Taiwo Kolajo - Profile on Academia.edu (original) (raw)
Papers by Taiwo Kolajo
Legal Frameworks Regulating Computational Models in Wireless Communication Systems
CRC Press eBooks, Jul 1, 2024
OSISA journal, Jan 13, 2024
Speech has long been recognized as the main form of communication between people and computers. T... more Speech has long been recognized as the main form of communication between people and computers. Technology made it possible for humans and computers to interact through the development of humancomputer interfaces. Although speech emotion recognition systems have advanced quickly in recent years, many difficulties have also arisen during this development, such as the inability to recognize emotions that lead to depression and mood swings, which can be used by therapists to track their patients' moods. It is necessary to create a model that detects the many emotions that contribute to depression to improve doctor-patient relationships and increase the effectiveness of spoken emotion recognition models. In this paper, over 2000 audio files were compiled. We curated a local dataset that accounts for 60% of the total dataset acquired, 40% of the dataset used was obtained from RAVDESS. To extract the proper vocal features, we employed the Mel-Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), and Root Mean Square (RMS). The Tensorflow CONV1D with Relu activation function and several sequential layers was used to build the model. The batch size was 64 and the epoch size was 50. Seven emotional states, including anger, disgust, sadness, happiness, surprise, fear, and neutrality were extracted. The accuracy of the confusion matrix, which served as the performance metrics, is 96%.
Government regulatory policies on telehealth data protection using artificial intelligence and blockchain technology
UMYU Scientifica, Jun 29, 2023
According to Numnonda (2018), there are four different types of recommendation systems that are f... more According to Numnonda (2018), there are four different types of recommendation systems that are frequently used: content-based, collaborative filtering-based,
Journal of Applied Artificial Intelligence
The issue of identifying the prevalence of sickness that is linked to the population of a nation,... more The issue of identifying the prevalence of sickness that is linked to the population of a nation, state, neighborhood, organization, or school has not been taken into consideration by the majority of prior studies on the prediction of illness among populations. They frequently merely choose any sickness based on assumption, while those that determined the prevalence of the condition before developing their framework utilized survey data or data from web repositories, which removes idiosyncrasies from those data. In order to increase performance, this research suggests an enhanced data analytics framework for the predictive diagnosis of common illnesses affecting university students. In order to do this, exploratory data analysis (EDA) using a multivariate analytic technique was conducted using a high-level model methodology using CRISP-DM stages. When the suggested strategy was evaluated on support vector machines, ensemble gradient boosting, random forest, decision tree, K-neighbor...
Journal of Big Data
Interactions via social media platforms have made it possible for anyone, irrespective of physica... more Interactions via social media platforms have made it possible for anyone, irrespective of physical location, to gain access to quick information on events taking place all over the globe. However, the semantic processing of social media data is complicated due to challenges such as language complexity, unstructured data, and ambiguity. In this paper, we proposed the Social Media Analysis Framework for Event Detection (SMAFED). SMAFED aims to facilitate improved semantic analysis of noisy terms in social media streams, improved representation/embedding of social media stream content, and improved summarization of event clusters in social media streams. For this, we employed key concepts such as integrated knowledge base, resolving ambiguity, semantic representation of social media streams, and Semantic Histogram-based Incremental Clustering based on semantic relatedness. Two evaluation experiments were conducted to validate the approach. First, we evaluated the impact of the data enr...
Leveraging big data to combat terrorism in developing countries
2017 Conference on Information Communication Technology and Society (ICTAS), 2017
Terrorism is a matter of great concern in many nations because of its impact on sustainable devel... more Terrorism is a matter of great concern in many nations because of its impact on sustainable development, which is critical for developing countries. Efforts on the part of security agencies need to stay a step ahead of threats of terrorism to effectively prevent their occurrence. Many research efforts that sought to combat terrorism using big data have been reported in the literature. However, most of them have targeted data from only one type of social media per time. This paper proposes a model that harnesses data from multiple social media sources in order to detect terrorist activities by using Apache Spark technology for implementation. This paper describes the Social Media Analysis for Combating Terrorism (SMACT) model as a plausible approach that leverages Big Data analytics to address terrorism problems in developing nations. SMACT is further illustrated by a practical use case from the Nigerian context in order to depict its viability as a potential panacea for handling terrorism threats.
Streaming Data and Data Streams
Wiley StatsRef: Statistics Reference Online, 2021
Interactions via social media platforms have made it possible for anyone, irrespective of physica... more Interactions via social media platforms have made it possible for anyone, irrespective of physical location, to gain access to quick information on events taking place all over the globe. However, the semantic processing of social media data is complicated due to challenges such as language complexity, unstructured data, and ambiguity. In this paper, we proposed the Social Media Analysis Framework for Event Detection (SMAFED). SMAFED aims to facilitate improved semantic analysis of noisy terms in social media streams, improved representation/embedding of social media stream content, and improved summarisation of event clusters in social media streams. For this, we employed key concepts such as integrated knowledge base, resolving ambiguity, semantic representation of social media streams, and Semantic Histogram-based Incremental Clustering based on semantic relatedness. Two evaluation experiments were conducted to validate the approach. First, we evaluated the impact of the data enr...
A lot of advancements have taken place and still taking place in computing. Gone are the days tha... more A lot of advancements have taken place and still taking place in computing. Gone are the days that people had to rely on inevitable standalone computer to meet their needs. With advancement in technology, computing is turning the world to a better place. Even physical objects are now connected to the internet with the help of wireless sensor networks. This paper traces the historical linkage of different computing frameworks from computer networks to cloud of things with a view to helping researchers and organisations understand the various evolution phases of computer networks. The progression of ideas from the advent of computer networks to six (6) different computer connectivity frameworks like distributed computing, cluster computing, grid computing, cloud computing, internet of things and the cloud of things was examined making all the developments that have taken place to be easily seen in a single medium. The emergence of each framework as well as the strengths, weaknesses an...
Sentiment Analysis on Twitter Health News
Microblogging has become a generally accepted way of expressing opinions and sentiments about pro... more Microblogging has become a generally accepted way of expressing opinions and sentiments about products, services, media, institutions to mention but few. A lot of research has focused on analyzing Twitter health news for topic modelling using various clustering approaches, but few have reported it for sentiment analysis. The fact that such data contains potential information for revealing the opinion of people about health services and behaviours make it an interesting study. In this paper, general sentiments about Twitter health news was investigated. Natural language processing and text mining tool, AYLIEN API was used to extract sentiments subjectivities and polarities from a previously uncategorized dataset. The result shows that most of the tweets in Twitter health news are objective, that is, expressing facts with an average of 64% objective while 34% are personal views or opinions (subjective) with subjectivity confidence of 0.9. Sentiment polarity reveals 9% positive, 19% ne...
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...
Employing both descriptive and predictive algorithms toward improving prediction accuracy
The research describes the use of both descriptive and predictive algorithms for better accurate ... more The research describes the use of both descriptive and predictive algorithms for better accurate prediction. The current research has focused on the use of either descriptive or predictive algorithm for prediction, but this research work employed the two algorithms. Clustering technique was used in the descriptive stage while classification technique was used in the predictive stage. K-Means and Expected Maximization (EM) were used for clustering while models from three classifiers (Decision Stump, M5P and RepTree) were used for classification. The result of using each of the two algorithms individually was presented as well as the result of combination of both algorithms. It was discovered that utilizing both algorithms for prediction provided more accurate result. Keywords : Data Mining, Clustering, Classification, Expected Maximization, M5P
Career Guidance through Admission Procedures in Nigerian Universities Using Artificial Neural Networks
The study describes the application of Artificial Neural Network model in guiding students for su... more The study describes the application of Artificial Neural Network model in guiding students for suitable career through admission procedures in Nigerian universities. Before a candidate can be admitted to any of the Nigerian universities, he/she must be appraised based on some factors. The researchers looked into and identified various factors that may likely influence the performance of a student. These factors include ordinary level subjects' scores and subjects' combination, universal tertiary matriculation examination scores, post-universal tertiary matriculation examination scores, age on admission, parental background, types and location of secondary school attended, gender, number of sitting for Senior Secondary Certificate Examination, among others. These factors then served as input variables for the Artificial Neural Network model. A model based on the Multilayer Perceptron Topology was deployed and trained using final year students' data from faculty of Science...
Information Processing & Management, 2020
Most of the previous studies on the semantic analysis of social media feeds have not considered t... more Most of the previous studies on the semantic analysis of social media feeds have not considered the issue of ambiguity that is associated with slangs, abbreviations, and acronyms that are embedded in social media posts. These noisy terms have implicit meanings and form part of the rich semantic context that must be analysed to gain complete insights from social media feeds. This paper proposes an improved framework for pre-processing of social media feeds for better performance. To do this, the use of an integrated knowledge base (ikb) which comprises a local knowledge source (Naijalingo), urban dictionary and internet slang was combined with the adapted Lesk algorithm to facilitate semantic analysis of social media feeds. Experimental results showed that the proposed approach performed better than existing methods when it was tested on three machine learning models, which are support vector Preprint submitted to Journal of Information Processing and Management machines, multilayer perceptron, and convolutional neural networks. The framework had an accuracy of 94.07% on a standardized dataset, and 99.78% on localised dataset when used to extract sentiments from tweets. The improved performance on the localised dataset reveals the advantage of integrating the use of local knowledge sources into the process of analysing social media feeds particularly in interpreting slangs/acronyms/abbreviations that have contextually rooted meanings.
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 2019
Examining sentiments in social media poses a challenge to natural language processing because of ... more Examining sentiments in social media poses a challenge to natural language processing because of the intricacy and variability in the dialect articulation, noisy terms in form of slang, abbreviation, acronym, emoticon, and spelling error coupled with the availability of real-time content. Moreover, most of the knowledgebased approaches for resolving slang, abbreviation, and acronym do not consider the issue of ambiguity that evolves in the usage of these noisy terms. This research work proposes an improved framework for social media feed pre-processing that leverages on the combination of integrated local knowledge bases and adapted Lesk algorithm to facilitate pre-processing of social media feeds. The results from the experimental evaluation revealed an improvement over existing methods when applied to supervised learning algorithms in the task of extracting sentiments from Nigeria-origin tweets with an accuracy of 99.17%.
Journal of Big Data, 2019
Advances in information technology have facilitated large volume, high-velocity of data, and the ... more Advances in information technology have facilitated large volume, high-velocity of data, and the ability to store data continuously leading to several computational challenges. Due to the nature of big data in terms of volume, velocity, variety, variability, veracity, volatility, and value [1] that are being generated recently, big data computing is a new trend for future computing. Big data computing can be generally categorized into two types based on the processing requirements, which are big data batch computing and big data stream computing Recently, big data streams have become ubiquitous due to the fact that a number of applications generate a huge amount of data at a great velocity. This made it difficult for existing data mining tools, technologies, methods, and techniques to be applied directly on big data streams due to the inherent dynamic characteristics of big data. In this paper, a systematic review of big data streams analysis which employed a rigorous and methodical approach to look at the trends of big data stream tools and technologies as well as methods and techniques employed in analysing big data streams. It provides a global view of big data stream tools and technologies and its comparisons. Three major databases, Scopus, ScienceDirect and EBSCO, which indexes journals and conferences that are promoted by entities such as IEEE, ACM, SpringerLink, and Elsevier were explored as data sources. Out of the initial 2295 papers that resulted from the first search string, 47 papers were found to be relevant to our research questions after implementing the inclusion and exclusion criteria. The study found that scalability, privacy and load balancing issues as well as empirical analysis of big data streams and technologies are still open for further research efforts. We also found that although, significant research efforts have been directed to real-time analysis of big data stream not much attention has been given to the preprocessing stage of big data streams. Only a few big data streaming tools and technologies can do all of the batch, streaming, and iterative jobs; there seems to be no big data tool and technology that offers all the key features required for now and standard benchmark dataset for big data streaming analytics has not been widely adopted. In conclusion, it was recommended that research efforts should be geared towards developing scalable frameworks and algorithms that will accommodate data stream computing mode, effective resource allocation strategy and parallelization issues to cope with the ever-growing size and complexity of data.
The Internet has continued to span great geographical space and generality interests. It has prov... more The Internet has continued to span great geographical space and generality interests. It has provided enough space for social interaction and information exchange. It is hard to imagine a world without the internet. Like other fields of human endeavours, the internet is no doubt revolutionising the act of researching, especially in the sciences. Regardless of any viewpoint, research outlines formal, methodical and rigorous processes, specifically the application of scientific methods of problem recognition, definition, solution development, data collection, analysis and conclusions. Expectedly, the introduction of the Internet heralded the upswing of the new soft form of learning; with the aim of achieving speedy and cost effective diffusion of knowledge. Secondly, the internet has also helped in aggregating with ease such knowledge which can be shared amongst geographically-detached partners. So, whether it involves fundamental/pure or basic distributed research, action, applied re...
In recent years, there has been a surge in interest in artificial intelligent systems that can pr... more In recent years, there has been a surge in interest in artificial intelligent systems that can provide human-centric explanations for decisions or predictions. No matter how good and efficient a model is, users or practitioners find it difficult to trust such model if they cannot understand the model or its behaviours. Incorporating explainability that is human-centric in event detection systems is significant for building a decision-making process that is more trustworthy and sustainable. Human-centric and semantics-based explainable event detection will achieve trustworthiness, explainability, and reliability, which are currently lacking in AI systems. This paper provides a survey on the human-centric explainable AI, explainable event detection, and semantics-based explainable event detection by answering some research questions that bother on the characteristics of human-centric explanations, the state of explainable AI, methods for human-centric explanations, the essence of huma...
Legal Frameworks Regulating Computational Models in Wireless Communication Systems
CRC Press eBooks, Jul 1, 2024
OSISA journal, Jan 13, 2024
Speech has long been recognized as the main form of communication between people and computers. T... more Speech has long been recognized as the main form of communication between people and computers. Technology made it possible for humans and computers to interact through the development of humancomputer interfaces. Although speech emotion recognition systems have advanced quickly in recent years, many difficulties have also arisen during this development, such as the inability to recognize emotions that lead to depression and mood swings, which can be used by therapists to track their patients' moods. It is necessary to create a model that detects the many emotions that contribute to depression to improve doctor-patient relationships and increase the effectiveness of spoken emotion recognition models. In this paper, over 2000 audio files were compiled. We curated a local dataset that accounts for 60% of the total dataset acquired, 40% of the dataset used was obtained from RAVDESS. To extract the proper vocal features, we employed the Mel-Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), and Root Mean Square (RMS). The Tensorflow CONV1D with Relu activation function and several sequential layers was used to build the model. The batch size was 64 and the epoch size was 50. Seven emotional states, including anger, disgust, sadness, happiness, surprise, fear, and neutrality were extracted. The accuracy of the confusion matrix, which served as the performance metrics, is 96%.
Government regulatory policies on telehealth data protection using artificial intelligence and blockchain technology
UMYU Scientifica, Jun 29, 2023
According to Numnonda (2018), there are four different types of recommendation systems that are f... more According to Numnonda (2018), there are four different types of recommendation systems that are frequently used: content-based, collaborative filtering-based,
Journal of Applied Artificial Intelligence
The issue of identifying the prevalence of sickness that is linked to the population of a nation,... more The issue of identifying the prevalence of sickness that is linked to the population of a nation, state, neighborhood, organization, or school has not been taken into consideration by the majority of prior studies on the prediction of illness among populations. They frequently merely choose any sickness based on assumption, while those that determined the prevalence of the condition before developing their framework utilized survey data or data from web repositories, which removes idiosyncrasies from those data. In order to increase performance, this research suggests an enhanced data analytics framework for the predictive diagnosis of common illnesses affecting university students. In order to do this, exploratory data analysis (EDA) using a multivariate analytic technique was conducted using a high-level model methodology using CRISP-DM stages. When the suggested strategy was evaluated on support vector machines, ensemble gradient boosting, random forest, decision tree, K-neighbor...
Journal of Big Data
Interactions via social media platforms have made it possible for anyone, irrespective of physica... more Interactions via social media platforms have made it possible for anyone, irrespective of physical location, to gain access to quick information on events taking place all over the globe. However, the semantic processing of social media data is complicated due to challenges such as language complexity, unstructured data, and ambiguity. In this paper, we proposed the Social Media Analysis Framework for Event Detection (SMAFED). SMAFED aims to facilitate improved semantic analysis of noisy terms in social media streams, improved representation/embedding of social media stream content, and improved summarization of event clusters in social media streams. For this, we employed key concepts such as integrated knowledge base, resolving ambiguity, semantic representation of social media streams, and Semantic Histogram-based Incremental Clustering based on semantic relatedness. Two evaluation experiments were conducted to validate the approach. First, we evaluated the impact of the data enr...
Leveraging big data to combat terrorism in developing countries
2017 Conference on Information Communication Technology and Society (ICTAS), 2017
Terrorism is a matter of great concern in many nations because of its impact on sustainable devel... more Terrorism is a matter of great concern in many nations because of its impact on sustainable development, which is critical for developing countries. Efforts on the part of security agencies need to stay a step ahead of threats of terrorism to effectively prevent their occurrence. Many research efforts that sought to combat terrorism using big data have been reported in the literature. However, most of them have targeted data from only one type of social media per time. This paper proposes a model that harnesses data from multiple social media sources in order to detect terrorist activities by using Apache Spark technology for implementation. This paper describes the Social Media Analysis for Combating Terrorism (SMACT) model as a plausible approach that leverages Big Data analytics to address terrorism problems in developing nations. SMACT is further illustrated by a practical use case from the Nigerian context in order to depict its viability as a potential panacea for handling terrorism threats.
Streaming Data and Data Streams
Wiley StatsRef: Statistics Reference Online, 2021
Interactions via social media platforms have made it possible for anyone, irrespective of physica... more Interactions via social media platforms have made it possible for anyone, irrespective of physical location, to gain access to quick information on events taking place all over the globe. However, the semantic processing of social media data is complicated due to challenges such as language complexity, unstructured data, and ambiguity. In this paper, we proposed the Social Media Analysis Framework for Event Detection (SMAFED). SMAFED aims to facilitate improved semantic analysis of noisy terms in social media streams, improved representation/embedding of social media stream content, and improved summarisation of event clusters in social media streams. For this, we employed key concepts such as integrated knowledge base, resolving ambiguity, semantic representation of social media streams, and Semantic Histogram-based Incremental Clustering based on semantic relatedness. Two evaluation experiments were conducted to validate the approach. First, we evaluated the impact of the data enr...
A lot of advancements have taken place and still taking place in computing. Gone are the days tha... more A lot of advancements have taken place and still taking place in computing. Gone are the days that people had to rely on inevitable standalone computer to meet their needs. With advancement in technology, computing is turning the world to a better place. Even physical objects are now connected to the internet with the help of wireless sensor networks. This paper traces the historical linkage of different computing frameworks from computer networks to cloud of things with a view to helping researchers and organisations understand the various evolution phases of computer networks. The progression of ideas from the advent of computer networks to six (6) different computer connectivity frameworks like distributed computing, cluster computing, grid computing, cloud computing, internet of things and the cloud of things was examined making all the developments that have taken place to be easily seen in a single medium. The emergence of each framework as well as the strengths, weaknesses an...
Sentiment Analysis on Twitter Health News
Microblogging has become a generally accepted way of expressing opinions and sentiments about pro... more Microblogging has become a generally accepted way of expressing opinions and sentiments about products, services, media, institutions to mention but few. A lot of research has focused on analyzing Twitter health news for topic modelling using various clustering approaches, but few have reported it for sentiment analysis. The fact that such data contains potential information for revealing the opinion of people about health services and behaviours make it an interesting study. In this paper, general sentiments about Twitter health news was investigated. Natural language processing and text mining tool, AYLIEN API was used to extract sentiments subjectivities and polarities from a previously uncategorized dataset. The result shows that most of the tweets in Twitter health news are objective, that is, expressing facts with an average of 64% objective while 34% are personal views or opinions (subjective) with subjectivity confidence of 0.9. Sentiment polarity reveals 9% positive, 19% ne...
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...
Employing both descriptive and predictive algorithms toward improving prediction accuracy
The research describes the use of both descriptive and predictive algorithms for better accurate ... more The research describes the use of both descriptive and predictive algorithms for better accurate prediction. The current research has focused on the use of either descriptive or predictive algorithm for prediction, but this research work employed the two algorithms. Clustering technique was used in the descriptive stage while classification technique was used in the predictive stage. K-Means and Expected Maximization (EM) were used for clustering while models from three classifiers (Decision Stump, M5P and RepTree) were used for classification. The result of using each of the two algorithms individually was presented as well as the result of combination of both algorithms. It was discovered that utilizing both algorithms for prediction provided more accurate result. Keywords : Data Mining, Clustering, Classification, Expected Maximization, M5P
Career Guidance through Admission Procedures in Nigerian Universities Using Artificial Neural Networks
The study describes the application of Artificial Neural Network model in guiding students for su... more The study describes the application of Artificial Neural Network model in guiding students for suitable career through admission procedures in Nigerian universities. Before a candidate can be admitted to any of the Nigerian universities, he/she must be appraised based on some factors. The researchers looked into and identified various factors that may likely influence the performance of a student. These factors include ordinary level subjects' scores and subjects' combination, universal tertiary matriculation examination scores, post-universal tertiary matriculation examination scores, age on admission, parental background, types and location of secondary school attended, gender, number of sitting for Senior Secondary Certificate Examination, among others. These factors then served as input variables for the Artificial Neural Network model. A model based on the Multilayer Perceptron Topology was deployed and trained using final year students' data from faculty of Science...
Information Processing & Management, 2020
Most of the previous studies on the semantic analysis of social media feeds have not considered t... more Most of the previous studies on the semantic analysis of social media feeds have not considered the issue of ambiguity that is associated with slangs, abbreviations, and acronyms that are embedded in social media posts. These noisy terms have implicit meanings and form part of the rich semantic context that must be analysed to gain complete insights from social media feeds. This paper proposes an improved framework for pre-processing of social media feeds for better performance. To do this, the use of an integrated knowledge base (ikb) which comprises a local knowledge source (Naijalingo), urban dictionary and internet slang was combined with the adapted Lesk algorithm to facilitate semantic analysis of social media feeds. Experimental results showed that the proposed approach performed better than existing methods when it was tested on three machine learning models, which are support vector Preprint submitted to Journal of Information Processing and Management machines, multilayer perceptron, and convolutional neural networks. The framework had an accuracy of 94.07% on a standardized dataset, and 99.78% on localised dataset when used to extract sentiments from tweets. The improved performance on the localised dataset reveals the advantage of integrating the use of local knowledge sources into the process of analysing social media feeds particularly in interpreting slangs/acronyms/abbreviations that have contextually rooted meanings.
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 2019
Examining sentiments in social media poses a challenge to natural language processing because of ... more Examining sentiments in social media poses a challenge to natural language processing because of the intricacy and variability in the dialect articulation, noisy terms in form of slang, abbreviation, acronym, emoticon, and spelling error coupled with the availability of real-time content. Moreover, most of the knowledgebased approaches for resolving slang, abbreviation, and acronym do not consider the issue of ambiguity that evolves in the usage of these noisy terms. This research work proposes an improved framework for social media feed pre-processing that leverages on the combination of integrated local knowledge bases and adapted Lesk algorithm to facilitate pre-processing of social media feeds. The results from the experimental evaluation revealed an improvement over existing methods when applied to supervised learning algorithms in the task of extracting sentiments from Nigeria-origin tweets with an accuracy of 99.17%.
Journal of Big Data, 2019
Advances in information technology have facilitated large volume, high-velocity of data, and the ... more Advances in information technology have facilitated large volume, high-velocity of data, and the ability to store data continuously leading to several computational challenges. Due to the nature of big data in terms of volume, velocity, variety, variability, veracity, volatility, and value [1] that are being generated recently, big data computing is a new trend for future computing. Big data computing can be generally categorized into two types based on the processing requirements, which are big data batch computing and big data stream computing Recently, big data streams have become ubiquitous due to the fact that a number of applications generate a huge amount of data at a great velocity. This made it difficult for existing data mining tools, technologies, methods, and techniques to be applied directly on big data streams due to the inherent dynamic characteristics of big data. In this paper, a systematic review of big data streams analysis which employed a rigorous and methodical approach to look at the trends of big data stream tools and technologies as well as methods and techniques employed in analysing big data streams. It provides a global view of big data stream tools and technologies and its comparisons. Three major databases, Scopus, ScienceDirect and EBSCO, which indexes journals and conferences that are promoted by entities such as IEEE, ACM, SpringerLink, and Elsevier were explored as data sources. Out of the initial 2295 papers that resulted from the first search string, 47 papers were found to be relevant to our research questions after implementing the inclusion and exclusion criteria. The study found that scalability, privacy and load balancing issues as well as empirical analysis of big data streams and technologies are still open for further research efforts. We also found that although, significant research efforts have been directed to real-time analysis of big data stream not much attention has been given to the preprocessing stage of big data streams. Only a few big data streaming tools and technologies can do all of the batch, streaming, and iterative jobs; there seems to be no big data tool and technology that offers all the key features required for now and standard benchmark dataset for big data streaming analytics has not been widely adopted. In conclusion, it was recommended that research efforts should be geared towards developing scalable frameworks and algorithms that will accommodate data stream computing mode, effective resource allocation strategy and parallelization issues to cope with the ever-growing size and complexity of data.
The Internet has continued to span great geographical space and generality interests. It has prov... more The Internet has continued to span great geographical space and generality interests. It has provided enough space for social interaction and information exchange. It is hard to imagine a world without the internet. Like other fields of human endeavours, the internet is no doubt revolutionising the act of researching, especially in the sciences. Regardless of any viewpoint, research outlines formal, methodical and rigorous processes, specifically the application of scientific methods of problem recognition, definition, solution development, data collection, analysis and conclusions. Expectedly, the introduction of the Internet heralded the upswing of the new soft form of learning; with the aim of achieving speedy and cost effective diffusion of knowledge. Secondly, the internet has also helped in aggregating with ease such knowledge which can be shared amongst geographically-detached partners. So, whether it involves fundamental/pure or basic distributed research, action, applied re...
In recent years, there has been a surge in interest in artificial intelligent systems that can pr... more In recent years, there has been a surge in interest in artificial intelligent systems that can provide human-centric explanations for decisions or predictions. No matter how good and efficient a model is, users or practitioners find it difficult to trust such model if they cannot understand the model or its behaviours. Incorporating explainability that is human-centric in event detection systems is significant for building a decision-making process that is more trustworthy and sustainable. Human-centric and semantics-based explainable event detection will achieve trustworthiness, explainability, and reliability, which are currently lacking in AI systems. This paper provides a survey on the human-centric explainable AI, explainable event detection, and semantics-based explainable event detection by answering some research questions that bother on the characteristics of human-centric explanations, the state of explainable AI, methods for human-centric explanations, the essence of huma...