Sengul Bayrak^1,2,*, Eylem Yucel², Hidayet Takci³, Ruya Samli²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1427-1446, 2021, DOI:10.32604/cmc.2021.015524 - 21 July 2021

Abstract Today, electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor. These signals are frequently used to obtain information about brain neurons and may detect disorders that affect the brain, such as epilepsy. Electroencephalogram (EEG) signals are however prone to artefacts. These artefacts must be removed to obtain accurate and meaningful signals. Currently, computer-aided systems have been used for this purpose. These systems provide high computing power, problem-specific development, and other advantages. In this study, a new clinical decision support system was developed for individuals to detect epileptic seizures… More >

V. Sudha^1,*, T. R. Ganesh Babu², N. Vikram¹, R. Raja²

Molecular & Cellular Biomechanics, Vol.18, No.3, pp. 139-145, 2021, DOI:10.32604/mcb.2021.016056 - 15 July 2021

Abstract Diabetic Retinopathy (DR) is a disease that occurs in the eye which results in blindness as it passes to proliferative stage. Diabetes can significantly result in symptoms like blurring of vision, kidney failure, nervous damage. Hence it has become necessary to identify retinal damage that occurs in diabetic eye due to raised glucose level in its initial stage itself. Hence automated detection of anamoly has become very essential. The appearance of crimson and yellow lesions is considered as the earliest symptoms of DR which are called as hemorrhages and exudates. If DR is analysed at… More >

Sajib Sarker, Ling Tan^*, Wenjie Ma, Shanshan Rong, Osibo Benjamin Kwapong, Oscar Famous Darteh

Journal on Internet of Things, Vol.3, No.2, pp. 39-51, 2021, DOI:10.32604/jiot.2021.014877 - 15 July 2021

Abstract The novel coronavirus 2019 (COVID-19) rapidly spreading around the world and turns into a pandemic situation, consequently, detecting the coronavirus (COVID-19) affected patients are now the most critical task for medical specialists. The deficiency of medical testing kits leading to huge complexity in detecting COVID-19 patients worldwide, resulting in the number of infected cases is expanding. Therefore, a significant study is necessary about detecting COVID-19 patients using an automated diagnosis method, which hinders the spreading of coronavirus. In this paper, the study suggests a Deep Convolutional Neural Network-based multi-classification framework (COVMCNet) using eight different pre-trained… More >

Hanan A. Hosni Mahmoud^*, Amal H. Alharbi, Doaa S. Khafga

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 803-814, 2021, DOI:10.32604/iasc.2021.018607 - 01 July 2021

Abstract In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms. We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer. The Deep-CNN is utilized to extract features from mammograms. A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases. The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier. Two texture features are included: texture features of spatial dependency and gradient-based histograms.… More >

Mengyuan Liao¹, Jing Duan^2,3,*, Rong Zhang^2,3, Xu Zhou^2,3, Xi Wu¹, Xin Wang⁴, Jinrong Hu¹

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 787-801, 2021, DOI:10.32604/iasc.2021.018190 - 01 July 2021

Abstract In the field of weather modification, it is important to accurately identify the ice crystal particles in ice clouds. When ice crystal habits are correctly identified, cloud structure can be further understood and cloud seeding and other methods of weather modification can be used to change the microstructure of the cloud. Consequently, weather phenomena can be changed at an appropriate time to support human production and quality of life. However, ice crystal morphology is varied. Traditional ice crystal particle classification methods are based on expert experience, which is subjective and unreliable for the identification of More >

Majid Hussain^1,2, Dileep Kumar¹, Imtiaz Hussain Kalwar³, Tayab Din Memon^4,5, Zubair Ahmed Memon⁶, Kashif Nisar^7,*, Bhawani Shankar Chowdhry¹

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 869-883, 2021, DOI:10.32604/iasc.2021.017790 - 01 July 2021

Abstract Induction motors (IMs) are the workhorse of the industry and are subjected to a harsh environment. Due to their operating conditions, they are exposed to different kinds of unavoidable faults that lead to unscheduled downtimes and losses. These faults may be detected early through predictive maintenance (i.e., deployment of condition monitoring systems). Motor current signature analysis (MCSA) is the most widely used technique to detect various faults in industrial motors. The stator winding faults (SWF) are one of the major faults. In this paper, we present an induction motor fault detection and identification system using… More >

Jianping Yu^1,2,3, Zhuqing Qiao¹, Wensheng Tang^1,2,3,*, Danni Wang¹, Xiaojun Cao⁴

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 713-728, 2021, DOI:10.32604/iasc.2021.017154 - 01 July 2021

Abstract In a distributed system such as Internet of things, the data volume from each node may be limited. Such limited data volume may constrain the performance of the machine learning classification model. How to effectively improve the performance of the classification in a distributed system has been a challenging problem in the field of data mining. Sharing data in the distributed network can enlarge the training data volume and improve the machine learning classification model’s accuracy. In this work, we take data sharing and the quality of shared data into consideration and propose an efficient… More >

Yanling An¹, Shaohai Hu^1,*, Shuaiqi Liu^2,3, Jie Zhao^2,3,*, Yu-Dong Zhang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 203-222, 2021, DOI:10.32604/cmes.2021.015159 - 28 June 2021

Abstract This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs) and discriminate them as benign or malignant. The system firstly uses variational level set active contour with gradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images. Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction method based on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, this paper introduces a weight factor to improve support vector machine, offering different classes of More >

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 >

Yousef Sanajalwe^1,2,*, Mohammed Anbar¹, Salam Al-E’mari¹

Computer Systems Science and Engineering, Vol.39, No.1, pp. 15-35, 2021, DOI:10.32604/csse.2021.017191 - 10 June 2021

Abstract The novel coronavirus disease 2019 (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of people. The first step to mitigate and control its spread is to identify and isolate the infected people. But, because of the lack of reverse transcription polymerase chain reaction (RT-CPR) tests, it is important to discover suspected COVID-19 cases as early as possible, such as by scan analysis and chest X-ray by radiologists. However, chest X-ray analysis is relatively time-consuming since it requires more than 15 minutes per case. In this… More >