Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in ...Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, Naive Bayes, Decision Trees and Random Forest Algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.展开更多
Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are explorin...Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are exploring the potential of DC microgrids across var-ious configurations.However,despite the sustainability and accuracy offered by DC microgrids,they pose various challenges when integrated into modern power distribution systems.Among these challenges,fault diagnosis holds significant importance.Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads.A primary chal-lenge is the lack of standards and guidelines for the protection and safety of DC microgrids,including fault detection,location,and clear-ing procedures for both grid-connected and islanded modes.In response,this study presents a brief overview of various approaches for protecting DC microgrids.展开更多
Urban traffic congestion is a severe and widely studied problem over the decade because of the negative impacts. However, in recent years some approaches emerge as proper and suitable solutions. The Carpooling initiat...Urban traffic congestion is a severe and widely studied problem over the decade because of the negative impacts. However, in recent years some approaches emerge as proper and suitable solutions. The Carpooling initiative is one of the most representative efforts to propitiate a responsible use of particular vehicles. Thus, the paper introduces a carpooling model considering the users’ preference to reach an appropriate match among drivers and passengers. In particular, the paper conducts a study of 6 of the most avid classified techniques in machine learning to create a model for the selection of travel companions. The experimental results show the models’ precision and assess the best cases using Friedman’s test. Finally, the conclusions emphasize the relevance of the proposed study and suggest that it is necessary to extend the proposal with more drives and passengers’ data.展开更多
In this study, an integrated approach for diagenetic facies classification, reservoir quality analysis and quantitative wireline log prediction of tight gas sandstones(TGSs) is introduced utilizing a combination of fi...In this study, an integrated approach for diagenetic facies classification, reservoir quality analysis and quantitative wireline log prediction of tight gas sandstones(TGSs) is introduced utilizing a combination of fit-for-purpose complementary testing and machine learning techniques. The integrated approach is specialized for the middle Permian Shihezi Formation TGSs in the northeastern Ordos Basin, where operators often face significant drilling uncertainty and increased exploration risks due to low porosities and micro-Darcy range permeabilities. In this study, detrital compositions and diagenetic minerals and their pore type assemblages were analyzed using optical light microscopy, cathodoluminescence, standard scanning electron microscopy, and X-ray diffraction. Different types of diagenetic facies were delineated on this basis to capture the characteristic rock properties of the TGSs in the target formation.A combination of He porosity and permeability measurements, mercury intrusion capillary pressure and nuclear magnetic resonance data was used to analyze the mechanism of heterogeneous TGS reservoirs.We found that the type, size and proportion of pores considerably varied between diagenetic facies due to differences in the initial depositional attributes and subsequent diagenetic alterations;these differences affected the size, distribution and connectivity of the pore network and varied the reservoir quality. Five types of diagenetic facies were classified:(i) grain-coating facies, which have minimal ductile grains, chlorite coatings that inhibit quartz overgrowths, large intergranular pores that dominate the pore network, the best pore structure and the greatest reservoir quality;(ii) quartz-cemented facies,which exhibit strong quartz overgrowths, intergranular porosity and a pore size decrease, resulting in the deterioration of the pore structure and reservoir quality;(iii) mixed-cemented facies, in which the cementation of various authigenic minerals increases the micropores, resulting in a poor pore structure and reservoir quality;(iv) carbonate-cemented facies and(v) tightly compacted facies, in which the intergranular pores are filled with carbonate cement and ductile grains;thus, the pore network mainly consists of micropores with small pore throat sizes, and the pore structure and reservoir quality are the worst. The grain-coating facies with the best reservoir properties are more likely to have high gas productivity and are the primary targets for exploration and development. The diagenetic facies were then translated into wireline log expressions(conventional and NMR logging). Finally, a wireline log quantitative prediction model of TGSs using convolutional neural network machine learning algorithms was established to successfully classify the different diagenetic facies.展开更多
The integration of distributed generations (DGs) into distribution systems (DSs) is increasingly becoming a solution for compensating for isolated local energy systems (ILESs). Additionally, distributed generations ar...The integration of distributed generations (DGs) into distribution systems (DSs) is increasingly becoming a solution for compensating for isolated local energy systems (ILESs). Additionally, distributed generations are used for self-consumption with excess energy injected into centralized grids (CGs). However, the improper sizing of renewable energy systems (RESs) exposes the entire system to power losses. This work presents an optimization of a system consisting of distributed generations. Firstly, PSO algorithms evaluate the size of the entire system on the IEEE bus 14 test standard. Secondly, the size of the system is allocated using improved Particles Swarm Optimization (IPSO). The convergence speed of the objective function enables a conjecture to be made about the robustness of the proposed system. The power and voltage profile on the IEEE 14-bus standard displays a decrease in power losses and an appropriate response to energy demands (EDs), validating the proposed method.展开更多
The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizatio...The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizations,and governments,highlighting the urgent need for robust malware detection mechanisms.Conventional machine learning-based approaches rely on static and dynamicmalware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures.Although machine learning algorithms(MLAs)offer promising detection capabilities,their reliance on extensive feature engineering limits real-time applicability.Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,affecting deployment efficiency.This research evaluates classical MLAs and deep learningmodels to enhance malware detection performance across diverse datasets.The proposed approach integrates a novel text and imagebased detection framework,employing an optimized Support Vector Machine(SVM)for textual data analysis and EfficientNet-B0 for image-based malware classification.Experimental analysis,conducted across multiple train-test splits over varying timescales,demonstrates 99.97%accuracy on textual datasets using SVM and 96.7%accuracy on image-based datasets with EfficientNet-B0,significantly improving zero-day malware detection.Furthermore,a comparative analysis with existing competitive techniques,such as Random Forest,XGBoost,and CNN-based(Convolutional Neural Network)classifiers,highlights the superior performance of the proposed model in terms of accuracy,efficiency,and robustness.展开更多
Craniosynostosis(CsO)is characterized by premature fusion of skull sutures in infants.This early closure of one or more main sutures can lead to various skull and facial deformities and may cause developmental delay i...Craniosynostosis(CsO)is characterized by premature fusion of skull sutures in infants.This early closure of one or more main sutures can lead to various skull and facial deformities and may cause developmental delay in children.Early diagnosis,crucial for effective treatment,traditionally relies on physical examination and 3D cranial imaging,which are often inaccurate or with the risk of X-ray exposure.This study presents a fully-automated deep learning-based method for diagnosing common types of single suture CsO using routine digi-tal photographs of infants'heads.We employed a two-stage approach involving head segmentation and CsO type classification.First,mask region-based convolutional neural network(Mask R-CNN)was used for accurate head segmentation,achieving an average precision of 97.60%and an average recall of 96.20%.The segmented images were then classified into different CsO types using a modified VGG11 neural network.The classifier attained a training accuracy of 99.74%and a test accuracy of 94.44%,with high sensitivity and specificity for uni-coronal,metopic,and sagittal types.Our method illustrates high reliability and accuracy,offering non-invasive,accessible and accurate diagnostic instrument for early detection and patient screening.展开更多
The compressive strength of self-compacting concrete(SCC)needs to be determined during the construction design process.This paper shows that the compressive strength of SCC(CS of SCC)can be successfully predicted from...The compressive strength of self-compacting concrete(SCC)needs to be determined during the construction design process.This paper shows that the compressive strength of SCC(CS of SCC)can be successfully predicted from mix design and curing age by a machine learning(ML)technique named the Extreme Gradient Boosting(XGB)algorithm,including non-hybrid and hybrid models.Nine ML techniques,such as Linear regression(LR),K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Decision Trees(DTR),Random Forest(RF),Gradient Boosting(GB),and Artificial Neural Network using two training algorithms LBFGS and SGD(denoted as ANN_LBFGS and ANN_SGD),are also compared with the XGB model.Moreover,the hybrid models of eight ML techniques and Particle Swarm Optimization(PSO)are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model.The highest number of SCC samples available in the literature is collected for building the ML techniques.Compared with previously published works’performance,the proposed XGB method,both hybrid and non-hybrid models,is the most reliable and robust of the examined techniques,and is more accurate than existing ML methods(R2=0.9644,RMSE=4.7801,and MAE=3.4832).Therefore,the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.展开更多
The construction industry relies so heavily on concrete that it’s crucial to precisely forecast and optimize the strength and workability of concrete mixtures,while reducing costs as much as possible.For this objecti...The construction industry relies so heavily on concrete that it’s crucial to precisely forecast and optimize the strength and workability of concrete mixtures,while reducing costs as much as possible.For this objective,this study tries to predict and optimize the compressive strength and workability(slump)of concrete by using deterministic and robust optimization approaches,so as to determine the optimum concrete mixture proportions,while minimizing cost.Specifically,strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine(SVM),artificial neural network(ANN),fuzzy inference system(FIS),adaptive fuzzy inference system(ANIS),and genetic expression programming(GEP),based on a dataset comprising two hundred concrete mixtures,which has various levels of key ingredients,including cement,water,fine aggregate,coarse aggregate,and size of coarse aggregate,along with their associated measures of strength and workability.These ingredients were used as input parameters,while compressive strength and slump(representing workability)served as output parameters for each mix proportion.Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks,finding that ANFIS can yield the best results both for training and validation.This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions,thus helping to maximize strength and workability while minimizing costs.展开更多
This study delves into the applications,challenges,and future directions of deep learning techniques in the field of image recognition.Deep learning,particularly Convolutional Neural Networks(CNNs),Recurrent Neural Ne...This study delves into the applications,challenges,and future directions of deep learning techniques in the field of image recognition.Deep learning,particularly Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Generative Adversarial Networks(GANs),has become key to enhancing the precision and efficiency of image recognition.These models are capable of processing complex visual data,facilitating efficient feature extraction and image classification.However,acquiring and annotating high-quality,diverse datasets,addressing imbalances in datasets,and model training and optimization remain significant challenges in this domain.The paper proposes strategies for improving data augmentation,optimizing model architectures,and employing automated model optimization tools to address these challenges,while also emphasizing the importance of considering ethical issues in technological advancements.As technology continues to evolve,the application of deep learning in image recognition will further demonstrate its potent capability to solve complex problems,driving society towards more inclusive and diverse development.展开更多
Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of suc...Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.展开更多
Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability ...Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability lias emerged as an important research topic to address industry expectations for reducing costs,in particular,maintenance costs.Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning(ML)for better prediction of software maintainability.This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction(SPMP)using ML techniques.This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria:maintainability prediction techniques,validation methods,accuracy criteria,overall accuracy of ML techniques,and the techniques offering the best performance.The review process followed the well-known syslematic review process.The results show that ML techniques are frequently used in predicting maintainability.In particular,artificial neural network(ANN),support vector machine/regression(SVM/R).regression&decision trees(DT),and fuzzy neuro fuzzy(FNF)techniques are more accurate in terms of PRED and MMRE.The N-fold and leave-one-out cross-validation methods,and the MMRE and PRED accuracy criteria are frequently used in empirical studies.In general,ML techniques outperformed non-machine learning techniques,e.g.,regression analysis(RA)techniques,while FNF outperformed SVM/R.DT.and ANN in most experiments.However,while many techniques were reported superior,no specific one can be identified as the best.展开更多
Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbule...Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.展开更多
In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to t...In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.展开更多
Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus o...Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus often suffer from model misspecification or inadequacy.The on-rising deep learning(DL)techniques offer a powerful alternative.Deep learning employs multi-layered artificial neural networks to progressively transform input data into more abstract and complex representations.DL methods can autonomously uncover meaningful patterns from data,thereby bypassing potential biases introduced by predefined features(Franklin,2005;Murphy,2012).Recent efforts have aimed to apply deep neural networks(DNNs)to phylogenetics,with a growing number of applications in tree reconstruction(Suvorov et al.,2020;Zou et al.,2020;Nesterenko et al.,2022;Smith and Hahn,2023;Wang et al.,2023),substitution model selection(Abadi et al.,2020;Burgstaller-Muehlbacher et al.,2023),and diversification rate inference(Voznica et al.,2022;Lajaaiti et al.,2023;Lambert et al.,2023).In phylogenetic tree reconstruction,PhyDL(Zou et al.,2020)and Tree_learning(Suvorov et al.,2020)are two notable DNN-based programs designed to infer unrooted quartet trees directly from alignments of four amino acid(AA)and DNA sequences,respectively.展开更多
Cervical cancer is a serious public health issue worldwide, and early identification is crucial for better patient outcomes. Recent study has investigated how ML and DL approaches may be used to increase the accuracy ...Cervical cancer is a serious public health issue worldwide, and early identification is crucial for better patient outcomes. Recent study has investigated how ML and DL approaches may be used to increase the accuracy of vagina tests. In this piece, we conducted a thorough review of 50 research studies that applied these techniques. Our investigation compared the outcomes to well-known screening techniques and concentrated on the datasets used and performance measurements reported. According to the research, convolutional neural networks and other deep learning approaches have potential for lowering false positives and boosting screening precision. Although several research used small sample sizes or constrained datasets, this raises questions about how applicable the findings are. This paper discusses the advantages and disadvantages of the articles that were chosen, as well as prospective topics for future research, to further the application of ml and dl in cervical cancer screening. The development of cervical cancer screening technologies that are more precise, accessible, and can lead to better public health outcomes is significantly affected by these findings.展开更多
Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world.The number of landslides and the level of damage across the globe has been increasing over time.Ther...Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world.The number of landslides and the level of damage across the globe has been increasing over time.Therefore,landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region.Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study.The prime goal of the study is to prepare landslide susceptibility maps(LSMs)using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide,twenty factors,including triggering and causative factors,were selected.A deep learning algorithm viz.convolutional neural network model(CNN)and three popular machine learning techniques,i.e.,random forest model(RF),artificial neural network model(ANN),and bagging model,were employed to prepare the LSMs.Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points.A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor,and the role of individual conditioning factors was estimated using information gain ratio.The result reveals that there is no severe multicollinearity among the landslide conditioning factors,and the triggering factor rainfall appeared as the leading cause of the landslide.Based on the final prediction values of each model,LSM was constructed and successfully portioned into five distinct classes,like very low,low,moderate,high,and very high susceptibility.The susceptibility class-wise distribution of landslides shows that more than 90%of the landslide area falls under higher landslide susceptibility grades.The precision of models was examined using the area under the curve(AUC)of the receiver operating characteristics(ROC)curve and statistical methods like root mean square error(RMSE)and mean absolute error(MAE).In both datasets(training and validation),the CNN model achieved the maximum AUC value of 0.903 and 0.939,respectively.The lowest value of RMSE and MAE also reveals the better performance of the CNN model.So,it can be concluded that all the models have performed well,but the CNN model has outperformed the other models in terms of precision.展开更多
The uniaxial compressive strength(UCS)of rock is an essential property of rock material in different relevant applications,such as rock slope,tunnel construction,and foundation.It takes enormous time and effort to obt...The uniaxial compressive strength(UCS)of rock is an essential property of rock material in different relevant applications,such as rock slope,tunnel construction,and foundation.It takes enormous time and effort to obtain the UCS values directly in the laboratory.Accordingly,an indirect determination of UCS through conducting several rock index tests that are easy and fast to carry out is of interest and importance.This study presents powerful boosting trees evaluation framework,i.e.,adaptive boosting machine,extreme gradient boosting machine(XGBoost),and category gradient boosting machine,for estimating the UCS of sandstone.Schmidt hammer rebound number,P-wave velocity,and point load index were chosen as considered factors to forecast UCS values of sandstone samples.Taylor diagrams and five regression metrics,including coefficient of determination(R2),root mean square error,mean absolute error,variance account for,and A-20 index,were used to evaluate and compare the performance of these boosting trees.The results showed that the proposed boosting trees are able to provide a high level of prediction capacity for the prepared database.In particular,itwas worth noting that XGBoost is the best model to predict sandstone strength and it achieved 0.999 training R^(2) and 0.958 testing R^(2).The proposed model had more outstanding capability than neural network with optimization techniques during training and testing phases.The performed variable importance analysis reveals that the point load index has a significant influence on predicting UCS of sandstone.展开更多
The study analyzes the performance of bank-specific characteristics,macroeconomic indicators,and global factors to predict the bank lending in Turkey for the period 2002Q4–2019Q2.The objective of this study is first,...The study analyzes the performance of bank-specific characteristics,macroeconomic indicators,and global factors to predict the bank lending in Turkey for the period 2002Q4–2019Q2.The objective of this study is first,to clarify the possible nonlinear and nonparametric relationships between outstanding bank loans and bank-specific,macroeconomic,and global factors.Second,it aims to propose various machine learning algorithms that determine drivers of bank lending and benefits from the advantages of these techniques.The empirical findings indicate favorable evidence that the drivers of bank lending exhibit some nonlinearities.Additionally,partial dependence plots depict that numerous bank-specific characteristics and macroeconomic indicators tend to be important variables that influence bank lending behavior.The study’s findings have some policy implications for bank managers,regulatory authorities,and policymakers.展开更多
Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteris...Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.展开更多
文摘Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, Naive Bayes, Decision Trees and Random Forest Algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.
文摘Low-voltage direct current(DC)microgrids have recently emerged as a promising and viable alternative to traditional alternating cur-rent(AC)microgrids,offering numerous advantages.Consequently,researchers are exploring the potential of DC microgrids across var-ious configurations.However,despite the sustainability and accuracy offered by DC microgrids,they pose various challenges when integrated into modern power distribution systems.Among these challenges,fault diagnosis holds significant importance.Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads.A primary chal-lenge is the lack of standards and guidelines for the protection and safety of DC microgrids,including fault detection,location,and clear-ing procedures for both grid-connected and islanded modes.In response,this study presents a brief overview of various approaches for protecting DC microgrids.
文摘Urban traffic congestion is a severe and widely studied problem over the decade because of the negative impacts. However, in recent years some approaches emerge as proper and suitable solutions. The Carpooling initiative is one of the most representative efforts to propitiate a responsible use of particular vehicles. Thus, the paper introduces a carpooling model considering the users’ preference to reach an appropriate match among drivers and passengers. In particular, the paper conducts a study of 6 of the most avid classified techniques in machine learning to create a model for the selection of travel companions. The experimental results show the models’ precision and assess the best cases using Friedman’s test. Finally, the conclusions emphasize the relevance of the proposed study and suggest that it is necessary to extend the proposal with more drives and passengers’ data.
基金financially supported by the National Natural Science Foundation of China (No. 42272156)research on efficient exploration and development technology for tight stone gas of China United Coalbed Methane Corporation (No. ZZGSECCYWG 2021-322)。
文摘In this study, an integrated approach for diagenetic facies classification, reservoir quality analysis and quantitative wireline log prediction of tight gas sandstones(TGSs) is introduced utilizing a combination of fit-for-purpose complementary testing and machine learning techniques. The integrated approach is specialized for the middle Permian Shihezi Formation TGSs in the northeastern Ordos Basin, where operators often face significant drilling uncertainty and increased exploration risks due to low porosities and micro-Darcy range permeabilities. In this study, detrital compositions and diagenetic minerals and their pore type assemblages were analyzed using optical light microscopy, cathodoluminescence, standard scanning electron microscopy, and X-ray diffraction. Different types of diagenetic facies were delineated on this basis to capture the characteristic rock properties of the TGSs in the target formation.A combination of He porosity and permeability measurements, mercury intrusion capillary pressure and nuclear magnetic resonance data was used to analyze the mechanism of heterogeneous TGS reservoirs.We found that the type, size and proportion of pores considerably varied between diagenetic facies due to differences in the initial depositional attributes and subsequent diagenetic alterations;these differences affected the size, distribution and connectivity of the pore network and varied the reservoir quality. Five types of diagenetic facies were classified:(i) grain-coating facies, which have minimal ductile grains, chlorite coatings that inhibit quartz overgrowths, large intergranular pores that dominate the pore network, the best pore structure and the greatest reservoir quality;(ii) quartz-cemented facies,which exhibit strong quartz overgrowths, intergranular porosity and a pore size decrease, resulting in the deterioration of the pore structure and reservoir quality;(iii) mixed-cemented facies, in which the cementation of various authigenic minerals increases the micropores, resulting in a poor pore structure and reservoir quality;(iv) carbonate-cemented facies and(v) tightly compacted facies, in which the intergranular pores are filled with carbonate cement and ductile grains;thus, the pore network mainly consists of micropores with small pore throat sizes, and the pore structure and reservoir quality are the worst. The grain-coating facies with the best reservoir properties are more likely to have high gas productivity and are the primary targets for exploration and development. The diagenetic facies were then translated into wireline log expressions(conventional and NMR logging). Finally, a wireline log quantitative prediction model of TGSs using convolutional neural network machine learning algorithms was established to successfully classify the different diagenetic facies.
文摘The integration of distributed generations (DGs) into distribution systems (DSs) is increasingly becoming a solution for compensating for isolated local energy systems (ILESs). Additionally, distributed generations are used for self-consumption with excess energy injected into centralized grids (CGs). However, the improper sizing of renewable energy systems (RESs) exposes the entire system to power losses. This work presents an optimization of a system consisting of distributed generations. Firstly, PSO algorithms evaluate the size of the entire system on the IEEE bus 14 test standard. Secondly, the size of the system is allocated using improved Particles Swarm Optimization (IPSO). The convergence speed of the objective function enables a conjecture to be made about the robustness of the proposed system. The power and voltage profile on the IEEE 14-bus standard displays a decrease in power losses and an appropriate response to energy demands (EDs), validating the proposed method.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizations,and governments,highlighting the urgent need for robust malware detection mechanisms.Conventional machine learning-based approaches rely on static and dynamicmalware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures.Although machine learning algorithms(MLAs)offer promising detection capabilities,their reliance on extensive feature engineering limits real-time applicability.Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,affecting deployment efficiency.This research evaluates classical MLAs and deep learningmodels to enhance malware detection performance across diverse datasets.The proposed approach integrates a novel text and imagebased detection framework,employing an optimized Support Vector Machine(SVM)for textual data analysis and EfficientNet-B0 for image-based malware classification.Experimental analysis,conducted across multiple train-test splits over varying timescales,demonstrates 99.97%accuracy on textual datasets using SVM and 96.7%accuracy on image-based datasets with EfficientNet-B0,significantly improving zero-day malware detection.Furthermore,a comparative analysis with existing competitive techniques,such as Random Forest,XGBoost,and CNN-based(Convolutional Neural Network)classifiers,highlights the superior performance of the proposed model in terms of accuracy,efficiency,and robustness.
文摘Craniosynostosis(CsO)is characterized by premature fusion of skull sutures in infants.This early closure of one or more main sutures can lead to various skull and facial deformities and may cause developmental delay in children.Early diagnosis,crucial for effective treatment,traditionally relies on physical examination and 3D cranial imaging,which are often inaccurate or with the risk of X-ray exposure.This study presents a fully-automated deep learning-based method for diagnosing common types of single suture CsO using routine digi-tal photographs of infants'heads.We employed a two-stage approach involving head segmentation and CsO type classification.First,mask region-based convolutional neural network(Mask R-CNN)was used for accurate head segmentation,achieving an average precision of 97.60%and an average recall of 96.20%.The segmented images were then classified into different CsO types using a modified VGG11 neural network.The classifier attained a training accuracy of 99.74%and a test accuracy of 94.44%,with high sensitivity and specificity for uni-coronal,metopic,and sagittal types.Our method illustrates high reliability and accuracy,offering non-invasive,accessible and accurate diagnostic instrument for early detection and patient screening.
文摘The compressive strength of self-compacting concrete(SCC)needs to be determined during the construction design process.This paper shows that the compressive strength of SCC(CS of SCC)can be successfully predicted from mix design and curing age by a machine learning(ML)technique named the Extreme Gradient Boosting(XGB)algorithm,including non-hybrid and hybrid models.Nine ML techniques,such as Linear regression(LR),K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Decision Trees(DTR),Random Forest(RF),Gradient Boosting(GB),and Artificial Neural Network using two training algorithms LBFGS and SGD(denoted as ANN_LBFGS and ANN_SGD),are also compared with the XGB model.Moreover,the hybrid models of eight ML techniques and Particle Swarm Optimization(PSO)are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model.The highest number of SCC samples available in the literature is collected for building the ML techniques.Compared with previously published works’performance,the proposed XGB method,both hybrid and non-hybrid models,is the most reliable and robust of the examined techniques,and is more accurate than existing ML methods(R2=0.9644,RMSE=4.7801,and MAE=3.4832).Therefore,the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.
文摘The construction industry relies so heavily on concrete that it’s crucial to precisely forecast and optimize the strength and workability of concrete mixtures,while reducing costs as much as possible.For this objective,this study tries to predict and optimize the compressive strength and workability(slump)of concrete by using deterministic and robust optimization approaches,so as to determine the optimum concrete mixture proportions,while minimizing cost.Specifically,strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine(SVM),artificial neural network(ANN),fuzzy inference system(FIS),adaptive fuzzy inference system(ANIS),and genetic expression programming(GEP),based on a dataset comprising two hundred concrete mixtures,which has various levels of key ingredients,including cement,water,fine aggregate,coarse aggregate,and size of coarse aggregate,along with their associated measures of strength and workability.These ingredients were used as input parameters,while compressive strength and slump(representing workability)served as output parameters for each mix proportion.Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks,finding that ANFIS can yield the best results both for training and validation.This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions,thus helping to maximize strength and workability while minimizing costs.
文摘This study delves into the applications,challenges,and future directions of deep learning techniques in the field of image recognition.Deep learning,particularly Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Generative Adversarial Networks(GANs),has become key to enhancing the precision and efficiency of image recognition.These models are capable of processing complex visual data,facilitating efficient feature extraction and image classification.However,acquiring and annotating high-quality,diverse datasets,addressing imbalances in datasets,and model training and optimization remain significant challenges in this domain.The paper proposes strategies for improving data augmentation,optimizing model architectures,and employing automated model optimization tools to address these challenges,while also emphasizing the importance of considering ethical issues in technological advancements.As technology continues to evolve,the application of deep learning in image recognition will further demonstrate its potent capability to solve complex problems,driving society towards more inclusive and diverse development.
文摘Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.
文摘Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability lias emerged as an important research topic to address industry expectations for reducing costs,in particular,maintenance costs.Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning(ML)for better prediction of software maintainability.This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction(SPMP)using ML techniques.This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria:maintainability prediction techniques,validation methods,accuracy criteria,overall accuracy of ML techniques,and the techniques offering the best performance.The review process followed the well-known syslematic review process.The results show that ML techniques are frequently used in predicting maintainability.In particular,artificial neural network(ANN),support vector machine/regression(SVM/R).regression&decision trees(DT),and fuzzy neuro fuzzy(FNF)techniques are more accurate in terms of PRED and MMRE.The N-fold and leave-one-out cross-validation methods,and the MMRE and PRED accuracy criteria are frequently used in empirical studies.In general,ML techniques outperformed non-machine learning techniques,e.g.,regression analysis(RA)techniques,while FNF outperformed SVM/R.DT.and ANN in most experiments.However,while many techniques were reported superior,no specific one can be identified as the best.
基金supported by the National Natural Science Foundation of China(No.12104141).
文摘Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.
基金supported in part by the National Natural Science Foundation of China(62403396,62433018,62373113)the Guangdong Basic and Applied Basic Research Foundation(2023A1515011527,2023B1515120010)the Postdoctoral Fellowship Program of CPSF(GZB20240621)
文摘In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.
基金supported by the National Key R&D Program of China(2022YFD1401600)the National Science Foundation for Distinguished Young Scholars of Zhejang Province,China(LR23C140001)supported by the Key Area Research and Development Program of Guangdong Province,China(2018B020205003 and 2020B0202090001).
文摘Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus often suffer from model misspecification or inadequacy.The on-rising deep learning(DL)techniques offer a powerful alternative.Deep learning employs multi-layered artificial neural networks to progressively transform input data into more abstract and complex representations.DL methods can autonomously uncover meaningful patterns from data,thereby bypassing potential biases introduced by predefined features(Franklin,2005;Murphy,2012).Recent efforts have aimed to apply deep neural networks(DNNs)to phylogenetics,with a growing number of applications in tree reconstruction(Suvorov et al.,2020;Zou et al.,2020;Nesterenko et al.,2022;Smith and Hahn,2023;Wang et al.,2023),substitution model selection(Abadi et al.,2020;Burgstaller-Muehlbacher et al.,2023),and diversification rate inference(Voznica et al.,2022;Lajaaiti et al.,2023;Lambert et al.,2023).In phylogenetic tree reconstruction,PhyDL(Zou et al.,2020)and Tree_learning(Suvorov et al.,2020)are two notable DNN-based programs designed to infer unrooted quartet trees directly from alignments of four amino acid(AA)and DNA sequences,respectively.
文摘Cervical cancer is a serious public health issue worldwide, and early identification is crucial for better patient outcomes. Recent study has investigated how ML and DL approaches may be used to increase the accuracy of vagina tests. In this piece, we conducted a thorough review of 50 research studies that applied these techniques. Our investigation compared the outcomes to well-known screening techniques and concentrated on the datasets used and performance measurements reported. According to the research, convolutional neural networks and other deep learning approaches have potential for lowering false positives and boosting screening precision. Although several research used small sample sizes or constrained datasets, this raises questions about how applicable the findings are. This paper discusses the advantages and disadvantages of the articles that were chosen, as well as prospective topics for future research, to further the application of ml and dl in cervical cancer screening. The development of cervical cancer screening technologies that are more precise, accessible, and can lead to better public health outcomes is significantly affected by these findings.
文摘Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world.The number of landslides and the level of damage across the globe has been increasing over time.Therefore,landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region.Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study.The prime goal of the study is to prepare landslide susceptibility maps(LSMs)using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide,twenty factors,including triggering and causative factors,were selected.A deep learning algorithm viz.convolutional neural network model(CNN)and three popular machine learning techniques,i.e.,random forest model(RF),artificial neural network model(ANN),and bagging model,were employed to prepare the LSMs.Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points.A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor,and the role of individual conditioning factors was estimated using information gain ratio.The result reveals that there is no severe multicollinearity among the landslide conditioning factors,and the triggering factor rainfall appeared as the leading cause of the landslide.Based on the final prediction values of each model,LSM was constructed and successfully portioned into five distinct classes,like very low,low,moderate,high,and very high susceptibility.The susceptibility class-wise distribution of landslides shows that more than 90%of the landslide area falls under higher landslide susceptibility grades.The precision of models was examined using the area under the curve(AUC)of the receiver operating characteristics(ROC)curve and statistical methods like root mean square error(RMSE)and mean absolute error(MAE).In both datasets(training and validation),the CNN model achieved the maximum AUC value of 0.903 and 0.939,respectively.The lowest value of RMSE and MAE also reveals the better performance of the CNN model.So,it can be concluded that all the models have performed well,but the CNN model has outperformed the other models in terms of precision.
基金funded by Act 211 Government of the Russian Federation,Contract No.02.A03.21.0011.
文摘The uniaxial compressive strength(UCS)of rock is an essential property of rock material in different relevant applications,such as rock slope,tunnel construction,and foundation.It takes enormous time and effort to obtain the UCS values directly in the laboratory.Accordingly,an indirect determination of UCS through conducting several rock index tests that are easy and fast to carry out is of interest and importance.This study presents powerful boosting trees evaluation framework,i.e.,adaptive boosting machine,extreme gradient boosting machine(XGBoost),and category gradient boosting machine,for estimating the UCS of sandstone.Schmidt hammer rebound number,P-wave velocity,and point load index were chosen as considered factors to forecast UCS values of sandstone samples.Taylor diagrams and five regression metrics,including coefficient of determination(R2),root mean square error,mean absolute error,variance account for,and A-20 index,were used to evaluate and compare the performance of these boosting trees.The results showed that the proposed boosting trees are able to provide a high level of prediction capacity for the prepared database.In particular,itwas worth noting that XGBoost is the best model to predict sandstone strength and it achieved 0.999 training R^(2) and 0.958 testing R^(2).The proposed model had more outstanding capability than neural network with optimization techniques during training and testing phases.The performed variable importance analysis reveals that the point load index has a significant influence on predicting UCS of sandstone.
文摘The study analyzes the performance of bank-specific characteristics,macroeconomic indicators,and global factors to predict the bank lending in Turkey for the period 2002Q4–2019Q2.The objective of this study is first,to clarify the possible nonlinear and nonparametric relationships between outstanding bank loans and bank-specific,macroeconomic,and global factors.Second,it aims to propose various machine learning algorithms that determine drivers of bank lending and benefits from the advantages of these techniques.The empirical findings indicate favorable evidence that the drivers of bank lending exhibit some nonlinearities.Additionally,partial dependence plots depict that numerous bank-specific characteristics and macroeconomic indicators tend to be important variables that influence bank lending behavior.The study’s findings have some policy implications for bank managers,regulatory authorities,and policymakers.
文摘Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.
