Donghyun Lee, Hosung Park, Soonshin Seo, Changmin Kim, Hyunsoo Son, Gyujin Kim, Ji-Hwan Kim^*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 537-551, 2021, DOI:10.32604/cmc.2021.015430

Abstract A differentiable neural computer (DNC) is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism. Such DNC’s offer a generalized method for task-specific deep learning models and have demonstrated reliability with reasoning problems. In this study, we apply a DNC to a language model (LM) task. The LM task is one of the reasoning problems, because it can predict the next word using the previous word sequence. However, memory deallocation is a problem in DNCs as some information unrelated to the input sequence is not allocated and remains… More >

Yasir Shahzad^1,*, Huma Javed¹, Haleem Farman², Jamil Ahmad², Bilal Jan³, Abdelmohsen A. Nassani⁴

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2463-2480, 2021, DOI:10.32604/cmc.2021.014904

Abstract Smart healthcare integrates an advanced wave of information technology using smart devices to collect health-related medical science data. Such data usually exist in unstructured, noisy, incomplete, and heterogeneous forms. Annotating these limitations remains an open challenge in deep learning to classify health conditions. In this paper, a long short-term memory (LSTM) based health condition prediction framework is proposed to rectify imbalanced and noisy data and transform it into a useful form to predict accurate health conditions. The imbalanced and scarce data is normalized through coding to gain consistency for accurate results using synthetic minority oversampling technique. The proposed model is… More >

Rami Sihwail^*, Khairuddin Omar, Khairul Akram Zainol Ariffin

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2301-2320, 2021, DOI:10.32604/cmc.2021.014510

Abstract The study of malware behaviors, over the last years, has received tremendous attention from researchers for the purpose of reducing malware risks. Most of the investigating experiments are performed using either static analysis or behavior analysis. However, recent studies have shown that both analyses are vulnerable to modern malware files that use several techniques to avoid analysis and detection. Therefore, extracted features could be meaningless and a distraction for malware analysts. However, the volatile memory can expose useful information about malware behaviors and characteristics. In addition, memory analysis is capable of detecting unconventional malware, such as in-memory and fileless malware.… More >

Ayesha Jabeen¹, Sitara Afzal¹, Muazzam Maqsood¹, Irfan Mehmood², Sadaf Yasmin¹, Muhammad Tabish Niaz³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1191-1206, 2021, DOI:10.32604/cmc.2021.014598

Abstract Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the positive and negative effects of… More >

Nafisa Ahmed¹, Manar Abu Talib^2,*, Qassim Nasir³

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 23-49, 2021, DOI:10.32604/cmc.2021.014523

Abstract Security is one of the major challenges that devices connected to the Internet of Things (IoT) face today. Remote attestation is used to measure these devices’ trustworthiness on the network by measuring the device platform’s integrity. Several software-based attestation mechanisms have been proposed, but none of them can detect runtime attacks. Although some researchers have attempted to tackle these attacks, the proposed techniques require additional secured hardware parts to be integrated with the attested devices to achieve their aim. These solutions are expensive and not suitable in many cases. This paper proposes a dual attestation process, SAPEM, with two phases:… More >

Mohamed Abdelsabour Fahmy^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.1, pp. 175-199, 2021, DOI:10.32604/cmes.2021.012218

Abstract The main aim of this paper is to propose a new memory dependent derivative (MDD) theory which called threetemperature nonlinear generalized anisotropic micropolar-thermoelasticity. The system of governing equations of the problems associated with the proposed theory is extremely difficult or impossible to solve analytically due to nonlinearity, MDD diffusion, multi-variable nature, multi-stage processing and anisotropic properties of the considered material. Therefore, we propose a novel boundary element method (BEM) formulation for modeling and simulation of such system. The computational performance of the proposed technique has been investigated. The numerical results illustrate the effects of time delays and kernel functions on… More >

Youseef Alotaibi^*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1879-1895, 2021, DOI:10.32604/cmc.2020.013739

Abstract A new secured database management system architecture using intrusion detection systems (IDS) is proposed in this paper for organizations with no previous role mapping for users. A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm. A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’ profiles. Then, queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious. The IDS will stop query execution or… More >

Gopi Krishna Durbhaka¹, Barani Selvaraj¹, Mamta Mittal², Tanzila Saba^3,*, Amjad Rehman³, Lalit Mohan Goyal⁴

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2041-2059, 2021, DOI:10.32604/cmc.2020.013131

Abstract Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches, practices and technology during the last decade. Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect. This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the… More >

Yi Liang^1,*, Shaokang Zeng¹, Xiaoxian Xu², Shilu Chang¹, Xing Su¹

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1697-1717, 2021, DOI:10.32604/cmc.2020.012513

Abstract Spark is the most popular in-memory processing framework for big data analytics. Memory is the crucial resource for workloads to achieve performance acceleration on Spark. The extant memory capacity configuration approach in Spark is to statically configure the memory capacity for workloads based on user’s specifications. However, without the deep knowledge of the workload’s system-level characteristics, users in practice often conservatively overestimate the memory utilizations of their workloads and require resource manager to grant more memory share than that they actually need, which leads to the severe waste of memory resources. To address the above issue, SMConf, an automated memory… More >

Xinda Hao¹, Jianmin Zhou^2,*, Xueqi Shen¹, Yu Yang¹

Journal of Quantum Computing, Vol.2, No.2, pp. 97-104, 2020, DOI:10.32604/jqc.2020.010819

Abstract In recent years, machine learning technology has been widely used for timely network attack detection and classification. However, due to the large number of network traffic and the complex and variable nature of malicious attacks, many challenges have arisen in the field of network intrusion detection. Aiming at the problem that massive and high-dimensional data in cloud computing networks will have a negative impact on anomaly detection, this paper proposes a Bi-LSTM method based on attention mechanism, which learns by transmitting IDS data to multiple hidden layers. Abstract information and high-dimensional feature representation in network data messages are used to… More >