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Sabah Mohammed
Lakehead University, Thunder Bay, Canada

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Editorial
Published: 09 August 2021 in Computing
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Sabah Mohammed; Wai Chi Fang; Carlos Ramos. Special issue on ‘‘artificial intelligence in cloud computing’’. Computing 2021, 1 -5.

AMA Style

Sabah Mohammed, Wai Chi Fang, Carlos Ramos. Special issue on ‘‘artificial intelligence in cloud computing’’. Computing. 2021; ():1-5.

Chicago/Turabian Style

Sabah Mohammed; Wai Chi Fang; Carlos Ramos. 2021. "Special issue on ‘‘artificial intelligence in cloud computing’’." Computing , no. : 1-5.

Journal article
Published: 05 July 2021 in Computers & Electrical Engineering
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A new paradigm of IoT monitoring using sonar sensors and microphones is studied as a contactless alternative to the traditional devices for sleep disorders in this paper. A pair of sonar sensors are used to measure the distance from the bottom and side of the body to the bedside respectively, and the basic posture of the human body during sleep can be inferred by using machine learning. When a person sleeps still, two streams of sonar signals from two adjacent sides remain unchanged. Any movement would disrupt the stationary sonar streams. Hence it could be detected as change of posture from lying still. Different sonar patterns could be recognized as specific sleeping postures by some non-linear machine learning model. This simple and novel solution could potentially be used as an alternative or supplementary to video analytics which can feedback to the user about their sleeping pattern. A new data transformation method namely Kennard-stone Balance (KSB) algorithm is also proposed for simplifying the data streams and enhancing the accuracy of the machine learning model. Simulation results show the feasibility of this sonar method, and KSB is able to improve the pattern recognition performance.

ACS Style

Tengyue Li; Yaoyang Wu; Feng Wu; Sabah Mohammed; Raymond K. Wong; Kok-Leong Ong. Sleep pattern inference using IoT sonar monitoring and machine learning with Kennard-stone balance algorithm. Computers & Electrical Engineering 2021, 93, 107181 .

AMA Style

Tengyue Li, Yaoyang Wu, Feng Wu, Sabah Mohammed, Raymond K. Wong, Kok-Leong Ong. Sleep pattern inference using IoT sonar monitoring and machine learning with Kennard-stone balance algorithm. Computers & Electrical Engineering. 2021; 93 ():107181.

Chicago/Turabian Style

Tengyue Li; Yaoyang Wu; Feng Wu; Sabah Mohammed; Raymond K. Wong; Kok-Leong Ong. 2021. "Sleep pattern inference using IoT sonar monitoring and machine learning with Kennard-stone balance algorithm." Computers & Electrical Engineering 93, no. : 107181.

Conference paper
Published: 11 April 2021 in LISS 2020
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The growth of today’s cities and the increased population mobility are providing great challenge to manage vehicles on the roads. This challenge led to the need for new and innovative traffic management, including the mitigation of road congestion, accidents, and air pollution as well as many business oriented demands. Over the last decade, researchers have been focusing their efforts on leveraging the recent advances in Web Services and Multi-Agents to design new road traffic management systems (TMS) for resolving these important challenges in the future transportation. However, these new solutions are still be insufficient and complex to construct TMS systems that are capable of handling the anticipated influx of the population, vehicles and changing transportation scenarios. This paper is pointing to a new and emerging technology that can solve these challenges and develop more flexible TMS systems based on the notion of microservices offered by web frameworks like IFTTT, Zapier, Node-Red and WoTKit.

ACS Style

Sabah Mohammed; Jinan Fiaidhi; Mincong Tang. Towards Using Micro-services for Transportation Management Systems. LISS 2020 2021, 107 -117.

AMA Style

Sabah Mohammed, Jinan Fiaidhi, Mincong Tang. Towards Using Micro-services for Transportation Management Systems. LISS 2020. 2021; ():107-117.

Chicago/Turabian Style

Sabah Mohammed; Jinan Fiaidhi; Mincong Tang. 2021. "Towards Using Micro-services for Transportation Management Systems." LISS 2020 , no. : 107-117.

Editorial
Published: 09 March 2021 in Neural Computing and Applications
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Sabah Mohammed; Carlos Ramos; Wai Chi Fang; Tia-Hoon Kim. Emerging higher-level artificial neural network-based intelligent systems. Neural Computing and Applications 2021, 33, 4595 -4597.

AMA Style

Sabah Mohammed, Carlos Ramos, Wai Chi Fang, Tia-Hoon Kim. Emerging higher-level artificial neural network-based intelligent systems. Neural Computing and Applications. 2021; 33 (10):4595-4597.

Chicago/Turabian Style

Sabah Mohammed; Carlos Ramos; Wai Chi Fang; Tia-Hoon Kim. 2021. "Emerging higher-level artificial neural network-based intelligent systems." Neural Computing and Applications 33, no. 10: 4595-4597.

Journal article
Published: 18 November 2020 in IEEE Access
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During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture.

ACS Style

Sabah Mohammed; Hamid R. Arabnia; Xiaobo Qu; Dalin Zhang; Tai-Hoon Kim; Jiandong Zhao. IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation. IEEE Access 2020, 8, 201331 -201344.

AMA Style

Sabah Mohammed, Hamid R. Arabnia, Xiaobo Qu, Dalin Zhang, Tai-Hoon Kim, Jiandong Zhao. IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation. IEEE Access. 2020; 8 ():201331-201344.

Chicago/Turabian Style

Sabah Mohammed; Hamid R. Arabnia; Xiaobo Qu; Dalin Zhang; Tai-Hoon Kim; Jiandong Zhao. 2020. "IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation." IEEE Access 8, no. : 201331-201344.

Journal article
Published: 26 August 2020 in Computer Methods and Programs in Biomedicine
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Bayesian network is a probabilistic model of which the prediction accuracy may not be one of the highest in the machine learning family. Deep learning (DL) on the other hand possess of higher predictive power than many other models. How reliable the result is, how it is deduced, how interpretable the prediction by DL mean to users, remain obscure. DL functions like a black box. As a result, many medical practitioners are reductant to use deep learning as the only tool for critical machine learning application, such as aiding tool for cancer diagnosis. In this paper, a framework of white learning is being proposed which takes advantages of both black box learning and white box learning. Usually, black box learning will give a high standard of accuracy and white box learning will provide an explainable direct acyclic graph. According to our design, there are 3 stages of White Learning, loosely coupled WL, semi coupled WL and tightly coupled WL based on degree of fusion of the white box learning and black box learning. In our design, a case of loosely coupled WL is tested on breast cancer dataset. This approach uses deep learning and an incremental version of Naïve Bayes network. White learning is largely defied as a systemic fusion of machine learning models which result in an explainable Bayes network which could find out the hidden relations between features and class and deep learning which would give a higher accuracy of prediction than other algorithms. We designed a series of experiments for this loosely coupled WL model. The simulation results show that using WL compared to standard black-box deep learning, the levels of accuracy and kappa statistics could be enhanced up to 50%. The performance of WL seems more stable too in extreme conditions such as noise and high dimensional data. The relations by Bayesian network of WL are more concise and stronger in affinity too. The experiments results deliver positive signals that WL is possible to output both high classification accuracy and explainable relations graph between features and class.

ACS Style

Tengyue Li; Simon Fong; Shirley W.I. Siu; Xin-She Yang; Lian-Sheng Liu; Sabah Mohammed. White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment. Computer Methods and Programs in Biomedicine 2020, 197, 105724 .

AMA Style

Tengyue Li, Simon Fong, Shirley W.I. Siu, Xin-She Yang, Lian-Sheng Liu, Sabah Mohammed. White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment. Computer Methods and Programs in Biomedicine. 2020; 197 ():105724.

Chicago/Turabian Style

Tengyue Li; Simon Fong; Shirley W.I. Siu; Xin-She Yang; Lian-Sheng Liu; Sabah Mohammed. 2020. "White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment." Computer Methods and Programs in Biomedicine 197, no. : 105724.

Preprint content
Published: 01 February 2020
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This is a pre-print for the submission to IEEE LISS 2020 Conference, Budapest, Hungary.

ACS Style

Sabah Mohammed; Jinan Fiaidhi; Mincong Tang. Towards using Microservices for Transportation Management: The New TMS Development Trend. 2020, 1 .

AMA Style

Sabah Mohammed, Jinan Fiaidhi, Mincong Tang. Towards using Microservices for Transportation Management: The New TMS Development Trend. . 2020; ():1.

Chicago/Turabian Style

Sabah Mohammed; Jinan Fiaidhi; Mincong Tang. 2020. "Towards using Microservices for Transportation Management: The New TMS Development Trend." , no. : 1.

Preprint content
Published: 01 February 2020
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This is a pre-print for the submission to IEEE LISS 2020 Conference, Budapest, Hungary.

ACS Style

Sabah Mohammed; Jinan Fiaidhi; Mincong Tang. Towards using Microservices for Transportation Management: The New TMS Development Trend. 2020, 1 .

AMA Style

Sabah Mohammed, Jinan Fiaidhi, Mincong Tang. Towards using Microservices for Transportation Management: The New TMS Development Trend. . 2020; ():1.

Chicago/Turabian Style

Sabah Mohammed; Jinan Fiaidhi; Mincong Tang. 2020. "Towards using Microservices for Transportation Management: The New TMS Development Trend." , no. : 1.

Preprint content
Published: 06 December 2019
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This paper describes the author’s vision in developing a flexible workflow infrastructure for enforcing the pragmatic interoperability in industries like manufacturing and healthcare. This vision is based on business continuity planning, web services interoperability, Node-Red and IFTTT workflow technologies.

ACS Style

Sabah Mohammed; Jinan Fiaidhi. Pragmatic Interoperability for Extreme Automation and Healthcare Interoperability & Continuity. 2019, 1 .

AMA Style

Sabah Mohammed, Jinan Fiaidhi. Pragmatic Interoperability for Extreme Automation and Healthcare Interoperability & Continuity. . 2019; ():1.

Chicago/Turabian Style

Sabah Mohammed; Jinan Fiaidhi. 2019. "Pragmatic Interoperability for Extreme Automation and Healthcare Interoperability & Continuity." , no. : 1.

Journal article
Published: 11 September 2019 in IT Professional
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A framework of white learning is proposed in this article, which embraces three categories of white learning models where various levels of hybridization of Bayesian networks and neural networks are fused. At the algorithm level, Bayesian networks and neural computing are integrated tightly as a whole or partial redesigned entity of computing logics. Elements of Bayesian networks and neural computing co-exist in the design of program codes. This level of integration often requires a high extent of intellectual innovation, especially if the new hybrid after coupling the white-andblack box learning models would outperform either one of the original twomodels. On the other hand, loosely coupledmodels are those which run almost independently of each other; exemplars are those ensembles from which the results of the best performing model out of many are taken as the final results. These models are often taken as they are, without any modification in their codes.

ACS Style

Tengyue Li; Simon Fong; Lian-Sheng Liu; Xin-She Yang; Xingshi He; Jinan Fiaidhi; Sabah Mohammed. White Learning: A White-Box Data Fusion Machine Learning Framework for Extreme and Fast Automated Cancer Diagnosis. IT Professional 2019, 21, 71 -77.

AMA Style

Tengyue Li, Simon Fong, Lian-Sheng Liu, Xin-She Yang, Xingshi He, Jinan Fiaidhi, Sabah Mohammed. White Learning: A White-Box Data Fusion Machine Learning Framework for Extreme and Fast Automated Cancer Diagnosis. IT Professional. 2019; 21 (5):71-77.

Chicago/Turabian Style

Tengyue Li; Simon Fong; Lian-Sheng Liu; Xin-She Yang; Xingshi He; Jinan Fiaidhi; Sabah Mohammed. 2019. "White Learning: A White-Box Data Fusion Machine Learning Framework for Extreme and Fast Automated Cancer Diagnosis." IT Professional 21, no. 5: 71-77.

Journal article
Published: 15 July 2019 in IT Professional
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Jinan Fiaidhi; Sabah Mohammed. Security and Vulnerability of Extreme Automation Systems: The IoMT and IoA Case Studies. IT Professional 2019, 21, 48 -55.

AMA Style

Jinan Fiaidhi, Sabah Mohammed. Security and Vulnerability of Extreme Automation Systems: The IoMT and IoA Case Studies. IT Professional. 2019; 21 (4):48-55.

Chicago/Turabian Style

Jinan Fiaidhi; Sabah Mohammed. 2019. "Security and Vulnerability of Extreme Automation Systems: The IoMT and IoA Case Studies." IT Professional 21, no. 4: 48-55.

Journal article
Published: 22 May 2019 in IT Professional
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Jinan Fiaidhi; Sabah Mohammed. Thick Data: A New Qualitative Analytics for Identifying Customer Insights. IT Professional 2019, 21, 4 -13.

AMA Style

Jinan Fiaidhi, Sabah Mohammed. Thick Data: A New Qualitative Analytics for Identifying Customer Insights. IT Professional. 2019; 21 (3):4-13.

Chicago/Turabian Style

Jinan Fiaidhi; Sabah Mohammed. 2019. "Thick Data: A New Qualitative Analytics for Identifying Customer Insights." IT Professional 21, no. 3: 4-13.

Journal article
Published: 27 March 2019 in IT Professional
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Jinan Fiaidhi; Sabah Mohammed. Empowering Extreme Automation via Zero-Touch Operations and GPU Parallelization. IT Professional 2019, 21, 27 -32.

AMA Style

Jinan Fiaidhi, Sabah Mohammed. Empowering Extreme Automation via Zero-Touch Operations and GPU Parallelization. IT Professional. 2019; 21 (2):27-32.

Chicago/Turabian Style

Jinan Fiaidhi; Sabah Mohammed. 2019. "Empowering Extreme Automation via Zero-Touch Operations and GPU Parallelization." IT Professional 21, no. 2: 27-32.

Journal article
Published: 27 March 2019 in IT Professional
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Tengyue Li; Simon Fong; Richard C. Millham; Sabah Mohammed; Jinan Fiaidhi. Fast Incremental Learning With Swarm Decision Table and Stochastic Feature Selection in an IoT Extreme Automation Environment. IT Professional 2019, 21, 14 -26.

AMA Style

Tengyue Li, Simon Fong, Richard C. Millham, Sabah Mohammed, Jinan Fiaidhi. Fast Incremental Learning With Swarm Decision Table and Stochastic Feature Selection in an IoT Extreme Automation Environment. IT Professional. 2019; 21 (2):14-26.

Chicago/Turabian Style

Tengyue Li; Simon Fong; Richard C. Millham; Sabah Mohammed; Jinan Fiaidhi. 2019. "Fast Incremental Learning With Swarm Decision Table and Stochastic Feature Selection in an IoT Extreme Automation Environment." IT Professional 21, no. 2: 14-26.

Journal article
Published: 01 March 2019 in IT Professional
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Reports on the concept of the Internet of Everything (IoE). IoE is a notion for intelligently connecting people, processes, and data in one uniform way, enabling communications between machines (M2M), machine-topeople and technology- assisted peopleto- people interactions. IoE is expected to reinvent the business and the automation wheel all-together. From processes, models to business and manufacturing frameworks everything is expected to change with the change in data available and the smart connectivity between people and machines for critical decision making. It is bringing productivity and competitiveness to higher levels along with opening up many doors to new and exciting opportunities. IoE expands on the concept of the “Internet of Things” by connecting devices and people in one network. This connection goes beyond the basic M2Mcommunications to enable a democratization of skill and how it is being delivered globally. An integral part of this is to be able to transmit touch in perceived real-time,which is enabled by suitable robotics and haptics equipment at the edges, along with an unprecedented communications network capabilities.

ACS Style

Jinan Fiaidhi; Sabah Mohammed. Internet of Everything as a Platform for Extreme Automation. IT Professional 2019, 21, 21 -25.

AMA Style

Jinan Fiaidhi, Sabah Mohammed. Internet of Everything as a Platform for Extreme Automation. IT Professional. 2019; 21 (1):21-25.

Chicago/Turabian Style

Jinan Fiaidhi; Sabah Mohammed. 2019. "Internet of Everything as a Platform for Extreme Automation." IT Professional 21, no. 1: 21-25.

Journal article
Published: 18 December 2018 in Bioinformatics
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Summary The Pathogen–Host Analysis Tool (PHAT) is an application for processing and analyzing next-generation sequencing (NGS) data as it relates to relationships between pathogens and their hosts. Unlike custom scripts and tedious pipeline programming, PHAT provides an integrative platform encompassing raw and aligned sequence and reference file input, quality control (QC) reporting, alignment and variant calling, linear and circular alignment viewing, and graphical and tabular output. This novel tool aims to be user-friendly for life scientists studying diverse pathogen–host relationships. Availability and implementation The project is available on GitHub (https://github.com/chgibb/PHAT) and includes convenient installers, as well as portable and source versions, for both Windows and Linux (Debian and RedHat). Up-to-date documentation for PHAT, including user guides and development notes, can be found at https://chgibb.github.io/PHATDocs/. We encourage users and developers to provide feedback (error reporting, suggestions and comments).

ACS Style

Christopher M Gibb; Robert Jackson; Sabah Mohammed; Jinan Fiaidhi; Ingeborg Zehbe. Pathogen–Host Analysis Tool (PHAT): an integrative platform to analyze next-generation sequencing data. Bioinformatics 2018, 35, 2665 -2667.

AMA Style

Christopher M Gibb, Robert Jackson, Sabah Mohammed, Jinan Fiaidhi, Ingeborg Zehbe. Pathogen–Host Analysis Tool (PHAT): an integrative platform to analyze next-generation sequencing data. Bioinformatics. 2018; 35 (15):2665-2667.

Chicago/Turabian Style

Christopher M Gibb; Robert Jackson; Sabah Mohammed; Jinan Fiaidhi; Ingeborg Zehbe. 2018. "Pathogen–Host Analysis Tool (PHAT): an integrative platform to analyze next-generation sequencing data." Bioinformatics 35, no. 15: 2665-2667.

Journal article
Published: 01 November 2018 in IT Professional
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It is argued that digitalization and the implementation of extreme automation as inspired by the Industry 4.0 will bring fundamental changes to industrial production and necessitate new products, solutions, and concepts. Robotization is the new reality that is going to change the manufacturing sector as well as our life. Robots are becoming smarter and cost-effective with time. Today a business may own few classical robots, but the day is not far when the significant parts of an industry would be managed by the new generation of robots. However, the main question that everyone asks is that we want robots to make our lives easier and safer, yet we do not want them to control our life and cause catastrophic loss of jobs. The challenge is to strike a balance between our fears and the benefits of such innovative technology, which can offer the enhancement to our life quality. This column has touched the surface of this exciting topic and we are encouraging you to contribute to this column by writing to the editor of this column via email.

ACS Style

Jinan Fiaidhi; Sabah Mohammed; Sami Mohammed. The Robotization of Extreme Automation: The Balance Between Fear and Courage. IT Professional 2018, 20, 87 -93.

AMA Style

Jinan Fiaidhi, Sabah Mohammed, Sami Mohammed. The Robotization of Extreme Automation: The Balance Between Fear and Courage. IT Professional. 2018; 20 (6):87-93.

Chicago/Turabian Style

Jinan Fiaidhi; Sabah Mohammed; Sami Mohammed. 2018. "The Robotization of Extreme Automation: The Balance Between Fear and Courage." IT Professional 20, no. 6: 87-93.

Journal article
Published: 25 October 2018 in IT Professional
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Collaborative spaces such as fab labs (fabrication labs), stemming from a desire to share knowledge and resources, offer inventors and makers the opportunity to produce almost anything. Fab labs represent manufacturing spaces that use digitally controlled machines, representing not only a possibility for decentralized production and design for individuals but also offering open production spaces for both small and large companies.

ACS Style

Jinan Fiaidhi; Sabah Mohammed. Fab Labs: A Platform for Innovation and Extreme Automation. IT Professional 2018, 20, 83 -90.

AMA Style

Jinan Fiaidhi, Sabah Mohammed. Fab Labs: A Platform for Innovation and Extreme Automation. IT Professional. 2018; 20 (5):83-90.

Chicago/Turabian Style

Jinan Fiaidhi; Sabah Mohammed. 2018. "Fab Labs: A Platform for Innovation and Extreme Automation." IT Professional 20, no. 5: 83-90.

Journal article
Published: 08 August 2018 in IT Professional
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Blockchain has enormous potential to advance modern services, but it is important to remember that no new technology succeeds with the rip-and-replace method. Organizations using this technology will have a greater impact if they augment existing, well-established technologies such as electronic data interchange (EDI) systems.

ACS Style

Jinan Fiaidhi; Sabah Mohammed; Sami Mohammed. EDI with Blockchain as an Enabler for Extreme Automation. IT Professional 2018, 20, 66 -72.

AMA Style

Jinan Fiaidhi, Sabah Mohammed, Sami Mohammed. EDI with Blockchain as an Enabler for Extreme Automation. IT Professional. 2018; 20 (4):66-72.

Chicago/Turabian Style

Jinan Fiaidhi; Sabah Mohammed; Sami Mohammed. 2018. "EDI with Blockchain as an Enabler for Extreme Automation." IT Professional 20, no. 4: 66-72.

Journal article
Published: 01 August 2018 in Applied Soft Computing
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Jinyan Li; Simon Fong; Raymond K. Wong; Sabah Mohammed; Jinan Fiaidhi; Yunsick Sung. A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets. Applied Soft Computing 2018, 69, 784 -805.

AMA Style

Jinyan Li, Simon Fong, Raymond K. Wong, Sabah Mohammed, Jinan Fiaidhi, Yunsick Sung. A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets. Applied Soft Computing. 2018; 69 ():784-805.

Chicago/Turabian Style

Jinyan Li; Simon Fong; Raymond K. Wong; Sabah Mohammed; Jinan Fiaidhi; Yunsick Sung. 2018. "A suite of swarm dynamic multi-objective algorithms for rebalancing extremely imbalanced datasets." Applied Soft Computing 69, no. : 784-805.