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  • Open Access

    ARTICLE

    Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

    Shafaq Abbas1, Muhammad Attique Khan1, Majed Alhaisoni2, Usman Tariq3, Ammar Armghan4, Fayadh Alenezi4, Arnab Majumdar5, Orawit Thinnukool6,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1139-1159, 2023, DOI:10.32604/cmc.2023.028824

    Abstract Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

    V. Nirmala1,*, B. Gomathy2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2585-2601, 2023, DOI:10.32604/csse.2023.027512

    Abstract In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method based on Convolutional Neural Network… More >

  • Open Access

    ARTICLE

    Brain Tumor Detection and Segmentation Using RCNN

    Maham Khan1, Syed Adnan Shah1, Tenvir Ali2, Quratulain2, Aymen Khan2, Gyu Sang Choi3,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5005-5020, 2022, DOI:10.32604/cmc.2022.023007

    Abstract Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification and segmentation using MR images,… More >

  • Open Access

    ARTICLE

    Efficient Urban Green Space Destruction and Crop Stress Yield Assessment Model

    G. Chamundeeswari1, S. Srinivasan1,*, S. Prasanna Bharathi1,2

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 515-534, 2022, DOI:10.32604/iasc.2022.023449

    Abstract Remote sensing (RS) is a very reliable and effective way to monitor the environment and landscape changes. In today’s world topographic maps are very important in science, research, planning and management. It is quite possible to detect the changes based on RS data which is obtained at two different times. In this paper, we propose an optimal technique that handles problems like urban green space destruction and detection of crop stress assessment. Firstly, the optimal preprocessing is performed on the given RS dataset, for image enhancement using geometric correction and image registration. Secondly, we propose the improved cat swarm optimization… More >

  • Open Access

    ARTICLE

    Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning

    Khalid Mahmood Aamir1, Muhammad Ramzan1,2, Saima Skinadar1, Hikmat Ullah Khan3, Usman Tariq4, Hyunsoo Lee5, Yunyoung Nam5,*, Muhammad Attique Khan6

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 17-33, 2022, DOI:10.32604/cmc.2022.018613

    Abstract This paper focuses on detecting diseased signals and arrhythmias classification into two classes: ventricular tachycardia and premature ventricular contraction. The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person. The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency (IF). Once a signal taken from a patient is detected, then the classifier takes that signal as input and classifies the target disease by predicting the class label. While applying the classifier, templates are designed separately for ventricular tachycardia and… More >

  • Open Access

    ARTICLE

    Multi-Model Detection of Lung Cancer Using Unsupervised Diffusion Classification Algorithm

    N. Jayanthi1,*, D. Manohari2, Mohamed Yacin Sikkandar3, Mohamed Abdelkader Aboamer3, Mohamed Ibrahim Waly3, C. Bharatiraja4

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1317-1329, 2022, DOI:10.32604/iasc.2022.018974

    Abstract Lung cancer is a curable disease if detected early, and its mortality rate decreases with forwarding treatment measures. At first, an easy and accurate way to detect is by using image processing techniques on the cancer-affected images captured from the patients. This paper proposes a novel lung cancer detection method. Firstly, an adaptive median filter algorithm (AMF) is applied to preprocess those images for improving the quality of the affected area. Then, a supervised image edge detection algorithm (SIED) is presented to segment those images. Then, feature extraction is employed to extract the mean, standard deviation, energy, contrast, etc., of… More >

  • Open Access

    Malaria Blood Smear Classification Using Deep Learning and Best Features Selection

    Talha Imran1, Muhammad Attique Khan2, Muhammad Sharif1, Usman Tariq3, Yu-Dong Zhang4, Yunyoung Nam5,*, Yunja Nam5, Byeong-Gwon Kang5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1875-1891, 2022, DOI:10.32604/cmc.2022.018946

    Abstract Malaria is a critical health condition that affects both sultry and frigid region worldwide, giving rise to millions of cases of disease and thousands of deaths over the years. Malaria is caused by parasites that enter the human red blood cells, grow there, and damage them over time. Therefore, it is diagnosed by a detailed examination of blood cells under the microscope. This is the most extensively used malaria diagnosis technique, but it yields limited and unreliable results due to the manual human involvement. In this work, an automated malaria blood smear classification model is proposed, which takes images of… More >

  • Open Access

    ARTICLE

    COVID19 Classification Using CT Images via Ensembles of Deep Learning Models

    Abdul Majid1, Muhammad Attique Khan1, Yunyoung Nam2,*, Usman Tariq3, Sudipta Roy4, Reham R. Mostafa5, Rasha H. Sakr6

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 319-337, 2021, DOI:10.32604/cmc.2021.016816

    Abstract The recent COVID-19 pandemic caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a significant impact on human life and the economy around the world. A reverse transcription polymerase chain reaction (RT-PCR) test is used to screen for this disease, but its low sensitivity means that it is not sufficient for early detection and treatment. As RT-PCR is a time-consuming procedure, there is interest in the introduction of automated techniques for diagnosis. Deep learning has a key role to play in the field of medical imaging. The most important issue in this area is the… More >

  • Open Access

    ARTICLE

    Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

    J. Jayanthi1,*, M. Kavitha2, T. Jayasankar3, A. Sagai Francis Britto4, N. B. Prakash5, Mohamed Yacin Sikkandar6, C. Bharathiraja7

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4191-4203, 2021, DOI:10.32604/cmc.2021.012252

    Abstract Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era. Among various cancers identified so far, glioma, a type of brain tumor, is one of the deadliest cancers, and it remains challenging to the medicinal world. The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease. Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time. From the captured image, the… More >

  • Open Access

    ARTICLE

    Single-Choice Aided Marking System Research Based on Back Propagation Neural Network

    Yunzuo Zhang*, Yi Li, Wei Guo, Lei Huo, Jiayu Zhang, Kaina Guo

    Journal of Cyber Security, Vol.3, No.1, pp. 45-54, 2021, DOI:10.32604/jcs.2021.017071

    Abstract In the field of educational examination, automatic marking technology plays an essential role in improving the efficiency of marking and liberating the labor force. At present, the implementation of the policy of expanding erolments has caused a serious decline in the teacher-student ratio in colleges and universities. The traditional marking system based on Optical Mark Reader technology can no longer meet the requirements of liberating the labor force of teachers in small and medium-sized examinations. With the development of image processing and artificial neural network technology, the recognition of handwritten character in the field of pattern recognition has attracted the… More >

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