Daxing Zhang^1,*, Erguan Cai²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3931-3943, 2021, DOI:10.32604/cmc.2021.012302

Abstract Previous research in the area of using deep learning algorithms to forecast stock prices was focused on news headlines, company reports, and a mix of daily stock fundamentals, but few studies achieved excellent results. This study uses a convolutional neural network (CNN) to predict stock prices by considering a great amount of data, consisting of financial news headlines. We call our model N-CNN to distinguish it from a CNN. The main concept is to narrow the diversity of specific stock prices as they are impacted by news headlines, then horizontally expand the news headline data to a higher level for… More >

Jing Zhang^1,2, Xin Qi^3,*, San Hlaing Myint³, Zheng Wen⁴

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2809-2809, 2021, DOI:10.32604/cmc.2021.17410

Abstract This article has no abstract. More >

Linguo Li^1,2, Shujing Li^1,*, Jian Su³

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2355-2366, 2021, DOI:10.32604/cmc.2021.019409

Abstract Brain tumor is one of the most common tumors with high mortality. Early detection is of great significance for the treatment and rehabilitation of patients. The single channel convolution layer and pool layer of traditional convolutional neural network (CNN) structure can only accept limited local context information. And most of the current methods only focus on the classification of benign and malignant brain tumors, multi classification of brain tumors is not common. In response to these shortcomings, considering that convolution kernels of different sizes can extract more comprehensive features, we put forward the multi-size convolutional kernel module. And considering that… More >

Tao Zhang^1,2, Zhanjie Zhang^1,2,*, Wenjing Jia³, Xiangjian He³, Jie Yang⁴

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2733-2747, 2021, DOI:10.32604/cmc.2021.019305

Abstract The generative adversarial network (GAN) is first proposed in 2014, and this kind of network model is machine learning systems that can learn to measure a given distribution of data, one of the most important applications is style transfer. Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image. CYCLE-GAN is a classic GAN model, which has a wide range of scenarios in style transfer. Considering its unsupervised learning characteristics, the mapping is easy to be learned between an input image and an output… More >

Munir Ahmad¹, Majed Alfayad², Shabib Aftab^1,3, Muhammad Adnan Khan^4,*, Areej Fatima⁵, Bilal Shoaib⁶, Mohammad Sh. Daoud⁷, Nouh Sabri Elmitwally^2,8

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2717-2731, 2021, DOI:10.32604/cmc.2021.019013

Abstract Heart disease, which is also known as cardiovascular disease, includes various conditions that affect the heart and has been considered a major cause of death over the past decades. Accurate and timely detection of heart disease is the single key factor for appropriate investigation, treatment, and prescription of medication. Emerging technologies such as fog, cloud, and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes, cancer, and cardiovascular disease. Cloud computing provides a cost-efficient infrastructure for data processing, storage, and retrieval, with much of the extant research recommending machine learning (ML) algorithms for… More >

Naila Rafiq¹, Saima Akram², Mudassir Shams^3,*, Nazir Ahmad Mir¹

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2635-2651, 2021, DOI:10.32604/cmc.2021.018955

Abstract In this research article, we construct a family of derivative free simultaneous numerical schemes to approximate all real zero of non-linear polynomial equation. We make a comparative analysis of the newly constructed numerical schemes with a well-known existing simultaneous method for determining all the distinct real zeros of polynomial equations using computer algebra system Mat Lab. Lower bound of convergence of simultaneous schemes is calculated using Mathematica. Global convergence property of the numerical schemes is presented by taking random starting initial approximation and their convergence history are graphically presented. Some real life engineering applications along with some higher degree polynomials… More >

Zakariae Dlimi^*, Abdellah Ezzati, Saïd Ben Alla

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2687-2703, 2021, DOI:10.32604/cmc.2021.018942

Abstract The smart city is a technological framework that connects the city’s different components to create new opportunities. This connection is possible with the help of the Internet of Things (IoT), which provides a digital personality to physical objects. Some studies have proposed integrating Blockchain technology with IoT in different use cases as access, orchestration, or replicated storage layer. The majority of connected objects’ capacity limitation makes the use of Blockchain inadequate due to its redundancy and its conventional processing-intensive consensus like PoW. This paper addresses these challenges by proposing a NOVEL model of a lightweight Blockchain framework (IoT-SmartChain), with a… More >

Muhammad Aslam Mohd Safari¹, Nurulkamal Masseran^2,*, Muhammad Hilmi Abdul Majid²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2807-2824, 2021, DOI:10.32604/cmc.2021.018815

Abstract In modeling reliability data, the exponential distribution is commonly used due to its simplicity. For estimating the parameter of the exponential distribution, classical estimators including maximum likelihood estimator represent the most commonly used method and are well known to be efficient. However, the maximum likelihood estimator is highly sensitive in the presence of contamination or outliers. In this study, a robust and efficient estimator of the exponential distribution parameter was proposed based on the probability integral transform statistic. To examine the robustness of this new estimator, asymptotic variance, breakdown point, and gross error sensitivity were derived. This new estimator offers… More >

Faisal Jamil¹, Faiza Qayyum¹, Soha Alhelaly², Farjeel Javed³, Ammar Muthanna^4,5,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2513-2530, 2021, DOI:10.32604/cmc.2021.018809

Abstract Nowadays, the blockchain, Internet of Things, and artificial intelligence technology revolutionize the traditional way of data mining with the enhanced data preprocessing, and analytics approaches, including improved service platforms. Nevertheless, one of the main challenges is designing a combined approach that provides the analytics functionality for diverse data and sustains IoT applications with robust and modular blockchain-enabled services in a diverse environment. Improved data analytics model not only provides support insights in IoT data but also fosters process productivity. Designing a robust IoT-based secure analytic model is challenging for several purposes, such as data from diverse sources, increasing data size,… More >

Anhao Xiang, Jun Zheng^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2141-2158, 2021, DOI:10.32604/cmc.2021.018808

Abstract Distribution feeder microgrid (DFM) built based on existing distributed feeder (DF), is a promising solution for modern microgrid. DFM contains a large number of heterogeneous devices that generate heavy network traffice and require a low data delivery latency. The information-centric networking (ICN) paradigm has shown a great potential to address the communication requirements of smart grid. However, the integration of advanced information and communication technologies with DFM make it vulnerable to cyber attacks. Adequate authentication of grid devices is essential for preventing unauthorized accesses to the grid network and defending against cyber attacks. In this paper, we propose a new… More >