On the fourth-order hybrid beta polynomial kernels in kernel density estimation

A note on the behaviour of a kernel-smoothed kernel density estimator

Final Comments by the Authors

Recently Hazelton and Turlach (2009) proposed a weighted kernel density estimatorfor the deconvolution problem. In the case of Gaussian kernels and measurement er-ror, they argued that the weighted kernel density estimator is a competitive estimatorover the classical deconvolution kernel estimator. In this paper we consider weightedkernel density estimators when sample observations are contaminated by double expo-nentially distributed errors. The performance of the weighted kernel density estimatorsis compared over the classical deconvolution kernel estimator and the kernel density es-timator based on the support vector regression method by means of a simulation study.The weighted density estimator with the Gaussian kernel shows numerical instabilityin practical implementation of optimization function. However the weighted densityestimates with the double exponential kernel has very similar patterns to the classicalkernel density estimates in the simulations, but the shape is less satisfactory than theclassical kernel density estimator with the Gaussian kernel.Keywords: Deconvolution, kernel density estimator, support vector regression, weightedkernel density estimator.

A family of kernels (with the sinc kernel as the simplest member) is introduced for which the associated deconvolving kernels (assuming normally distributed measurement errors) can be represented by relatively simple analytic functions. For this family, deconvolving kernel density estimation is not more sophisticated than ordinary kernel density estimation. Application examples suggest that it may be advantageous to overestimate the measurement error, because the resulting deconvolving kernels can partially compensate for the blurring inherent to the density estimation itself. A corollary of this proposition is that, even without error, it may be rational to use deconvolving rather than ordinary kernels.

The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these signals remains a challenge due to their inherent complexity and non-linearity. This study introduces a novel method, PCA-ANFIS, which integrates Principal Component Analysis (PCA) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to enhance cognitive pattern recognition in multimodal brain signal analysis. PCA reduces the dimensionality of EEG data while retaining salient features, enabling computational efficiency. ANFIS combines the adaptability of neural networks with the interpretability of fuzzy logic, making it well-suited to model the non-linear relationships within brain signals. Performance metrics of our proposed method, such as accuracy, sensitivity, and computational efficiency. These additions highlight the effectiveness of the method and provide a concise summary of the findings. The proposed method achieves superior classification performance, with an unprecedented accuracy of 99.5%, significantly outperforming existing approaches. Comprehensive experiments were conducted using a diverse multimodal EEG dataset, demonstrating the method’s robustness and sensitivity. The integration of PCA and ANFIS addresses key challenges in multimodal brain signal analysis, such as EEG artifact contamination and non-stationarity, ensuring reliable feature extraction and classification. This research has significant implications for both cognitive neuroscience and clinical practice. By advancing the understanding of cognitive processes, the PCA-ANFIS method facilitates accurate diagnosis and treatment of cognitive disorders and neurological conditions. Future work will focus on testing the approach with larger and more diverse datasets and exploring its applicability in domains such as neurofeedback, neuromarketing, and brain-computer interfaces. This study establishes PCA-ANFIS as a capable tool for the precise and efficient classification of cognitive patterns in brain signal processing.

In health research discourse, there is the assumption that a controlled, planned clinical research naturally produces cleaner datasets, while real-world data suffers from quality deficiencies due to its spontaneous collection without anticipation of secondary research use. However, this binary view simplifies a complex reality. Both clinical research and real-world data exhibit quality issues, challenging the convention that data origin determines data integrity. What proves to be more vital is the way data quality (DQ) is being assessed—particularly when examining the exhibited relationships between data items that reflect the true complexity of healthcare information. Health data is inherently interconnected e.g., height relates to weight and age; medication dosages connect to patient characteristics and medical conditions. Yet most data quality assessment (DQA) studies limit their logical evaluations to only a handful of interdependent items, missing the broader web of relationships that could reveal critical quality issues. Contradictions within datasets serve as an important DQ indicator (measurable attribute of data) that extend far beyond simple data entry errors. When values contradict each other, they reveal misunderstandings of domain knowledge, failures in data capture infrastructure, and problems in data transformation processes. Given the complexity of the contradiction patterns, a comprehensive assessment becomes essential. Comprehensive DQA in health research requires evaluating data on multiple dimensions, such as completeness, conformance, and plausibility, to determine the suitability of data for intended use. These dimensions are used to group related measurable attributes of data referred to as indicators. While some DQ indicators are implemented as generic rules (e.g., completeness), contradiction, which denotes pair(s) of measurements or facts that cannot logically coexist, are multi-dimensional. Some contradictions require common knowledge for their evaluation—e.g., the disparity in age information at baseline and during follow-up visits. However, contradictions in the pre-analytic states of blood samples require knowledge of experts. Although various tools have been developed to assess contradictions in health data, the evolving nature of contradiction rules presents the following challenges for existing tools: 1) limited reusability across diverse health datasets due to incompatible predefined rules, 2) insufficient contextual information required for comprehensive contradiction assessment, and 3) lack of consistent representation of contradiction patterns to support transparent rule implementations and computational efficiency. To address these gaps in a systematic manner, the first step of this thesis was to adapt an existing tool developed based on an existing framework to assess broader contradiction rules introduced in the German Corona Consensus (GECCO) dataset. Building on the experience from the modification of Schmidt et al.'s tool, this thesis developed a new framework that generalizes the DQA tool to accept custom contradiction rules compatible with diverse health datasets. This is intended to empower domain experts in defining effective contradiction rules on an ad-hoc basis while maintaining alignment with established DQ indicators. Although the primary dependencies between health data items form the basis for contradiction rule definition, the required context for a comprehensive analysis is equally relevant. For example, diabetes mellitus (DM) and insulin medication (INS) are two interdependent items, however, it is the timestamps that indicate if the INS was administered before the DM diagnosis, which is a prelude for a conclusive contradiction finding. Through a qualitative grading scheme for analyzing metadata defined in a study database, this work created a mechanism to measure the availability of the contextual information required to support a context-aware contradiction analysis. However, having access to rich contextual information is only part of the solution—the way contradiction rules are structured and implemented is equally critical for effective DQA. While researchers have worked extensively on harmonizing DQ indicators, the challenge extends beyond categorization to implementation efficiency. Though the taxonomies for contradictions, such as logical and empirical contradictions, reflect the domain description, the rules implemented in the DQA systems do not rely on semantics. Extracting the structure of the varied contradiction patterns ensures transparency and consistency in rule implementation. This work evaluated the performance of different rule implementations and offered an optimization method that integrates the fastest and traceable units of the rules in one unified implementation. This is relevant because broadly defined DQA rules can cause performance degradation in large database infrastructures. With these several perspectives on contradictions as a DQ indicator, the contributions in this thesis enhance contradiction assessment by improving reusability across diverse health datasets, enabling context-aware assessments, and optimizing rule implementation for transparency and computational efficiency. This systematic approach supports high-quality health data assessment while maintaining traceability to support data cleansing efforts.

Efficiency Metrics and Numerical Simulation Procedure for Atmosphere-Breathing Electric Propulsion (ABEP) Intake Designs

ABSTRACTThis research was carried out to analyse the efficiency on University International Research Collaboration (IRC) and examines disciplines that are popular areas for collaboration. Based on data for the years 2015–2021, an empirical study was conducted to determine how to implement Data Envelopment Analysis (DEA) in 56 research partner countries in Indonesia among selected Universities. A total of two inputs and four different outputs were defined for this assessment. Only three international partners have an efficiency score of 1, except for the fact that only one IRC has a lower efficiency score than Decision Making Units (DMU), that do not have an IRC. The Social sciences, health and medicine, and food and agriculture receive the majority of government funding for the IRCs. The originality of this study lies in from the quantitative methology typically used in efficiency analysis, but its use to evaluate research collaboration, particularly on global levels, adds a unique dimension to the study. In addition, the geographical focus on higher education in Indonesia provides a fresh perspective, as does the thematic investigation related to the identification of popular collaborative disciplines. The importance of efficiency metrics in assessing the effectiveness of research collaborations is emphasised in this discussion. There is need for closer collaboration with academics from these partner countries as evidenced by the limited presence of Indonesian academics in a number of sectors. In addition, the study offers recommendations for maximising resources and promoting more productive methods of collaboration in order to increase the output and significance of research.

This article proposes a Data-based condition monitoring technique (CM) to observe the operational status of a Photovoltaic (PV) system. Without any need for more sensors than traditional measurements installed in the DC Combiner Box which are one Voltage sensor for the array and one Current sensor for each string, Kernel-based Gaussian process regression (GPR) is capable of nonlinear approximation, So it is utilized to empirically model the DC power of PV generator and compute residuals which are used for detecting faults. The residuals go via an exponentially weighted moving averages filter. Kernel density estimation (KDE) is employed in this study to detect faults by examining the univariate residuals. The four failure situations that have received the most attention in this study are the PV string open circuit fault, partial shading, shorted PV modules, and module deterioration or degradation. Using information from a 5, 310Wp gridconnected solar power system simulated on Matlab Simulink. Through this study, six kernel functions were tested for the GPR model where two of them were customized, These functions are Quadratic, Matern, Exponential, Squared Exponential, Dot product Plus White kernel, and Matern kernel Plus quadratic kernel, by performing advanced preprocessing, and comparing results and performance metrics we found that MATERN and Customized Matern Kernelbased GPR models perform better than other kernel-based GPR to monitor PV systems.

Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities alongside normal signals. This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on diverse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting CVD. The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analysis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection.

8577 Background: Accurate and timely assessment of immunotherapy response is vital for optimizing lung cancer management. This study evaluates the efficacy of a large language model (LLM), Gemini 1.5 Pro, in automating response assessment using positron emission tomography/computed tomography (PET/CT) reports based on the European Organization for Research and Treatment of Cancer (EORTC) criteria. Methods: Google Gemini 1.5 Pro was selected due to its large context window capacity and its free availability via the web interface.The model was utilized with explicit instructions on applying EORTC criteria and fine-tuned using few-shot prompting. Pre- and post-immunotherapy PET-CT reports in text format from 33 lung cancer patients, anonymized in compliance with HIPAA regulations, were independently classified by the LLM and an experienced nuclear medicine specialist. Performance metrics, including precision, recall, F1-score, and support, were calculated for each response category. Inter-rater agreement was assessed using Cohen's Kappa. Results: The nuclear medicine specialist classified 5, 21, 6, and 1 cases as complete metabolic response (CMR), progressive metabolic disease (PMD), partial metabolic response (PMR), and stable metabolic disease (SMD), respectively, while Gemini 1.5 Pro classified 5, 20, 7, and 1 cases accordingly. The LLM achieved an overall accuracy of 97% and demonstrated excellent agreement with the expert (overall Cohen's Kappa: 0.945). F1-scores were 1.00 for CMR and SMD, 0.98 for PMD, and 0.92 for PMR, with per-label Kappa scores ranging from 0.904 (PMR) to 1.00 (CMR and SMD) (Table 1). Conclusions: Gemini 1.5 Pro exhibits strong potential for automating accurate immunotherapy response assessment in lung cancer using PET-CT reports. Its high concordance with expert evaluations underscores its utility in streamlining clinical workflows and improving efficiency. Validation with larger, more diverse datasets is warranted to support its clinical implementation. Performance metrics of Gemini 1.5 Pro for immunotherapy response assessment. Response Precision Recall F1-score Support Cohen’s Kappa CMR 1.00 1.00 1.00 5 1.000 PMD 1.00 0.95 0.98 21 0.936 PMR 0.86 1.00 0.92 6 0.904 SMD 1.00 1.00 1.00 1 1.000 CMR, Complete Metabolic Response; PMD, Progressive Metabolic Disease; PMR, Partial Metabolic Response; SMD, Stable Metabolic Disease.

We propose a semantic kernel for Support Vector Machines (SVM) that takes advantage of higher-order relations between the words and between the documents. Conventional approach in text categorization systems is to represent documents as a “Bag of Words” (BOW) in which the relations between the words and their positions are lost. Additionally, traditional machine learning algorithms assume that instances, in our case documents, are independent and identically distributed. This approach simplifies the underlying models, but nevertheless it ignores the semantic connections between words as well as the semantic relations between documents that stem from the words. In this study, we improve the semantic knowledge capture capability of a previous work in [1], which is called χ-Sim Algorithm and use this method in the SVM as a semantic kernel. The proposed approach is evaluated on different benchmark textual datasets. Experiment results show that classification performance improves over the well-known traditional kernels used in the SVM such as the linear kernel (one of the state-of-the-art algorithms for text classification system), the polynomial kernel and the Radial Basis Function (RBF) kernel.

This paper compares the well-known locally optimum (LO) performance metrics of efficacy (/spl xi/) and asymptotic relative efficiency (ARE) for two types of robust LO detectors. In particular, closed-form expressions for these metrics are derived for the histogram and Fourier series approximation (FSA) implementation algorithms. Additionally, they are evaluated for the case of Cauchy noise and compared to results given in the literature. Thus, simulation analysis is no longer the only method for determining robust LO detector performance.

We discuss designs and interpretable metrics of bias and statistical efficiency of "externally controlled" trials (ECT) and compare ECT performance to randomized and single-arm designs. We specify an ECT design that leverages information from real-world data (RWD) and prior clinical trials to reduce bias associated with interstudy variations of the enrolled populations. We then used a collection of clinical studies in glioblastoma (GBM) and RWD from patients treated with the current standard of care to evaluate ECTs. Validation is based on a "leave one out" scheme, with iterative selection of a single-arm from one of the studies, for which we estimate treatment effects using the remaining studies as external control. This produces interpretable and robust estimates on ECT bias and type I errors. We developed a model-free approach to evaluate ECTs based on collections of clinical trials and RWD. For GBM, we verified that inflated false positive error rates of standard single-arm trials can be considerably reduced (up to 30%) by using external control data. The use of ECT designs in GBM, with adjustments for the clinical profiles of the enrolled patients, should be preferred to single-arm studies with fixed efficacy thresholds extracted from published results on the current standard of care.

<div>AbstractPurpose:<p>We discuss designs and interpretable metrics of bias and statistical efficiency of “externally controlled” trials (ECT) and compare ECT performance to randomized and single-arm designs.</p>Experimental Design:<p>We specify an ECT design that leverages information from real-world data (RWD) and prior clinical trials to reduce bias associated with interstudy variations of the enrolled populations. We then used a collection of clinical studies in glioblastoma (GBM) and RWD from patients treated with the current standard of care to evaluate ECTs. Validation is based on a “leave one out” scheme, with iterative selection of a single-arm from one of the studies, for which we estimate treatment effects using the remaining studies as external control. This produces interpretable and robust estimates on ECT bias and type I errors.</p>Results:<p>We developed a model-free approach to evaluate ECTs based on collections of clinical trials and RWD. For GBM, we verified that inflated false positive error rates of standard single-arm trials can be considerably reduced (up to 30%) by using external control data.</p>Conclusions:<p>The use of ECT designs in GBM, with adjustments for the clinical profiles of the enrolled patients, should be preferred to single-arm studies with fixed efficacy thresholds extracted from published results on the current standard of care.</p></div>