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  • Open Access

    ARTICLE

    Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

    Swati Singh1, Isha Gupta2, Sheifali Gupta2, Deepika Koundal3,*, Sultan Aljahdali4, Shubham Mahajan5, Amit Kant Pandit5

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1849-1866, 2022, DOI:10.32604/cmc.2022.021875 - 03 November 2021

    Abstract In Agriculture Sciences, detection of diseases is one of the most challenging tasks. The mis-interpretations of plant diseases often lead to wrong pesticide selection, resulting in damage of crops. Hence, the automatic recognition of the diseases at earlier stages is important as well as economical for better quality and quantity of fruits. Computer aided detection (CAD) has proven as a supportive tool for disease detection and classification, thus allowing the identification of diseases and reducing the rate of degradation of fruit quality. In this research work, a model based on convolutional neural network with 19… More >

  • Open Access

    ARTICLE

    Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

    Visvasam Devadoss Ambeth Kumar1, Chetan Swarup2, Indhumathi Murugan1, Abhishek Kumar3, Kamred Udham Singh4, Teekam Singh5, Ramu Dubey6,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 855-869, 2022, DOI:10.32604/cmc.2022.021582 - 03 November 2021

    Abstract Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main… More >

  • Open Access

    ARTICLE

    Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

    K. S. Bhuvaneshwari1, J. Uma2, K. Venkatachalam3, Mehedi Masud4, Mohamed Abouhawwash5,6,*, T. Logeswaran7

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 683-695, 2022, DOI:10.32604/cmc.2022.021477 - 03 November 2021

    Abstract Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic… More >

  • Open Access

    ARTICLE

    Heart Failure Patient Survival Analysis with Multi Kernel Support Vector Machine

    R. Sujatha1, Jyotir Moy Chatterjee2, NZ Jhanjhi3, Thamer A. Tabbakh4, Zahrah A. Almusaylim5,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 115-129, 2022, DOI:10.32604/iasc.2022.019133 - 26 October 2021

    Abstract Heart failure (HF) is an intercontinental pandemic influencing in any event 26 million individuals globally and is expanding in commonness. HF healthiness consumptions are extensive and will increment significantly with a maturing populace. As per the World Health Organization (WHO), Cardiovascular diseases (CVDs) are the major reason for all-inclusive death, taking an expected 17.9 million lives per year. CVDs are a class of issues of the heart, blood vessels and include coronary heart sickness, cerebrovascular illness, rheumatic heart malady, and various other conditions. In the medical care industry, a lot of information is as often… More >

  • Open Access

    ARTICLE

    Optimal Data Placement and Replication Approach for SIoT with Edge

    B. Prabhu Shankar1,*, S. Chitra2

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 661-676, 2022, DOI:10.32604/csse.2022.019507 - 25 October 2021

    Abstract Social networks (SNs) are sources with extreme number of users around the world who are all sharing data like images, audio, and video to their friends using IoT devices. This concept is the so-called Social Internet of Things (SIot). The evolving nature of edge-cloud computing has enabled storage of a large volume of data from various sources, and this task demands an efficient storage procedure. For this kind of large volume of data storage, the usage of data replication using edge with geo-distributed cloud service area is suited to fulfill the user’s expectations with low… More >

  • Open Access

    ARTICLE

    Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold

    Muhammad Tariq Mahmood*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4867-4882, 2022, DOI:10.32604/cmc.2022.019544 - 11 October 2021

    Abstract Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type, scenarios and level of blurriness. In this paper, we propose an effective method for blur detection and segmentation based on transfer learning concept. The proposed method consists of two separate steps. In the first step, genetic programming (GP) model is developed that quantify the amount of blur for each pixel in the image. The GP model method uses the multi-resolution features of the image and it provides an improved blur More >

  • Open Access

    A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

    Shubha Sumesh1, John Yearwood1, Shamsul Huda1 and Shafiq Ahmad2,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4503-4521, 2022, DOI:10.32604/cmc.2022.015474 - 11 October 2021

    Abstract

    Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model

    More >

  • Open Access

    ARTICLE

    Modeling the Spread of COVID-19 by Leveraging Machine and Deep Learning Models

    Muhammad Adnan1, Maryam Altalhi2, Ala Abdulsalam Alarood3, M.Irfan Uddin1,*

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1857-1872, 2022, DOI:10.32604/iasc.2022.020606 - 09 October 2021

    Abstract Corona Virus disease 2019 (COVID-19) has caused a worldwide pandemic of cough, fever, headache, body aches, and respiratory ailments. COVID- 19 has now become a severe disease and one of the leading causes of death globally. Modeling and prediction of COVID-19 have become inevitable as it has affected people worldwide. With the availability of a large-scale universal COVID-19 dataset, machine learning (ML) techniques and algorithms occur to be the best choice for the analysis, modeling, and forecasting of this disease. In this research study, we used one deep learning algorithm called Artificial Neural Network (ANN)… More >

  • Open Access

    ARTICLE

    Hybrid Online Model for Predicting Diabetes Mellitus

    C. Mallika1,*, S. Selvamuthukumaran2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1873-1885, 2022, DOI:10.32604/iasc.2022.020543 - 09 October 2021

    Abstract Modern healthcare systems have become smart by synergizing the potentials of wireless sensors, the medical Internet of things, and big data science to provide better patient care while decreasing medical expenses. Large healthcare organizations generate and accumulate an incredible volume of data continuously. The already daunting volume of medical information has a massive amount of diagnostic features and logged details of patients for certain diseases such as diabetes. Diabetes mellitus has emerged as along-haul fatal disease across the globe and particularly in developing countries. Exact and early diagnosis of diabetes from big medical data is… More >

  • Open Access

    ARTICLE

    Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

    Naeem Ali1, Taher M. Ghazal2,3, Alia Ahmed1, Sagheer Abbas4, M. A. Khan5, Haitham M. Alzoubi6, Umar Farooq7, Munir Ahmad4, Muhammad Adnan Khan8,*

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1671-1687, 2022, DOI:10.32604/iasc.2022.019892 - 09 October 2021

    Abstract Supply Chain Collaboration is the network of various entities that work cohesively to make up the entire process. The supply chain organizations’ success is dependent on integration, teamwork, and the communication of information. Every day, supply chain and business players work in a dynamic setting. They must balance competing goals such as process robustness, risk reduction, vulnerability reduction, real financial risks, and resilience against just-in-time and cost-efficiency. Decision-making based on shared information in Supply Chain Collaboration constitutes the recital and competitiveness of the collective process. Supply Chain Collaboration has prompted companies to implement the perfect… More >

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