A fusion framework for occupancy estimation in office buildings based on environmental sensor data

Comparison of the performance of multiclass classifiers in chemical data: Addressing the problem of overfitting with the permutation test

The rapid growth of wavelets and machine learning algorithms in the field of medical sciences like cardiology have encouraged researchers for fast and effective classification of heart irregularities. Electrocardiogram (ECG) is a non-linear pattern which confines critical information regarding cardiac functions. It has to be analyzed and examined profoundly for equitable diagnosis. In this paper, Dual Tree Complex Wavelet Transform (DTCWT) and Linear Discriminant Analysis (LDA) are used as a hybrid pair for ECG feature extraction. The Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are effectually employed as classifiers to distinguish ECG as a normal and abnormal class. The simulated outcome shows that linear behavior of SVM and ELM results in better ECG analysis as compared to radial basis function (RBF). In addition, the performance comparison illustrates that SVM outperforms ELM in terms of accuracy while ELM clearly outperforms SVM in the time taken for training and testing of MIT/BIH dataset.

This study aims to draw a scientific framework for plotting soil erosion susceptibility in the Chittagong Hill Tracts of Bangladesh by comparing existing approaches. Data-driven machine learning techniques (including Classification and Regression Tree (CART), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF)) and a knowledge-based approach (AHP) are used in this study to pinpoint areas of Chittagong that are particularly susceptible to soil erosion while taking into account 18 soil erosion-regulating parameters. Furthermore, the effectiveness of the selected data-driven machine learning models and knowledge-based models was assessed by utilizing soil erosion and non-erosion sites. When evaluating the fidelity of each model using the ROC and AUC, the RF model was shown to be the most accurate and predictive. There is no poor performer among these models; all have AUCs greater than 67 % (RF = 0.86, ANN = 0.73, SVM = 0.67, CART = 0.67, and AHP = 0.82). According to the findings of the Random Forest model, approximately 71.55 percent of the area exhibited a moderate level of susceptibility to soil erosion. In relation to the land area, the high and low zones accounted for 16.91 percent and 11.54 percent, respectively. The specific area shares of 2256.25, 9548.08, and 1539.67 square kilometers were attributed to the high, moderate, and low danger zones, respectively. The best models' results after comparing models of data-driven and knowledge-based approaches can help to estimate soil erosion risk zones and provide insight into establishing appropriate policies to minimize this issue. In addition, the methods used in this research might be applicable to assessing the vulnerability and risk of soil erosion events in other areas. As they begin long-term planning to reduce soil erosion, local authorities and policymakers will find the study's results on practical policies and management options quite helpful.

Context Correct diagnosis of the state of soil compaction is a challenge in continuous no-tillage (NT). Aims and methods The aim of this study was to evaluate the performance of four machine learning algorithms to diagnose the state of soil compaction (NT and occasional tillage – OT). For these purposes, data from a field experiment conducted in a clayey Typic Hapludox with mechanical (chiselling and subsoiling) and chemical (gypsum and limestone) methods for mitigation of soil compaction were used. To diagnose the state of soil compaction, soil physical properties [soil bulk density, penetration resistance, macroporosity (MAC), microporosity (MIC), air capacity (AC), available water content, relative field capacity and total porosity (TP)] in addition to crop yield (Rel_Yield) were used as predictor variables for Classification and Regression Trees (CART), Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms. Key results The most important variables for predicting the state of soil compaction were Rel_Yield and soil porosity (MAC, TP, MIC and AC). The machine learning algorithms had satisfactory performance in diagnosing which sites were compacted and which were not. The decision tree algorithms (CART and RF) performed better than ANN and SVM, reaching accuracy = 0.90, Kappa index = 0.76 and sensitivity = 0.83. Conclusions and implications The machine learning algorithm approach proved to be an efficient tool in diagnosing soil compaction in continuous NT, improving decision-making concerning the use of OT.

Parkinson's disease (PD) is a progressive disorder of the nervous system that affects movement. It develops gradually, often starting with a barely noticeable tremor in just one hand. But while tremor may be the most well-known sign of Parkinson's disease, the disorder also commonly causes a slowing or freezing of movement. Parkinson's disease is the second most common Neurodegenerative action only surpassed by Alzheimer's disease. However, a proper diagnosis at an early stage can result in significant life saving. A system for automated medical diagnosis would enhance the accuracy of the diagnosis and reduce the cost effects. The present study compares the accuracy of several machine learning methods including Bayesian Networks, Regression, Classification and Regression Trees (CART), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) for proposing a decision support system for diagnosis of parkinson's disease. The proposed system achieved an accuracy of 93.7% using classification and regression tree. Sensitivity analysis via classification and regression tree was also used to find importance of input variables.

Objective: Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Approach: Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. Main results: HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Significance: Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

Generating high-resolution daily soil moisture by using spatial downscaling techniques: a comparison of six machine learning algorithms

Malware forensics is a field in digital forensics that is dedicated to collecting evidence of a malware attack on a system. Malware is also executable code, but unlike regular executables, it is designed to perform unauthorized actions on victim computers. Being an executable, the malware also performs designated tasks through several processes that can leave its traces (digital footprints) throughout different components of the operating system, such as registries, file systems, DLL calls, API calls, memory usage, CPU utilization, network traffic, etc. Some of these traces can exist even after the removal of the malware and can be used as evidence to verify the occurrence of an attack. The proposed malware forensic analysis technique combines both the static and dynamic features of the malware to trace its evidence. This information is collected using the Cuckoo sandbox, which executes the malware sample files in a virtual environment to generate a JSON-formatted analysis report. The Python script is used to extract the non-volatile features from the JSON format analysis report. However, combining both features results in a very long and sparse feature vector, which negatively affects the classifier’s performance. Therefore, the principal component analysis (PCA) algorithm is adopted to reduce the dimensionality of the feature vectors. The generated reduced dimensionality feature vectors are used to train the extreme learning machine (ELM). The ELM can be trained in a fraction of the time compared to the support vector machine (SVM), while achieving similar accuracy. This makes it a favorable choice, especially for malware forensic analysis, where the classifier needs to train with a large amount of data and requires quick decision-making capabilities. The performance of the proposed techniques is evaluated using 90 malicious code samples containing 41 Trojans, 28 worms, and 21 bots. To demonstrate the superiority of the proposed algorithm, a comparison with some state-of-the-art classification methods, named Hidden Markov Model (HMM), Support Vector Machine (SVM), Artificial Neural Network (ANN), and ELM, was also performed. The comparison results show that the proposed technique achieves approximately 11% higher precision, 10% higher recall, and a 10.5% higher F-score than the competitors.

Simulation of daily maize evapotranspiration at different growth stages using four machine learning models in semi-humid regions of northwest China

North America sees 5% growth in powder sales

Recent advances in machine learning techniques have significantly influenced the development of early diabetes detection systems. In this paper, different machine learning (ML) algorithms are employed for diabetes detection based on hand movement. The spatial and temporal information for horizontal and vertical hand movement deviations for both diabetic and non-diabetic subjects are obtained using a data acquisition system. A dataset is constructed based on the obtained hand movement information along with other related attributes such as age, gender, weight and vision health. Binary classification of diabetic and non-diabetic subjects is achieved using several machine learning algorithms. These algorithms include Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminate Analysis (LDA), Classification and Regression Tree (CART), k-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). The considered ML algorithms are evaluated and ranked according to a set of key performance metrics that include accuracy, precision, recall and F-1 score. Algorithm ranking results demonstrate that LR, CART, KNN and MLP algorithms have achieved higher ranking positions as compared to other algorithms. The best ranked ML algorithms are then validated and thoroughly investigated to prove their ability to discriminate between diabetic and non-diabetic subjects. Validation results show that the best-ranked ML algorithms have achieved high classification accuracy, precision, recall and F-1 scores. Therefore, the considered machine learning algorithms can serve as efficient tools for diabetes detection based on the constructed dataset.

The U.S. Tox21 and EPA ToxCast program screen thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors, then used supervised machine learning to predict in vivo hepatotoxic effects. A set of 677 chemicals was represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PaDEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naïve Bayes (NB), support vector machines (SVM), classification and regression trees (CART), k-nearest neighbors (KNN), and an ensemble of these classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure descriptors, ToxCast bioactivity descriptors, and hybrid descriptors. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.84 ± 0.08), injury (0.80 ± 0.09), and proliferative lesions (0.80 ± 0.10). Though chemical and bioactivity classifiers had a similar balanced accuracy, the former were more sensitive, and the latter were more specific. CART, ENSMB, and SVM classifiers performed the best, and nuclear receptor activation and mitochondrial functions were frequently found in highly predictive classifiers of hepatotoxicity. ToxCast and ToxRefDB provide the largest and richest publicly available data sets for mining linkages between the in vitro bioactivity of environmental chemicals and their adverse histopathological outcomes. Our findings demonstrate the utility of high-throughput assays for characterizing rodent hepatotoxicants, the benefit of using hybrid representations that integrate bioactivity and chemical structure, and the need for objective evaluation of classification performance.

The study was intended to eliminate the noise in three-dimensional transvaginal ultrasound (3D-TVS) images and improve the diagnostic accuracy in intrauterine adhesion (IUA). The extreme learning machine (ELM) algorithm was introduced first for statement. One hundred and thirty cases of suspected IUA patients were taken as the research subjects. The denoising effects of ELM algorithm were evaluated in terms of mean square errors (MSE), peak signal-to-noise ratio (PSNR), and running time, and its diagnostic efficiency of IUA was identified from precise, specificity, and sensitivity. Furthermore, the support vector machine (SVM) algorithm was introduced for comparison. It was found that the MSE and PSNR of the ELM algorithm were 0.0021 and 64.5, respectively, and its average operation time was 11.22 ± 0.89s, that the MSE values of SVM algorithm and ELM algorithm were 0.0045 and 0.0021 and the PSNR values were 52.3 and 64.5, respectively, and that the average running time of SVM algorithm was 16.35 ± 1.33s, and the average running time of ELM algorithm was 11.22 ± 0.89s, superior to the SVM algorithm in denoising effects. Moreover, the ELM algorithm showed excellent diagnostic efficiency for patients with various degrees of IUA. In conclusion, ELM can effectively eliminate noise in 3D-TVS images and demonstrates excellent diagnostic efficiency on IUA, which is worthy of clinical application.

Depression or Major Depressive Disorder (MDD) is a psychiatric disorder. It is the major contributor to overall global burden of disease. Any deterioration in brain functioning is reflected in Electroencephalogram (EEG) signals. EEG signals are highly complex, nonstationary and nonlinear. So, it is very difficult to analyze visually and identify changes in the waveform in order to classify MDD and normal subjects. Thus, computerized quantitative EEG is used for the analysis of signals. Support Vector Machine (SVM) using band power feature reported an accuracy of 98.33% and using Kernel Eigen-Filter-Bank Common Spatial Patterns (KEFB-CSP) gave an accuracy of 91.67% [1, 2]. Logistic Regression (LR) using band power feature reported an accuracy of 98.33%, using 4 nonlinear features combination provided an accuracy of 90%, using wavelet transform provided an accuracy of 87.5% and using only alpha power gave an accuracy of 73.3% [1, 3, 4]. Naive Bayesian (NB) using band power feature provided an accuracy of 96.8% [1]. Artificial Neural Network using Relative Wavelet Energy (RWE) reported an accuracy of 98.11%, using power spectrum feature gave accuracy of 84% and using Lep–Ziv complexity accuracy of 60–80% was reported [5, 6, 7]. Linear Discriminant Analysis (LDA) reported an accuracy of 91.2% using SASI (Spectral Asymmetry Index) and DFA (Detrended Fluctuation Analysis) [8]. Decision Tree provided an accuracy of 80% using EEG band power as feature [9]. The study reveals that, in general, high classification accuracy is achieved by SVM, LR and ANN and highest classification accuracy of 98.33% is achieved by SVM. Highest accuracy is achieved by SVM because it is more robust and computationally more efficient due to maximal margin gap between separating hyper planes and kernel trick. The study gives some ideas which could be helpful for guiding and improving future researches. Since linear and nonlinear method for feature extraction are both equally efficient. So any of the linear/nonlinear feature can be used. For feature selection and reduction, Genetic Algorithm (GA), Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) can be used. Since SVM, LR and ANN give high classification accuracy and any of them can be used or any hybrid technique like GA-SVM, GA-ANN can be used. This chapter compares various EEG signal analysis techniques, compares their accuracy and methodology used and finally recommends the most suitable technique based on the accuracy for detection of depression. The chapter also summarizes some of the key finding related to EEG features based on present state of art. These results could be helpful for guiding and improving the future research in this area.

Savannah grazing lawns are a key food resource for large herbivores such as blue wildebeest (Connochaetes taurinus), hippopotamus (Hippopotamus amphibius) and white rhino (Ceratotherium simum), and impact herbivore densities, movement and recruitment rates. They also exert a strong influence on fire behaviour including frequency, intensity and spread. Thus, variation in grazing lawn cover can have a profound impact on broader savannah ecosystem dynamics. However, knowledge of their present cover and distribution is limited. Importantly, we lack a robust, broad-scale approach for detecting and monitoring grazing lawns, which is critical to enhancing understanding of the ecology of these vital grassland systems. We selected two sites in the Lower Sabie and Satara regions of Kruger National Park, South Africa with mesic and semiarid conditions, respectively. Using spectral and texture features derived from WorldView-3 imagery, we (i) parameterised and assessed the quality of Random Forest (RF), Support Vector Machines (SVM), Classification and Regression Trees (CART) and Multilayer Perceptron (MLP) models for general discrimination of plant functional types (PFTs) within a sub-area of the Lower Sabie landscape, and (ii) compared model performance for probabilistic mapping of grazing lawns in the broader Lower Sabie and Satara landscapes. Further, we used spatial metrics to analyse spatial patterns in grazing lawn distribution in both landscapes along a gradient of distance from waterbodies. All machine learning models achieved high F-scores (F1) and overall accuracy (OA) scores in general savannah PFTs classification, with RF (F1 = 95.73±0.004%, OA = 94.16±0.004%), SVM (F1 = 95.64±0.002%, OA = 94.02±0.002%) and MLP (F1 = 95.71±0.003%, OA = 94.27±0.003%) forming a cluster of the better performing models and marginally outperforming CART (F1 = 92.74±0.006%, OA = 90.93±0.003%). Grazing lawn detection accuracy followed a similar trend within the Lower Sabie landscape, with RF, SVM, MLP and CART achieving F-scores of 0.89, 0.93, 0.94 and 0.81, respectively. Transferring models to the Satara landscape however resulted in relatively lower but high grazing lawn detection accuracies across models (RF = 0.87, SVM = 0.88, MLP = 0.85 and CART = 0.75). Results from spatial pattern analysis revealed a relatively higher proportion of grazing lawn cover under semiarid savannah conditions (Satara) compared to the mesic savannah landscape (Lower Sabie). Additionally, the results show strong negative correlation between grazing lawn spatial structure (fractional cover, patch size and connectivity) and distance from waterbodies, with larger and contiguous grazing lawn patches occurring in close proximity to waterbodies in both landscapes. The proposed machine learning approach provides a novel and robust workflow for accurate and consistent landscape-scale monitoring of grazing lawns, while our findings and research outputs provide timely information critical for understanding habitat heterogeneity in southern African savannahs.