Khomdet Phapatanaburi¹, Kasidit kokkhunthod², Longbiao Wang³, Talit Jumphoo², Monthippa Uthansakul², Anyaporn Boonmahitthisud⁴, Peerapong Uthansakul^2,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3029-3044, 2021, DOI:10.32604/cmc.2021.014433

Abstract A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD… More >

Miaomiao Yang, Wentao Ma, Yongbin Ge^*

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.1, pp. 25-54, 2021, DOI:10.32604/cmes.2021.012575

Abstract In this paper, Chebyshev interpolation nodes and barycentric Lagrange interpolation basis function are used to deduce the scheme for solving the Helmholtz equation. First of all, the interpolation basis function is applied to treat the spatial variables and their partial derivatives, and the collocation method for solving the second order differential equations is established. Secondly, the differential equations on a given test node. Finally, based on three kinds of test nodes, numerical experiments show that the present scheme can not only calculate the high wave numbers problems, but also calculate the variable wave numbers problems. In addition, the algorithm has… More >

Amir Haider¹, Muhammad Adnan Khan², Abdur Rehman³, Muhib Ur Rahman⁴, Hyung Seok Kim^1,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1785-1798, 2021, DOI:10.32604/cmc.2020.013910

Abstract In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in this article an intrusion detection… More >

Muhammad Adnan Khan^1,*, Abdur Rehman², Khalid Masood Khan¹, Mohammed A. Al Ghamdi³, Sultan H. Almotiri³

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 467-480, 2021, DOI:10.32604/cmc.2020.013121

Abstract Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular… More >

Dildar Hussain¹, Muhammad Adnan Khan^2,*, Sagheer Abbas³, Rizwan Ali Naqvi⁴, Muhammad Faheem Mushtaq⁵, Abdur Rehman³, Afrozah Nadeem²

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 141-156, 2021, DOI:10.32604/cmc.2020.013458

Abstract The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries, including the transportation sector. The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features. One of these tasks is to ensure that vehicles are autonomous, intelligent and able to grow their repository of information. Machine learning has recently been implemented in wireless networks, as a major artificial intelligence branch, to solve historically challenging problems through a data-driven approach. In this article, we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try… More >

Iftikhar Ahmad^{1, *}, Rayan Atteah Alsemmeari¹

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1097-1111, 2020, DOI:10.32604/cmc.2020.011732

Abstract An IDS (intrusion detection system) provides a foremost front line mechanism to guard networks, systems, data, and information. That’s why intrusion detection has grown as an active study area and provides significant contribution to cyber-security techniques. Multiple techniques have been in use but major concern in their implementation is variation in their detection performance. The performance of IDS lies in the accurate detection of attacks, and this accuracy can be raised by improving the recognition rate and significant reduction in the false alarms rate. To overcome this problem many researchers have used different machine learning techniques. These techniques have limitations… More >

Muhammad Adnan Khan^{1, *}, Sagheer Abbas², Khalid Masood Khan¹, Mohammad A. Al Ghamdi³, Abdur Rehman²

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1329-1342, 2020, DOI:10.32604/cmc.2020.011155

Abstract An epidemic is a quick and widespread disease that threatens many lives and damages the economy. The epidemic lifetime should be accurate so that timely and remedial steps are determined. These include the closing of borders schools, suspension of community and commuting services. The forecast of an outbreak effectively is a very necessary but difficult task. A predictive model that provides the best possible forecast is a great challenge for machine learning with only a few samples of training available. This work proposes and examines a prediction model based on a deep extreme learning machine (DELM). This methodology is used… More >

Kue-Hong Chen^{1, *}, Cheng-Tsung Chen^{2, 3}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 145-160, 2020, DOI:10.32604/cmc.2020.08864

Abstract In this study, we applied a defined auxiliary problem in a novel error estimation technique to estimate the numerical error in the method of fundamental solutions (MFS) for solving the Helmholtz equation. The defined auxiliary problem is substituted for the real problem, and its analytical solution is generated using the complementary solution set of the governing equation. By solving the auxiliary problem and comparing the solution with the quasianalytical solution, an error curve of the MFS versus the source location parameters can be obtained. Thus, the optimal location parameter can be identified. The convergent numerical solution can be obtained and… More >

Fatih M. Tok¹, Sibel Dervis^2,*, Halit Yetisir³

Phyton-International Journal of Experimental Botany, Vol.89, No.2, pp. 303-314, 2020, DOI:10.32604/phyton.2020.08801

Abstract Forty-four V. dahliae isolates were collected from symptomatic vascular tissues of okra plants each from a different field in eight provinces located in the eastern Mediterranean and western Anatolia regions of Turkey during 2006- 2009. Nitrate-nonutilizing (nit) mutants of V. dahliae from okra were used to determine heterokaryosis and genetic relatedness among isolates. All isolates from okra plants were grouped into two vegetative compatibility groups (VCGs) (1 and 2) and three subgroups as 1A (13.6%, 6/44), 2A (20.5%, 9/44) and 2B (65.9%, 29/44) according to international criteria. Pathogenicity tests were performed on a susceptible local okra (A. esculentus) landrace in… More >

C. DAMBORENEA*, F. BRUSA*, A. PAOLA**

BIOCELL, Vol.30, No.3, pp. 457-468, 2006, DOI:10.32604/biocell.2006.30.457

Abstract Pomacea canaliculata is a common gastropod in freshwater habitats from Central and Northern Argentina, extending northwards into the Amazon basin. Several Platyhelminthes have been reported associated to P. canaliculata, sharing an intimate relationship with this gastropod host. The objectives of this study were to describe the symbiotic species assemblages associated to P. canaliculata in the study area, and to disclose differences among them. Samples were taken in three typical small streams and one artificial lentic lagoon, all connected with the Río de la Plata estuary. The 81.53% were infested with different symbiotic (sensu lato) species. Among the Platyhelminthes, the commensal… More >