Anchal Tyagi¹, Salem Alelyani², Sapna Katiyar³, Mohammad Rashid Hussain^2,*, Rijwan Khan³, Mohammed Saleh Alsaqer²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1423-1438, 2023, DOI:10.32604/csse.2023.026057 - 03 November 2022

Abstract An image can be degraded due to many environmental factors like foggy or hazy weather, low light conditions, extra light conditions etc. Image captured under the poor light conditions is generally known as non-uniform illumination image. Non-uniform illumination hides some important information present in an image during the image capture Also, it degrades the visual quality of image which generates the need for enhancement of such images. Various techniques have been present in literature for the enhancement of such type of images. In this paper, a novel architecture has been proposed for enhancement of poor… More >

Husam Yahya¹, Dumitru Baleanu^2,3,4, Rabha W. Ibrahim^5,*, Nadia M.G. Al-Saidi⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2531-2539, 2023, DOI:10.32604/cmc.2023.033282 - 31 October 2022

Abstract The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values. The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone. The brain Magnetic Resonance Imaging (MRI) scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer. Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease. To solve this issue, this research… More >

Safi Ullah^1,2, Seong-Ho Song^1,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2999-3014, 2023, DOI:10.32604/cmc.2023.032326 - 31 October 2022

Abstract Due to highly underdetermined nature of Single Image Super-Resolution (SISR) problem, deep learning neural networks are required to be more deeper to solve the problem effectively. One of deep neural networks successful in the Super-Resolution (SR) problem is ResNet which can render the capability of deeper networks with the help of skip connections. However, zero padding (ZP) scheme in the network restricts benefits of skip connections in SRResNet and its performance as the ratio of the number of pure input data to that of zero padded data increases. In this paper. we consider the ResNet More >

Hamayun Khan^1,*, Irfan Ud Din², Arshad Ali³, Mohammad Husain³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2097-2113, 2023, DOI:10.32604/cmc.2023.032999 - 22 September 2022

Abstract Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities on-chip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of… More >

Rashid Jahangir^1,*, Muhammad Asif Nauman², Roobaea Alroobaea³, Jasem Almotiri³, Muhammad Mohsin Malik¹, Sabah M. Alzahrani³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1069-1091, 2023, DOI:10.32604/cmc.2023.032719 - 22 September 2022

Abstract Environmental sound classification (ESC) involves the process of distinguishing an audio stream associated with numerous environmental sounds. Some common aspects such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording make the ESC task much more complicated and complex. This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources. In this research, the performance of transformer and convolutional neural networks (CNN) are investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz,… More >

Swaleha Zubair¹, Anjani Kumar Singha¹, Nitish Pathak², Neelam Sharma³, Shabana Urooj^4,*, Samia Rabeh Larguech⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2005-2019, 2023, DOI:10.32604/cmc.2023.031481 - 22 September 2022

Abstract Deep learning is the process of determining parameters that reduce the cost function derived from the dataset. The optimization in neural networks at the time is known as the optimal parameters. To solve optimization, it initialize the parameters during the optimization process. There should be no variation in the cost function parameters at the global minimum. The momentum technique is a parameters optimization approach; however, it has difficulties stopping the parameter when the cost function value fulfills the global minimum (non-stop problem). Moreover, existing approaches use techniques; the learning rate is reduced during the iteration… More >

Lin Zhou^1,*, Xijin Chen¹, Chaoyan Wu¹, Qiuyue Zhong¹, Xu Cheng², Yibin Tang³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1259-1277, 2023, DOI:10.32604/cmc.2023.027379 - 22 September 2022

Abstract Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network (DNN). But the mapping-based methods only utilizes the phase of noisy speech, which limits the upper bound of speech enhancement performance. Masking-based methods need to accurately estimate the masking which is still the key problem. Combining the advantages of above two types of methods, this paper proposes the speech enhancement algorithm MM-RDN (masking-mapping residual dense network) based on masking-mapping (MM) and residual dense network (RDN). Using the logarithmic power spectrogram (LPS) of consecutive frames, MM estimates the ideal… More >

Aleksandr Lekomtsev^1,*, Vitaliy Bakaneev¹, Ivan Stepanenko¹, Petr Maximov¹, Yulia Rozhkova¹, Alexey Dengaev², Wanli Kang³

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.2, pp. 285-301, 2023, DOI:10.32604/fdmp.2022.019809 - 29 August 2022

Abstract A new integrated oil production enhancement technology based on water-flooding energy recovery is proposed. After providing an extensive review of the existing scientific and technical literature on this subject, the proposed integrated technology is described together with the related process flow diagram, the criteria used to select a target facility for its implementation and the outcomes of the laboratory studies conducted to analyze emulsion formation and separation kinetics. Moreover, the outcomes of numerical simulations performed using Ansys CFX software are also presented. According to these results, using the proposed approach the incremental oil production may More >

P. Ganesan^1,*, S. Arockia Edwin Xavier²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2979-2996, 2023, DOI:10.32604/iasc.2023.029264 - 17 August 2022

Abstract Typically, smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks. These vulnerabilities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems. Thus, for this purpose, Intrusion detection system (IDS) plays a pivotal part in offering a reliable and secured range of services in the smart grid framework. Several existing approaches are there to detect the intrusions in smart grid framework, however they are utilizing an old dataset to detect anomaly thus… More >

B. Murugeshwari^1,*, S. Rajalakshmi¹, K. Sudharson²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2293-2307, 2023, DOI:10.32604/csse.2023.029074 - 01 August 2022

Abstract Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the… More >