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  • Open Access


    An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

    Muhammad Asif Saleem1, Muhammad Aamir1,2, * ,*, 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 More >

  • Open Access


    RDA- CNN: Enhanced Super Resolution Method for Rice Plant Disease Classification

    K. Sathya1,*, M. Rajalakshmi2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 33-47, 2022, DOI:10.32604/csse.2022.022206

    Abstract In the field of agriculture, the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants. This research focuses on identifying the plant diseases and detecting them promptly through the advancements in the field of computer vision. The images obtained from in-field farms are typically with less visual information. However, there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images. We propose a novel Reconstructed Disease Aware–Convolutional Neural Network (RDA-CNN), inspired by recent CNN… More >

  • Open Access


    Heart Disease Classification Using Multiple K-PCA and Hybrid Deep Learning Approach

    S. Kusuma*, Dr. Jothi K. R

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1273-1289, 2022, DOI:10.32604/csse.2022.021741

    Abstract One of the severe health problems and the most common types of heart disease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart attack occurs without any symptoms, it cannot be cured by an intelligent detection system. An effective diagnosis and detection of CHD should prevent human casualties. Moreover, intelligent systems employ clinical-based decision support approaches to assist physicians in providing another option for diagnosing and detecting HD. This paper aims to introduce a heart disease prediction model including phases like… More >

  • Open Access


    Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

    Fahd N. Al-Wesabi1,2,*, Amani Abdulrahman Albraikan3, Anwer Mustafa Hilal4, Majdy M. Eltahir1, Manar Ahmed Hamza4, Abu Sarwar Zamani4

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6223-6238, 2022, DOI:10.32604/cmc.2022.021299

    Abstract Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure… More >

  • Open Access


    A Secure IoT-Cloud Based Healthcare System for Disease Classification Using Neural Network

    M. Vedaraj*, P. Ezhumalai

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 95-108, 2022, DOI:10.32604/csse.2022.019976

    Abstract The integration of the Internet of Things (IoT) and cloud computing is the most popular growing technology in the IT world. IoT integrated cloud computing technology can be used in smart cities, health care, smart homes, environmental monitoring, etc. In recent days, IoT integrated cloud can be used in the health care system for remote patient care, emergency care, disease prediction, pharmacy management, etc. but, still, security of patient data and disease prediction accuracy is a major concern. Numerous machine learning approaches were used for effective early disease prediction. However, machine learning takes more time… More >

  • Open Access


    Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

    R. H. Aswathy1,*, P. Suresh1, Mohamed Yacin Sikkandar2, S. Abdel-Khalek3, Hesham Alhumyani4, Rashid A. Saeed4, Romany F. Mansour5

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2097-2111, 2022, DOI:10.32604/cmc.2022.019790

    Abstract In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the deadliest diseases that has high mortality rate across the globe. The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as FPA-DNN. The steps involved in the… More >

  • Open Access


    Arrhythmia and Disease Classification Based on Deep Learning Techniques

    Ramya G. Franklin1,*, B. Muthukumar2

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 835-851, 2022, DOI:10.32604/iasc.2022.019877

    Abstract Electrocardiography (ECG) is a method for monitoring the human heart’s electrical activity. ECG signal is often used by clinical experts in the collected time arrangement for the evaluation of any rhythmic circumstances of a topic. The research was carried to make the assignment computerized by displaying the problem with encoder-decoder methods, by using misfortune appropriation to predict standard or anomalous information. The two Convolutional Neural Networks (CNNs) and the Long Short-Term Memory (LSTM) fully connected layer (FCL) have shown improved levels over deep learning networks (DLNs) across a wide range of applications such as speech… More >

  • Open Access


    Classification of Citrus Plant Diseases Using Deep Transfer Learning

    Muhammad Zia Ur Rehman1, Fawad Ahmed1, Muhammad Attique Khan2, Usman Tariq3, Sajjad Shaukat Jamal4, Jawad Ahmad5,*, Iqtadar Hussain6

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1401-1417, 2022, DOI:10.32604/cmc.2022.019046

    Abstract In recent years, the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits. This in turn has helped in improving the quality and production of vegetables and fruits. Citrus fruits are well known for their taste and nutritional values. They are one of the natural and well known sources of vitamin C and planted worldwide. There are several diseases which severely affect the quality and yield of citrus fruits. In this paper, a new deep learning based technique is proposed for citrus disease classification.… More >

  • Open Access


    Plant Disease Classification Using Deep Bilinear CNN

    D. Srinivasa Rao1, Ramesh Babu Ch2, V. Sravan Kiran1, N. Rajasekhar3,*, 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


    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

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  • Open Access


    Cardiac Arrhythmia Disease Classification Using LSTM Deep Learning Approach

    Muhammad Ashfaq Khan, Yangwoo Kim*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 427-443, 2021, DOI:10.32604/cmc.2021.014682

    Abstract Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the… More >

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