Proposed Classification Scheme Research Articles

Effect of Binder on Oxidation Properties of Tungsten Carbides: A Review by a Conceptual Classification Approach

This study presents a conceptual classification scheme to review the literature on improving the oxidation resistance of tungsten carbide by modifying the binder. The first parts of the article are dedicated to the specification of the databases, the search method, and the description of the criteria chosen to classify the articles. Then, the data collected are presented in statistical graphs according to the proposed classification scheme. The data analyzed show that most of the significant improvements in oxidation resistance are achieved with advanced production processes, especially HIP and SPS, which eliminate porosity to a very high degree. In addition, statistical studies showed that the use of new replacement binders, Ni3Al, Fe–based alloys, FeAl, and Al2O3, improved the oxidation properties in 75–100% of cases. Meanwhile, the use of high–entropy alloys (HEAs) as cermet binders may be the subject of future research for oxidation, given the recently published results of good mechanical properties.

Classification of second harmonic generation effect in magnetically ordered materials

The relationship between magnetic order and the second harmonic generation (SHG) effect is a fundamental area of study in condensed matter physics with significant practical implications. In order to gain a clearer understanding of this intricate relation, this study presents a comprehensive classification scheme for the SHG effect in magnetically ordered materials. This framework offers a straightforward approach to connecting magnetic order and the SHG effect. The characteristics of the SHG tensors in all magnetic point groups are studied using the isomorphic group method, followed by a comprehensive SHG effect classification scheme that includes seven types based on the symmetries of the magnetic phases and their corresponding parent phases. In addition, a tensor dictionary containing the SHG and linear magneto-optic (LMO) effect is established. Furthermore, an extensive SHG database of magnetically ordered materials is also built up. This classification strategy exposes an anomalous SHG effect with even characteristics (i.e., invariant) under time-reversal symmetry, which is solely contributed by magnetic structure. Moreover, the proposed classification scheme facilitates the determination of magnetic structures through the SHG effect.

Privacy-Preserving Electricity Data Classification Scheme Based on CNN Model With Fully Homomorphism

Data classification of users' electricity consump- tion provides an in-depth analysis for users' electricity con- sumption status, which plays a vital role in the management and distribution of electric energy. So, some data classification methods have been proposed to solve the classification problem of electricity consumption data. However, plaintext-based data classification may bring about the privacy leakage of electric- ity consumption data. In this paper, we propose a privacy- preserving classification scheme for electricity consumption da- ta under fog computing-based smart metering system, which is based on convolutional neural network (CNN) model with fully homomorphic method (CKKS). The target of our proposed scheme is to solve the leakage problem of private electricity consumption data during the classification procedure. In our scheme, an improved K-means-based labeling algorithm is constructed to process historical electricity consumption data, which is used as the sample data to train the CNN classification model by cloud server. Also, the fog nodes are only permitted to obtain the related ciphertext parameters of the trained CNN model, and perform the classification of ciphertext-based electricity consumption data generated by fully homomorphic method. Based on the classical testing data, the experimental results show that our proposed classification scheme can provide the high classification accuracy of electricity data while protecting the privacy of electricity data.

Object-oriented polarimetric SAR image classification via the combination of a pixel-based classifier and a region growing technique

ABSTRACT Land-cover type interpretation by the use of remote sensing image classification techniques is always a hot topic. In this paper, an object-oriented method is presented for fully polarimetric synthetic aperture radar (SAR) image classification. Differing from most of the traditional object-oriented classification algorithms, the proposed method employs an innovative classification strategy that combines a pixel-based classifier and a region growing technique. Firstly, taking each individual pixel as a seed pixel, the homogeneous areas are extracted by a region growing technique. Then, using the information of the pixel-based classification result, the pixels located in each homogeneous area are all assigned to a certain class. Finally, the majority voting strategy is deployed to determine the final class label of each pixel. The experiments conducted on two fully polarimetric SAR images reveal that the proposed classification scheme can obtain pleasing classification accuracy and can provide the classification maps with more homogeneous regions than pixel-based classification.

Steady Flow through Gated Circular Culverts: Hydraulic Operation and Experiments

Culverts are ubiquitous hydraulic structures used in cross-drainage and agricultural water systems, especially at farm outlets in irrigation systems. Gate control adds hydraulic complexity to culvert water flow, yet it has hardly been studied. This study has two objectives: (1) to classify the operational conditions of gated culverts, and (2) to establish an experimental data set for analyzing discharge relationships based on that classification. A theoretical inlet–outlet control–based classification resulted in 22 inlet control and 19 outlet control cases, organized in 4 inlet and 5 outlet control groups. A total of 230 laboratory tests were conducted. The laboratory setup reproduced the conditions of inlet submerged with outlet in free surface (one of four inlet control groups) and all conditions except inlet in free surface with outlet submerged (four of five outlet control groups) of the inlet and outlet control groups, respectively. Comparison with existing classification schemes for gated culverts confirmed the suitability of the proposed scheme and provided further insight into flow classification. The discharge grouped by narrow ranges of gate openings was found to follow a power-law relationship of head with an exponent between 0 and 1. Flow transition regions between groups of conditions were examined, suggesting parabolic transition curves. The gate opening was found to be the dominant variable, resulting in a direct proportional effect on discharge, i.e., data corresponding to greater gate openings enveloped those with smaller openings. The slope’s influence could not be discerned within the slope range tested. Discharge estimation based on a dimensionless, empirical model produced reasonable results, although its case-dependent nature requires further research to develop operational hydraulic models for determining discharge through gated culverts. Application of the proposed classification scheme could help develop these hydraulic tools with sufficient accuracy.

BILE: A Literature Review Based Novel Clinical Classification and Treatment Algorithm of Iatrogenic Bile Duct Injuries.

The management of patients with iatrogenic bile duct injuries (IBDI) is a challenging field, often with dismal medico legal projections. Attempts to classify IBDI have been made repeatedly and the final results were either analytical and extensive but not useful in everyday clinical practice systems, or simple and user friendly but with limited clinical correspondence approaches. The purpose of the present review is to propose a novel, clinical classification system of IBDI by reviewing the relevant literature. A systematic literature review was conducted by performing bibliographic searches in the available electronic databases, including PubMed, Scopus, and the Cochrane Library. Based on the literature results, we propose a five (5) stage (A, B, C, D and E) classification system for IBDI (BILE Classification). Each stage is correlated with the recommended and most appropriate treatment. Although the proposed classification scheme is clinically oriented, the anatomical correspondence of each IBDI stage has been incorporated as well, using the Strasberg classification. BILE classification represents a novel, simple, and dynamic in nature classification system of IBDI. The proposed classification focuses on the clinical consequences of IBDI and provides an action map that can appropriately guide the treatment plan.

Saint Petersburg 3D: Creating a Large-Scale Hybrid Mobile LiDAR Point Cloud Dataset for Geospatial Applications

At the present time, many publicly available point cloud datasets exist, which are mainly focused on autonomous driving. The objective of this study is to develop a new large-scale mobile 3D LiDAR point cloud dataset for outdoor scene semantic segmentation tasks, which has a classification scheme suitable for geospatial applications. Our dataset (Saint Petersburg 3D) contains both real-world (34 million points) and synthetic (34 million points) subsets that were acquired using real and virtual sensors with the same characteristics. An original classification scheme is proposed that contains a set of 10 universal object categories into which any scene represented by dense outdoor mobile LiDAR point clouds can be divided. The evaluation procedure for semantic segmentation of point clouds for geospatial applications is described. An experiment with the Kernel Point Fully Convolution Neural Network model trained on the proposed dataset was carried out. We obtained an overall 92.56% mIoU, which demonstrates the high efficiency of using deep learning models for point cloud semantic segmentation for geospatial applications in accordance with the proposed classification scheme.

Leukocyte subtype classification with multi-model fusion.

Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers.

183 Proposed Classification System of Radiographic Bone Changes After Cervical Disc Replacement

INTRODUCTION: Cervical Disc Replacement (CDR) is an increasingly utilized procedure to address cervical degenerative conditions, as an alternative to discectomy and fusion, for the purpose of preserving natural segmental motion. METHODS: Using serial radiographs from patients, a panel of experts assembled for the purpose of creating a classification system to aid in reproducibly and consistently identifying boney changes and assessing CDR device radiographic appearance. Subdividing the superior and inferior vertebral bodies into 3 equal sections, observed bone loss such as endplate rounding, cystic erosion adjacent to the endplate, and cystic erosion not adjacent to the endplate, is recorded. Determining if bone loss is progressive and estimating severity of bone loss (measured by the percentage of end plate involved) is recorded. Additional relevant boney changes and device observations include radiolucent lines, heterotopic ossification, loss of core implant height, and presence of device migration, subsidence, and olisthesis. RESULTS: Serial radiographs from 26 patients implanted with a variety of CDR products were assessed by 6 investigators including clinicians and scientists experienced in CDR or appendicular skeleton joint replacement. After several iterations, a classification system emerged that had improved concordance among the participating investigators. Next steps include assessing reliability with a broad group of clinicians, using serial radiographs from a significant number of patients, to confirm the proposed classification scheme. CONCLUSIONS: A standardized nomenclature for boney changes following CDR will facilitate accurate and reproducible scientific communications regarding the clinical outcomes of this procedure. The novel system proposed demonstrated good concordance among experienced investigators in this field and represents an important advancement.

Semiempirical model for the estimation of site amplification in Northern South America

This article proposes a semiempirical model to estimate seismic site effects based on a predominant-period classification scheme for application in earthquake ground-motion models (GMMs). The proposed model introduces the use of the peak amplitude of the average horizontal-to-vertical ratio of the spectral response of the site obtained from earthquake records. The site effects model is implemented within a GMM estimated using ground-motion records from northern South America. The research compares the performance of the proposed site function with other formulations based on VS30 or site predominant period. This approach yields a smaller within-event variability than alternative methodologies, providing confidence for the reliability of the proposed site classification scheme as a viable alternative for the estimation of site effects, especially for sites for which VS30 measurements are not available.

A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing.

In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).

Quantum collisional classifier driven by information reservoirs

We investigate the open dynamics of a probe qubit weakly interacting with distinct qubit environments bearing quantum information. We show that the proposed dissipative model yields a binary classification of the reservoir qubits' quantum information in the steady state in the Bloch qubit parameter space, depending on the coupling rates. To describe the dissipation model dynamics, we have adopted the collision model, in which the input information parameters of the reservoir qubits are easily determined. We develop a generalized classification rule based on the results of the micromaserlike master equation where the classification can be described in terms of the Bloch parameters. Moreover, we show that the proposed classification scheme can also be achieved through quantum parameter estimation. Finally, we demonstrate that the proposed dissipative classification scheme is suitable for gradient descent-based supervised learning tasks.

Forensic Classification of Subjects Counteracting Investigation of High-Tech Crimes

The paper defines the concept of counteraction to the investigation of high-tech crimes employing the author’s definition of high-tech crimes. A forensic classification of the subjects of this counteraction is proposed, in which the procedural status of these subjects is used as a formal classification criterion. A hierarchical classification scheme is described, which includes groups and subgroups of counteraction subjects of varying degrees of similarity. The subjects of «internal» opposition include participants in criminal proceedings, divided into four main groups: on the part of the defense; by the prosecution; administering justice; other subjects. For the subjects of «external» opposition, the following groups are identified: law enforcement officers who are not subjects of criminal proceedings in a specific criminal case; persons associated with the accused (defendants); heads of government bodies; media workers. For each group of subjects, forensically significant features of counteraction to the investigation of high-tech crimes are analyzed, typical motives and methods of counteraction are given. It is noted that the proposed classification scheme can be used not only for high-tech crimes, but also for other types of criminal acts.

Experimentally Shock‐Induced Melt Veins in Basalt: Improving the Shock Classification of Eucrites

AbstractBasaltic rocks occur widely on the terrestrial planets and differentiated asteroids, including the asteroid 4 Vesta. We conducted a shock recovery experiment with decaying compressive pulses on a terrestrial basalt at the Chiba Institute of Technology, Japan. The sample recorded a range of pressures, and shock physics modeling was conducted to add a pressure scale to the observed shock features. The shocked sample was examined by optical and electron microscopy, electron back‐scattered diffractometry, and Raman spectroscopy. We found that localized melting occurs at a lower pressure (∼10 GPa) than previously thought (>20 GPa). The shocked basalt near the epicenter represents “shock degree C” of a recently proposed classification scheme for basaltic eucrites and, as such, our results provide a pressure scale for the classification scheme. Finally, we estimated the total fraction of the basaltic eucrites classified as shock degree C to be ∼15% by assuming the impact velocity distribution onto Vesta.

Reply to the comment on “A new classification system for mixed (turbidite-contourite) depositional systems: Examples, conceptual models and diagnostic criteria for modern and ancient records” by Sara Rodrigues, F. Javier Hernández-Molina, Marco Fonnesu, Elda Miramontes, Michele Rebesco, D. Calvin Campbell [Earth-Science Reviews (2022), 104030

We appreciate the comments by G. Shanmugam on the paper “ A new classification system for mixed (turbidite-contourite) depositional systems: Examples, conceptual models and diagnostic criteria for modern and ancient records ” by S. Rodrigues, F.J. Hernández-Molina, M. Fonnesu, E. Miramontes, M. Rebesco, D. C. Campbell [Earth-Science Reviews (2022), 104,030] ”. Most of the comments from G. Shanmugam refer to the sedimentary facies scale, with special emphasis on the distinct types of deposits and their definitions. It was quite a surprise that G. Shanmugam's comments did not include any remarks about the descriptions or discussions elaborated in Rodrigues et al. (2022a), such as the large-scale morphological features recognized in mixed (turbidite-contourite) depositional systems through bathymetric or seismic data or the newly proposed classification scheme. Rodrigues et al. (2022a) has a very brief sub-section on the sedimentary facies recognized in mixed systems (e.g., Section 5.4 Sedimentary facies and deposits of mixed systems , page 22), which is based on a few key contributions from past literature (such as Mutti and Carminatti, 2012; Fonnesu et al., 2020; Rodrigues et al., 2022b). His comments appear to be a compilation of his own assertions and do not provide a clear link to the contribution of Rodrigues et al. (2022a) or to the study of mixed depositional systems. Initially, G. Shanmugam focuses his comments on the multitude of turbidite and bottom current processes that occur in contourite and turbidite settings, as well as their definitions and models. He states that Rodrigues et al. (2022a) proposed a new classification for mixed depositional systems “ based on their notion that there is only one type of turbidite and that there is only one type of contourite in deep-marine systems” , but the authors have never made such a statement in their manuscript. He also reports that Rodrigues et al. (2022a) “ failed to provide a clear and precise definition of the terms turbidite, contourite, and bottom current” , but nevertheless the authors have provided definitions and references for those terms. Accordingly, G. Shanmugam outlined four distinct problems in Rodrigues et al. (2022a) review paper, namely (1) the turbidite problem, (2) the contourite problem, (3) the bottom-current problem, and (4) the seismic geometry vs. sedimentology problem. As indicated above, G. Shanmugam's criticism is focused on turbidite and bottom current processes in non-mixed settings, while very few points are made about these processes in mixed depositional systems. As clearly stated in the Introduction of Rodrigues et al. (2022a), the aim of this review paper was to “ compile all currently known cases” , “ present new undocumented examples” and “ compare their lateral migration, stratigraphic stacking patterns and seismic facies” . In addition, “ a new classification system is proposed, along with clear diagnostic criteria, to address some of the previous inconsistencies and to characterize the spatial and temporal variability of mixed systems” . Therefore, we believe G. Shanmugam has misinterpreted the aims of this review paper and raised issues unrelated to or not covered in this study. He uses this comment to highlight points that are controversial in literature or that follow opinions different from his own publications, in order to open a discussion that is not fully relevant to the identification or recognition of mixed systems. In fact, the problems raised in Shanmugam's comments have been a recurrent point of debate for several years now. Deep-water processes, their definitions and models have been profusely discussed by him and other authors since the 50s (Kuenen and Migliorini, 1950; Bouma, 1962; Middleton and Hampton, 1973; Lowe, 1982; Cullis et al., 2018). We do agree that there is a high degree of variability in deep-water turbidite and bottom current processes, both in contourite and turbidite settings, as well as in mixed depositional settings. However, the paper by Rodrigues et al. (2022a) focuses solely on reviewing the interactions between turbidite and bottom currents in mixed depositional systems to improve their recognition and classification and, therefore, does not approach these processes separately in other depositional environments (e.g., in pure contourite or turbidite settings). The authors will therefore not partake in the debate surrounding the definitions and models of these processes in non-mixed depositional environments. However, the authors will reply to the points raised about these processes in mixed depositional systems, specifically points 1–4 in problem 1, points 1–2 in problem 2 , points 7–9 in problem 3 and point 12 in problem 4 .

Validation of a proposed radiographic bone defect classification system

ObjectivesTo clinically validate a recently proposed classification scheme of post-traumatic bone defects. Methods and materialsOpen fractures were classified utilising a newly introduced classification system. This classification system is based on plain radiographs, assessing the extent and local geometry of bone loss, including: D1 - Incomplete Defects; D2 - Minor/Sub-Critical (Complete) Defects (<2 cm); and D3 - Segmental/Critical Sized Defects (≥2 cm). Reliability was assessed among six independent assessors (three trauma orthopaedic surgeons and three orthopaedic training surgeons) using Fleiss’ kappa tests. 43 open fractures from a tertiary referral trauma centre and their radiographic series were analysed. ResultsInterobserver reliability testing demonstrated the proposed classification system had substantial agreement between the 6 observers, κ = 0.623 (z = 33.8), p < 0.001. Intraobserver variability showed a range of substantial to almost perfect agreement of each observer following a three-week interval between repeat assessments, κ range 0.69–0.914, p < 0.001. ConclusionsIn this representative validation cohort there was substantial agreement between observers when assessing a diverse range of bone defects following long bone open trauma, with highly reproducible assessments by both orthopaedic surgeons and trainee orthopaedic surgeons alike on an internal level. The classification scheme is based on conventional orthogonal radiographs and requires no sophisticated technology, and is therefore pragmatic and applicable to secondary, tertiary and quaternary levels of care for trauma patients. Level of EvidenceIII

Overview of Filicide: Characteristics and Classification

Child murder is most common at the hands of a biologic or stepparent. Attempts to better understand filicide, or child murder by a parent, has resulted in several proposed classification schemes. Like most of the research in this area, the proposed classification schemes provide a conceptualization to understand why filicide occurs but have several limitations. Resnick's classification by motive, developed in 1969, is the most widely cited. A child is at highest risk of filicide within the first 24 hours of life. Mothers are responsible for more neonaticides than fathers, but filicides after the first day of life are committed equally by mothers and fathers. In many cases mental illness was present in filicides as well as suicide attempts following the filicidal act. This paper provides an overview of filicide including classification schemes and offender characteristics.

Identification of saline landscapes from an integrated SVM approach from a novel 3-D classification schema using Sentinel-1 dual-polarized SAR data

This study presented an integrated SVM classification method that investigated the relationship between intrinsic scattering attributes of the surface features and the conductivity characteristics of the soil in the newly proposed two- and three-dimensional classification (2D and 3D) schema to classify the saline landscape. The study was conducted in the Vellore district, Tamil Nadu, which is composed of heterogeneous saline landforms due to the active geological processes and anthropogenic activities. The intensity products of Sentinel-1 data (VV + VH) of C-band frequency were employed in the present study. The SVM with linear kernel has outperformed the other models, namely random forest (RF), naive Bayes (NB), and K-Nearest Neighbour (k−NN), in performing the broad level of classification from the 2D classification schema (SVMOA = 84.2%, RFOA = 80.4%, k-NNOA = 78.8%, NBOA = 68.4%). The soil EC values derived from the dielectric loss measurements (R2 = 0.79, ρ = 0.018, and α=95%) were used to introduce the integrated SVM approach in the 3D schema to further breakdown the mapped classes into non-saline (NS) (soil EC ≤ 2 ds/cm), slightly saline (SS) (soil EC = 2.1 to 4 ds/cm) and moderately saline (MS) (soil EC = 4.1 to 8 ds/cm) categories. The overall performance of the integrated SVM approach implemented for the 3D classification schema (F1 = 0.80) was found to be satisfactory, but with an associated uncertainty majorly from MS (Precision = 0.52, F1 = 0.69), SS (Recall = 0.09, F1 = 0.15), and NS waterbodies (Recall = 0.18, F1 = 0.29) as shown in the Precision-Recall graph (AUCPR3D = 0.62). However, with the promising performance level demonstrated for the other nine classes such as NS, SS, and MS wet soil (F1 = 0.92, 0.92, 0.96), healthy plants (F1 = 0.83), salt-tolerant plants under SS and MS conditions (F1 = 0.83, 0.88), and waterlogged vegetation under NS, SS, and MS conditions (F1 = 0.82, 0.83, 0.83), the proposed classification scheme becomes an effective method to map saline and non-saline features from dual-polarimetric SAR data.

A Review of the Hydrologic Response Mechanisms During Mountain Rain-on-Snow

Mountain rain-on-snow (ROS) generates large flooding events worldwide. Climate warming will enhance the frequency, magnitude, and widespread nature of these events. Past studies indicate rainfall, not snowmelt, typically drives much of the runoff response during ROS. However, there is substantial event-to-event variability—resulting from shifting atmospheric drivers and nuanced physical mechanisms governing water flow through a snowpack. Historically, turbulent fluxes were assumed to dominate the energy balance for snowmelt during ROS. Recent research nonetheless suggests that other components of the energy balance might be larger drivers depending on: 1) the time of year; 2) the elevation; and 3) the aspect of the slope. This mini review summarizes the literature on the physical processes governing ROS and proposes that moving forward we utilize the terms “active” and “passive” to describe a snowpack’s contribution (via snowmelt) to terrestrial water input (TWI) during ROS. Active snowpacks readily contribute meltwater to TWI via the energy balance, bolstering rainfall-runoff totals. Passive snowpacks do not melt, but simply convey rainwater through the snow matrix. In both snowpack cases, preferential flow paths enhance transmissivity. This proposed classification scheme will help researchers and water managers better communicate and interpret past findings, and aid in forecasting discussions of future events.

Long–Short Ensemble Network for Bipolar Manic-Euthymic State Recognition Based on Wrist-Worn Sensors

Manic episodes of bipolar disorder can lead to uncritical behavior and delusional psychosis, often with destructive consequences for those affected and their surroundings. Early detection and intervention of a manic episode are crucial to prevent escalation, hospital admission, and premature death. However, people with bipolar disorder may not recognize that they are experiencing a manic episode and symptoms, such as euphoria and increased productivity can also deter affected individuals from seeking help. This work proposes to perform user-independent, automatic mood-state detection based on actigraphy and electrodermal activity acquired from a wrist-worn device during mania and after recovery (euthymia). This article proposes a new deep learning-based ensemble method leveraging long (20 h) and short (5 min) time intervals to discriminate between the mood states. When tested on 47 bipolar patients, the proposed classification scheme achieves an average accuracy of 91.59% in euthymic/manic mood-state recognition.