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  • Open Access

    ARTICLE

    A Comparative Analysis of Machine Learning Algorithms to Predict Liver Disease

    Mounita Ghosh1, Md. Mohsin Sarker Raihan1, M. Raihan2, Laboni Akter1, Anupam Kumar Bairagi3, Sultan S. Alshamrani4, Mehedi Masud5,*

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 917-928, 2021, DOI:10.32604/iasc.2021.017989

    Abstract The liver is considered an essential organ in the human body. Liver disorders have risen globally at an unprecedented pace due to unhealthy lifestyles and excessive alcohol consumption. Chronic liver disease is one of the principal causes of death affecting large portions of the global population. An accumulation of liver-damaging factors deteriorates this condition. Obesity, an undiagnosed hepatitis infection, alcohol abuse, coughing or vomiting blood, kidney or hepatic failure, jaundice, liver encephalopathy, and many more disorders are responsible for it. Thus, immediate intervention is needed to diagnose the ailment before it is too late. Therefore,… More >

  • Open Access

    ARTICLE

    Early Detection of Lung Carcinoma Using Machine Learning

    A. Sheryl Oliver1, T. Jayasankar2, K. R. Sekar3,*, T. Kalavathi Devi4, R. Shalini5, S. Poojalaxmi5, N. G. Viswesh5

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 755-770, 2021, DOI:10.32604/iasc.2021.016242

    Abstract Lung cancer is a poorly understood disease. Smokers may develop lung cancer due to the inhalation of carcinogenic substances while smoking, but non-smokers may develop this disease as well. Lung cancer can spread to other parts of the body and this process is called metastasis. Because the lung cancer is difficult to identify in the initial stages. The objective of this work is to reduce the mortality rate of the disease by identifying it at an earlier stage based on the existing symptoms. Artificial intelligence plays active roles in tasks such as entropy extraction through… More >

  • Open Access

    ARTICLE

    Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

    Jiaming Mao1,*, Mingming Zhang1, Mu Chen2, Lu Chen2, Fei Xia1, Lei Fan1, ZiXuan Wang3, Wenbing Zhao4

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 373-390, 2021, DOI:10.32604/csse.2021.018086

    Abstract The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning… More >

  • Open Access

    REVIEW

    Review of Computational Techniques for the Analysis of Abnormal Patterns of ECG Signal Provoked by Cardiac Disease

    Revathi Jothiramalingam1, Anitha Jude2, Duraisamy Jude Hemanth2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 875-906, 2021, DOI:10.32604/cmes.2021.016485

    Abstract The 12-lead ECG aids in the diagnosis of myocardial infarction and is helpful in the prediction of cardiovascular disease complications. It does, though, have certain drawbacks. For other electrocardiographic anomalies such as Left Bundle Branch Block and Left Ventricular Hypertrophy syndrome, the ECG signal with Myocardial Infarction is difficult to interpret. These diseases cause variations in the ST portion of the ECG signal. It reduces the clarity of ECG signals, making it more difficult to diagnose these diseases. As a result, the specialist is misled into making an erroneous diagnosis by using the incorrect therapeutic More >

  • Open Access

    ARTICLE

    The Design of Intelligent Wastebin Based on AT89S52

    Juan Guo*, Xiaoying Yu

    Journal of Information Hiding and Privacy Protection, Vol.3, No.2, pp. 61-68, 2021, DOI:10.32604/jihpp.2021.017451

    Abstract Mainly introduces intelligent classification trash can be dedicated to solving indoor household garbage classification. The trash can is based on AT89S52 single-chip microcomputer as the main control chip. The single-chip microcomputer realizes the intelligent classification of garbage by controlling the voice module, mechanical drive module, and infrared detection module. The use of voice control technology and infrared detection technology makes the trash can have voice control and overflow alarm functions. The design has the advantages of simple and intelligent operation, simple structure, stable performance, low investment, etc., which can further effectively isolate people and garbage, More >

  • Open Access

    ARTICLE

    A Multi-Task Network for Cardiac Magnetic Resonance Image Segmentation and Classification

    Jing Peng1,2,4, Chaoyang Xia2, Yuanwei Xu3, Xiaojie Li2, Xi Wu2, Xiao Han1,4, Xinlai Chen5, Yucheng Chen3, Zhe Cui1,4,*

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 259-272, 2021, DOI:10.32604/iasc.2021.016749

    Abstract Cardiomyopathy is a group of diseases that affect the heart and can cause serious health problems. Segmentation and classification are important for automating the clinical diagnosis and treatment planning for cardiomyopathy. However, this automation is difficult because of the poor quality of cardiac magnetic resonance (CMR) imaging data and varying dimensions caused by movement of the ventricle. To address these problems, a deep multi-task framework based on a convolutional neural network (CNN) is proposed to segment the left ventricle (LV) myocardium and classify cardiopathy simultaneously. The proposed model consists of a longitudinal encoder–decoder structure that… More >

  • Open Access

    ARTICLE

    A Multi-Category Brain Tumor Classification Method Bases on Improved ResNet50

    Linguo Li1,2, Shujing Li1,*, Jian Su3

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2355-2366, 2021, DOI:10.32604/cmc.2021.019409

    Abstract Brain tumor is one of the most common tumors with high mortality. Early detection is of great significance for the treatment and rehabilitation of patients. The single channel convolution layer and pool layer of traditional convolutional neural network (CNN) structure can only accept limited local context information. And most of the current methods only focus on the classification of benign and malignant brain tumors, multi classification of brain tumors is not common. In response to these shortcomings, considering that convolution kernels of different sizes can extract more comprehensive features, we put forward the multi-size convolutional More >

  • Open Access

    ARTICLE

    Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

    Mohamed Esmail Karar1,2, Omar Reyad1,3, Mohammed Abd-Elnaby4, Abdel-Haleem Abdel-Aty5,6, Marwa Ahmed Shouman7,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2295-2312, 2021, DOI:10.32604/cmc.2021.018671

    Abstract Lightweight deep convolutional neural networks (CNNs) present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients. Recently, advantages of portable Ultrasound (US) imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases. In this paper, a new framework of lightweight deep learning classifiers, namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images. Compared to traditional deep learning models, lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources. Four main lightweight deep learning… More >

  • Open Access

    ARTICLE

    An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification

    Ahsan Aziz1, Muhammad Attique1, Usman Tariq2, Yunyoung Nam3,*, Muhammad Nazir1, Chang-Won Jeong4, Reham R. Mostafa5, Rasha H. Sakr6

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2653-2670, 2021, DOI:10.32604/cmc.2021.018606

    Abstract Owing to technological developments, Medical image analysis has received considerable attention in the rapid detection and classification of diseases. The brain is an essential organ in humans. Brain tumors cause loss of memory, vision, and name. In 2020, approximately 18,020 deaths occurred due to brain tumors. These cases can be minimized if a brain tumor is diagnosed at a very early stage. Computer vision researchers have introduced several techniques for brain tumor detection and classification. However, owing to many factors, this is still a challenging task. These challenges relate to the tumor size, the shape… More >

  • Open Access

    ARTICLE

    Toward Robust Classifiers for PDF Malware Detection

    Marwan Albahar*, Mohammed Thanoon, Monaj Alzilai, Alaa Alrehily, Munirah Alfaar, Maimoona Algamdi, Norah Alassaf

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2181-2202, 2021, DOI:10.32604/cmc.2021.018260

    Abstract Malicious Portable Document Format (PDF) files represent one of the largest threats in the computer security space. Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction. These approaches are time consuming, require substantial prior knowledge, and the list of features must be updated with each newly discovered vulnerability individually. In this study, we propose two models for PDF malware detection. The first model is a convolutional neural network (CNN) integrated into a standard deviation based regularization model to detect malicious PDF documents. The second model is a More >

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