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Applications of machine learning in Knowledge Management System: A Comprehensive Review

JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2022年第2期21卷

作者： Simon, Casper Gihes Kaun Jhanjhi, Noor Zaman Goh, Wei Wei Sukumaran, Sanath Taylors Univ 1 Jalan Taylors Subang Jaya 47500 Selangor Malaysia Agile Management Consultancy Petaling Jaya Selangor Malaysia

As new generations of technology appear, legacy knowledge management solutions and applications become increasingly out of date, necessitating a paradigm shift. machine learning presents an opportunity by foregoing rule-based knowledge intensive systems inundating the marketplace. An extensive review was made on the literature pertaining to machine learning which common machine learning algorithms were identified. This study has analysed more than 200 papers extracted from Scopus and IEEE databases. Searches ranged with the bulk of the articles from 2018 to 2021, while some articles ranged from 1959 to 2017. The research gap focusses on implementing machine learning algorithm to knowledge management systems, specifically knowledge management attributes. By investigating and reviewing each algorithm extensively, the usability of each algorithm is identified, with its advantages and disadvantages. From there onwards, these algorithms were mapped for what area of knowledge management it may be beneficial. Based on the findings, it is evidently seen how these algorithms are applicable in knowledge management and how it can enhance knowledge management system further. Based on the findings, the paper aims to bridge the gap between the literature in knowledge management and machine learning. A knowledge management-machine learning framework is conceived based on the review done on each algorithm earlier and to bridge the gap between the two literatures. The framework highlights how machine learning algorithm can play a part in different areas of knowledge management. From the framework, it provides practitioners how and where to implement machine learning in knowledge management.

关键词： Knowledge management machine learning supervised learning unsupervised learning classification regression clustering association rule machine learning algorithm

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Identification of TYR, TYRP1, DCT and LARP7 as related biomarkers and immune infiltration characteristics of vitiligo via comprehensive strategies

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BIOENGINEERED 2021年第1期12卷 2214-2227页

作者： Zhang, Jiayu Yu, Rongguo Guo, Xiaoyu Zou, Yuanxia Chen, Sixuan Zhou, Kai Chen, Yi Li, YongRong Gao, Su Wu, Yifei Kunming Univ Sci & Technol Peoples Hosp Yunnan Prov 1 Dept Dermatol Affiliated Hosp Kunming 650032 Yunnan Peoples R China Yunnan Univ Tradit Chinese Med Clin Med Coll 1 Dept Dermatol Kunming Yunnan Peoples R China Xiamen Univ Dept Orthoped Fuzhou Hosp 2 Xiamen Fujian Peoples R China Sun Yat Sen Univ Canc Ctr Collaborat Innovat Ctr Canc Med Dept Neurosurg Neurooncol State Key Lab Oncol South China Guangzhou Peoples R China Southwest Med Univ Dept Newborn Med Hosp TCM Luzhou Sichuan Peoples R China

This study aims to explore biomarkers associated with vitiligo and analyze the pathological role of immune cell infiltration in the disease. We used the robust rank aggregation (RRA) method to integrate three vitiligo data sets downloaded from gene expression omnibus database, identify the differentially expressed genes (DEGs) and analyze the functional correlation. Then, the comprehensive strategy of combined weighted gene coexpression network analysis (WGCNA) and logical regression of the selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) machine learning algorithm are employed to screen and biomarkers associated with vitiligo. Finally, the immune cell infiltration of vitiligo was evaluated by CIBERSORT, and the correlation between biomarkers and infiltrating immune cells was analyzed. Herein, we identified 131 robust DEGs, and enrichment analysis results showed that robust DEGs and melanogenesis were closely associated with vitiligo development and progression. TYR, TYRP1, DCT and LARP7 were identified as vitiligo-related biomarkers. Immune infiltration analysis demonstrated that CD4 T Cell, CD8 T Cell, Tregs, NK cells, dendritic cells, and macrophages were involved in vitiligo's pathogenesis. In summary, we adopted a comprehensive strategy to screen biomarkers related to vitiligo and explore the critical role of immune cell infiltration in vitiligo. [GRAPHICS] .

关键词： Vitiligo machine learning algorithm immune cells biomarkers cibersort

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Investigating the application of artificial intelligence for earthquake prediction in Terengganu

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NATURAL HAZARDS 2021年第1期108卷 977-999页

作者： Marhain, Suzlyana Ahmed, Ali Najah Murti, Muhammad Ary Kumar, Pavitra El-Shafie, Ahmed Univ Tenaga Nas Coll Engn Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Tenaga Nas Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia Telkom Univ Bandung Indonesia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr Al Ain U Arab Emirates

Earthquake is one of the devastating and frightening natural disasters that caused big casualties in a small duration. Earthquake caused lots of damage in just a few minutes and the casualties of the earthquake increase as the population increase which also contribute to higher amount of property and buildings. Therefore, by developing model capable of detecting the recurrence behaviour of earthquake helps in predicting earthquake as well as minimizing the casualties caused by the earthquake. In this report, a few of artificial intelligence algorithms such as support vector machine, boosted decision tree regression, random forest and multivariate adaptive regression spline will be used in the development of best model algorithm in earthquake prediction. Meteorological data are collected from several stations in Terengganu and processed for normalization and the data will be analysed using algorithms and its performance will be evaluated. Terengganu is situated on the east coast of Peninsular Malaysia and is bordered on the north-west and south-west by Kelantan and Pahang. Terengganu's east side is bordered by the South China Sea. Terengganu is located within the vicinity of the South China Sea, which is possible to be affected by the Marina Trench Earthquake. The subduction zone of Manila Trench is capable of producing a high magnitude of earthquake activity that can create a deadliest tsunami disaster. Therefore, Terengganu is studied for the investigation of artificial intelligence in earthquake prediction. The model algorithms are then analysed to measure its sensitivity and accuracy in prediction and consistency of the result.

关键词： Earthquake prediction Artificial intelligence machine learning algorithm Support vector machine Boosted decision tree regression Random forest Multivariate adaptive regression spline Pearson correlation coefficient Uncertainty analysis

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Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Peak Shear Strength Along Soil-Geocomposite Drainage Layer Interfaces

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INTERNATIONAL JOURNAL OF GEOSYNTHETICS AND GROUND ENGINEERING 2021年第3期7卷 1-19页

作者： Chao, Zhiming Fowmes, Gary Dassanayake, S. M. Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England Monash Univ Malaysia Sch Engn Jalan Lagoon Selatan Bandar Sunway Selangor Darul Malaysia

Peak shear strength of soil-Geocomposite Drain Layer (GDL) interfaces is an important parameter in the designing and operating related engineering structures. In this paper, a database compiled from 316 large direct shear tests on soil-GDL interfaces has been established. Based on this database, five different machine learning models: Back Propagation Artificial Neural Network (BPANN) and Support Vector machine (SVM), with hyperparameters optimised by Particle Swarm Optimisation algorithm (PSO) and Genetic algorithm (GA), respectively, and Extreme learning machine (ELM) optimised by Exhaustive Method, were adopt to assess the peak shear strength of soil-GDL interfaces. Then, a comprehensive investigation and comparison of the predictive performance for the models was conducted. Also, based on the selected optimal machine learning model, sensitivity analysis was conducted, and an empirical equation developed based on it. The research indicated that GA and PSO could significantly increase forecasting precision in a small number of iterations. The BPANN model optimised by PSO has the highest forecasting precision based on the statistics criteria: Root-Mean-Square Error, Correlation Coefficient, Coefficient of Determination, Wilmot's Index of Agreement, and Mean Absolute Percentage Error. The normal stress has the biggest impact on the peak shear strength, followed by drainage core type, moisture saturation of the soil layer, shearing surface, soil type, consolidation condition, geotextile specification, soil density and drainage core thickness, and the ranking is affected partly by the data distribution of input parameters in the database based on mechanism analysis. An empirical equation developed from the optimal model was proposed to estimate the peak shear strength, which provides convenience for geotechnical engineering personnel with limited knowledge of machine learning technique.

关键词： Geosynthetics Geocomposite drainage layer Soil-Interface shear strength machine learning algorithm Optimisation algorithm

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Impacts of use PID control and artificial intelligence methods for solar air heater energy performance

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JOURNAL OF BUILDING ENGINEERING 2023年 65卷

作者： Das, Mehmet Catalkaya, Murat Akay, O. Erdal Akpinar, Ebru Kavak Firat Univ Engn Fac Mechatron Engn Dept TR-23119 Elazig Turkiye Kahramanmaras Sutcuimam Univ Mech Engn Dept TR-46100 K Maras Turkiye Firat Univ Engn Fac Mech Engn Dept TR-23279 Elazig Turkiye Firat Univ Mechatron Eng Dept TR-23119 Elazig Turkiye

Solar air heaters generally operate at constant mass flow rates achieved with constant fan speed. This situation causes continuous energy consumption in solar air heater (SAH) systems. This study aims to reduce fan energy consumption by controlling the fan speed according to the outlet temperature of the SAH and, to obtain regression-based artificial intelligence models that can calculate the SAH energy efficiency value. For these purposes, the fan speed values of a SAH were modeled using three different system identification (SI) function models according to the set outlet temperature value. A proportional integral derivative (PID) control was applied to the SU function, which had fewer error values. The energy consumption of SAH, which worked with constant fan speed, was reduced by an average of 16% with the help of PID control application. In the second part of the study, a data set consisting of only environmental parameters (temperature, air velocity, solar radiation, relative humidity) was created for SAH's thermal efficiency value modeling. Using the data sets, mathematical equations that can calculate SAH energy efficiency were obtained with the help of regression algorithms (Pace and ***). The obtained equations calculated the energy efficiency value with an error of 3%. The originality of this study is that a PID control was applied to a SAH system for the first time in the literature, and energy savings were achieved. In addition, utility models with equations calculating SAH energy per-formance only according to environmental parameters were obtained.

关键词： Solar air heater Energy consumption Energy efficiency PID control Equation derivation machine learning algorithm

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CBDC-PUF: A Novel Physical Unclonable Function Design Framework Utilizing Configurable Butterfly Delay Chain Against Modeling Attack

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ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS 2023年第5期28卷 78-78页

作者： Liu, Yanjiang Li, Junwei Qu, Tongzhou Dai, Zibin Informat Engn Univ Shangcheng Rd 12 Zhengzhou 450000 Henan Peoples R China

Physical unclonable function (PUF) is a promising security-based primitive, which provides an extremely large number of responses for key generation and authentication applications. Various PUFs have been developed as central building blocks in cryptographic protocols and security architectures, however, the existing PUFs and their improvements are still vulnerable to modeling attacks (MA) with refined machine learning algorithms. In this article, a configurable butterfly delay chain-based PUF design framework is proposed to meet the requirements of randomness, reliability, uniqueness, and MA-resistance metrics. A configurable butterfly delay chain is introduced to create multiple pairs of symmetric paths and a strong PUF relying on the intrinsic delay fluctuations of two identical paths is built. Furthermore, a secure hash function is used to insert non-linearities into the PUF, and a BCH-based error correction algorithm is utilized to recover the actual responses under noisy environments. The proposed PUF is implemented on Xilinx FPGAs and three machine learning algorithms are used to evaluate the resistance against MA. Experimental results show that the randomness, reliability, and uniqueness of the proposed PUF are close to the ideal value (49.6%, 99.9%, and 49.9%, respectively), and the prediction accuracy reaches 50% that indicating a desirable resilient to MA.

关键词： Physical unclonable function modeling attack machine learning algorithm cryptographic hash function

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machine learning-Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis

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JOURNAL OF THE AMERICAN MEDICAL DIRECTORS ASSOCIATION 2022年第10期23卷 1655-+页

作者： Xie, Qi Wang, Xinglei Pei, Juhong Wu, Yinping Guo, Qiang Su, Yujie Yan, Hui Nan, Ruiling Chen, Haixia Dou, Xinman Lanzhou Univ Sch Nursing 28 Yanxi Rd Lanzhou Gansu Peoples R China Lanzhou Univ Second Hosp Dept Liver Dis Branch Lanzhou Gansu Peoples R China Lanzhou Univ Clin Med Coll 1 Lanzhou Gansu Peoples R China Lanzhou Univ Second Hosp Med Dept Neurol Lanzhou Gansu Peoples R China Gansu Prov Ctr Dis Control & Prevent Dept TB Prevent & Control Lanzhou Gansu Peoples R China Lanzhou Univ Second Hosp Dept Nursing Lanzhou Gansu Peoples R China Lanzhou Univ Second Hosp Dept EICU Lanzhou Gansu Peoples R China Lanzhou Univ Second Hosp Dept Oncol Surg Lanzhou Gansu Peoples R China

Objective: To critically appraise and quantify the performance studies by employing machine learning (ML) to predict delirium. Design: A systematic review and meta-analysis. Setting and Participants: Articles reporting the use of ML to predict delirium in adult patients were included. Studies were excluded if (1) the primary goal was only the identification of various risk factors for delirium;(2) the full-text article was not found;and (3) the article was published in a language other than English/Chinese. Methods: PubMed, Embase, Cochrane Library database, Web of Science, Grey literature, and other relevant databases for the related publications were searched (from inception to December 15, 2021). The data were extracted using a standard checklist, and the risk of bias was assessed through the prediction model risk of bias assessment tool. Meta-analysis with the area under the receiver operating characteristic curve, sensitivity, and specificity as effect measures, was performed with Metadisc software. Cochran Q and I2 statistics were used to assess the heterogeneity. Meta-regression was performed to determine the potential effect of adjustment for the key covariates. Results: A total of 22 studies were included. Only 4 of 22 studies were quantitatively analyzed. The studies varied widely in reporting about the study participants, features and selection, handling of missing data, sample size calculations, and the intended clinical application of the model. For ML models, the overall pooled area under the receiver operating characteristic curve for predicting delirium was 0.89, sensitivity 0.85 (95% confidence interval 0.84-0.85), and specificity 0.80 (95% confidence interval 0.81-0.80). Conclusions and Implications: We found that the ML model showed excellent performance in predicting delirium. This review highlights the potential shortcomings of the current approaches, including low comparability and reproducibility. Finally, we present the various recommendation

关键词： machine learning algorithm predictive model delirium systematic review meta-analysis

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Application of machine learning techniques in the prediction of excess lifetime cancer risks of agricultural byproducts used as building and construction materials

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CLEANER WASTE SYSTEMS 2023年 5卷

作者： Oyebisi, Solomon Owamah, Hilary Covenant Univ Dept Civil Engn PMB 1023 Km 10 Idiroko Rd Ota Nigeria Delta State Univ Dept Civil & Environm Engn Abraka Nigeria

Recycling improves the circular economy and resource sustainability by using agricultural waste to create new products. However, agricultural byproducts resulting from the recycling of agricultural waste materials contain naturally occurring radionuclides with potential risks to human health and the environment. Therefore, this article provides an overview of relevant literature on radiological properties of agricultural byproducts (rice husk ash, mussel shell, palm oil clinker, and palm oil fuel ash), prioritizing their specific activities (226Ra, 232Th and 40K). Consequently, absorbed gamma dose rates (AGDR), annual effective dose rates (AEDR) and excess lifetime cancer risks (ELCR) of the agricultural byproducts studied were determined. The specific concentrations, AGDR, AEDR, and ECLR were trained, validated, and tested using various machine learning algorithms. An evaluation of the radiological properties of all agricultural byproducts examined revealed that they pose no risk of cancer. Additionally, compared to support vector machine, regression trees, ensemble trees, Gaussian process regression, and neural networks, linear regression yielded the best performance metrics, making it the most suitable technique for predicting excess lifetime cancer risks of the surveyed agricultural byproducts.

关键词： Cancer machine learning algorithm Radioactivity Recycling Sustainable production Waste management

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Characterization and prediction of soil organic matter content in reclaimed mine soil using visible and near-infrared diffuse spectroscopy

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ARID LAND RESEARCH AND MANAGEMENT 2021年第3期35卷 276-291页

作者： Bao, Nisha Liu, Shanjun Yang, Tianhong Cao, Yue Northeastern Univ Sch Resources & Civil Engn Shenyang Peoples R China

An accurate determination of the soil organic matter (SOM) levels present in reclaimed mine soil is necessary to evaluate the success of the ecological reclamation of mines. Visible and near-infrared diffuse spectroscopy is a fast and efficient method for collecting data for soil management during the reclamation of soil removed from mines. In this work, we used spectroscopy to characterize and estimate the SOM of lands from different reclamation years after coal mining in semi-arid grasslands of North China. Our goals were: (1) to explore the SOM characteristics and the spectra of reclaimed mine soil with different reclamation ages, and (2) to establish a reliable and accurate SOM prediction model by comparing support vector machine (SVM), partial least-squares regression (PLSR), and random forest (RF) modeling methods by determining the optimal preprocessing method and input spectral region. The results showed that spectral characteristics are useful indicators for understanding progressive SOM changes in the topsoil at different reclamation ages. RF is a more appropriate method for assessing the performance of a selected spectral region and provides more accurate results with a root mean square error (RMSE) of 4.34 g center dot kg(-1) between predicted and observed SOM values. This study provides an alternative method that uses spectroscopy to estimate reclaimed soil conditions for the environmental monitoring of mining sites.

关键词： machine learning algorithm mine reclamation reclamation years reclaimed soil VIS-NIR spectra

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TBM penetration rate prediction ensemble model based on full-scale linear cutting test

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2023年 131卷

作者： Wang, Yaxu Wang, Jiwen Wang, Ruirui Liu, Bin Li, Yao Shandong Univ Geotech & Struct Engn Res Ctr Jinan 250061 Peoples R China Shandong Univ Sch Qilu Transportat Jinan 250061 Peoples R China Shandong Jianzhu Univ Sch Civil Engn Jinan 250101 Peoples R China Shandong Univ Sch Civil Engn Jinan 250061 Peoples R China Shandong Univ Data Sci Inst Jinan 250061 Peoples R China Shandong Univ Geotech & Struct Engn Res Ctr 17923 Jingshi Rd Jinan 250061 Peoples R China

Accurate and effective prediction of TBM penetration rate (PR) is significant for ensuring TBM tunneling safety, improving tunneling efficiency and optimizing construction cost. Establishing a prediction model is usually an effective method to solve this critical problem. The type of TBM penetration rate prediction models can be divided into two categories: theoretical models and empirical models. Each model has its advantages, but there are also shortcomings. To make the advantages of the two models complement each other and further improve the accuracy and application scope of the prediction model, this paper proposes a prediction ensemble model of PR based on a full-scale linear cutting test and machine learning algorithm. A semi-theoretical TBM penetration rate prediction model is established through the full-scale linear cutting test and the rock-breaking analysis of granite and sandstone with different strengths. Based on the XGBoost intelligent algorithm, an empirical model of the penetration rate prediction is also established. On this basis, the Bagging algorithm combines these two models to form an ensemble prediction model of TBM penetration rate. We collected rock mass and operational parameters in the Hangzhou Second Water Source Water Transfer Channel Project to verify the semi-theoretical, empirical, and ensemble models, respectively. The results show that the three models' prediction errors are all less than 15%, and the ensemble model has a smaller error of 10.2%. This result confirms that the ensemble model can effectively improve prediction accuracy and has good engineering applicability.

关键词： TBM performance prediction Linear cutting test machine learning algorithm Engineering verification

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