Hyun Yoo¹, Ji-Won Baek², Kyungyong Chung^3,*

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 319-334, 2021, DOI:10.32604/iasc.2021.018115 - 16 June 2021

Abstract With the convergence and development of the Internet of things (IoT) and artificial intelligence, closed-circuit television, wearable devices, and artificial neural networks have been combined and applied to crime prevention and follow-up measures against crimes. However, these IoT devices have various limitations based on the physical environment and face the fundamental problem of privacy violations. In this study, voice data are collected and emotions are classified based on an acoustic sensor that is free of privacy violations and is not sensitive to changes in external environments, to overcome these limitations. For the classification of emotions… More >

Kentaro Mori^1,*, Kotaro Kitaya², Tomomoto Ishikawa², Yutaka Hata³

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 335-352, 2021, DOI:10.32604/iasc.2021.01010 - 16 June 2021

Abstract In infertility treatment, it is required to improve a success rate of the treatment. A purpose of this study is to develop a prediction system for pregnancy outcomes using ultrasonic images. In infertility treatment, it is typical to evaluate the endometrial shape by using ultrasonic images. The convolutional neural network (CNN) system developed in the current study predicted pregnancy outcome by velocity information. The velocity information has a movement feature of uterine. It is known that a uterine movement is deep related to infertility. Experiments compared the velocity-based and shape-based systems. The shape-based systems predict… 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 >

Walid El-Shafai¹, Noha A. El-Hag², Ghada M. El-Banby³, Ashraf A. M. Khalaf², Naglaa F. Soliman^4,*, Abeer D. Algarni⁴, Fathi E. Abd El-Samie^1,4

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1323-1341, 2021, DOI:10.32604/cmc.2021.017385 - 04 June 2021

Abstract Corona Virus Disease-2019 (COVID-19) continues to spread rapidly in the world. It has dramatically affected daily lives, public health, and the world economy. This paper presents a segmentation and classification framework of COVID-19 images based on deep learning. Firstly, the classification process is employed to discriminate between COVID-19, non-COVID, and pneumonia by Convolutional Neural Network (CNN). Then, the segmentation process is applied for COVID-19 and pneumonia CT images. Finally, the resulting segmented images are used to identify the infected region, whether COVID-19 or pneumonia. The proposed CNN consists of four Convolutional (Conv) layers, four batch More >

Romany F. Mansour^1,*, Moheb R. Girgis²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4069-4085, 2021, DOI:10.32604/cmc.2021.017064 - 06 May 2021

Abstract Steganography is one of the best techniques to hide secret data. Several steganography methods are available that use an image as a cover object, which is called image steganography. In image steganography, the major features are the cover object quality and hiding data capacity. Due to poor image quality, attackers could easily hack the secret data. Therefore, the hidden data quantity should be improved, while keeping stego-image quality high. The main aim of this study is combining several steganography techniques, for secure transmission of data without leakage and unauthorized access. In this paper, a technique,… More >

Kathiravan Srinivasan¹, Lalit Garg^2,*, Debajit Datta³, Abdulellah A. Alaboudi⁴, N. Z. Jhanjhi⁵, Rishav Agarwal³, Anmol George Thomas¹

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4109-4124, 2021, DOI:10.32604/cmc.2021.016736 - 06 May 2021

Abstract According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semi-automated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver’s distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an More >

Saleem Ibraheem Saleem^*, Adnan Mohsin Abdulazeez

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3353-3372, 2021, DOI:10.32604/cmc.2021.016342 - 06 May 2021

Abstract Writer identification (WI) based on handwritten text structures is typically focused on digital characteristics, with letters/strokes representing the information acquired from the current research in the integration of individual writing habits/styles. Previous studies have indicated that a word’s attributes contribute to greater recognition than the attributes of a character or stroke. As a result of the complexity of Arabic handwriting, segmenting and separating letters and strokes from a script poses a challenge in addition to WI schemes. In this work, we propose new texture features for WI based on text. The histogram of oriented gradient… More >

Mohammad Yamin^1,*, Adnan Ahmed Abi Sen², Zenah Mahmoud AlKubaisy¹, Rahaf Almarzouki¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2283-2298, 2021, DOI:10.32604/cmc.2021.017433 - 13 April 2021

Abstract COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose… More >

Sidharth Samanta¹, Mrutyunjaya Panda¹, Somula Ramasubbareddy², S. Sankar³, Daniel Burgos^4,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1937-1948, 2021, DOI:10.32604/cmc.2021.014406 - 13 April 2021

Abstract Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) More >

Yang Wu¹, Lihong Xu^1,*, Erik D. Goodman²

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 561-576, 2021, DOI:10.32604/iasc.2021.016415 - 01 April 2021

Abstract Deep convolutional neural network (DCNN) requires a lot of data for training, but there has always been data vacuum in agriculture, making it difficult to label all existing data accurately. Therefore, a lightweight tomato leaf disease identification network supported by Variational auto-Encoder (VAE) is proposed to improve the accuracy of crop leaf disease identification. In the lightweight network, multi-scale convolution can expand the network width, enrich the extracted features, and reduce model parameters such as deep separable convolution. VAE makes full use of a large amount of unlabeled data to achieve unsupervised learning, and then… More >