Displaying publications 1 - 20 of 908 in total

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  1. Nandi AK, Randhawa KK, Chua HS, Seera M, Lim CP
    PLoS One, 2022 01 20;17(1):e0260579.
    PMID: 35051184 DOI: 10.1371/journal.pone.0260579
    With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.
    Matched MeSH terms: Machine Learning*
  2. Sim JH, Hassan H
    Acad Med, 2018 01;93(1):11-12.
    PMID: 29278593 DOI: 10.1097/ACM.0000000000002007
    Matched MeSH terms: Learning*
  3. Murti MA, Junior R, Ahmed AN, Elshafie A
    Sci Rep, 2022 Dec 08;12(1):21200.
    PMID: 36482200 DOI: 10.1038/s41598-022-25098-1
    Earthquake is one of the natural disasters that have a big impact on society. Currently, there are many studies on earthquake detection. However, the vibrations that were detected by sensors were not only vibrations caused by the earthquake, but also other vibrations. Therefore, this study proposed an earthquake multi-classification detection with machine learning algorithms that can distinguish earthquake and non-earthquake, and vandalism vibration using acceleration seismic waves. In addition, velocity and displacement as integration products of acceleration have been considered additional features to improve the performances of machine learning algorithms. Several machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) have been used to develop the best algorithm for earthquake multi-classification detection. The results of this study indicate that the ANN algorithm is the best algorithm to distinguish between earthquake and non-earthquake, and vandalism vibrations. Moreover, it's also more resistant to various input features. Furthermore, using velocity and displacement as additional features has been proven to increase the performance of every model.
    Matched MeSH terms: Machine Learning*
  4. Sivakumar I, Arunachalam S, Buzayan MM, Chidambaram R
    J Dent Educ, 2022 Dec;86 Suppl 3:1742-1744.
    PMID: 35412660 DOI: 10.1002/jdd.12945
    Matched MeSH terms: Learning*
  5. Gong C, Xue B, Jing C, He CH, Wu GC, Lei B, et al.
    Math Biosci Eng, 2022 Sep 13;19(12):13276-13293.
    PMID: 36654046 DOI: 10.3934/mbe.2022621
    Brain community detection is an efficient method to represent the communities of brain networks. However, time-variable functions of the brain and the intricate brain community structure impose a great challenge on it. In this paper, a time-sequential graph adversarial learning (TGAL) framework is proposed to detect brain communities and characterize the structure of communities from brain networks. In the framework, a novel time-sequential graph neural network is designed as an encoder to extract efficient graph representations by spatio-temporal attention mechanism. Since it is difficult to capture the community structure, the measurable modularity loss is used to optimize by maximizing the modularity of the community. In addition, the framework employs an adversarial scheme to guide the learning of representation. The effectiveness of our model is shown through experiments on the real-world brain network datasets, and the great performance of brain community detection demonstrates the advantage of the proposed framework.
    Matched MeSH terms: Learning*
  6. Jalil-Masir H, Fattahi R, Ghanbari-Adivi E, Asadi Aghbolaghi M, Ehteram M, Ahmed AN, et al.
    Environ Sci Pollut Res Int, 2022 Sep;29(44):67180-67213.
    PMID: 35522411 DOI: 10.1007/s11356-022-20472-y
    Predicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR-vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull optimization algorithm (SEOA), the crow optimization algorithm (COA), the firefly algorithm (FFA), and particle swarm optimization (PSO) to estimate the STR in the presence of vegetation cover. The rigidity index, D50/wave height, Newton number, drag coefficient, and cover density were used as inputs to the models. The root mean square error (RMSE), the mean absolute error (MAE), and percentage of bias (PBIAS) were used to evaluate the capability of models. This study applied the novel ensemble model, and the inclusive multiple model (IMM), to assemble the outputs of the KELM models. In addition, the innovations of this study were the introduction of a new IMM model, and the use of new hybrid KELM models for predicting STR and investigating the effects of various parameters on the STR. At the testing level, the MAE of the IMM model was 22, 60, 68, 73, and 76% lower than those of the KELM-SEOA, KELM-COA, KELM-PSO, and KELM models, respectively. The IMM had a PBIAS of 5, whereas the KELM-SEOA, KELM-COA, KELM-PSOA, and KELM had PBIAS of 9, 12, 14, 18, and 21%, respectively. The results indicated that the increasing drag coefficient and D50/wave height had decreased the STR. From the findings, it was revealed that the IMM and KELM-SEOA had higher predictive ability for STR. Since the sediment is one of the most important sources of environmental pollution, therefore, this study is useful for monitoring and controlling environmental pollution.
    Matched MeSH terms: Learning*
  7. Abdullah JM
    Malays J Med Sci, 2014 Jul;21(4):1-3.
    PMID: 25977614
    The Dunning-Kruger effect is a cognitive bias in which unskilled people make poor decisions and reach erroneous conclusions, but their incompetence denies them the metacognitive ability to recognise their mistakes. These unskilled people therefore suffer from illusory superiority, rating their ability as above average, much higher than it actually is, while the highly skilled underrate their own abilities, suffering from illusory inferiority.
    Matched MeSH terms: Learning
  8. Azer SA
    Kaohsiung J. Med. Sci., 2009 Mar;25(3):109-15.
    PMID: 19419915 DOI: 10.1016/S1607-551X(09)70049-X
    Lectures are of great value to students. However, with the introduction of hybrid problem-based learning (PBL) curricula into most medical schools, the emphasis on lectures has decreased. This paper discusses how lectures can be used in a PBL curriculum, what makes a great lecture, and how to deliver a lecture that fits with these changes.
    Matched MeSH terms: Problem-Based Learning*
  9. Anbananthen KSM, Subbiah S, Chelliah D, Sivakumar P, Somasundaram V, Velshankar KH, et al.
    F1000Res, 2021;10:1143.
    PMID: 34987773 DOI: 10.12688/f1000research.73009.1
    Background: In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used. Results: Based on the experimental results done on the agricultural data, the following observations have been made. The performance of the individual algorithm and hybrid ML algorithms are compared using cross-validation to identify the most promising performers for the agricultural dataset.  The accuracy of random forest regressor, gradient boosted tree regression, and stacked generalization ensemble methods are 87.71%, 86.98%, and 88.89% respectively. Conclusions: The proposed stacked generalization ML algorithm statistically outperforms with an accuracy of 88.89% and hence demonstrates that the proposed approach is an effective algorithm for predicting crop yield. The system also gives fast and accurate responses to the farmers.
    Matched MeSH terms: Machine Learning*
  10. Chughtai JU, Haq IU, Islam SU, Gani A
    Sensors (Basel), 2022 Dec 12;22(24).
    PMID: 36560104 DOI: 10.3390/s22249735
    Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages-initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.
    Matched MeSH terms: Machine Learning*
  11. Saxena K, Arunachalam S, Banavar SR, Dhanaraj S
    J Dent Educ, 2022 Dec;86 Suppl 3:1731-1733.
    PMID: 34958485 DOI: 10.1002/jdd.12872
    Matched MeSH terms: Learning*
  12. Mondal PP, Galodha A, Verma VK, Singh V, Show PL, Awasthi MK, et al.
    Bioresour Technol, 2023 Feb;370:128523.
    PMID: 36565820 DOI: 10.1016/j.biortech.2022.128523
    Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.
    Matched MeSH terms: Machine Learning*
  13. Dixit R, Khambhati K, Supraja KV, Singh V, Lederer F, Show PL, et al.
    Bioresour Technol, 2023 Feb;370:128522.
    PMID: 36565819 DOI: 10.1016/j.biortech.2022.128522
    Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.
    Matched MeSH terms: Machine Learning*
  14. Sutradhar A, Al Rafi M, Shamrat FMJM, Ghosh P, Das S, Islam MA, et al.
    Sci Rep, 2023 Dec 18;13(1):22874.
    PMID: 38129433 DOI: 10.1038/s41598-023-48486-7
    Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.
    Matched MeSH terms: Machine Learning*
  15. Brotcorne F, Holzner A, Jorge-Sales L, Gunst N, Hambuckers A, Wandia IN, et al.
    Anim Cogn, 2020 Mar;23(2):311-326.
    PMID: 31820148 DOI: 10.1007/s10071-019-01335-5
    Animals use social information, available from conspecifics, to learn and express novel and adaptive behaviours. Amongst social learning mechanisms, response facilitation occurs when observing a demonstrator performing a behaviour temporarily increases the probability that the observer will perform the same behaviour shortly after. We studied "robbing and bartering" (RB), two behaviours routinely displayed by free-ranging long-tailed macaques (Macaca fascicularis) at Uluwatu Temple, Bali, Indonesia. When robbing, a monkey steals an inedible object from a visitor and may use this object as a token by exchanging it for food with the temple staff (bartering). We tested whether the expression of RB-related behaviours could be explained by response facilitation and was influenced by model-based biases (i.e. dominance rank, age, experience and success of the demonstrator). We compared video-recorded focal samples of 44 witness individuals (WF) immediately after they observed an RB-related event performed by group members, and matched-control focal samples (MCF) of the same focal subjects, located at similar distance from former demonstrators (N = 43 subjects), but in the absence of any RB-related demonstrations. We found that the synchronized expression of robbing and bartering could be explained by response facilitation. Both behaviours occurred significantly more often during WF than during MCF. Following a contagion-like effect, the rate of robbing behaviour displayed by the witness increased with the cumulative rate of robbing behaviour performed by demonstrators, but this effect was not found for the bartering behaviour. The expression of RB was not influenced by model-based biases. Our results support the cultural nature of the RB practice in the Uluwatu macaques.
    Matched MeSH terms: Social Learning*; Learning
  16. Motlagh O, Papageorgiou E, Tang S, Zamberi Jamaludin
    Sains Malaysiana, 2014;43:1781-1790.
    Soft computing is an alternative to hard and classic math models especially when it comes to uncertain and incomplete data. This includes regression and relationship modeling of highly interrelated variables with applications in curve fitting, interpolation, classification, supervised learning, generalization, unsupervised learning and forecast. Fuzzy cognitive map (FCM) is a recurrent neural structure that encompasses all possible connections including relationships among inputs, inputs to outputs and feedbacks. This article examines a new methods for nonlinear multivariate regression using fuzzy cognitive map. The main contribution is the application of nested FCM structure to define edge weights in form of meaningful functions rather than crisp values. There are example cases in this article which serve as a platform to modelling even more complex engineering systems. The obtained results, analysis and comparison with similar techniques are included to show the robustness and accuracy of the developed method in multivariate regression, along with future lines of research.
    Matched MeSH terms: Supervised Machine Learning; Unsupervised Machine Learning; Learning
  17. Ammu K. Radhakrishnan
    MyJurnal
    In the last decade or so, Medical education all over the world has been inundated with innovations in education, which include innovations in curricular design, delivery as well as assessments. There is a need to reflect on the effectives of these innovations
    on the learner. Hence the theme chosen for the 2009 International Medical Education Conference (IMEC 2009) was “Reflections on Innovations”. The Organising Committee felt that it was timely for medical educators everywhere to reflect and evaluate the effect of the many innovations adopted by their schools. (Copied from article)
    Matched MeSH terms: Learning
  18. Siti Nurma Hanim Hadie, Asma' Hassan, Zul Izhar Mohd Ismail, Mohd Asnizam Asari, Aaijaz Ahmed Khan, Fazlina Kasim, et al.
    MyJurnal
    Anatomy is an important knowledge for medical practice. Insufficient anatomy knowledge leading to errors in identification of anatomical structures during medical practices has been reported in many countries. Many medical students seem to have difficulties in learning anatomy and retaining the knowledge for future practice, thus this might reflect the possible flaws in anatomy education. In order to achieve optimum anatomy education environment and to close the gaps in education, measuring the students' perception on anatomy teaching and learning is a pre-emptive measure needed by educationists. At present, there is no valid and reliable inventory available to specifically evaluate the anatomy education environment. Therefore, this article highlights the importance of having such inventory.
    Matched MeSH terms: Learning
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