Application of KRR, K-NN and GPR algorithms for predicting the soaked CBR of fine-grained plastic soils
- Verma, Gaurav, Kumar, Brind, Kumar, Chintoo, Ray, Arunava, Khandelwal, Manoj
- Authors: Verma, Gaurav , Kumar, Brind , Kumar, Chintoo , Ray, Arunava , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: Arabian Journal for Science and Engineering Vol. 48, no. 10 (2023), p. 13901-13927
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- Description: California bearing ratio (CBR) test is one of the comprehensive tests used for the last few decades to design the pavement thickness of roadways, railways and airport runways. Laboratory-performed CBR test is considerably rigorous and time-taking. In a quest for an alternative solution, this study utilizes novel computational approaches, including the kernel ridges regression, K-nearest neighbor and Gaussian process regression (GPR), to predict the soaked CBR value of soils. A vast quantity of 1011 in situ soil samples were collected from an ongoing highway project work site. Two data divisional approaches, i.e., K-Fold and fuzzy c-means (FCM) clustering, were used to separate the dataset into training and testing subsets. Apart from the numerous statistical performance measurement indices, ranking and overfitting analysis were used to identify the best-fitted CBR prediction model. Additionally, the literature models were also tried to validate through present study datasets. From the results of Pearson’s correlation analysis, Sand, Fine Content, Plastic Limit, Plasticity Index, Maximum Dry Density and Optimum Moisture Content were found to be most influencing input parameters in developing the soaked CBR of fine-grained plastic soils. Experimental results also establish the proficiency of the GPR model developed through FCM and K-Fold data division approaches. The K-Fold data division approach was found to be helpful in removing the overfitting of the models. Furthermore, the predictive ability of any model is considerably influenced by the geological location of the soils/materials used for the model development. © 2023, The Author(s).
- Authors: Verma, Gaurav , Kumar, Brind , Kumar, Chintoo , Ray, Arunava , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: Arabian Journal for Science and Engineering Vol. 48, no. 10 (2023), p. 13901-13927
- Full Text:
- Reviewed:
- Description: California bearing ratio (CBR) test is one of the comprehensive tests used for the last few decades to design the pavement thickness of roadways, railways and airport runways. Laboratory-performed CBR test is considerably rigorous and time-taking. In a quest for an alternative solution, this study utilizes novel computational approaches, including the kernel ridges regression, K-nearest neighbor and Gaussian process regression (GPR), to predict the soaked CBR value of soils. A vast quantity of 1011 in situ soil samples were collected from an ongoing highway project work site. Two data divisional approaches, i.e., K-Fold and fuzzy c-means (FCM) clustering, were used to separate the dataset into training and testing subsets. Apart from the numerous statistical performance measurement indices, ranking and overfitting analysis were used to identify the best-fitted CBR prediction model. Additionally, the literature models were also tried to validate through present study datasets. From the results of Pearson’s correlation analysis, Sand, Fine Content, Plastic Limit, Plasticity Index, Maximum Dry Density and Optimum Moisture Content were found to be most influencing input parameters in developing the soaked CBR of fine-grained plastic soils. Experimental results also establish the proficiency of the GPR model developed through FCM and K-Fold data division approaches. The K-Fold data division approach was found to be helpful in removing the overfitting of the models. Furthermore, the predictive ability of any model is considerably influenced by the geological location of the soils/materials used for the model development. © 2023, The Author(s).
Application of various robust techniques to study and evaluate the role of effective parameters on rock fragmentation
- Mehrdanesh, Amirhossein, Monjezi, Masoud, Khandelwal, Manoj, Bayat, Parichehr
- Authors: Mehrdanesh, Amirhossein , Monjezi, Masoud , Khandelwal, Manoj , Bayat, Parichehr
- Date: 2023
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 39, no. 2 (2023), p. 1317-1327
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- Description: In this paper, an attempt has been made to implement various robust techniques to predict rock fragmentation due to blasting in open pit mines using effective parameters. As rock fragmentation prediction is very complex and complicated, and due to that various artificial intelligence-based techniques, such as artificial neural network (ANN), classification and regression tree and support vector machines were selected for the modeling. To validate and compare the prediction results, conventional multivariate regression analysis was also utilized on the same data sets. Since accuracy and generality of the modeling is dependent on the number of inputs, it was tried to collect enough required information from four different open pit mines of Iran. According to the obtained results, it was revealed that ANN with a determination coefficient of 0.986 is the most precise method of modeling as compared to the other applied techniques. Also, based on the performed sensitivity analysis, it was observed that the most prevailing parameters on the rock fragmentation are rock quality designation, Schmidt hardness value, mean in-situ block size and the minimum effective ones are hole diameter, burden and spacing. The advantage of back propagation neural network technique for using in this study compared to other soft computing methods is that they are able to describe complex and nonlinear multivariable problems in a transparent way. Furthermore, ANN can be used as a first approach, where much knowledge about the influencing parameters are missing. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Mehrdanesh, Amirhossein , Monjezi, Masoud , Khandelwal, Manoj , Bayat, Parichehr
- Date: 2023
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 39, no. 2 (2023), p. 1317-1327
- Full Text:
- Reviewed:
- Description: In this paper, an attempt has been made to implement various robust techniques to predict rock fragmentation due to blasting in open pit mines using effective parameters. As rock fragmentation prediction is very complex and complicated, and due to that various artificial intelligence-based techniques, such as artificial neural network (ANN), classification and regression tree and support vector machines were selected for the modeling. To validate and compare the prediction results, conventional multivariate regression analysis was also utilized on the same data sets. Since accuracy and generality of the modeling is dependent on the number of inputs, it was tried to collect enough required information from four different open pit mines of Iran. According to the obtained results, it was revealed that ANN with a determination coefficient of 0.986 is the most precise method of modeling as compared to the other applied techniques. Also, based on the performed sensitivity analysis, it was observed that the most prevailing parameters on the rock fragmentation are rock quality designation, Schmidt hardness value, mean in-situ block size and the minimum effective ones are hole diameter, burden and spacing. The advantage of back propagation neural network technique for using in this study compared to other soft computing methods is that they are able to describe complex and nonlinear multivariable problems in a transparent way. Furthermore, ANN can be used as a first approach, where much knowledge about the influencing parameters are missing. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Applications of Computed Tomography (CT) in environmental soil and plant sciences
- Zhang, Huan, He, Hailong, Gao, Yanjun, Mady, Ahmed, Filipović, Vilim, Dyck, Miles, Lv, Jialong, Liu, Yang
- Authors: Zhang, Huan , He, Hailong , Gao, Yanjun , Mady, Ahmed , Filipović, Vilim , Dyck, Miles , Lv, Jialong , Liu, Yang
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Soil and Tillage Research Vol. 226, no. (2023), p.
- Full Text: false
- Reviewed:
- Description: Computed tomography (CT) in combination with advanced image processing can be used to non-invasively and non-destructively visualize complex interiors of living and non-living media in 2 and 3-dimensional space. In addition to medical applications, CT has also been widely used in soil and plant science for visual and quantitative descriptions of physical, chemical, and biological properties and processes. The technique has been used successfully on numerous applications. However, with a rapidly evolving CT technologies and expanding applications, a renewed review is desirable. Only a few attempts have been made to collate and review examples of CT applications involving the integrated field of soil and plant research in recent years. Therefore, the objectives of this work were to: (1) briefly introduce the basic principles of CT and image processing; (2) identify the research status and hot spots of CT using bibliometric analysis based on Web of Science literature over the past three decades; (3) provide an overall review of CT applications in soil science for measuring soil properties (e.g., porous soil structure, soil components, soil biology, heat transfer, water flow, and solute transport); and (4) give an overview of applications of CT in plant science to detect morphological structures, plant material properties, and root-soil interaction. Moreover, the limitations of CT and image processing are discussed and future perspectives are given. © 2022 Elsevier B.V.
Applications of machine learning and deep learning in antenna design, optimization, and selection : a review
- Sarker, Nayan, Podder, Prajoy, Mondal, M., Shafin, Sakib, Kamruzzaman, Joarder
- Authors: Sarker, Nayan , Podder, Prajoy , Mondal, M. , Shafin, Sakib , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 11, no. (2023), p. 103890-103915
- Full Text:
- Reviewed:
- Description: This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process. © 2013 IEEE.
- Authors: Sarker, Nayan , Podder, Prajoy , Mondal, M. , Shafin, Sakib , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 11, no. (2023), p. 103890-103915
- Full Text:
- Reviewed:
- Description: This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process. © 2013 IEEE.
Apprenticeships : the problem of attractiveness and the hindrance of heterogeneity
- Authors: Smith, Erica
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Training and Development Vol. 27, no. 1 (2023), p. 18-38
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- Description: This paper examines a question posed in 2019 in the International Journal on Training and Development: ‘How do we solve a problem like apprenticeship?’ Data sources covering a substantial number of countries are used to present findings on, and analyse, initiatives that have been implemented or that have been considered, and then to develop some analytical constructs to help address the question. Fundamental issues such as the status of vocational education and training and the status of apprenticed occupations are important, but the nature of the apprenticeship arrangements, within countries and within industries are also major factors affecting perceived attractiveness. The paper therefore argues that the heterogeneity of apprenticeship systems and arrangements is a major barrier to solving the attractiveness problem. Moreover, the heterogeneity of potential apprenticeship applicants means that marketing campaigns or other efforts to attract more, and higher quality, apprentices need to be cognisant of individuals’ backgrounds, characteristics, and aspirations. Some tentative ways of addressing these matters are presented, but the conclusion is that the topic needs large-scale research. © 2022 The Authors. International Journal of Training and Development published by Brian Towers (BRITOW) and John Wiley & Sons Ltd.
- Authors: Smith, Erica
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Training and Development Vol. 27, no. 1 (2023), p. 18-38
- Full Text:
- Reviewed:
- Description: This paper examines a question posed in 2019 in the International Journal on Training and Development: ‘How do we solve a problem like apprenticeship?’ Data sources covering a substantial number of countries are used to present findings on, and analyse, initiatives that have been implemented or that have been considered, and then to develop some analytical constructs to help address the question. Fundamental issues such as the status of vocational education and training and the status of apprenticed occupations are important, but the nature of the apprenticeship arrangements, within countries and within industries are also major factors affecting perceived attractiveness. The paper therefore argues that the heterogeneity of apprenticeship systems and arrangements is a major barrier to solving the attractiveness problem. Moreover, the heterogeneity of potential apprenticeship applicants means that marketing campaigns or other efforts to attract more, and higher quality, apprentices need to be cognisant of individuals’ backgrounds, characteristics, and aspirations. Some tentative ways of addressing these matters are presented, but the conclusion is that the topic needs large-scale research. © 2022 The Authors. International Journal of Training and Development published by Brian Towers (BRITOW) and John Wiley & Sons Ltd.
- Gomez, Rapson, Watson, Shaun, Brown, Taylor
- Authors: Gomez, Rapson , Watson, Shaun , Brown, Taylor
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of Psychopathology and Behavioral Assessment Vol. 45, no. 3 (2023), p. 650-658
- Full Text: false
- Reviewed:
- Description: Using individual differences constructs, the current study used cross-sectional data to examine the mediating role of negative self-statements during public speaking on the relationship between fear of negative evaluation and public speaking anxiety (a type of performance anxiety), and how this relationship was moderated by positive self-statements during public performance. The sample comprised 319 adults (men = 105, women = 214) from the general Australian community, with ages ranging from 18 years to 65 years. All participants completed questionnaires covering the different study variables. The findings showed that there was partial mediation by negative self-statements on the relationship between fear of negative evaluation and performance anxiety. There were also moderation effects by positive self-statements for this relationship. Additionally, moderation by positive self-statements was evident at all levels of positive self-statements. The theoretical and clinical implications of the findings for public speaking anxiety are discussed. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Associations between smartphone keystroke metadata and mental health symptoms in adolescents: findings from the future proofing study
- Braund, Taylor, O'Dea, Bridianne, Bal, Debopriyo, Maston, Kate, Larsen, Mark, Werner-Seidler, Aliza, Tillman, Gabriel, Christensen, Helen
- Authors: Braund, Taylor , O'Dea, Bridianne , Bal, Debopriyo , Maston, Kate , Larsen, Mark , Werner-Seidler, Aliza , Tillman, Gabriel , Christensen, Helen
- Date: 2023
- Type: Text , Journal article
- Relation: JMIR Mental Health Vol. 10, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. Objective: In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. Methods: A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children's Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. Results: Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. Conclusions: Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. ©Taylor A Braund, Bridianne O'Dea, Debopriyo Bal, Kate Maston, Mark Larsen, Aliza Werner-Seidler, Gabriel Tillman, Helen Christensen.
- Authors: Braund, Taylor , O'Dea, Bridianne , Bal, Debopriyo , Maston, Kate , Larsen, Mark , Werner-Seidler, Aliza , Tillman, Gabriel , Christensen, Helen
- Date: 2023
- Type: Text , Journal article
- Relation: JMIR Mental Health Vol. 10, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. Objective: In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. Methods: A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children's Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. Results: Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. Conclusions: Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. ©Taylor A Braund, Bridianne O'Dea, Debopriyo Bal, Kate Maston, Mark Larsen, Aliza Werner-Seidler, Gabriel Tillman, Helen Christensen.
- Gomez, Rapson, Watson, Shaun, Stavropoulos, Vasileios, Typuszak, Natasha
- Authors: Gomez, Rapson , Watson, Shaun , Stavropoulos, Vasileios , Typuszak, Natasha
- Date: 2023
- Type: Text , Journal article
- Relation: Current Psychology Vol. 42, no. 17 (2023), p. 14159-14170
- Full Text: false
- Reviewed:
- Description: Background: Using Kimbrel’s (2008) mediation model of social anxiety as a theoretical framework, the primary aim of the current study was to use path analysis to examine how biased cognitions for negative and threatening social information mediated the relationships for the personality constructs of the reinforcement sensitivity theory (RST) with generalized and specific social anxiety (target mediation model). A secondary aim was to examine reverse mediation testing (RMT) models, in which the social anxiety constructs were viewed as mediating the relations between RST constructs and biased social cognition constructs. Methods: A total of 302 (males = 101, females = 201) adults (age ranging from 18 to 65 years) from the general community completed questionnaires measuring the behavioral inhibition system/fight-flight-freeze system (BIS/FFFS), the behavioral approach system (BAS), social comparison (SC), social ineptness (SI), and generalized and specific social anxiety. Results: The findings for the target mediation model showed that there was support for indirect effects for the BIS/FFFS and the BAS on generalized and specific social anxiety through SC and SI. For the RMT model, there was support for the indirect effect of the RST constructs with SI through generalized social anxiety. However, specific generalized anxiety did not mediate the relations of the BIS/FFFS and BAS to SC. Conclusions: The findings highlight the importance of cognitive therapy that targets SC and SI in the treatment of social anxiety, especially among those with high BIS/FFFS and low BAS. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Associations of UPPS-P negative urgency and positive urgency with ADHD dimensions : moderation by lack of premeditation and lack of perseverance in men and women
- Gomez, Rapson, Watson, Shaun
- Authors: Gomez, Rapson , Watson, Shaun
- Date: 2023
- Type: Text , Journal article
- Relation: Personality and Individual Differences Vol. 206, no. (2023), p.
- Full Text:
- Reviewed:
- Description: The study examined how dimensions of Whiteside and Lynam's (2003) UPPS-P model of impulsivity (lack of premeditation, lack of perseverance, negative urgency, and positive urgency) were associated directly and interactively with the attention-deficit/hyperactivity disorder (ADHD) dimensions of inattention and hyperactivity/impulsivity in men and women separately. A total of 550 adults (men = 147, women = 403), ages ranging from 18 to 65 years, from the general community completed questionnaires covering the study variables. For women, there was support for the additive model for the prediction of inattention, and both inattention and hyperactivity/impulsivity were predicted by lack of premeditation × positive urgency. For men, inattention was predicted by lack of premeditation × negative urgency, and lack of premeditation × positive urgency. In all instances, low levels of premeditation reduced the relationships between the urgency dimensions and ADHD dimensions. The theoretical and clinical implications of the findings are discussed. © 2023 The Author(s)
- Authors: Gomez, Rapson , Watson, Shaun
- Date: 2023
- Type: Text , Journal article
- Relation: Personality and Individual Differences Vol. 206, no. (2023), p.
- Full Text:
- Reviewed:
- Description: The study examined how dimensions of Whiteside and Lynam's (2003) UPPS-P model of impulsivity (lack of premeditation, lack of perseverance, negative urgency, and positive urgency) were associated directly and interactively with the attention-deficit/hyperactivity disorder (ADHD) dimensions of inattention and hyperactivity/impulsivity in men and women separately. A total of 550 adults (men = 147, women = 403), ages ranging from 18 to 65 years, from the general community completed questionnaires covering the study variables. For women, there was support for the additive model for the prediction of inattention, and both inattention and hyperactivity/impulsivity were predicted by lack of premeditation × positive urgency. For men, inattention was predicted by lack of premeditation × negative urgency, and lack of premeditation × positive urgency. In all instances, low levels of premeditation reduced the relationships between the urgency dimensions and ADHD dimensions. The theoretical and clinical implications of the findings are discussed. © 2023 The Author(s)
Attributes of expert anticipation should inform the design of virtual reality simulators to accelerate learning and transfer of skill
- Müller, Sean, Dekker, Evan, Morris-Binelli, Khaya, Piggott, Benjamin, Hoyne, Gerard, Christensen, Wayne, Fadde, Peter, Zaichkowsky, Leonard, Brenton, John, Hambrick, David
- Authors: Müller, Sean , Dekker, Evan , Morris-Binelli, Khaya , Piggott, Benjamin , Hoyne, Gerard , Christensen, Wayne , Fadde, Peter , Zaichkowsky, Leonard , Brenton, John , Hambrick, David
- Date: 2023
- Type: Text , Journal article
- Relation: Sports Medicine Vol. 53, no. 2 (2023), p. 301-309
- Full Text:
- Reviewed:
- Description: Expert sport performers cope with a multitude of visual information to achieve precise skill goals under time stress and pressure. For example, a major league baseball or cricket batter must read opponent variations in actions and ball flight paths to strike the ball in less than a second. Crowded playing schedules and training load restrictions to minimise injury have limited opportunity for field-based practice in sports. As a result, many sports organisations are exploring the use of virtual reality (VR) simulators. Whilst VR synthetic experiences can allow greater control of visual stimuli, immersion to create presence in an environment, and interaction with stimuli, compared to traditional video simulation, the underpinning mechanisms of how experts use visual information for anticipation have not been properly incorporated into its content design. In themes, this opinion article briefly explains the mechanisms underpinning expert visual anticipation, as well as its learning and transfer, with a view that this knowledge can better inform VR simulator content design. In each theme, examples are discussed for improved content design of VR simulators taking into consideration its advantages and limitations relative to video simulation techniques. Whilst sport is used as the exemplar, the points discussed have implications for skill learning in other domains, such as military and law enforcement. It is hoped that our paper will stimulate improved content design of VR simulators for future research and skill enhancement across several domains. © 2022, The Author(s). Correction to: Sports Medicine https://doi.org/10.1007/s40279-022-01735-7, Page 1: The affiliation for Evan Dekker, which previously read: 2Academic Services and Support Directorate, University Drive, Mt. Helen, Ballarat, VIC 3350, Australia has now been updated to read: Academic Services and Support Directorate, Federation University, University Drive, Mt. Helen, Ballarat, VIC 3350, Australia. The original article has been corrected.
- Authors: Müller, Sean , Dekker, Evan , Morris-Binelli, Khaya , Piggott, Benjamin , Hoyne, Gerard , Christensen, Wayne , Fadde, Peter , Zaichkowsky, Leonard , Brenton, John , Hambrick, David
- Date: 2023
- Type: Text , Journal article
- Relation: Sports Medicine Vol. 53, no. 2 (2023), p. 301-309
- Full Text:
- Reviewed:
- Description: Expert sport performers cope with a multitude of visual information to achieve precise skill goals under time stress and pressure. For example, a major league baseball or cricket batter must read opponent variations in actions and ball flight paths to strike the ball in less than a second. Crowded playing schedules and training load restrictions to minimise injury have limited opportunity for field-based practice in sports. As a result, many sports organisations are exploring the use of virtual reality (VR) simulators. Whilst VR synthetic experiences can allow greater control of visual stimuli, immersion to create presence in an environment, and interaction with stimuli, compared to traditional video simulation, the underpinning mechanisms of how experts use visual information for anticipation have not been properly incorporated into its content design. In themes, this opinion article briefly explains the mechanisms underpinning expert visual anticipation, as well as its learning and transfer, with a view that this knowledge can better inform VR simulator content design. In each theme, examples are discussed for improved content design of VR simulators taking into consideration its advantages and limitations relative to video simulation techniques. Whilst sport is used as the exemplar, the points discussed have implications for skill learning in other domains, such as military and law enforcement. It is hoped that our paper will stimulate improved content design of VR simulators for future research and skill enhancement across several domains. © 2022, The Author(s). Correction to: Sports Medicine https://doi.org/10.1007/s40279-022-01735-7, Page 1: The affiliation for Evan Dekker, which previously read: 2Academic Services and Support Directorate, University Drive, Mt. Helen, Ballarat, VIC 3350, Australia has now been updated to read: Academic Services and Support Directorate, Federation University, University Drive, Mt. Helen, Ballarat, VIC 3350, Australia. The original article has been corrected.
Australian men’s sheds and their role in the health and wellbeing of men : a systematic review
- Barbagallo, Michael, Brito, Sara, Porter, Joanne
- Authors: Barbagallo, Michael , Brito, Sara , Porter, Joanne
- Date: 2023
- Type: Text , Journal article
- Relation: Health & Social care in the Community Vol. 2023, no. (2023), p. 1-9
- Full Text:
- Reviewed:
- Description: Men’s sheds are a community-based organisation that allows a space for a community of men to interact and engage with one another with hands-on activities. As such, men’s sheds form an appropriate setting to deliver health and wellbeing initiatives. This review aims to understand the role of Australian men’s sheds with respect to the health and wellbeing of their male participants. This review was conducted in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) following a three-step process of planning, conducting, and reporting the review. All three authors reviewed all the eligible articles. There was significant methodological heterogeneity between the sources identified (n = 11). A narrative synthesis identified three key themes: health promotion, wellbeing, and socialisation intergenerational mentoring and Aboriginal and Torres Strait Islander men’s health. Men’s sheds serve as ideal locations for the delivery of initiatives that can positively impact on the health and wellbeing of their male participants. Furthermore, research is needed to explore the implementation and evaluation of these health and wellbeing initiatives for men in their respective communities.
- Authors: Barbagallo, Michael , Brito, Sara , Porter, Joanne
- Date: 2023
- Type: Text , Journal article
- Relation: Health & Social care in the Community Vol. 2023, no. (2023), p. 1-9
- Full Text:
- Reviewed:
- Description: Men’s sheds are a community-based organisation that allows a space for a community of men to interact and engage with one another with hands-on activities. As such, men’s sheds form an appropriate setting to deliver health and wellbeing initiatives. This review aims to understand the role of Australian men’s sheds with respect to the health and wellbeing of their male participants. This review was conducted in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) following a three-step process of planning, conducting, and reporting the review. All three authors reviewed all the eligible articles. There was significant methodological heterogeneity between the sources identified (n = 11). A narrative synthesis identified three key themes: health promotion, wellbeing, and socialisation intergenerational mentoring and Aboriginal and Torres Strait Islander men’s health. Men’s sheds serve as ideal locations for the delivery of initiatives that can positively impact on the health and wellbeing of their male participants. Furthermore, research is needed to explore the implementation and evaluation of these health and wellbeing initiatives for men in their respective communities.
Automated methods for diagnosis of Parkinson’s disease and predicting severity level
- Ayaz, Zainab, Naz, Saeeda, Khan, Naila, Razzak, Imran, Imran, Muhammad
- Authors: Ayaz, Zainab , Naz, Saeeda , Khan, Naila , Razzak, Imran , Imran, Muhammad
- Date: 2023
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 35, no. 20 (2023), p. 14499-14534
- Full Text: false
- Reviewed:
- Description: The recent advancements in information technology and bioinformatics have led to exceptional contributions in medical sciences. Extensive developments have been recorded for digital devices, thermometers, digital equipments and health monitoring systems for the automated disease diagnosis of different diseases. These automated systems assist doctors with accurate and efficient disease diagnosis. Parkinson’s disease is a neurodegenerative disorder that affects the nervous system. Over the years, numerous efforts have been reported for the efficient automatic detection of Parkinson’s disease. Different datasets including voice data samples, radiology images, and handwriting samples and gait specimens have been used for analysis and detection. Techniques such as machine learning and deep learning have been used broadly and reported promising results. This review paper aims to provide a comprehensive survey of the use of artificial intelligence for Parkinson’s disease diagnosis. The available datasets and their various properties are discussed in detail. Further, a thorough overview is provided for the existing algorithms, methods and approaches utilizing different datasets. Several key peculiarities and challenges are also provided based on the comprehensive literature review to diagnose a healthy or unhealthy person. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
- Younas, Ahtisham, Porr, Caroline, Maddigan, Joy, Moore, Julia, Navarro, Pablo, Whitehead, Dean
- Authors: Younas, Ahtisham , Porr, Caroline , Maddigan, Joy , Moore, Julia , Navarro, Pablo , Whitehead, Dean
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of Clinical Nursing Vol. 32, no. 13-14 (2023), p. 4024-4036
- Full Text: false
- Reviewed:
- Description: Aims and objectives: To explore behavioural indicators of compassionate nursing care from the perspectives of individuals with multimorbidities and complex needs. Background: Complex patients are individuals with multimorbidity and/or mental health concerns, andoften with medication and drug-related problems requiring ongoing person-centered care, mental health interventions, and family and community resources. They are frequent consumers of health-care services and it is documented that these patients experience discrimination and substandard care. Compassionate care can improve patient care experiences and health outcomes. However, missing is the guidance on how to provide compassionate care for this population from the perspectives of complex patients. Design: A qualitative descriptive approach was conducted in eastern Canada from December 2020–April 2021. The COREQ guidelines were followed for reporting. Methods: Data from in-person and virtual semi-structured interviews with 23 individuals having experiences as complex patients were analysed using reflexive thematic analysis. Among them 19 were homeless and lived in a shelter. Findings: Six indicators of compassionate nursing care were generated: sensitivity, awareness, a non-judgmental approach, a positive demeanour, empathic understanding, and altruism. Conclusions: Individuals perceived that nurses who acknowledge personal biases are better at providing compassionate care by manifesting compassion through their genuine and selfless interest in the complicated health problems and underlying socio-cultural determinants of each patient. Kindness, positivity, and a respectful nursing approach elicit openness and the sharing of heartfelt concerns. Relevance to clinical practice: Comprehensive health assessment, dedicated efforts to know the patient as a human being, and listening to the patient's preferences can improve health outcomes among individuals with complex needs. Healthcare administrators can effect the change by supporting nurses to address complex health and social care needs with compassion. Patient or public contribution: Patients and healthcare professionals helped in data collection at the community care centre. © 2022 John Wiley & Sons Ltd.
Being private, big 4 auditors, and debt raising
- Sharpe, Wen, Carey, Peter, Zhang, Hong
- Authors: Sharpe, Wen , Carey, Peter , Zhang, Hong
- Date: 2023
- Type: Text , Journal article
- Relation: Accounting and Finance Vol. 63, no. 2 (2023), p. 2295-2345
- Full Text:
- Reviewed:
- Description: This study investigates the role of auditor choice (Big 4/Non-Big 4) in debt financing for private and public firms. We find private firms have less access to debt than public firms, and Big 4 auditors support debt raising in both private and public firms. Consistent with private firms facing greater information asymmetry, Big 4 auditors are more important for debt raising in private firms than in public firms. The benefit of appointing Big 4 auditors for private firms' debt raising is greater in the opaque information environment of the global financial crisis. It is also greater when firms are smaller, younger, or have poorer financial reporting quality. We also find evidence consistent with Big 4 auditors mitigating agency conflicts and enhancing debt raising when ownership concentration is higher in private firms. © 2022 The Authors. Accounting & Finance published by John Wiley & Sons Australia, Ltd on behalf of Accounting and Finance Association of Australia and New Zealand.
- Authors: Sharpe, Wen , Carey, Peter , Zhang, Hong
- Date: 2023
- Type: Text , Journal article
- Relation: Accounting and Finance Vol. 63, no. 2 (2023), p. 2295-2345
- Full Text:
- Reviewed:
- Description: This study investigates the role of auditor choice (Big 4/Non-Big 4) in debt financing for private and public firms. We find private firms have less access to debt than public firms, and Big 4 auditors support debt raising in both private and public firms. Consistent with private firms facing greater information asymmetry, Big 4 auditors are more important for debt raising in private firms than in public firms. The benefit of appointing Big 4 auditors for private firms' debt raising is greater in the opaque information environment of the global financial crisis. It is also greater when firms are smaller, younger, or have poorer financial reporting quality. We also find evidence consistent with Big 4 auditors mitigating agency conflicts and enhancing debt raising when ownership concentration is higher in private firms. © 2022 The Authors. Accounting & Finance published by John Wiley & Sons Australia, Ltd on behalf of Accounting and Finance Association of Australia and New Zealand.
Benchmarking Australian enabling programs for a national framework of standards, a practice report
- Davis, Charmaine, Cook, Chris, Syme, Suzi, Dempster, Sarah, Duffy, Lisa, Hattam, Sarah, Lambrinidis, George, Lawson, Kathy, Levy, Stuart
- Authors: Davis, Charmaine , Cook, Chris , Syme, Suzi , Dempster, Sarah , Duffy, Lisa , Hattam, Sarah , Lambrinidis, George , Lawson, Kathy , Levy, Stuart
- Date: 2023
- Type: Text , Journal article
- Relation: Student Success Vol. 14, no. 2 (2023), p. 41-49
- Full Text:
- Reviewed:
- Description: Enabling education programs in Australia assist students, who would otherwise have been excluded from higher education, to transition into undergraduate study. These programs emerged independently in response to the needs of individual universities and the varying cohorts of students they serve. The exclusion of these programs from the Australian Qualifications Framework (AQF) has meant they remain unregulated, with no national framework for standards. The development of academic standards is a dynamic, consensus driven process, and benchmarking provides a method through which academics from across institutions can work in partnership to reach shared understandings and improve and align practices. This practice report outlines the results of the first comprehensive cross-institutional benchmarking project involving nine Australian universities and demonstrates there is shared understanding of the standards of enabling programs between institutions. These findings will contribute to the establishment of national standards for enabling programs in Australia. ©The Author/s 2023.
- Authors: Davis, Charmaine , Cook, Chris , Syme, Suzi , Dempster, Sarah , Duffy, Lisa , Hattam, Sarah , Lambrinidis, George , Lawson, Kathy , Levy, Stuart
- Date: 2023
- Type: Text , Journal article
- Relation: Student Success Vol. 14, no. 2 (2023), p. 41-49
- Full Text:
- Reviewed:
- Description: Enabling education programs in Australia assist students, who would otherwise have been excluded from higher education, to transition into undergraduate study. These programs emerged independently in response to the needs of individual universities and the varying cohorts of students they serve. The exclusion of these programs from the Australian Qualifications Framework (AQF) has meant they remain unregulated, with no national framework for standards. The development of academic standards is a dynamic, consensus driven process, and benchmarking provides a method through which academics from across institutions can work in partnership to reach shared understandings and improve and align practices. This practice report outlines the results of the first comprehensive cross-institutional benchmarking project involving nine Australian universities and demonstrates there is shared understanding of the standards of enabling programs between institutions. These findings will contribute to the establishment of national standards for enabling programs in Australia. ©The Author/s 2023.
Beyond survival : strengthening community-based support for parents receiving a family service intervention
- Goff, Rachel, Sadowski, Christina, Bagley, Kerryn
- Authors: Goff, Rachel , Sadowski, Christina , Bagley, Kerryn
- Date: 2023
- Type: Text , Journal article
- Relation: Child and Family Social Work Vol. 28, no. 2 (2023), p. 491-502
- Full Text:
- Reviewed:
- Description: This paper presents parents' experiences of community support and their recommendations for how their communities, and the services within them, might support their families. Generated through a human-centred design methodology and using a desire-centred framework, the findings suggest that parents receiving a family service require support invoking feelings of intimacy, trust, reciprocity, inclusivity, connection and belonging. Parents' recommendations for community support include addressing material and attitudinal constraints impacting on engagement with services; creating non-judgmental services tailored to their needs but accessed as a last resort; and creating peer-based opportunities to support each other. Parents reflect that moving beyond basic survival of risk and vulnerability to a position where thriving is possible requires purposeful integration of parent's existing and desired community into service interventions. Facilitating deliberate change at the intersection of community and service support is pertinent to current and future social work policy and practice. Wider opportunities for understanding and enabling the needs and aspirations of parents, which are often overlooked because of a focus on addressing risk and vulnerability, are considered. © 2022 The Authors. Child & Family Social Work published by John Wiley & Sons Ltd.
- Authors: Goff, Rachel , Sadowski, Christina , Bagley, Kerryn
- Date: 2023
- Type: Text , Journal article
- Relation: Child and Family Social Work Vol. 28, no. 2 (2023), p. 491-502
- Full Text:
- Reviewed:
- Description: This paper presents parents' experiences of community support and their recommendations for how their communities, and the services within them, might support their families. Generated through a human-centred design methodology and using a desire-centred framework, the findings suggest that parents receiving a family service require support invoking feelings of intimacy, trust, reciprocity, inclusivity, connection and belonging. Parents' recommendations for community support include addressing material and attitudinal constraints impacting on engagement with services; creating non-judgmental services tailored to their needs but accessed as a last resort; and creating peer-based opportunities to support each other. Parents reflect that moving beyond basic survival of risk and vulnerability to a position where thriving is possible requires purposeful integration of parent's existing and desired community into service interventions. Facilitating deliberate change at the intersection of community and service support is pertinent to current and future social work policy and practice. Wider opportunities for understanding and enabling the needs and aspirations of parents, which are often overlooked because of a focus on addressing risk and vulnerability, are considered. © 2022 The Authors. Child & Family Social Work published by John Wiley & Sons Ltd.
Blockchain technology and application : an overview
- Dong, Shi, Abbas, Khushnood, Li, Meixi, Kamruzzaman, Joarder
- Authors: Dong, Shi , Abbas, Khushnood , Li, Meixi , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article
- Relation: PeerJ Computer Science Vol. 9, no. (2023), p.
- Full Text:
- Reviewed:
- Description: In recent years, with the rise of digital currency, its underlying technology, blockchain, has become increasingly well-known. This technology has several key characteristics, including decentralization, time-stamped data, consensus mechanism, traceability, programmability, security, and credibility, and block data is essentially tamper-proof. Due to these characteristics, blockchain can address the shortcomings of traditional financial institutions. As a result, this emerging technology has garnered significant attention from financial intermediaries, technology-based companies, and government agencies. This article offers an overview of the fundamentals of blockchain technology and its various applications. The introduction defines blockchain and explains its fundamental working principles, emphasizing features such as decentralization, immutability, and transparency. The article then traces the evolution of blockchain, from its inception in cryptocurrency to its development as a versatile tool with diverse potential applications. The main body of the article explores fundamentals of block chain systems, its limitations, various applications, applicability etc. Finally, the study concludes by discussing the present state of blockchain technology and its future potential, as well as the challenges that must be surmounted to unlock its full potential. © Copyright 2023 Dong et al
- Authors: Dong, Shi , Abbas, Khushnood , Li, Meixi , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article
- Relation: PeerJ Computer Science Vol. 9, no. (2023), p.
- Full Text:
- Reviewed:
- Description: In recent years, with the rise of digital currency, its underlying technology, blockchain, has become increasingly well-known. This technology has several key characteristics, including decentralization, time-stamped data, consensus mechanism, traceability, programmability, security, and credibility, and block data is essentially tamper-proof. Due to these characteristics, blockchain can address the shortcomings of traditional financial institutions. As a result, this emerging technology has garnered significant attention from financial intermediaries, technology-based companies, and government agencies. This article offers an overview of the fundamentals of blockchain technology and its various applications. The introduction defines blockchain and explains its fundamental working principles, emphasizing features such as decentralization, immutability, and transparency. The article then traces the evolution of blockchain, from its inception in cryptocurrency to its development as a versatile tool with diverse potential applications. The main body of the article explores fundamentals of block chain systems, its limitations, various applications, applicability etc. Finally, the study concludes by discussing the present state of blockchain technology and its future potential, as well as the challenges that must be surmounted to unlock its full potential. © Copyright 2023 Dong et al
Bundle enrichment method for nonsmooth difference of convex programming problems
- Gaudioso, Manilo, Taheri, Sona, Bagirov, Adil, Karmitsa, Napsu
- Authors: Gaudioso, Manilo , Taheri, Sona , Bagirov, Adil , Karmitsa, Napsu
- Date: 2023
- Type: Text , Journal article
- Relation: Algorithms Vol. 16, no. 8 (2023), p.
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
- Description: The Bundle Enrichment Method (BEM-DC) is introduced for solving nonsmooth difference of convex (DC) programming problems. The novelty of the method consists of the dynamic management of the bundle. More specifically, a DC model, being the difference of two convex piecewise affine functions, is formulated. The (global) minimization of the model is tackled by solving a set of convex problems whose cardinality depends on the number of linearizations adopted to approximate the second DC component function. The new bundle management policy distributes the information coming from previous iterations to separately model the DC components of the objective function. Such a distribution is driven by the sign of linearization errors. If the displacement suggested by the model minimization provides no sufficient decrease of the objective function, then the temporary enrichment of the cutting plane approximation of just the first DC component function takes place until either the termination of the algorithm is certified or a sufficient decrease is achieved. The convergence of the BEM-DC method is studied, and computational results on a set of academic test problems with nonsmooth DC objective functions are provided. © 2023 by the authors.
- Authors: Gaudioso, Manilo , Taheri, Sona , Bagirov, Adil , Karmitsa, Napsu
- Date: 2023
- Type: Text , Journal article
- Relation: Algorithms Vol. 16, no. 8 (2023), p.
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
- Description: The Bundle Enrichment Method (BEM-DC) is introduced for solving nonsmooth difference of convex (DC) programming problems. The novelty of the method consists of the dynamic management of the bundle. More specifically, a DC model, being the difference of two convex piecewise affine functions, is formulated. The (global) minimization of the model is tackled by solving a set of convex problems whose cardinality depends on the number of linearizations adopted to approximate the second DC component function. The new bundle management policy distributes the information coming from previous iterations to separately model the DC components of the objective function. Such a distribution is driven by the sign of linearization errors. If the displacement suggested by the model minimization provides no sufficient decrease of the objective function, then the temporary enrichment of the cutting plane approximation of just the first DC component function takes place until either the termination of the algorithm is certified or a sufficient decrease is achieved. The convergence of the BEM-DC method is studied, and computational results on a set of academic test problems with nonsmooth DC objective functions are provided. © 2023 by the authors.
Caffeine does not influence persistent inward current contribution to motoneuron firing
- Mackay, Karen, Orssatto, Lucas, Polman, Remco, Van der Pols, Jolieke, Trajano, Gabriel
- Authors: Mackay, Karen , Orssatto, Lucas , Polman, Remco , Van der Pols, Jolieke , Trajano, Gabriel
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of Neurophysiology Vol. 130, no. 6 (2023), p. 1529-1540
- Full Text: false
- Reviewed:
- Description: The purpose of this study was to investigate whether caffeine consumption would change persistent inward current (PIC) contribution to motoneuron firing at increased contraction intensities and after repetitive sustained maximal contractions. Before and after the consumption of 6 mg·kg-1 of caffeine or placebo, 16 individuals performed isometric triangular-shaped ramp dorsiflexion contractions (to 20% and 40% of peak torque), followed by four maximal contractions sustained until torque production dropped to 60% of maximum, and consecutive 20% triangular-shaped contractions. Tibialis anterior motor unit firing frequencies were analyzed from high-density surface electromyograms. PIC contribution to motor unit firing was estimated by calculating the delta frequency (DF) using the paired motor unit technique. Motoneuron peak firing frequencies at 20% and 40% contractions and total torque-time integral during the repetitive sustained maximal contractions were also assessed. DF increased 0.69 peaks per second (pps) (95% CI ¼ -0.98, -0.405; d ¼ -0.87) from 20% to 40% contraction intensities and reduced 0.85 pps (95% CI ¼ 0.66, 1.05; d ¼ 0.99) after the repetitive sustained maximal contractions, regardless of caffeine consumption. Participants produced 337 Nm·s (95% CI ¼ 49.9, 624; d ¼ 0.63) more torque integral during the repetitive sustained maximal contractions after caffeine consumption. A strong repeated-measures correlation (r ¼ 0.61; 95% CI ¼ 0.49, 0.69) was observed between reductions of DF and peak firing frequencies after the repetitive sustained maximal contractions. PIC contribution to motoneuron firing increases from 20% to 40% contraction intensities, with no effect of caffeine (on rested tibialis anterior). Repetitive sustained maximal contractions reduced PIC contribution to motoneuron firing, regardless of caffeine or placebo consumption, evidencing that changes in intrinsic motoneuron properties contributed to performance loss. Caffeine-attenuated reduction of torque production capacity was unlikely mediated by PICs. © 2023 American Physiological Society. All rights reserved.
Cancer classification utilizing voting classifier with ensemble feature selection method and transcriptomic data
- Khatun, Rabea, Akter, Maksuda, Islam, Md Manowarul, Uddin, Md Ashraf, Talukder, Md Alamin, Kamruzzaman, Joarder, Azad, Akm, Paul, Bikash, Almoyad, Muhammad, Aryal, Sunil, Moni, Mohammad
- Authors: Khatun, Rabea , Akter, Maksuda , Islam, Md Manowarul , Uddin, Md Ashraf , Talukder, Md Alamin , Kamruzzaman, Joarder , Azad, Akm , Paul, Bikash , Almoyad, Muhammad , Aryal, Sunil , Moni, Mohammad
- Date: 2023
- Type: Text , Journal article
- Relation: Genes Vol. 14, no. 9 (2023), p.
- Full Text:
- Reviewed:
- Description: Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods. © 2023 by the authors.
- Authors: Khatun, Rabea , Akter, Maksuda , Islam, Md Manowarul , Uddin, Md Ashraf , Talukder, Md Alamin , Kamruzzaman, Joarder , Azad, Akm , Paul, Bikash , Almoyad, Muhammad , Aryal, Sunil , Moni, Mohammad
- Date: 2023
- Type: Text , Journal article
- Relation: Genes Vol. 14, no. 9 (2023), p.
- Full Text:
- Reviewed:
- Description: Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods. © 2023 by the authors.