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  • Open Access


    Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

    Muhammad Irfan1, Ali Raza2,*, Faisal Althobiani3, Nasir Ayub4,5, Muhammad Idrees6, Zain Ali7, Kashif Rizwan4, Abdullah Saeed Alwadie1, Saleh Mohammed Ghonaim3, Hesham Abdushkour3, Saifur Rahman1, Omar Alshorman1, Samar Alqhtani8

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4249-4265, 2022, DOI:10.32604/cmc.2022.025863

    Abstract In the Smart Grid (SG) residential environment, consumers change their power consumption routine according to the price and incentives announced by the utility, which causes the prices to deviate from the initial pattern. Thereby, electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability. Due to the massive amount of data, big data analytics for forecasting becomes a hot topic in the SG domain. In this paper, the changing and non-linearity of consumer consumption pattern complex data is taken as input. To minimize the computational cost and complexity of the data, the… More >

  • Open Access


    Computer Aided Coronary Atherosclerosis Plaque Detection and Classification

    S. Deivanayagi1,*, P. S. Periasamy2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 639-653, 2022, DOI:10.32604/iasc.2022.025632

    Abstract Coronary artery disease (CAD) remains a major reason for increased mortality over the globe, comprising myocardial infarction and ischemic cardiomyopathy. The CAD is highly linked to coronary stenosis owing to the encumbrance of atherosclerotic plaques. Particularly, diversified atherosclerotic plaques are highly responsible for major cardiac adverse events over the calcified and non-calcified plaques. There, the recognition and classification of atherosclerotic plaques play a vital role to prevent and intervene in CAD. The process of detecting various class labels of the atherosclerotic plaques is significant to identify the disease at the earlier stages. Since several automated coronary plaque recognition models are… More >

  • Open Access


    Efficient Classification of Remote Sensing Images Using Two Convolution Channels and SVM

    Khalid A. AlAfandy1, Hicham Omara2, Hala S. El-Sayed3, Mohammed Baz4,*, Mohamed Lazaar5, Osama S. Faragallah6, Mohammed Al Achhab1

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 739-753, 2022, DOI:10.32604/cmc.2022.022457

    Abstract Remote sensing image processing engaged researchers’ attentiveness in recent years, especially classification. The main problem in classification is the ratio of the correct predictions after training. Feature extraction is the foremost important step to build high-performance image classifiers. The convolution neural networks can extract images’ features that significantly improve the image classifiers’ accuracy. This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels’ outputs as a features extraction using two classic convolution models; these convolution models are the ResNet 50 and the DenseNet 169. These elicited features have been used by… More >

  • Open Access


    An Enhanced Re-Ranking Model for Person Re-Identification

    Jayavarthini Chockalingam*, Malathy Chidambaranathan

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 697-710, 2022, DOI:10.32604/iasc.2022.024142

    Abstract Presently, Person Re-IDentification (PRe-ID) acts as a vital part of real time video surveillance to ensure the rising need for public safety. Resolving the PRe-ID problem includes the process of matching observations of persons among distinct camera views. Earlier models consider PRe-ID as a unique object retrieval issue and determine the retrieval results mainly based on the unidirectional matching among the probe and gallery images. But the accurate matching might not be present in the top-k ranking results owing to the appearance modifications caused by the difference in illumination, pose, viewpoint, and occlusion. For addressing these issues, a new Hyper-parameter… More >

  • Open Access


    Deep Transfer Learning Based Rice Plant Disease Detection Model

    R. P. Narmadha1,*, N. Sengottaiyan2, R. J. Kavitha3

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1257-1271, 2022, DOI:10.32604/iasc.2022.020679

    Abstract In agriculture, plant diseases are mainly accountable for reduction in productivity and leads to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily affected by different kinds of diseases. Because of the advent of computer vision and deep learning (DL) techniques, the rice plant diseases can be detected and reduce the burden of the farmers to save the crops. To achieve this, a new DL based rice plant disease diagnosis is developed using Densely Convolution Neural Network (DenseNet) with multilayer perceptron (MLP), called DenseNet169-MLP. The proposed model aims to classify the rice plant… More >

  • Open Access


    Sentiment Analysis of Short Texts Based on Parallel DenseNet

    Luqi Yan1, Jin Han1,*, Yishi Yue2, Liu Zhang2, Yannan Qian3

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 51-65, 2021, DOI:10.32604/cmc.2021.016920

    Abstract Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks (CNNs). However, most of these CNN models focus only on learning local features while ignoring global features. In this paper, based on traditional densely connected convolutional networks (DenseNet), a parallel DenseNet is proposed to realize sentiment analysis of short texts. First, this paper proposes two novel feature extraction blocks that are based on DenseNet and a multi-scale convolutional neural network. Second, this paper solves the problem of ignoring global features in traditional CNN models by combining the… More >

  • Open Access


    An Optimized Deep Residual Network with a Depth Concatenated Block for Handwritten Characters Classification

    Gibrael Abosamra*, Hadi Oqaibi

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1-28, 2021, DOI:10.32604/cmc.2021.015318

    Abstract Even though much advancements have been achieved with regards to the recognition of handwritten characters, researchers still face difficulties with the handwritten character recognition problem, especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset (EMNIST). The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class variability. Inter-class similarity exists because of the similarity between the shapes of certain characters in the dataset. The presence of intra-class variability is mainly due to different shapes written by different writers for the same character. In this… More >

  • Open Access


    Vehicle Re-Identification Model Based on Optimized DenseNet121 with Joint Loss

    Xiaorui Zhang1,2,*, Xuan Chen1, Wei Sun2, Xiaozheng He3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3933-3948, 2021, DOI:10.32604/cmc.2021.016560

    Abstract With the increasing application of surveillance cameras, vehicle re-identification (Re-ID) has attracted more attention in the field of public security. Vehicle Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar appearances. Plentiful existing methods focus on local attributes by marking local locations. However, these methods require additional annotations, resulting in complex algorithms and insufferable computation time. To cope with these challenges, this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint loss. This model applies the SE block to automatically… More >

  • Open Access


    Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

    Saleh Albahli1, Tahira Nazir2,*, Aun Irtaza2, Ali Javed3

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1333-1351, 2021, DOI:10.32604/cmc.2021.014691

    Abstract Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe. DR usually has no clear symptoms before the onset, thus making disease identification a challenging task. The healthcare industry may face unfavorable consequences if the gap in identifying DR is not filled with effective automation. Thus, our objective is to develop an automatic and cost-effective method for classifying DR samples. In this work, we present a custom Faster-RCNN technique for the recognition and classification of DR lesions from retinal images. After… More >

  • Open Access


    Image-Based Automatic Diagnostic System for Tomato Plants Using Deep Learning

    Shaheen Khatoon1,*, Md Maruf Hasan1, Amna Asif1, Majed Alshmari1, Yun-Kiam Yap2

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 595-612, 2021, DOI:10.32604/cmc.2021.014580

    Abstract Tomato production is affected by various threats, including pests, pathogens, and nutritional deficiencies during its growth process. If control is not timely, these threats affect the plant-growth, fruit-yield, or even loss of the entire crop, which is a key danger to farmers’ livelihood and food security. Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost. Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss. Recent developments in Artificial Intelligence (AI) and computer vision allow researchers… More >

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