Khalid A. AlAfandy¹, Hicham Omara², Hala S. El-Sayed³, Mohammed Baz^4,*, 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 - 24 February 2022

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… More >

Jayavarthini Chockalingam^*, Malathy Chidambaranathan

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 697-710, 2022, DOI:10.32604/iasc.2022.024142 - 08 February 2022

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… More >

R. P. Narmadha^1,*, N. Sengottaiyan², R. J. Kavitha³

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1257-1271, 2022, DOI:10.32604/iasc.2022.020679 - 22 September 2021

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… More >

Min-Jun Kim¹, Seng-Phil Hong², Mingoo Kang¹, Jeongwook Seo^1,*

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 743-753, 2021, DOI:10.32604/iasc.2021.019329 - 20 August 2021

Abstract In this paper, we present an oneM2M-compliant system including an artificial intelligence of things (AIoT) edge device whose principal function is to estimate human poses by using two PoseNet models built on MobileNet v1 and ResNet-50 backbone architectures. Although MobileNet v1 is generally known to be much faster but less accurate than ResNet50, it is necessary to analyze the performances of whole PoseNet models carefully and select one of them suitable for the AIoT edge device. For this reason, we first investigate the computational complexity of the models about their neural network layers and parameters… More >

Luqi Yan¹, Jin Han^1,*, Yishi Yue², Liu Zhang², Yannan Qian³

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 51-65, 2021, DOI:10.32604/cmc.2021.016920 - 04 June 2021

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 More >

Gibrael Abosamra^*, Hadi Oqaibi

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1-28, 2021, DOI:10.32604/cmc.2021.015318 - 22 March 2021

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… More >

Xiaorui Zhang^1,2,*, Xuan Chen¹, Wei Sun², Xiaozheng He³

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3933-3948, 2021, DOI:10.32604/cmc.2021.016560 - 01 March 2021

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… More >

Saleh Albahli¹, Tahira Nazir^2,*, Aun Irtaza², Ali Javed³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1333-1351, 2021, DOI:10.32604/cmc.2021.014691 - 05 February 2021

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… More >

Shaheen Khatoon^1,*, 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 - 12 January 2021

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)… More >

Haifeng Song¹, Weiwei Yang^1,*, Haiyan Yuan², Harold Bufford³

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1441-1458, 2020, DOI:10.32604/iasc.2020.011988 - 24 December 2020

Abstract There are two main problems that lead to unsatisfactory classification performance for hyperspectral remote sensing images (HSIs). One issue is that the HSI data used for training in deep learning is insufficient, therefore a deeper network is unfavorable for spatial-spectral feature extraction. The other problem is that as the depth of a deep neural network increases, the network becomes more prone to overfitting. To address these problems, a dual-channel 3D-Multiscale DenseNet (3DMSS) is proposed to boost the discriminative capability for HSI classification. The proposed model has several distinct advantages. First, the model consists of dual… More >