Search Results (63)

Ovarian cancer is a gynecological cancer, which, if metastasized and not detected early, can cause death among women. Therefore, accurate prediction of drug responses to ovarian cancer is needed. A gynecological pathologist inspects abnormality in tissues and provides a report for patients; however, this diagnostic process (1) is difficult to undertake; (2) requires experience; and (3) is time-consuming. Moreover, existing tools are imperfect. Hence, we present a computational pipeline to improve predictions of drug response pertaining to ovarian cancer. First, we downloaded digital pathology images pertaining to ovarian responses to bevacizumab from the Cancer Imaging Archive Repository. We employed a histogram of oriented gradients for images, constructed feature vectors, and used Fisher’s linear discriminant analysis to alter data representations through dimensionality reduction. This reduced-dimensionality data was used for regression analysis, employing support vector regression coupled with various kernels and calculating the area under the ROC curve (AUC). Experimental results were validated using transformer-based models (ViT and Swin) and other deep learning (DL) models (VGG16, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB6). Our approach using a radial kernel (named SVRD + R) improved AUC performance by 17% compared to the best-performing transformer-based model (ViT). Likewise, AUC performance improved by 14.9% when compared against the best DL-based model (MobileNetV2). These results demonstrate feasibility, showing that induced models via the presented AI-based pipeline can lead to superior performance when investigating prediction problems pertaining to gynecologic cancer studies. Full article

Dry-type electrical transformers are essential components in commercial, industrial, and residential power distribution systems, as they adapt voltage levels required by a broad range of load types. Although they are robustly constructed, they are exposed to adverse operational and environmental conditions such as dust, humidity, and electrical disturbances that may cause premature winding damage, such as inter-turn short circuits. This study focuses on the detection of inter-turn short-circuit faults in a 15 kVA commercial dry-type transformer, where a fault equivalent to 11.54% of short-circuited turns was induced in the tap changers. Axial, radial, and rotational leakage magnetic flux signals were captured using a low-cost, non-invasive triaxial Hall-effect magnetic flux sensor. During data processing, Fisher Score feature selection was applied to identify the most relevant indicators. Subsequently, feature extraction techniques, including Linear Discriminant Analysis, Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection, and Isometric Mapping, were evaluated. The technique that best preserved global and local data structures was selected using Trustworthiness, Spearman’s correlation, and Kruskal’s stress metrics. PCA was selected as the optimal technique based on these quality metrics, achieving the highest classification performance. The resulting subspace data were classified using support vector machines and applying K-fold cross-validation. The proposed system achieved classification accuracies above 95%, with high recall and F1-score values, for inter-turn fault detection in each winding, confirming its effectiveness for reliable inter-turn fault detection in each transformer winding. Full article

Rapid and accurate identification of water inrush sources is essential for the prevention and control of coal mine water hazards. Fisher discriminant analysis and random forest are widely applied, but their performance comparison and applicability under single-mine and multi-mine scenarios remain to be investigated. This study takes the Tunlan Mine in Shanxi Province, China, as an example and evaluates both models using accuracy, precision, recall, F1-score, and confusion matrix. A joint discrimination scheme is used to explore their generalization ability. In the single-mine scenario, the improved Fisher algorithm achieves an overall accuracy of 93% and the random forest model achieves 87%, indicating that the former has greater advantages when data distribution is relatively linear. In the multi-mine joint discrimination scenario, the random forest model yields accuracies of 77–98%, far exceeding those of the Fisher algorithm and demonstrating clear superiority in handling complex nonlinear data. The results show that model performance depends primarily on data quality and feature distribution rather than solely on sample size. This study provides a scientific basis for selecting water source identification algorithms in different scenarios and has practical value for improving coal mine water hazard prevention and control. Full article

As the global aging population increases, the early detection and prevention of Alzheimer’s disease (AD) have become important in public health. To solve the problems of subjectivity and low timeliness of traditional assessment methods, this paper proposes a multimodal dementia prevention system that combines physiological sensing, a gamification interface, and a classification model. The system includes an interactive joystick to measure pulse and blood pressure. A Chinese music game app increases the participation of the elderly and reduces their sense of rejection through gamification interaction. After the physiological data were standardized by Z-score, they were input into three small sample classifiers (Gaussian Naïve Bayes, Fisher Linear Discriminant Analysis, and Logistic Regression) for the binary classification of AD. The system performance was evaluated using the Leave-One-Out cross-validation method. Experimental results show that Logistic Regression performed best in situations with extremely small samples and class imbalance, with an F1-score of 0.700, which was higher than the other two. Dynamic features and model fusion technologies need to be integrated to further enhance the clinical application potential of the system in the early prediction of dementia. Full article

Background/Objectives: An ASD diagnosis from EEG is challenging due to non-stationary, low-SNR signals and small cohorts. We propose a compact, interpretable pipeline that pairs a shift-invariant Stationary Wavelet Transform (SWT) with Fisher’s Linear Discriminant (FLDA) as a supervised projection method, delivering band-level insight and subject-wise evaluation suitable for resource-constrained clinics. Methods: EEG from the KAU dataset (eight ASD, eight controls; 256 Hz) was decomposed with SWT (db4). We retained levels 3, 4, and 6 (γ/β/θ) as features. FLDA learned a low-dimensional discriminant subspace, followed by a linear decision rule. Evaluation was conducted using a subject-wise 70/30 split (no subject overlap) with accuracy, precision, recall, F1, and confusion matrices. Results: The β band (Level 4) achieved the best performance (accuracy/precision/recall/F1 = 0.95), followed by γ (0.92) and θ (0.85). Despite partial overlap in FLDA scores, the projection maximized between-class separation relative to within-class variance, yielding robust linear decisions. Conclusions: Unlike earlier FLDA-only pipelines and wavelet–entropy–ANN approaches, our study (1) employs SWT (undecimated, shift-invariant) rather than DWT to stabilize sub-band features on short resting segments, (2) uses FLDA as a supervised projection to mitigate small-sample covariance pathologies before classification, (3) provides band-specific discriminative insight (β > γ/θ) under a subject-wise protocol, and (4) targets low-compute deployment. These choices yield a reproducible baseline with competitive accuracy and clear clinical interpretability. Future work will benchmark kernel/regularized discriminants and lightweight deep models as cohort size and compute permit. Full article

This study introduces a promising authentication framework utilizing brain–computer interface (BCI) technology to enhance both security protocols and user experience. A key strength of this approach lies in its reliance on objective, physiological signals—specifically, brainwave patterns—which are inherently difficult to replicate or forge, thereby providing a robust foundation for secure authentication. The authentication system was developed and implemented in four sequential stages: signal acquisition, preprocessing, feature extraction, and classification. Objective feature extraction methods, including Fisher’s Linear Discriminant (FLD) and Discrete Wavelet Transform (DWT), were employed to isolate meaningful brainwave features. These features were then classified using advanced machine learning techniques, with Quadratic Discriminant Analysis (QDA) and Convolutional Neural Networks (CNN) achieving accuracy rates exceeding 99%. These results highlight the effectiveness of the proposed BCI-based system and underscore the value of objective, data-driven methodologies in developing secure and user-friendly authentication solutions. To further address usability and efficiency, the number of BCI channels was systematically reduced from 64 to 32, and then to 16, resulting in accuracy rates of 92.64% and 80.18%, respectively. This reduction streamlined the authentication process, demonstrating that objective methods can maintain high performance even with simplified hardware and pointing to future directions for practical, real-world implementation. Additionally, we developed a real-time application using our custom dataset, reaching 99.75% accuracy with a CNN model. Full article

Gesture recognition based on conventional machine learning is the main control approach for advanced prosthetic hand systems. Its primary limitation is the need for feature extraction, which must meet real-time control requirements. On the other hand, deep learning models could potentially overfit when trained on small datasets. For these reasons, we propose a hybrid Linear Discriminant Analysis–convolutional neural network (LDA-CNN) framework to improve the gesture recognition performance of sEMG-based prosthetic hand control systems. Within this framework, 1D-CNN filters are trained to generate latent representation that closely approximates Fisher’s (LDA’s) discriminant subspace, constructed from handcrafted features. Under the train-one-test-all evaluation scheme, our proposed hybrid framework consistently outperformed the 1D-CNN trained with cross-entropy loss only, showing improvements from 4% to 11% across two public datasets featuring hand gestures recorded under various limb positions and arm muscle contraction levels. Furthermore, our framework exhibited advantages in terms of induced spectral regularization, which led to a state-of-the-art recognition error of 22.79% with the extended 23 feature set when tested on the multi-limb position dataset. The main novelty of our hybrid framework is that it decouples feature extraction in regard to the inference time, enabling the future incorporation of a more extensive set of features, while keeping the inference computation time minimal. Full article

The tropism of the Human Immunodeficiency Virus type 1 (HIV-1) is determined by the use of either or both chemokine coreceptors CCR5 (R5) and CXCR4 (X4) for entry into the target cell. The ability of HIV-1 to bind R5 or X4 is determined primarily by the third variable loop (V3) of the viral envelope glycoprotein gp120. HIV-1 strains of pandemic group M contain an antisense gene termed asp, which overlaps env outside the region encoding the V3 loop. We previously showed that the ASP protein localizes on the envelope of infectious HIV-1 virions, suggesting that it may play a role in viral entry. In this study, we first developed a statistical method to predict coreceptor tropism based on Fisher’s linear discriminant analysis. We obtained three linear discriminant functions able to predict coreceptor tropism with high accuracy (94.4%) when applied to a training dataset of V3 sequences of known tropism. Using these functions, we predicted the tropism in a dataset of HIV-1 strains containing a full-length asp gene. In the amino acid sequence of ASP proteins expressed from these asp genes, we identified five positions with substitutions significantly associated with viral tropism. Interestingly, we found that these substitutions correlate significantly with substitutions at six amino acid positions of the V3 loop domain associated with tropism. Altogether, our computational analyses identify ASP amino acid signatures coevolving with V3 and potentially affecting HIV-1 tropism, which can be validated through in vitro and in vivo experiments. Full article

Remote Sensing plays a fundamental role in acquiring crucial information about the Earth’s surface from a distance, especially through fully polarimetric data, which offers a rich source of information for diverse applications. However, extracting meaningful insights from this intricate data necessitates sophisticated techniques. In addressing this challenge, one predominant trend that has emerged is known as target decomposition techniques. These techniques can be broadly classified into coherent and non-coherent methods. Each of these methods provides high-quality information using different procedures. In this context, this paper introduces innovative feature fusion techniques, amalgamating coherent and non-coherent information. While coherent techniques excel in detailed exploration and specific feature extraction, non-coherent methods offer a broader perspective. Our feature fusion techniques aim to harness the strengths of both approaches, providing a comprehensive and high-quality fusion of information. In the first approach, features derived from Pauli coherent decomposition, Freeman–Durden non-coherent technique, and the Symmetry criterion from Cameron’s stepwise algorithm are combined to construct a sophisticated feature vector. This fusion is achieved using the well-established Fisher Linear Discriminant Analysis algorithm. In the second approach, the Symmetry criterion serves as the basis for fusing coherent and non-coherent coefficients, resulting in the creation of a new feature vector. Both approaches aim to exploit information simultaneously extracted from coherent and non-coherent methods in feature extraction from Remote Sensing data through fusion at the feature level. To evaluate the effectiveness of the feature generated by the proposed fusion techniques, we employ a land cover classification procedure. This involves utilizing a basic classifier, achieving overall accuracies of approximately 82% and 86% for each of the two proposed techniques. Furthermore, the accuracy in individual classes surpasses 92%. The evaluation aims to gauge the effectiveness of the fusion methods in enhancing feature extraction from fully polarimetric data and opens avenues for further exploration in the integration of coherent and non-coherent features for remote sensing applications. Full article

The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gears has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used to solve nonlinear feature extraction problems, but the kernel function selection and its blind parameter setting greatly affect the performance of the algorithm. For the optimization of the kernel parameters, it is very urgent to study the theoretical modeling to improve the performance of kernel principal component analysis. Aiming at the deficiency of kernel principal component analysis using the single-kernel function for the nonlinear mapping of feature extraction, a dual-kernel function based on the flexible linear combination of a radial basis kernel function and polynomial kernel function is proposed. In order to increase the scientificity of setting the kernel parameters and the flexible weight coefficient, a mathematical model for dual-kernel parameter optimization was constructed based on a Fisher criterion discriminant analysis. In addition, this paper puts forward a swarm intelligent fusion algorithm to increase this method’s advantages for optimization problems, involving the shuffled frog leaping algorithm combined with particle swarm optimization (SFLA-PSO). The new fusion algorithm was applied to optimize the kernel parameters to improve the performance of KPCA nonlinear mapping. The optimized dual-kernel function KPCA (DKKPCA) was applied to the feature extraction of planetary gear wear damage, and had a good identification effect on the fuzzy damage boundary of the planetary gearbox. The conclusion is that the DKKPCA optimized by the SFLA-PSO swarm intelligent fusion algorithm not only effectively improves the performance of feature extraction, but also enables the adaptive selection of parameters for the dual-kernel function and the adjustment of weights for the basic kernel function through a certain degree of optimization; so, this method has great potential for practical use. Full article

This paper introduces a novel method for enhancing fault classification and diagnosis in dynamic nonlinear processes. The method focuses on dynamic feature extraction within multivariate time series data and utilizes dynamic reconstruction errors to augment the feature set. A fault classification procedure is then developed, using the weighted maximum scatter difference (WMSD) dimensionality reduction criterion and quadratic discriminant analysis (QDA) classifier. This method addresses the challenge of high-dimensional, sample-limited fault classification, offering early diagnosis capabilities for online samples with smaller amplitudes than the training set. Validation is conducted using a cold rolling mill simulation model, with performance compared to classical methods like linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD). The results demonstrate the superiority of the proposed method for reliable industrial process monitoring and fault diagnosis. Full article

Microsampling elemental analysis is widely used for gemstone and mineralogy traceability. Using laser ablation inductively coupled plasma mass spectrometry combined with chemometrics, the contents of 56 elements in turquoise samples from 5 distinct producing areas in 3 nations were measured. An origin identification model for turquoise samples from various producing areas was established through random forest importance analysis, principal component analysis, and linear discriminant analysis. When combined with random forest importance screening, the traceability efficiency of principal component analysis is significantly improved. Moreover, by taking 48 elements as characteristic variables and introducing them into the discriminant model, a Fisher discriminant model for identifying the origin of turquoise was successfully established. The effective element fingerprint information of turquoise species is closely related to the species origin, and the accuracy of cross-validation reaches 99.5%, demonstrating the feasibility of the proposed model for the identification of the origin of turquoise samples. Full article

In biometrics, it is desirable to distinguish a person using only a short sample of his handwriting. This problem is treated in the present work using only a short word with three letters. It is shown that short words can contribute to high-performance writer identification if line characteristics are extracted using morphological directional transformations. Thus, directional morphological structuring elements are used as a tool for extracting this kind of information with the morphological opening operation. The line characteristics are organized based on Markov chains so that the elements of the transition matrix are used as feature vectors for identification. The Markov chains describe the alternation in the directional line features along the word. The analysis of the feature space is carried out using the Fisher linear discriminant method. The identification performance is assessed using neural networks, where the simplest neural structures are sought. The capabilities of these simple neural structures are investigated theoretically concerning the achieved separability into the feature space. The identification capabilities of the neural networks are further assessed using the leave-one-out method. It is proved that the neural methods achieve identification performance that approaches 100%. The significance of the proposed method is that it is the only one in the literature that presents high identification performance using only one short word. Furthermore, the features used as well as the classifiers are simple and robust. The method is independent of the language used regardless of the direction of writing. The NIST database is used for extracting short-length words having only three letters each. Full article

Current microprocessor-based relay protection and automation (RPA) devices supported by IEC 61850 provide access to a large amount of information on the protected or controlled electric power facility in real time. The issue of using such information (Big Data) in order to improve the parameters of technical modification of intelligent electronic devices at digital substations remains unaddressed. Prerequisites arise for designing modern power systems with relay protection devices of a new generation based on new information algorithms. In particular, it is expedient to develop multi-parameter protections using more than one information parameter: modules of current, voltage, derivatives thereof, phase angles, active and reactive resistances, etc. An information approach based on multiple modeling and statistical processing of modeling results is also promising. This article explores the issues of enhanced sensitivity of multi-parameter relay protection using long-range redundancy protection as an example. Transition to “generalized features” is proposed in order to simplify multi-parameter protection and reduction in the computational load on the RPA device. Out of a large number of analyzed indicators (currents, voltages, their derivatives, resistances, increments of currents, angles between current and voltage, etc.), we specify the most informative by using the method of “data compression”. The transition to generalized features simplifies the parameterization of settings, and the process of making a decision by the relay protection device is reduced to obtaining a generalized feature and comparing it with a dimensionless setting in relative terms. For the formation of generalized information features, two mathematical methods are studied: the method of principal components and Fisher’s linear discriminant. Full article

Background: Persistent infection with human papillomavirus (HPV) can lead to cervical cancer (CxCa). During the progression to CxCa, the expression of HPV oncogenes E6 and E7 is upregulated. In turn, cellular proteins such as p16^INK4a are also modulated. The combined detection of HPV oncogenes and cellular biomarkers indicative for dysplasia could be informative and convey better specificity than the current HPV tests that cannot discriminate transient infection from dysplastic changes. Methods: The QuantiGene^TM 2.0 Plex Assay platform was chosen for the effective multiplexing and quantitative detection of seven HPV-E7 mRNA targets (HPV6, 16, 18, 31, 45, 59, and 68) and the cellular mRNA of p16^INK4a as a biomarker for HPV-induced transformation. Actin-beta (ACTB) and hypoxanthine-guanine phosphoribosyltransferase 1 (HPRT1) were included as reference markers. Sequences for the specific capture and detector probes were customized and developed by ThermoFisher and formulated as a QuantiGene proof-of-concept (QG-POC) plex-set. The crude lysates of the HPV-positive cervical cancer cell lines CaSki (HPV16), HeLa (HPV18), MRHI-215 (HPV45), Erin59 (HPV59), ME180 (HPV68), and the HPV-negative cell line C33A, as well as liquid-based cytology smear samples (n = 441) were analyzed. The study was a proof-of-concept evaluating the feasibility of the platform. Logistic regression and receiver operating characteristic (ROC) analyses were performed to test for the sensitivity and specificity of HPV detection and dysplastic stage discrimination. Results: A QG-POC assay specifically and sensitively detects the HPV-E7 mRNA of seven different genotypes with an assay linearity between 20 and 13,000 cells. Cellular mRNA was detected from the crude lysates of cell lines and of cellular material from clinical liquid-based cytology smear samples. By combining HPV-E7 and p16^INK4a expression normalized to ACTB, high-grade dysplasia (HCIN) and invasive cervical cancer (CxCa) were detectable, discriminable, and correlated to the biomarker expression strength. The ROC analysis from the multivariate logistic regression model including HPV-E7 and p16 ^INK4a resulted in an AUC of 0.74, at the optimal cut-off (sensitivity: 70.4%; specificity: 66.0%) for HCIN detection. CxCa was detected with an AUC of 0.77 (sensitivity: 81.8%, specificity: 77.4%). Conclusions: The QG-POC assay is sufficiently sensitive to detect and quantify HPV-E7 and cellular mRNA species. Multiplexing allows the specific detection of at least 10 analytes in a single reaction. Determining the abundance of E7 and p16^INK4a transcripts when normalized to ACTB is informative about the presence of cervical dysplasia and potentially discriminates between low-grade and high-grade dysplasia and invasive cervical cancer. Further studies including more HPV genotypes and biomarkers are warranted. Full article