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Islam Ali¹, Musawwer Khan¹, Waqar Mehmood¹, Wasif Nisar¹, Waqar Aslam², Muhammad Qaiser Saleem³, Majzoob K. Omer³, Muhammad Shafiq^4,*
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 605-623, 2021, DOI:10.32604/iasc.2021.014639
（This article belongs to this Special Issue: Soft Computing Methods for Innovative Software Practices)
Abstract Capability Maturity Model Integration (CMMI) is a world-renowned framework for software process improvement, which specifies “What-To-Do” in terms of requirements. However, it leaves the “How-To-Do” part regarding implementation to implementers. The software industry especially software SMEs (SSMEs) faces difficulties in implementing the Specific Practices (SPs) of Various Process Areas (PAs). Configuration Management Process Area (CM-PA) is usually ignored despite its acknowledged importance in the software development process. Establishing integrity is one of the three Specific Goals (SGs) that CMMI ver. 1.3 requires for successful implementation of CM-PA. This goal is achieved through the implementation of two SPs (i.e., 3.1 and… More >

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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
（This article belongs to this Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)
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 >

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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 >

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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
（This article belongs to this Special Issue: Soft Computing Technologies for COVID 19 Assessment, Analysis and Control)
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 >

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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 >

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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 >

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Aaqib Inam^1,*, Zhuli¹, Ayesha Sarwar¹, Salah-ud-din², Ayesha Atta³, Iftikhar Naaseer⁴, Shahan Yamin Siddiqui^5,6, Muhammad Adnan Khan⁷
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 701-712, 2021, DOI:10.32604/iasc.2021.014235
（This article belongs to this Special Issue: Machine Learning and Computational Methods for Disease Detection and Prediction)
Abstract The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for the diagnosis of disease are… More >

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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
（This article belongs to this Special Issue: Recent Advances in Intelligent Systems and Communication)
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 >

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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 >

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Xiaohong Ji¹, Junjun Du¹, Guoqing Jia^1,*, Weidong Fang^2,3
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 737-746, 2021, DOI:10.32604/iasc.2021.013009
Abstract As a new non-orthogonal multiple access technology for 5G massive machine type communication scenario, the sparse code multiple access (SCMA) has greatly improved the spectrum efficiency due to the high connection density. SCMA combines QAM (Quadrature Amplitude Modulation) modulation and sparse spreading into a codebook set to obtain forming gain. The user binary bit data is directly mapped into multi-dimensional codewords in the transmitter. The receiver uses the message passing algorithm (MPA) for multi-user detection to achieve efficient decoding. However, MPA is a good solution for SCMA, though its high complexity limits the application in practical systems. In order to… More >

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Charles Arputham^1,*, Krishnaraj Nagappan², Lenin Babu Russeliah³, AdalineSuji Russeliah⁴
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 747-759, 2021, DOI:10.32604/iasc.2021.012077
Abstract Breast cancer detection is a crucial topic in the healthcare sector. Breast cancer is a major reason for the increased mortality rate in recent years among women, specifically in developed and underdeveloped countries around the world. The incidence rate is less in India than in developed countries, but awareness must be increased. This paper focuses on an efficient deep learning-based diagnosis and classification technique to detect breast cancer from mammograms. The model includes preprocessing, segmentation, feature extraction, and classification. At the initial level, Laplacian filtering is applied to identify the portions of edges in mammogram images that are highly sensitive… More >

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Anuj Kumar Singh¹, Mohammed Alshehri^2,*, Shashi Bhushan³, Manoj Kumar³, Osama Alfarraj⁴, Kamal Raj Pardarshani⁵
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 761-769, 2021, DOI:10.32604/iasc.2021.012806
Abstract Wireless sensor networks (WSNs) have been used in numerous delicate checking, observation, and surveillance systems that use sensitive data. When a WSN utilizes various data clustering techniques, data is moved to the cluster head (CH) of the corresponding cluster area. Cluster Head further communicates the information to the sink node. In a WSN, a network owner (NO) does not validate a sensor before connecting to the network, so faulty nodes may likely attach to the network to sense the data and attempt to release the information to unauthorized persons. Further, a malicious node may become a mobile node (MN) equipped… More >

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Krishnan Muthumayil^1,*, Thangaiyan Jayasankar², Nagappan Krishnaraj³, Mohamed Yacin Sikkandar⁴, Prakash Nattanmai Balasubramanian⁵, Chokkalingam Bharatiraja⁶
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 771-784, 2021, DOI:10.32604/iasc.2021.012462
Abstract Wireless Multimedia Sensor Networks (WMSN) provides valuable information for scalar data, images, audio, and video processing in monitoring and surveillance applications. Multimedia streaming, however, is highly challenging for networks as energy restriction sensor nodes limit the potential data transmission bandwidth and lead to reduced throughput. WMSN’s two key design challenges, which can be achieved by the clustering process, are energy efficiency and throughput maximization. The use of the clustering technique helps to organise the sensor nodes into clusters, and between each cluster a cluster head (CH) will be chosen. This paper introduces a new Artificial Fish Swarm Optimization Algorithm (AFSA)… More >

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Mazhar Javed Awan^1,2,*, Mohd Shafry Mohd Rahim², Haitham Nobanee^3,4,5, Awais Yasin⁶, Osamah Ibrahim Khalaf⁷, Umer Ishfaq²
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 785-797, 2021, DOI:10.32604/iasc.2021.014216
Abstract Retail companies recognize the need to analyze and predict their sales and customer behavior against their products and product categories. Our study aims to help retail companies create personalized deals and promotions for their customers, even during the COVID-19 pandemic, through a big data framework that allows them to handle massive sales volumes with more efficient models. In this paper, we used Black Friday sales data taken from a dataset on the Kaggle website, which contains nearly 550,000 observations analyzed with 10 features: qualitative and quantitative. The class label is purchases and sales (in U.S. dollars). Because the predictor label… More >

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Hassan Ashraf¹, Asim Waris^1,*, Syed Omer Gilani¹, Muhammad Umair Tariq¹, Hani Alquhayz²
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 799-815, 2021, DOI:10.32604/iasc.2021.014765
（This article belongs to this Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)
Abstract Empirical Mode Decomposition (EMD) is a data-driven and fully adaptive signal decomposition technique to decompose a signal into its Intrinsic Mode Functions (IMF). EMD has attained great attention due to its capabilities to process a signal in the frequency-time domain without altering the signal into the frequency domain. EMD-based signal denoising techniques have shown great potential to denoise nonlinear and nonstationary signals without compromising the signal’s characteristics. The denoising procedure comprises three steps, i.e., signal decomposition, IMF thresholding, and signal reconstruction. Thresholding is performed to assess which IMFs contain noise. In this study, Interval Thresholding (IT), Iterative Interval Thresholding (IIT),… More >

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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
（This article belongs to this Special Issue: Recent Trends in Computational Methods for Differential Equations)
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 >

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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 >

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Chung Le Van¹, Trinh Hiep Hoa¹, Nguyen Minh Duc¹, Vikram Puri¹, Tung Sanh Nguyen², Dac-Nhuong Le^3,4,*
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 853-871, 2021, DOI:10.32604/iasc.2021.013732
（This article belongs to this Special Issue: Machine Learning and Computational Methods for Disease Detection and Prediction)
Abstract Background: Augmented Reality (AR) incorporates both real and virtual objects in real-time environments and allows single and multi-users to interact with 3D models. It is often tricky to adopt multi-users in the same environment because of the devices’ latency and model position accuracy in displaying the models simultaneously. Method: To address this concern, we present a multi-user sharing technique in the AR of the human anatomy that increases learning with high quality, high stability, and low latency in multiple devices. Besides, the multi-user interactive display (HoloLens) merges with the human body anatomy application (AnatomyNow) to teach and train students, academic… More >

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Xiaorui Zhang^1,2,*, Hailun Wu¹, Wei Sun¹, Aiguo Song³, Sunil Kumar Jha⁴
Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 873-889, 2021, DOI:10.32604/iasc.2021.013541
Abstract Virtual surgery simulation is indispensable for virtual vascular interventional training system, which provides the doctor with visual scene between catheter and vascular. Soft tissue deformation, as the most significant part, determines the success or failure of the virtual surgery simulation. However, most soft tissue deformation model cannot simultaneously meet the requirement of high deformation accuracy and real-time interaction. To solve the challenge mentioned above, this paper proposes a fast and accurate vascular tissue simulation model based on point primitive method. Firstly, the proposed model simulates a deformation of the internal structure of the vascular tissue by adopting a point primitive… More >

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933

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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 >

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