Khudran M. Alzhrani^*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 483-500, 2022, DOI:10.32604/iasc.2022.023914

Abstract Individuals inadvertently allow emotions to drive their rational thoughts to predetermined conclusions regarding political partiality issues. Being well-informed about the subject in question mitigates emotions’ influence on humans’ cognitive reasoning, but it does not eliminate bias. By nature, humans tend to pick a side based on their beliefs, personal interests, and principles. Hence, journalists’ political leaning is defining factor in the rise of the polarity of political news coverage. Political bias studies usually align subjects or controversial topics of the news coverage to a particular ideology. However, politicians as private citizens or public officials are also consistently in the media… More >

Xiaoting Zhang¹, Weidong Gao^2,*, Ruru Pan²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1533-1546, 2022, DOI:10.32604/cmes.2022.018763

Abstract Historically, yarn-dyed plaid fabrics (YDPFs) have enjoyed enduring popularity with many rich plaid patterns, but production data are still classified and searched only according to production parameters. The process does not satisfy the visual needs of sample order production, fabric design, and stock management. This study produced an image dataset for YDPFs, collected from 10,661 fabric samples. The authors believe that the dataset will have significant utility in further research into YDPFs. Convolutional neural networks, such as VGG, ResNet, and DenseNet, with different hyperparameter groups, seemed the most promising tools for the study. This paper reports on the authors’ exhaustive… More >

Abhishek Kumar Shukla^*, Sujoy Das

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3557-3570, 2022, DOI:10.32604/cmc.2022.022411

Abstract The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining, Natural language processing, Image processing, and Information retrieval etc. Word embedding has been applied by many researchers for Information retrieval tasks. In this paper word embedding-based skip-gram model has been developed for the query expansion task. Vocabulary terms are obtained from the top “k” initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user query. The performance of the… More >

Ahmed Elaraby^1,*, Walid Hamdy², Madallah Alruwaili³

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4019-4031, 2022, DOI:10.32604/cmc.2022.022161

Abstract Plant diseases are a major impendence to food security, and due to a lack of key infrastructure in many regions of the world, quick identification is still challenging. Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities, motivating our mission. Because of the large range of diseases, identifying and classifying diseases with human eyes is not only time-consuming and labor intensive, but also prone to being mistaken with a high error rate. Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis. The proposed work describes… More >

N. Venkateswaran^1,*, K. Umadevi²

Computer Systems Science and Engineering, Vol.42, No.1, pp. 1-14, 2022, DOI:10.32604/csse.2022.021217

Abstract Huge data over the cloud computing and big data are processed over the network. The data may be stored, send, altered and communicated over the network between the source and destination. Once data send by source to destination, before reaching the destination data may be attacked by any intruders over the network. The network has numerous routers and devices to connect to internet. Intruders may attack any were in the network and breaks the original data, secrets. Detection of attack in the network became interesting task for many researchers. There are many intrusion detection feature selection algorithm has been suggested… More >

Kexin Zhang¹, Bin Lin^2,*, Jixin Chen¹, Xinlong Wu¹, Chao Lu³, Desheng Zheng¹, Lulu Tian⁴

Computer Systems Science and Engineering, Vol.42, No.1, pp. 351-360, 2022, DOI:10.32604/csse.2022.021132

Abstract Deep learning techniques have outstanding performance in feature extraction and model fitting. In the field of aero-engine fault diagnosis, the introduction of deep learning technology is of great significance. The aero-engine is the heart of the aircraft, and its stable operation is the primary guarantee of the aircraft. In order to ensure the normal operation of the aircraft, it is necessary to study and diagnose the faults of the aero-engine. Among the many engine failures, the one that occurs more frequently and is more hazardous is the wheeze, which often poses a great threat to flight safety. On the basis… More >

Xing Guo^1,#, Yu-Dong Zhang^2,#, Siyuan Lu², Zhihai Lu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 23-71, 2022, DOI:10.32604/cmes.2021.017679

Abstract Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What’s more, the advantages and limitations of different diagnosis methods are compared. Although the… More >

S. Ushasukhanya^*, M. Karthikeyan

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1261-1275, 2022, DOI:10.32604/iasc.2022.022654

Abstract Electricity conservation systems are designed to conserve electricity to manage the bridge between the high raising demand and the production. Such systems have been so far using sensors to detect the necessity which adds an additional cost to the setup. Closed-circuit Television (CCTV) has been installed in almost everywhere around us especially in commercial places. Interpretation of these CCTV images is being carried out for various reasons to elicit the information from it. Hence a framework for electricity conservation that enables the electricity supply only when required, using existing resources would be a cost effective conservation system. Such a framework… More >

V. V. S. Vijaya Krishna^1,*, N. Pappa¹, S. P. Joy Vasantharani²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 781-794, 2022, DOI:10.32604/iasc.2022.022181

Abstract Industries use soft sensors for estimating output parameters that are difficult to measure on-line. These parameters can be determined by laboratory analysis which is an offline task. Now a days designing Soft sensors for complex nonlinear systems using deep learning training techniques has become popular, because of accuracy and robustness. There is a need to find pertinent hardware for realizing soft sensors to make it portable and can be used in the place of general purpose PC. This paper aims to propose a new strategy for realizing a soft sensor using deep neural networks (DNN) on appropriate hardware which can… More >

Erssa Arif^1,*, Syed Khuram Shahzad², Rehman Mustafa¹, Muhammad Arfan Jaffar³, Muhammad Waseem Iqbal⁴

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 909-922, 2022, DOI:10.32604/iasc.2022.021061

Abstract The conventional surveillance and control system of Closed-Circuit Television (CCTV) cameras require human resource supervision. Almost all the criminal activities take place using weapons mostly handheld gun, revolver, or pistol. Automatic gun detection is a vital requirement now-a-days. The use of real-time object detection system for the improvement of surveillance is a promising application of Convolutional Neural Networks (CNN). We are concerned about the real-time detection of weapons for the surveillance cameras, so we focused on the implementation and comparison of faster approaches such as Region (R-CNN) and Region Fully Convolutional Networks (R-FCN) with feature extractor Visual Geometry Group (VGG)… More >