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 >

Junghyun Bum¹, Hyunseung Choo¹, Joyce Jiyoung Whang^2,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1963-1977, 2021, DOI:10.32604/cmc.2021.014931

Abstract Lifelog is a digital record of an individual’s daily life. It collects, records, and archives a large amount of unstructured data; therefore, techniques are required to organize and summarize those data for easy retrieval. Lifelogging has been utilized for diverse applications including healthcare, self-tracking, and entertainment, among others. With regard to the image-based lifelogging, even though most users prefer to present photos with facial expressions that allow us to infer their emotions, there have been few studies on lifelogging techniques that focus upon users’ emotions. In this paper, we develop a system that extracts users’ own photos from their smartphones… 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 >

Ji Qian¹, Fang Liu^2,*, Donghui Li³, Xin Jin⁴, Feng Li⁴

Journal of Cyber Security, Vol.2, No.4, pp. 157-166, 2020, DOI:10.32604/jcs.2020.011169

Abstract Anomaly detection using KPI (Key Performance Indicator) is critical for Internet-based services to maintain high service availability. However, given the velocity, volume, and diversified nature of monitoring data, it is difficult to obtain enough labelled data to build an accurate anomaly detection model for using supervised machine leaning methods. In this paper, we propose an automatic and generic transfer learning strategy: Detecting anomalies on a new KPI by using pretrained model on existing selected labelled KPI. Our approach, called KADT (KPI Anomaly Detection based on Transfer Learning), integrates KPI clustering and model pretrained techniques. KPI clustering is used to obtain… More >

Chung Le Van¹, Vikram Puri¹, Nguyen Thanh Thao², Dac-Nhuong Le^3,4,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 17-33, 2021, DOI:10.32604/cmc.2020.013125

Abstract With the rapid growth of the autonomous system, deep learning has become integral parts to enumerate applications especially in the case of healthcare systems. Human body vertebrae are the longest and complex parts of the human body. There are numerous kinds of conditions such as scoliosis, vertebra degeneration, and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone. Early detection of these problems is very important otherwise patients will suffer from a disease for a lifetime. In this proposed system, we developed an autonomous system that detects lumbar implants and diagnoses scoliosis from the… More >

Xing Deng^1,2, Haijian Shao^1,2,*, Liang Shi³, Xia Wang^4,5, Tongling Xie⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 579-596, 2020, DOI:10.32604/cmes.2020.011920

Abstract The Coronavirus Disease 2019 (COVID-19) is wreaking havoc around the world, bring out that the enormous pressure on national health and medical staff systems. One of the most effective and critical steps in the fight against COVID-19, is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging. In this paper, five keras-related deep learning models: ResNet50, InceptionResNetV2, Xception, transfer learning and pre-trained VGGNet16 is applied to formulate an classification–detection approaches of COVID-19. Two benchmark methods SVM (Support Vector Machine), CNN (Convolutional Neural Networks) are provided to compare with the classification–detection approaches based on… More >

Inzamam Mashood Nasir¹, Muhammad Attique Khan^1,*, Majed Alhaisoni², Tanzila Saba³, Amjad Rehman³, Tassawar Iqbal⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1017-1033, 2020, DOI:10.32604/cmes.2020.010943

Abstract Comic character detection is becoming an exciting and growing research area in the domain of machine learning. In this regard, recently, many methods are proposed to provide adequate performance. However, most of these methods utilized the custom datasets, containing a few hundred images and fewer classes, to evaluate the performances of their models without comparing it, with some standard datasets. This article takes advantage of utilizing a standard publicly dataset taken from a competition, and proposes a generic data balancing technique for imbalanced dataset to enhance and enable the in-depth training of the CNN. In addition, to classify the superheroes… More >

Mohammad Shorfuzzaman^{1, *}, Mehedi Masud¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1359-1381, 2020, DOI:10.32604/cmc.2020.011326

Abstract Coronavirus disease (COVID-19) is an extremely infectious disease and possibly causes acute respiratory distress or in severe cases may lead to death. There has already been some research in dealing with coronavirus using machine learning algorithms, but few have presented a truly comprehensive view. In this research, we show how convolutional neural network (CNN) can be useful to detect COVID-19 using chest X-ray images. We leverage the CNN-based pre-trained models as feature extractors to substantiate transfer learning and add our own classifier in detecting COVID-19. In this regard, we evaluate performance of five different pre-trained models with fine-tuning the weights… More >

Hongbo Wang^{1, *}, Qian Xue¹, Tong Cui¹, Yangyang Li², Huacheng Zeng³

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 337-351, 2020, DOI:10.32604/cmc.2020.07290

Abstract As a major function of smart transportation in smart cities, vehicle model recognition plays an important role in intelligent transportation. Due to the difference among different vehicle models recognition datasets, the accuracy of network model training in one scene will be greatly reduced in another one. However, if you don’t have a lot of vehicle model datasets for the current scene, you cannot properly train a model. To address this problem, we study the problem of cold start of vehicle model recognition under cross-scenario. Under the condition of small amount of datasets, combined with the method of transfer learning, load… More >

M. Vilasini^{1, *}, P. Ramamoorthy²

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1445-1472, 2020, DOI:10.32604/cmc.2020.08857

Abstract Leaf species identification leads to multitude of societal applications. There is enormous research in the lines of plant identification using pattern recognition. With the help of robust algorithms for leaf identification, rural medicine has the potential to reappear as like the previous decades. This paper discusses CNN based approaches for Indian leaf species identification from white background using smartphones. Variations of CNN models over the features like traditional shape, texture, color and venation apart from the other miniature features of uniformity of edge patterns, leaf tip, margin and other statistical features are explored for efficient leaf classification. More >