Eunmok Yang¹, Gyanendra Prasad Joshi², Changho Seo^3,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1647-1663, 2021, DOI:10.32604/cmc.2020.013210 - 26 November 2020

Abstract In network-based intrusion detection practices, there are more regular instances than intrusion instances. Because there is always a statistical imbalance in the instances, it is difficult to train the intrusion detection system effectively. In this work, we compare intrusion detection performance by increasing the rarely appearing instances rather than by eliminating the frequently appearing duplicate instances. Our technique mitigates the statistical imbalance in these instances. We also carried out an experiment on the training model by increasing the instances, thereby increasing the attack instances step by step up to 13 levels. The experiments included not… More >

Fouzia Nawaz¹, Muhammad Ramzan¹, Khalid Mehmood¹, Hikmat Ullah Khan², Saleem Hayat Khan^3,4, Muhammad Raheel Bhutta^5,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1631-1645, 2021, DOI:10.32604/cmc.2020.012887 - 26 November 2020

Abstract Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness. DR occurs due to the high blood sugar level of the patient, and it is clumsy to be detected at an early stage as no early symptoms appear at the initial level. To prevent blindness, early detection and regular treatment are needed. Automated detection based on machine intelligence may assist the ophthalmologist in examining the patients’ condition more accurately and efficiently. The purpose of this study is to produce an automated screening system for recognition and grading of diabetic retinopathy using machine learning through More >

Zia Ul Rehman^1,2, Sagheer Abbas¹, Muhammad Adnan Khan^3,*, Ghulam Mustafa², Hira Fayyaz⁴, Muhammad Hanif^1,2, Muhammad Anwar Saeed⁵

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1075-1090, 2021, DOI:10.32604/cmc.2020.012770 - 26 November 2020

Abstract The internet, particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals. Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization. Consequently, social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare. Thus, recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle. The aim of this research work is to identify radical text in social media. Our contributions are… More >

Yuming Chu^1,2, Muhannad A. Shallal³, Seyed Mehdi Mirhosseini-Alizamini⁴, Hadi Rezazadeh⁵, Shumaila Javeed^6,*, Dumitru Baleanu^7,8

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1369-1378, 2021, DOI:10.32604/cmc.2020.012611 - 26 November 2020

Abstract The purpose of this work is to find new soliton solutions of the complex Ginzburg–Landau equation (GLE) with Kerr law non-linearity. The considered equation is an imperative nonlinear partial differential equation (PDE) in the field of physics. The applications of complex GLE can be found in optics, plasma and other related fields. The modified extended tanh technique with Riccati equation is applied to solve the Complex GLE. The results are presented under a suitable choice for the values of parameters. Figures are shown using the three and two-dimensional plots to represent the shape of the… More >

Ahmed Bashar Fakhri¹, Saleem Latteef Mohammed¹, Imran Khan², Ali Safaa Sadiq^3,4, Basem Alkazemi⁵, Prashant Pillai⁴, Bong Jun Choi^6,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1175-1194, 2021, DOI:10.32604/cmc.2020.012587 - 26 November 2020

Abstract The development of science and technology has led to the era of Industry 4.0. The core concept is the combination of “material and informationization”. In the supply chain and manufacturing process, the “material” of the physical entity world is realized by data, identity, intelligence, and information. Industry 4.0 is a disruptive transformation and upgrade of intelligent industrialization based on the Internet-of-Things and Big Data in traditional industrialization. The goal is “maximizing production efficiency, minimizing production costs, and maximizing the individual needs of human beings for products and services.” Achieving this goal will surely bring about 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 >

Yan Li¹, Renquan Huang¹, Jianrong Li¹, Xiaozhen Huang¹, Xiaofang Zeng^1,*, Degang Zhao^1,2,*

Phyton-International Journal of Experimental Botany, Vol.90, No.1, pp. 111-127, 2021, DOI:10.32604/phyton.2020.013933 - 20 November 2020

Abstract Plant architecture is a vital agronomic trait to control yield in rice (Oryza sativa L.). A dwarf and small seed 1 (dss1) mutant were obtained from the ethyl methanesulfonate (EMS) mutagenized progeny of a Guizhou glutinous landrace cultivar, Lipingzabianhe. The dss1 mutant displayed phenotypes similar to those of brassinosteroid (BR) deficient mutants, such as dwarfing, dark green and rugose erect leaves, small seeds, and loner neck internode panicles with primary branching. In our previous study, the underlying DSS1 gene was isolated, a novel allele of OsDWARF (OsBR6ox) that encodes a cytochrome P450 protein involved in the BR biosynthetic pathway by More >

Qin Liu^*, Ruliang Zhang

Energy Engineering, Vol.118, No.1, pp. 173-187, 2021, DOI:10.32604/EE.2020.012788 - 17 November 2020

Abstract With the continuous advancement of economic globalization, energy demand is expanding and energy consumption is excessive, which leads to energy shortage. Unreasonable energy use also brings great challenges to the environment and affects the balance of the ecosystem seriously. The rise of the third industrial revolution has injected new vitality into energy system. The construction of energy Internet system, which integrates Internet technology and energy technology, has become a new energy system of sustainable development. It has put forward the reform scheme for the mismatch of energy demand points and environmental pollution. The deepening of… More >

Hongchun Shu^1,2, Xiaohan Jiang^1,2,*, Pulin Cao², Na An², Xincui Tian², Bo Yang²

Energy Engineering, Vol.118, No.1, pp. 53-71, 2021, DOI:10.32604/EE.2020.012381 - 17 November 2020

Abstract The system impedance instability, high-order harmonics, and frequency offset are main fault characteristics of wind power system. Moreover, the measurement angle of faulty phase is affected by rotation speed frequency component, which causes traditional directional protections based on angle comparison between voltage and current to operate incorrectly. In this paper, a time-domain protection for connected to wind power plant based on model matching is proposed, which compares the calculated current and the measured current to identify internal faults and external faults. Under external faults, the calculated current and measured current waveform are quite similar because… More >

H. M. Rehan Afzal^1,2,*, Suhuai Luo¹, Saadallah Ramadan^1,2, Jeannette Lechner-Scott^1,2,3, Mohammad Ruhul Amin³, Jiaming Li⁴, M. Kamran Afzal⁵

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 977-991, 2021, DOI:10.32604/cmc.2020.012448 - 30 October 2020

Abstract The diagnosis of multiple sclerosis (MS) is based on accurate detection of lesions on magnetic resonance imaging (MRI) which also provides ongoing essential information about the progression and status of the disease. Manual detection of lesions is very time consuming and lacks accuracy. Most of the lesions are difficult to detect manually, especially within the grey matter. This paper proposes a novel and fully automated convolution neural network (CNN) approach to segment lesions. The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly. The first CNN network is… More >