Advances in Multidisciplinary and Scientific Research Journal, Mar 30, 2024
Heart disease stands as the foremost contributor to global mortality in the present era. While a ... more Heart disease stands as the foremost contributor to global mortality in the present era. While a range of medical tests aids in diagnosing various manifestations of heart disease, anticipating its occurrence without resorting to these tests presents a formidable challenge. Machine learning has revealed a potential path for comprehending extensive medical datasets, unearthing concealed insights that elude human observation. Random Forest, Logistic Regression, XG Boost, k-nearest neighbor, and Gradient boosting was used to predict instances of heart diseases based on age and gender in this study. Regardless of the dataset's generalization or gender-specific characteristics, we observed that Random Forest, XG Boost, and K-Nearest Neighbors models exhibit high robustness and minimal sensitivity to hyperparameters. While the prediction accuracy remains consistent among Random Forest, Xgboost, and K-Nearest Neighbors, the crucial features responsible for achieving that accuracy vary among them. This observation indicates that it is important to test different machine learning algorithms when the interest is in prediction and understanding the most important feature(s).
The goals of this study are to describe machine learning techniques employing computer-vision mov... more The goals of this study are to describe machine learning techniques employing computer-vision movement algorithms to automatically evaluate infants' general movements (GMs) in the writhing stage. This is a retrospective study of infants admitted 07/2019 to 11/2021 to a level IV neonatal intensive care unit (NICU). Infant GMs, classified by certified expert, were analyzed in two-steps (1) determination of anatomic key point location using a NICU-trained pose estimation model [accuracy determined using object key point similarity (OKS)]; (2) development of a preliminary movement model to distinguish normal versus cramped-synchronized (CS) GMs using cosine similarity and autocorrelation of major joints. GMs were analyzed using 85 videos from 74 infants; gestational age at birth 28.9 ± 4.1 weeks and postmenstrual age (PMA) at time of video 35.9 ± 4.6 weeks The NICU-trained pose estimation model was more accurate (0.91 ± 0.008 OKS) than a generic model (0.83 ± 0.032 OKS, p < 0.001). Autocorrelation values in the lower limbs were significantly different between normal (5 videos) and CS GMs (5 videos, p < 0.05). These data indicate that automated pose estimation of anatomical key points is feasible in NICU patients and that a NICU-trained model can distinguish between normal and CS GMs. These preliminary data indicate that machine learning techniques may represent a promising tool for earlier CP risk assessment in the writhing stage and prior to hospital discharge. Cerebral palsy (CP) is the leading childhood motor neuro-disability, affecting 1 in 325 infants in the United States 1 . Advances in neonatal care have led to increased survival of preterm infants, even those born extremely preterm. However, the risk of neurodevelopmental disability and CP, in particular, remains high 2 with an increased incidence and severity of CP in infants born at younger gestational ages (GA) . Research shows that early identification of CP risk followed by early intervention at favorable stages of brain development are critical steps in improving outcomes . Infant general movements (GMs) are a reliable predictor of later motor development and have been used with high accuracy for the early identification of CP risk in high-risk infants . Infants typically exhibit these spontaneous GMs through 20 weeks post-term and progress from writhing GMs (9 weeks gestation to 8 weeks post-term) to fidgety GMs (6 to 20 weeks post-term) before transitioning to goal oriented movements 10 . Infants with abnormal GMs are known to be at higher risk of neuro-disability and CP in particular. The Prechtl General Movement Assessment (GMA) is a validated qualitative diagnostic tool developed for the classification of GMs by certified providers. The GMA is non-invasive and can be used shortly after birth to monitor GMs longitudinally with the goal to detect high-risk GM patterns . The presence of cramped-synchronized (CS) GMs in the writhing stage followed by absent fidgety GMs in the fidgety stage has a specificity of 96% for the identification of high-risk CP . Persistent CS GMs are also predictive of later diagnosis of CP with a specificity of 92.5% 12 . The GMA is typically used in combination with other assessments such as brain imaging and physical evaluation in order to make a diagnosis of CP 6 . While the GMA is an effective tool for the early detection of CP 6 , there are barriers to its widespread implementation into routine neonatal intensive care unit (NICU) clinical care, including associated cost of training and re-training, as well as resource allocation considerations to obtain and score videos 15 . As such, technologies aiming to decrease the impact of these barriers may lead to more neonates benefiting from the GMA. Many
In this study a predictive model using Multi-layer Perceptron of Artificial Neural Network archit... more In this study a predictive model using Multi-layer Perceptron of Artificial Neural Network architecture was developed to predict customer churn in financial institution. Previous researches have used supervised machine learning classifiers such as Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Random Forest. These classifiers require human effort to perform feature engineering which lead to over-specified and incomplete feature selection. Therefore, this research developed a model to eliminate manual feature engineering in data preprocessing stage. Fifty thousand customers' data were extracted from the database of one of the leading financial institution in Nigeria for the study. The multi-layer perceptron model was built with python programming language and used two overfitting techniques (Dropout and L2 regularization). The implementation done in python was compared with another model in Neuro solution infinity software. The results showed that the Artificial Neural Network software development (Python) had comparable performance with that obtained from the Neuro Solution Infinity software. The accuracy rates are 97.53% and 97.4% while ROC (Receiver Operating Characteristic) curve graphs are 0.89 and 0.85 respectively.
Journal of Internet and Information Systems, Jan 31, 2011
Nigeria is a country that has always posed a paradox to the international community in terms of i... more Nigeria is a country that has always posed a paradox to the international community in terms of its level of development vis-a-vis its economic potential. This is also reflected in the low ranking index it gets in international surveys despite the effort to develop its infrastructure and human capital. Nigeria has the fastest growing and most lucrative telecommunications, and Information and Communications Technology (ICT) market in Africa, yet in spite of this obvious and significant progress is still being ranked low in e-government provision to its citizen. This paper reviews the aim and methodology used by the ranking agencies, takes a survey of Nigeria's performance in global e-government surveys, identifies areas of deficiencies which have contributed to the low e-service delivery in the country despite the success recorded in the country's ICT and telecommunications sector and suggests how this situation may be improved.
Clustering algorithms generally suffer from some well-known problems for which the Self Organizin... more Clustering algorithms generally suffer from some well-known problems for which the Self Organizing Maps (SOM) algorithms are adept at handling. While there are many variants of the SOM algorithm, software programmes that implement the SOM algorithms have tended to give varying results even when tested on the same data sets. This can have serious implications when the goal of the clustering is novelty detection. In this study a comparison of the performance of some SOM clustering software was carried out and results presented. • General and reference ➝ -computing tools and techniques ➝ Empirical studies
The concept of the Data warehouse was developed to provide a single access point to data from a v... more The concept of the Data warehouse was developed to provide a single access point to data from a variety of sources. There is a need to have a single location for the storage and sharing of data that users can easily utilize to make effective and quality business decisions, rather than trying to traverse the multiple data sources that exist today. Although many frameworks have been developed to integrate these sources into a single database, a reliable framework has yet to be developed. A major hindrance to achieving a reliable warehouse is the poor quality of data obtained from the data transformation stage in the extract, transfer and load process. This poor quality of data contributes to inaccurate and unreliable results and if this data is used for decision making, unforeseen critical business errors can occur. This work reviews the data integration and transformation process in dimensional data warehouses and proposes a dual structure for data integration and metadata of multi-formatted data used for the design of dimensional data warehouse using Agrarian data collected from Ondo State, Nigeria as a case study.
The Weather is a continuous, data-intensive, multidimensional, dynamic process that makes weather... more The Weather is a continuous, data-intensive, multidimensional, dynamic process that makes weather forecasting a formidable challenge. Weather forecasting involves predicting how the present state of the atmosphere will change. Climate is the long-term effect of the sun's radiation on the rotating earth's varied surface and atmosphere. The Day-byday variations in a given area constitute the weather, whereas climate is the long-term synthesis of such variations. A simple, long-term summary of weather changes, however, is still not a true picture of climate. To obtain this requires the analysis of daily, monthly, and yearly patterns. This paper presents the use of Self Organizing Maps (SOM) and CoActive Neuro Fuzzy Inference System (CANFIS) soft computing techniques for weather and climate change studies using historical data collected from the city of Ibadan, Nigeria between 1951 and 2009. The results show that the soft computing techniques can be used for knowledge discovery in weather prediction and climate change studies.
Employee turnover is a serious concern in knowledge based organizations. When employees leave an ... more Employee turnover is a serious concern in knowledge based organizations. When employees leave an organization, they carry with them invaluable tacit knowledge which is often the source of competitive advantage for the business. In order for an organization to continually have a higher competitive advantage over its competition, it should make it a duty to minimize employee attrition. This study identifies employee related attributes that contribute to the prediction of employees' attrition in organizations. Three hundred and nine (309) complete records of employees of one of the Higher Institutions in Nigeria who worked in and left the institution between 1978 and 2006 were used for the study. The demographic and job related records of the employee were the main data which were used to classify the employee into some predefined attrition classes. Waikato Environment for Knowledge Analysis (WEKA) and See5 for Windows were used to generate decision tree models and rule-sets. The results of the decision tree models and rule-sets generated were then used for developing a a predictive model that was used to predict new cases of employee attrition. A framework for a software tool that can implement the rules generated in this study was also proposed.
In this study a predictive model using Multi-layer Perceptron of Artificial Neural Network archit... more In this study a predictive model using Multi-layer Perceptron of Artificial Neural Network architecture was developed to predict customer churn in financial institution. Previous researches have used supervised machine learning classifiers such as Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Random Forest. These classifiers require human effort to perform feature engineering which lead to over-specified and incomplete feature selection. Therefore, this research developed a model to eliminate manual feature engineering in data preprocessing stage. Fifty thousand customers' data were extracted from the database of one of the leading financial institution in Nigeria for the study. The multi-layer perceptron model was built with python programming language and used two overfitting techniques (Dropout and L2 regularization). The implementation done in python was compared with another model in Neuro solution infinity software. The results showed that the Artificial Neural Network software development (Python) had comparable performance with that obtained from the Neuro Solution Infinity software. The accuracy rates are 97.53% and 97.4% while ROC (Receiver Operating Characteristic) curve graphs are 0.89 and 0.85 respectively.
Journal of Internet and Information Systems, Jan 31, 2011
Nigeria is a country that has always posed a paradox to the international community in terms of i... more Nigeria is a country that has always posed a paradox to the international community in terms of its level of development vis-a-vis its economic potential. This is also reflected in the low ranking index it gets in international surveys despite the effort to develop its infrastructure and human capital. Nigeria has the fastest growing and most lucrative telecommunications, and Information and Communications Technology (ICT) market in Africa, yet in spite of this obvious and significant progress is still being ranked low in e-government provision to its citizen. This paper reviews the aim and methodology used by the ranking agencies, takes a survey of Nigeria's performance in global e-government surveys, identifies areas of deficiencies which have contributed to the low e-service delivery in the country despite the success recorded in the country's ICT and telecommunications sector and suggests how this situation may be improved.
Industrial pollution is often considered to be one of the prime factors contributing to air, wate... more Industrial pollution is often considered to be one of the prime factors contributing to air, water and soil pollution. Sectoral pollution loads (ton/yr) into different media (i.e. air, water and land) in Lagos were estimated using Industrial Pollution Projected System (IPPS). These were further studied using Artificial neural Networks (ANNs), a data mining technique that has the ability of detecting and describing patterns in large data sets with variables that are non-linearly related. Time Lagged Recurrent Network (TLRN) appeared as the best Neural Network model among all the neural networks considered which includes Multilayer Perceptron (MLP) Network, Generalized Feed Forward Neural Network (GFNN), Radial Basis Function (RBF) Network and Recurrent Network (RN). TLRN modelled the data-sets better than the others in terms of the mean average error (MAE) (0.14), time (39 s) and linear correlation coefficient (0.84). The results showed that Artificial Neural Networks (ANNs) technique (i.e., Time Lagged Recurrent Network) is also applicable and effective in environmental assessment study.
Signature recognition is the process of verifying a writer’s identity by checking the signature a... more Signature recognition is the process of verifying a writer’s identity by checking the signature against samples previously stored in the database. Several techniques such as the distance-based and statistical classifiers used for feature extraction on a signature image are not invariant to scaling and rotation and the Scale invariant feature transform (SIFT) though invariant to scaling and rotation cannot cater for intra-class variation (Transposition) among set of genuine signature images. This paper proposed a SIFT-SURF algorithm which is used for enhanced offline signature recognition. The SIFT-SURF algorithm computes integral image; obtains hessian data and interest point for each computed integral image; applied neuro-scaling PCA based radial basis function neural network to compute the optimal features for each signature image to come up with an algorithm that is invariant to scaling and rotation as well as reliably match transposition among genuine samples of a signature imag...
As markets have become increasingly saturated, companies have acknowledged that their business st... more As markets have become increasingly saturated, companies have acknowledged that their business strategies need to focuson identifying those customers who are most likely to churn. It is becoming common knowledge in business, that retainingexisting customers is the best core marketing strategy to survive in industry. In this research, both descriptive and predictivedata mining techniques were used to determine the calling behaviour of subscribers and to recognise subscribers with highprobability of churn in a telecommunications company subscriber database. First a data model for the input data variablesobtained from the subscriber database was developed. Then Simple K-Means and Expected Maximization (EM) clusteringalgorithms were used for the clustering stage, while Decision Stump, M5P and RepTree Decision Tree algorithms were usedfor the classification stage. The best algorithms in both the clustering and classification stages were used for the predictionprocess where customers that...
Data Mining also known as Knowledge Discovery from Databases (KDD) is the computer assisted proce... more Data Mining also known as Knowledge Discovery from Databases (KDD) is the computer assisted process of digging through and analyzing enormous or massive set of data stored either in data warehouse or information repositories and extracting the meaning of the data. Data Mining is a powerful technology that has a great potential to help governments, organizations and companies with wide range of industries including retail, finance, health care, manufacturing, transportation and aerospace focus on the most important information in the data they have collected about the behaviour of their clients and potential clients. However, Data Mining is only possible when the analyst or researcher has access to data and the right infrastructure. This paper reviews the Data Mining process, identifies some of the practical challenges of implementing Data Mining projects in the Nigerian environment and how some of them have been managed.
PurposeCollaborative-based national cybersecurity incident management benefits from the huge size... more PurposeCollaborative-based national cybersecurity incident management benefits from the huge size of incident information, large-scale information security devices and aggregation of security skills. However, no existing collaborative approach has been able to cater for multiple regulators, divergent incident views and incident reputation trust issues that national cybersecurity incident management presents. This paper aims to propose a collaborative approach to handle these issues cost-effectively.Design/methodology/approachA collaborative-based national cybersecurity incident management architecture based on ITU-T X.1056 security incident management framework is proposed. It is composed of the cooperative regulatory unit with cooperative and third-party management strategies and an execution unit, with incident handling and response strategies. Novel collaborative incident prioritization and mitigation planning models that are fit for incident handling in national cybersecurity in...
International Journal of Intelligent Systems Technologies and Applications, 2020
Recommender system is a type of information filtering system that is designed to curtail the diff... more Recommender system is a type of information filtering system that is designed to curtail the difficulties of information overload by automatically suggesting relevant items to users tailored to their preferences. Bayesian personalised smart linear methods (BPRSLIM) is a variant of item-based collaborative filtering technique used in information filtering system. Although, this algorithm has shown outstanding performance in a range of applications, nevertheless it suffers serious limitation of inability to provide accurate and reliable recommendations when the user-item matrix contains insufficient rating information, this always reduces its accuracy. In this paper, we propose a framework that integrates multi-item attribute information besides the classic information of users and items into BPRSLIM model in order to ease the sparsity problem associated with it and hence improves its performance accuracy. The enhanced model is expected to outperform the original BPRSLIM model.
This paper applies forensic data mining to determine the true status of employees and thereafter ... more This paper applies forensic data mining to determine the true status of employees and thereafter provide useful evidences for proper administration of administrative rules in a Typical Nigerian Teaching Service. The conventional technique of personnel audit was studied and a new technique for personnel audit was modeled using Artificial Neural Networks and Decision Tree algorithms. Atwolayer classifier architecture was modeled. The outcome of the experiment proved that Radial Basis Function Artificial Neural Network is better than Feed-forward Multilayer Perceptron in modeling of appointment and promotion audit in layer 1 while Logitboost Multiclass Alternating Decision Tree in Layer 2 is best in modeling suspicious appointment audit and abnormal promotion audit among the tested Decision Trees. The evidential rules derived from the decision trees for determining the suspicious appointment and abnormal promotion were also presented.
Advances in Multidisciplinary and Scientific Research Journal, Mar 30, 2024
Heart disease stands as the foremost contributor to global mortality in the present era. While a ... more Heart disease stands as the foremost contributor to global mortality in the present era. While a range of medical tests aids in diagnosing various manifestations of heart disease, anticipating its occurrence without resorting to these tests presents a formidable challenge. Machine learning has revealed a potential path for comprehending extensive medical datasets, unearthing concealed insights that elude human observation. Random Forest, Logistic Regression, XG Boost, k-nearest neighbor, and Gradient boosting was used to predict instances of heart diseases based on age and gender in this study. Regardless of the dataset's generalization or gender-specific characteristics, we observed that Random Forest, XG Boost, and K-Nearest Neighbors models exhibit high robustness and minimal sensitivity to hyperparameters. While the prediction accuracy remains consistent among Random Forest, Xgboost, and K-Nearest Neighbors, the crucial features responsible for achieving that accuracy vary among them. This observation indicates that it is important to test different machine learning algorithms when the interest is in prediction and understanding the most important feature(s).
The goals of this study are to describe machine learning techniques employing computer-vision mov... more The goals of this study are to describe machine learning techniques employing computer-vision movement algorithms to automatically evaluate infants' general movements (GMs) in the writhing stage. This is a retrospective study of infants admitted 07/2019 to 11/2021 to a level IV neonatal intensive care unit (NICU). Infant GMs, classified by certified expert, were analyzed in two-steps (1) determination of anatomic key point location using a NICU-trained pose estimation model [accuracy determined using object key point similarity (OKS)]; (2) development of a preliminary movement model to distinguish normal versus cramped-synchronized (CS) GMs using cosine similarity and autocorrelation of major joints. GMs were analyzed using 85 videos from 74 infants; gestational age at birth 28.9 ± 4.1 weeks and postmenstrual age (PMA) at time of video 35.9 ± 4.6 weeks The NICU-trained pose estimation model was more accurate (0.91 ± 0.008 OKS) than a generic model (0.83 ± 0.032 OKS, p < 0.001). Autocorrelation values in the lower limbs were significantly different between normal (5 videos) and CS GMs (5 videos, p < 0.05). These data indicate that automated pose estimation of anatomical key points is feasible in NICU patients and that a NICU-trained model can distinguish between normal and CS GMs. These preliminary data indicate that machine learning techniques may represent a promising tool for earlier CP risk assessment in the writhing stage and prior to hospital discharge. Cerebral palsy (CP) is the leading childhood motor neuro-disability, affecting 1 in 325 infants in the United States 1 . Advances in neonatal care have led to increased survival of preterm infants, even those born extremely preterm. However, the risk of neurodevelopmental disability and CP, in particular, remains high 2 with an increased incidence and severity of CP in infants born at younger gestational ages (GA) . Research shows that early identification of CP risk followed by early intervention at favorable stages of brain development are critical steps in improving outcomes . Infant general movements (GMs) are a reliable predictor of later motor development and have been used with high accuracy for the early identification of CP risk in high-risk infants . Infants typically exhibit these spontaneous GMs through 20 weeks post-term and progress from writhing GMs (9 weeks gestation to 8 weeks post-term) to fidgety GMs (6 to 20 weeks post-term) before transitioning to goal oriented movements 10 . Infants with abnormal GMs are known to be at higher risk of neuro-disability and CP in particular. The Prechtl General Movement Assessment (GMA) is a validated qualitative diagnostic tool developed for the classification of GMs by certified providers. The GMA is non-invasive and can be used shortly after birth to monitor GMs longitudinally with the goal to detect high-risk GM patterns . The presence of cramped-synchronized (CS) GMs in the writhing stage followed by absent fidgety GMs in the fidgety stage has a specificity of 96% for the identification of high-risk CP . Persistent CS GMs are also predictive of later diagnosis of CP with a specificity of 92.5% 12 . The GMA is typically used in combination with other assessments such as brain imaging and physical evaluation in order to make a diagnosis of CP 6 . While the GMA is an effective tool for the early detection of CP 6 , there are barriers to its widespread implementation into routine neonatal intensive care unit (NICU) clinical care, including associated cost of training and re-training, as well as resource allocation considerations to obtain and score videos 15 . As such, technologies aiming to decrease the impact of these barriers may lead to more neonates benefiting from the GMA. Many
In this study a predictive model using Multi-layer Perceptron of Artificial Neural Network archit... more In this study a predictive model using Multi-layer Perceptron of Artificial Neural Network architecture was developed to predict customer churn in financial institution. Previous researches have used supervised machine learning classifiers such as Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Random Forest. These classifiers require human effort to perform feature engineering which lead to over-specified and incomplete feature selection. Therefore, this research developed a model to eliminate manual feature engineering in data preprocessing stage. Fifty thousand customers' data were extracted from the database of one of the leading financial institution in Nigeria for the study. The multi-layer perceptron model was built with python programming language and used two overfitting techniques (Dropout and L2 regularization). The implementation done in python was compared with another model in Neuro solution infinity software. The results showed that the Artificial Neural Network software development (Python) had comparable performance with that obtained from the Neuro Solution Infinity software. The accuracy rates are 97.53% and 97.4% while ROC (Receiver Operating Characteristic) curve graphs are 0.89 and 0.85 respectively.
Journal of Internet and Information Systems, Jan 31, 2011
Nigeria is a country that has always posed a paradox to the international community in terms of i... more Nigeria is a country that has always posed a paradox to the international community in terms of its level of development vis-a-vis its economic potential. This is also reflected in the low ranking index it gets in international surveys despite the effort to develop its infrastructure and human capital. Nigeria has the fastest growing and most lucrative telecommunications, and Information and Communications Technology (ICT) market in Africa, yet in spite of this obvious and significant progress is still being ranked low in e-government provision to its citizen. This paper reviews the aim and methodology used by the ranking agencies, takes a survey of Nigeria's performance in global e-government surveys, identifies areas of deficiencies which have contributed to the low e-service delivery in the country despite the success recorded in the country's ICT and telecommunications sector and suggests how this situation may be improved.
Clustering algorithms generally suffer from some well-known problems for which the Self Organizin... more Clustering algorithms generally suffer from some well-known problems for which the Self Organizing Maps (SOM) algorithms are adept at handling. While there are many variants of the SOM algorithm, software programmes that implement the SOM algorithms have tended to give varying results even when tested on the same data sets. This can have serious implications when the goal of the clustering is novelty detection. In this study a comparison of the performance of some SOM clustering software was carried out and results presented. • General and reference ➝ -computing tools and techniques ➝ Empirical studies
The concept of the Data warehouse was developed to provide a single access point to data from a v... more The concept of the Data warehouse was developed to provide a single access point to data from a variety of sources. There is a need to have a single location for the storage and sharing of data that users can easily utilize to make effective and quality business decisions, rather than trying to traverse the multiple data sources that exist today. Although many frameworks have been developed to integrate these sources into a single database, a reliable framework has yet to be developed. A major hindrance to achieving a reliable warehouse is the poor quality of data obtained from the data transformation stage in the extract, transfer and load process. This poor quality of data contributes to inaccurate and unreliable results and if this data is used for decision making, unforeseen critical business errors can occur. This work reviews the data integration and transformation process in dimensional data warehouses and proposes a dual structure for data integration and metadata of multi-formatted data used for the design of dimensional data warehouse using Agrarian data collected from Ondo State, Nigeria as a case study.
The Weather is a continuous, data-intensive, multidimensional, dynamic process that makes weather... more The Weather is a continuous, data-intensive, multidimensional, dynamic process that makes weather forecasting a formidable challenge. Weather forecasting involves predicting how the present state of the atmosphere will change. Climate is the long-term effect of the sun's radiation on the rotating earth's varied surface and atmosphere. The Day-byday variations in a given area constitute the weather, whereas climate is the long-term synthesis of such variations. A simple, long-term summary of weather changes, however, is still not a true picture of climate. To obtain this requires the analysis of daily, monthly, and yearly patterns. This paper presents the use of Self Organizing Maps (SOM) and CoActive Neuro Fuzzy Inference System (CANFIS) soft computing techniques for weather and climate change studies using historical data collected from the city of Ibadan, Nigeria between 1951 and 2009. The results show that the soft computing techniques can be used for knowledge discovery in weather prediction and climate change studies.
Employee turnover is a serious concern in knowledge based organizations. When employees leave an ... more Employee turnover is a serious concern in knowledge based organizations. When employees leave an organization, they carry with them invaluable tacit knowledge which is often the source of competitive advantage for the business. In order for an organization to continually have a higher competitive advantage over its competition, it should make it a duty to minimize employee attrition. This study identifies employee related attributes that contribute to the prediction of employees' attrition in organizations. Three hundred and nine (309) complete records of employees of one of the Higher Institutions in Nigeria who worked in and left the institution between 1978 and 2006 were used for the study. The demographic and job related records of the employee were the main data which were used to classify the employee into some predefined attrition classes. Waikato Environment for Knowledge Analysis (WEKA) and See5 for Windows were used to generate decision tree models and rule-sets. The results of the decision tree models and rule-sets generated were then used for developing a a predictive model that was used to predict new cases of employee attrition. A framework for a software tool that can implement the rules generated in this study was also proposed.
In this study a predictive model using Multi-layer Perceptron of Artificial Neural Network archit... more In this study a predictive model using Multi-layer Perceptron of Artificial Neural Network architecture was developed to predict customer churn in financial institution. Previous researches have used supervised machine learning classifiers such as Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Random Forest. These classifiers require human effort to perform feature engineering which lead to over-specified and incomplete feature selection. Therefore, this research developed a model to eliminate manual feature engineering in data preprocessing stage. Fifty thousand customers' data were extracted from the database of one of the leading financial institution in Nigeria for the study. The multi-layer perceptron model was built with python programming language and used two overfitting techniques (Dropout and L2 regularization). The implementation done in python was compared with another model in Neuro solution infinity software. The results showed that the Artificial Neural Network software development (Python) had comparable performance with that obtained from the Neuro Solution Infinity software. The accuracy rates are 97.53% and 97.4% while ROC (Receiver Operating Characteristic) curve graphs are 0.89 and 0.85 respectively.
Journal of Internet and Information Systems, Jan 31, 2011
Nigeria is a country that has always posed a paradox to the international community in terms of i... more Nigeria is a country that has always posed a paradox to the international community in terms of its level of development vis-a-vis its economic potential. This is also reflected in the low ranking index it gets in international surveys despite the effort to develop its infrastructure and human capital. Nigeria has the fastest growing and most lucrative telecommunications, and Information and Communications Technology (ICT) market in Africa, yet in spite of this obvious and significant progress is still being ranked low in e-government provision to its citizen. This paper reviews the aim and methodology used by the ranking agencies, takes a survey of Nigeria's performance in global e-government surveys, identifies areas of deficiencies which have contributed to the low e-service delivery in the country despite the success recorded in the country's ICT and telecommunications sector and suggests how this situation may be improved.
Industrial pollution is often considered to be one of the prime factors contributing to air, wate... more Industrial pollution is often considered to be one of the prime factors contributing to air, water and soil pollution. Sectoral pollution loads (ton/yr) into different media (i.e. air, water and land) in Lagos were estimated using Industrial Pollution Projected System (IPPS). These were further studied using Artificial neural Networks (ANNs), a data mining technique that has the ability of detecting and describing patterns in large data sets with variables that are non-linearly related. Time Lagged Recurrent Network (TLRN) appeared as the best Neural Network model among all the neural networks considered which includes Multilayer Perceptron (MLP) Network, Generalized Feed Forward Neural Network (GFNN), Radial Basis Function (RBF) Network and Recurrent Network (RN). TLRN modelled the data-sets better than the others in terms of the mean average error (MAE) (0.14), time (39 s) and linear correlation coefficient (0.84). The results showed that Artificial Neural Networks (ANNs) technique (i.e., Time Lagged Recurrent Network) is also applicable and effective in environmental assessment study.
Signature recognition is the process of verifying a writer’s identity by checking the signature a... more Signature recognition is the process of verifying a writer’s identity by checking the signature against samples previously stored in the database. Several techniques such as the distance-based and statistical classifiers used for feature extraction on a signature image are not invariant to scaling and rotation and the Scale invariant feature transform (SIFT) though invariant to scaling and rotation cannot cater for intra-class variation (Transposition) among set of genuine signature images. This paper proposed a SIFT-SURF algorithm which is used for enhanced offline signature recognition. The SIFT-SURF algorithm computes integral image; obtains hessian data and interest point for each computed integral image; applied neuro-scaling PCA based radial basis function neural network to compute the optimal features for each signature image to come up with an algorithm that is invariant to scaling and rotation as well as reliably match transposition among genuine samples of a signature imag...
As markets have become increasingly saturated, companies have acknowledged that their business st... more As markets have become increasingly saturated, companies have acknowledged that their business strategies need to focuson identifying those customers who are most likely to churn. It is becoming common knowledge in business, that retainingexisting customers is the best core marketing strategy to survive in industry. In this research, both descriptive and predictivedata mining techniques were used to determine the calling behaviour of subscribers and to recognise subscribers with highprobability of churn in a telecommunications company subscriber database. First a data model for the input data variablesobtained from the subscriber database was developed. Then Simple K-Means and Expected Maximization (EM) clusteringalgorithms were used for the clustering stage, while Decision Stump, M5P and RepTree Decision Tree algorithms were usedfor the classification stage. The best algorithms in both the clustering and classification stages were used for the predictionprocess where customers that...
Data Mining also known as Knowledge Discovery from Databases (KDD) is the computer assisted proce... more Data Mining also known as Knowledge Discovery from Databases (KDD) is the computer assisted process of digging through and analyzing enormous or massive set of data stored either in data warehouse or information repositories and extracting the meaning of the data. Data Mining is a powerful technology that has a great potential to help governments, organizations and companies with wide range of industries including retail, finance, health care, manufacturing, transportation and aerospace focus on the most important information in the data they have collected about the behaviour of their clients and potential clients. However, Data Mining is only possible when the analyst or researcher has access to data and the right infrastructure. This paper reviews the Data Mining process, identifies some of the practical challenges of implementing Data Mining projects in the Nigerian environment and how some of them have been managed.
PurposeCollaborative-based national cybersecurity incident management benefits from the huge size... more PurposeCollaborative-based national cybersecurity incident management benefits from the huge size of incident information, large-scale information security devices and aggregation of security skills. However, no existing collaborative approach has been able to cater for multiple regulators, divergent incident views and incident reputation trust issues that national cybersecurity incident management presents. This paper aims to propose a collaborative approach to handle these issues cost-effectively.Design/methodology/approachA collaborative-based national cybersecurity incident management architecture based on ITU-T X.1056 security incident management framework is proposed. It is composed of the cooperative regulatory unit with cooperative and third-party management strategies and an execution unit, with incident handling and response strategies. Novel collaborative incident prioritization and mitigation planning models that are fit for incident handling in national cybersecurity in...
International Journal of Intelligent Systems Technologies and Applications, 2020
Recommender system is a type of information filtering system that is designed to curtail the diff... more Recommender system is a type of information filtering system that is designed to curtail the difficulties of information overload by automatically suggesting relevant items to users tailored to their preferences. Bayesian personalised smart linear methods (BPRSLIM) is a variant of item-based collaborative filtering technique used in information filtering system. Although, this algorithm has shown outstanding performance in a range of applications, nevertheless it suffers serious limitation of inability to provide accurate and reliable recommendations when the user-item matrix contains insufficient rating information, this always reduces its accuracy. In this paper, we propose a framework that integrates multi-item attribute information besides the classic information of users and items into BPRSLIM model in order to ease the sparsity problem associated with it and hence improves its performance accuracy. The enhanced model is expected to outperform the original BPRSLIM model.
This paper applies forensic data mining to determine the true status of employees and thereafter ... more This paper applies forensic data mining to determine the true status of employees and thereafter provide useful evidences for proper administration of administrative rules in a Typical Nigerian Teaching Service. The conventional technique of personnel audit was studied and a new technique for personnel audit was modeled using Artificial Neural Networks and Decision Tree algorithms. Atwolayer classifier architecture was modeled. The outcome of the experiment proved that Radial Basis Function Artificial Neural Network is better than Feed-forward Multilayer Perceptron in modeling of appointment and promotion audit in layer 1 while Logitboost Multiclass Alternating Decision Tree in Layer 2 is best in modeling suspicious appointment audit and abnormal promotion audit among the tested Decision Trees. The evidential rules derived from the decision trees for determining the suspicious appointment and abnormal promotion were also presented.
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