ORCID Profile
0000-0002-9403-7140
Current Organisations
University of Wollongong
,
Organisation
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Communication Technology and Digital Media Studies | Communication and Media Studies | Artificial Intelligence and Image Processing | Neural, Evolutionary and Fuzzy Computation | Pattern Recognition and Data Mining | Decision Support and Group Support Systems | Software Engineering | Visual Arts and Crafts not elsewhere classified
ANZSRC Socio-Economic Objective (SEO)
Learner and Learning Processes | Management of Education and Training Systems | Electronic Information Storage and Retrieval Services | Expanding Knowledge in Law and Legal Studies | The Creative Arts (incl. Graphics and Craft) | Finance Services |
Related Links
Publications
Virtual-reality and intelligent hardware in digital twins
Publisher: Elsevier BV
Date: 08-2022
An Evaluation of the Dynamics of Diluted Neural Network
Publisher: Springer Science and Business Media LLC
Date: 2016
From computer vision to short text understanding: Applying similar approaches into different disciplines
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Consensus in Concatenated Opinion Dynamics With Stubborn Agents
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
FVW: Finding Valuable Weight on Deep Neural Network for Model Pruning
Publisher: ACM
Date: 21-10-2023
A Machine Learning Based Framework for Adaptive Mobile Learning
Publisher: Springer Berlin Heidelberg
Date: 2009
Guest Editorial: Secure Communications Over the Internet of Artificially Intelligent Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Embedding the development of graduate qualities of international postgraduate IT students in the disciplinary curriculum
Publisher: University of New England, Armidale
Date: 12-2021
DOI: 10.14742/ASCILITE2021.0128
Abstract: There has been a steady increase in international students pursuing postgraduate coursework education in English speaking countries. Like first-year undergraduate students, these international students need assistance transitioning into the new educational environment and preparing for self-directed, collaborative learning throughout their careers. Drawing on the social constructivist pedagogical approaches, we developed learning tasks that foster self-regulation and collaboration among postgraduate coursework IT students, aligning these tasks with the learning outcomes of the subject Information Design and Content Management. This paper presents the rationale and method for the design of the learning tasks, and how these learning tasks to not only align with the subject learning outcomes but also facilitate self-regulation. A study involving preand post-subject surveys and interviews with 133 subject students will provide us with further insights into the effectiveness of the learning task design and the areas for improvement.
QoS-Aware Service Selection in P2P-Based Business Process Frameworks
Publisher: IEEE
Date: 07-2007
Emerging Insights of Health Informatics Research: A Literature Analysis for Outlining New Themes
Publisher: Australian Journal of Information Systems
Date: 12-02-2019
Abstract: This paper presents a contemporary literature review to provide insights into the current health informatics literature. The objective of this study is to identify emerging directions of current health informatics research from the latest and existing studies in the health informatics domain. We analyse existing health informatics studies using a thematic analysis, so that justified sets of research agenda can be outlined on the basis of these findings. We selected articles that are published in the science direct online database. The selected 73 s le articles (published from 2014 to 2018 in premier health informatics journals) are considered as representative s les of health informatics studies. The analysis revealed ten topic areas and themes that would be of paramount importance for researchers and practitioners to follow. The findings provide an important foundational understanding for new health informatics studies.
Fast Anomaly Detection in Multiple Multi-Dimensional Data Streams
Publisher: IEEE
Date: 12-2019
Profiling and Supporting Adaptive Micro Learning on Open Education Resources
Publisher: IEEE
Date: 08-2017
DOI: 10.1109/CBD.2016.037
Towards Industrial Internet of Things in Steel Manufacturing: A Multiple-Factor-based Detection System of Longitudinal Surface Cracks
Publisher: IEEE
Date: 10-12-2020
A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
Publisher: MDPI AG
Date: 30-12-2022
DOI: 10.3390/FI15010022
Abstract: Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving the accuracy of electricity load forecasting is vital for public resources management departments. Traditional neural network methods such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) have been widely used in electricity load forecasting. However, LSTM and its variants are not sensitive to the dynamic change of inputs and miss the internal nonperiodic rules of series, due to their discrete observation interval. In this paper, a novel neural ordinary differential equation (NODE) method, which can be seen as a continuous version of residual network (ResNet), is applied to electricity load forecasting to learn dynamics of time series. We design three groups of models based on LSTM and BiLSTM and compare the accuracy between models using NODE and without NODE. The experimental results show that NODE can improve the prediction accuracy of LSTM and BiLSTM. It indicates that NODE is an effective approach to improving the accuracy of electricity load forecasting.
Transient receptor potential vanilloid 4 inhibits mouse colonic motility by activating NO-dependent enteric neurotransmission
Publisher: Springer Science and Business Media LLC
Date: 02-09-2015
DOI: 10.1007/S00109-015-1336-5
Abstract: Recent studies implicate TRPV4 receptors in visceral pain signaling and intestinal inflammation. Our aim was to evaluate the role of TRPV4 in the control of gastrointestinal (GI) motility and to establish the underlying mechanisms. We used immunohistochemistry and PCR to study TRPV4 expression in the GI tract. The effect of TRPV4 activation on GI motility was characterized using in vitro and in vivo motility assays. Calcium and nitric oxide (NO) imaging were performed to study the intracellular signaling pathways. Finally, TRPV4 expression was examined in the colon of healthy human subjects. We demonstrated that TRPV4 can be found on myenteric neurons of the colon and is co-localized with NO synthase (NOS-1). In vitro, the TRPV4 agonist GSK1016790A reduced colonic contractility and increased inhibitory neurotransmission. In vivo, TRPV4 activation slowed GI motility and reduced stool production in mouse models mimicking pathophysiological conditions. We also showed that TRPV4 activation inhibited GI motility by reducing NO-dependent Ca(2+) release from enteric neurons. In conclusion, TRPV4 is involved in the regulation of GI motility in health and disease. • Recent studies implicate TRPV4 in pain signaling and intestinal inflammation. • Our aim was to characterize the role of TRPV4 in the control of GI motility. • We found that TRPV4 activation reduced colonic contractility. • Our studies also showed altered TRPV4 mRNA expression in IBS-C patients. • TRPV4 may be a novel pharmacological target in functional GI diseases.
Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service
Publisher: Springer International Publishing
Date: 2020
Estimation of the mass of microplastics ingested – A pivotal first step towards human health risk assessment
Publisher: Elsevier BV
Date: 02-2021
Solving arithmetic word problems by synergizing syntax-semantics extractor for explicit relations and neural network miner for implicit relations
Publisher: Springer Science and Business Media LLC
Date: 29-07-2022
DOI: 10.1007/S40747-022-00828-0
Abstract: This paper presents a relation-centric algorithm for solving arithmetic word problems (AWPs) by synergizing a syntax-semantics extractor for extracting explicit relations, and a neural network miner for mining implicit relations. This is the first algorithm that has a specific component to acquire implicit knowledge items for solving AWPs. This paper proposes a three-phase scheme to decompose the challenging task of designing an algorithm for solving AWPs into three smaller tasks. The first phase proposes a state-action paradigm the second phase instantiates the paradigm into a relation-centric approach and the third phase implements a relation-centric algorithm for solving AWPs. There are two main steps in the proposed algorithm: problem understanding and symbolic solver. By adopting the relation-centric approach, problem understanding becomes a task of relation acquisition. For conducting the task of relation acquisition, a relaxed syntax-semantics method first extracts a group of explicit relation candidates. In parallel, a neural network miner acquires implicit relation candidates. The miner computes the vectors encoded by BERT to determine which implicit relations should be added. Thus, problem understanding can acquire both explicit relations and implicit relations, which addresses the challenge of building a problem understanding method that can acquire all the knowledge items to find the solution. In the subsequent step of symbolic solver, a fusion procedure forms a distilled set of relations from all the candidates by discarding unnecessary relations. Experimentation on nine benchmark datasets validates the superiority of the proposed algorithm that outperforms the state-of-the-art algorithms.
Children’s E-Learning Interactions and Perceived Outcomes with Educational Key Opinion Leaders in China
Publisher: Association for Information Systems
Date: 2023
DOI: 10.17705/1CAIS.05317
Knowledge Sharing in Digital Learning Communities: A Comparative Review of Issues between Education and Industry
Publisher: IEEE
Date: 12-2018
Accelerating Skycube Computation with Partial and Parallel Processing for Service Selection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
GaiaFGK benchmark stars: abundances ofαand iron-peak elements
Publisher: EDP Sciences
Date: 10-2015
Towards Bio-inspired Cost Minimisation for Data-Intensive Service Provision
Publisher: IEEE
Date: 06-2012
DOI: 10.1109/SE.2012.14
Guest Editorial: Secure Communications Over the Internet of Artificially Intelligent Things: Part 2
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
SATP-GAN: self-attention based generative adversarial network for traffic flow prediction
Publisher: Informa UK Limited
Date: 2021
Short-Term Electricity Demand Forecasting Based on Multiple LSTMs
Publisher: Springer International Publishing
Date: 2020
Guest editorial notes for selected papers from SOCA 2010
Publisher: Springer Science and Business Media LLC
Date: 06-05-2012
Feasibility Analysis of Data Transmission in Partially Damaged IoT Networks of Vehicles
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
A GTFS data acquisition and processing framework and its application to train delay prediction
Publisher: Elsevier BV
Date: 03-2023
Energy-Efficient Data Consistency based Sampling Rate Optimization and Aggregation Method for IoT
Publisher: IEEE
Date: 24-05-2023
An HSM-based scalable network service model
Publisher: IEEE
Date: 09-2014
Micro learning adaptation in MOOC: A software as a service and a personalized learner model
Publisher: Springer International Publishing
Date: 2015
Towards Bringing Adaptive Micro Learning into MOOC Courses
Publisher: IEEE
Date: 07-2015
Cloud Failure Prediction based on Traditional Machine Learning and Deep Learning
Publisher: Research Square Platform LLC
Date: 18-05-2022
DOI: 10.21203/RS.3.RS-1657073/V1
Abstract: Cloud failure is one of the critical issues since it can cost millions to the cloud service providers in addition to the loss of productivity being suffered by the industrial users using different cloud-based applications and services. As the cloud system grows larger, the process of failure prediction is still a challenge for both practitioners and academic researchers. In this study, we tackle the challenge of predicting job and task failure using the benchmarking Google Cluster Traces Dataset. We first propose a conceptual model to prepare, construct and evaluate five traditional machine learning algorithms and three variants of the latest deep learning algorithms for predicting job and task failures. We then perform a series of experiments on two datasets (1) Dataset A for the job failure prediction models and (2) Dataset B for the task failure prediction models. In the case of job failure prediction experiments, we observe that Extreme Gradient Boosting is the best performance with 94.35% accuracy with the F-Score score of 0.9310, 91.92% sensitivity, and 96.07% specificity. For Extreme Gradient Boosting, the disk space request and CPU request are the most important features in determining the outcome of the job prediction. In the case of task failure prediction, we observe that Decision Tree and Random Forest have achieved the highest 89.75% accuracy and 0.9145 for F-Score with 98% sensitivity and 78% specificity. The priority of the task is the most important feature for determining the task prediction outcome for both, the Decision Tree and Random Forest.
Facilitating an ant colony algorithm for multi-objective data-intensive service provision
Publisher: Elsevier BV
Date: 06-2015
Engineering Blockchain-based Software Systems: Foundations, Survey, and Future Directions
Publisher: Association for Computing Machinery (ACM)
Date: 07-12-2022
DOI: 10.1145/3530813
Abstract: Many scientific and practical areas have shown increasing interest in reaping the benefits of blockchain technology to empower software systems. However, the unique characteristics and requirements associated with Blockchain-based Software (BBS) systems raise new challenges across the development lifecycle that entail an extensive improvement of conventional software engineering. This article presents a systematic literature review of the state-of-the-art in BBS engineering research from the perspective of the software engineering discipline. We characterize BBS engineering based on the key aspects of theoretical foundations, processes, models , and roles . Based on these aspects, we present a rich repertoire of development tasks, design principles, models, roles, challenges, and resolution techniques. The focus and depth of this survey not only give software engineering practitioners and researchers a consolidated body of knowledge about current BBS development but also underpin a starting point for further research in this field.
Enhanced learner model for adaptive mobile learning
Publisher: ACM
Date: 08-11-2010
Structural Principles Analysis of Host-Pathogen Protein-Protein Interactions: A Structural Bioinformatics Survey
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Revealing safety impact of bus stops on passenger-cyclist interactions – Evidence from Nanjing, China
Publisher: Elsevier BV
Date: 07-2023
Blockchain Developments and Innovations
Publisher: arXiv
Date: 2021
Ensemble Neural Network Method for Wind Speed Forecasting
Publisher: IEEE
Date: 10-2019
A Decentralized Latency-Aware Task Allocation and Group Formation Approach With Fault Tolerance for IoT Applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Pairwise-Based Hierarchical Gating Networks for Sequential Recommendation
Publisher: Springer International Publishing
Date: 2020
A Consumer-Oriented Decision-Making Approach for Selecting the Cloud Storage Service: From PAPRIKA Perspective
Publisher: Springer International Publishing
Date: 2017
Exploration of the Misalignment Between Business and IT Strategic Objectives in Public-Sector Organisations: An Empirical Study in Saudi Arabia
Publisher: Springer International Publishing
Date: 2018
Search for Higgs boson pair production in events with two bottom quarks and two tau leptons in proton–proton collisions at
Publisher:
Elsevier BV
Date:
03-2018
Publisher: Elsevier BV
Date: 03-2018
Multiplicities in early childhood reform engagement in Victorian long day care centres: Discourse, position and practice
Publisher: SAGE Publications
Date: 18-06-2019
Abstract: Change is not a new concept in the Australian early childhood sector. However, the rate of change has significantly increased throughout the last decade, specifically with the introduction of the curriculum and quality frameworks, changes to regulations, and subsequent reviews (some particularly affecting the Victorian long day care sector). The rapid timeline of these reforms created challenges for early childhood professionals who needed to understand, interpret and translate multiple changes to their practice. This paper presents some key findings from a poststructural study involving 11 participants from the Victorian long day care sector. Foucauldian Discourse Analysis has been applied to explore how reform discourses shape and reshape the positioning and engagement of professionals within the reform process. These findings reveal how specific subject positions and discursive practices within available discourses of knowledge, teacher education and workplace can either challenge and/or support early childhood professionals in their ability to engage in reform.
Incremental service level agreements violation handling with time impact analysis
Publisher: Elsevier BV
Date: 06-2013
A Framework of MLaaS for Facilitating Adaptive Micro Learning through Open Education Resources in Mobile Environment
Publisher: IGI Global
Date: 10-2017
Abstract: Micro learning becomes popular in online open learning and it is effective and helpful for learning in mobile environment. However, the delivery of open education resources (OERs) is scarcely supported by the current online systems. In this research, the authors introduce an approach to bridge the gap by providing adaptive micro open education resources for in idual learners to carry out learning activities in a short time span. They propose a framework for micro learning resource customization and a personalized learner model, which are supported by education data mining (EDM) and learning analysis (LA). A service-oriented architecture for Micro Learning as a Service (MLaaS) is designed to integrate all necessary procedures together as a complete Service for delivering micro OERs, providing a platform for resource sharing and exchanging in peer-to-peer learning environment. Working principle of a key step, namely the computational decision-making of micro OER adaptation, is also introduced.
A new context-aware approach for automatic Chinese poetry generation
Publisher: Elsevier BV
Date: 11-2021
Comparison of bio-inspired algorithms for peer selection in services composition
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/SCC.2011.29
Ensemble Machine Learning Systems for the Estimation of Steel Quality Control
Publisher: IEEE
Date: 12-2018
GaiaFGK benchmark stars: opening the black box of stellar element abundance determination
Publisher: EDP Sciences
Date: 26-04-2017
A Hybrid LSTM-CPS Approach for Long-Term Prediction of Train Delays in Multivariate Time Series
Publisher: MDPI AG
Date: 03-12-2021
DOI: 10.3390/FUTURETRANSP1030042
Abstract: In many big cities, train delays are among the most complained-about events by the public. Although various models have been proposed for train delay prediction, prior studies on both primary and secondary train delay prediction are limited in number. Recent advances in deep learning approaches and increasing availability of various data sources has created new opportunities for more efficient and accurate train delay prediction. In this study, we propose a hybrid deep learning solution by integrating long short-term memory (LSTM) and Critical Point Search (CPS). LSTM deals with long-term prediction tasks of trains’ running time and dwell time, while CPS uses predicted values with a nominal timetable to identify primary and secondary delays based on the delay causes, run-time delay, and dwell time delay. To validate the model and analyse its performance, we compare the standard LSTM with the proposed hybrid model. The results demonstrate that new variants outperform the standard LSTM, based on predicting time steps of dwell time feature. The experiment results also showed many irregularities of historical trends, which draws attention for further research.
Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Combined General Vector Machine for Single Point Electricity Load Forecast
Publisher: Springer Singapore
Date: 2020
An offer generation approach to SLA negotiation support in service oriented computing
Publisher: Springer Science and Business Media LLC
Date: 19-11-2010
Parameter Identification for Memristive Chaotic System Using Modified Sparrow Search Algorithm
Publisher: Frontiers Media SA
Date: 28-06-2022
Abstract: A memristor is a non-linear element. The chaotic system constructed by it can improve its unpredictability and complexity. Parameter identification of a memristive chaotic system is the primary task to implement chaos control and synchronization. To identify the unknown parameters accurately and quickly, we introduce the Sine Pareto Sparrow Search Algorithm (SPSSA), a modified sparrow search algorithm (SSA). in this research. Firstly, we introduce the Pareto distribution to alter the scroungers’ location in the SSA. Secondly, we use a sine-cosine strategy to improve the producers’ position update. These measures can effectively accelerate the convergence speed and avoid local optimization. Thirdly, the SPSSA is used to identify the parameters of a memristive chaotic system. The proposed SPSSA exceeds the classic SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC) in simulations based on the five benchmark functions. The simulation results of parameter identification of a memristive chaotic system show that the method is feasible, and the algorithm has a fast convergence speed and high estimation accuracy.
Software Engineering for Blockchain Based Software Systems: Foundations, Survey, and Future Directions
Publisher: arXiv
Date: 2021
AN ANCIENT EXTRASOLAR SYSTEM WITH FIVE SUB-EARTH-SIZE PLANETS
Publisher: American Astronomical Society
Date: 03-01-0003
A novel Monte Carlo based neural network model for electricity load forecasting
Publisher: Inderscience Publishers
Date: 2018
A novel independent job rescheduling strategy for cloud resilience in the cloud environment
Publisher: Emerald
Date: 10-02-2022
Abstract: Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance. A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed. The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches. This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.
A hybrid unsupervised clustering-based anomaly detection method
Publisher: Tsinghua University Press
Date: 04-2021
Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2012
DOI: 10.1109/TLT.2011.36
A Groundwater Management Tool for Solving the Pumping Yields Minimization Problem: A Case Study in the Heihe River Basin
Publisher: IEEE
Date: 08-2016
DOI: 10.1109/CBD.2016.057
Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison
Publisher: EJournal Publishing
Date: 04-2018
Distributed GAN: Toward a Faster Reinforcement-Learning-Based Architecture Search
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
A Petri Net Model for Session Services
Publisher: Springer Berlin Heidelberg
Date: 2002
Multi-features fusion for short-term photovoltaic power prediction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Potential for biomonitoring metals and metalloids using fish condition and tissue analysis in an agricultural and coal mining region
Publisher: Elsevier BV
Date: 07-2018
DOI: 10.1016/J.CHEMOSPHERE.2018.03.080
Abstract: Agricultural and mining activities contribute to metal inputs in freshwater ecosystems around the world, which can in turn bioaccumulate in biota such as fish. Monitoring of metals loads in biota thus provides insight into the concentrations of bioavailable metals within the environment. Little research has been conducted on the potential of Australian freshwater fish for biomonitoring metals. Within the Fitzroy Basin of Central Queensland, a major agricultural and coal mining region, three commonly-encountered fish taxa were analysed for tissue metal loads. Arsenic concentrations in Nematalosa erebi and Melanotaenia splendida splendida tissue were elevated (above Food Standards Australia and New Zealand (FSANZ) guidelines), with highest concentrations in N. erebi liver tissue (up to 5.14 μg/g). Lead concentrations were above the FSANZ guidelines in all three fish taxa analysed, with highest concentrations in Hypseleotrid full-body tissue (up to 5.99 μg/g). Selenium in M. s. splendida and N. erebi tissue was positively correlated with total selenium in water (p < 0.05 r = 0.68 and 0.87 respectively). Environmental boron, selenium and nickel concentrations were positively correlated with N. erebi liver tissue metals. N. erebi hepatosomatic index was negatively correlated with dissolved arsenic, manganese, and total phosphorus (in water). The results highlight that M. s. splendida and N. erebi yield bioindicators which are responsive to environmental metals, and thus have potential for use in biomonitoring metals. The two species are also widespread along the east coast of Australia, there is thus a strong potential for applying the results to other regions within Australia.
Effective visualisation of workflow enactment
Publisher: Springer Berlin Heidelberg
Date: 2004
A Comprehensive Framework for Analyzing IoT Platforms: A Smart City Industrial Experience
Publisher: MDPI AG
Date: 28-04-2021
DOI: 10.3390/SMARTCITIES4020031
Abstract: The compliance of IoT platforms to quality is paramount to achieve users’ satisfaction. Currently, we do not have a comprehensive set of guidelines to appraise and select the most suitable IoT platform architectures that meet relevant criteria. This paper is a tentative response to this critical knowledge gap where we adopted the design science research approach to develop a novel evaluation framework. Our research, on the one hand, stimulates an unbiased competition among IoT platform providers and, on the other hand, establishes a solid foundation for IoT platform consumers to make informed decisions in this multiplicity. The application of the framework is illustrated in ex le scenarios. Moreover, lessons learned from applying design science research are shared.
Multi-agent deep reinforcement learning for traffic signal control with Nash Equilibrium
Publisher: IEEE
Date: 12-2021
Towards the readiness of learning analytics data for micro learning
Publisher: Springer International Publishing
Date: 2019
Automatic ventricular nuclear magnetic resonance image processing with deep learning
Publisher: Springer Science and Business Media LLC
Date: 18-04-2020
Knowledge Management for Cloud Computing Field
Publisher: arXiv
Date: 2022
TheGaia-ESO Survey: Sodium and aluminium abundances in giants and dwarfs
Publisher: EDP Sciences
Date: 21-04-2016
A Systematic Review of Bio-Inspired Service Concretization
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Analysis of Student Course Evaluation Data for an IT Subject: Implications for Improving STEM Education
Publisher: EJournal Publishing
Date: 2017
Dynamic Service Selection for Service Composition with Time Constraints
Publisher: IEEE
Date: 2009
A Comprehensive Framework for Analyzing IoT Platforms: A Smart City Industrial Experience
Publisher: arXiv
Date: 2021
Multiagent Consensus Over Time-Invariant and Time-Varying Signed Digraphs via Eventual Positivity
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Industrial IoT Intrusion Detection via Evolutionary Cost-Sensitive Learning and Fog Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-11-2022
Optimising Automatic Text Classification Approach in Adaptive Online Collaborative Discussion - A perspective of Attention Mechanism-Based Bi-LSTM
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions.
Publisher: Oxford University Press (OUP)
Date: 27-05-2020
DOI: 10.1093/BIB/BBAA068
Abstract: In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein–protein interaction identification, including human–pathogen protein–protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human–bacterium protein–protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on ‘host–pathogen interactions’ whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.
Economical Data-Intensive Service Provision Supported with a Modified Genetic Algorithm
Publisher: IEEE
Date: 06-2013
A framework towards data analytics on host–pathogen protein–protein interactions
Publisher: Springer Science and Business Media LLC
Date: 20-01-2020
Assisting Open Education Resource Providers and Instructors to Deal with Cold Start Problem in Adaptive Micro Learning: A Service Oriented Solution
Publisher: IEEE
Date: 06-2017
DOI: 10.1109/SCC.2017.32
The Spatio-temporal Hybrid Development Methodology for Smart IoT: A Review based Study
Publisher: IEEE
Date: 18-12-2022
A Survey of Segmentation, Annotation, and Recommendation Techniques in Micro Learning for Next Generation of OER
Publisher: IEEE
Date: 05-2019
Photovoltaic Power Forecasting in Dunhuang based on Recurrent Neural Network
Publisher: IEEE
Date: 12-2021
The benefits of introducing electronic health records in residential aged care facilities: A multiple case study
Publisher: Elsevier BV
Date: 10-2012
DOI: 10.1016/J.IJMEDINF.2012.05.013
Abstract: Information and communications technology solutions have been introduced into the residential aged care system in order to improve the effectiveness and efficiency of aged care, however to date, the actual benefits have not been systematically analysed. The aim of this study was to identify the benefits of electronic health records (EHR) in residential aged care services and to examine how the benefits have been achieved. A qualitative interview study was conducted in nine residential aged care facilities (RACFs) belonging to three organisations in the Australian Capital Territory (ACT), New South Wales (NSW) and Queensland, Australia. A longitudinal investigation after the implementation of the aged care EHR systems was conducted at two data points: January 2009 to December 2009 and December 2010 to February 2011. Semi-structured interviews were conducted with 110 care staff members selected through theoretical s ling, representing all levels of care staff who worked in those facilities. Three categories of benefits were perceived by the care staff members according to who gain the benefits: the benefits to in idual care staff members, to residents and to the RACFs. The benefits to in idual care staff members include an improvement of documentation efficiency, information and knowledge growth as well as empowering the staff the benefits to residents are an improvement in the quality of in idual residents' health records, the higher quality of care and smoother communication between the residents and aged care staff the RACFs gain an increased ability to manage information and acquire funding, an increase in their ability to control the care quality and improvements in the working environment and educational benefits. Three factors leading to these benefits were examined: the nature of the aged care EHR systems in comparison with paper-based records the way the systems were used by the staff and one benefit that could lead to another. In this study, EHR systems were perceived to have substantial benefits for care staff, residents and the aged care organisations introducing the systems. The benefits were derived from the nature of the aged care EHR systems, staff members' continuous use of the systems, and one benefit led to the other.
Object-aware Policy Network in Deep Recommender Systems
Publisher: Springer Science and Business Media LLC
Date: 13-05-2022
DOI: 10.1007/S11265-022-01773-4
Abstract: Deep learning has been successfully applied in the recommender system. Low-dimensional dense embedding is typically used to represent the feature of users and items. To optimize the model, some models propose to dynamically search the embedding size based on the popularity of different users and items. However, these models ignore the interaction between the user and the item which will hinder the optimization of the features in embedding. In this paper, we propose Object-aware Policy Network (OPN) and introduces an object-aware method that is used for optimizing the features in embedding. We evaluate our model on the two real-world benchmark datasets. With less than 10% increased time consumption in all experiments, the results show that our proposed model is able to improve the performance of binary classification task by a margin of 0.30 and multiclass classification task by a margin of 0.35 compared to the best accuracies achieced by baselines on different datasests.
Mining e‐services in P2P‐based workflow enactments
Publisher: Emerald
Date: 11-04-2008
DOI: 10.1108/14684520810879809
Abstract: This paper aims to explore the feasible mining and proper selection of QoS‐aware services for a P2P‐based business process enactment framework, and enhance the relevant workflow prototype. Design/methodology/approach – Through observing and analysing unpredictable dynamic changes that commonly exist in e‐service workflow, a set of QoS specifications and monitoring mechanisms in a P2P workflow framework is proposed. These specifications are an evolving extension of services' description with semantic web facilitators for P2P‐based e‐services. It has been demonstrated that the QoS‐OWL approach can be effectively used to describe and exploit e‐services, particularly in a decentralised environment. QoS‐OWL is also able to provide the deliberation of geographic information for e‐services, and facilitates the peers' effective cooperation and automates business processes. It is recommended that the approach be adopted in the dynamic online information system environment to increase efficiency and reduce costs in running e‐services. The QoS attributes discussed in this paper are relatively typical for benchmarking purposes, so the extension of the QoS metrics specifications and related protocols need to be discussed in future work. The authors propose an approach to dynamically selecting appropriate service‐oriented peers in a P2P network, where the composition of e‐services in a business process can be enhanced significantly. The approach is based on ontology and QoS perspectives, and the descriptions of services have rich semantic features in an attempt to make the service selection more intelligent and reliable. Therefore the proposal offers e‐service providers innovative initiatives to re‐engineer their business services.
The Syncretic Effect of Dual-Source Data on Affective Computing in Online Learning Contexts: A Perspective From Convolutional Neural Network With Attention Mechanism
Publisher: SAGE Publications
Date: 10-11-2022
DOI: 10.1177/07356331221115663
Abstract: Affective computing (AC) has been regarded as a relevant approach to identifying online learners’ mental states and predicting their learning performance. Previous research mainly used one single-source data set, typically learners’ facial expression, to compute learners’ affection. However, a single facial expression may represent different affections in various head poses. This study proposed a dual-source data approach to solve the problem. Facial expression and head pose are two typical data sources that can be captured from online learning videos. The current study collected a dual-source data set of facial expressions and head poses from an online learning class in a middle school. A deep learning neural network using AlexNet with an attention mechanism was developed to verify the syncretic effect on affective computing of the proposed dual-source fusion strategy. The results show that the dual-source fusion approach significantly outperforms the single-source approach based on the AC recognition accuracy between the two approaches (dual-source approach using Attention-AlexNet model 80.96% single-source approach, facial expression 76.65% and head pose 64.34%). This study contributes to the theoretical construction of the dual-source data fusion approach, and the empirical validation of the effect of the Attention-AlexNet neural network approach on affective computing in online learning contexts.
Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
Publisher: MDPI AG
Date: 22-07-2022
DOI: 10.3390/APP12157369
Abstract: Service recommendation is key to improving users’ online experience. The development of the Internet has accelerated the creation of many services, and whether users can obtain good experiences among the massive number of services mainly depends on the quality of service recommendation. It is commonly believed that deep learning has excellent nonlinear fitting ability in capturing the complex interactions between users and items. The advantage in learning intricacy relationships enables deep learning to become an important technology for present service recommendation. Recently, it is noticed that linear models can perform almost as well as the state-of-the-art deep learning models, suggesting that capturing linear relationships between users and items is also very important for recommender systems. Therefore, numerous deep learning systems combined with linear models have been proposed. However, existing models are incapable of considering the size of the embedding. When the embedding dimension is too large, it leads to overfitting and thus influences the model’s ability to capture linear relationships. In this paper, a neural network based on two-layer matrix factorization and multi-layer perceptron—Two-layer Matrix factorization and Multi-layer perceptron Neural Network (TMMNN)—is proposed. To solve the problem of overfitting caused by an oversized embedding dimension, multi-size embedding technology has been integrated into the model. Matrix factorization and the multi-layer perceptron are placed in the upper and lower layers respectively, and they both receive embedding vectors dynamically adjusted for dimensions. In the upper layer, the matrix factorization is responsible for receiving the embedding of users and items, capturing linear relationships, and then yielding the generated new vectors as input to the multi-layer perceptron in the lower layer. Compared to other previously proposed models, the experimental results on the standard datasets MovieLens 20M and MovieLens Latest show that the TMMNN model is evidently better in terms of prediction accuracy.
A Blockchain-based Multi-Users Oblivious Data Sharing Scheme for Digital Twin System in Industrial Internet of Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Analyzing Fault-Impact Region of Composite Service for Supporting Fault Handling Process
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/SCC.2011.51
Misalignment between Business and IT Strategic Objectives in Saudi Arabia Public Sector Organisations
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2018
A Utility-Based Fault Handling Approach for Efficient Job Rescue in Clouds
Publisher: Springer International Publishing
Date: 2020
Association of Thyroid Function Test Abnormalities and Thyroid Autoimmunity With Preterm Birth
Publisher: American Medical Association (AMA)
Date: 20-08-2019
From BPEL4WS to OWL-S: integrating e-business process descriptions
Publisher: IEEE
Date: 2005
DOI: 10.1109/SCC.2005.54
A heuristic approach for new-item cold start problem in recommendation of micro open education resources
Publisher: Springer International Publishing
Date: 2018
TheGaia-ESO Survey: Insights into the inner-disc evolution from open clusters
Publisher: EDP Sciences
Date: 08-2015
Observational study on multi-type conflicts between passengers and cyclists at the bus stop – A case study in Nanjing
Publisher: Elsevier BV
Date: 10-2022
Evolutionary Learner Profile Optimization Using Rare and Negative Association Rules for Micro Open Learning
Publisher: Springer International Publishing
Date: 2020
New trends and innovative methods in cloud computing and big data
Publisher: Wiley
Date: 08-08-2017
DOI: 10.1002/CPE.4279
Signed Social Networks With Biased Assimilation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Distributed agent-based deep reinforcement learning for large scale traffic signal control
Publisher: Elsevier BV
Date: 04-2022
Uncertainty quantification for operators in online reinforcement learning
Publisher: Elsevier BV
Date: 12-2022
SmartFilter: An Edge System for Real-Time Application-Guided Video Frames Filtering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Cost and Time Aware Ant Colony Algorithm for Data Replica in Alpha Magnetic Spectrometer Experiment
Publisher: IEEE
Date: 06-2013
Editorial: Digital twins of plant and forest
Publisher: Frontiers Media SA
Date: 23-12-2022
Successfully sharing knowledge: Empirical evidence on the influence of managerial factors
Publisher: Wiley
Date: 28-03-2022
DOI: 10.1002/KPM.1703
Abstract: Knowledge is an intangible and vital resource that is an important source of competitive advantage however, the technologies that help create, store, and transfer knowledge are hindered by unrealistic expectations and ambiguity, and the measurement of knowledge‐sharing activities is both difficult and complex. Compounding this is the deficit of empirical studies on the factors that influence the knowledge‐sharing process. We endeavored to provide empirical evidence on these interactions using a survey developed from a prior extensive systematic literature review. The previously identified factors that were in the current study tested comprised (1) organizational culture, (2) formal processes, (3) top‐down support, (4) motivation, (5) clear strategy, and (6) quality of technology. In order, the most influential factors were organizational culture, top‐down support, motivation, and quality of technology. This study is a promising start to the exploration of the factors used in knowledge sharing and should be expanded to include new industries and contexts.
An efficient video watermark method using blockchain
Publisher: Elsevier BV
Date: 2023
An intelligent blockchain-based system for safe vaccine supply and supervision
Publisher: Elsevier BV
Date: 06-2020
Verification of Composite Services with Temporal Consistency Checking and Temporal Satisfaction Estimation
Publisher: Springer Berlin Heidelberg
Date: 2009
TheGaia-ESO Survey: the first abundance determination of the pre-main-sequence cluster gamma Velorum
Publisher: EDP Sciences
Date: 07-2014
Colonic immune cells in irritable bowel syndrome: A systematic review and meta‐analysis
Publisher: Wiley
Date: 29-08-2017
DOI: 10.1111/NMO.13192
Abstract: Increases in mucosal immune cells have frequently been observed in irritable bowel syndrome (IBS) patients. However, this finding is not completely consistent between studies, possibly due to a combination of methodological variability, population differences and small s le sizes. We performed a meta-analysis of case-control studies that compared immune cell counts in colonic biopsies of IBS patients and controls. PubMed and Embase were searched in February 2017. Results were pooled using standardized mean difference (SMD) and were considered significant when zero was not within the 95% confidence interval (CI). Heterogeneity was assessed based on I Twenty-two studies on 706 IBS patients and 401 controls were included. Mast cells were increased in the rectosigmoid (SMD: 0.38 [95% CI: 0.06-0.71] P = .02) and descending colon (SMD: 1.69 [95% CI: 0.65-2.73] P = .001) of IBS patients. Increased mast cells were observed in both constipation (IBS-C) and diarrhea predominant IBS (IBS-D). CD3 Mast cells and CD3
Innovation event model for STEM education: A constructivism perspective
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2021
Fast multi-resource allocation with patterns in large scale cloud data center
Publisher: Elsevier BV
Date: 05-2018
A Bandwidth and Latency Based Replica Selection Mechanism for Data-Intensive Workflow Applications in the Multi-Cloud Environment
Publisher: ACM
Date: 04-02-2020
Towards A More Effective Bidirectional LSTM-Based Learning Model for Human-Bacterium Protein-Protein Interactions
Publisher: Springer International Publishing
Date: 23-07-2021
HIME: Mining and Ensembling Heterogeneous Information for Protein-Protein Interactions Prediction
Publisher: IEEE
Date: 07-2020
MSIS 2016: Global Competency Model for Graduate Degree Programs in Information Systems
Publisher: ACM
Date: 23-05-2017
DOI: 10.1145/3129538
Logit Calibration for Non-IID and Long-Tailed Data in Federated Learning
Publisher: IEEE
Date: 12-2022
DOI: 10.1109/ISPA-BDCLOUD-SOCIALCOM-SUSTAINCOM57177.2022.00105
Enhanced ant colony algorithm for cost-aware data-intensive service provision
Publisher: IEEE
Date: 06-2013
Investigating Sequence Patterns of Collaborative Problem-Solving Behavior in Online Collaborative Discussion Activity
Publisher: MDPI AG
Date: 15-10-2020
DOI: 10.3390/SU12208522
Abstract: Collaborative problem solving (CPS) is an influential human behavior affecting working performance and well-being. Previous studies examined CPS behavior from the perspective of either social or cognitive dimensions, which leave a research gap from the interactive perspective. In addition, the traditional sequence analysis method failed to combine time sequences and sub-problem sequences together while analyzing behavioral patterns in CPS. This study proposes a developed schema for the multidimensional analysis of CPS. A combination sequential analysis approach that comprises time sequences and sub-problem sequences is also employed to explore CPS patterns. A total of 191 students were recruited and randomly grouped into 38 teams (four to six students per team) in the online collaborative discussion activity. Their discussion transcripts were coded while they conducted CPS, followed by the assessment of high- and low- performance groups according to the developed schema and sequential analysis. With the help of the new analysis method, the findings indicate that a deep exploratory discussion is generated from conflicting viewpoints, which promotes improved problem-solving outcomes and perceptions. In addition, evidence-based rationalization can motivate collaborative behavior effectively. The results demonstrated the potential power of automatic sequential analysis with multidimensional behavior and its ability to provide quantitative descriptions of group interactions in the investigated threaded discussions.
Parallel fast Fourier transform in SPMD style of CILK
Publisher: Inderscience Publishers
Date: 2018
QDG: A unified model for automatic question-distractor pairs generation
Publisher: Springer Science and Business Media LLC
Date: 25-07-2022
Supervised Machine Learning Model for High Dimensional Gene Data in Colon Cancer Detection
Publisher: IEEE
Date: 06-2015
Empirical Investigation on Conflicts between Bus Passengers and Cyclists at Different Types of Bus Stops
Publisher: IEEE
Date: 08-10-2022
GVM Based Copy-Dynamics Model for Electricity Load Forecast
Publisher: Springer Singapore
Date: 2019
Conditional Generative Adversarial Networks for Domain Transfer: A Survey
Publisher: MDPI AG
Date: 21-08-2022
DOI: 10.3390/APP12168350
Abstract: Generative Adversarial Network (GAN), deemed as a powerful deep-learning-based silver bullet for intelligent data generation, has been widely used in multi-disciplines. Furthermore, conditional GAN (CGAN) introduces artificial control information on the basis of GAN, which is more practical for many specific fields, though it is mostly used in domain transfer. Researchers have proposed numerous methods to tackle erse tasks by employing CGAN. It is now a timely and also critical point to review these achievements. We first give a brief introduction to the principle of CGAN, then focus on how to improve it to achieve better performance and how to evaluate such performance across the variants. Afterward, the main applications of CGAN in domain transfer are presented. Finally, as another major contribution, we also list the current problems and challenges of CGAN.
A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image
Publisher: MDPI AG
Date: 03-11-2021
DOI: 10.3390/MATH9212790
Abstract: At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the ersity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.
Sun‐like stars unlike the Sun: Clues for chemical anomaliesof cool stars
Publisher: Wiley
Date: 05-2017
Effective long-term travel time prediction with fuzzy rules for tollway
Publisher: Springer Science and Business Media LLC
Date: 22-02-2017
Achieving consensus in multilateral international negotiations: The case study of the 2015 Paris Agreement on climate change
Publisher: American Association for the Advancement of Science (AAAS)
Date: 17-12-2021
Abstract: A dynamical model describes the negotiation process that led to the 2015 Paris Agreement on climate change.
MultiselfGAN: A Self-Guiding Neural Architecture Search Method for Generative Adversarial Networks With Multicontrollers
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Toward gradient bandit-based selection of candidate architectures in AutoGAN
Publisher: Springer Science and Business Media LLC
Date: 02-01-2021
Uncertainties and effects on geocenter motion estimates from global GPS observations
Publisher: Elsevier BV
Date: 07-2014
GScheduler: Optimizing resource provision by using GPU usage pattern extraction in cloud environments
Publisher: IEEE
Date: 10-2017
Microlearning and computer-supported collaborative learning: An agenda towards a comprehensive online learning system
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2021
Abstract: style='text-indent:20px ' With the rise of the COVID-19 pandemic and its inevitable consequences in education, increased demand for robust online learning frameworks has occurred at all levels of the education system. Given the transformative power of Artificial Intelligence (AI) and machine learning algorithms, there have been determined attempts through the design and application of intelligent tools to overcome existing challenges in online learning platforms. Accordingly, educational providers and researchers are investigating and developing intelligent online learning environments which share greater commonalities with real-world classroom conditions in order to better meet learners' needs. However, short attention spans and the widespread use of smart devices and social media bring about new e-learning systems known as microlearning (ML). While there has been le research investigating ML and developing micro-content, pedagogical challenges and a general lack of alternative frameworks, theories and practices still exist. The present models have little to say about the connections between social interaction, including learner–content, learner–instructor and learner–learner communication. This has prompted us to investigate the complementary aspects of Computer-supported Collaborative Learning (CSCL) as an interactive learning model, along with an embedded ML module in the design and development of a comprehensive learning platform. The purpose of this study is to explore the pedagogical frameworks and challenges with reference to interaction and retention in online learning environments, as well as the theoretical and pedagogical foundations of ML and its applications. In addition, we delve into the theories and principles behind CSCL, the main elements in CSCL, identifying the issues and challenges to be faced in improving the efficacy of collaboration processes and outcomes. In short, we aim to synthesize how microlearning and CSCL can be applied as effective modules within a comprehensive online learning platform, thereby offering STEM educators a relevant roadmap towards progress that has yet to be offered in previous studies.
Cost-Effective Big Data Mining in the Cloud: A Case Study with K-means
Publisher: IEEE
Date: 06-2017
A review of the application of machine learning in adult obesity studies
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2022
DOI: 10.3934/ACI.2022002
Abstract: abstract In obesity studies, several researchers have been applying machine learning tools to identify factors affecting human body weight. However, a proper review of strength, limitations and evaluation metrics of machine learning algorithms in obesity is lacking. This study reviews the status of application of machine learning algorithms in obesity studies and to identify strength and weaknesses of these methods. A scoping review of paper focusing on obesity was conducted. PubMed and Scopus databases were searched for the application of machine learning in obesity using different keywords. Only English papers in adult obesity between 2014 and 2019 were included. Also, only papers that focused on controllable factors (e.g., nutrition intake, dietary pattern and/or physical activity) were reviewed in depth. Papers on genetic or childhood obesity were excluded. Twenty reviewed papers used machine learning algorithms to identify the relationship between the contributing factors and obesity. Regression algorithms were widely applied. Other algorithms such as neural network, random forest and deep learning were less exploited. Limitations regarding data priori assumptions, overfitting and hyperparameter optimization were discussed. Performance metrics and validation techniques were identified. Machine learning applications are positively impacting obesity research. The nature and objective of a study and available data are key factors to consider in selecting the appropriate algorithms. The future research direction is to further explore and take advantage of the modern methods, i.e., neural network and deep learning, in obesity studies. /abstract
Derivative-based acceleration of general vector machine
Publisher: Springer Science and Business Media LLC
Date: 25-09-2017
A collaborative and open solution for large-scale online learning
Publisher: Wiley
Date: 07-08-2018
DOI: 10.1002/CAE.22040
From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning
Publisher: Springer Science and Business Media LLC
Date: 23-10-2020
A High Availability Application Service Platform for nuclear power enterprises
Publisher: IEEE
Date: 05-2016
Distributional Knowledge Transfer for Heterogeneous Federated Learning
Publisher: IEEE
Date: 12-2022
DOI: 10.1109/ISPA-BDCLOUD-SOCIALCOM-SUSTAINCOM57177.2022.00101
Data-Intensive Service Provision Based on Particle Swarm Optimization
Publisher: Springer Science and Business Media LLC
Date: 2018
Blockchain Developments and Innovations – An Analytical Evaluation of Software Engineering Approaches
Publisher: Springer International Publishing
Date: 2022
Service-Oriented Computing – ICSOC 2018 Workshops
Publisher: Springer International Publishing
Date: 2019
Facilitating Collaborative Learning in TaaS: A Mobile Cloud System for Enhancing Teamwork Performance
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/SMC.2013.121
Ensemble machine learning approaches for webshell detection in Internet of things environments
Publisher: Wiley
Date: 18-08-2020
DOI: 10.1002/ETT.4085
Abstract: The Internet of things (IoT), made up of a massive number of sensor devices interconnected, can be used for data exchange, intelligent identification, and management of interconnected “things.” IoT devices are proliferating and playing a crucial role in improving the living quality and living standard of the people. However, the real IoT is more vulnerable to attack by countless cyberattacks from the Internet, which may cause privacy data leakage, data t ering and also cause significant harm to society and in iduals. Network security is essential in the IoT system, and Web injection is one of the most severe security problems, especially the webshell. To develop a safe IoT system, in this article, we apply essential machine learning models to detect webshell to build secure solutions for IoT network. Future, ensemble methods including random forest (RF), extremely randomized trees (ET), and Voting are used to improve the performances of these machine learning models. We also discuss webshell detection in lightweight and heavyweight computing scenarios for different IoT environments. Extensive experiments have been conducted on these models to verify the validity of webshell intrusion. Simulation results show that RF and ET are suitable for lightweight IoT scenarios, and Voting method is effective for heavyweight IoT scenarios.
Multi-objective Ant Colony System for Data-Intensive Service Provision
Publisher: IEEE
Date: 11-2014
DOI: 10.1109/CBD.2014.15
Coverless Text Information Hiding Method Based on the Word Rank Map
Publisher: Springer International Publishing
Date: 2016
Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland
Publisher: ACM
Date: 31-01-2017
Adapting P2P based decentralised workflow system SwinDeW-S with Web service profile support
Publisher: IEEE
Date: 2005
An object-oriented net graph model for agent group-based network management
Publisher: IEEE Comput. Soc
Date: 1999
A survey on bio-inspired algorithms for web service composition
Publisher: IEEE
Date: 05-2012
Cloud Computing Adoption Decision Modelling for SMEs: From the PAPRIKA Perspective
Publisher: Springer Singapore
Date: 2016
Frontier Computing
Publisher: Springer Singapore
Date: 2016
How accident causation theory can facilitate smart safety management: An application of the 24Model
Publisher: Elsevier BV
Date: 06-2022
A novel cloud based auxiliary medical system for hypertension management
Publisher: Emerald
Date: 07-2019
Analysis of business process integration in Web service context
Publisher: Elsevier BV
Date: 03-2007
Measurement of prompt and nonprompt charmonium suppression in $$\text {PbPb}$$ collisions at 5.02$$\,\text {Te}\text {V}$$
Publisher: Springer Science and Business Media LLC
Date: 06-2018
DOI: 10.1140/EPJC/S10052-018-5950-6
Abstract: The nuclear modification factors of $${\mathrm {J}/\psi }$$ J / ψ and $$\psi \text {(2S)}$$ ψ (2S) mesons are measured in $$\text {PbPb}$$ PbPb collisions at a centre-of-mass energy per nucleon pair of $$\sqrt{\smash [b]{s_{_{\text {NN}}}}} = 5.02\,\text {Te}\text {V} $$ s NN = 5.02 TeV . The analysis is based on $$\text {PbPb}$$ PbPb and $$\mathrm {p}\mathrm {p}$$ p p data s les collected by CMS at the LHC in 2015, corresponding to integrated luminosities of 464 $$\,\mu \mathrm {b}^{-1}$$ μ b - 1 and 28 $$\,\text {pb}^\text {-1}$$ pb -1 , respectively. The measurements are performed in the dimuon rapidity range of $$|y | 2.4$$ | y | 2.4 as a function of centrality, rapidity, and transverse momentum ( $$p_{\mathrm {T}}$$ p T ) from $$p_{\mathrm {T}} =3$$ p T = 3 $${\,\text {Ge}\text {V}/}\text {c}$$ GeV / c in the most forward region and up to 50 $${\,\text {Ge}\text {V}/}\text {c}$$ GeV / c . Both prompt and nonprompt (coming from b hadron decays) $${\mathrm {J}/\psi }$$ J / ψ mesons are observed to be increasingly suppressed with centrality, with a magnitude similar to the one observed at $$\sqrt{\smash [b]{s_{_{\text {NN}}}}} = 2.76\,\text {Te}\text {V} $$ s NN = 2.76 TeV for the two $${\mathrm {J}/\psi }$$ J / ψ meson components. No dependence on rapidity is observed for either prompt or nonprompt $${\mathrm {J}/\psi }$$ J / ψ mesons. An indication of a lower prompt $${\mathrm {J}/\psi }$$ J / ψ meson suppression at $$p_{\mathrm {T}} 25$$ p T 25 $${\,\text {Ge}\text {V}/}\text {c}$$ GeV / c is seen with respect to that observed at intermediate $$p_{\mathrm {T}}$$ p T . The prompt $$\psi \text {(2S)}$$ ψ (2S) meson yield is found to be more suppressed than that of the prompt $${\mathrm {J}/\psi }$$ J / ψ mesons in the entire $$p_{\mathrm {T}}$$ p T range.
Reform and development of coal mine safety in China: An analysis from government supervision, technical equipment, and miner education
Publisher: Elsevier BV
Date: 08-2022
Organisational-Level Assessment of Cloud Computing Adoption
Publisher: IGI Global
Date: 04-2020
Abstract: Cloud Computing (CC) is an emerging technology that can potentially revolutionise the application and delivery of IT. There has been little research, however, into the adoption of CC in Small and Medium-Sized Enterprises (SMEs). The indicators show that CC has been adopted very slowly. There is also a significant research gap in the investigation of the adoption of this innovation in SMEs. This article explores how the adoption of CC in Australia is related to technological factors, risk factors, and environmental factors. The study provides useful insights that can be utilised practically by SMEs, policymakers, and cloud vendors.
Smart fog based workflow for traffic control networks
Publisher: Elsevier BV
Date: 08-2019
Adapting New Learners and New Resources to Micro Open Learning via Online Computation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
RDF-Based Knowledge Models for Network Management
Publisher: Springer US
Date: 2003
Improving Adversarial Transferability via Frequency-based Stationary Point Search
Publisher: ACM
Date: 21-10-2023
Drawing micro learning into MOOC: Using fragmented pieces of time to enable effective entire course learning experiences
Publisher: IEEE
Date: 05-2015
A research of Monte Carlo optimized neural network for electricity load forecast
Publisher: Springer Science and Business Media LLC
Date: 23-03-2019
Ensemble Machine Learning Method for Photovoltaic Power Forecasting
Publisher: IEEE
Date: 24-05-2023
The Item Response Theory Model for an AI-based Adaptive Learning System
Publisher: IEEE
Date: 09-2019
An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control
Publisher: MDPI AG
Date: 31-07-2020
DOI: 10.3390/S20154291
Abstract: With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment.
An MAS-Based Implementation for Semantic Web Services Composition.
Publisher: Springer New York
Date: 2013
Data-centric intelligent computing
Publisher: Springer Science and Business Media LLC
Date: 2018
An edge-aided parallel evolutionary privacy-preserving algorithm for Internet of Things
Publisher: Elsevier BV
Date: 10-2023
A high performance peer to cloud and peer model augmented with hierarchical secure communications
Publisher: Elsevier BV
Date: 07-2013
A Service Computing Framework for Proteomics Analysis and Collaboration of Pathogenic Mechanism Studies
Publisher: IEEE
Date: 11-2020
MLaaS: a Cloud-based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2018
A p2p based service flow system with advanced ontology-based service profiles
Publisher: Elsevier BV
Date: 04-2007
Aligning ontology-based development with service oriented systems
Publisher: Elsevier BV
Date: 03-2014
A Formal Study of Backward Compatible Dynamic Software Updates
Publisher: Springer International Publishing
Date: 2015
The Bounds of Improvements Toward Real-Time Forecast of Multi-Scenario Train Delays
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Teamwork as a Service: A Cloud-Based System for Enhancing Teamwork Performance in Mobile Learning
Publisher: IEEE
Date: 07-2013
Leveraging Stacked Denoising Autoencoder in Prediction of Pathogen-Host Protein-Protein Interactions
Publisher: IEEE
Date: 06-2017
Multi-Layer Efficient Data Classification Methods for Enterprise Business Applications
Publisher: IEEE
Date: 11-2022
APEX2S: A two‐layer machine learning model for discovery of host‐pathogen protein‐protein interactions on cloud‐based multiomics data
Publisher: Wiley
Date: 02-06-2020
DOI: 10.1002/CPE.5846
Assessing the Determinants of Business Value Related to IT Projects: A Strategic Alignment Perspective of Public-Sector Organisations in Saudi Arabia
Publisher: University of Technology, Sydney
Date: 2018
DOI: 10.5130/ACIS2018.CT
Managerial factors that influence the success of knowledge management systems: A systematic literature review
Publisher: Wiley
Date: 16-01-2020
DOI: 10.1002/KPM.1622
Impaired Hypothalamic Microglial Activation in Offspring of Antibiotic-Treated Pregnant/Lactating Rats Is Attenuated by Prebiotic Oligofructose Co-Administration
Publisher: MDPI AG
Date: 21-07-2020
DOI: 10.3390/MICROORGANISMS8071085
Abstract: Microbial colonization of the gut early in life is crucial for the development of the immune and nervous systems, as well as influencing metabolism and weight gain. While early life exposure to antibiotics can cause microbial dysbiosis, prebiotics are non-digestible substrates that selectively promote the growth of beneficial gut microbiota. Our objective was to examine the effects of dietary prebiotic administration on the consequences of maternal antibiotic intake on offspring body weight, behavior, and neuroimmune responses later in life. Sprague-Dawley rat dams were given low-dose penicillin (LDP), prebiotic fiber (10% oligofructose), or both, during the third week of pregnancy and throughout lactation. Anxiety-like behavior, weight gain, body composition, cecal microbiota composition, and microglial responses to lipopolysaccharide (LPS) were assessed in offspring. Male and female prebiotic offspring had lower body weight compared to antibiotic offspring. Maternal antibiotic exposure resulted in lasting effects on select offspring microbiota including a lower relative abundance of Streptococcus, Lactococcus, and Eubacterium at 10 weeks of age. Maternal antibiotic use impaired microglial response to LPS in the hypothalamus compared to control, and this phenotype was reversed with prebiotic. Prebiotic fiber warrants further investigation as an adjunct to antibiotic use during pregnancy.
Extending RDF in distributed knowledge-intensive applications
Publisher: Elsevier BV
Date: 2004
A Pragmatic GIS-Oriented Ontology for Location Based Services
Publisher: IEEE
Date: 03-2008
Facilitating Social Collaboration in Mobile Cloud-Based Learning: A Teamwork as a Service (TaaS) Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Formulating Cost-Effective Data Distribution Strategies Online for Edge Cache Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Enhancing Teamwork Performance In Mobile Cloud-Based Learning
Publisher: Springer Berlin Heidelberg
Date: 2015
Benchmarking bioplastics
Publisher: Springer Science and Business Media LLC
Date: 02-08-2020
Towards organizing smart collaboration and enhancing teamwork performance: a GA-supported system oriented to mobile learning through cloud-based online course
Publisher: Springer Science and Business Media LLC
Date: 25-05-2014
(WIP) Evaluation of a Cloud-Based System for Delivering Adaptive Micro Open Education Resource to Fresh Learners
Publisher: IEEE
Date: 07-2018
Facilitating Application-aware Bandwidth Allocation in the Cloud with One-step-ahead Traffic Information
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Bidirectional GRU networks‐based next POI category prediction for healthcare
Publisher: Hindawi Limited
Date: 11-10-2021
DOI: 10.1002/INT.22710
Real-Time Management of Groundwater Resources Based on Wireless Sensors Networks
Publisher: MDPI AG
Date: 13-01-2018
DOI: 10.3390/JSAN7010004
Refinement and augmentation for data in micro open learning activities with an evolutionary rule generator
Publisher: Wiley
Date: 19-07-2020
DOI: 10.1111/BJET.12997
Designing an optimization model for the vaccine supply chain during the COVID-19 pandemic
Publisher: Elsevier BV
Date: 03-2023
Advances in cloud computing and big data analytics
Publisher: Wiley
Date: 31-08-2018
DOI: 10.1002/CPE.4960
A Comparison of Stellar Elemental Abundance Techniques and Measurements
Publisher: American Astronomical Society
Date: 31-08-2016
MOOC Student Dropout Rate Prediction via Separating and Conquering Micro and Macro Information
Publisher: Springer International Publishing
Date: 2021
GVM based intuitive simulation web application for collision detection
Publisher: Elsevier BV
Date: 03-2018
Learning Path Adaptation in Online Learning Systems
Publisher: IEEE
Date: 05-2016
Educational Process Mining for Discovering Students' Problem-Solving Ability in Computer Programming Education
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Attention-Based High-Order Feature Interactions to Enhance the Recommender System for Web-Based Knowledge-Sharing Service
Publisher: Springer International Publishing
Date: 2020
TreeNet Based Fast Task Decomposition for Resource-Constrained Edge Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
The coupling effect between the environment and strategies drives the emergence of group cooperation
Publisher: Elsevier BV
Date: 11-2023
Targeting the endocannabinoid system for the treatment of abdominal pain in irritable bowel syndrome.
Publisher: Springer Science and Business Media LLC
Date: 27-09-2022
DOI: 10.1038/S41575-022-00682-Y
Abstract: The management of visceral pain in patients with disorders of gut-brain interaction, notably irritable bowel syndrome, presents a considerable clinical challenge, with few available treatment options. Patients are increasingly using cannabis and cannabinoids to control abdominal pain. Cannabis acts on receptors of the endocannabinoid system, an endogenous system of lipid mediators that regulates gastrointestinal function and pain processing pathways in health and disease. The endocannabinoid system represents a logical molecular therapeutic target for the treatment of pain in irritable bowel syndrome. Here, we review the physiological and pathophysiological functions of the endocannabinoid system with a focus on the peripheral and central regulation of gastrointestinal function and visceral nociception. We address the use of cannabinoids in pain management, comparing them to other treatment modalities, including opioids and neuromodulators. Finally, we discuss emerging therapeutic candidates targeting the endocannabinoid system for the treatment of pain in irritable bowel syndrome.
Spatial Goal Refinement Patterns for IoT Applications
Publisher: IEEE
Date: 07-2022
Adaptive E-Services Selection in P2P-Based Workflow with Multiple Property Specifications
Publisher: Springer Berlin Heidelberg
Date: 2009
Adult Learners’ Online Engagement and Perceived Outcomes with Educational Key Opinion Leaders: a Two-Phase Study
Publisher: IEEE
Date: 24-05-2023
A Cost-Sensitive Machine Learning Model With Multitask Learning for Intrusion Detection in IoT
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Bio-Inspired Cost-Effective Access to Big Data
Publisher: University of Wollongong, SMART Infrastructure Facility
Date: 2014
Data Augmentation for Small Sample Iris Image Based on a Modified Sparrow Search Algorithm
Publisher: Springer Science and Business Media LLC
Date: 16-12-2022
DOI: 10.1007/S44196-022-00173-7
Abstract: Training convolutional neural networks (CNN) often require a large amount of data. However, for some biometric data, such as fingerprints and iris, it is often difficult to obtain a large amount of data due to privacy issues. Therefore, training the CNN model often suffers from specific problems, such as overfitting, low accuracy, poor generalization ability, etc. To solve them, we propose a novel image augmentation algorithm for small s le iris image in this article. It is based on a modified sparrow search algorithm (SSA) called chaotic Pareto sparrow search algorithm (CPSSA), combined with contrast limited adaptive histogram equalization (CLAHE). The CPSSA is used to search for a group of clipping limit values. Then a set of iris images that satisfies the constraint condition is produced by CLAHE. In the fitness function, cosine similarity is used to ensure that the generated images are in the same class as the original one. We select 200 categories of iris images from the CASIA-Iris-Thousand dataset and test the proposed augmentation method on four CNN models. The experimental results show that, compared with the some standard image augmentation methods such as flipping, mirroring and clipping, the accuracy and Equal Error Rate (EER)of the proposed method have been significantly improved. The accuracy and EER of the CNN models with the best recognition performance can reach 95.5 and 0.6809 respectively. This fully shows that the data augmentation method proposed in this paper is effective and quite simple to implement.
Ingestion of marine debris plastic by the wedge-tailed shearwater Ardenna pacifica in the Great Barrier Reef, Australia
Publisher: Elsevier BV
Date: 07-2013
DOI: 10.1016/J.MARPOLBUL.2013.03.017
Abstract: We present the first evidence of ingestion of plastic by seabirds from the southern Great Barrier Reef (GBR), Australia. The occurrence of marine debris ingestion in the wedge-tailed shearwater, Ardenna pacifica, on Heron Island was the focus of this preliminary research. Our findings indicate that 21% of surveyed chicks are fed plastic fragments by their parents, having ingested 3.2 fragments on average. The most common colours of ingested plastic fragments were off/white (37.5%) and green (31.3%). Ingested fragments had a mean size of 10.17±4.55 mm and a mean weight of 0.056±0.051 g. Our results indicate that further research is critical to understanding the extent of ingestion, colour preferences, and what impacts ingestion may have on these and other seabird populations in the GBR.
Characterisation and strategies for mitigation of the contact surface unevenness in dry-stack masonry
Publisher: Elsevier BV
Date: 04-2018
Optimal learning group formation: A multi-objective heuristic search strategy for enhancing inter-group homogeneity and intra-group heterogeneity
Publisher: Elsevier BV
Date: 03-2019
Multi-Phase Ant Colony System for Multi-Party Data-Intensive Service Provision
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
TreeNet: A hierarchical deep learning model to facilitate edge intelligence for resource-constrained devices
Publisher: IEEE
Date: 05-2021
Ant Inspired Scalable Peer Selection in Ontology-Based Service Composition
Publisher: IEEE
Date: 09-2009
A Novel PageRank-Based Fault Handling Strategy for Workflow Scheduling in Cloud Data Centers
Publisher: IGI Global
Date: 10-2021
Abstract: Unexpected faults result in unscheduled cloud outage, which negatively affects the completion of workflow tasks in the cloud. This paper presents a novel PageRank-based fault handling strategy to rescue workflow tasks at the faulty data center. The proposed approach uses a holistic view and considers the task attributes, the timeline scenario, and the overall cloud performance. A priority assignment system is developed based on the modified PageRank algorithm to prioritise workflow tasks. A min-max normalization method is applied to select the target data center and match the timeline at this data center. Additionally, a dynamic PageRank-constrained task scheduling algorithm is proposed to generate the task scheduling solution. The simulation results show that the proposed approach can achieve better fault handling performance, measured by task resilience ratio, workflow resilience ratio, and workflow continuity ratio in both the traditional 3-replica and the image backup cloud environment.
UV-C irradiation as an alternative disinfection technique: Study of its effect on polyphenols and antioxidant activity of apple juice
Publisher: Elsevier BV
Date: 04-2016
Text classification based on machine learning for Tibetan social network
Publisher: IEEE
Date: 11-2022
A privacy-aware data sharing framework for Internet of Things through edge computing platform
Publisher: IEEE
Date: 07-2022
Expressing Business Process Models as OWL-S Ontologies
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11837862_38
Privacy-preserving Offloading in Edge Intelligence Systems with Inductive Learning and Local Differential Privacy
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Can Edu-Metaverse Reshape Virtual Teaching Community (VTC) to Promote Educational Equity? An Exploratory Study
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
'Half of it's Out the Window': Exploring Tensions, Hierarchies and Positionalities Amidst the Changing Knowledge Base of Early Childhood Teacher Education Discourses
Publisher: Edith Cowan University
Date: 2023
An Energy Aware Offloading Scheme for Interdependent Applications in Software-Defined IoV With Fog Computing Architecture
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
A Practical Geographic Ontology for Spatial Web Services
Publisher: IEEE
Date: 07-2008
DOI: 10.1109/SCC.2008.15
Organizing Online Computation for Adaptive Micro Open Education Resource Recommendation
Publisher: Springer International Publishing
Date: 2017
Enabling intelligent business processes with context awareness
Publisher: IEEE
Date: 07-2018
Crop and weed classification based on AutoML
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2021
DOI: 10.3934/ACI.2021003
Abstract: abstract CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective functions are not thoroughly compatible with agricultural farming tasks as the corresponding models suffer from misclassifying crop to weed, often more likely than in other deep learning application domains. In this paper, we applied autonomous machine learning with a new objective function for crop and weed classification, achieving higher accuracy and lower crop killing rate (rate of identifying a crop as a weed). The experimental results show that our method outperforms state-of-the-art applications, for ex le, ResNet and VGG19. /abstract
SwinDeW-S: extending P2P workflow systems for adaptive composite Web services
Publisher: IEEE
Date: 2006
Collaborative data analytics towards prediction on pathogen-host protein-protein interactions
Publisher: IEEE
Date: 04-2017
Synchro-net system: A Petri net model for higher-layer protocols
Publisher: Informa UK Limited
Date: 10-2003
Towards Better ML-Based Software Services: An Investigation of Source Code Engineering Impact
Publisher: IEEE
Date: 07-2023
Ontological Learner Profile Identification for Cold Start Problem in Micro Learning Resources Delivery
Publisher: IEEE
Date: 07-2017
Parallel fast Fourier transform in SPMD style of Cilk
Publisher: Inderscience Publishers
Date: 2019
TheGaia-ESO Survey: The analysis of high-resolution UVES spectra of FGK-type stars
Publisher: EDP Sciences
Date: 10-2014
Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys
Publisher: MDPI AG
Date: 12-06-2020
DOI: 10.3390/S20123354
Abstract: Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data-driven travel time prediction model for bus journeys based on open data. We explore a convolutional long short-term memory (ConvLSTM) model with a self-attention mechanism that accurately predicts the running time of each segment of the trips and the waiting time at each station. The model is more robust to capture long-range dependence in time series data as well.
Towards Minimising Perturbation Rate for Adversarial Machine Learning with Pruning
Publisher: Springer Nature Switzerland
Date: 2023
Seabirds and plastics don't mix: Examining the differences in marine plastic ingestion in wedge-tailed shearwater chicks at near-shore and offshore locations
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.MARPOLBUL.2018.08.016
Abstract: Plastic ingestion by wedge-tailed shearwaters (WTS) nesting at near-shore and offshore sites along the east coast of Australia were investigated. Ingestion rates were at 20% in near-shore lavaged WTS, where the beaches were significantly more polluted, compared to 8% in birds at offshore sites. The material and colour of recovered plastics at offshore sites differed significantly between beach surveys and that ingested by seabirds in the same area. This pattern was not evident near-shore. Hence, in near-shore environments birds may feed locally and are influenced by nearby plastics, compared to birds offshore. The origins of marine debris between near-shore and offshore beaches differed with land-based sources unsurprisingly having more influence on near-shore sites. The findings of this study indicate the need for localised data to address and manage this pollutant, with nesting seabirds at greater risk in near-shore environments. A preliminary modified ecological quality objective for WTS is presented.
Dynamic Selection of Service Peers with Multiple Property Specifications
Publisher: Springer Berlin Heidelberg
Date: 2009
A Microservices Oriented Edge Computing Framework for LVC Simulation in the IoT Era
Publisher: ACM
Date: 16-01-2019
Digital Transformation: Challenges and Opportunities
Publisher: Springer International Publishing
Date: 2018
An Improved Genetic Algorithm for Cost-Effective Data-Intensive Service Composition
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/SKG.2013.19
Towards Cost Reduction in Cloud-Based Workflow Management through Data Replication
Publisher: IEEE
Date: 08-2017
DOI: 10.1109/CBD.2017.24
Research and Its Implementation of a Remote Teaching Application System Model Based on Self-Organization Components
Publisher: IEEE
Date: 2009
Cloud failure prediction based on traditional machine learning and deep learning
Publisher: Springer Science and Business Media LLC
Date: 19-09-2022
DOI: 10.1186/S13677-022-00327-0
Abstract: Cloud failure is one of the critical issues since it can cost millions of dollars to cloud service providers, in addition to the loss of productivity suffered by industrial users. Fault tolerance management is the key approach to address this issue, and failure prediction is one of the techniques to prevent the occurrence of a failure. One of the main challenges in performing failure prediction is to produce a highly accurate predictive model. Although some work on failure prediction models has been proposed, there is still a lack of a comprehensive evaluation of models based on different types of machine learning algorithms. Therefore, in this paper, we propose a comprehensive comparison and model evaluation for predictive models for job and task failure. These models are built and trained using five traditional machine learning algorithms and three variants of deep learning algorithms. We use a benchmark dataset, called Google Cloud Traces, for training and testing the models. We evaluated the performance of models using multiple metrics and determined their important features, as well as measured their scalability. Our analysis resulted in the following findings. Firstly, in the case of job failure prediction, we found that Extreme Gradient Boosting produces the best model where the disk space request and CPU request are the most important features that influence the prediction. Second, for task failure prediction, we found that Decision Tree and Random Forest produce the best models where the priority of the task is the most important feature for both models. Our scalability analysis has determined that the Logistic Regression model is the most scalable as compared to others.
Learning network storage curriculum with experimental case based on embedded systems
Publisher: Wiley
Date: 28-09-2015
DOI: 10.1002/CAE.21696
Exploratory Research on Satisfaction Degree in Distance Education
Publisher: MDPI AG
Date: 06-08-2022
DOI: 10.3390/APP12157889
Abstract: In order to solve the education problems caused by teachers and students’ unavoidable absence in school during the COVID-19 pandemic, a series of online education activities were carried out by Nanjing University of Posts and Telecommunication in early March. To explore students and teachers’ degree of satisfaction with distance education, this paper investigates multiple dimensions such as students’ degree of satisfaction with teachers, the regional living standard, educational resources and negative factors that reduce the students’ degree of satisfaction, etc. Furthermore, the attitude of teachers toward distance education may be partially reflected by the arrangement of live classes. All of the statistics are analyzed by comparing the distribution of votes. The results show that the degree of satisfaction by students and teachers with distance education is generally high but varies in areas with different living standards. In addition, we find that students are more sensitive to the lack of a learning atmosphere.
Editorial note: Learning management systems and big data technologies for higher education
Publisher: Springer Science and Business Media LLC
Date: 04-02-2020
Towards optimal service composition upon QoS in agent cooperation
Publisher: Inderscience Publishers
Date: 2013
Determining learning direction via multi-controller model for stably searching generative adversarial networks
Publisher: Elsevier BV
Date: 11-2021
EfficientAutoGAN: Predicting the Rewards in Reinforcement-Based Neural Architecture Search for Generative Adversarial Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Attention-based knowledge tracing with heterogeneous information network embedding
Publisher: Springer International Publishing
Date: 2020
A Document-Level Relation Extraction Framework with Dynamic Pruning
Publisher: Springer Nature Switzerland
Date: 2023
Mapping web services specifications to process ontology: opportunities and limitations
Publisher: IEEE
Date: 2004
CAKT: Coupling contrastive learning with attention networks for interpretable knowledge tracing
Publisher: IEEE
Date: 18-06-2023
A Data Dependency and Access Threshold Based Replication Strategy for Multi-cloud Workflow Applications
Publisher: Springer International Publishing
Date: 2019
E-learning Meets Educational Key Opinion Leaders (Edu-KOLs): A Close-Up Look on Exuberant Platforms and Their Success Ingredients
Publisher: The International Academic Forum(IAFOR)
Date: 02-2023
Crop and weed classification based on AutoML
Publisher: arXiv
Date: 2020
Towards Biological Sequence Data Service with Insights
Publisher: IEEE
Date: 12-2018
Tobacco Smoking and Associated Factors among In-school Adolescents in Vietnam in 2013, 2019
Publisher: Cold Spring Harbor Laboratory
Date: 04-04-2021
DOI: 10.1101/2021.03.28.21254501
Abstract: Tobacco smoking is one of the most dangerous risk behaviors, leading to many adverse human health consequences. The aims of this study is to estimate the prevalence of tobacco smoking and related factor among adolescents aged 13–17 years in Vietnam. The data were from two rounds of the Vietnam Global School-based Student Health Survey (GSHS) that is the nationally representative survey conducted in 2013 and 2019. The logistic regressions were carried out to identify factors associated with tobacco smoking among study participants. We found the prevalence of current smoking (water pipe and cigarettes) reduced significantly from 5.4% (95% CI: 4.0–7.2) in 2013 to 2.8% (95% CI: 2.2–3.6) in 2019. In 2019, 2.6% of students used electronic cigarette products in the last 30 days. Factors associated with higher odds of current smoking status included study year, gender, parental monitoring, loneliness, suicide attempt, sexual intercourse, truancy, alcohol drinking. Similar patterns were found in e-cigarette use. Smoking among adolescents in Vietnam reduced between 2013 and 2019. Further follow-up studies are needed to confirm the causal factors of the reduction and e-cigarettes use.
KOI-3158: The oldest known system of terrestrial-size planets
Publisher: EDP Sciences
Date: 2015
QoS-Aware Peer Services Selection Using Ant Colony Optimisation
Publisher: Springer Berlin Heidelberg
Date: 2009
Ant-inspired multi-phase and multi-party negotiations in the data-intensive service provision
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/SCC.2014.36
A light weight approach for ontology generation and change synchronization between ontologies and source relational databases
Publisher: IEEE
Date: 06-2011
Bio-inspired cost-aware optimization for data-intensive service provision
Publisher: Wiley
Date: 27-07-2015
DOI: 10.1002/CPE.3589
Data Privacy and System Security for Banking and Financial Services Industry based on Cloud Computing Infrastructure
Publisher: IEEE
Date: 05-2018
Hyperparameter Estimation in SVM with GPU Acceleration for Prediction of Protein-Protein Interactions
Publisher: IEEE
Date: 12-2019
Using an air cycle heat pump system with a turbocharger to supply heating for full electric vehicles
Publisher: Elsevier BV
Date: 05-2017
A Personalized Hybrid Recommendation System Oriented to E-Commerce Mass Data in the Cloud
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/SMC.2013.178
GPU Based Simulation of Collision Detection of Irregular Vessel Walls
Publisher: Springer Singapore
Date: 27-09-2017
An effective data aggregation based adaptive long term CPU load prediction mechanism on computational grid
Publisher: Elsevier BV
Date: 07-2012
Towards massive data and sparse data in adaptive micro open educational resource recommendation: A study on semantic knowledge base construction and cold start problem
Publisher: MDPI AG
Date: 26-05-2017
DOI: 10.3390/SU9060898
Deep Sequence Labelling Model for Information Extraction in Micro Learning Service
Publisher: IEEE
Date: 07-2020
A Cost-Sensitive Deep Learning-Based Approach for Network Traffic Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
MSIS 2016: Global Competency Model for Graduate Degree Programs in Information Systems
Publisher: Association for Information Systems
Date: 2017
DOI: 10.17705/1CAIS.04018
Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Impacts of Pheromone Modification Strategies in Ant Colony for Data-Intensive Service Provision
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/ICWS.2014.36
Parallel GPU-based collision detection of irregular vessel wall for massive particles
Publisher: Springer Science and Business Media LLC
Date: 18-02-2017
Data Fusion for MaaS: Opportunities and Challenges
Publisher: IEEE
Date: 05-2018
Adaptive Task Allocation for P2P-Based e-Services Composition
Publisher: IEEE
Date: 10-2008
PGLR: Pseudo Graph and Label Reuse for Entity Relation Extraction
Publisher: IEEE
Date: 18-06-2023
Robust Iris-Localization Algorithm in Non-Cooperative Environments Based on the Improved YOLO v4 Model
Publisher: MDPI AG
Date: 16-12-2022
DOI: 10.3390/S22249913
Abstract: Iris localization in non-cooperative environments is challenging and essential for accurate iris recognition. Motivated by the traditional iris-localization algorithm and the robustness of the YOLO model, we propose a novel iris-localization algorithm. First, we design a novel iris detector with a modified you only look once v4 (YOLO v4) model. We can approximate the position of the pupil center. Then, we use a modified integro-differential operator to precisely locate the iris inner and outer boundaries. Experiment results show that iris-detection accuracy can reach 99.83% with this modified YOLO v4 model, which is higher than that of a traditional YOLO v4 model. The accuracy in locating the inner and outer boundary of the iris without glasses can reach 97.72% at a short distance and 98.32% at a long distance. The locating accuracy with glasses can obtained at 93.91% and 84%, respectively. It is much higher than the traditional Daugman’s algorithm. Extensive experiments conducted on multiple datasets demonstrate the effectiveness and robustness of our method for iris localization in non-cooperative environments.
Customizable Data Exchange Based on Web Service
Publisher: IEEE
Date: 2009
Modeling Mobile Learning System Using ANFIS
Publisher: IEEE
Date: 07-2011
Towards a General Prediction System for the Primary Delay in Urban Railways
Publisher: IEEE
Date: 10-2019
A novel Monte Carlo-based neural network model for electricity load forecasting
Publisher: Inderscience Publishers
Date: 2020
A Novel Instructional Design Based on Cognitive Apprenticeship Model to Enhance Teaching Network Management
Publisher: IEEE
Date: 05-12-2021
Virtual-reality and intelligent hardware in Digital Twins
Publisher: Elsevier BV
Date: 12-2022
An integrated and cooperative architecture for multi-intersection traffic signal control
Publisher: Maximum Academic Press
Date: 2023
A Local Field Correlated and Monte Carlo Based Shallow Neural Network Model for Nonlinear Time Series Prediction
Publisher: European Alliance for Innovation n.o.
Date: 09-08-2016
Big Data Management and Processing
Publisher: Chapman and Hall/CRC
Date: 19-05-2017
Marine debris is selected as nesting material by the brown booby (Sula leucogaster) within the Swain Reefs, Great Barrier Reef, Australia
Publisher: Elsevier BV
Date: 10-2014
DOI: 10.1016/J.MARPOLBUL.2014.07.060
Abstract: Many seabirds are impacted by marine debris through its presence in foraging and nesting areas. To determine the extent of this problem, marine debris use in nest material of the brown booby (Sula leucogaster) in the Great Barrier Reef, Australia, was investigated. Nine cays were examined using beach and nest surveys. On average, four marine debris items were found per nest (n=96) with 58.3% of surveyed nests containing marine debris. The source of marine debris in nests and transects were primarily oceanic. Hard plastic items dominated both nest (56.8%) and surveyed beaches (72.8%), however only two item types were significantly correlated between these surveys. Nest surveys indicated higher levels of black and green items compared to beach transects. This selectivity for colours and items suggest these nests are not good indicators of environmental loads. This is the first study to examine S. leucogaster nests for marine debris in this location.
Space sharing between pedestrians and micro-mobility vehicles: A systematic review
Publisher: Elsevier BV
Date: 03-2023
MLaaS: A Cloud System for Mobile Micro Learning in MOOC
Publisher: IEEE
Date: 06-2015
TransformerLight: A Novel Sequence Modeling Based Traffic Signaling Mechanism via Gated Transformer
Publisher: ACM
Date: 04-08-2023
Towards Improving the Anti-attack Capability of the RangeNet++
Publisher: Springer Nature Switzerland
Date: 2023
Collaborative Learning through TaaS: A Mobile System for Courses over the Cloud
Publisher: IEEE
Date: 07-2014
The Gaia-ESO survey: the inner disk intermediate-age open cluster NGC 6802
Publisher: EDP Sciences
Date: 28-04-2017
Research of Botnet Situation Awareness Based on Big Data
Publisher: Springer International Publishing
Date: 2015
Distributed Secondary Control Strategy Based on <italic>Q</italic>-learning and Pinning Control for Droop-controlled Microgrids
Publisher: Journal of Modern Power Systems and Clean Energy
Date: 2022
An Integration Life Cycle for Semantic Web Services Composition
Publisher: IEEE
Date: 04-2007
Towards Data Analytics of Pathogen-Host Protein-Protein Interaction: A survey
Publisher: IEEE
Date: 06-2016
Towards Elucidating the Structural Principles of Host-Pathogen Protein-Protein Interaction Networks
Publisher: IEEE
Date: 06-2017
DeDu: Building a deduplication storage system over cloud computing
Publisher: IEEE
Date: 06-2011
The influence of riparian vegetation on water quality in a mixed land use river basin
Publisher: Wiley
Date: 15-02-2019
DOI: 10.1002/RRA.3410
RDF-based knowledge models for network management
Publisher: Kluwer Academic Publishers
Date: 2003
A Dynamic Sensitivity Model for Unidirectional Pedestrian Flow With Overtaking Behaviour and Its Application on Social Distancing’s Impact During COVID-19
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Cloud computing adoption decision modelling for SMEs: a conjoint analysis
Publisher: Inderscience Publishers
Date: 2016
High-Dimensional Feature Selection Based on Improved Binary Ant Colony Optimization Combined with Hybrid Rice Optimization Algorithm
Publisher: Hindawi Limited
Date: 10-07-2023
DOI: 10.1155/2023/1444938
Abstract: In the realm of high-dimensional data analysis, numerous fields stand to benefit from its applications, including the biological and medical sectors that are crucial for computer-aided disease diagnosis and prediction systems. However, the presence of a significant number of redundant or irrelevant features can adversely affect system accuracy and real-time diagnosis efficiency. To mitigate this issue, this paper proposes two innovative wrapper feature selection (FS) methods that integrate the ant colony optimization (ACO) algorithm and hybrid rice optimization (HRO). HRO is a recently developed metaheuristic that mimics the breeding process of the three-line hybrid rice, which is yet to be thoroughly explored in the context of solving high-dimensional FS problems. In the first hybridization, ACO is embedded as an evolutionary operator within HRO and updated alternately with it. In the second form of hybridization, two subpopulations evolve independently, sharing the local search results to assist in idual updating. In the initial stage preceding hybridization, a problem-oriented heuristic factor assignment strategy based on the importance of the knee point feature is introduced to enhance the global search capability of ACO in identifying the smallest and most representative features. The performance of the proposed algorithms is evaluated on fourteen high-dimensional biomedical datasets and compared with other recently advanced FS methods. Experimental results suggest that the proposed methods are efficient and computationally robust, exhibiting superior performance compared to the other algorithms involved in this study.
Towards dynamic formation of temporal constraints for the service level agreements negotiation
Publisher: IEEE
Date: 12-2009
Leveraging SMOTE in a Two-Layer Model for Prediction of Protein-Protein Interactions
Publisher: IEEE
Date: 09-2019
SBAR: A framework to support learning path adaptation in mobile learning
Publisher: Springer Singapore
Date: 27-09-2018
Towards minimizing cost for composite data-intensive services
Publisher: IEEE
Date: 06-2013
Evaluations of heuristic algorithms for teamwork-enhanced task allocation in mobile cloud-based learning
Publisher: IEEE
Date: 06-2013
Influence of deposition rates on the mode I fracture toughness of in-situ consolidated thermoplastic composites
Publisher: Elsevier BV
Date: 02-2023
Towards an assessment framework of reuse: a knowledge level analysis approach
Publisher: Springer Science and Business Media LLC
Date: 11-07-2020
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