Jingjun Zhou^1,2, Jing Liu³, Jingbing Li^1,2,*, Mengxing Huang^1,2, Jieren Cheng⁴, Yen-Wei Chen⁵, Yingying Xu^3,6, Saqib Ali Nawaz¹

Computer Systems Science and Engineering, Vol.39, No.1, pp. 133-146, 2021, DOI:10.32604/csse.2021.016633 - 10 June 2021

Abstract Recent applications of convolutional neural networks (CNNs) in single image super-resolution (SISR) have achieved unprecedented performance. However, existing CNN-based SISR network structure design consider mostly only channel or spatial information, and cannot make full use of both channel and spatial information to improve SISR performance further. The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information. Specifically, we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a More >

Yuchen Cao, Yongwen Yang, Xianglong Zhao, Qifen Li^*

Journal of Renewable Materials, Vol.9, No.11, pp. 1823-1842, 2021, DOI:10.32604/jrm.2021.015722 - 04 June 2021

Abstract The temporal and spatial characteristics of seasonal hydrogen storage will play a very important role in the coupling of multi-energy systems. This essay believes that there are several key issues worth noting in the seasonal hydrogen storage coupled multi-energy system, namely, hydrogen storage methods, coupling models, and benefit evaluation. Through research, this article innovatively divides seasonal hydrogen storage into two types: space transfer hydrogen storage technology and time transfer physical property conversion hydrogen storage technology. Then sort out the two most typical seasonal hydrogen storage multi-energy system application scenarios and their hydrogen storage unit models. More > Graphic Abstract

J. Hemalatha¹, S. Geetha², R. Geetha³, C. Balasubramanian⁴, Daniela Elena Popescu⁵, D. Jude Hemanth^6,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 283-300, 2021, DOI:10.32604/cmc.2021.017203 - 04 June 2021

Abstract A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article. Using varying radix, variable sum of data may be embedded in various pixels of images. This scheme is made adaptive using the correlation of the neighboring pixels. Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image. A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image. The size of the embedded data depends on the statistics… More >

Muhammad Shaheen^1,*, Umair Abdullah²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3305-3322, 2021, DOI:10.32604/cmc.2021.016766 - 06 May 2021

Abstract This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm (CBPNARM). CBPNARM was developed to extract positive and negative association rules from Spatio-temporal (space-time) data only, while the proposed algorithm can be applied to both spatial and non-spatial data. The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative… More >

Amena Mahmoud^1,*, Mahmoud Y. Shams², O. M. Elzeki³, Nancy Awadallah Awad⁴

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2711-2726, 2021, DOI:10.32604/cmc.2021.015293 - 13 April 2021

Abstract Semantic Web (SW) provides new opportunities for the study and application of big data, massive ranges of data sets in varied formats from multiple sources. Related studies focus on potential SW technologies for resolving big data problems, such as structurally and semantically heterogeneous data that result from the variety of data formats (structured, semi-structured, numeric, unstructured text data, email, video, audio, stock ticker). SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured data. In the current research, we implement a new… More >

Muhammad Usman Ghani Khan^1,2, Zubaira Naz¹, Javeria Khan¹, Tanzila Saba³, Ibrahim Abunadi³, Amjad Rehman³, Usman Tariq^4,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2363-2376, 2021, DOI:10.32604/cmc.2021.014550 - 13 April 2021

Abstract The National Health Interview Survey (NHIS) shows that there are 13.2% of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder (ADHD), globally. The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy. These traditional methods, namely therapy, need a large number of visits to hospitals and include medication. Neurogames could be used for the effective treatment of ADHD. It could be a helpful tool in improving children and ADHD patients’ cognitive skills by using Brain–Computer Interfaces (BCI). BCI enables the user to interact with the… More >

Sidharth Samanta¹, Mrutyunjaya Panda¹, Somula Ramasubbareddy², S. Sankar³, Daniel Burgos^4,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1937-1948, 2021, DOI:10.32604/cmc.2021.014406 - 13 April 2021

Abstract Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) More >

Mohamed Abd-Elbasseer^*, Hatem Kh Mohamed

Sound & Vibration, Vol.55, No.1, pp. 43-55, 2021, DOI:10.32604/sv.2021.09417 - 19 January 2021

Abstract This research presents a thorough evaluation of the reverberation room at Acoustics Laboratory in National Institute of Standards (NIS) according to the related international standards. The evaluation aims at examining the room performance and exploring its effectiveness in the frequency range from 125 Hz to 10000 Hz according to the international standard requirements. The room, which was designed and built several years ago, is an irregular rectangular shape free from diffusers. Its volume is about 158.84 m³, which meets the requirement of the ISO 354 standard L_max < 1.9V^1/3. Cut-off frequencies of one and one-third octave are… More >

Mohammad Masroor Ahmed^1,*, Saleh Al Shehri², Jawad Usman Arshed³, Mahmood Ul Hassan⁴, Muzammil Hussain⁵, Mehtab Afzal⁶

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 171-185, 2021, DOI:10.32604/cmc.2021.014141 - 12 January 2021

Abstract Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field (MAP-MRF). It developed its own maximization step to be used within this framework. This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images (MRIs). The improved model is named as the Weighted Spatially Constrained Finite Mixture Model (WSCFMM). To compare the performance of SCMM and WSCFMM, simulated T1-Weighted normal MRIs were segmented. A region of interest (ROI) was extracted from segmented images. The similarity level More >

Choo-Yee Ting^1,*, Helmi Zakariah², Fadzilah Kamaludin², Darryl Lin-Wei Cheng¹, Nicholas Yu-Zhe Tan¹, Hui-Jia Yee²

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 835-848, 2021, DOI:10.32604/cmc.2021.013327 - 12 January 2021

Abstract Ever since the COVID-19 pandemic started in Wuhan, China, much research work has been focusing on the clinical aspect of SARS-CoV-2. Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus. Limited studies have, however, reported on COVID-19 transmission pattern analysis, and using geography features for prediction of potential outbreak sites. Predicting the next most probable outbreak site is crucial, particularly for optimizing the planning of medical personnel and supply resources. To tackle the challenge, this work proposed distance-based similarity measures to predict the next most… More >