Wael Alosaimi^1,*, Hashem Alyami², M. Irfan Uddin³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1665-1677, 2021, DOI:10.32604/cmc.2021.014950

Abstract To meet the food requirements of the seven billion people on Earth, multiple advancements in agriculture and industry have been made. The main threat to food items is from diseases and pests which affect the quality and quantity of food. Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food. Still these mechanisms require manual efforts and human expertise to diagnose diseases. In the current decade Artificial Intelligence is used to automate different processes, including agricultural processes, such as automatic harvesting. Machine Learning techniques are becoming… More >

Sally M. Elghamrawy¹, Aboul Ella Hassnien^2,*, Vaclav Snasel³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2353-2371, 2021, DOI:10.32604/cmc.2021.014767

Abstract Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization… More >

Huaning Song¹, Jiansheng Peng^1,2,*, Nianyang Tuo¹, Haiying Xia², Yiyun Peng³

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 581-592, 2021, DOI:10.32604/iasc.2021.014152

Abstract Accurate identification of sugarcane buds, as one of the key technologies in sugarcane plantation, becomes very necessary for its mechanized and intelligent advancements. In the traditional methods of sugarcane bud recognition, a significant amount of algorithms work goes into the location and recognition of sugarcane bud images. A Convolutional Neural Network (CNN) for classifying the bud conditions is proposed in this paper. Firstly, we convert the colorful sugarcane images into gray ones, unify the size of them, and make a TFRecord format data set, which contains 1100 positive samples and 1100 negative samples. Then, a CNN mode is developed to… 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 >

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 >

C. Sivamani¹, M. Rajeswari², E. Golden Julie³, Y. Harold Robinson⁴, Vimal Shanmuganathan⁵, Seifedine Kadry⁶, Yunyoung Nam^7,*

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 1-13, 2021, DOI:10.32604/iasc.2021.013705

Abstract Road accident is a potential risk to the lives of both drivers and passers-by. Many road accidents occur due to the improper condition of the vehicle tyres after long term usage. Thus, tyres need to be inspected and analyzed while manufacturing to avoid serious road problems. However, tyre wear is a multifaceted happening. It normally needs the non-linearly on many limitations, like tyre formation and plan, vehicle category, conditions of the road. Yet, tyre wear has numerous profitable and environmental inferences particularly due to maintenance costs and traffic safety implications. Thus, the risk to calculate tyre wear is therefore of… 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 >