Zeyad Ghaleb Al-Mekhlafi¹, Badiea Abdulkarem Mohammed^1,2,*, Mohammed Al-Sarem³, Faisal Saeed³, Tawfik Al-Hadhrami⁴, Mohammad T. Alshammari¹, Abdulrahman Alreshidi¹, Talal Sarheed Alshammari¹

Computer Systems Science and Engineering, Vol.41, No.1, pp. 109-125, 2022, DOI:10.32604/csse.2022.020414 - 08 October 2021

Abstract Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic More >

Kunkun Wang^1,2, Xianda Liu^2,3,4,*

Journal on Artificial Intelligence, Vol.3, No.1, pp. 9-19, 2021, DOI:10.32604/jai.2021.016706 - 02 April 2021

Abstract With the development of Internet technology, the computing power of data has increased, and the development of machine learning has become faster and faster. In the industrial production of industrial control systems, quality inspection and safety production of process products have always been our concern. Aiming at the low accuracy of anomaly detection in process data in industrial control system, this paper proposes an anomaly detection method based on stacking integration using the machine learning algorithm. Data are collected from the industrial site and processed by feature engineering. Principal component analysis (PCA) and integrated rule… More >

Ali M. Aseere^1,*, Ayodele Lasisi²

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 683-699, 2021, DOI:10.32604/iasc.2021.014811 - 01 March 2021

Abstract In the absence of fast and adequate measures to combat them, life-threatening diseases are catastrophic to human health. Computational intelligent algorithms characterized by their adaptability, robustness, diversity, and recognition abilities allow for the diagnosis of medical diseases. This enhances the decision-making process of physicians. The objective is to predict and classify diseases accurately. In this paper, we proposed a multi-agent stacked ensemble classifier based on a vaguely quantified rough set, simple logistic algorithm, sequential minimal optimization (SMO), and JRip. The vaguely quantified rough set (VQRS) is used for feature selection and eradicating noise in the More >

Tabrej Khan¹, Mohamed Thariq Hameed Sultan^1,2,3,*, Mohammad Jawaid², Syafiqah Nur Azrie Safri², Ain Umaira Md Shah², Mohd Shukry Abdul Majid^4,5,*, Nik Noriman Zulkepli⁶, Haliza Jaya⁶

Journal of Renewable Materials, Vol.9, No.1, pp. 73-84, 2021, DOI:10.32604/jrm.2021.011385 - 30 November 2020

Abstract This research focused on the dynamic mechanical and thermal properties of woven mat jute/kenaf/jute (J/K/J) and kenaf/jute/kenaf (K/J/K) hybrid composites. Dynamic mechanical analysis (DMA) and Thermo-gravimetric Analysis (TGA) were used to study the effect of layering sequence on the thermal properties of kenaf/jute hybrid composites. The DMA results; it was found that the differences in the stacking sequence between the kenaf/jute composites do not affect their storage modulus, loss modulus and damping factor. From the TGA and DMA results, it has been shown that stacking sequence has given positive effect to the kenaf/jute hybrid composite… More >

Lewis Nkenyereye¹, Bayu Adhi Tama², Sunghoon Lim^3,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2217-2227, 2021, DOI:10.32604/cmc.2020.012432 - 26 November 2020

Abstract An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study More >

Lianping Wu, Wenshan Yu*, Shuling Hu, Shengping Shen*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.1, pp. 101-101, 2019, DOI:10.32604/icces.2019.05016

Abstract Multiple collision cascades (MCC) of nanotwinned (nt) Cu with three different twin spacings are performed to model the response of nt Cu upon a radiation dose of 1 displacement per atom (dpa). The microstructural evolutions during the radiation process shows that the main radiation defect in Cu is stacking fault tetrahedron (SFT). Smaller size of defect clusters and lower defect density are seen in the nt Cu with smaller twin spacing. Besides, the potential formation and elimination mechanisms of SF are found to be due to the climb of Frank partial dislocation and glide of… More >

Khouloud Cheour^1,*, Mustapha Assarar¹, Daniel Scida¹, Rezak Ayad¹, Xiaolu Gong²

Journal of Renewable Materials, Vol.7, No.9, pp. 877-889, 2019, DOI:10.32604/jrm.2019.06826

Abstract The aim of this study is to show the interest of the mechanical and dynamical properties of glass-flax hybrid composites. Therefore, various staking sequences of glass-flax hybrid composites were manufactured and tested in free vibrations. The damping coefficients were identified by fitting the experimental responses of free-free bending vibrations. The obtained results show that the staking sequences and the position of flax fiber layers in the hybrid composites changed the properties, so a classification of different stacking sequences was established. In fact, the hybrid laminate made of two glass external layers placed on both sides More >

Xuefeng Xi¹, Victor S. Sheng^1,2,*, Binqi Sun², Lei Wang¹, Fuyuan Hu¹

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 185-198, 2018, DOI:10.3970/cmc.2018.03694

Abstract Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It has received relatively small attention from the Machine Learning community. However, multi-target regression exists in many real-world applications. In this paper we conduct extensive experiments to investigate the performance of three representative multi-target regression learning algorithms (i.e. Multi-Target Stacking (MTS), Random Linear Target Combination (RLTC), and Multi-Objective Random Forest (MORF)), comparing the baseline single-target learning. Our experimental results show that all three multi-target regression learning algorithms do improve the performance of the single-target More >

Zhengong Zhou¹, Shuang Tian^1,2, Jiawei Zhang³

CMC-Computers, Materials & Continua, Vol.52, No.2, pp. 105-121, 2016, DOI:10.3970/cmc.2016.052.105

Abstract The methods of numerical simulation and test are combined to analyze the impact behavior of glass fiber reinforced aluminum alloy laminate (GLARE). A new failure criteria is proposed to obtain the impact failure of GLARE, and combined with material progressive damage method by writing code of LS-DYNA. Low velocity impact test of GLARE is employed to validate the feasibility of the finite element model established. The simulation results have been shown that progressive damage finite element model established is reliable. Through the application of the finite element model established, the delamination of GLARE evolution progress More >

W.H. Chen¹, H.C. Cheng^2,3, C.F. Yu^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.109-110, No.1, pp. 1-13, 2015, DOI:10.3970/cmes.2015.109.001

Abstract This study aims at exploring the interfacial adhesion strength between solder bump and four typical under bump metallurgies (UBMs), i.e., Cu/Ni, Cu/TiW, Cu/Ni/Cr and /Cu/V/Cr, at atomistic scale. The average bond length and interfacial adhesion stress of the Sn-3.5Ag/Cu/Ni, Sn-3.5Ag/Cu/TiW, Sn-3.5Ag/Cu/Ni/Cr and Sn-3.5Ag/Cu/V/Cr micro-bump interconnects are calculated through the firstprinciples density functional theory (DFT) calculation to estimate the interfacial adhesion strength between the solder bump and UBMs. In addition, by investigating the electric field effect on the average bond length and adhesive stress, the combination of solder bump and UBM with better interfacial adhesion strength… More >