Accuracy Of Classifier Research Articles

Prediction model of subacromial pain syndrome in assembly workers using shoulder range of motion and muscle strength based on support vector machine

Subacromial pain syndrome (SAPS) is the most common upper-extremity musculoskeletal problem among workers. In this study, a machine learning model was built to predict and classify the presence or absence of SAPS in assembly workers with shoulder joint range of motion (ROM) and muscle strength data using support vector machine (SVM). Permutation importance was used to determine important variables for predicting workers with or without SAPS. The accuracy of the support vector classifier (SVC) polynomial model for classifying workers with SAPS was 82.4%. The important variables in model construction were internal rotation and abduction of shoulder ROM and internal rotation of shoulder muscle strength. It is possible to accurately perform SAPS classification of workers with relatively easy-to-obtain shoulder ROM and muscle strength data using this model. In addition, preventing SAPS in workers is possible by adjusting the factors affecting model building using exercise or rehabilitation programs. Practitioner summary: This study aimed to create a machine learning model that can predict and classify SAPS using shoulder ROM and muscle strength and identify the variables that are of high importance in model construction. This model could be used to predict or classify workers’ SAPS and manage or prevent SAPS.

Distinguishing Laparoscopic Surgery Experts from Novices Using EEG Topographic Features.

The study aimed to differentiate experts from novices in laparoscopic surgery tasks using electroencephalogram (EEG) topographic features. A microstate-based common spatial pattern (CSP) analysis with linear discriminant analysis (LDA) was compared to a topography-preserving convolutional neural network (CNN) approach. Expert surgeons (N = 10) and novice medical residents (N = 13) performed laparoscopic suturing tasks, and EEG data from 8 experts and 13 novices were analysed. Microstate-based CSP with LDA revealed distinct spatial patterns in the frontal and parietal cortices for experts, while novices showed frontal cortex involvement. The 3D CNN model (ESNet) demonstrated a superior classification performance (accuracy > 98%, sensitivity 99.30%, specificity 99.70%, F1 score 98.51%, MCC 97.56%) compared to the microstate based CSP analysis with LDA (accuracy ~90%). Combining spatial and temporal information in the 3D CNN model enhanced classifier accuracy and highlighted the importance of the parietal-temporal-occipital association region in differentiating experts and novices.

OPTIMIZING LANTANA CLASSIFICATION: HIGH-ACCURACY MODEL UTILIZING FEATURE EXTRACTION

As an invasive and poisonous plant, Lantana has become a pest in the agricultural world. Still, on the other hand, it becomes an ornamental plant with different positive potentials. Lantana flower datasets are not yet widely available for open image classification research, given that the research needs are still broad in remote sensing. This study aims to provide a model with classifier accuracy that outperforms similar studies and Lantana datasets for classification needs using several algorithms that can be run on small source computers. This study used five types of lantana colors, red, white, yellow, purple, and orange, as the primary dataset, which had 411 instances. VGG16 assisted feature extraction in preparing datasets for the data training using three classifiers: decision tree, AdaBoost, and k-NN. 2-fold cross-validation, 5-fold cross-validation, and a self-organizing map are used to help validate each process. The experiment to measure the classifier's performance resulted in a good figure of 99.8% accuracy for 2-fold cross-validation, 100% for 5-fold cross-validation, and a primary dataset of lantana interest that can be accessed freely on the IEEE Data port. This study outperformed other related studies in terms of classifier accuracy.

Rapid identification of the geographical origin of Baimudan tea using a Multi-AdaBoost model integrated with Raman Spectroscopy

The potential of Multi-AdaBoost in spectral analysis is substantial, particularly when combined with weak classifiers and trained to develop into a robust classifier. Given the variable quality of Baimudan tea sourced from diverse regions, the novel application of Raman spectroscopy in conjunction with the Multi-AdaBoost model to analyze the geographic origin of Baimudan tea was introduced. Initially, Raman spectra of Baimudan tea from four distinct origins in Fujian province were gathered, namely Fuan (FA), Fuding (FD), Zhenghe (ZH), and Songxi (SX). Decision Tree (DT) and Support Vector Machine (SVM) models were employed as fitting classifiers to construct the Multi-AdaBoost-DT and Multi-AdaBoost-SVM models. The results demonstrated that the Multi-AdaBoost-DT model exhibited significantly improved recognition rates for FA, FD, ZH, and SX origin compared to the DT model, with the average recognition rate increasing from 86.46% to 91.67%. In contrast, the recognition rates for FA and SX origin in the Multi-AdaBoost-SVM model remained unchanged, attributed to the model having reached a local optimum. The recognition rates of FD origin increased from 91.67% to 95.83%, a significant improvement, while those of ZH origin escalated from 83.33% to 87.50%. The average recognition rate increased from 92.71% to 94.79%. Additionally, Multi-AdaBoost-SVM and Multi-AdaBoost-DT enhanced the sensitivity and specificity of the discrimination outcomes. These results corroborated the effectiveness of the proposed Multi-AdaBoost-SVM model in identifying the geographical origin of Baimudan tea. Moreover, the Multi-AdaBoost model demonstrates potential in elevating the discrimination accuracy of weak classifiers, which bodes well for its application in food authentication and quality control.

A differential diagnosis between uterine leiomyoma and leiomyosarcoma using transcriptome analysis

BackgroundThe objective of this study was to estimate the accuracy of transcriptome-based classifier in differential diagnosis of uterine leiomyoma and leiomyosarcoma. We manually selected 114 normal uterine tissue and 31 leiomyosarcoma samples from publicly available transcriptome data in UCSC Xena as training/validation sets. We developed pre-processing procedure and gene selection method to sensitively find genes of larger variance in leiomyosarcoma than normal uterine tissues. Through our method, 17 genes were selected to build transcriptome-based classifier. The prediction accuracies of deep feedforward neural network (DNN), support vector machine (SVM), random forest (RF), and gradient boosting (GB) models were examined. We interpret the biological functionality of selected genes via network-based analysis using GeneMANIA. To validate the performance of trained model, we additionally collected 35 clinical samples of leiomyosarcoma and leiomyoma as a test set (18 + 17 as 1st and 2nd test sets).ResultsWe discovered genes expressed in a highly variable way in leiomyosarcoma while these genes are expressed in a conserved way in normal uterine samples. These genes were mainly associated with DNA replication. As gene selection and model training were made in leiomyosarcoma and uterine normal tissue, proving discriminant of ability between leiomyosarcoma and leiomyoma is necessary. Thus, further validation of trained model was conducted in newly collected clinical samples of leiomyosarcoma and leiomyoma. The DNN classifier performed sensitivity 0.88, 0.77 (8/9, 7/9) while the specificity 1.0 (8/8, 8/8) in two test data set supporting that the selected genes in conjunction with DNN classifier are well discriminating the difference between leiomyosarcoma and leiomyoma in clinical sample.ConclusionThe transcriptome-based classifier accurately distinguished uterine leiomyosarcoma from leiomyoma. Our method can be helpful in clinical practice through the biopsy of sample in advance of surgery. Identification of leiomyosarcoma let the doctor avoid of laparoscopic surgery, thus it minimizes un-wanted tumor spread.

Research on pipeline leakage signal denoising using variational mode decomposition and energy value

In order to separate the effective components and the noise components after variational mode decomposition (VMD) and improve the denoising effect of VMD, a denoising approach combining VMD with energy value (EV) and VMD (VMD-EV-VMD) is proposed. First, VMD decomposes the original signal into K intrinsic mode functions (IMF) and then calculates the EV of the probability density function of each IMF component. According to the changing trend of energy value, the effective components and the noise components are distinguished. Subsequently, each noise component is decomposed by VMD again, the effective components are selected by calculating the correlation coefficient (CC) between the input component of the second VMD and the IMF obtained from the second VMD. The effective components selected by VMD twice are used for signal reconstruction. Simulation experiments and pipeline leakage signal processing show that the method of VMD-EV-VMD achieves a greater SNR than the methods of EEMD, VMD-EV, and VMD-WT. and it has a smaller MSE and MAE, A better denoising effect is achieved and the pipeline leakage characteristics are more accurately preserved. In addition, the accuracy of the SVM classifier is used to verify the effectiveness of the proposed method.

A Large Scale Study and Classification of VirusTotal Reports on Phishing and Malware URLs

VirusTotal (VT) is a widely used scanning service for researchers and practitioners to label malicious entities and predict new security threats. Unfortunately, it is little known to the end-users how VT URL scanners decide on the maliciousness of entities and the attack types they are involved in (e.g., phishing or malware-hosting websites). In this paper, we conduct a systematic comparative study on VT URL scanners' behavior for different attack types of malicious URLs, in terms of 1) detection specialties, 2) stability, 3) correlations between scanners, and 4) lead/lag behaviors. Our findings highlight that the VT scanners commonly disagree with each other on their detection and attack type classification, leading to challenges in ascertaining the maliciousness of a URL and taking prompt mitigation actions according to different attack types. This motivates us to present a new highly accurate classifier that helps correctly identify the attack types of malicious URLs at the early stage. This in turn assists practitioners in performing better threat aggregation and choosing proper mitigation actions for different attack types

Detection of Abnormality in Coronary Artery Magnetic Resonance Imaging using Bit Plane Slicing and Deep Learning.

This paper presents a novel approach for detecting abnormality in coronary arteries using MRI data in RGB images. The study evaluates the test accuracy of the weak classifiers and the test accuracy and F1 score of the strong classifier. The method involves separating the image into information planes, including R, G, and B color space, or bit-planes, and training a VGG-like convolutional neural network model on each plane separately, referred to as a "weak classifier." The classification results of these planes are aggregated using a proposed soft voting method, forming a "strong classifier," with the weights for the aggregation determined by the model's performance on the training set. The results indicate that the strong classifier achieves a test accuracy and F1 score of around 68% to 74% on our private coronary artery dataset. Moreover, by aggregating the top three highest bit-plane levels in a grayscale image, the accuracy is slightly lower than that of the three color spaces but requires a significantly smaller CNN model of nearly 4M parameters. The potential of bit-planes in reducing model storage costs is suggested. This approach holds promise for improving the detection of abnormalities in coronary arteries using MRI data.

IoT and ML approach for ornamental fish behaviour analysis

Ornamental fish keeping is the second most preferred hobby in the world and it provides a great opportunity for entrepreneurship development and income generation. Controlling the environment in ornamental fish farm is a considerable challenge because it is affected by a variety of parameters like water temperature, dissolved oxygen, pH, and disease occurrences. One particular interesting ornamental fish species is goldfish (Carassius auratus). Machine learning (ML) and deep learning technique have significant potential in analysing voluminous data collected from fish farm. Through this technique, the fish farmers can get insight on feeding behaviour, fish growth patterns, predict diseases/stress, and environmental factors affecting fish health. The aim of the study is to analyze the behavioural changes in goldfish due to alterations in environmental parameters (water temperature and dissolved oxygen). Decision tree, Naïve Bayes classifier, K-nearest neighbour (KNN), and linear discriminant analysis (LDA) were used to analyse the behavioural change data. To compare the performance between all four classifiers, cross validation and confusion matrix used. The cross-validation error of LDA, Naïve Bayes classification, KNN and decision tree was 19.86, 28.08, 30.14 and 13.78 respectively. Decision tree was proved to be the most accurate and effective classifier. Different temperature and DO range were taken to predict fish behaviour. Some findings are, the behaviour of fish was rest between temperature 37.85 °C and 40.535 °C, erratic when temperature was greater than or equal to 40.535 °C, gasping when temperature was between 37.85 and 40.535 °C and when DO concentration was less than 6.58 mg/L. Blood parameter analysis has been done to validate the change in external behaviours with change in physiological parameters.

IMPROVING THE ACCURACY OF AN OIL SPILL DETECTION AND CLASSIFICATION MODEL WITH FAKE DATASETS

Abstract. Deep learning is a popular tool for object detection, including oil spill detection. However, acquiring sufficient data for training deep learning models can be challenging, particularly for offshore oil spill accidents. Data augmentation is an effective solution to this issue. This study proposes a data augmentation method using a conditional-GAN model, specifically Pix2Pix, to generate dummy datasets of oil spills. These datasets were used to train the DaNet model for oil detection and classification. Results show that using the dummy datasets improves the mIoU and f1-score to 2.56% and 1.69%, respectively, and enhances the accuracy of classifying of each oil in the model. This approach not only improves the accuracy of the deep learning model but also presents a direction for data enhancement in detection or segmentation tasks for formless objects, such as oil spills, cracks, water seepage, and mildew.

Separating Alzheimer’s disease from Dementia with Lewy bodies using a volumetric MRI classifier

AbstractBackgroundSeparating Alzheimer’s dementia (AD) from dementia with Lewy bodies (DLB) can be challenging, especially in DLB patients with concomitant AD pathology. It is imperative to distinguish the diseases for optimal patient management. To date, no single MRI feature can accurately separate the two diseases. This study examines whether a linear combination of regional atrophy measures can differentiate cognitively normal (CN), AD, and DLB patients.MethodWe computed volumes of 70 brain regions using an automatic segmentation tool. For each region, we derived age, sex, and head size‐dependent z‐scores. A linear classifier with all brain regions was used to predict if a patient belonged to CN, AD, or DLB. Diagnoses were based on international consensus criteria and CSF‐biomarkers. The classifier was tested in 327 CN (mean age 67±10y, 48%F), 501 AD (70±9y, 49%F), and 99 DLB (69±7y, 15%F) patients from three cohorts (ADNI, PredictND, ADC). The ADC cohort was assessed separately, as almost all DLB patients were from the ADC. In addition, we assessed classification performance in a subgroup of DLB patients with AD‐like CSF (DLB+, n = 57). Performance was evaluated using 10‐fold cross‐validation.ResultPerformance measures for the classifier are summarized in Table 1. The classifier accuracy was assessed in the three‐class classification task and in two‐class classifications where one of the groups was excluded from the analysis. The balanced accuracy in the three‐class classification was 67% for the DLB group and the DLB+ subgroup. For AD vs. DLB, the AUC was 0.77(95% CI:0.70‐0.82), for AD vs. DLB+ AUC was 0.72(0.64‐0.80). The classifier correctly classified CN and AD patients more often than DLB patients (Table 2). Most DLB+ patients were misclassified as AD. The hippocampus was the best single region for distinguishing AD vs. DLB, with an AUC of 0.70(0.64‐0.76).ConclusionThe joint classifier achieved high accuracy in separating AD vs. DLB patients. Although the accuracy was lower, the classifier could distinguish DLB patients with concomitant AD pathology from AD. Next, we will combine different brain regions to discern disease‐specific patterns and add clinical data to the classifier to improve differential diagnostic accuracy for DLB and DLB with concomitant AD pathology.

Effective Spam Detection with Machine Learning

Abstract This paper aims to provide results of empirical experiments on the accuracy of different machine learning algorithms for detecting spam messages, using a public dataset of spam messages. The originality of our study lies in the integration of topic modeling, specifically employing Latent Dirichlet Allocation (LDA) alongside machine learning algorithms for spam detection. By extracting hidden topics and uncovering patterns in spam and non-spam messages, we provide unique insights into the distinguishing characteristics of spam messages. Moreover, the integration of machine learning is a powerful tool in bolstering risk control measures ensuring the sustainability of digital platforms and communication channels. The research tests the accuracy of spam detection classifiers on an open-source dataset of spam messages. The key findings of this study reveal that the Logistic Regression classifier achieved the highest F score of 0.986, followed by the Support Vector Machine classifier with a score of 0.98 and the Naive Bayes classifier with a score of 0.955. The study concludes that Logistic Regression outperforms Naive Bayes and Support Vector Machine in text classification, particularly in spam detection, emphasizing the role of machine learning techniques in optimizing risk management strategies for sustained digital ecosystems. This capability stems from Logistic Regression’s adeptness in modeling complex relationships, enabling it to achieve high accuracy on training and test datasets.

Differentiation of frontotemporal dementia subtypes using neuroimaging‐based multi‐type parallel feature embedding

AbstractBackgroundFrontotemporal dementia (FTD) represents a collection of neurocognitive syndromes with frontotemporal lobar degeneration (FTLD) neuropathology, and is associated with significant clinical, pathological, and genetic heterogeneity. We trained a deep neural network (DNN) classifier to differentiate behavioral‐variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA), and non‐fluent variant (nfvPPA) patients using MRI scans drawn from two multi‐site neuroimaging consortiums.MethodBvFTD (N = 173), nfvPPA (N = 63), and svPPA (N = 41) patients with T1‐MRI were extracted from the FTLD Neuroimaging Initiative (FTLDNI) and the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) databases. MRI data were preprocessed with FreeSurfer, with cortical thickness, cortical volume, and subcortical volumes extracted. Cortical measures were parcellated into 360 patches (i.e., ROIs) according to the HCP‐MMP1 atlas. Both the patch‐based cortical thickness and volume features were harmonized to control confounding effects of sex, age, total intracranial volume (TIV), cohort, and scanner. Multi‐type features were parallelly fed into a multi‐layer‐perceptron (MLP)‐based classifier. Weighted cross‐entropy loss function was used to account for unbalanced sample sizes across FTD subtypes. 10‐fold nested cross‐validation was used to evaluate the robustness of the classification model, with data split into 80/10/10 of training/validation/test data sets.ResultVisual evaluation z‐scores of cortical features among FTLDNI and ALLFTD groups revealed site‐specific differences significantly reduced by feature harmonization, especially for volume (Figure 1). The balanced accuracy of the ensembled DNN classifier of each FTD subtype on test sets across all ten folds reached 0.76 ± 0.09 for bvFTD, 0.79 ± 0.07 for nfvPPA, and 0.88 ± 0.07 for svPPA (Figure 2). Figure 3 shows confusion matrices for classification performance in the test set across ten cross‐validation folds.ConclusionWe developed a deep‐learning‐based framework to classify three FTD subtypes: bvFTD, nfvPPA, and svPPA. The combination of feature harmonization and parallel multi‐type feature embedding framework showed promising differentiation power. This work can be used to recognize at‐risk populations for early and precise diagnosis, to aid intervention planning. Future studies will compare classification based on different input features and use visualization methods to identify the most discriminative regions and explore their clinical relevance.

Machine Learning Approach to Identify Dysregulation in the Plasma Proteome of CADASIL

AbstractBackgroundThe plasma proteome provides information to identify dysregulated molecular pathways underlying disease. Cerebrovascular diseases often manifest a significantly different plasma proteomic signature. Cerebral‐Autosomal‐Dominant‐Arteriopathy‐Subcortical‐Infarcts‐and‐Leukoencephalopathy (CADASIL) is one such vascular brain disease. The CADASIL plasma proteome has yet to be investigated.MethodTo investigate the plasma proteome in the context of CADASIL, we used large‐scale proteomics, measuring over 7,000 proteins via aptamer‐based technology (SomaLogic) from 53 study participants (nCADASIL = 25; nControl = 28). We employed machine learning (ML) methods as an unbiased approach to uncover disease‐associated change in proteomic networks. This approach is based on the premise that protein sets that best classify disease state could be important biological drivers of disease. We developed a novel ML method: coupling recursive feature extraction with logistic regression (LR) and XGBoost evaluators, as well maximum‐relevance‐minimum‐redundancy with Random‐Forest and F‐Statistic evaluators. The results of all four models were selectively aggregated in order to delineate the CADASIL plasma proteome signature while minimizing overfitting to the low sample size data. We developed a 45 protein model of the CADASIL proteomic signature from these results. To evaluate the classification ability, we tested the Repeated‐Stratified‐10‐fold‐Cross‐Validation accuracy of a LR classifier. Next, we investigated whether the CADASIL proteomic signature was relevant for other neurodegenerative diseases with vascular components; the classifier was applied to an Alzheimer’s Disease (AD) dataset. Finally, the functional signature of the 45 proteins was investigated through gene ontology (GO).ResultThe LR classifier in CADASIL is 99.8% accurate. In AD, the accuracy is 71.2%. This implies the CADASIL signature shares commonalities as well as pertinent differences with other neurodegenerative diseases. The GO of the 45 proteins resulted in GO terms corresponding to extracellular matrix and collagen, cellular membrane lipoproteins and glycolipids, and proteasome biological pathways.ConclusionThe proposed method allows for an initial unbiased discovery of important proteomic changes associated with disease state. The identified proteins would provide starting points for mechanistic studies. In addition, a panel of proteins could be used for screening at population level for risk stratification and more in‐depth phenotyping. Next steps include investigating the biological relevance of findings in mechanistic models for therapeutic developments.

Machine Learning Approach to Identify Dysregulation in the Plasma Proteome of CADASIL

AbstractBackgroundThe plasma proteome provides information to identify dysregulated molecular pathways underlying disease. Cerebrovascular diseases often manifest a significantly different plasma proteomic signature. Cerebral‐Autosomal‐Dominant‐Arteriopathy‐Subcortical‐Infarcts‐and‐Leukoencephalopathy (CADASIL) is one such vascular brain disease. The CADASIL plasma proteome has yet to be investigated.MethodTo investigate the plasma proteome in the context of CADASIL, we used large‐scale proteomics, measuring over 7,000 proteins via aptamer‐based technology (SomaLogic) from 53 study participants (nCADASIL = 25; nControl = 28).We employed machine learning (ML) methods as an unbiased approach to uncover disease‐associated change in proteomic networks. This approach is based on the premise that protein sets that best classify disease state could be important biological drivers of disease. We developed a novel ML method: coupling recursive feature extraction with logistic regression (LR) and XGBoost evaluators, as well maximum‐relevance‐minimum‐redundancy with Random‐Forest and F‐Statistic evaluators.The results of all four models were selectively aggregated in order to delineate the CADASIL plasma proteome signature while minimizing overfitting to the low sample size data. We developed a 45 protein model of the CADASIL proteomic signature from these results. To evaluate the classification ability, we tested the Repeated‐Stratified‐10‐fold‐Cross‐Validation accuracy of a LR classifier. Next, we investigated whether the CADASIL proteomic signature was relevant for other neurodegenerative diseases with vascular components; the classifier was applied to an Alzheimer’s Disease (AD) dataset. Finally, the functional signature of the 45 proteins was investigated through gene ontology (GO).ResultsThe LR classifier in CADASIL is 99.8% accurate. In AD, the accuracy is 71.2%. This implies the CADASIL signature shares commonalities as well as pertinent differences with other neurodegenerative diseases.The GO of the 45 proteins resulted in GO terms corresponding to extracellular matrix and collagen, cellular membrane lipoproteins and glycolipids, and proteasome biological pathways.ConclusionThe proposed method allows for an initial unbiased discovery of important proteomic changes associated with disease state. The identified proteins would provide starting points for mechanistic studies. In addition, a panel of proteins could be used for screening at population level for risk stratification and more in‐depth phenotyping. Next steps include investigating the biological relevance of findings in mechanistic models for therapeutic developments.

Associations of APOE4 and Alzheimer’s disease biomarkers with cortical reinstatement and episodic memory in clinically unimpaired older adults

AbstractBackgroundCortical reinstatement is a key neural correlate of episodic remembering across the lifespan and explains significant variability in memory performance among older adults. We assessed whether Alzheimer’s disease (AD) risk factors – cerebrospinal fluid (CSF) Aβ42/Aβ40, CSF pTau181, plasma pTau181, and APOE genotype – impact cortical reinstatement in clinically unimpaired older adults.MethodParticipants were 150 clinically unimpaired older adults enrolled in the Stanford Aging and Memory Study (SAMS). APOE genotype was determined from whole‐genome sequencing and participants were classified as ε4 carriers (N = 38) or non‐carriers (N = 112). CSF Aβ42/Aβ40 and pTau181 and plasma pTau181 were quantified using the automated Lumipulse G system. Cortical reinstatement was quantified during successful retrieval of word‐picture associations using fMRI multivoxel pattern analysis (MVPA) in five regions of interest, including parahippocampal gyrus (PHG), ventral temporal cortex (VTC), middle temporal gyrus (MTG), angular gyrus (ANG), and posterior medial cortex (PMC). Regression analyses examined cross‐sectional relationships between regional cortical reinstatement and a) APOE genotype (N = 150), b) CSF Aβ42/Aβ40 and pTau181 (N = 113), and (c) plasma pTau181 (N = 140), as well as associations of these variables with associative memory discrimination (d’). All models included age, sex, and regional MVPA classifier accuracy during memory encoding.ResultAPOE ε4 genotype was negatively associated with reinstatement strength in VTC (β = ‐0.54, p < .005), MTG (β = ‐0.32, p < .05), and ANG (β = ‐0.32, p < .05) and CSF Aβ42/Aβ40 burden was linked to weaker reinstatement in ANG (β = 0.16, p < .05). In VTC, the effect of APOE ε4 on reinstatement remained significant after controlling for CSF Aβ42/Aβ40 (β = ‐0.47, p < .05); a similar pattern was observed in MTG and ANG. Although CSF pTau181 was unrelated to reinstatement strength, both pTau181 and cortical reinstatement explained unique variance in memory performance (pTau181: β = ‐0.26, p < .005, reinstVTC: β = 0.39, p < .001). Analogous findings were observed with plasma pTau181.ConclusionThese results suggest that APOE ε4 impacts cortical reinstatement, and that this effect may be independent of amyloid burden. These effects of APOE ε4 may be distinct from tau‐mediated effects on episodic memory in human aging.

Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis

Land use and land cover change constitute a significant driver of land degradation worldwide, and machine-learning algorithms are providing new opportunities for effectively classifying land use and land cover changes over time. The aims of this study are threefold: Firstly, we aim to compare the accuracies of the parametric classifier Naïve Bayes with the non-parametric classifier Extreme Gradient Boosting Random Forest algorithm on the 2020 LULC dataset. Secondly, we quantify land use and land cover changes in the Cradle of Humankind from 1990 to 2020 using the Extreme Gradient Boosting Random Forest algorithm and post-classification change detection. Thirdly, the study uses landscape metrics to examine landscape structural changes occurring in the same area due to fragmentation. The classification results show that while Naïve Bayers and XGB Random Forest produce classification results of high accuracy, the XGB Random Forest Classifier produced superior results compared to the Naïve Bayers Classifier. From 1990 to 2020, bare ground/rock outcrop significantly increased by 39%, and open bush by 32%. Indigenous forests and natural grasslands lost area (26% and 12%, respectively). The results from this study indicate increasing land cover fragmentation and attest to land degradation, as shown by increases in bare ground and a reduction in indigenous forest and natural grassland. The decline in indigenous forests and natural grassland indicates the degradation of native vegetation, considered as prehistoric plant food sources. The high classification results also attest to the efficacy of the XGBRFClassifier executed in GEE. Land degradation evident in the nature reserve has long-term ecological consequences, such as loss of habitat, biodiversity decline, soil erosion, and alteration of local ecosystems, which together diminish the aesthetic value of the heritage site and negatively impact its tourism value. Consequently, it destroys crucial local economies and threatens sustainable tourism.

Hybrid resampling and weighted majority voting for multi-class anomaly detection on imbalanced malware and network traffic data

In a large skewed dataset, the data imbalance is severe and the classifier's accuracy is biased towards the majority class. Insufficient data makes it challenging for the classifier to learn the feature of the minority classes. Moreover, some existing techniques of binary classification cannot directly apply to multi-classification. Plain oversampling may generate redundant and irrelevant data for the minority classes while undersampling may eliminate too many features for the majority class. This paper proposes a combination of constrained undersampling, oversampling, noise cleaning, and a weighted majority voting classifier (WMVC) to detect multi-classification obfuscated malware via memory and network traffic anomalies. As a constraint on undersampling and oversampling, the proposed framework divides the total sample number by the class number to obtain the average class size. According to the constraint, one or more majority classes are down-sampled using Random Undersampling, while all the minority classes are up-sampled using Adaptive Synthetic Sampling Approach, which is followed by Tomek Link to remove the noisy data. Then a weighted majority voting classifier aggregated tree-based ensemble algorithms is designed and compared to the XGBoost and Convolutional Neural Network (CNN) classifiers and six state-of-the-art ensemble algorithms. The comparison results show that the performance of the classifiers on balanced data outperforms those on imbalanced data, and the WMVC outperforms XGBoost, CNN, and the six other ensemble algorithms. Our approach can alleviate the classifier's bias towards the majority class while improving its performance for the difficult minority class.

User Sentiment Analysis Methods for Elderly Social Media Networks

For the analysis of user sentiment in social media networks for the elderly population, emotional sentences are first extracted to classify movie reviews. Afterwards, social network data of the elderly population based on user search behavior is analyzed. The movie reviews of elderly social media users are analyzed for rating prediction. The research results indicate that the accuracy of sentiment classification results is in descending order of Dirichlet, maximum entropy, and support vector machine. The highest classification accuracy of the three algorithms is 87.1%, 86.9%, and 86.5%, respectively. The classification accuracy of the first level classifiers of Dirichlet, maximum entropy, and support vector machine are 90.7%, 88.7%, and 87.4%, respectively. The classification accuracy of the second level classifier is 86.7%, 83.7%, and 80.4%, respectively. The predictive analysis results of the research method are superior to those generated by using Slope One. The method proposed in the study can promote emotional analysis of film review texts, improving the analysis accuracy.

On effectively predicting autism spectrum disorder therapy using an ensemble of classifiers

An ensemble of classifiers combines several single classifiers to deliver a final prediction or classification decision. An increasingly provoking question is whether such an ensemble can outperform the single best classifier. If so, what form of ensemble learning system (also known as multiple classifier learning systems) yields the most significant benefits in the size or diversity of the ensemble? In this paper, the ability of ensemble learning to predict and identify factors that influence or contribute to autism spectrum disorder therapy (ASDT) for intervention purposes is investigated. Given that most interventions are typically short-term in nature, henceforth, developing a robotic system that will provide the best outcome and measurement of ASDT therapy has never been so critical. In this paper, the performance of five single classifiers against several multiple classifier learning systems in exploring and predicting ASDT is investigated using a dataset of behavioural data and robot-enhanced therapy against standard human treatment based on 3000 sessions and 300 h, recorded from 61 autistic children. Experimental results show statistically significant differences in performance among the single classifiers for ASDT prediction with decision trees as the more accurate classifier. The results further show multiple classifier learning systems (MCLS) achieving better performance for ASDT prediction (especially those ensembles with three core classifiers). Additionally, the results show bagging and boosting ensemble learning as robust when predicting ASDT with multi-stage design as the most dominant architecture. It also appears that eye contact and social interaction are the most critical contributing factors to the ASDT problem among children.