Alternative Classification Methods Research Articles

The path from mood symptoms to substance use: A longitudinal examination in individuals with and at risk for bipolar spectrum disorders

BackgroundAdolescent substance use poses a critical public health challenge, intertwined with risk-taking behavior, criminality, functional impairment, and comorbid mental and physical health issues. Adolescents with bipolar spectrum disorders (BSD) exhibit heightened susceptibility to substance use, necessitating a nuanced exploration of the bipolar-substance use relationship. MethodsThis study addressed gaps in the literature by employing a prospective, longitudinal design with 443 Philadelphia-area adolescents, tracking BSD symptoms and substance use. We predicted that BSD symptoms would be associated with increases in substance use, and that these effects would be more pronounced for individuals with a BSD and those with high reward sensitivity. ResultsHypomanic symptoms predicted subsequent substance use, with a stronger association observed in individuals diagnosed with BSD. Contrary to expectations, depressive symptoms did not exhibit a similar relationship. Although the hypothesized moderating role of reward sensitivity was not supported, higher reward sensitivity predicted increased substance use. LimitationsSymptoms and substance use are only captured for the month prior to each session due to the assessment timeline. This highlights the benefits of frequent assessments over a shorter time frame to monitor real-time changes. Alternative classification methods for reward sensitivity, such as brain or behavior-based assessments, might yield different results. ConclusionsThis study's contributions include evaluating substance use broadly, utilizing a longitudinal design for temporal clarity, and shifting the focus from substance use predicting mood symptoms to the inverse. The findings underscore the need for continued exploration of mood symptom predictors of substance use, emphasizing the role of reward sensitivity.

Boosted Nutcracker optimizer and Chaos Game Optimization with Cross Vision Transformer for medical image classification

This paper presents an alternative breast cancer classification method based on enhancing the efficiency of the Nutcracker optimizer (NO) algorithm using Chaos Game Optimization (CGO). In addition, we use the Cross Vision Transformer to extract features from breast images. After that, the relevant features are allocated using the modified version of NO based on CGO. This modification aims to enhance the exploration ability of the NO algorithm to discover the region of a feasible solution (an optimal subset of features). The performance of the developed model is validated by using twelve functions from the CEC2022 benchmark and comparing the results with traditional CGO and NO algorithms. In addition, to assess the applicability of the developed technique, a set of three datasets, and the results were compared with other techniques. The results illustrate the high ability of the developed method to enhance the detection of breast cancer and find the optimal solution of CEC2022 functions according to different performance measures.

LDBM-PFR: Local Directional Binary Pattern Mixed Filtering for 2D and 3D Palmprint Fusion Recognition

<p>Single-dimensional palmprint images are susceptible to interference from noise, lighting, and various factors, leading to information loss and subpar recognition performance. To address the problem, a fusion recognition scheme of 2D and 3D palmprints based on Hybrid Filter Local Orientation Binary Pattern (HFLOBP) is proposed. Firstly, the HFLOBP algorithm is employed to extract 2D features and 3D Shape Index (SI) features respectively to improve the recognition effect. Secondly, Principal Component Analysis (PCA) is utilized to improve the recognition efficiency, to reduce the dimension of 2D features and 3D Surface Type (ST) features of palmprint, to improve the recognition efficiency. Finally, the multi-dimensional palmprint features are fused by Double Weight Threshold Algorithm (DWTA), and classified and recognized by Collaborative Representation (CR). Experimental results conducted on the 3D palmprint database of Hong Kong Polytechnic University reveal that, in comparison with alternative classification methods, the average recognition rate reaches 99.98%, with an average recognition time of 0.64 seconds. The proposed method not only demonstrates a commendable recognition effect but also meets the real-time requirements, underscoring its practical application value.</p> <p> </p>

First record of Trypanosoma (Ornithotrypanum) infecting Neotropical birds.

Parasites play a pivotal role in ecosystem health, influencing human and zoonotic diseases, as well as biodiversity preservation. The genus Trypanosoma comprises approximately 500 species mostly found in wildlife animals. This study focuses on identifying trypanosomes found in the white-necked thrush (Turdus albicollis) and the yellow-legged thrush (Turdus flavipes) in the Neotropics. First, we demonstrate the utility of an 18S rDNA sequence-structure phylogeny as an alternative method for trypanosome classification, especially when gGAPDH sequences are unavailable. Subsequently, the sequence-structure phylogeny is employed to classify new trypanosome sequences discovered in wild birds, placing them within the Ornithotrypanum subgenus. This marks the first identification of Ornithotrypanum in Neotropical birds, contributing to the understanding of the distribution and ecological adaptation of avian trypanosomes. Beyond taxonomy, this study broadens our comprehension of the ecological implications of avian trypanosomes in the Neotropics, emphasizing the need for continued research in this field. These findings underscore the importance of alternative classification methods, which are essential to unravel the complex interactions between parasites, wildlife hosts, and their ecosystems.

An Alternative Fast Seismic Site Classification Method Based on Geological Maps: A Case Study in Shandong Province, North China

An Alternative Fast Seismic Site Classification Method Based on Geological Maps: A Case Study in Shandong Province, North China

Developing an alternative classification method for predicting ham composition using linear measurements from the cross-sectional ham surface

Digital image analysis based on the ham cross-sectional face was used to measure two lean muscle and three subcutaneous fat locations from 248 bone-in hams. Linear measurements of the two selected fat locations were used to predict dual-energy X-ray (DXA) fat or lean percentages with prediction accuracies (R2) of 0.7 in a stepwise regression eq. A classification system was built based on the prediction equations, and the linear measurements aimed to classify extremes at the threshold of the 10th percentile of DXA fat percentage (> 32.0%) and lean percentage (< 60.2%). When using either DXA fat or lean percentage, lean ham prediction accuracy dropped by 18%, but fat ham prediction accuracy increased by 60% when the threshold was changed from the 10th percentile to the 30th percentile. This classification approach has the potential to be converted into a manual tool with several useful applications for commercial pork processors.

DE_PSO_SVM: An Alternative Wine Classification Method Based on Machine Learning

Accurate classification of wine quality may help to improve making technology of wine. For achieving more effective quality classification, a classification method named DE_PSO_SVM (dataset enhancement (DE)_particle swarm optimization (PSO)_support vector machine (SVM)) is proposed. The correlation between feature attributes and classification labels of wine samples were analyzed to achieve dimension reduction. DE was achieved by calculating the different weight sums of adjacent odd and even rows, both of which belong to the same class of samples. PSO was used to search for the optimal parameters of a Gaussian kernel function, which were substituted in the SVM model to classify wine. K-nearest-neighbor (KNN), random forest (RF) and classification and regression tree (CART) were also used to test the wine classification. In 7-fold cross-validation on three wine datasets, the average Precision, Recall, and F1score of DE_PSO_SVM were best. The results show that enhancing datasets with small samples and searching for the optimal super parameters by PSO improved the performance of the wine classification model.

Spectral classification by generative adversarial linear discriminant analysis

Generative adversarial linear discriminant analysis (GALDA) is formulated as a broadly applicable tool for increasing classification accuracy and reducing overfitting in spectrochemical analysis. Although inspired by the successes of generative adversarial neural networks (GANs) for minimizing overfitting artifacts in artificial neural networks, GALDA was built around an independent linear algebra framework distinct from those in GANs. In contrast to feature extraction and data reduction approaches for minimizing overfitting, GALDA performs data augmentation by identifying and adversarially excluding the regions in spectral space in which genuine data do not reside. Relative to non-adversarial analogs, loading plots for dimension reduction showed significant smoothing and more prominent features aligned with spectral peaks following generative adversarial optimization. Classification accuracy was evaluated for GALDA together with other commonly available supervised and unsupervised methods for dimension reduction in simulated spectra generated using an open-source Raman database (Romanian Database of Raman Spectroscopy, RDRS). Spectral analysis was then performed for microscopy measurements of microsphereroids of the blood thinner clopidogrel bisulfate and in THz Raman imaging of common constituents in aspirin tablets. From these collective results, the potential scope of use for GALDA is critically evaluated relative to alternative established spectral dimension reduction and classification methods.

Has urbanization slowed down in Pakistan?

ABSTRACT Pakistan is the seventh largest contributor to world urban growth and exhibits high levels of urbanization. The recent 2017 Population Census results show a slowing of urban growth. We question whether this apparent slowdown reflects lowering of the rate of natural increase and migration, or is the result of a disconnect between the administrative definition of urban and actual reality. Alternative estimates presented here suggest that the 2017 Census may have underestimated urbanization by as much as 22.2 per cent, that is, the actual level of urbanization may be 44.5 per cent compared to 36.4 per cent reported in the census. We decompose the urban growth to assess the relative impact of natural increase, reclassification and migration and we utilize alternative methods of classification of urban areas to assess urbanization levels for Pakistan and its provinces. Continuing high levels of urban fertility and natural increase are the major contributors to urban growth. Internal migration is the next biggest contributor, however directions of movements may be changing. These findings have implications for the forthcoming 2022 census to improve the enumeration of urban areas and for urban planning to take advantage of the beneficial effects of building connectivity between small, medium, and large cities.

Double contrast-enhanced ultrasound for the preoperative gross classification of gastric cancer: a comparison with multidetector computed tomography

PurposeTo compare the diagnostic performance of double contrast-enhanced ultrasound (DCEUS) and multi-detector row computed tomography (MDCT) in the gross classification of gastric cancer (GC) preoperatively.Methods54 patients with histology proved GC were included in this retrospective study. The sensitivity and specificity of DCEUS and MDCT for the gross classification of GC was calculated and compared. The area under the curve (AUC) from a receiver operating characteristic curve analysis was used to evaluate the difference of the diagnostic performance between these two methods.ResultsThere were no significant differences between DCEUS and MDCT in terms of AUC for early gastric cancer (EGC), Borrmann I, II, III and Borrmann (III + IV) (P = 0.248, 0.317, 0.717, 0.464 and 0.594, respectively). The accuracy of DCEUS in diagnosing EGC, Borrmann I, II and Borrmann (III + IV) was higher than that of MDCT (96% vs 92%; 96% vs 94%; 87% vs 80%; 83% vs 73%), while in determining Borrmann III and IV, that of DCEUS was lower than that of MDCT (72% vs 74%; 89% vs 96%).ConclusionConsidering the revolution in clinical decision, prognosis evaluation, safety and non-invasion aspects, DCEUS can be used as the main alternative method for Borrmann classification of GC preoperatively.

EEG-dependent automatic speech recognition using deep residual encoder based VGG net CNN

Speech difficulties are common in children and teenagers, but they can also occur in adults as a result of physical problems. A speech disorder is a situation in which an individual struggles to produce or construct the spoken sounds necessary for interpersonal communication. As a result, it could be challenging to comprehend the person's speech. Articulation abnormalities are typical speech problems. In this situation, automatic speech recognition (ASR) technology may be used to detect and further rectify such deficiencies. The first attempts to detect speech abnormalities were made in the early 1970s, and they appear to have followed the same path as those on the ASR. These early experiments did rely heavily on signal processing techniques. As time goes on, more ideas from ASR technology are being incorporated into systems that deal with speech impairments. Many traditional techniques are executed in the ASR system. In this paper, we developed an automatic speech recognition technology based on deep learning techniques. In this paper, we research alternative extraction and classification methods of electroencephalography (EEG) to help diagnose speech disorders (SD). The EEG data is prepared before degradation into numerous EEG sub-strands with a discrete wavelet transformation to eliminate unimportant errors. For sharpening signals, the Eigenvector crack curvature wavelet method was used. A hyper-similarity abnormality coder is used for feature extraction in the EEG recording and to detect synchronization between EEG channels, which may show abnormalities in communication. The recovered functions are then categorized using the Deep Residual–encoder–based VGG net CNN Classification Method. Thus, the techniques proposed to produce the most promising outcome aren't the suggested technique attained better classification accuracy when compared to the traditional methodologies.

Classification and Total Carbon Determination of the Soils Using RGB Digital Images Combined with Machine Learning

ABSTRACT An alternative approach for soil characterization is proposed, using digital images and machine learning. The present work investigated the Red, Green, and Blue (RGB) pattern with Artificial Neural Network (ANN) and Random Forest (RF) in the classification of Oxisol and Inceptisol collected in South Brazil. Furthermore, we also built ANN, RF, Partial Least Squares (PLS), and Support Vector Machine (SVM) models from digital images to quantify Total Carbon (TC) content in soil samples. Preliminary evaluations considered the hypothesis that alternative methods of classification and quantification of soil properties might provide results comparable to traditional methods. The TC content of 91 soil samples, 40 from Oxisol and 51 from Inceptisol were determined by dry combustion. ANN and RF as machine learning algorithms were applied to classify the two kinds of soils. The classification quality was assessed by the percentage of correctly classified instances. The results suggest using digital images and machine learning algorithms as a new methodology to soil classification. The best machine learning method was RF which can be used to classify soil types by the RGB channel colors of the soil images. The use of digital images and RF is a clean (eco-friendly), rapid, and affordable strategy analysis for TC quantification in soils. Therefore, the proposed methods represent an advancement in soil classification, such as the determination of the TC content of soils.

N-exterior isoclinic Lie crossed modules

Exterior isoclinism gives an alternative classification method for finite Lie algebras and Lie crossed modules. In this paper, we define the notions of exterior isoclinism of Lie algebras and Lie crossed modules. Also, we generalize these notions for n-th dimension.

A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

Cardiovascular disease is the number one cause of death in humans. Therefore, cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug development. Recently, cell-based techniques developed for arrhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large datasets. We carried out the first report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm (MNDA) analysis for drug-induced arrhythmia recognition and classification. The biosensing system cultured cardiomyocytes as physiologically relevant models, used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes, and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating recording. The best performing MNDA parameter, approximate entropy, enabled the system to recognize the appearance of sertindole- and norepinephrine-induced arrhythmia in the recording. The MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of arrhythmia. This new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

Combination of UAV Images and DSM for Land Cover Classification using Convolutional Neural Network

Machine learning applies predominantly to the classification of the satellite images, aerial photo, unmanned aerial vehicle (UAV) data, point clouds with considerable achievements. However, the dynamic and complex structures of land surface prevent accurate land cover segregation through built-in models, and there is a crucial need to investigate novel ones. This study integrates Catboost into a Convolutional neural network for land cover classification from UAV images, with a case study in Hanoi. The combination of these images and Digital surface model to form the input datasets. The results show that the overall accuracy reaches 91,5%, which is relatively higher than other comparing methods. The proposal model can be used as an alternative method for land cover classification.&#x0D;

Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects

Familial Hypercholesterolemia (FH) is an inherited disorder of lipid metabolism, characterized by increased low density lipoprotein cholesterol (LDLc) levels. The main purpose of the current work was to explore alternative classification methods to traditional clinical criteria for FH diagnosis, based on several biochemical and biological indicators. Logistic regression (LR), decision tree (DT), random forest (RF) and naive Bayes (NB) algorithms were developed for this purpose, and thresholds were optimized by maximization of Youden index (YI). All models presented similar accuracy (Acc), specificity (Spec) and positive predictive values (PPV). Sensitivity (Sens) and G-mean values were significantly higher in LR and RF models, compared to the DT. When compared to Simon Broome (SB) biochemical criteria for FH diagnosis, all models presented significantly higher Acc, Spec and G-mean values (p < 0.01), and lower negative predictive value (NPV, p < 0.05). Moreover, LR and RF models presented comparable Sens values. Adjustment of the cut-off point by maximizing YI significantly increased Sens values, with no significant loss in Acc. The obtained results suggest such classification algorithms can be a viable alternative to be used as a widespread screening method. An online application has been developed to assess the performance of the LR model in a wider population.

Cardiovascular manifestations in obstructive sleep apnea: current evidence and potential mechanisms.

Obstructive sleep apnea (OSA) is an increasingly prevalent health concern characterized by repeated episodes of pharyngeal collapse during sleep. It is frequently associated with daytime sleepiness and impaired functional capacity, but it is also linked to cardiovascular disease by a growing body of epidemiological, clinical, and translational research. The severity of OSA is traditionally evaluated by the apnea‑hypopnea index (AHI), but the value of this marker as a predictor of cardiovascular outcomes is limited. Thus, there is an increasing focus on alternative classification methods such as the hypoxic burden, other polysomnographic traits, and phenotypic subgroups based on clinical symptoms. There is a need to identify subgroups of patients with OSA who will benefit most from treatment, as recent large randomized controlled trials in selected populations have failed to show benefit in reducing overall cardiovascular mortality. Obstructive sleep apnea adversely affects cardiovascular structure and function by several distinct mechanisms such as intermittent hypoxia, sleep fragmentation, and intrathoracic pressure swings. These mechanisms lead to sympathetic activation, inflammation, and oxidative stress, which may result in the clinical consequences of OSA such as hypertension, coronary artery disease, heart failure, and cerebrovascular disease. This review focuses on the epidemiology and potential mechanisms of cardiovascular diseases in OSA. Furthermore, we will briefly discuss the role of personalized medicine, alternative treatment options, and precise phenotyping to optimize treatment of this complex condition and its associated cardiovascular risk.

Does Industry Classification Matter in IT Business Value Research?

Studies which examine the effect of IT on firm performance make the implicit assumption that the choice of industry classification (e.g., SIC instead of NAICS) and the level of aggregation (e.g., 2-digit SIC instead of 4-digit SIC) is unlikely to affect the results. Given that practically none of the studies in our literature review has tested the robustness of their results to an alternative industry classification method, we do not know whether this choice matters (i.e., we don’t know whether the evidence generated from this stream of research is robust). To answer this question, we replicated three studies (Bharadwaj 2000; Chae et al. 2014; Santhanam and Hartono 2003) and found that results are likely to differ if we change the classification method and/or the level of aggregation within a given industry classification method. We encourage IT researchers to replicate prior studies to assess the sensitivity of their results to such choices as well as encouraging future studies to consider implementing robustness checks. The need to consider such robustness checks spans all fields (e.g., accounting, strategic management, and marketing) that use statistical analysis which requires control for role of industry.

Hyperspectral Image Classification Based on Stacked Contractive Autoencoder Combined With Adaptive Spectral-Spatial Information

Hyperspectral image (HSI) contain abundant spectral and spatial information, enabling the accurate classification of ground objects. However, many existing machine learning methods have poor performance, and some existing CNN-based methods require high computational power, which considerably limits their real-world applications. To address these issues, in this paper, we propose an alternative HSI classification method based on the stacked contractive autoencoder (SCAE) and adaptive spectral-spatial information to improve the accuracy of HSI classification. Specifically, the non-subsampled shearlet transform (NSST) with the guided filtering (NG) enhances spatial structure information. Subsequently, we present an adaptive spatial information extraction method to extract the spatial information of pixels. Furthermore, we propose an HSI classification network, called SCAE-LR, for feature extraction and classification. The SCAE is implemented to extract the adaptive spectral-spatial feature, and a logistic regression (LR) layer is employed for classification. Extensive experiments on the Indian Pines data set and the Pavia University data set demonstrate the superior performance of our method.

Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests

BackgroundBlack alder (Alnus glutinosa) forests are in severe decline across their area of distribution due to a disease caused by the soil-borne pathogenic Phytophthora alni species complex (class Oomycetes), “alder Phytopththora”. Mapping of the different types of damages caused by the disease is challenging in high density ecosystems in which spectral variability is high due to canopy heterogeneity. Data obtained by unmanned aerial vehicles (UAVs) may be particularly useful for such tasks due to the high resolution, flexibility of acquisition and cost efficiency of this type of data. In this study, A. glutinosa decline was assessed by considering four categories of tree health status in the field: asymptomatic, dead and defoliation above and below a 50% threshold. A combination of multispectral Parrot Sequoia and UAV unmanned aerial vehicles -red green blue (RGB) data were analysed using classical random forest (RF) and a simple and robust three-step logistic modelling approaches to identify the most important forest health indicators while adhering to the principle of parsimony. A total of 34 remote sensing variables were considered, including a set of vegetation indices, texture features from the normalized difference vegetation index (NDVI) and a digital surface model (DSM), topographic and digital aerial photogrammetry-derived structural data from the DSM at crown level.ResultsThe four categories identified by the RF yielded an overall accuracy of 67%, while aggregation of the legend to three classes (asymptomatic, defoliated, dead) and to two classes (alive, dead) improved the overall accuracy to 72% and 91% respectively. On the other hand, the confusion matrix, computed from the three logistic models by using the leave-out cross-validation method yielded overall accuracies of 75%, 80% and 94% for four-, three- and two-level classifications, respectively.DiscussionThe study findings provide forest managers with an alternative robust classification method for the rapid, effective assessment of areas affected and non-affected by the disease, thus enabling them to identify hotspots for conservation and plan control and restoration measures aimed at preserving black alder forests.