImageNet data set were transferred to the Xception model. Then a global average pooling layer replaced the full connection layer of the Xception model. Finally, the XGBoost classifier was added to the top layer of the model to output results. The performance of the proposed model… More >

Abdulmalik AlSalman¹, Abdu Gumaei^1,*, Amani AlSalman², Suheer Al-Hadhrami¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3847-3864, 2021, DOI:10.32604/cmc.2021.015614

Abstract Braille-assistive technologies have helped blind people to write, read, learn, and communicate with sighted individuals for many years. These technologies enable blind people to engage with society and help break down communication barriers in their lives. The Optical Braille Recognition (OBR) system is one example of these technologies. It plays an important role in facilitating communication between sighted and blind people and assists sighted individuals in the reading and understanding of the documents of Braille cells. However, a clear gap exists in current OBR systems regarding asymmetric multilingual conversion of Braille documents. Few systems allow sighted people to read and… More >

Haikel Alhichri^*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3581-3599, 2021, DOI:10.32604/cmc.2021.015399

Abstract In January 2020, the World Health Organization declared a global health emergency concerning the spread of a new coronavirus disease, which was later named COVID-19. Early and fast diagnosis and isolation of COVID-19 patients have proven to be instrumental in limiting the spread of the disease. Computed tomography (CT) is a promising imaging method for fast diagnosis of COVID-19. In this study, we develop a unique preprocessing step to resize CT chest images to a fixed size (256 × 256 pixels) that preserves the aspect ratio and reduces image loss. Then, we present a deep learning (DL) method to classify… More >

Mustaqeem, Soonil Kwon^*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 4039-4059, 2021, DOI:10.32604/cmc.2021.015070

Abstract Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications, such as robotics, virtual reality, behavior assessments, and emergency call centers. Recently, researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches, but the recognition rate is still not convincing. Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations. In this paper, we suggested a new technique, which is a one-dimensional dilated convolutional neural network (1D-DCNN) for… More >

K. Lakshminarayanan¹, N. Muthukumaran¹, Y. Harold Robinson², Vimal Shanmuganathan³, Seifedine Kadry⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3045-3055, 2021, DOI:10.32604/cmc.2021.014370

Abstract Hookworm is an illness caused by an internal sponger called a roundworm. Inferable from deprived cleanliness in the developing nations, hookworm infection is a primary source of concern for both motherly and baby grimness. The current framework for hookworm detection is composed of hybrid convolutional neural networks; explicitly an edge extraction framework alongside a hookworm classification framework is developed. To consolidate the cylindrical zones obtained from the edge extraction framework and the trait map acquired into the hookworm scientific categorization framework, pooling layers are proposed. The hookworms display different profiles, widths, and bend directions. These challenges make it difficult for… More >

Emad-ul-Haq Qazi, Muhammad Hussain^*, Hatim A. AboAlsamh

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3329-3348, 2021, DOI:10.32604/cmc.2021.013589

Abstract The detection of alcoholism is of great importance due to its effects on individuals and society. Automatic alcoholism detection system (AADS) based on electroencephalogram (EEG) signals is effective, but the design of a robust AADS is a challenging problem. AADS’ current designs are based on conventional, hand-engineered methods and restricted performance. Driven by the excellent deep learning (DL) success in many recognition tasks, we implement an AAD system based on EEG signals using DL. A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain… More >

Sheng Bai, Feng Bao^*, Fengzhi Zhao

FDMP-Fluid Dynamics & Materials Processing, Vol.17, No.1, pp. 39-47, 2021, DOI:10.32604/fdmp.2021.010376

Abstract The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology. Assuming a problem of aircraft design as the workhorse, a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet (Visual Geometry Group Net) and FCN (Fully Convolutional Network) techniques. As shown by the results, the model displays a strong fitting ability, and there is almost no over-fitting in training. Moreover, the model has good accuracy and convergence. For different input data and different grids, the model basically achieves convergence, showing… More >

Imtisal Qadeer^1,2, Muhammad Khurram Ehsan^3,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2619-2630, 2021, DOI:10.32604/cmc.2021.015641

Abstract To secure the wireless connection between devices with low computational power has been a challenging problem due to heterogeneity in operating devices, device to device communication in Internet of Things (IoTs) and 5G wireless systems. Physical layer key generation (PLKG) tackles this secrecy problem by introducing private keys among two connecting devices through wireless medium. In this paper, relative calibration is used as a method to enhance channel reciprocity which in turn increases the performance of the key generation process. Channel reciprocity based key generation is emerged as better PLKG methodology to obtain secure wireless connection in IoTs and 5G… More >

Hamid A. Jalab¹, Ala’a R. Al-Shamasneh¹, Hadil Shaiba², Rabha W. Ibrahim^3,4,*, Dumitru Baleanu^5,6,7

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2061-2075, 2021, DOI:10.32604/cmc.2021.015170

Abstract Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed… More >

Gulzar Ahmad¹, Saad Alanazi², Madallah Alruwaili², Fahad Ahmad^3,6, Muhammad Adnan Khan^4,*, Sagheer Abbas¹, Nadia Tabassum⁵

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2585-2600, 2021, DOI:10.32604/cmc.2021.015080

Abstract Security is a significant issue for everyone due to new and creative ways to commit cybercrime. The Closed-Circuit Television (CCTV) systems are being installed in offices, houses, shopping malls, and on streets to protect lives. Operators monitor CCTV; however, it is difficult for a single person to monitor the actions of multiple people at one time. Consequently, there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study, we have designed a new Intelligent Ammunition Detection and Classification (IADC) system using… More >