Ebola, also known as Ebola virus disease or Ebola hemorrhagic fever, is a viral hemorrhagic fever... more Ebola, also known as Ebola virus disease or Ebola hemorrhagic fever, is a viral hemorrhagic fever of humans and other primates caused by ebolaviruses. The Ebola virus and the disease in effect tend to randomly move individuals in the population around susceptible, infected, quarantined, hospitalized, recovered, and dead subpopulation. Motivated by the effectiveness in propagating the disease through the virus, a new bio-inspired and population-based optimization algorithm is proposed. This paper presents a novel metaheuristic algorithm named Ebola optimization algorithm (EOSA). To correctly achieve this, this study models the propagation mechanism of the Ebola virus disease, emphasising all consistent states of the propagation. The model was further represented using a mathematical model based on first-order differential equations. After that, the combined propagation and mathematical models were adapted for developing the new metaheuristic algorithm. To evaluate the proposed method...
The need for an accurate reasoning algorithm is usually necessitated by the sensitivity of domain... more The need for an accurate reasoning algorithm is usually necessitated by the sensitivity of domain of (medicine as example) application of such algorithms. Most reasoning algorithms for medical diagnosis are either limited by their techniques or accuracy and efficiency. Even the Select and Test (ST) algorithm which is considered a more approximate reasoning algorithm is also limited by its approach of using bipartite graph in modeling domain knowledge and making inference through the use of orthogonal vector projection for estimating likelihood of diagnosis at the clinical decision stage (induction). While the bipartite graph knowledge base lacks n-ary use of predicate on concepts, orthogonal vector projection on the other hand has high computation for the inference process. The aim of this paper is to enhance ST algorithm for improved performance and accuracy. First, we propose the use of ontologies and semantic web based rule for knowledge representation so as to provide support for inference making. Furthermore, three major improvements were added to ST algorithm to aid the improvement of its approximation. Secondly, we designed an inference making procedure to enable interaction with the knowledge base mentioned earlier. Thirdly, we model Hill's Criteria of Causation into clinical decision stage of ST to overcome the limitation of orthogonal vector projection. Lastly, the improved ST algorithm was largely represented and described using set notations (though implemented as linked-list and queues) and mathematical notations. The result of the improved ST algorithm revealed a sensitivity of 0.81 and 0.89 and specificity of 0.82 and 1.0 in the Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. In addition, the accuracy obtained from the proposed algorithm was 86.0% and 88.72% for the Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. This enhancement in accuracy was obtained at a slowdown time due to the reasoning process and ontology parsing task added to the enhanced system. However, there was an improvement in the accuracy and inference power of the resulting system.
Breast cancer is a major terminal disease that occurs largely among females. This disease stems f... more Breast cancer is a major terminal disease that occurs largely among females. This disease stems from abnormal mutations in the genes of normal cells, thereby resulting in development of cancerous cells. Though there have being several research breakthroughs in the field of medicine in taming this disease, however, computer aided diagnosis on the other hand has proven very supportive in the quest. Techniques such as Machine Learning (ML) and Medical Expert Systems (MES) algorithms have added impetus to the use of artificial intelligence in detecting and diagnosing breast cancer. While MES may seem promising in machine based diagnostic systems, their accuracy is often impaired by inefficient medical reasoning algorithms employed. This paper therefore seeks to address the limitation of one such reasoning algorithm known as Select and Test (ST). The approach in this paper is to first create an efficient input mechanism that enables the system to read, filter and clean input from datasets. Secondly, semantic web languages (ontologies and rule languages) were used to create a coordinated rule set and a knowledge representation framework was created to aid the reasoning algorithm. As a result, the reasoning structures of ST were modified to accommodate this enhancement. Thereafter, the input generating mechanism was used to transform instances of the databases of Breast Cancer Wisconsin Data set retrieved from UCI Learning Repository. The generated inputs were passed into the improved ST algorithm to diagnose breast cancer in patients captured in the datasets. Experiments were carried out, and result show that 26.60%, 56.17%, and 54.05% were diagnosed of breast cancer in Wisconsin Breast Cancer Database (WBCD), Wisconsin Diagnostic Breast Cancer (WDBC), and Wisconsin Prognostic Breast Cancer (WPBC) respectively.
Information gathering from patient by clinicians during diagnostic procedures may sometimes requi... more Information gathering from patient by clinicians during diagnostic procedures may sometimes require some skills to adequately collect required information that will be sufficient for the procedure. A situation where this information gathering may proof difficult in when a diagnostic decision making support system (DDSS) will have to gather such information from patient before carrying out the diagnostic procedure. Research has proven that it is more challenging to ensure user or patient inputs, in their raw form, maps into the list of acceptable medical terms for diagnostic tasks. This paper therefore proposes a formalized input generating model that addresses this shortcoming through the creation of an inference process, breast cancer lexicon, rule set and natural language processing (NLP). We developed an input generation algorithm which uses the python natural language processing capability in first filtering and generation the first pre-input collection. Furthermore, this algorithm then feeds in the pre-input word collection as input into the inference engine which has in its memory the rule set and ontology-based lexicon developed. Finally, this generates a list of acceptable tokens that will be sent into the medical expert system or DDSS for the diagnosing breast cancer. This proposed model was tested on a breast cancer based DDSS earlier designed by this authors, and result shows that the inference support of this model generates additional input of about 64% compared to when the patient's input where sent in as input in is state.
Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organizatio... more Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases' specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to interpret and solve new cases even at their early stage to the advanced stage. The approach adopted in this study employs a natural language processing (NLP) technique to parse records of cases and thereafter formalize each case which is represented as a mini-ontology file. The formalized case is therefore parsed into a CBR model to allow for classification of the case into positive or negative to COVID-19. Meanwhile, feature extraction for each case is done by classifying tokens extracted by the NLP approach into special, temporal and thematic classes before encoding them using an ontology modeling method. The CBR model therefore leverages on the formalized features to compute the similarity of the new case with extracted similar cases from the archive of the CBR model. The proposed framework was populated with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach leverages on locations (spatial) and time (temporal) of contagion to successfully detect cases even in their early stages of two days onward before the incubation period of fourteen days. The proposed framework achieved an accuracy of 97.10%, sensitivity of 0.98 and specificity of .066. The study found that the proposed model can assist physicians to easily diagnose and isolate cases, thereby minimizing the rate of contagion and reducing false diagnosis as observed in some parts of the globe.
There exist several terminal diseases whose fatality rate escalates with time of which breast can... more There exist several terminal diseases whose fatality rate escalates with time of which breast cancer is a frontline disease among such. Computer aided systems have also been well researched through the use intelligent algorithms capable of detecting, diagnosing, and proffering treatment for breast cancer. While good research breakthrough has been attained in terms of algorithmic solution towards diagnosis of breast cancer, however, not much has been done to sufficiently model knowledge frameworks for diagnostic algorithms that are knowledgebased. While Select and Test (ST) algorithm have proven relevant for implementing diagnostic systems, through support for reasoning, however the knowledge representation pattern that enables inference of missing or ambiguous data still limits the effectiveness of ST algorithm. This paper therefore proposes a knowledge representation model to systematically model knowledge to aid the performance of ST algorithm. Our proposal is specifically targeted at developing systematic knowledge representation for breast cancer. The approach uses the ontology web language (OWL) to implement the design of the knowledge model proposed. This study aims at carefully crafting a knowledge model whose implementation seamlessly work with ST algorithm. Furthermore, this study adapted the proposed model into an implementation of ST algorithm an obtained an improved performance compared to the simple knowledge model proposed by the author of ST algorithm. Our knowledge mode resulted in an accuracy gain of 23.5% and obtained and AUC of (0.49, 1.0). This proposed model has therefore shown that combining an inference-oriented knowledge model with an inference-oriented reasoning algorithm improves the performance of computer aided diagnostic (CADx) systems. In future, we intend to enhance the proposed model to support rules.
Clinical reasoning involves coordinated thinking strategies targeted at gathering and analyzing r... more Clinical reasoning involves coordinated thinking strategies targeted at gathering and analyzing relevant information to arrive at a high-quality clinical solution to patients' medical problems. The correctness and proper choice of clinical reasoning approach chosen by a clinician or machine affect the accuracy and acceptability of the diagnosis; moreover, another challenge that usually surfaces during clinical reasoning is the burden of reasoning out what is known as missing data. This implies that failure to measure-up to the requirement of an acceptable clinical reasoning procedure and inability to gather enough data will have adverse impact on patient's health. A major motivating factor necessitating research of models for automation and formalization of in clinical reasoning is due to its sensitivity and complexity. Although different formalism now exists, such as approximate reasoning and use of models or logical inferences, to curtail the issue of incompleteness of data and as well attain a high-quality clinical problem solving. However most of these formalisms have the drawback associated with their underlying methods of approximation of reasoning structures that usually mar the procedure and result of clinical problem solving process. The Select and Test (ST) algorithm offers close solution to the problems earlier stated. This paper presents an improved model of ST algorithm for clinical diagnosis and monitoring. This paper is focused on redesign of the ST model and the formalization of the approximate clinical reasoning. The redesigned ST model incorporates a monitoring module and mechanism for interacting with an ontology-based knowledge store. Specifically, the ST algorithm was modified to accommodate these additions to the ST model. Data was sourced through a retrospective study on breast cancer patients' records and through the administration of questionnaire at the Ahmadu Bello University Teaching Hospital (ABUTH) Zaria, Nigeria. In addition, Wisconsin datasets were used to test the resulting formalism. Empirical analysis of breast cancer diagnosis using the proposed model revealed that the accuracy of 88.72% was achieved. Similarly, a sensitivity of 1.0, specificity of 0.51 and an ROC point of (0.49, 1) was also attained. This paper presents an inference model and its approximate reasoning representation to diagnose and monitor the presence of a disease.
Social network sites have become de factor in establishing and maintaining good relationships acr... more Social network sites have become de factor in establishing and maintaining good relationships across different continents and countries. Associations or connectivity has proven beneficial to people who share same interests professionally, research wise, in casual social connectivity, and companies have employed the social network in advancing their marketing strategies and it which have proven profitable. However, like other web applications that attract a great influx of visitors, the security of personal information of users in social network sites had suffered different security threats. This paper focuses on highlighting social network users on some security issues that are peculiar with social network. It proposes an algorithm and a model to circumventing security threats which are categorized into user authentication, data confidentiality and integrity.
Intelligent agents have been employed in the field of artificial intelligence to develop their co... more Intelligent agents have been employed in the field of artificial intelligence to develop their cognitive capability so as to populate their knowledge base and as well carry out tasks that proves their ability to carry act or respond to stimuli just like a human would have done. Furthermore, agents with different functionalities that may be harmonized to render a given service are been correlated together to form multi-agent. However, this multi-agent systems are been further categorized into those that carry out decisions and those that carry out simulation. This paper researches into decision making applications (also called decision support systems- DSS) that are driven by semantic web technologies, and also looks into their common components and functionalities. Then it points out the prospect that awaits future decision support systems with respect to the growing research in the semantic web technologies.
Abstract-Knowledge representation enabling inference making has been greatly promoted through the... more Abstract-Knowledge representation enabling inference making has been greatly promoted through the Semantic Web. The Semantic Web provides and supports some ontology languages that have been used in representing machine readable knowledge base. Furthermore, both classical and newer ontology development tools abounds correspondingly with the available ontology languages. Also, storage models and systems have been proposed and used in storing ontology files. This research first reviews some ontology languages and their development tools, and then discusses some ontology repositories. Finally, a proposed model for unifying the task of ontology creation and editor, and as well as incorporating this development tool with ontology repository is presented. We indicate that storing such ontology in the proposed repository does make it lose its semantics.
The initial Web was called static web
because only it allowed users to follow
clickable links in ... more The initial Web was called static web because only it allowed users to follow clickable links in a bid to view information. That primitive Web platform provided means for propagating electronic commerce (E-commerce). However, the Semantic Web Initiative and its supporting technologies further improved the Web, which have enhanced E-commerce. Some of the Semantic Web technologies that have been employed to enhance the primitive Web are Semantic Web technologies such as languages like XML, RDF/RDFS, OWL and also, query and rule languages like SPARQL, SQWRL and SWRL. In this paper, the goal is to demonstrate how data modeling with OWL and as well queried with SQWRL will enhance both more hits for products search of products in B2C scenario. Also, SWRL will be used to add entailments to the OWL ontology so that the weakness of SPARQL may be clearly shown compared to SQWRL, and by so doing, improve the efficiency of B2C. We seek to a mobile phone based Semantic Web application to implement the goal.
The Semantic Web Initiative advanced the web from a syntactically oriented web to semantically or... more The Semantic Web Initiative advanced the web from a syntactically oriented web to semantically oriented web. Different ontology languages are used in modeling information about specific domain in the Semantic Web and the choice of this ontology language will influence the degree of expressivity of concepts of such domain. However, the choice of ontology language does not affect the representation of the model alone, but as well the efficiency of the chosen Semantic Web enabled query language that is to be used on the desired ontology language. Furthermore, the Semantic Web also enables ontology developer to implements rules that will foster ontological reasoning upon the underlying ontology. This research seeks to demonstrate how performance of reasoning and query output can be improved using Web Ontology Language (OWL) as ontology language used to model the concepts of a domain of choice. This was chosen from other ontology languages such as Resource Description Framework/ Resource Description Framework Schema (RDF/RDFS), due to it's in depth expressivity power. Also, Semantic Queryenhanced Web Rule Language (SQWRL) is chosen from among other Semantic Web enabled query languages, such as OWL Query Language (OWL-QL) and SPARQL RDF Query Language (SPARQL), due to its feature of rightly interpreting the semantics of OWL constructs. An implementation of this reasoning and retrieval performance was further illustrated by the application developed in the research work.
Ebola, also known as Ebola virus disease or Ebola hemorrhagic fever, is a viral hemorrhagic fever... more Ebola, also known as Ebola virus disease or Ebola hemorrhagic fever, is a viral hemorrhagic fever of humans and other primates caused by ebolaviruses. The Ebola virus and the disease in effect tend to randomly move individuals in the population around susceptible, infected, quarantined, hospitalized, recovered, and dead subpopulation. Motivated by the effectiveness in propagating the disease through the virus, a new bio-inspired and population-based optimization algorithm is proposed. This paper presents a novel metaheuristic algorithm named Ebola optimization algorithm (EOSA). To correctly achieve this, this study models the propagation mechanism of the Ebola virus disease, emphasising all consistent states of the propagation. The model was further represented using a mathematical model based on first-order differential equations. After that, the combined propagation and mathematical models were adapted for developing the new metaheuristic algorithm. To evaluate the proposed method...
The need for an accurate reasoning algorithm is usually necessitated by the sensitivity of domain... more The need for an accurate reasoning algorithm is usually necessitated by the sensitivity of domain of (medicine as example) application of such algorithms. Most reasoning algorithms for medical diagnosis are either limited by their techniques or accuracy and efficiency. Even the Select and Test (ST) algorithm which is considered a more approximate reasoning algorithm is also limited by its approach of using bipartite graph in modeling domain knowledge and making inference through the use of orthogonal vector projection for estimating likelihood of diagnosis at the clinical decision stage (induction). While the bipartite graph knowledge base lacks n-ary use of predicate on concepts, orthogonal vector projection on the other hand has high computation for the inference process. The aim of this paper is to enhance ST algorithm for improved performance and accuracy. First, we propose the use of ontologies and semantic web based rule for knowledge representation so as to provide support for inference making. Furthermore, three major improvements were added to ST algorithm to aid the improvement of its approximation. Secondly, we designed an inference making procedure to enable interaction with the knowledge base mentioned earlier. Thirdly, we model Hill's Criteria of Causation into clinical decision stage of ST to overcome the limitation of orthogonal vector projection. Lastly, the improved ST algorithm was largely represented and described using set notations (though implemented as linked-list and queues) and mathematical notations. The result of the improved ST algorithm revealed a sensitivity of 0.81 and 0.89 and specificity of 0.82 and 1.0 in the Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. In addition, the accuracy obtained from the proposed algorithm was 86.0% and 88.72% for the Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. This enhancement in accuracy was obtained at a slowdown time due to the reasoning process and ontology parsing task added to the enhanced system. However, there was an improvement in the accuracy and inference power of the resulting system.
Breast cancer is a major terminal disease that occurs largely among females. This disease stems f... more Breast cancer is a major terminal disease that occurs largely among females. This disease stems from abnormal mutations in the genes of normal cells, thereby resulting in development of cancerous cells. Though there have being several research breakthroughs in the field of medicine in taming this disease, however, computer aided diagnosis on the other hand has proven very supportive in the quest. Techniques such as Machine Learning (ML) and Medical Expert Systems (MES) algorithms have added impetus to the use of artificial intelligence in detecting and diagnosing breast cancer. While MES may seem promising in machine based diagnostic systems, their accuracy is often impaired by inefficient medical reasoning algorithms employed. This paper therefore seeks to address the limitation of one such reasoning algorithm known as Select and Test (ST). The approach in this paper is to first create an efficient input mechanism that enables the system to read, filter and clean input from datasets. Secondly, semantic web languages (ontologies and rule languages) were used to create a coordinated rule set and a knowledge representation framework was created to aid the reasoning algorithm. As a result, the reasoning structures of ST were modified to accommodate this enhancement. Thereafter, the input generating mechanism was used to transform instances of the databases of Breast Cancer Wisconsin Data set retrieved from UCI Learning Repository. The generated inputs were passed into the improved ST algorithm to diagnose breast cancer in patients captured in the datasets. Experiments were carried out, and result show that 26.60%, 56.17%, and 54.05% were diagnosed of breast cancer in Wisconsin Breast Cancer Database (WBCD), Wisconsin Diagnostic Breast Cancer (WDBC), and Wisconsin Prognostic Breast Cancer (WPBC) respectively.
Information gathering from patient by clinicians during diagnostic procedures may sometimes requi... more Information gathering from patient by clinicians during diagnostic procedures may sometimes require some skills to adequately collect required information that will be sufficient for the procedure. A situation where this information gathering may proof difficult in when a diagnostic decision making support system (DDSS) will have to gather such information from patient before carrying out the diagnostic procedure. Research has proven that it is more challenging to ensure user or patient inputs, in their raw form, maps into the list of acceptable medical terms for diagnostic tasks. This paper therefore proposes a formalized input generating model that addresses this shortcoming through the creation of an inference process, breast cancer lexicon, rule set and natural language processing (NLP). We developed an input generation algorithm which uses the python natural language processing capability in first filtering and generation the first pre-input collection. Furthermore, this algorithm then feeds in the pre-input word collection as input into the inference engine which has in its memory the rule set and ontology-based lexicon developed. Finally, this generates a list of acceptable tokens that will be sent into the medical expert system or DDSS for the diagnosing breast cancer. This proposed model was tested on a breast cancer based DDSS earlier designed by this authors, and result shows that the inference support of this model generates additional input of about 64% compared to when the patient's input where sent in as input in is state.
Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organizatio... more Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases' specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to interpret and solve new cases even at their early stage to the advanced stage. The approach adopted in this study employs a natural language processing (NLP) technique to parse records of cases and thereafter formalize each case which is represented as a mini-ontology file. The formalized case is therefore parsed into a CBR model to allow for classification of the case into positive or negative to COVID-19. Meanwhile, feature extraction for each case is done by classifying tokens extracted by the NLP approach into special, temporal and thematic classes before encoding them using an ontology modeling method. The CBR model therefore leverages on the formalized features to compute the similarity of the new case with extracted similar cases from the archive of the CBR model. The proposed framework was populated with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach leverages on locations (spatial) and time (temporal) of contagion to successfully detect cases even in their early stages of two days onward before the incubation period of fourteen days. The proposed framework achieved an accuracy of 97.10%, sensitivity of 0.98 and specificity of .066. The study found that the proposed model can assist physicians to easily diagnose and isolate cases, thereby minimizing the rate of contagion and reducing false diagnosis as observed in some parts of the globe.
There exist several terminal diseases whose fatality rate escalates with time of which breast can... more There exist several terminal diseases whose fatality rate escalates with time of which breast cancer is a frontline disease among such. Computer aided systems have also been well researched through the use intelligent algorithms capable of detecting, diagnosing, and proffering treatment for breast cancer. While good research breakthrough has been attained in terms of algorithmic solution towards diagnosis of breast cancer, however, not much has been done to sufficiently model knowledge frameworks for diagnostic algorithms that are knowledgebased. While Select and Test (ST) algorithm have proven relevant for implementing diagnostic systems, through support for reasoning, however the knowledge representation pattern that enables inference of missing or ambiguous data still limits the effectiveness of ST algorithm. This paper therefore proposes a knowledge representation model to systematically model knowledge to aid the performance of ST algorithm. Our proposal is specifically targeted at developing systematic knowledge representation for breast cancer. The approach uses the ontology web language (OWL) to implement the design of the knowledge model proposed. This study aims at carefully crafting a knowledge model whose implementation seamlessly work with ST algorithm. Furthermore, this study adapted the proposed model into an implementation of ST algorithm an obtained an improved performance compared to the simple knowledge model proposed by the author of ST algorithm. Our knowledge mode resulted in an accuracy gain of 23.5% and obtained and AUC of (0.49, 1.0). This proposed model has therefore shown that combining an inference-oriented knowledge model with an inference-oriented reasoning algorithm improves the performance of computer aided diagnostic (CADx) systems. In future, we intend to enhance the proposed model to support rules.
Clinical reasoning involves coordinated thinking strategies targeted at gathering and analyzing r... more Clinical reasoning involves coordinated thinking strategies targeted at gathering and analyzing relevant information to arrive at a high-quality clinical solution to patients' medical problems. The correctness and proper choice of clinical reasoning approach chosen by a clinician or machine affect the accuracy and acceptability of the diagnosis; moreover, another challenge that usually surfaces during clinical reasoning is the burden of reasoning out what is known as missing data. This implies that failure to measure-up to the requirement of an acceptable clinical reasoning procedure and inability to gather enough data will have adverse impact on patient's health. A major motivating factor necessitating research of models for automation and formalization of in clinical reasoning is due to its sensitivity and complexity. Although different formalism now exists, such as approximate reasoning and use of models or logical inferences, to curtail the issue of incompleteness of data and as well attain a high-quality clinical problem solving. However most of these formalisms have the drawback associated with their underlying methods of approximation of reasoning structures that usually mar the procedure and result of clinical problem solving process. The Select and Test (ST) algorithm offers close solution to the problems earlier stated. This paper presents an improved model of ST algorithm for clinical diagnosis and monitoring. This paper is focused on redesign of the ST model and the formalization of the approximate clinical reasoning. The redesigned ST model incorporates a monitoring module and mechanism for interacting with an ontology-based knowledge store. Specifically, the ST algorithm was modified to accommodate these additions to the ST model. Data was sourced through a retrospective study on breast cancer patients' records and through the administration of questionnaire at the Ahmadu Bello University Teaching Hospital (ABUTH) Zaria, Nigeria. In addition, Wisconsin datasets were used to test the resulting formalism. Empirical analysis of breast cancer diagnosis using the proposed model revealed that the accuracy of 88.72% was achieved. Similarly, a sensitivity of 1.0, specificity of 0.51 and an ROC point of (0.49, 1) was also attained. This paper presents an inference model and its approximate reasoning representation to diagnose and monitor the presence of a disease.
Social network sites have become de factor in establishing and maintaining good relationships acr... more Social network sites have become de factor in establishing and maintaining good relationships across different continents and countries. Associations or connectivity has proven beneficial to people who share same interests professionally, research wise, in casual social connectivity, and companies have employed the social network in advancing their marketing strategies and it which have proven profitable. However, like other web applications that attract a great influx of visitors, the security of personal information of users in social network sites had suffered different security threats. This paper focuses on highlighting social network users on some security issues that are peculiar with social network. It proposes an algorithm and a model to circumventing security threats which are categorized into user authentication, data confidentiality and integrity.
Intelligent agents have been employed in the field of artificial intelligence to develop their co... more Intelligent agents have been employed in the field of artificial intelligence to develop their cognitive capability so as to populate their knowledge base and as well carry out tasks that proves their ability to carry act or respond to stimuli just like a human would have done. Furthermore, agents with different functionalities that may be harmonized to render a given service are been correlated together to form multi-agent. However, this multi-agent systems are been further categorized into those that carry out decisions and those that carry out simulation. This paper researches into decision making applications (also called decision support systems- DSS) that are driven by semantic web technologies, and also looks into their common components and functionalities. Then it points out the prospect that awaits future decision support systems with respect to the growing research in the semantic web technologies.
Abstract-Knowledge representation enabling inference making has been greatly promoted through the... more Abstract-Knowledge representation enabling inference making has been greatly promoted through the Semantic Web. The Semantic Web provides and supports some ontology languages that have been used in representing machine readable knowledge base. Furthermore, both classical and newer ontology development tools abounds correspondingly with the available ontology languages. Also, storage models and systems have been proposed and used in storing ontology files. This research first reviews some ontology languages and their development tools, and then discusses some ontology repositories. Finally, a proposed model for unifying the task of ontology creation and editor, and as well as incorporating this development tool with ontology repository is presented. We indicate that storing such ontology in the proposed repository does make it lose its semantics.
The initial Web was called static web
because only it allowed users to follow
clickable links in ... more The initial Web was called static web because only it allowed users to follow clickable links in a bid to view information. That primitive Web platform provided means for propagating electronic commerce (E-commerce). However, the Semantic Web Initiative and its supporting technologies further improved the Web, which have enhanced E-commerce. Some of the Semantic Web technologies that have been employed to enhance the primitive Web are Semantic Web technologies such as languages like XML, RDF/RDFS, OWL and also, query and rule languages like SPARQL, SQWRL and SWRL. In this paper, the goal is to demonstrate how data modeling with OWL and as well queried with SQWRL will enhance both more hits for products search of products in B2C scenario. Also, SWRL will be used to add entailments to the OWL ontology so that the weakness of SPARQL may be clearly shown compared to SQWRL, and by so doing, improve the efficiency of B2C. We seek to a mobile phone based Semantic Web application to implement the goal.
The Semantic Web Initiative advanced the web from a syntactically oriented web to semantically or... more The Semantic Web Initiative advanced the web from a syntactically oriented web to semantically oriented web. Different ontology languages are used in modeling information about specific domain in the Semantic Web and the choice of this ontology language will influence the degree of expressivity of concepts of such domain. However, the choice of ontology language does not affect the representation of the model alone, but as well the efficiency of the chosen Semantic Web enabled query language that is to be used on the desired ontology language. Furthermore, the Semantic Web also enables ontology developer to implements rules that will foster ontological reasoning upon the underlying ontology. This research seeks to demonstrate how performance of reasoning and query output can be improved using Web Ontology Language (OWL) as ontology language used to model the concepts of a domain of choice. This was chosen from other ontology languages such as Resource Description Framework/ Resource Description Framework Schema (RDF/RDFS), due to it's in depth expressivity power. Also, Semantic Queryenhanced Web Rule Language (SQWRL) is chosen from among other Semantic Web enabled query languages, such as OWL Query Language (OWL-QL) and SPARQL RDF Query Language (SPARQL), due to its feature of rightly interpreting the semantics of OWL constructs. An implementation of this reasoning and retrieval performance was further illustrated by the application developed in the research work.
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Papers by Oyelade Olaide
Web provides and supports some ontology languages that have been used in representing machine readable knowledge base.
Furthermore, both classical and newer ontology development tools abounds correspondingly with the available ontology languages. Also,
storage models and systems have been proposed and used in storing ontology files. This research first reviews some ontology languages
and their development tools, and then discusses some ontology repositories. Finally, a proposed model for unifying the task of ontology
creation and editor, and as well as incorporating this development tool with ontology repository is presented. We indicate that storing
such ontology in the proposed repository does make it lose its semantics.
because only it allowed users to follow
clickable links in a bid to view information.
That primitive Web platform provided
means for propagating electronic commerce
(E-commerce). However, the Semantic Web
Initiative and its supporting technologies
further improved the Web, which have
enhanced E-commerce. Some of the
Semantic Web technologies that have been
employed to enhance the primitive Web are
Semantic Web technologies such as
languages like XML, RDF/RDFS, OWL and
also, query and rule languages like
SPARQL, SQWRL and SWRL. In this
paper, the goal is to demonstrate how data
modeling with OWL and as well queried
with SQWRL will enhance both more hits
for products search of products in B2C
scenario. Also, SWRL will be used to add
entailments to the OWL ontology so that the
weakness of SPARQL may be clearly shown
compared to SQWRL, and by so doing,
improve the efficiency of B2C. We seek to a
mobile phone based Semantic Web
application to implement the goal.
Web provides and supports some ontology languages that have been used in representing machine readable knowledge base.
Furthermore, both classical and newer ontology development tools abounds correspondingly with the available ontology languages. Also,
storage models and systems have been proposed and used in storing ontology files. This research first reviews some ontology languages
and their development tools, and then discusses some ontology repositories. Finally, a proposed model for unifying the task of ontology
creation and editor, and as well as incorporating this development tool with ontology repository is presented. We indicate that storing
such ontology in the proposed repository does make it lose its semantics.
because only it allowed users to follow
clickable links in a bid to view information.
That primitive Web platform provided
means for propagating electronic commerce
(E-commerce). However, the Semantic Web
Initiative and its supporting technologies
further improved the Web, which have
enhanced E-commerce. Some of the
Semantic Web technologies that have been
employed to enhance the primitive Web are
Semantic Web technologies such as
languages like XML, RDF/RDFS, OWL and
also, query and rule languages like
SPARQL, SQWRL and SWRL. In this
paper, the goal is to demonstrate how data
modeling with OWL and as well queried
with SQWRL will enhance both more hits
for products search of products in B2C
scenario. Also, SWRL will be used to add
entailments to the OWL ontology so that the
weakness of SPARQL may be clearly shown
compared to SQWRL, and by so doing,
improve the efficiency of B2C. We seek to a
mobile phone based Semantic Web
application to implement the goal.