This study deployed k-means clustering to formulate earthquake categories based on magnitude and consequence,using global earthquake data spanning from 1900 to 2021.Based on patterns within the historical data,numeric...This study deployed k-means clustering to formulate earthquake categories based on magnitude and consequence,using global earthquake data spanning from 1900 to 2021.Based on patterns within the historical data,numeric boundaries were extracted to categorize the magnitude,deaths,injuries,and damage caused by earthquakes into low,medium,and high classes.Following a future earthquake incident,the classification scheme can be utilized to assign earthquakes into appropriate categories by inputting the magnitude,number of fatalities and injuries,and monetary estimates of total damage.The resulting taxonomy provides a means of classifying future earthquake incidents,thereby guiding the allocation and deployment of disaster management resources in proportion to the specific characteristics of each incident.Furthermore,the scheme can serve as a reference tool for auditing the utilization of earthquake management resources.展开更多
This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagno...This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state cells.These models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases.Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed decisions.Our goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic support.We propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies effectively.Additionally,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN models.We further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and imbalance.Comprehensive validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’reliability.Moreover,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model interpretability.Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.展开更多
The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data.The capability to process these gigantic amounts of data in real-time with Big D...The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data.The capability to process these gigantic amounts of data in real-time with Big Data Analytics(BDA)tools and Machine Learning(ML)algorithms carries many paybacks.However,the high number of free BDA tools,platforms,and data mining tools makes it challenging to select the appropriate one for the right task.This paper presents a comprehensive mini-literature review of ML in BDA,using a keyword search;a total of 1512 published articles was identified.The articles were screened to 140 based on the study proposed novel taxonomy.The study outcome shows that deep neural networks(15%),support vector machines(15%),artificial neural networks(14%),decision trees(12%),and ensemble learning techniques(11%)are widely applied in BDA.The related applications fields,challenges,and most importantly the openings for future research,are detailed.展开更多
Knowledge propagation is a necessity,both in academics and in the industry.The focus of this work is on how to achieve rapid knowledge propaga-tion using collaborative study groups.The practice of knowledge sharing in...Knowledge propagation is a necessity,both in academics and in the industry.The focus of this work is on how to achieve rapid knowledge propaga-tion using collaborative study groups.The practice of knowledge sharing in study groupsfinds relevance in conferences,workshops,and class rooms.Unfortu-nately,there appears to be only few researches on empirical best practices and techniques on study groups formation,especially for achieving rapid knowledge propagation.This work bridges this gap by presenting a workflow driven compu-tational algorithm for autonomous and unbiased formation of study groups.The system workflow consists of a chronology of stages,each made of distinct steps.Two of the most important steps,subsumed within the algorithmic stage,are the algorithms that resolve the decisional problem of number of study groups to be formed,as well as the most effective permutation of the study group participants to form collaborative pairs.This work contributes a number of new algorithmic concepts,such as autonomous and unbiased matching,exhaustive multiplication technique,twisted round-robin transversal,equilibrium summation,among others.The concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’performances in an examination,rather than through any external process.As part of practical demon-stration of this work,study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty(40)participants,and partially for study groups of 50,60 and 80 participants.The quantitative proof of this work was done through the technique called equilibrium summation,as well as the calculation of inter-study group Pearson Correlation Coefficients,which resulted in values higher than 0.9 in all cases.Real life experimentation was carried out while teaching Object-Oriented Programming to forty(40)under-graduates between February and May 2021.Empirical result showed that the per-formance of the learners was improved appreciably.This work will therefore be of immense benefit to the industry,academics and research community involved in collaborative learning.展开更多
This paper proposed an improved Naïve Bayes Classifier for sentimental analysis from a large-scale dataset such as in YouTube.YouTube contains large unstructured and unorganized comments and reactions,which carry...This paper proposed an improved Naïve Bayes Classifier for sentimental analysis from a large-scale dataset such as in YouTube.YouTube contains large unstructured and unorganized comments and reactions,which carry important information.Organizing large amounts of data and extracting useful information is a challenging task.The extracted information can be considered as new knowledge and can be used for deci sion-making.We extract comments from YouTube on videos and categorized them in domain-specific,and then apply the Naïve Bayes classifier with improved techniques.Our method provided a decent 80%accuracy in classifying those comments.This experiment shows that the proposed method provides excellent adaptability for large-scale text classification.展开更多
Anemia is one of the public health issues that affect children and pregnant women globally.Anemia occurs when the level of red blood cells within the body is reduced.Detecting anemia requires expert blood draw for cli...Anemia is one of the public health issues that affect children and pregnant women globally.Anemia occurs when the level of red blood cells within the body is reduced.Detecting anemia requires expert blood draw for clinical analysis of hemoglobin quantity.Although this standard method is accurate,it is costive and consumes enough time,unlike the non-invasive approach which is cost-effective and takes less time.This study focused on pallor analysis and used images of the conjunctiva of the eyes to detect anemia using machine learning techniques.This study used a publicly available dataset of 710 images of the conjunctiva of the eyes acquired with a unique tool that eliminates any interference from ambient light.We combined Convolutional Neural Networks,Logistic Regression,and Gaussian Blur algorithm to develop a conjunctiva detection model and an anemia detection model which runs on a Fast API server connected to a frontend mobile app built with React Native.The developed model was embedded into a smartphone application that can detect anemia by capturing and processing a patient's conjunctiva with a sensitivity of 90%,a specificity of 95%,and an accuracy of 92.50%on average performance in about 50 s.展开更多
Anemia is a public health issue with serious ramifications for human health globally.Anemia particularly affects pregnant women and children from 6 to 59 months old even though every individual is at risk.Anemia occur...Anemia is a public health issue with serious ramifications for human health globally.Anemia particularly affects pregnant women and children from 6 to 59 months old even though every individual is at risk.Anemia occurs when the Hb level is below its normal threshold or when the red blood cells are weakened or destroyed.To discover medical remedies on time,early detection or diagnosis of anemia assist patients to understand their condition.The invasive approach for anemia detection is costive and time-consuming as compared to the non-invasive approach which is reliable and suitable for developing communities where medical resources and personnel are inadequate.This study uses palpable palm images(dataset)collected from 710 participants in selected hospitals in Ghana.The images were extracted,segmented and converted into RGB percentile to train,validate and tested the machine learning models.A hybrid model was developed with the application of ensemble learning models using the R programming language on the R Studio platform.Stacking,voting,boosting and bagging ensemble model techniques were used to build the hybrid models,the stacking ensemble model achieved an accuracy of 99.73%.The study justifies that ensemble models are efficient for medical disease diagnosis or detection such as anemia.展开更多
Identification of bird species from their sounds has become an important area in biodiversity-related research due to the relative ease of capturing bird sounds in the commonly challenging habitat. Audio features have...Identification of bird species from their sounds has become an important area in biodiversity-related research due to the relative ease of capturing bird sounds in the commonly challenging habitat. Audio features have a massive impact on the classification task since they are the fundamental elements used to differentiate classes. As such, the extraction of informative properties of the data is a crucial stage of any classification-based application. Therefore, it is vital to identify the most significant feature to represent the actual bird sounds. In this paper, we propose a novel feature that can advance classification accuracy with modified features, which are most suitable for classifying birds from its audio sounds. Modified Gammatone frequency cepstral coefficient(GTCC) features have been extracted with their frequency banks adjusted to suit bird sounds. The features are then used to train and test a support vector machine(SVM) classifier. It has been shown that the modified GTCC features are able to give 86% accuracy with twenty Bornean birds. Furthermore, in this paper, we are proposing a novel probability enhanced entropy(PEE) feature, which, when combined with the modified GTCC features, is able to improve accuracy further to 89.5%. These results are significant as the relatively low-resource intensive SVM with the proposed modified GTCC, and the proposed novel PEE feature can be implemented in a real-time system to assist researchers,scientists, conservationists, and even eco-tourists in identifying bird species in the dense forest.展开更多
In the present era of big data,web page searching and ranking in an efficient manner on the World Wide Web to satisfy the specific search needs of the modern user is undoubtedly a major challenge for search engines.Ev...In the present era of big data,web page searching and ranking in an efficient manner on the World Wide Web to satisfy the specific search needs of the modern user is undoubtedly a major challenge for search engines.Even though a large number of web search techniques have been developed,some problems still exist while searching with generic search engines as none of the search engines can index the entire web.The issue is not just the volume but also the relevance concerning the user’s requirements.Moreover,if the search query is partially incomplete or is ambiguous,then most of the modern search engines tend to return the result by interpreting all possible meanings of the query.Concerning search quality,more than half of the retrieved web pages have been reported to be irrelevant.Hence web search personalization is required to retrieve search results while incorporating the user’s interests.In the proposed research work we have highlighted the strengths and weakness of various studies as proposed in the literature for web search personalization by carrying out a detailed comparison among them.The in-depth comparative study with baselines leads to the recommendation of Intelligent Meta Search System(IMSS)and Advanced Cluster Vector Page Ranking(ACVPR)algorithm as one of the best approaches as proposed in the literature for web search personalization.Furthermore,the detailed discussion about the comparative analysis of all categories gives new opportunities to think in different research areas.展开更多
文摘This study deployed k-means clustering to formulate earthquake categories based on magnitude and consequence,using global earthquake data spanning from 1900 to 2021.Based on patterns within the historical data,numeric boundaries were extracted to categorize the magnitude,deaths,injuries,and damage caused by earthquakes into low,medium,and high classes.Following a future earthquake incident,the classification scheme can be utilized to assign earthquakes into appropriate categories by inputting the magnitude,number of fatalities and injuries,and monetary estimates of total damage.The resulting taxonomy provides a means of classifying future earthquake incidents,thereby guiding the allocation and deployment of disaster management resources in proportion to the specific characteristics of each incident.Furthermore,the scheme can serve as a reference tool for auditing the utilization of earthquake management resources.
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and granted financial resources from the Ministry of Trade,Industry,and Energy,Korea(No.20204010600090).
文摘This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state cells.These models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases.Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed decisions.Our goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic support.We propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies effectively.Additionally,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN models.We further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and imbalance.Comprehensive validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’reliability.Moreover,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model interpretability.Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.
文摘The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data.The capability to process these gigantic amounts of data in real-time with Big Data Analytics(BDA)tools and Machine Learning(ML)algorithms carries many paybacks.However,the high number of free BDA tools,platforms,and data mining tools makes it challenging to select the appropriate one for the right task.This paper presents a comprehensive mini-literature review of ML in BDA,using a keyword search;a total of 1512 published articles was identified.The articles were screened to 140 based on the study proposed novel taxonomy.The study outcome shows that deep neural networks(15%),support vector machines(15%),artificial neural networks(14%),decision trees(12%),and ensemble learning techniques(11%)are widely applied in BDA.The related applications fields,challenges,and most importantly the openings for future research,are detailed.
文摘Knowledge propagation is a necessity,both in academics and in the industry.The focus of this work is on how to achieve rapid knowledge propaga-tion using collaborative study groups.The practice of knowledge sharing in study groupsfinds relevance in conferences,workshops,and class rooms.Unfortu-nately,there appears to be only few researches on empirical best practices and techniques on study groups formation,especially for achieving rapid knowledge propagation.This work bridges this gap by presenting a workflow driven compu-tational algorithm for autonomous and unbiased formation of study groups.The system workflow consists of a chronology of stages,each made of distinct steps.Two of the most important steps,subsumed within the algorithmic stage,are the algorithms that resolve the decisional problem of number of study groups to be formed,as well as the most effective permutation of the study group participants to form collaborative pairs.This work contributes a number of new algorithmic concepts,such as autonomous and unbiased matching,exhaustive multiplication technique,twisted round-robin transversal,equilibrium summation,among others.The concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’performances in an examination,rather than through any external process.As part of practical demon-stration of this work,study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty(40)participants,and partially for study groups of 50,60 and 80 participants.The quantitative proof of this work was done through the technique called equilibrium summation,as well as the calculation of inter-study group Pearson Correlation Coefficients,which resulted in values higher than 0.9 in all cases.Real life experimentation was carried out while teaching Object-Oriented Programming to forty(40)under-graduates between February and May 2021.Empirical result showed that the per-formance of the learners was improved appreciably.This work will therefore be of immense benefit to the industry,academics and research community involved in collaborative learning.
文摘This paper proposed an improved Naïve Bayes Classifier for sentimental analysis from a large-scale dataset such as in YouTube.YouTube contains large unstructured and unorganized comments and reactions,which carry important information.Organizing large amounts of data and extracting useful information is a challenging task.The extracted information can be considered as new knowledge and can be used for deci sion-making.We extract comments from YouTube on videos and categorized them in domain-specific,and then apply the Naïve Bayes classifier with improved techniques.Our method provided a decent 80%accuracy in classifying those comments.This experiment shows that the proposed method provides excellent adaptability for large-scale text classification.
文摘Anemia is one of the public health issues that affect children and pregnant women globally.Anemia occurs when the level of red blood cells within the body is reduced.Detecting anemia requires expert blood draw for clinical analysis of hemoglobin quantity.Although this standard method is accurate,it is costive and consumes enough time,unlike the non-invasive approach which is cost-effective and takes less time.This study focused on pallor analysis and used images of the conjunctiva of the eyes to detect anemia using machine learning techniques.This study used a publicly available dataset of 710 images of the conjunctiva of the eyes acquired with a unique tool that eliminates any interference from ambient light.We combined Convolutional Neural Networks,Logistic Regression,and Gaussian Blur algorithm to develop a conjunctiva detection model and an anemia detection model which runs on a Fast API server connected to a frontend mobile app built with React Native.The developed model was embedded into a smartphone application that can detect anemia by capturing and processing a patient's conjunctiva with a sensitivity of 90%,a specificity of 95%,and an accuracy of 92.50%on average performance in about 50 s.
文摘Anemia is a public health issue with serious ramifications for human health globally.Anemia particularly affects pregnant women and children from 6 to 59 months old even though every individual is at risk.Anemia occurs when the Hb level is below its normal threshold or when the red blood cells are weakened or destroyed.To discover medical remedies on time,early detection or diagnosis of anemia assist patients to understand their condition.The invasive approach for anemia detection is costive and time-consuming as compared to the non-invasive approach which is reliable and suitable for developing communities where medical resources and personnel are inadequate.This study uses palpable palm images(dataset)collected from 710 participants in selected hospitals in Ghana.The images were extracted,segmented and converted into RGB percentile to train,validate and tested the machine learning models.A hybrid model was developed with the application of ensemble learning models using the R programming language on the R Studio platform.Stacking,voting,boosting and bagging ensemble model techniques were used to build the hybrid models,the stacking ensemble model achieved an accuracy of 99.73%.The study justifies that ensemble models are efficient for medical disease diagnosis or detection such as anemia.
文摘Identification of bird species from their sounds has become an important area in biodiversity-related research due to the relative ease of capturing bird sounds in the commonly challenging habitat. Audio features have a massive impact on the classification task since they are the fundamental elements used to differentiate classes. As such, the extraction of informative properties of the data is a crucial stage of any classification-based application. Therefore, it is vital to identify the most significant feature to represent the actual bird sounds. In this paper, we propose a novel feature that can advance classification accuracy with modified features, which are most suitable for classifying birds from its audio sounds. Modified Gammatone frequency cepstral coefficient(GTCC) features have been extracted with their frequency banks adjusted to suit bird sounds. The features are then used to train and test a support vector machine(SVM) classifier. It has been shown that the modified GTCC features are able to give 86% accuracy with twenty Bornean birds. Furthermore, in this paper, we are proposing a novel probability enhanced entropy(PEE) feature, which, when combined with the modified GTCC features, is able to improve accuracy further to 89.5%. These results are significant as the relatively low-resource intensive SVM with the proposed modified GTCC, and the proposed novel PEE feature can be implemented in a real-time system to assist researchers,scientists, conservationists, and even eco-tourists in identifying bird species in the dense forest.
文摘In the present era of big data,web page searching and ranking in an efficient manner on the World Wide Web to satisfy the specific search needs of the modern user is undoubtedly a major challenge for search engines.Even though a large number of web search techniques have been developed,some problems still exist while searching with generic search engines as none of the search engines can index the entire web.The issue is not just the volume but also the relevance concerning the user’s requirements.Moreover,if the search query is partially incomplete or is ambiguous,then most of the modern search engines tend to return the result by interpreting all possible meanings of the query.Concerning search quality,more than half of the retrieved web pages have been reported to be irrelevant.Hence web search personalization is required to retrieve search results while incorporating the user’s interests.In the proposed research work we have highlighted the strengths and weakness of various studies as proposed in the literature for web search personalization by carrying out a detailed comparison among them.The in-depth comparative study with baselines leads to the recommendation of Intelligent Meta Search System(IMSS)and Advanced Cluster Vector Page Ranking(ACVPR)algorithm as one of the best approaches as proposed in the literature for web search personalization.Furthermore,the detailed discussion about the comparative analysis of all categories gives new opportunities to think in different research areas.
