Zhen-Zhen Li¹, Zi-Chen Li^1,*, Xiu-Bo Chen², Zhiguo Qu³, Xiaojun Wang⁴, Haizhu Pan⁵

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2651-2663, 2021, DOI:10.32604/cmc.2021.016960

Abstract

In many earlier works, perfect quantum state transmission over the butterfly network can be achieved via quantum network coding protocols with the assist of maximally entangled states. However, in actual quantum networks, a maximally entangled state as auxiliary resource is hard to be obtained or easily turned into a non-maximally entangled state subject to all kinds of environmental noises. Therefore, we propose a more practical quantum network coding scheme with the assist of non-maximally entangled states. In this paper, a practical quantum network coding protocol over grail network is proposed, in which the non-maximally entangled resource is assisted and even… More >

Van-Truong Truong¹, Anand Nayyar^2,*, Showkat Ahmad Lone³

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1615-1635, 2021, DOI:10.32604/cmc.2021.016922

Abstract Wireless sensor network (WSN) is considered as the fastest growing technology pattern in recent years because of its applicability in varied domains. Many sensor nodes with different sensing functionalities are deployed in the monitoring area to collect suitable data and transmit it to the gateway. Ensuring communications in heterogeneous WSNs, is a critical issue that needs to be studied. In this research paper, we study the system performance of a heterogeneous WSN using LoRa–Zigbee hybrid communication. Specifically, two Zigbee sensor clusters and two LoRa sensor clusters are used and combined with two Zigbee-to-LoRa converters to communicate in a network managed… More >

Hanan Abdullah Mengash^1,*, Hanan A. Hosni Mahmoud^2,3

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1551-1563, 2021, DOI:10.32604/cmc.2021.016907

Abstract Detection of brain tumors in MRI images is the first step in brain cancer diagnosis. The accuracy of the diagnosis depends highly on the expertise of radiologists. Therefore, automated diagnosis of brain cancer from MRI is receiving a large amount of attention. Also, MRI tumor detection is usually followed by a biopsy (an invasive procedure), which is a medical procedure for brain tumor classification. It is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive procedures. Convolutional neural network (CNN) is deemed to be one of the best machine learning… More >

Samina Amin^1,*, M. Irfan Uddin¹, Heyam H. Al-Baity², M. Ali Zeb¹, M. Abrar Khan¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2231-2247, 2021, DOI:10.32604/cmc.2021.016896

Abstract Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this… More >

Javaria Amin¹, Muhammad Almas Anjum², Muhammad Sharif³, Seifedine Kadry⁴, Yunyoung Nam^5,*, ShuiHua Wang⁶

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2693-2709, 2021, DOI:10.32604/cmc.2021.016871

Abstract Recognition of human gait is a difficult assignment, particularly for unobtrusive surveillance in a video and human identification from a large distance. Therefore, a method is proposed for the classification and recognition of different types of human gait. The proposed approach is consisting of two phases. In phase I, the new model is proposed named convolutional bidirectional long short-term memory (Conv-BiLSTM) to classify the video frames of human gait. In this model, features are derived through convolutional neural network (CNN) named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information. In phase II,… More >

Baiyan Zhang¹, Hefei Ling^1,*, Ping Li¹, Qian Wang¹, Yuxuan Shi¹, Lei Wu¹, Runsheng Wang¹, Jialie Shen²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1505-1517, 2021, DOI:10.32604/cmc.2021.016851

Abstract The majority of existing graph-network-based few-shot models focus on a node-similarity update mode. The lack of adequate information intensifies the risk of overtraining. In this paper, we propose a novel Multi-head Attention Graph Network to excavate discriminative relation and fulfill effective information propagation. For edge update, the node-level attention is used to evaluate the similarities between the two nodes and the distribution-level attention extracts more in-deep global relation. The cooperation between those two parts provides a discriminative and comprehensive expression for edge feature. For node update, we embrace the label-level attention to soften the noise of irrelevant nodes and optimize… More >

Mohammed H. Alsharif¹, Kannadasan Raju², Abu Jahid³, Mahmoud A. Albreem⁴, Peerapong Uthansakul^5,*, Jamel Nebhen⁶, Venkatesan Chandrasekaran²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2665-2680, 2021, DOI:10.32604/cmc.2021.016836

Abstract

Green wireless networks or energy-efficient wireless networks have gained popularity as a research topic due to the ecological and economic concerns of cellular operators. The specific power supply requirements for the cellular base station, such as cost-effectiveness, efficiency, sustainability, and reliability, can be met by utilizing the technological advances in renewable energy. There are numerous drivers for the deployment of renewable energy technologies and the transition towards green energy. Renewable energy is free, clean, and abundant in most locations throughout the year. Accordingly, this work proposes a novel framework for energy-efficient solar-powered base stations for the Oman site, specifically for… More >

Huiping Jiang^1,*, Rui Jiao¹, Demeng Wu¹, Wenbo Wu²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2315-2327, 2021, DOI:10.32604/cmc.2021.016832

Abstract With the rapid development of deep learning and artificial intelligence, affective computing, as a branch field, has attracted increasing research attention. Human emotions are diverse and are directly expressed via non-physiological indicators, such as electroencephalogram (EEG) signals. However, whether emotion-based or EEG-based, these remain single-modes of emotion recognition. Multi-mode fusion emotion recognition can improve accuracy by utilizing feature diversity and correlation. Therefore, three different models have been established: the single-mode-based EEG-long and short-term memory (LSTM) model, the Facial-LSTM model based on facial expressions processing EEG data, and the multi-mode LSTM-convolutional neural network (CNN) model that combines expressions and EEG. Their… More >

R. Sowmyalakshmi¹, T. Jayasankar^1,*, V. Ayyem Pillai², Kamalraj Subramaniyan³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, K. Shankar⁶

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1751-1767, 2021, DOI:10.32604/cmc.2021.016825

Abstract Internet of Things (IoT) paves a new direction in the domain of smart farming and precision agriculture. Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent. In smart farming, IoT devices are linked among one another with new technologies to improve the agricultural practices. Smart farming makes use of IoT devices and contributes in effective decision making. Rice is the major food source in most of the countries. So, it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and… More >

Ikhlas Ahmad¹, Haris Dildar¹, Wasi Ur Rehman Khan¹, Sadiq Ullah¹, Shakir Ullah¹, Mahmoud A. Albreem², Mohammed H. Alsharif³, Peerapong Uthansakul^4,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2563-2578, 2021, DOI:10.32604/cmc.2021.016813

Abstract In this paper, low profile frequency reconfigurable monopole antenna is designed on FR-4 substrate with a compact size of 30 mm³ × 20 mm³ × 1.6 mm³. The antenna is tuned to four different modes through three pin diode switches. In Mode 1 (SW1 to SW3 = OFF), antenna covers a wideband of 3.15–8.51 GHz. For Mode 2 (SW1 = ON, SW2 to SW3 = OFF), the proposed antenna resonates at 3.5 GHz. The antenna shows dual band behavior and covers 2.6 and 6.4 GHz in Mode 3 (SW1 and SW2 = ON, SW3 = OFF). The same antenna covers… More >