Mamona Arshad¹, Ahmad Karim¹, Salman Naseer², Shafiq Ahmad³, Mejdal Alqahtani³, Akber Abid Gardezi⁴, Muhammad Shafiq^5,*, Jin-Ghoo Choi⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2123-2135, 2023, DOI:10.32604/cmc.2022.028680

Abstract The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming, Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat in the research area. A… More >

Deepak Kumar¹, Vinay Kukreja¹, Ayush Dogra^1,*, Bhawna Goyal², Talal Taha Ali³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2097-2121, 2023, DOI:10.32604/cmc.2023.044287

Abstract Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20% every year. The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques. The experienced evaluators take time to identify the disease which is highly laborious and too costly. If wheat rust diseases are predicted at the development stages, then fungicides are sprayed earlier which helps to increase wheat yield quality. To solve the experienced evaluator issues, a combined region extraction and cross-entropy support vector machine (CE-SVM) model is proposed for wheat rust disease identification. In the proposed… More >

Yanlei Xu, Xue Cong, Yuting Zhai, Zhiyuan Gao, Helong Yu^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3019-3037, 2023, DOI:10.32604/iasc.2023.040330

Abstract To solve inefficient water stress classification of spinach seedlings under complex background, this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-MobileNetXt (NCAM+MobileNetXt) network. Firstly, this study reconstructed the Sandglass Block to effectively increase the model accuracy; secondly, this study introduced the group convolution module and a two-dimensional adaptive average pool, which can significantly compress the model parameters and enhance the model robustness separately; finally, this study innovatively proposed the Normalization-based Channel Attention Module (NCAM) to enhance the image features obviously. The experimental results showed that the classification accuracy of N-MobileNetXt… More >

Alaa Alsaheel, Reem Alhassoun, Reema Alrashed, Noura Almatrafi, Noura Almallouhi, Saleh Albahli^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2461-2482, 2023, DOI:10.32604/cmc.2023.040257

Abstract With the increasing use of deep learning technology, there is a growing concern over creating deep fake images and videos that can potentially be used for fraud. In healthcare, manipulating medical images could lead to misdiagnosis and potentially life-threatening consequences. Therefore, the primary purpose of this study is to explore the use of deep learning algorithms to detect deep fake images by solving the problem of recognizing the handling of samples of cancer and other diseases. Therefore, this research proposes a framework that leverages state-of-the-art deep convolutional neural networks (CNN) and a large dataset of authentic and deep fake medical… More >

Zhibo Song^1,*, Hao Yang¹, Wei Shen², Yixin Ren¹, Yawei Li³, Jianlong Ma¹

Energy Engineering, Vol.120, No.9, pp. 2097-2113, 2023, DOI:10.32604/ee.2023.029115

Abstract Pollution flashover is one of the major insulation accidents that can occur in modern power systems. If the local arc is not sufficiently elongated by the creepage distance, the flashover performance of the insulator will decrease. The insulator structure and equivalent salt deposition density (ESDD) are the main factors affecting the arc path and length. To investigate the forms of arc paths under different insulator structures and their influence on flashover characteristics, a platform for recording arc paths is established. This study presents a method to identify the pattern and distribution of insulator arc paths based on the effect of… More >

Bilal Bataineh^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1941-1961, 2023, DOI:10.32604/iasc.2023.039868

Abstract Autonomous vehicles are currently regarded as an interesting topic in the AI field. For such vehicles, the lane where they are traveling should be detected. Most lane detection methods identify the whole road area with all the lanes built on it. In addition to having a low accuracy rate and slow processing time, these methods require costly hardware and training datasets, and they fail under critical conditions. In this study, a novel detection algorithm for a lane where a car is currently traveling is proposed by combining simple traditional image processing with lightweight machine learning (ML) methods. First, a preparation… More >

Anuj Kumar, Shakti Kumar^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2175-2198, 2023, DOI:10.32604/iasc.2023.039093

Abstract Melanoma is a perfidious form of skin cancer. The study offers a hybrid framework for the automatic classification of melanoma. An Automatic Melanoma Detection System (AMDS) is used for identifying melanoma from the infected area of the skin image using image processing techniques. A larger number of pre-existing automatic melanoma detection systems are either commercial or their accuracy can be further improved. The research problem is to identify the best preprocessing technique, feature extractor, and classifier for melanoma detection using publically available MED-NODE data set. AMDS goes through four stages. The preprocessing stage is for noise removal; the segmentation stage… More >

Safa Ridene¹ , Reda Yaagoubi¹, Imane Sebari¹, Audrey Alajouanine²

Revue Internationale de Géomatique, Vol.29, No.3, pp. 241-253, 2019, DOI:10.3166/rig.2019.00091

Abstract While shadow can give useful information about size and shape of objects, it can pose problems in feature detection and object detection, thereby, it represents one of the major perturbator phenomenons frequently occurring on images and unfortunately, it is inevitable. “Shadows may lead to the failure of image analysis processes and also cause a poor quality of information which in turn leads to problems in implementation of algorithms.” (Mahajan and Bajpayee, 2015). It also affects multiple image analysis applications, whereby shadow cast by buildings deteriorate the spectral values of the surfaces. Therefore, its presence causes a deterioration in the visual… More >

A. Manju¹, A. Revathi², M. Arivukarasi¹, S. Hariharan³, V. Umarani⁴, Shih-Yu Chen^5,*, Jin Wang⁶

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 905-920, 2023, DOI:10.32604/iasc.2023.038444

Abstract With the proliferation of the internet, big data continues to grow exponentially, and video has become the largest source. Video big data introduces many technological challenges, including compression, storage, transmission, analysis, and recognition. The increase in the number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. The integration between Semantic link Networks and multimedia resources provides a new prospect for organizing them with their semantics. The tags and surrounding texts of multimedia resources are used to measure their semantic association. Two evaluation methods including clustering and retrieval are performed to measure… More >

Muwei Jian^1,2,#,*, Ronghua Wu^1,#, Hongyu Chen¹, Lanqi Fu³, Chengdong Yang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 705-716, 2023, DOI:10.32604/cmes.2023.027425

Abstract In intelligent perception and diagnosis of medical equipment, the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases (e.g., diabetes and hypertension). Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results. To address this challenge, we design a Dual-Branch-UNet framework, which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation. To be more explicit, we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of the original U-Net. Then, image… More >