Muhammad Asif Saleem¹, Muhammad Aamir^{1,2, * ,*}, Rosziati Ibrahim¹, Norhalina Senan¹, Tahir Alyas³

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6053-6067, 2022, DOI:10.32604/cmc.2022.022215

Abstract Plant disease classification based on digital pictures is challenging. Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize, identify, and diagnose plant diseases in the previous decade. Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries. However, some diseases that are blocking the improvement in paddy production are considered as an ominous threat. Convolution Neural Network (CNN) has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology.… More >

G. Sriram¹, T. R. Ganesh Babu², R. Praveena^2,*, J. V. Anand³

Molecular & Cellular Biomechanics, Vol.19, No.1, pp. 29-40, 2022, DOI:10.32604/mcb.2022.016966

Abstract Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts… More >

Fayadh Alenezi^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3425-3443, 2022, DOI:10.32604/cmc.2022.023339

Abstract Image dehazing is still an open research topic that has been undergoing a lot of development, especially with the renewed interest in machine learning-based methods. A major challenge of the existing dehazing methods is the estimation of transmittance, which is the key element of haze-affected imaging models. Conventional methods are based on a set of assumptions that reduce the solution search space. However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image. In this paper we reduce the number of simplified hypotheses in order to attain… More >

Erssa Arif^1,*, Syed Khuram Shahzad², Rehman Mustafa¹, Muhammad Arfan Jaffar³, Muhammad Waseem Iqbal⁴

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 909-922, 2022, DOI:10.32604/iasc.2022.021061

Abstract The conventional surveillance and control system of Closed-Circuit Television (CCTV) cameras require human resource supervision. Almost all the criminal activities take place using weapons mostly handheld gun, revolver, or pistol. Automatic gun detection is a vital requirement now-a-days. The use of real-time object detection system for the improvement of surveillance is a promising application of Convolutional Neural Networks (CNN). We are concerned about the real-time detection of weapons for the surveillance cameras, so we focused on the implementation and comparison of faster approaches such as Region (R-CNN) and Region Fully Convolutional Networks (R-FCN) with feature extractor Visual Geometry Group (VGG)… More >

Sang-Hyun Lee^*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1987-1998, 2022, DOI:10.32604/cmc.2022.022554

Abstract Currently, there are many limitations to classify images of small objects. In addition, there are limitations such as error detection due to external factors, and there is also a disadvantage that it is difficult to accurately distinguish between various objects. This paper uses a convolutional neural network (CNN) algorithm to recognize and classify object images of very small moths and obtain precise data images. A convolution neural network algorithm is used for image data classification, and the classified image is transformed into image data to learn the topological structure of the image. To improve the accuracy of the image classification… More >

K. Prabhu^1,*, S. SathishKumar², M. Sivachitra³, S. Dineshkumar², P. Sathiyabama⁴

Computer Systems Science and Engineering, Vol.41, No.1, pp. 415-426, 2022, DOI:10.32604/csse.2022.019749

Abstract Facial Expression Recognition (FER) has been an interesting area of research in places where there is human-computer interaction. Human psychology, emotions and behaviors can be analyzed in FER. Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces. Recently, Deep Learning Techniques (DLT) have gained popularity in applications of real-world problems including recognition of human emotions. The human face reflects emotional states and human intentions. An expression is the most natural and powerful way of communicating non-verbally. Systems which form communications between the two are termed Human Machine Interaction (HMI)… More >

Mohamed Ibrahim Waly¹, Mohamed Yacin Sikkandar¹, Mohamed Abdelkader Aboamer¹, Seifedine Kadry², Orawit Thinnukool^3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3295-3309, 2022, DOI:10.32604/cmc.2022.020713

Abstract Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical… More >

H. Varun Chand^*, J. Karthikeyan

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 717-728, 2022, DOI:10.32604/iasc.2022.020008

Abstract

The drowsiness of the driver and rash driving are the major causes of road accidents, which result in loss of valuable life, and deteriorate the safety in the road traffic. Reliable and precise driver drowsiness systems are required to prevent road accidents and to improve road traffic safety. Various driver drowsiness detection systems have been designed with different technologies which have an affinity towards the unique parameter of detecting the drowsiness of the driver. This paper proposes a novel model of multi-level distribution of detecting the driver drowsiness using the Convolution Neural Networks (CNN) followed by the emotion analysis. The… More >

Mohammed A. Alghassab^*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 637-652, 2022, DOI:10.32604/cmc.2022.019527

Abstract Printed Circuit Boards (PCBs) are very important for proper functioning of any electronic device. PCBs are installed in almost all the electronic device and their functionality is dependent on the perfection of PCBs. If PCBs do not function properly then the whole electric machine might fail. So, keeping this in mind researchers are working in this field to develop error free PCBs. Initially these PCBs were examined by the human beings manually, but the human error did not give good results as sometime defected PCBs were categorized as non-defective. So, researchers and experts transformed this manual traditional examination to automated… More >

D. Srinivasa Rao¹, Ramesh Babu Ch², V. Sravan Kiran¹, N. Rajasekhar^3,*, Kalyanapu Srinivas⁴, P. Shilhora Akshay¹, G. Sai Mohan¹, B. Lalith Bharadwaj¹

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 161-176, 2022, DOI:10.32604/iasc.2022.017706

Abstract

Plant diseases have become a major threat in farming and provision of food. Various plant diseases have affected the natural growth of the plants and the infected plants are the leading factors for loss of crop production. The manual detection and identification of the plant diseases require a careful and observative examination through expertise. To overcome manual testing procedures an automated identification and detection can be implied which provides faster, scalable and precisive solutions. In this research, the contributions of our work are threefold. Firstly, a bi-linear convolution neural network (Bi-CNNs) for plant leaf disease identification and classification is proposed.… More >