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 - 26 November 2020

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 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 - 30 October 2020

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… 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 - 30 October 2020

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… 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 - 20 August 2020

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… 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 - 30 June 2020

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… 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 - 20 May 2020

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 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 - 22 April 2020

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… More >

Jingen Xiao^1,*, Chengyu Ku^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.4, pp. 177-177, 2019, DOI:10.32604/icces.2019.05068

Abstract This paper presents a novel boundary-type meshless method for solving the two-dimensional modified Helmholtz equation in multiply connected regions. Numerical approximation is obtained by the superposition principle of the non-singular basis functions satisfied the governing equation. The advantage of the proposed method is that the locations of the source points are not sensitive to the results. The novel concept may resolve the major issue for the method of fundamental solutions (MFS). In contrast to the collocation Trefftz method (CTM), the Trefftz order of the non-singular basis functions can be reduced since the multiple source points More >

Yajie Sun^1,2,*, Yanqing Yuan², Qi Wang², Sai Ji^1,2, Lihua Wang³, Shaoen Wu⁴, Jie Chen², Qin Zhang²

Journal of Quantum Computing, Vol.1, No.1, pp. 1-8, 2019, DOI:10.32604/jqc.2019.05788

Abstract A method is proposed based on the transmissibility function and the Online Sequence Extreme Learning Machine (OS-ELM) algorithm, which is applied to the impact damage of composite materials. First of all, the transmissibility functions of the undamaged signals and the damage signals at different points are calculated. Secondly, the difference between them is taken as the damage index. Finally, principal component analysis (PCA) is used to reduce the noise feature. And then, input to the online sequence limit learning neural network classification to identify damage and confirm the damage location. Taking the amplitude of the More >

Yi TANG^1,*, Carlos Alberto BUSSO²

BIOCELL, Vol.42, No.2, pp. 61-66, 2018, DOI:10.32604/biocell.2018.07015

Abstract Elm (Ulmus pumila L.) is the dominant tree species in the sparse elm woodland, the original vegetation in the Horqin Sandy Land. The effects of changes in precipitation on U. pumila trees have not been fully studied. We determined a dynamic model by considering the five stages in the U. pumila life cycle, i.e. seed, seedling, and juvenile, mature and over-mature tree stages. The effects of changes in precipitation on population density and age structure were then evaluated. Population density, after averaging all study developmental morphology stages, ranged from 16.67 individuals/m2 to 25.01 individuals/m2 under More >