Abdullah Muhammad, Salwani Abdullah, Nor Samsiah Sani^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1783-1799, 2021, DOI:10.32604/cmc.2021.018593

Abstract Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is… More >

Yong-Woon Hwang, Im-Yeong Lee^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1747-1764, 2021, DOI:10.32604/cmc.2021.018549

Abstract Recently, with the advancement of Information and Communications Technology (ICT), Internet of Things (IoT) has been connected to the cloud and used in industrial sectors, medical environments, and smart grids. However, if data is transmitted in plain text when collecting data in an IoT-cloud environment, it can be exposed to various security threats such as replay attacks and data forgery. Thus, digital signatures are required. Data integrity is ensured when a user (or a device) transmits data using a signature. In addition, the concept of data aggregation is important to efficiently collect data transmitted from multiple users (or a devices)… More >

Buddhadeva Sahoo^1,*, Sangram Keshari Routray², Pravat Kumar Rout², Mohammed M. Alhaider³

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1851-1875, 2021, DOI:10.32604/cmc.2021.018535

Abstract This paper suggests a combined novel control strategy for DFIG based wind power systems (WPS) under both nonlinear and unbalanced load conditions. The combined control approach is designed by coordinating the machine side converter (MSC) and the load side converter (LSC) control approaches. The proposed MSC control approach is designed by using a model predictive control (MPC) approach to generate appropriate real and reactive power. The MSC controller selects an appropriate rotor voltage vector by using a minimized optimization cost function for the converter operation. It shows its superiority by eliminating the requirement of transformation, switching table, and the PWM… More >

Rohit Samkria¹, Mohammed Abd-Elnaby², Rajesh Singh³, Anita Gehlot³, Mamoon Rashid^4,*, Moustafa H. Aly⁵, Walid El-Shafai⁶

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1709-1723, 2021, DOI:10.32604/cmc.2021.018525

Abstract Fault detection of the photovoltaic (PV) grid is necessary to detect serious output power reduction to avoid PV modules’ damage. To identify the fault of the PV arrays, there is a necessity to implement an automatic system. In this IoT and LabVIEW-based automatic fault detection of 3 × 3 solar array, a PV system is proposed to control and monitor Internet connectivity remotely. Hardware component to automatically reconfigure the solar PV array from the series-parallel (SP) to the complete cross-linked array underneath partial shading conditions (PSC) is centered on the Atmega328 system to achieve maximum power. In the LabVIEW environment,… More >

Prince Waqas Khan, Yung-Cheol Byun^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1893-1913, 2021, DOI:10.32604/cmc.2021.018523

Abstract Despite the advancement within the last decades in the field of smart grids, energy consumption forecasting utilizing the metrological features is still challenging. This paper proposes a genetic algorithm-based adaptive error curve learning ensemble (GA-ECLE) model. The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach. A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy. This approach combines three models, namely CatBoost (CB), Gradient Boost (GB), and Multilayer Perceptron (MLP). The ensembled CB-GB-MLP model’s inner mechanism consists of generating… More >

Simon Crase^1,2, Benjamin Hall², Suresh N. Thennadil^3,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1945-1965, 2021, DOI:10.32604/cmc.2021.018517

Abstract Supervised machine learning techniques have become well established in the study of spectroscopy data. However, the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis. This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data. In addition, we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain. This includes practices in data pre-processing, feature extraction, clustering distance metrics, clustering algorithms and validation techniques. Special consideration is given to the specific characteristics of IR and… More >

Saddam Bekhet^1,*, Monagi H. Alkinani², Reinel Tabares-Soto³, M. Hassaballah⁴

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2475-2491, 2021, DOI:10.32604/cmc.2021.018514

Abstract The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary. The situation is very complex as the COVID-19 test kits are limited, therefore, more diagnostic methods must be developed urgently. A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography (CT), where any chest anomalies (e.g., lung inflammation) can be easily identified. Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19. Motivated by this, various artificial intelligence (AI) techniques have been developed… More >

Liangbo Xie¹, Yan Ren¹, Mu Zhou¹, Xiaolong Yang^1,*, Zhengwen Huang²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1597-1609, 2021, DOI:10.32604/cmc.2021.018502

Abstract This paper presents an energy efficient successive-approximation register (SAR) analog-to-digital converter (ADC) for low-power applications. To improve the overall energy-efficiency, a skipping-window technique is used to bypass corresponding conversion steps when the input falls in a window indicated by a time-domain comparator, which can provide not only the polarity of the input, but also the amount information of the input. The time-domain comparator, which is based on the edge pursing principle, consists of delay cells, two NAND gates, two D-flip-flop register-based phase detectors and a counter. The digital characteristic of the comparator makes the design more flexible, and the comparator… More >

Nahida Islam¹, Fahiba Farhin¹, Ishrat Sultana¹, M. Shamim Kaiser¹, Md. Sazzadur Rahman¹, Mufti Mahmud², A. S. M. Sanwar Hosen³, Gi Hwan Cho^3,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1801-1821, 2021, DOI:10.32604/cmc.2021.018466

Abstract The Internet of Things (IoT) integrates billions of self-organized and heterogeneous smart nodes that communicate with each other without human intervention. In recent years, IoT based systems have been used in improving the experience in many applications including healthcare, agriculture, supply chain, education, transportation and traffic monitoring, utility services etc. However, node heterogeneity raised security concern which is one of the most complicated issues on the IoT. Implementing security measures, including encryption, access control, and authentication for the IoT devices are ineffective in achieving security. In this paper, we identified various types of IoT threats and shallow (such as decision… More >

Bin Xu^1,2,3, Lu Zhang¹, Zipeng Xu¹, Yichuan Liu¹, Jinming Chai¹, Sichong Qin⁴, Yanfei Sun^1,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1671-1686, 2021, DOI:10.32604/cmc.2021.018395

Abstract In the IoT (Internet of Things) system, the introduction of UAV (Unmanned Aerial Vehicle) as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy. However, the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system. In this work, to deal with the problem, a deployment model of a mobile edge computing (MEC) system based on multi-UAV is proposed. The goal of the model is to minimize the energy consumption of the… More >