Pub Date : 2024-03-11DOI: 10.46481/jnsps.2024.1911
Muhammad Dahiru Liman, S. Osanga, Esther Samuel Alu, Sa'adu Zakariya
This research examines the impact of three widely utilized regularization approaches -- data augmentation, weight decay, and dropout --on mitigating overfitting, as well as various amalgamations of these methods. Employing a Convolutional Neural Network (CNN), the study assesses the performance of these strategies using two distinct datasets: a flower dataset and the CIFAR-10 dataset. The findings reveal that dropout outperforms weight decay and augmentation on both datasets. Additionally, a hybrid of dropout and augmentation surpasses other method combinations in effectiveness. Significantly, integrating weight decay with dropout and augmentation yields the best performance among all tested method blends. Analyses were conducted in relation to dataset size and convergence time (measured in epochs). Dropout consistently showed superior performance across all dataset sizes, while the combination of dropout and augmentation was the most effective across all sizes, and the triad of weight decay, dropout, and augmentation excelled over other combinations. The epoch-based analysis indicated that the effectiveness of certain techniques scaled with dataset size, with varying results.
{"title":"Regularization Effects in Deep Learning Architecture","authors":"Muhammad Dahiru Liman, S. Osanga, Esther Samuel Alu, Sa'adu Zakariya","doi":"10.46481/jnsps.2024.1911","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1911","url":null,"abstract":"This research examines the impact of three widely utilized regularization approaches -- data augmentation, weight decay, and dropout --on mitigating overfitting, as well as various amalgamations of these methods. Employing a Convolutional Neural Network (CNN), the study assesses the performance of these strategies using two distinct datasets: a flower dataset and the CIFAR-10 dataset. The findings reveal that dropout outperforms weight decay and augmentation on both datasets. Additionally, a hybrid of dropout and augmentation surpasses other method combinations in effectiveness. Significantly, integrating weight decay with dropout and augmentation yields the best performance among all tested method blends. Analyses were conducted in relation to dataset size and convergence time (measured in epochs). Dropout consistently showed superior performance across all dataset sizes, while the combination of dropout and augmentation was the most effective across all sizes, and the triad of weight decay, dropout, and augmentation excelled over other combinations. The epoch-based analysis indicated that the effectiveness of certain techniques scaled with dataset size, with varying results.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"78 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140251679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-06DOI: 10.46481/jnsps.2024.1897
A. Ibiyemi, O. Akinrinola, G. T. Yusuf, S. Olaniyan, J. Lawal, M. Orojo, B. Osuporu
The rheological characteristics of manganese zinc (Mn-Zn) ferrite magnetic nanofluid synthesized using co-precipitation technique were examined in the absence and presence of magnetic fields. The research formulates required conditions needed for the formation of a gelly-like structure. The impact of magnetic field and temperature on the rheological properties of Mn-Zn ferrite ferrofluid is investigated. When a magnetic field was applied, higher magnetoviscoelasticity and magnetoviscosity were formed. Analysis was also done on other rheological parameters, such as the damping factor, which is crucial for regulating and restricting vibrations in a system. A stiff, gel-like structure is produced when a magnetic field is applied, and the gel-like quality grows as the magnetic field increases; when the magnetic field is removed, the gel-like and rigidity of the structure is lost. At low temperatures, the liquid phase is dominated by solid-like particles, whereas at high temperatures, the liquid-like structure is dominant. This study reveals the conditions required for the creation of high viscous effect and the viscoelastic behavior induced by the field offers important insights for optimizing the Mn-Zn ferrite ferrofluid for a range of applications. Other criterial for gel-like structure formation such as low torque and deflection angle of the ferrofluid were also established.
{"title":"Advance effect of magnetic field on the rheological properties of manganese zinc ferrite ferrofluid","authors":"A. Ibiyemi, O. Akinrinola, G. T. Yusuf, S. Olaniyan, J. Lawal, M. Orojo, B. Osuporu","doi":"10.46481/jnsps.2024.1897","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1897","url":null,"abstract":"The rheological characteristics of manganese zinc (Mn-Zn) ferrite magnetic nanofluid synthesized using co-precipitation technique were examined in the absence and presence of magnetic fields. The research formulates required conditions needed for the formation of a gelly-like structure. The impact of magnetic field and temperature on the rheological properties of Mn-Zn ferrite ferrofluid is investigated. When a magnetic field was applied, higher magnetoviscoelasticity and magnetoviscosity were formed. Analysis was also done on other rheological parameters, such as the damping factor, which is crucial for regulating and restricting vibrations in a system. A stiff, gel-like structure is produced when a magnetic field is applied, and the gel-like quality grows as the magnetic field increases; when the magnetic field is removed, the gel-like and rigidity of the structure is lost. At low temperatures, the liquid phase is dominated by solid-like particles, whereas at high temperatures, the liquid-like structure is dominant. This study reveals the conditions required for the creation of high viscous effect and the viscoelastic behavior induced by the field offers important insights for optimizing the Mn-Zn ferrite ferrofluid for a range of applications. Other criterial for gel-like structure formation such as low torque and deflection angle of the ferrofluid were also established. ","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"94 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.46481/jnsps.2024.1726
A. Y. Jimoh, M. B. Saadu, A. A. Adetoro, J. Ajadi, T. Issa, U. Issa
Grain size analysis, geochemistry, and petrography of sandstones of the Ilaro Formation exposed at the Ajegunle area were investigated to infer provenance, transportation history, tectonic setting, paleoenvironment, and degree of palaeoweathering of the sediments. Selected sandstones were analyzed, and the major, trace, and rare earth elements were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Results from the granulometric analysis showed that sandstones were deposited in fluvial conditions. The sandstones exhibit a coarse-grained texture, displaying poor sorting and being texturally immature. The petrographic analysis indicated that quartz was predominant, whereas opaque minerals, muscovite, and ferruginous ground mass were present in smaller quantities. The sandstones can be geochemically classified as arkose and subarenite. The sandstones have an average composition of SiO2 (82.87%) and Al2O3 (9.49%), while K2O, Na2O, MgO, CaO, and P2O5 have <1% each. The elevated Al2O3 content is associated with the lithic fragment composition, whereas the low concentrations of MgO (mean 0.03%), Na2O (mean 0.008%), and K2O (mean 0.04%) suggest chemical destruction in an oxidizing environment. The angularity of the grains indicated a short transportation history very close to the provenance. Bivariate and discriminant plots from major elements and trace elements suggest the sandstones were non-marine and sourced from intermediate rocks. The sandstones were deposited in an oxic-dyoxic condition under a humid climate and passive or active continental margins. The average values of the weathering indices indicate an intense degree of chemical weathering.
{"title":"Sedimentological and geochemical evaluation of sandstones of the Ilaro formation, Dahomey Basin, Southwestern Nigeria : Insights into paleoenvironments, provenance, and tectonic settings","authors":"A. Y. Jimoh, M. B. Saadu, A. A. Adetoro, J. Ajadi, T. Issa, U. Issa","doi":"10.46481/jnsps.2024.1726","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1726","url":null,"abstract":"Grain size analysis, geochemistry, and petrography of sandstones of the Ilaro Formation exposed at the Ajegunle area were investigated to infer provenance, transportation history, tectonic setting, paleoenvironment, and degree of palaeoweathering of the sediments. Selected sandstones were analyzed, and the major, trace, and rare earth elements were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Results from the granulometric analysis showed that sandstones were deposited in fluvial conditions. The sandstones exhibit a coarse-grained texture, displaying poor sorting and being texturally immature. The petrographic analysis indicated that quartz was predominant, whereas opaque minerals, muscovite, and ferruginous ground mass were present in smaller quantities. The sandstones can be geochemically classified as arkose and subarenite. The sandstones have an average composition of SiO2 (82.87%) and Al2O3 (9.49%), while K2O, Na2O, MgO, CaO, and P2O5 have <1% each. The elevated Al2O3 content is associated with the lithic fragment composition, whereas the low concentrations of MgO (mean 0.03%), Na2O (mean 0.008%), and K2O (mean 0.04%) suggest chemical destruction in an oxidizing environment. The angularity of the grains indicated a short transportation history very close to the provenance. Bivariate and discriminant plots from major elements and trace elements suggest the sandstones were non-marine and sourced from intermediate rocks. The sandstones were deposited in an oxic-dyoxic condition under a humid climate and passive or active continental margins. The average values of the weathering indices indicate an intense degree of chemical weathering.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140415363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-19DOI: 10.46481/jnsps.2024.1692
S. Akinwunmi, Garba Risqot Ibrahim, A. Adeniji, D. Oyewola
The application of graph theory has gained significant traction within the realm of the algebraic theory of semigroups. This study delves into exploring the geometric properties of the star-like transformation semigroup alphaomega_n^*, a distinctive category of transformation, and delineates a tropical graph (curve) by elucidating its algebraic and tropical structure. Through this investigation, various tropical properties are established, offering insights into the graph theory aspects of star-like spinnable Tomega_n^* transformation semigroups. Consequently, the objective of this research is to delineate and characterize several tropical and combinatorial functions applicable to Tomega_n^*.
{"title":"On the multiplicity order of spinnable star-like transformation semigroup Tw*n","authors":"S. Akinwunmi, Garba Risqot Ibrahim, A. Adeniji, D. Oyewola","doi":"10.46481/jnsps.2024.1692","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1692","url":null,"abstract":"The application of graph theory has gained significant traction within the realm of the algebraic theory of semigroups. This study delves into exploring the geometric properties of the star-like transformation semigroup alphaomega_n^*, a distinctive category of transformation, and delineates a tropical graph (curve) by elucidating its algebraic and tropical structure. Through this investigation, various tropical properties are established, offering insights into the graph theory aspects of star-like spinnable Tomega_n^* transformation semigroups. Consequently, the objective of this research is to delineate and characterize several tropical and combinatorial functions applicable to Tomega_n^*.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"86 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140449198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-18DOI: 10.46481/jnsps.2024.1909
A. Ibiyemi, G. T. Yusuf, O. Akirinola, M. Orojo, B. Osuporu, J. Lawal
The impact of transition metals on ferrite (iron (III) oxide) compounds is investigated in this study. Ferrite samples were synthesized using the co-precipitation method. X-ray analysis unveiled the presence of the Fe-phase in the trivalent state, showcasing a single-phased cubic spinel framework with a preferred orientation along the (311) reflection plane. Crystallite sizes were determined for CdFe3O4, ZnFe3O4, and CoFe3O4 utilizing the Scherer equation, yielding values of 10.54 nm, 18.76 nm, and 32.63 nm, respectively. Zinc ferrite displayed an intermediate photonic nature compared to cobalt and cadmium ferrite, with cadmium ferrite showing high optical losses and cobalt ferrite exhibiting minimal optical losses. EDX analysis confirmed the presence of Zn2+, Co2+, Fe3+, Cd2+, and O2? ions in the correct ratios, supporting the intended stoichiometric composition. Optical assessment revealed that CoFe3O4 nanoparticles are well-suited for optoelectronic devices, ultraviolet detectors, and infrared (IR) detectors. VSM measurements of cobalt ferrite exhibited higher coercivity and magnetic saturation compared to other samples. Photoluminescence (PL) spectroscopy revealed multiple colors, including cyan, green, and yellow, at different wavelengths for the ferrite samples. These findings suggest that the synthesized samples are suitable materials for high-frequency devices owing to their robust magnetic properties. Cadmium ferrite displayed a multi-magnetic domain structure, contrasting with the single-magnetic domain structure observed in zinc and cobalt ferrite.
{"title":"Investigating the magnetic domain structure and photonics characters of Singled Phased hard ferromagnetic Ferrite MFe3O4 (M= Co2+, Zn2+, Cd2+) Compounds","authors":"A. Ibiyemi, G. T. Yusuf, O. Akirinola, M. Orojo, B. Osuporu, J. Lawal","doi":"10.46481/jnsps.2024.1909","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1909","url":null,"abstract":"The impact of transition metals on ferrite (iron (III) oxide) compounds is investigated in this study. Ferrite samples were synthesized using the co-precipitation method. X-ray analysis unveiled the presence of the Fe-phase in the trivalent state, showcasing a single-phased cubic spinel framework with a preferred orientation along the (311) reflection plane. Crystallite sizes were determined for CdFe3O4, ZnFe3O4, and CoFe3O4 utilizing the Scherer equation, yielding values of 10.54 nm, 18.76 nm, and 32.63 nm, respectively. Zinc ferrite displayed an intermediate photonic nature compared to cobalt and cadmium ferrite, with cadmium ferrite showing high optical losses and cobalt ferrite exhibiting minimal optical losses. EDX analysis confirmed the presence of Zn2+, Co2+, Fe3+, Cd2+, and O2? ions in the correct ratios, supporting the intended stoichiometric composition. Optical assessment revealed that CoFe3O4 nanoparticles are well-suited for optoelectronic devices, ultraviolet detectors, and infrared (IR) detectors. VSM measurements of cobalt ferrite exhibited higher coercivity and magnetic saturation compared to other samples. Photoluminescence (PL) spectroscopy revealed multiple colors, including cyan, green, and yellow, at different wavelengths for the ferrite samples. These findings suggest that the synthesized samples are suitable materials for high-frequency devices owing to their robust magnetic properties. Cadmium ferrite displayed a multi-magnetic domain structure, contrasting with the single-magnetic domain structure observed in zinc and cobalt ferrite.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-18DOI: 10.46481/jnsps.2024.1092
O. Adesina, A. F. Adedotuun, K. S. Adekeye, O. F. Imaga, Adeleke J. Adeyiga, T. J. Akingbade
The performance of two classification techniques, logistic regression and Support Vector Machines (SVMs), in assessing vaccination data is investigated in this study. The model was trained based on leave-out-one cross validation to obtain an accurate result. Simulated with ten thousand replications, a life data set was used to establish a better model. The findings from the simulation revealed that the logistic regression model slightly outperformed the SVM while the life data shows that the tuned SVM outperformed both the logistic and the SVM. This demonstrates the practical utility of advanced approaches such as SVMs in difficult categorization scenarios such as vaccination prediction. The study emphasizes the superiority of the customized SVM model in this setting, as well as the potential of machine learning approaches to increase comprehension of complicated healthcare scenarios and guide data-driven decision-making for influencing vaccination plans and public health. The study recommends the use of logistic regression if the data point is high.
{"title":"On logistic regression versus support vectors machine using vaccination dataset","authors":"O. Adesina, A. F. Adedotuun, K. S. Adekeye, O. F. Imaga, Adeleke J. Adeyiga, T. J. Akingbade","doi":"10.46481/jnsps.2024.1092","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1092","url":null,"abstract":"The performance of two classification techniques, logistic regression and Support Vector Machines (SVMs), in assessing vaccination data is investigated in this study. The model was trained based on leave-out-one cross validation to obtain an accurate result. Simulated with ten thousand replications, a life data set was used to establish a better model. The findings from the simulation revealed that the logistic regression model slightly outperformed the SVM while the life data shows that the tuned SVM outperformed both the logistic and the SVM. This demonstrates the practical utility of advanced approaches such as SVMs in difficult categorization scenarios such as vaccination prediction. The study emphasizes the superiority of the customized SVM model in this setting, as well as the potential of machine learning approaches to increase comprehension of complicated healthcare scenarios and guide data-driven decision-making for influencing vaccination plans and public health. The study recommends the use of logistic regression if the data point is high.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"202 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-18DOI: 10.46481/jnsps.2024.1823
O. Ige, K. H. Gan
Following the increasing number of high dimensional data, selecting relevant features has always been better handled by filter feature selection techniques due to its improved generalization, faster training time, dimensionality reduction, less prone to overfitting, and improved model performance. However, the most used feature selection methods are unstable; a feature selection method chooses different subsets of characteristics that produce different classification accuracy. Selecting an appropriate hybrid harnesses the local feature relevant to the discriminative power of filter methods for improved text classification, which is lacking in past literature. In this paper, we proposed a novel multi-univariate hybrid feature selection method (MUNIFES) for enhanced discriminative power between the features and the target class. The proposed method utilizes multi-iterative processes to select the best feature sets from each univariate feature selection method. MUNIFES has employed the ensemble of multi-filter discriminative strength of Chi-Square (Chi2), Analysis of Variance (ANOVA), and Infogain methods to select optimal feature subsets. To evaluate the success of the proposed method, several experiments were performed on the 20newsgroup dataset and its variant (17newsgroup) with 10 classifiers (including ensemble, classification and optimization algorithms, and Artificial Neural Network (ANN)), compared with the state-of-the-art feature selection methods. The MUNIFES results indicated a better accuracy classification performance.
{"title":"Ensemble feature selection using weighted concatenated voting for text classification","authors":"O. Ige, K. H. Gan","doi":"10.46481/jnsps.2024.1823","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1823","url":null,"abstract":"Following the increasing number of high dimensional data, selecting relevant features has always been better handled by filter feature selection techniques due to its improved generalization, faster training time, dimensionality reduction, less prone to overfitting, and improved model performance. However, the most used feature selection methods are unstable; a feature selection method chooses different subsets of characteristics that produce different classification accuracy. Selecting an appropriate hybrid harnesses the local feature relevant to the discriminative power of filter methods for improved text classification, which is lacking in past literature. In this paper, we proposed a novel multi-univariate hybrid feature selection method (MUNIFES) for enhanced discriminative power between the features and the target class. The proposed method utilizes multi-iterative processes to select the best feature sets from each univariate feature selection method. MUNIFES has employed the ensemble of multi-filter discriminative strength of Chi-Square (Chi2), Analysis of Variance (ANOVA), and Infogain methods to select optimal feature subsets. To evaluate the success of the proposed method, several experiments were performed on the 20newsgroup dataset and its variant (17newsgroup) with 10 classifiers (including ensemble, classification and optimization algorithms, and Artificial Neural Network (ANN)), compared with the state-of-the-art feature selection methods. The MUNIFES results indicated a better accuracy classification performance.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"5 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.46481/jnsps.2024.1631
Benson Ade Eniola Afere
This paper introduces a novel family of fourth-order hybrid beta polynomial kernels tailored for statistical analysis. The efficacy of these kernels is evaluated using two principal performance metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Comprehensive assessments were conducted using both simulated and real-world datasets, enabling a thorough comparison with conventional fourth-order polynomial kernels. The evaluation process entailed computing AMISE and efficiency metrics for both the hybrid and classical kernels. Consistently, the results illustrated the superior performance of the hybrid kernels over their classical counterparts across diverse datasets, underscoring the robustness and effectiveness of the hybrid approach. By leveraging these performance metrics and conducting evaluations on simulated and real world data, this study furnishes compelling evidence supporting the superiority of the proposed hybrid beta polynomial kernels. The heightened performance, evidenced by lower AMISE values and elevated efficiency scores, strongly advocates for the adoption of the proposed kernels in statistical analysis tasks, presenting a marked improvement over traditional kernels.
{"title":"On the fourth-order hybrid beta polynomial kernels in kernel density estimation","authors":"Benson Ade Eniola Afere","doi":"10.46481/jnsps.2024.1631","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1631","url":null,"abstract":"This paper introduces a novel family of fourth-order hybrid beta polynomial kernels tailored for statistical analysis. The efficacy of these kernels is evaluated using two principal performance metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Comprehensive assessments were conducted using both simulated and real-world datasets, enabling a thorough comparison with conventional fourth-order polynomial kernels. The evaluation process entailed computing AMISE and efficiency metrics for both the hybrid and classical kernels. Consistently, the results illustrated the superior performance of the hybrid kernels over their classical counterparts across diverse datasets, underscoring the robustness and effectiveness of the hybrid approach. By leveraging these performance metrics and conducting evaluations on simulated and real world data, this study furnishes compelling evidence supporting the superiority of the proposed hybrid beta polynomial kernels. The heightened performance, evidenced by lower AMISE values and elevated efficiency scores, strongly advocates for the adoption of the proposed kernels in statistical analysis tasks, presenting a marked improvement over traditional kernels.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"75 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139774922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.46481/jnsps.2024.1631
Benson Ade Eniola Afere
This paper introduces a novel family of fourth-order hybrid beta polynomial kernels tailored for statistical analysis. The efficacy of these kernels is evaluated using two principal performance metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Comprehensive assessments were conducted using both simulated and real-world datasets, enabling a thorough comparison with conventional fourth-order polynomial kernels. The evaluation process entailed computing AMISE and efficiency metrics for both the hybrid and classical kernels. Consistently, the results illustrated the superior performance of the hybrid kernels over their classical counterparts across diverse datasets, underscoring the robustness and effectiveness of the hybrid approach. By leveraging these performance metrics and conducting evaluations on simulated and real world data, this study furnishes compelling evidence supporting the superiority of the proposed hybrid beta polynomial kernels. The heightened performance, evidenced by lower AMISE values and elevated efficiency scores, strongly advocates for the adoption of the proposed kernels in statistical analysis tasks, presenting a marked improvement over traditional kernels.
{"title":"On the fourth-order hybrid beta polynomial kernels in kernel density estimation","authors":"Benson Ade Eniola Afere","doi":"10.46481/jnsps.2024.1631","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1631","url":null,"abstract":"This paper introduces a novel family of fourth-order hybrid beta polynomial kernels tailored for statistical analysis. The efficacy of these kernels is evaluated using two principal performance metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Comprehensive assessments were conducted using both simulated and real-world datasets, enabling a thorough comparison with conventional fourth-order polynomial kernels. The evaluation process entailed computing AMISE and efficiency metrics for both the hybrid and classical kernels. Consistently, the results illustrated the superior performance of the hybrid kernels over their classical counterparts across diverse datasets, underscoring the robustness and effectiveness of the hybrid approach. By leveraging these performance metrics and conducting evaluations on simulated and real world data, this study furnishes compelling evidence supporting the superiority of the proposed hybrid beta polynomial kernels. The heightened performance, evidenced by lower AMISE values and elevated efficiency scores, strongly advocates for the adoption of the proposed kernels in statistical analysis tasks, presenting a marked improvement over traditional kernels.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"413 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.46481/jnsps.2024.1881
A. Sangotola, S. B. Adeyemo, O. A. Nuga, A. E. Adeniji, A. J. Adigun
The dynamics of tuberculosis within a population cannot be adequately represented by a single infectious class. Therefore, this study develops a compartmental model encompassing latent, active, and drug-resistant populations to better capture tuberculosis dynamics in a community. Model analysis reveals that the disease-free equilibrium point is locally asymptotically stable when the basic reproduction number is below one. Moreover, the use of a suitable Lyapunov function demonstrates global asymptotic stability of the disease-free equilibrium point. An endemic equilibrium emerges when the basic reproduction number exceeds one. Sensitivity analysis is conducted for each parameter associated with the basic reproduction number, and optimal control analysis is employed to assess the impact of various control strategies on disease containment. Numerical simulations are conducted to supplement theoretical findings, illustrating the practical implications of the proposed control strategies.
{"title":"A tuberculosis model with three infected classes","authors":"A. Sangotola, S. B. Adeyemo, O. A. Nuga, A. E. Adeniji, A. J. Adigun","doi":"10.46481/jnsps.2024.1881","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1881","url":null,"abstract":"The dynamics of tuberculosis within a population cannot be adequately represented by a single infectious class. Therefore, this study develops a compartmental model encompassing latent, active, and drug-resistant populations to better capture tuberculosis dynamics in a community. Model analysis reveals that the disease-free equilibrium point is locally asymptotically stable when the basic reproduction number is below one. Moreover, the use of a suitable Lyapunov function demonstrates global asymptotic stability of the disease-free equilibrium point. An endemic equilibrium emerges when the basic reproduction number exceeds one. Sensitivity analysis is conducted for each parameter associated with the basic reproduction number, and optimal control analysis is employed to assess the impact of various control strategies on disease containment. Numerical simulations are conducted to supplement theoretical findings, illustrating the practical implications of the proposed control strategies.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"289 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：