Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing capacity.Despite the popularity of ML tech...Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing capacity.Despite the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering(RE)activities to solve the problems that occur in RE activities.The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023.The authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this period.Forty-five research studies were selected based on our exclusion and inclusion criteria.The results show that the scientific community used 57 algorithms.Among those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random Forest.The results show that researchers used these algorithms in eight major RE activities.Those activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural language.Our selected research studies used 32 private and 41 public data sources.The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software Engineering,and iTrust Electronic Health Care System.展开更多
A supervised genetic algorithm (SGA) is proposed to solve the quality of service (QoS) routing problems in computer networks. The supervised rules of intelligent concept are introduced into genetic algorithms (GAs) to...A supervised genetic algorithm (SGA) is proposed to solve the quality of service (QoS) routing problems in computer networks. The supervised rules of intelligent concept are introduced into genetic algorithms (GAs) to solve the constraint optimization problem. One of the main characteristics of SGA is its searching space can be limited in feasible regions rather than infeasible regions. The superiority of SGA to other GAs lies in that some supervised search rules in which the information comes from the problems are incorporated into SGA. The simulation results show that SGA improves the ability of searching an optimum solution and accelerates the convergent process up to 20 times.展开更多
The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple bottom-line.Routine temporal assessments of coal mine dumps are a compliant requirement to ensure design r...The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple bottom-line.Routine temporal assessments of coal mine dumps are a compliant requirement to ensure design reconciliation as spoil off-loading continues over time.Generally,the conventional in-situ coal spoil characterisation is inefficient,laborious,hazardous,and prone to experts'observation biases.To this end,this study explores a novel approach to develop automated coal spoil characterisation using unmanned aerial vehicle(UAV)based optical remote sensing.The textural and spectral properties of the high-resolution UAV images were utilised to derive lithology and geotechnical parameters(i.e.,fabric structure and relative density/consistency)in the proposed workflow.The raw images were converted to an orthomosaic using structure from motion aided processing.Then,structural descriptors were computed per pixel to enhance feature modalities of the spoil materials.Finally,machine learning algorithms were employed with ground truth from experts as training and testing data to characterise spoil rapidly with minimal human intervention.The characterisation accuracies achieved from the proposed approach manifest a digital solution to address the limitations in the conventional characterisation approach.展开更多
The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Indi...The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Individuals can quickly fabricate comments and news on social media.The most difficult challenge is determining which news is real or fake.Accordingly,tracking down programmed techniques to recognize fake news online is imperative.With an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media content.This study shows past,current,and possible methods that can be used in the future for fake news classification.Two different publicly available datasets containing political news are utilized for performing experiments.Sixteen supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text classification.In contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer text.Additionally,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.展开更多
A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and...A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.展开更多
Accurate and timely surveying of airfield pavement distress is crucial for cost-effective air-port maintenance.Deep learning(DL)approaches,leveraging advancements in computer science and image acquisition techniques,h...Accurate and timely surveying of airfield pavement distress is crucial for cost-effective air-port maintenance.Deep learning(DL)approaches,leveraging advancements in computer science and image acquisition techniques,have become the mainstream for automated air-field pavement distress detection.However,fully-supervised DL methods require a large number of manually annotated ground truth labels to achieve high accuracy.To address the challenge of limited high-quality manual annotations,we propose a novel end-to-end distress detection model called class activation map informed weakly-supervised dis-tress detection(WSDD-CAM).Based on YOLOv5,WSDD-CAM consists of an efficient back-bone,a classification branch,and a localization network.By utilizing class activation map(CAM)information,our model significantly reduces the need for manual annotations,auto-matically generating pseudo bounding boxes with a 71%overlap with the ground truth.To evaluate WSDD-CAM,we tested it on a self-made dataset and compared it with other weakly-supervised and fully-supervised models.The results show that our model achieves 49.2%mean average precision(mAP),outperforming other weakly-supervised methods and even approaching state-of-the-art fully-supervised methods.Additionally,ablation experiments confirm the effectiveness of our architecture design.In conclusion,our WSDD-CAM model offers a promising solution for airfield pavement distress detection,reducing manual annotation time while maintaining high accuracy.This efficient and effec-tive approach can significantly contribute to cost-effective airport maintenance management.展开更多
The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be lethal.The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches...The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be lethal.The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches,including deep learning.We used a dataset with 13 primary characteristics to carry out the research.Support vector machine and logistic regression algorithms are used to process the datasets,and the latter displays the highest accuracy in predicting coronary disease.Python programming is used to process the datasets.Multiple research initiatives have used machine learning to speed up the healthcare sector.We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks.Using the accuracy and confusion matrix has resulted in some favorable outcomes.To get the best results,the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine(SVM)classification.展开更多
基金Research Center of the College of Computer and Information Sciences,King Saud University,Grant/Award Number:RSPD2024R947King Saud University。
文摘Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing capacity.Despite the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering(RE)activities to solve the problems that occur in RE activities.The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023.The authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this period.Forty-five research studies were selected based on our exclusion and inclusion criteria.The results show that the scientific community used 57 algorithms.Among those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random Forest.The results show that researchers used these algorithms in eight major RE activities.Those activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural language.Our selected research studies used 32 private and 41 public data sources.The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software Engineering,and iTrust Electronic Health Care System.
基金China Postdoctoral Foundation (No2005037529)Doctoral Foundation of Education Ministry of China (No2003005607)Tianjin High Education Science Development Foundation (No20041325)
文摘A supervised genetic algorithm (SGA) is proposed to solve the quality of service (QoS) routing problems in computer networks. The supervised rules of intelligent concept are introduced into genetic algorithms (GAs) to solve the constraint optimization problem. One of the main characteristics of SGA is its searching space can be limited in feasible regions rather than infeasible regions. The superiority of SGA to other GAs lies in that some supervised search rules in which the information comes from the problems are incorporated into SGA. The simulation results show that SGA improves the ability of searching an optimum solution and accelerates the convergent process up to 20 times.
基金supported by the Australian Coal Industry's Research Program(ACARP)[C29048].
文摘The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple bottom-line.Routine temporal assessments of coal mine dumps are a compliant requirement to ensure design reconciliation as spoil off-loading continues over time.Generally,the conventional in-situ coal spoil characterisation is inefficient,laborious,hazardous,and prone to experts'observation biases.To this end,this study explores a novel approach to develop automated coal spoil characterisation using unmanned aerial vehicle(UAV)based optical remote sensing.The textural and spectral properties of the high-resolution UAV images were utilised to derive lithology and geotechnical parameters(i.e.,fabric structure and relative density/consistency)in the proposed workflow.The raw images were converted to an orthomosaic using structure from motion aided processing.Then,structural descriptors were computed per pixel to enhance feature modalities of the spoil materials.Finally,machine learning algorithms were employed with ground truth from experts as training and testing data to characterise spoil rapidly with minimal human intervention.The characterisation accuracies achieved from the proposed approach manifest a digital solution to address the limitations in the conventional characterisation approach.
基金Abu Dhabi University’s Office of sponsored programs in the United Arab Emirates(Grant Number:19300752)funded this endeavor.
文摘The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Individuals can quickly fabricate comments and news on social media.The most difficult challenge is determining which news is real or fake.Accordingly,tracking down programmed techniques to recognize fake news online is imperative.With an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media content.This study shows past,current,and possible methods that can be used in the future for fake news classification.Two different publicly available datasets containing political news are utilized for performing experiments.Sixteen supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text classification.In contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer text.Additionally,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.
文摘A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.
基金support of the National Natural Science Foundation of China(Nos.52008311,51878499,and 52178433)the Science and Technology Commission of Shanghai Municipality(No.21ZR1465700)the Fundamental Research Funds for the Central Universities(No.22120230196).
文摘Accurate and timely surveying of airfield pavement distress is crucial for cost-effective air-port maintenance.Deep learning(DL)approaches,leveraging advancements in computer science and image acquisition techniques,have become the mainstream for automated air-field pavement distress detection.However,fully-supervised DL methods require a large number of manually annotated ground truth labels to achieve high accuracy.To address the challenge of limited high-quality manual annotations,we propose a novel end-to-end distress detection model called class activation map informed weakly-supervised dis-tress detection(WSDD-CAM).Based on YOLOv5,WSDD-CAM consists of an efficient back-bone,a classification branch,and a localization network.By utilizing class activation map(CAM)information,our model significantly reduces the need for manual annotations,auto-matically generating pseudo bounding boxes with a 71%overlap with the ground truth.To evaluate WSDD-CAM,we tested it on a self-made dataset and compared it with other weakly-supervised and fully-supervised models.The results show that our model achieves 49.2%mean average precision(mAP),outperforming other weakly-supervised methods and even approaching state-of-the-art fully-supervised methods.Additionally,ablation experiments confirm the effectiveness of our architecture design.In conclusion,our WSDD-CAM model offers a promising solution for airfield pavement distress detection,reducing manual annotation time while maintaining high accuracy.This efficient and effec-tive approach can significantly contribute to cost-effective airport maintenance management.
文摘The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be lethal.The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches,including deep learning.We used a dataset with 13 primary characteristics to carry out the research.Support vector machine and logistic regression algorithms are used to process the datasets,and the latter displays the highest accuracy in predicting coronary disease.Python programming is used to process the datasets.Multiple research initiatives have used machine learning to speed up the healthcare sector.We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks.Using the accuracy and confusion matrix has resulted in some favorable outcomes.To get the best results,the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine(SVM)classification.
