Changhui You^1,2, Hong Zheng^1,2,*, Zhongyuan Guo², Tianyu Wang², Jianping Ju³, Xi Li³

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3431-3452, 2023, DOI:10.32604/cmc.2023.035897

Abstract The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment, photographing habits, camera resolution and other factors, resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits. Developing an anti-counterfeiting code authentication algorithm based on mobile phones is of great commercial value. Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes, the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy. To address the small differences in texture features, low response speed and excessively large deep learning… More >

Jikang Xu^1,4, Ruichuan Li^1,2,*, Yi Cheng², Yanchao Li³, Junru Yang³, Chenyu Feng⁴, Xinkai Ding², Huazhong Zhang⁵

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.7, pp. 1817-1836, 2023, DOI:10.32604/fdmp.2023.022602

Abstract Electro-hydraulic proportional valve is the core control valve in many hydraulic systems used in agricultural and engineering machinery. To address the problem related to the large throttling losses and poor stability typically associated with these valves, here, the beneficial effects of a triangular groove structure on the related hydraulic response are studied. A mathematical model of the pressure compensation system based on the power-bond graph method is introduced, and the AMESim software is used to simulate its response. The results show that the triangular groove structure increases the jet angle and effectively compensates for the hydrodynamic force. The steady-state differential… More > Graphic Abstract

Somasundaram Devaraj^*, Senthil Kumar Arunachalam

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1283-1299, 2023, DOI:10.32604/iasc.2023.033200

Abstract Retinal fundus images are used to discover many diseases. Several Machine learning algorithms are designed to identify the Glaucoma disease. But the accuracy and time consumption performance were not improved. To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regressive Segmentation based Radial Basis Image Classifier (MPCNKFTRS-RBIC) Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time. In MPCNKFTRS-RBIC Model, the retinal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuan filter. Then, preprocessed retinal fundus is given for hidden… More >

Usman Ahmad¹, Muhammad Junaid Ali², Faizan Ahmed Khan³, Arfat Ahmad Khan⁴, Arif Ur Rehman¹, Malik Muhammad Ali Shahid⁵, Mohd Anul Haq^6,*, Ilyas Khan⁷, Zamil S. Alzamil⁶, Ahmed Alhussen⁸

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2125-2140, 2023, DOI:10.32604/csse.2023.031008

Abstract Building an automatic fish recognition and detection system for large-scale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species. However, it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes. To solve these issues, we propose a transfer learning-based technique in which we use Efficient-Net, which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database, which is a large scale dataset. Furthermore, prior to the activation layer, we use Global Average Pooling (GAP) instead of dense layer with… More >

Jun Liu¹, Geng Yuan², Changdi Yang², Houbing Song³, Liang Luo^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1571-1587, 2023, DOI:10.32604/cmes.2022.023195

Abstract The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research. The safety criteria for medical imaging are highly stringent, and models are required for an explanation. However, existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs. Thus, the interpretability of CNNs has come into the spotlight. Since medical imaging data are limited, many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public ImageNet datasets by the transfer learning method. Unfortunately, this generates many unreliable parameters and makes… More >

Swarna Sudha Muppudathi¹, Valarmathi Krishnasamy^2,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1027-1042, 2023, DOI:10.32604/iasc.2023.031165

Abstract Social Networking Sites (SNSs) are nowadays utilized by the whole world to share ideas, images, and valuable contents by means of a post to reach a group of users. The use of SNS often inflicts the physical and the mental health of the people. Nowadays, researchers often focus on identifying the illegal behaviors in the SNS to reduce its negative influence. The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide annotated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy. To overcome these issues, the proposed… More >

M. Indirani^*, S. Shankar

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1657-1674, 2023, DOI:10.32604/iasc.2023.026148

Abstract Recent developments in computer vision applications have enabled detection of significant visual objects in video streams. Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization (SPSOM) and Incremental Deep Convolution Neural Networks (IDCNN) for detecting multiple objects. However, the study considered optical flows resulting in assessing motion contrasts. Existing methods have issue with accuracy and error rates in motion contrast detection. Hence, the overall object detection performance is reduced significantly. Thus, consideration of object motions in videos efficiently is a critical issue to be solved. To overcome the above mentioned problems, this research work… More >

Adel Hafeezallah¹, Ahlam Al-Dhamari^2,3,*, Syed Abd Rahman Abu-Bakar²

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3879-3903, 2022, DOI:10.32604/cmc.2022.028289

Abstract Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering. Moreover, since the crowd images in this case can range from low density to high density, detection-based approaches are hard to apply for crowd counting. Recently, deep learning-based regression has become the prominent approach for crowd counting problems, where a density-map is estimated, and its integral is further computed to acquire the final count result. In this paper, we put forward a novel multi-scale network (named 2U-Net)… More >

D. Karthik Prabhu^1,*, R. V. Maheswari², B. Vigneshwaran²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1441-1454, 2022, DOI:10.32604/iasc.2022.024128

Abstract Measurement and recognition of Partial Discharge (PD) in power apparatus is considered a protuberant tool for condition monitoring and assessing the state of a dielectric system. During operating conditions, PD may occur either in the form of single and multiple patterns in nature. Currently, for PD pattern recognition, deep learning approaches are used. To evaluate spatial order less features from the large-scale patterns, a pre-trained network is used. The major drawback of traditional approaches is that they generate high dimensional data or requires additional steps like dictionary learning and dimensionality reduction. However, in real-time applications, interference incorporated in the measured… More >

Mohamed W. Abo El-Soud^1,2,*, Tarek Gaber^2,3, Mohamed Tahoun², Abdullah Alourani¹

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1109-1123, 2022, DOI:10.32604/cmc.2022.028561

Abstract The prevalence of melanoma skin cancer has increased in recent decades. The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins. Thus, the early diagnosis of melanoma is a key factor in improving the prognosis of the disease. Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images. Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases. This paper proposes a new method which can be used for… More >