Zhiguo Chen^1,*, Xiaorui Zhang^1,2, Sungryul Kim³

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 891-908, 2021, DOI:10.32604/iasc.2021.016933

Abstract The rapid growth of malware poses a significant threat to the security of computer systems. Analysts now need to examine thousands of malware samples daily. It has become a challenging task to determine whether a program is a benign program or malware. Making accurate decisions about the program is crucial for anti-malware products. Precise malware detection techniques have become a popular issue in computer security. Traditional malware detection uses signature-based strategies, which are the most widespread method used in commercial anti-malware software. This method works well against known malware but cannot detect new malware. To overcome the deficiency of the… More >

Abdul-Wahid A. Saif¹, Mohammad Ataur-Rahman¹, Sami Elferik¹, Muhammad F. Mysorewala¹, Mujahed Al-Dhaifallah^1,*, Fouad Yacef²

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 817-834, 2021, DOI:10.32604/iasc.2021.015932

Abstract In this paper, the formation control problem of a group of unmanned air vehicle (UAV) quadrotors is solved using the Takagi–Sugeno (T–S) multi-model approach to linearize the nonlinear model of UAVs. The nonlinear model sof the quadrotor is linearized first around a set of operating points using Taylor series to get a set of local models. Our approach’s novelty is in considering the difference between the nonlinear model and the linearized ones as disturbance. Then, these linear models are interpolated using the fuzzy T–S approach to approximate the entire nonlinear model. Comparison of the nonlinear and the T–S model shows… More >

Mohammed Baz¹, Hosam Alhakami², Alka Agrawal³, Abdullah Baz⁴, Raees Ahmad Khan^3,*

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 641-652, 2021, DOI:10.32604/iasc.2021.015845

Abstract Inspite of the world being at a complete standstill in the wake of unprecedented health emergency of COVID-19 pandemic, people have managed to retain their digital interactions through Information Technology. Cloud networks, departmental servers, data centres, and the digital devices have ensured that businesses and industries as well as workers across the world remain associated with each other and are connected to the organizations’ data. In such a scenario, the requirements placed on digital frames have increased rapidly. While this has proved to be a boon in the combat against the spread of Coronavirus, alarming increase in the instances of… More >

Arun Kumar¹, Manoj Gupta¹, Dac-Nhuong Le^2,3,*, Ayman A. Aly⁴

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 713-722, 2021, DOI:10.32604/iasc.2021.015793

Abstract Non-orthogonal multiple access (NOMA) will play an imperative part in an advanced 5G radio arrangement, owing to its numerous benefits such as improved spectrum adeptness, fast data rate, truncated spectrum leakage, and, so on. So far, NOMA undergoes from peak to average power ratio (PAPR) problem, which shrinks the throughput of the scheme. In this article, we propose a hybrid method, centered on the combination of advanced Partial transmission sequence (PTS), Selective mapping (SLM), and bacteria foraging optimization (BFO), known as PTS-BFO and SLM-PTS. PTS and SLM are utilized at the sender side and divide the NOMA into several sub-blocks.… More >

Shakeel Ahmed^*, Abdulaziz Alhumam

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 835-851, 2021, DOI:10.32604/iasc.2021.015789

Abstract Most of the patients diagnosed with COVID-19 pandemic usually suffer from mild-to-serious respiratory illness and become stable without any specific care. In fact, in some countries like India the mortality rate is as low. Those who are amongst the most vulnerable groups are the elderly and the ones with chronic ailments like diabetes, heart ailments, and respiratory ailments. However, apart from the impact on the physical health of the patients, this disease has had a more debilitating affect on the mental as well as emotional well-being of the people. Due to continuous watching and protection programs to fight the pandemic,… More >

Hakimah Yaacob, Qaisar Ali^*, Nur Anissa Sarbini, Abdul Nasir Rani, Zaki Zaini, Nurul Nabilah Ali, Norliza Mahalle

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 653-668, 2021, DOI:10.32604/iasc.2021.015442

Abstract Since the beginning of the Covid-19 pandemic, big data analytics (BDA) remains a signatory medium in the battle against it. Governments and policymakers alike are yet to leverage on this scalable technology in an attempt to curb the economic effects of Covid-19. The primary objective of this study is to leverage on BDA to identify economic shocks, and propose a strategic solution for economic recovery in ASEAN member states (AMS). The findings of this study suggest that BDA techniques, frameworks, and architectures are effective tools in predicting and tracking economic shocks, as well as in designing and implementing an effective… More >

Yanlei Xu¹, Yuting Zhai¹, Bin Zhao¹, Yubin Jiao², ShuoLin Kong¹, Yang Zhou^1,*, Zongmei Gao³

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 669-682, 2021, DOI:10.32604/iasc.2021.015225

Abstract For improving the accuracy of weed recognition under complex field conditions, a weed recognition method using depthwise separable convolutional neural network based on deep transfer learning was proposed in this study. To improve the model classification accuracy, the Xception model was refined by using model transferring and fine-tuning. Specifically, the weight parameters trained by ImageNet data set were transferred to the Xception model. Then a global average pooling layer replaced the full connection layer of the Xception model. Finally, the XGBoost classifier was added to the top layer of the model to output results. The performance of the proposed model… More >

Gurparsad Singh Suri¹, Gurleen Kaur¹, Sara Moein^2,*

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 723-735, 2021, DOI:10.32604/iasc.2021.015049

Abstract Schizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data are high dimensional. In this… More >

Fatmah Baothman^*, Kawther Saeedi, Khulood Aljuhani, Safaa Alkatheri, Mashael Almeatani, Nourah Alothman

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 625-639, 2021, DOI:10.32604/iasc.2021.014827

Abstract Blockchain is an innovative technology that disrupts different industries and offers decentralized, secure, and immutable platforms. Its first appearance is connected with monetary cryptocurrency transactions, followed by adaptation in several domains. We believe that blockchain can provide a reliable environment by utilizing its unique characteristics to offer a more secure, costless, and robust mechanism suitable for a voting application. Although the technology has captured the interest of governments worldwide, blockchain as a service is still limited due to lack of application development experience, technology complexity, and absence of standardized design, architecture, and best practices. Therefore, this study aims to build… 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

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 data. There are two classifier… More >