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    Browsing KINDI Center for Computing Research by Type "Article"

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    Now showing items 1-20 of 168

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        A cultural evolution with a modified selection function and adaptive α-cognition procedure for numerical optimization 

        Ali, Mostafa Z.; Abdel-Nabi, Heba; Alazrai, Rami; AlHijawi, Bushra; AlWadi, Mazen G.; Al-Badarneh, Amer F.; Suganthan, Ponnuthurai N.; Daoud, Mohammad I.; Reynolds, Robert G.... more authors ... less authors ( Elsevier , 2023 , Article)
        In recent years, several population-based evolutionary and swarm algorithms have been developed and used in the literature. This work introduces an improved Cultural Algorithm with a modified selection function and a dynamic ...
      • A Greedy Cooperative Co-Evolutionary Algorithm With Problem-Specific Knowledge for Multiobjective Flowshop Group Scheduling Problems 

        He, Xuan; Pan, Quan-Ke; Gao, Liang; Wang, Ling; Suganthan, Ponnuthurai Nagaratnam ( Institute of Electrical and Electronics Engineers Inc. , 2023 , Article)
        The flowshop sequence-dependent group scheduling problem (FSDGSP) with the production efficiency measures has been extensively studied due to its wide industrial applications. However, energy efficiency indicators are often ...
      • A problem-specific knowledge based artificial bee colony algorithm for scheduling distributed permutation flowshop problems with peak power consumption 

        Yuan-Zhen, Li; Gao, Kaizhou; Meng, Lei-Lei; Suganthan, Ponnuthurai Nagaratnam ( Elsevier , 2023 , Article)
        A distributed permutation flowshop scheduling problem (DPFSP) with peak power consumption is addressed in this work. The instantaneous energy consumption of each factory cannot exceed a threshold. First, a mathematical ...
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        A resource provisioning framework for bioinformatics applications in multi-cloud environments 

        Senturk, Izzet F.; Balakrishnan, P.; Abu-Doleh, Anas; Kaya, Kamer; Malluhi, Qutaibah; Çatalyürek, Ümit V.... more authors ... less authors ( Elsevier , 2018 , Article)
        The significant advancement in Next Generation Sequencing (NGS) have enabled the generation of several gigabytes of raw data in a single sequencing run. This amount of raw data introduces new scalability challenges in ...
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        A Service-Oriented Approach for Sensing in the Internet of Things: Intelligent Transportation Systems and Privacy Use Cases 

        Hammoudeh, Mohammad; Epiphaniou, Gregory; Belguith, Sana; Unal, Devrim; Adebisi, Bamidele; Baker, Thar; Kayes, A. S. M.; Watters, Paul... more authors ... less authors ( Institute of Electrical and Electronics Engineers Inc. , 2021 , Article)
        This paper presents a Sensing-as-a-Service run-time Service Oriented Architecture (SOA), called 3SOA, for the development of Internet of Things (IoT) applications. 3SOA aims to allow interoperability among various IoT ...
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        A spectral-ensemble deep random vector functional link network for passive brain-computer interface 

        Li, Ruilin; Gao, Ruobin; Suganthan, Ponnuthurai N.; Cui, Jian; Sourina, Olga; Wang, Lipo... more authors ... less authors ( Elsevier , 2023 , Article)
        Randomized neural networks (RNNs) have shown outstanding performance in many different fields. The superiority of having fewer training parameters and closed-form solutions makes them popular in small datasets analysis. ...
      • Accurate parameters extraction of photovoltaic models with multi-strategy gaining-sharing knowledge-based algorithm 

        Guojiang, Xiong; Gu, Zaiyu; Mohamed, Ali Wagdy; Bouchekara, Houssem R.E.H.; Suganthan, Ponnuthurai Nagaratnam ( Elsevier , 2024 , Article)
        The determination of photovoltaic (PV) model parameters has essential theoretical and practical significance for the performance evaluation, power monitoring, and power generation efficiency calculation of PV systems. In ...
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        Active Computing Toward 5G Internet of Things 

        Liu, Chunpeng; Meng, Wei Xiao; Li, Cheng; Ma, Maode ( Institute of Electrical and Electronics Engineers Inc. , 2022 , Article)
        The Internet of Things (IoT) contains enormous computing resources. How to fully and efficiently use computing power is a very important research issue. This article proposes an active computing (AC) concept toward IoT. ...
      • Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection 

        Fang, Xiang; Easwaran, Arvind; Genest, Blaise; Suganthan, Ponnuthurai Nagaratnam ( Institute of Electrical and Electronics Engineers Inc. (IEEE) , 2025 , Article)
        This paper focuses on a significant yet challenging task: out-of-distribution detection (OOD detection), which aims to distinguish and reject test samples with semantic shifts, so as to prevent models trained on in-distribution ...
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        Advanced Deep Learning for Resource Allocation and Security Aware Data Offloading in Industrial Mobile Edge Computing 

        Elgendy, Ibrahim A.; Muthanna, Ammar; Hammoudeh, Mohammad; Shaiba, Hadil; Unal, Devrim; Khayyat, Mashael... more authors ... less authors ( Mary Ann Liebert Inc. , 2021 , Article)
        The Internet of Things (IoT) is permeating our daily lives through continuous environmental monitoring and data collection. The promise of low latency communication, enhanced security, and efficient bandwidth utilization ...
      • AI-powered malware detection with Differential Privacy for zero trust security in Internet of Things networks 

        Faria, Nawshin; Unal, Devrim; Hammoudeh, Mohammad; Suganthan, Ponnuthurai N. ( Elsevier , 2024 , Article)
        The widespread usage of Android-powered devices in the Internet of Things (IoT) makes them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks, such as smart watches, smart thermometers, ...
      • Alzheimer’s disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier 

        Tripti, Goel; Verma, Shradha; Tanveer, M.; Suganthan, P.N. ( Elsevier , 2025 , Article)
        Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that involves gradual memory loss and eventually leads to severe cognitive decline at the final stage. Advanced neuroimaging modalities, including magnetic ...
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        An enhanced ensemble deep random vector functional link network for driver fatigue recognition 

        Li, Ruilin; Gao, Ruobin; Yuan, Liqiang; Suganthan, P.N.; Wang, Lipo; Sourina, Olga... more authors ... less authors ( Elsevier , 2023 , Article)
        This work investigated the use of an ensemble deep random vector functional link (edRVFL) network for electroencephalogram (EEG)-based driver fatigue recognition. Against the low feature learning capability of the edRVFL ...
      • An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification 

        Liang, Jing; Zhang, Yuyang; Chen, Ke; Qu, Boyang; Yu, Kunjie; Yue, Caitong; Suganthan, Ponnuthurai Nagaratnam... more authors ... less authors ( Science China Press , 2024 , Article)
        Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and ...
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        Anonymizing transactional datasets 

        AL Bouna, Becharaa; Clifton, Chrisc; Malluhi, Qutaibah ( IOS Press , 2015 , Article)
        In this paper, we study the privacy breach caused by unsafe correlations in transactional data where individuals have multiple tuples in a dataset. We provide two safety constraints to guarantee safe correlation of the ...
      • An archive-assisted multi-modal multi-objective evolutionary algorithm 

        Peng, Chen; Li, Zhimeng; Qiao, Kangjia; Suganthan, P.N.; Ban, Xuanxuan; Yu, Kunjie; Yue, Caitong; Liang, Jing... more authors ... less authors ( Elsevier , 2024 , Article)
        The multi-modal multi-objective optimization problems (MMOPs) pertain to characteristic of the decision space that exhibit multiple sets of Pareto optimal solutions that are either identical or similar. The resolution of ...
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        Assessing the effects of data selection and representation on the development of reliable E. coli sigma 70 promoter region predictors 

        Abbas, Mostafa M.; Mohie-Eldin, Mostafa M.; El-Manzalawy, Yasser ( Public Library of Science , 2015 , Article)
        As the number of sequenced bacterial genomes increases, the need for rapid and reliable tools for the annotation of functional elements (e.g., transcriptional regulatory elements) becomes more desirable. Promoters are the ...
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        Automated layer-wise solution for ensemble deep randomized feed-forward neural network 

        Hu, Minghui; Gao, Ruobin; Suganthan, Ponnuthurai N.; Tanveer, M. ( Elsevier B.V. , 2022 , Article)
        The randomized feed-forward neural network is a single hidden layer feed-forward neural network that enables efficient learning by optimizing only the output weights. The ensemble deep learning framework significantly ...
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        Automatic variable reduction 

        Song, Aijuan; Wu, Guohua; Suganthan, P. N.; Pedrycz, Witold ( Institute of Electrical and Electronics Engineers Inc. , 2022 , Article)
        A variable reduction strategy (VRS) is an effective method to accelerate the optimization process of evolutionary algorithms (EAs) by simplifying the corresponding optimization problems. Unfortunately, the VRS is manually ...
      • Benchmark problems for large-scale constrained multi-objective optimization with baseline results 

        Qiao, Kangjia; Liang, Jing; Yu, Kunjie; Guo, Weifeng; Yue, Caitong; Qu, Boyang; Suganthan, P.N.... more authors ... less authors ( Elsevier , 2024 , Article)
        The interests in evolutionary constrained multiobjective optimization are rapidly increasing during the past two decades. However, most related studies are limited to small-scale problems, despite the fact that many practical ...

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