Wael M. S. Yafooz^*, Abdullah Alsaeedi

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 735-759, 2024, DOI:10.32604/cmc.2024.048270

Abstract In the last decade, technical advancements and faster Internet speeds have also led to an increasing number of mobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobile apps. The use of these apps eases our daily lives, and all customers who need any type of service can access it easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digital services to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services, particularly during two large occasions, Umrah and Hajj. However, pilgrims… More >

Jauzak Hussaini Windiatmaja, Delphi Hanggoro, Muhammad Salman, Riri Fitri Sari^*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2309-2339, 2023, DOI:10.32604/cmc.2023.041152

Abstract This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation (PoIR) as an alternative to traditional Proof of Work (PoW). PoIR addresses the limitations of existing reputation-based consensus mechanisms by proposing a more decentralized and fair node selection process. The proposed PoIR consensus combines Bidirectional Long Short-Term Memory (BiLSTM) with the Network Entity Reputation Database (NERD) to generate reputation scores for network entities and select authoritative nodes. NERD records network entity profiles based on various sources, i.e., Warden, Blacklists, DShield, AlienVault Open Threat Exchange (OTX), and MISP (Malware Information Sharing Platform). It summarizes these profile records into… More >

Min Hyung Park¹, Dongyan Nan^2,3, Yerin Kim¹, Jang Hyun Kim^1,2,3,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 121-134, 2023, DOI:10.32604/csse.2023.033247

Abstract After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized as sentiment analysis methods. The… More >

Manar Ahmed Hamza^1,*, Siwar Ben Haj Hassine², Souad Larabi-Marie-Sainte³, Mohamed K. Nour⁴, Fahd N. Al-Wesabi^5,6, Abdelwahed Motwakel¹, Anwer Mustafa Hilal¹, Mesfer Al Duhayyim⁷

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3055-3071, 2022, DOI:10.32604/cmc.2022.024490

Abstract Modulation signal classification in communication systems can be considered a pattern recognition problem. Earlier works have focused on several feature extraction approaches such as fractal feature, signal constellation reconstruction, etc. The recent advent of deep learning (DL) models makes it possible to proficiently classify the modulation signals. In this view, this study designs a chaotic oppositional satin bowerbird optimization (COSBO) with bidirectional long term memory (BiLSTM) model for modulation signal classification in communication systems. The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals. In addition, the fractal feature extraction process takes place by the use… More >

Shuo Liang¹, Dingcheng Wang^1,*, Jingrong Wu¹, Rui Wang¹, Ruiqi Wang²

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 701-714, 2021, DOI:10.32604/iasc.2021.020010

Abstract Atmospheric temperature forecast plays an important role in weather forecast and has a significant impact on human daily and economic life. However, due to the complexity and uncertainty of the atmospheric system, exploring advanced forecasting methods to improve the accuracy of meteorological prediction has always been a research topic for scientists. With the continuous improvement of computer performance and data acquisition technology, meteorological data has gained explosive growth, which creates the necessary hardware support conditions for more accurate weather forecast. The more accurate forecast results need advanced weather forecast methods suitable for hardware. Therefore, this paper proposes a deep learning… More >

Ziyong Ran¹, Desheng Zheng^{1, *}, Yanling Lai¹, Lulu Tian²

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 309-320, 2020, DOI:10.32604/cmc.2020.010102

Abstract Nowadays, Internet has become an indispensable part of daily life and is used in many fields. Due to the large amount of Internet traffic, computers are subject to various security threats, which may cause serious economic losses and even endanger national security. It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data. As machine learning technology matures, deep learning is widely used in various industries. Combining deep learning with network security and intrusion detection is the current trend. In this paper, the problem of data… More >

Bo Zhang¹, Haowen Wang^{1, #}, Longquan Jiang¹, Shuhan Yuan², Meizi Li^{1, *}

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1273-1288, 2020, DOI:10.32604/cmc.2020.07269

Abstract Deep learning models have been shown to have great advantages in answer selection tasks. The existing models, which employ encoder-decoder recurrent neural network (RNN), have been demonstrated to be effective. However, the traditional RNN-based models still suffer from limitations such as 1) high-dimensional data representation in natural language processing and 2) biased attentive weights for subsequent words in traditional time series models. In this study, a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory (Bi-LSTM) and attention mechanism. The proposed model is able to generate the more effective question-answer pair representation. Experiments on a question… More >

Liang Ding^1,*, Lunjie Li¹, Jianghong Han¹, Yuqi Fan^2,*, Donghui Hu¹

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1285-1304, 2019, DOI:10.32604/cmc.2019.06160

Abstract Botnets often use domain generation algorithms (DGA) to connect to a command and control (C2) server, which enables the compromised hosts connect to the C2 server for accessing many domains. The detection of DGA domains is critical for blocking the C2 server, and for identifying the compromised hosts as well. However, the detection is difficult, because some DGA domain names look normal. Much of the previous work based on statistical analysis of machine learning relies on manual features and contextual information, which causes long response time and cannot be used for real-time detection. In addition, when a new family of… More >