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  • Open Access

    ARTICLE

    A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

    José Escorcia-Gutierrez1,4,*, Jordina Torrents-Barrena4, Margarita Gamarra2, Natasha Madera1, Pedro Romero-Aroca3, Aida Valls4, Domenec Puig4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2971-2989, 2022, DOI:10.32604/cmc.2022.020074

    Abstract Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection

    Nazar Hussain1, Muhammad Attique Khan1, Usman Tariq2, Seifedine Kadry3,*, MuhammadAsfand E. Yar4, Almetwally M. Mostafa5, Abeer Ali Alnuaim6, Shafiq Ahmad7

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3281-3294, 2022, DOI:10.32604/cmc.2022.019036

    Abstract Agriculture is an important research area in the field of visual recognition by computers. Plant diseases affect the quality and yields of agriculture. Early-stage identification of crop disease decreases financial losses and positively impacts crop quality. The manual identification of crop diseases, which are mostly visible on leaves, is a very time-consuming and costly process. In this work, we propose a new framework for the recognition of cucumber leaf diseases. The proposed framework is based on deep learning and involves the fusion and selection of the best features. In the feature extraction phase, VGG (Visual… More >

  • Open Access

    ARTICLE

    A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

    Farrukh Zia1, Isma Irum1, Nadia Nawaz Qadri1, Yunyoung Nam2,*, Kiran Khurshid3, Muhammad Ali1, Imran Ashraf4, Muhammad Attique Khan4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2261-2276, 2022, DOI:10.32604/cmc.2022.017820

    Abstract Diabetes or Diabetes Mellitus (DM) is the upset that happens due to high glucose level within the body. With the passage of time, this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy (DR) which can cause a major loss of vision. The symptoms typically originate within the retinal space square in the form of enlarged veins, liquid dribble, exudates, haemorrhages and small scale aneurysms. In current therapeutic science, pictures are the key device for an exact finding of patients’ illness. Meanwhile, an assessment of new medicinal symbolisms stays complex. Recently, Computer Vision (CV)… More >

  • Open Access

    ARTICLE

    Selecting Dominant Features for the Prediction of Early-Stage Chronic Kidney Disease

    Vinothini Arumugam*, S. Baghavathi Priya

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 947-959, 2022, DOI:10.32604/iasc.2022.018654

    Abstract Nowadays, Chronic Kidney Disease (CKD) is one of the vigorous public health diseases. Hence, early detection of the disease may reduce the severity of its consequences. Besides, medical databases of any disease diagnosis may be collected from the blood test, urine test, and patient history. Nevertheless, medical information retrieved from various sources is diverse. Therefore, it is unadaptable to evaluate numerical and nominal features using the same feature selection algorithm, which may lead to fallacious analysis. Applying machine learning techniques over the medical database is a common way to help feature identification for CKD prediction.… More >

  • Open Access

    ARTICLE

    A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

    Ahmed Reda*, Sherif Barakat, Amira Rezk

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1381-1399, 2022, DOI:10.32604/cmc.2022.019809

    Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a More >

  • Open Access

    ARTICLE

    BHGSO: Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

    R. Manjula Devi1, M. Premkumar2, Pradeep Jangir3, B. Santhosh Kumar4, Dalal Alrowaili5, Kottakkaran Sooppy Nisar6,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 557-579, 2022, DOI:10.32604/cmc.2022.019611

    Abstract In machine learning and data mining, feature selection (FS) is a traditional and complicated optimization problem. Since the run time increases exponentially, FS is treated as an NP-hard problem. The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios. This paper presents two binary variants of a Hunger Games Search Optimization (HGSO) algorithm based on V- and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset.… More >

  • Open Access

    ARTICLE

    A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

    Faiz Ali Shah1, Muhammad Attique Khan2, Muhammad Sharif1, Usman Tariq3, Aimal Khan4, Seifedine Kadry5, Orawit Thinnukool6,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1491-1507, 2022, DOI:10.32604/cmc.2022.019490

    Abstract Fruit diseases seriously affect the production of the agricultural sector, which builds financial pressure on the country's economy. The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert. Hence, it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images. According to the literature, many automated methods have been developed for the recognition of fruit diseases at the early stage. However, these techniques still face some challenges, such as the similar symptoms… More >

  • Open Access

    ARTICLE

    Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

    Lijo Jacob Varghese1, K. Dhayalini2, Suma Sira Jacob3, Ihsan Ali4,*, Abdelzahir Abdelmaboud5, Taiseer Abdalla Elfadil Eisa6

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1053-1067, 2022, DOI:10.32604/cmc.2022.019435

    Abstract Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer. In recent times, numerous Machine Learning (ML)-enabled load predictive techniques have been developed, while most of the existing studies did not consider its implicit features, optimal parameter selection, and prediction stability. In order to overcome fulfill this research gap, the current research paper… More >

  • Open Access

    ARTICLE

    Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

    Awais Mahmood1,*, Muhammad Imran2, Aun Irtaza3, Qammar Abbas4, Habib Dhahri1,5, Esam Mohammed Asem Othman1, Arif Jamal Malik6, Aaqif Afzaal Abbasi6

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 963-979, 2022, DOI:10.32604/cmc.2022.019291

    Abstract Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that… More >

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