Kyamelia Roy¹, Sheli Sinha Chaudhuri¹, Sayan Pramanik², Soumen Banerjee^2,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 647-662, 2023, DOI:10.32604/csse.2023.024997 - 01 June 2022

Abstract In recent years, computer vision finds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture. Automatic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies. Waterways being an important medium of transport require continuous monitoring for protection of national security. The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea. This paper proposes a deep learning… More >

S. Eliza Femi Sherley^1,*, J. M. Karthikeyan¹, N. Bharath Raj¹, R. Prabakaran², A. Abinaya¹, S. V. V. Lakshmi³

Journal on Artificial Intelligence, Vol.4, No.4, pp. 215-227, 2022, DOI:10.32604/jai.2022.034931 - 25 May 2023

Abstract Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic (labels/categories) details before and after the timelines are analyzed. Periodical land change analysis is used for many real time applications for valuation purposes. Majority of the research works are focused on Convolutional Neural Networks (CNN) which tries to analyze changes alone. Semantic information of changes appears to be missing, there by absence of communication between the different semantic timelines and changes detected over the region happens.… More >

M. Premkumar¹, R. Sowmya², S. Sridhar³, C. Kumar⁴, Mohamed Abbas^5,6, Malak S. Alqahtani⁷, Kottakkaran Sooppy Nisar^8,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6289-6306, 2022, DOI:10.32604/cmc.2022.030490 - 28 July 2022

Abstract It is critical to have precise data about Lithium-ion batteries, such as the State-of-Charge (SoC), to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles. Numerous strategies for estimating battery SoC, such as by including the coulomb counting and Kalman filter, have been established. As a result of the differences in parameter values between each cell, when these methods are applied to high-capacity battery packs, it has difficulties sustaining the prediction accuracy of overall cells. As a result of aging, the variation in the parameters of each cell… More >

Wael Deabes^1,2,*, Kheir Eddine Bouazza^1,3

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6307-6326, 2022, DOI:10.32604/cmc.2022.030420 - 28 July 2022

Abstract Great achievements have been made during the last decades in the field of Electrical Capacitance Tomography (ECT) image reconstruction. However, there is still a need to make these image reconstruction results faster and of better quality. Recently, Deep Learning (DL) is flourishing and is adopted in many fields. The DL is very good at dealing with complex nonlinear functions and it is built using several series of Artificial Neural Networks (ANNs). An ECT image reconstruction model using DNN is proposed in this paper. The proposed model mainly uses Residual Autoencoder called (ECT_ResAE). A large-scale dataset… More >

Kavitha Chinniyan^*, Thamil Vani Samiyappan, Aishvarya Gopu, Narmatha Ramasamy

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1877-1891, 2022, DOI:10.32604/iasc.2022.027274 - 25 May 2022

Abstract Steganography is the technique of hiding secret data within ordinary data by modifying pixel values which appear normal to a casual observer. Steganography which is similar to cryptography helps in secret communication. The cryptography method focuses on the authenticity and integrity of the messages by hiding the contents of the messages. Sometimes, it is not only just enough to encrypt the message but also essential to hide the existence of the message itself. As this avoids misuse of data, this kind of encryption is less suspicious and does not catch attention. To achieve this, Stacked… More >

Abdallah Abdallah¹, Mohamad Khairi Ishak², Nor Samsiah Sani³, Imran Khan⁴, Fahad R. Albogamy⁵, Hirofumi Amano⁶, Samih M. Mostafa^7,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1125-1140, 2022, DOI:10.32604/cmc.2022.025810 - 18 May 2022

Abstract Software-defined networking (SDN) is a new paradigm that promises to change by breaking vertical integration, decoupling network control logic from the underlying routers and switches, promoting (logical) network control centralization, and introducing network programming. However, the controller is similarly vulnerable to a “single point of failure”, an attacker can execute a distributed denial of service (DDoS) attack that invalidates the controller and compromises the network security in SDN. To address the problem of DDoS traffic detection in SDN, a novel detection approach based on information entropy and deep neural network (DNN) is proposed. This approach… More >

Pranamita Nanda^1,*, N. Duraipandian²

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1099-1110, 2022, DOI:10.32604/csse.2022.024695 - 09 May 2022

Abstract In severe cases, diabetic retinopathy can lead to blindness. For decades, automatic classification of diabetic retinopathy images has been a challenge. Medical image processing has benefited from advances in deep learning systems. To enhance the accuracy of image classification driven by Convolutional Neural Network (CNN), balanced dataset is generated by data augmentation method followed by an optimized algorithm. Deep neural networks (DNN) are frequently optimized using gradient (GD) based techniques. Vanishing gradient is the main drawback of GD algorithms. In this paper, we suggest an innovative algorithm, to solve the above problem, Hypergradient Descent learning… More >

Vinay Gautam^1,*, Jyoti Rani²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 849-864, 2022, DOI:10.32604/iasc.2022.025113 - 03 May 2022

Abstract Mango is a widely growing and consumable fruit crop. The quantity and quality of production are most important to satisfy the needs of the huge population. Numerous research has been conducted to increase the yield of the crop. But a good number of crop harvests were destroyed due to various factors and leaf stress is one of them. The various types of stresses include biotic and abiotic that impact the mangoes productivity. But here the focus is on biotic stress factors such as fungus and bacteria. The effect of the stress can be reduced in… More >

Bareera Zafar¹, Syed Abbas Zilqurnain Naqvi¹, Muhammad Ahsan¹, Allah Ditta^2,*, Ummul Baneen¹, Muhammad Adnan Khan^3,4

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5099-5116, 2022, DOI:10.32604/cmc.2022.027874 - 21 April 2022

Abstract This research proposes a method called enhanced collaborative and geometric multi-kernel learning (E-CGMKL) that can enhance the CGMKL algorithm which deals with multi-class classification problems with non-linear data distributions. CGMKL combines multiple kernel learning with softmax function using the framework of multi empirical kernel learning (MEKL) in which empirical kernel mapping (EKM) provides explicit feature construction in the high dimensional kernel space. CGMKL ensures the consistent output of samples across kernel spaces and minimizes the within-class distance to highlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit… More >

S. Prabha Kumaresan¹, Chee Keong Tan^2,*, Yin Hoe Ng¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6119-6140, 2022, DOI:10.32604/cmc.2022.027223 - 21 April 2022

Abstract Non-orthogonal multiple access (NOMA) has been a key enabling technology for the fifth generation (5G) cellular networks. Based on the NOMA principle, a traditional neural network has been implemented for user clustering (UC) to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones. Consequently, the prediction of UC for the future ones is based on the current clustering information, which is never used again due to the lack of memory of the network. Therefore, to relate the input features of NOMA users and capture… More >