Bin Yang^1,*, Lingyun Xiang², Xianyi Chen³, Wenjing Jia⁴

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 951-964, 2021, DOI:10.32604/cmc.2021.014839

Abstract Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network. However, due to the complexity of the human body, there are still many challenges to face in that process. One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients, online. This paper presents a novel chronic disease prediction system based on an incremental deep neural network. The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner. With time, the system can predict diabetes more and more… More >

Samina Amin^1,*, M. Irfan Uddin¹, M. Ali Zeb¹, Ala Abdulsalam Alarood², Marwan Mahmoud³, Monagi H. Alkinani⁴

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1317-1332, 2021, DOI:10.32604/cmc.2021.014733

Abstract Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques. Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media. The amount of data generated by social media platforms, such as Twitter, that can be used to track diseases is increasing rapidly. This paper proposes a method for the classification of tweets related to the outbreak of dengue using machine learning algorithms. An artificial neural network (ANN)-based method is developed using Global Vector… More >

Shaheen Khatoon^1,*, Md Maruf Hasan¹, Amna Asif¹, Majed Alshmari¹, Yun-Kiam Yap²

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 595-612, 2021, DOI:10.32604/cmc.2021.014580

Abstract Tomato production is affected by various threats, including pests, pathogens, and nutritional deficiencies during its growth process. If control is not timely, these threats affect the plant-growth, fruit-yield, or even loss of the entire crop, which is a key danger to farmers’ livelihood and food security. Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost. Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss. Recent developments in Artificial Intelligence (AI) and computer vision allow researchers… More >

Shahan Yamin Siddiqui^1,2, Iftikhar Naseer³, Muhammad Adnan Khan⁴, Muhammad Faheem Mushtaq⁵, Rizwan Ali Naqvi^6,*, Dildar Hussain⁷, Amir Haider⁸

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1033-1049, 2021, DOI:10.32604/cmc.2021.013952

Abstract Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally. According to clinical statistics, one woman out of eight is under the threat of breast cancer. Lifestyle and inheritance patterns may be a reason behind its spread among women. However, some preventive measures, such as tests and periodic clinical checks can mitigate its risk thereby, improving its survival chances substantially. Early diagnosis and initial stage treatment can help increase the survival rate. For that purpose, pathologists can gather support from nondestructive and efficient computer-aided diagnosis (CAD) systems. This study explores… More >

Jingming Xia¹, Yi Lu¹, Ling Tan^2,*, Ping Jiang³

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 613-624, 2021, DOI:10.32604/cmc.2021.013457

Abstract Multi-source information can be obtained through the fusion of infrared images and visible light images, which have the characteristics of complementary information. However, the existing acquisition methods of fusion images have disadvantages such as blurred edges, low contrast, and loss of details. Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform (NSST). Furthermore, the low-frequency subbands were fused by convolutional sparse representation (CSR), and the high-frequency subbands were fused by an improved pulse coupled neural network (IPCNN) algorithm,… More >

Nakhim Chea¹, Yunyoung Nam^2,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 411-426, 2021, DOI:10.32604/cmc.2021.013390

Abstract Various techniques to diagnose eye diseases such as diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD), are possible through deep learning algorithms. A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects. However, multiple major eye diseases, such as DR, GLC, and AMD, could not be detected simultaneously by computer-aided systems to date. There were just high-performance-outcome researches on a pair of healthy and eye-diseased group, besides of four categories of fundus image classification. To have a better knowledge of multi-categorical classification of fundus photographs, we used optimal residual… More >

Mohamed Elhoseny¹, Mazin Abed Mohammed^2,*, Salama A. Mostafa³, Karrar Hameed Abdulkareem⁴, Mashael S. Maashi⁵, Begonya Garcia-Zapirain⁶, Ammar Awad Mutlag⁷, Marwah Suliman Maashi⁸

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 51-71, 2021, DOI:10.32604/cmc.2021.012632

Abstract Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy… More >

Dongfang Yu, Jinwei Wang^*

Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 165-174, 2020, DOI:10.32604/jihpp.2020.010466

Abstract Chemical spectral analysis is contemporarily undergoing a revolution and drawing much attention of scientists owing to machine learning algorithms, in particular convolutional networks. Hence, this paper outlines the major machine learning and especially deep learning methods contributed to interpret chemical images, and overviews the current application, development and breakthrough in different spectral characterization. Brief categorization of reviewed literatures is provided for studies per application apparatus: X-Ray spectra, UV-Vis-IR spectra, Micro-scope, Raman spectra, Photoluminescence spectrum. End with the overview of existing circumstances in this research area, we provide unique insight and promising directions for the chemical imaging field to fully couple… More >

Peizhu Gong, Jin Liu^*, Shiqi Lv

Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 155-163, 2020, DOI:10.32604/jihpp.2020.010453

Abstract Image denoising is often used as a preprocessing step in computer vision tasks, which can help improve the accuracy of image processing models. Due to the imperfection of imaging systems, transmission media and recording equipment, digital images are often contaminated with various noises during their formation, which troubles the visual effects and even hinders people’s normal recognition. The pollution of noise directly affects the processing of image edge detection, feature extraction, pattern recognition, etc., making it difficult for people to break through the bottleneck by modifying the model. Many traditional filtering methods have shown poor performance since they do not… More >

Jong-Yih Kuo^*, Hui-Chi Lin, Chien-Hung Liu

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 53-68, 2021, DOI:10.32604/iasc.2021.014437

Abstract Predicting salary trends of students after employment is vital for helping students to develop their career plans. Particularly, salary is not only considered employment information for students to pursue jobs, but also serves as an important indicator for measuring employability and competitiveness of graduates. This paper considers salary prediction as an ordinal regression problem and uses deep learning techniques to build a salary prediction model for determining the relative ordering between different salary grades. Specifically, to solve this problem, the model uses students’ personal information, grades, and family data as input features and employs a multi-output deep neural network to… More >