Hatice Jenkins¹, Siamand Hesami^1,*, Fulden Yesiltepe²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6167-6184, 2022, DOI:10.32604/cmc.2022.027293

Abstract This research investigates a broad range of possible factors affecting the adoption of new technology in the banking industry using adaptive LASSO and a standard logit model. The research integrated the adoption of the innovation framework and the technology acceptance theory to develop a conceptual framework for the analysis. Primary data was collected from 400 bank customers in North Cyprus. Risk perception and other customer-specific factors such as perceived risk index and negative attitude toward new technologies index were formulated for the proposed conceptual model. The findings indicated that individuals with a negative attitude toward new technology are least likely… More >

Yangqun Li^1,2,*, Jin Qi^1,2, Lijuan Min^1,2, Hongzhi Yang^1,2, Chenyang Zhou^1,2, Bonan Jin³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5079-5098, 2022, DOI:10.32604/cmc.2022.027281

Abstract Web of Things (WoT) resources are not only numerous, but also have a wide range of applications and deployments. The centralized WoT resource sharing mechanism lacks flexibility and scalability, and hence cannot satisfy requirement of distributed resource sharing in large-scale environment. In response to this problem, a trusted and secure mechanism for WoT resources sharing based on context and blockchain (CWoT-Share) was proposed. Firstly, the mechanism can respond quickly to the changes of the application environment by dynamically determining resource access control rules according to the context. Then, the flexible resource charging strategies, which reduced the fees paid by the… 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

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 the dependency in the clustering… More >

Letao Gao^1,*, Xiaoming Wang², Tao Wang³, Mengyu Chang⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4741-4754, 2022, DOI:10.32604/cmc.2022.027204

Abstract In the research field of bearing fault diagnosis, classical deep learning models have the problems of too many parameters and high computing cost. In addition, the classical deep learning models are not effective in the scenario of small data. In recent years, deep forest is proposed, which has less hyper parameters and adaptive depth of deep model. In addition, weighted deep forest (WDF) is proposed to further improve deep forest by assigning weights for decisions trees based on the accuracy of each decision tree. In this paper, weighted deep forest model-based bearing fault diagnosis method (WDBM) is proposed. The WDBM… More >

Wei Fang^1,2,*, Liang Shen¹, Victor S. Sheng³, Qiongying Xue¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4867-4877, 2022, DOI:10.32604/cmc.2022.027197

Abstract Precipitation nowcasting is of great significance for severe convective weather warnings. Radar echo extrapolation is a commonly used precipitation nowcasting method. However, the traditional radar echo extrapolation methods are encountered with the dilemma of low prediction accuracy and extrapolation ambiguity. The reason is that those methods cannot retain important long-term information and fail to capture short-term motion information from the long-range data stream. In order to solve the above problems, we select the spatiotemporal long short-term memory (ST-LSTM) as the recurrent unit of the model and integrate the 3D convolution operation in it to strengthen the model's ability to capture… More >

S. Vinson Joshua¹, A. Selwin Mich Priyadharson¹, Raju Kannadasan², Arfat Ahmad Khan³, Worawat Lawanont^3,*, Faizan Ahmed Khan⁴, Ateeq Ur Rehman⁵, Muhammad Junaid Ali⁶

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5663-5679, 2022, DOI:10.32604/cmc.2022.027178

Abstract The exponential growth of population in developing countries like India should focus on innovative technologies in the Agricultural process to meet the future crisis. One of the vital tasks is the crop yield prediction at its early stage; because it forms one of the most challenging tasks in precision agriculture as it demands a deep understanding of the growth pattern with the highly nonlinear parameters. Environmental parameters like rainfall, temperature, humidity, and management practices like fertilizers, pesticides, irrigation are very dynamic in approach and vary from field to field. In the proposed work, the data were collected from paddy fields… More >

Juan Fang^1,*, Yunfei Mao¹, Min Cai¹, Li’ang Zhao¹, Huijie Chen¹, Wei Xiang^2,3

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5531-5545, 2022, DOI:10.32604/cmc.2022.027177

Abstract Since the three-dimensional Network on Chip (3D NoC) uses through-silicon via technology to connect the chips, each silicon layer is conducted through heterogeneous thermal, and 3D NoC system suffers from thermal problems. To alleviate the seriousness of the thermal problem, the distribution of data packets usually relies on traffic information or historical temperature information. However, thermal problems in 3D NoC cannot be solved only based on traffic or temperature information. Therefore, we propose a Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) that applies traffic load and temperature information to routing. First, the STTAR dynamically adjusts the input and output buffer… More >

He Gong^1,3,4, Lin Chen¹, Haohong Pan¹, Shijun Li^2,5, Yin Guo¹, Lili Fu¹, Tianli Hu^1,3,4,*, Ye Mu^1,3, Thobela Louis Tyasi⁶

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6015-6027, 2022, DOI:10.32604/cmc.2022.027160

Abstract The scale of deer breeding has gradually increased in recent years and better information management is necessary, which requires the identification of individual deer. In this paper, a deer face dataset is produced using face images obtained from different angles, and an improved residual neural network (ResNet)-based recognition model is proposed to extract the features of deer faces, which have high similarity. The model is based on ResNet-50, which reduces the depth of the model, and the network depth is only 29 layers; the model connects Squeeze-and-Excitation (SE) modules at each of the four layers where the channel changes to… More >

Hanguang Luo¹, Tao Zou^1,*, Chunming Wu², Dan Li³, Shunbin Li¹, Chu Chu⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5031-5040, 2022, DOI:10.32604/cmc.2022.027118

Abstract In the emerging Industrial Internet of Things (IIoT), authentication problems have become an urgent issue for massive resource-constrained devices because traditional costly security mechanisms are not suitable for them. The security protocol designed for resource-constrained systems should not only be secure but also efficient in terms of usage of energy, storage, and processing. Although recently many lightweight schemes have been proposed, to the best of our knowledge, they are unable to address the problem of privacy preservation with the resistance of Denial of Service (DoS) attacks in a practical way. In this paper, we propose a lightweight authentication protocol based… More >

Yassir Edrees Almalki1¹, Ahmad Shaf², Tariq Ali², Muhammad Aamir², Sharifa Khalid Alduraibi³, Shoayea Mohessen Almutiri⁴, Muhammad Irfan⁵, Mohammad Abd Alkhalik Basha⁶, Alaa Khalid Alduraibi³, Abdulrahman Manaa Alamri⁷, Muhammad Zeeshan Azam⁸, Khalaf Alshamrani^9,*, Hassan A. Alshamrani⁹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4833-4851, 2022, DOI:10.32604/cmc.2022.027111

Abstract Breast cancer (BC) is the most common cause of women’s deaths worldwide. The mammography technique is the most important modality for the detection of BC. To detect abnormalities in mammographic images, the Breast Imaging Reporting and Data System (BI-RADs) is used as a baseline. The correct allocation of BI-RADs categories for mammographic images is always an interesting task, even for specialists. In this work, to detect and classify the mammogram images in BI-RADs, a novel hybrid model is presented using a convolutional neural network (CNN) with the integration of a support vector machine (SVM). The dataset used in this research… More >