Statistical Characteristics Research Articles

Anomaly detection in bridge structural health monitoring via 1D-LBP and statistical feature fusion

Over the past few years, numerous bridges have been equipped with structural health monitoring (SHM) systems to continuously monitor critical structural parameters, enabling early detection of potential issues and timely maintenance. However, the monitoring data frequently contain anomalies due to various interferences during the acquisition and transmission processes. To address this, this paper proposes a robust and efficient anomaly detection and classification method. The method extracts one-dimensional local binary pattern (1D-LBP) features and time-domain statistical features from the monitoring data, fusing them into a comprehensive feature representation. These fused features are then input into an extreme gradient boosting (XG-Boost) classifier for anomaly detection. Additionally, a 1D-LBP feature simplification method is introduced to enhance detection efficiency. The effectiveness of the proposed method was validated using monitoring data from a long-span cable-stayed bridge SHM system. Experimental results demonstrate the excellent performance of the method in terms of detection accuracy and efficiency.

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot. In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other datasets. In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated. 92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy. The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement over TCIA and BRaTS datasets respectively.

Classification of cyclic alternating patterns of sleep using EEG signals

Cyclic alternating patterns (CAP) occur in electroencephalogram (EEG) signals during non-rapid eye movement sleep. The analysis of CAP can offer insights into various sleep disorders. The first step is the identification of phases A and B for the CAP cycles. In this work, we develop an easy-to-implement accurate system to differentiate between CAP A and CAP B. Small segments of the EEG signal are processed using Gaussian filters to obtain sub-band components. Features are extracted using some statistical characteristics of these signal components. Minimum redundancy maximum relevance test is employed to identify the more significant features. Three different machine learning classifiers are considered and their performance is compared. The results are analyzed for both the balanced and unbalanced datasets. The k-nearest neighbour (kNN) classifier achieves 79.14% accuracy and F-1 score of 79.24% for the balanced dataset. The proposed method outperforms the existing methods for CAP classification. It is easy-to-implement and can be considered as a candidate for real-time deployment.

Probability density function based adaptive ensemble learning with global convergence for wind power prediction

Accurate wind power prediction is highly significant to the safety, stability, and economic operation of power grids. Currently, typical ensemble methods for wind power forecasting are widely designed based on the mean square error (MSE) loss, which are very suitable for the assumption that the error distribution obeys the Gaussian distribution. However, the complex nonlinear conversion of wind energy into wind power may change the statistical characteristics of errors, thus the prediction model based on the traditional MSE loss may lead to the performance degradation of the forecasting model. To address these problems, a probability density function based adaptive ensemble learning with global convergence is proposed for wind power prediction, which comprises three modules: a data preprocessing module, a prediction module, and a combination module. Firstly, an effective feature generation mechanism is employed to extract the multi-mode characteristics of wind data. Then, an auxiliary error based adaptive global convergence model is developed as benchmark predictor in the prediction module, where an adaptive updating algorithm is derived based on the Lyapunov approach to ensure the global convergence of the model weights. Moreover, considering the asymmetric characteristic of modeling error, a probability density function (PDF) based ensemble learning is created to integrate the results of benchmark predictors. Specifically, the ensemble model parameter updating is transformed into the shape control for the modeling error PDF, which can break through the limitation of MSE loss capturing only the second moment information, and emphasize the spatial distribution of errors to make an unbiased estimate of wind power. Experimental results show that the proposed ensemble model has significant advantages over other models involved in this study.

Objective classification for solid hydrometeor particles using deep learning

AbstractVarious small particles are present in the clouds. Hydrometeor videosonde (HYVIS) is an in situ instrument to observe such particles. The movies were used to capture hydrometeors with approximately 110,000 frames per sounding. When the particles in such movies were manually classified and measured sizes of particles for every several frames, it took an unrealistically long time to perform statistical analysis because of the large number of observed frames. Particle classification is subjective for the observers. This study developed a technique to classify cloud particles objectively using deep learning to overcome these problems and investigated the statistical microphysical characteristics of clouds. This study used the deep learning method You Only Looking Once for detection and classification. The training data were obtained using HYVIS in the Republic of Palau in 2013. The results trained using only HYVIS images showed low validity because some types of particles in the training data were insufficient. The data for typical particle shapes were augmented to improve the classification. Thus, the validity of classification using augmented data was improved. We used the Yonaguni Island HYVIS observation results from manual and artificial intelligence (AI) classification. The AI tended to classify the less irregular type than the manual, but no other significant differences were found. We believe this AI classification system will be for cloud microphysical studies on solid-phase particles.

Comparison of the performance of classical and quantum machine‐learning methods on the detection of sugar beet Cercospora leaf disease

AbstractImage processing and machine‐learning (ML) techniques are essential for the detection of diseases and pests in plants. This study explored the application of quantum ML (QML) algorithms for the early detection of Cercospora beticola leaf disease in sugar beet, which causes significant impact on global sugar production. Using a dataset of 1065 images (739 diseased and 326 healthy), we extracted 70 ML statistical features, including 10 from the grey‐level co‐occurrence matrix (GLCM) and 60 colour‐related features. Performance evaluations of classical ML algorithms, such as random forest (RF; 91.95% accuracy) and extreme gradient boosting (91.95% accuracy), demonstrated strong results compared to quantum approaches. Notably, the quantum support vector classifier (QSVC) achieved an accuracy of 85% with perfect recall of 1.00, while the variational quantum classifier (VQC) recorded an accuracy of 88.73%. Dimensionality reduction via principal component analysis reduced features from 70 to 5, enabling effective classification with competitive results: ML (RF) 91.41%, VQC with limited‐memory Broyden–Fletcher–Goldfarb–Shanno with box constraints (L_BFGS_B) 88.73% and QSVC 85%. These findings highlight the potential of QML algorithms in improving agricultural disease identification and aiding in the advancement of more efficient, sustainable farming techniques.

Statistical operating characteristics of current early phase dose finding designs with toxicity and efficacy in oncology.

Traditional phase I dose finding cancer clinical trial designs aim to determine the maximum tolerated dose (MTD) of the investigational cytotoxic agent based on a single toxicity outcome, assuming a monotone dose-response relationship. However, this assumption might not always hold for newly emerging therapies such as immuno-oncology therapies and molecularly targeted therapies, making conventional dose finding trial designs based on toxicity no longer appropriate. To tackle this issue, numerous early-phase dose finding clinical trial designs have been developed to identify the optimal biological dose (OBD), which takes both toxicity and efficacy outcomes into account. In this article, we review the current model-assisted dose finding designs, BOIN-ET, BOIN12, UBI, TEPI-2, PRINTE, STEIN, and uTPI to identify the OBD and compare their operating characteristics. Extensive simulation studies and a case study using a CAR T-cell therapy phase I trial have been conducted to compare the performance of the aforementioned designs under different possible dose-response relationship scenarios. The simulation results demonstrate that the performance of different designs varies depending on the particular dose-response relationship and the specific metric considered. Based on our simulation results and practical considerations, STEIN, PRINTE, and BOIN12 outperform the other designs from different perspectives.

Predicting Chronic Liver Disease and Jaundice Detection an Integrated Approach Using Statistical Feature Extraction and Machine Learning Algorithms

An important health concern with chronic liver disease is early identification, which is essential for management and therapy. This work suggests an integrated method to identify tattoo-induced jaundice and forecast chronic liver disease by combining statistical feature extraction with machine learning techniques. First, we use statistical techniques based on projections to extract pertinent aspects from patient data, such as test results and clinical markers. We then use Artificial Neural Networks (ANN), KNN, and Naive Bayes to categorize individuals according to the probability that they have chronic liver illness and to pinpoint jaundice episodes associated with tattooing. The KNN technique offers interpretability, managing categorical data is made simple by Naive Bayes, and complicated patterns are captured by ANN using layers of neural networks. These models' performance is assessed using the following metrics: F1 score, recall, accuracy, and precision. Our research aims to improve prediction accuracy and offer practical advice for early diagnosis and individualized treatment plans. By utilizing advanced data analysis and machine learning instruments, the integrated strategy shows promise in enhancing healthcare outcomes.

Rotor angle stability of a microgrid generator through polynomial approximation based on RFID data collection and deep learning.

The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable.

Image-processing-based model for surface roughness evaluation in titanium based alloys using dual tree complex wavelet transform and radial basis function neural networks.

In this study, we examine the assessment of surface roughness on turned surfaces of Ti 6Al 4V using a computer vision system. We utilize the Dual-Tree Complex Wavelet Transform (DTCWT) to break down the images of the turned surface into sub-images oriented in directions. Three different methods of feature generation have been compared, i.e., the use of Gray-Level Co-Occurrence Matrix (GLCM) and DTCWT-based extraction of second-order statistical features, DTCWT Image fusion, and the use of GLCM for feature extraction, and DTCWT image fusion using Particle Swarm Optimization (PSO) based GLCM features. Principal Component Analysis (PCA) was utilized to identify and select features. The model was developed using a Radial Basis Function Neural Network (RBFNN). Accordingly, six models were designed based on the three feature generation methods, considering all features and features selected using PCA. The RBFNN model, which incorporates DTCWT Image fusion and utilizes PSO with PCA features, achieved a training data prediction accuracy of 100% and a test data prediction accuracy of 99.13%.

Machine Learning-Driven GLCM Analysis of Structural MRI for Alzheimer’s Disease Diagnosis

Background: Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative condition that increasingly impairs cognitive functions and daily activities. Given the incurable nature of AD and its profound impact on the elderly, early diagnosis (at the mild cognitive impairment (MCI) stage) and intervention are crucial, focusing on delaying disease progression and improving patients’ quality of life. Methods: This work aimed to develop an automatic sMRI-based method to detect AD in three different stages, namely healthy controls (CN), mild cognitive impairment (MCI), and AD itself. For such a purpose, brain sMRI images from the ADNI database were pre-processed, and a set of 22 texture statistical features from the sMRI gray-level co-occurrence matrix (GLCM) were extracted from various slices within different anatomical planes. Different combinations of features and planes were used to feed classical machine learning (cML) algorithms to analyze their discrimination power between the groups. Results: The cML algorithms achieved the following classification accuracy: 85.2% for AD vs. CN, 98.5% for AD vs. MCI, 95.1% for CN vs. MCI, and 87.1% for all vs. all. Conclusions: For the pair AD vs. MCI, the proposed model outperformed state-of-the-art imaging source studies by 0.1% and non-imaging source studies by 4.6%. These results are particularly significant in the field of AD classification, opening the door to more efficient early diagnosis in real-world settings since MCI is considered a precursor to AD.

Adadelta-CSA: Adadelta-Chameleon Swarm Algorithm for EEG-Based Epileptic Seizure Detection

Epilepsy is referred to as a neurological disorder, which is detected via examination and manual comprehension of Electroencephalogram (EEG) signals. In deep learning schemes, various enhancements have emerged to efficiently address complex issues by end-to-end learning. The major objective of this research is to propose a new seizure detection approach from EEG signals using a deep learning-based classification technique. The pre-processing is the initial stage, where denoising is performed using a Short-Time Fourier Transform (STFT). Subsequently, the statistical features, time-domain features and spectral features are extracted from the pre-processed signal. Finally, an efficient optimization approach, named Adadelta-Chameleon Swarm Algorithm (Adadelta-CSA), is proposed and employed to train Deep Neural Network (DNN) to carry out the precise seizure prediction. Here, the integration of the Adadelta concept in the Chameleon Swarm Algorithm (CSA) has resulted in Adadelta-CSA. At last, the performance of the Adadelta-CSA scheme-based DNN is compared with the existing techniques by considering accuracy, sensitivity and specificity, and it is found to produce better values of 0.951, 0.966, and 0.935, respectively.

Stacked ensemble machine learning approach for electroencephalography based major depressive disorder classification using temporal statistics

Major depressive disorder (MDD) is a serious and widespread mental health condition that remains challenging to diagnose accurately. Traditional psychological assessments, which can be subjective and sometimes unreliable, emphasize the need for more objective diagnostic tools. In this study, we present a machine learning (ML) model designed to diagnose depression by analysing statistical time-domain features extracted from Electroencephalography (EEG) data. The model is built using a stacked ensemble ML approach, incorporating nine-base estimators with various meta-classifiers. Through multiple trials, the model achieved an accuracy of 98.01%, with precision and recall rates of 97.78% and 96.61%, respectively with Adaptive Boosting (AdaBoost) as the meta-classifer. We also investigated the effects of data sampling and the number of base classifiers on the model’s performance. The findings demonstrate that the stacked ensemble approach significantly enhances the accuracy of diagnosing MDD and that the proposed model outperforms the methods used in previous studies. This model offers a promising tool for psychologists and medical professionals to diagnose depression more reliably, potentially leading to better treatment outcomes for those affected by the disorder.

Precision Detection of Infrared Small Target in Ground-to-Air Scene

Reliable infrared small target detection plays an important role in infrared search and track systems. In recent years, most target detection methods usually use the statistical features of a rectangular window to represent the contrast between the target and the background. When the size of the target is small or the target is close to the background, the statistical features of the rectangular window would reduce the significance of the target. Moreover, such methods have limited effect on interfering targets, high brightness background, background edges, and clutter suppression in complex backgrounds, and are likely to misdetect the target or even miss it. This paper proposes a non-window, structured algorithm for precision detection of infrared small targets under ground-to-air complex scenes. The non-window, structured local grayscale descent intensity and local gradient watershed (LGDI-LGW) filter can detect a 1 × 1 pixel infrared small target, and effectively suppress interfering targets and background edges. By using the adaptive threshold and centroid algorithm on the target area, the precision of target coordinates reaches sub-pixel accuracy. The results of 9 simulation experiments show that the algorithm has the lowest false alarm rate and the highest detection rate compared with the eight baseline algorithms. It can effectively detect targets with Gaussian distribution of grayscale values and targets with grayscale values approximating tree stump structure. The results of 2 engineering experiments show that under simulated near-sun conditions, a uniform target is precisely detected, and the UAV point target is precisely detected in complex ground-to-air scenes.

Design of Travel Route Planning Model Based on Deep Learning

Tourism route planning is affected by the factors such as tourism destination and tourists’ preference, which leads to poor automatic matching of routes. A mathematical model of tourism route planning based on deep learning is proposed. Under the condition of total route constraints, according to the historical tourism preference information and prior information of tourists, a big data model for the distribution of personalized characteristics of tourists is established, spatial constraints and time constraints parameters are input, and the feature matching of geographic location information and tourist interest parameter information is carried out by using the deep learning method, and the statistical feature quantity of the parameters of the tourism route planning model is extracted. Under the control of the deep learning and geographic information data set, Carry out multi-constraint and multi-objective hierarchical analysis of tourists and tourism destinations, and realize the optimization design of tourism route planning algorithm. The simulation results show that the accuracy of this method is high and the deviation is small. It improves the satisfaction level of tourists and helps users complete a series of visual analysis tasks such as route mining, route planning and destination analysis.

V. S. Kozlov and his Tsifrar-diagrammometer

In the late 19th century a military statistician and a pioneers of the Russian cooperative movement, Viktor Semyonovich Kozlov (1842–1920), designed and built a unique mechanical analog computing device he named “tsifrar-diagrammometer”. It was intended for operational visual presentation of numerical results of observations, calculations, experiments, etc. (tsifrar) and for calculating their statistical characteristics (diagrammometer). At the 1889 Paris Exposition this device that represented Russia was awarded the bronze medal. Drawing on the archival records and the materials from the Russian and European periodicals, the history of the inventor’s work on this device is reconstructed for the first time and some facts from his biography are described. The authors also attempt to describe the design of the device and analyze the mechanism of its functioning. Since no sources contain a detailed description of the design with the diagrams, charts, etc. or the mechanism of its functioning while available information is often contradictory, the authors present their own version of the device’s description and operation. The tsifrar-diagrammometer aroused much interest among the most prominent Russian and French scientists of the time, including P. L. Chebyshev, D. I. Mendeleev, E. Lucas, and many others. They viewed Kozlov’s device as a forerunner of a future universal instrument for numerical data analysis and for discovering new laws of nature. The appraisals of the device by the contemporaries are also given and analyzed in the article.

Robust double encryption and watermarking algorithms for color watermark images

Abstract Current digital watermarking technologies mainly focus on the imperceptibility and robustness of watermark embedding, while the security of watermarking images is also worth further research. Considering nonlinear characteristics and the integration structure of storage and computation, memristors can be introduced into encryption algorithms to improve the effect of encryption. The paper proposes a double encryption algorithm for color watermark images based on MCNN (Memristive Cellular Neural Networks) and Arnold transform, generates chaotic sequences for watermark image encryption by introducing memristors to the CNN (Cellular Neural Networks) to construct MCNN, scrambles the images using the Arnold transform to achieve the double encryption of pixel values and pixel positions, and enhances the security of the watermark images. Adopting the SE (Spectral Entropy) complexity algorithm optimizes the parameters of MCNN, and improves the performance of the double encryption algorithm. The embedding and extraction of the encrypted watermark image is realized by the algorithm combining CT (Contourlet Transform) and SVD (Singular Value Decomposition), which enhances the ability to resist common attacks such as compression and rotation attacks. Experiment results show the proposed algorithms can better maintain the quality of the color watermark images, break the statistical characteristics of the original images, and the generated key has good randomness. In addition, the presented algorithms are highly sensitive to the key, and improve the ability to resist statistical attacks, differential attacks, exhaustive attacks and common image attacks with good security, robustness and imperceptibility.

NIMG-75. SIMILARITY DISTANCES-BASED DELTA RADIOMICS (SDDR) ENABLES LONGITUDINAL MRI COMPARISONS FOR SURVIVAL PREDICTION IN GLIOMAS: A MULTI-INSTITUTIONAL VALIDATION

Abstract Radiomics (computerized feature analysis) on treatment-naive MRI scans has demonstrated great value in outcome prediction for Glioblastoma (GBM). However, delta radiomics (analysis of radiomic feature variation between different events, e.g., before and after treatment) has not been explored on account of challenges with precise spatial correspondences between pre- and post-operative scans. This work presents a survival prediction model for GBM, Similarity Distances-based delta radiomics (SDDR), based on the hypothesis that similarity distance-measures between radiomic features on pre-, and post-treatment images can accurately capture temporal changes within-and-around the tumor, and provide improved prediction of overall survival, compared to models based on a single timepoint. 150 patients from 2 publicly-available Glioma repositories (University of California San Francisco “UCSF” (n=48), LUMIERE (n=67)) and 2 two proprietary datasets (from xCures (n=13), and Cleveland Clinic (CCF) (n=74)) were obtained. UCSF and LUMIERE cohorts were used as separate hold-out sets, in each of which the remaining collections were used as discovery set. Following pre-processing, tumor segmentation into Edema (ED) and Enhancing tumor (ET) were performed. SDDR was then computed, which is the distance between probability densities functions from 156 gray co-occurrence (GLCM) and 52 gradient-based co-occurrences statistics (COLLAGE) features extracted from pre and-post-treatment MRI. Six statistical distances (e.g., Earth mover distance) normalized by the scanning interval (in weeks) yielded n=2496 SDDR measurements per tumor region (1248/region, 936 delta-GLCM, and 312 delta-COLLAGE). The same set of features were computed from pretreatment MRI (Pre-R) for comparison. Features were fed to LASSO-Cox regression for survival analysis. SDDR for GLCM features extracted from ET outperformed Pre-R, and demonstrated significant differences between high and low risk groups for both hold out tests: UCSF (p=0.0069, HR =2.8, C-index=0.69) and LUMIERE (p=0.0061, HR = 1.9, C-index 0.65). SDDR-based multi-institutional survival analysis demonstrated promise in reliable longitudinal risk-stratification in GBM.

Impact of Harmonization on MRI Radiomics Feature Variability Across Preprocessing Methods for Parkinson's Disease Motor Subtype Classification.

This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple preprocessing methods for classifying Parkinson's disease (PD) motor subtypes and to evaluate the impact of ComBat harmonization on feature stability and machine learning performance. T1-weighted MRI scans from 140 PD patients (70 tremor-dominant and 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's Progression Markers Initiative (PPMI) database, acquired using different scanner models. Radiomic features were extracted from 16 brain regions using various preprocessing pipelines. ComBat harmonization was applied using a combined batch variable incorporating both scanner models and preprocessing methods. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Feature selection was performed using Linear Support Vector Classifier with L1 regularization. Support vector machine classifiers were used for PD subtype classification. ComBat harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC ≥ 0.90) increased from 40.2 to 56.3% after harmonization. First-order statistic features showed the highest robustness, with 71.11% demonstrating excellent ICC after harmonization. The proportion of features significantly affected by preprocessing methods was reduced following harmonization. Classification accuracy improved dramatically, from a range of 34-75% before harmonization to 89-96% after harmonization across all preprocessing methods. AUC values similarly increased from 0.28-0.87 to 0.95-0.99 after harmonization. ComBat harmonization significantly enhanced the reproducibility of radiomic features across preprocessing methods and improved PD motor subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD and suggests potential clinical applications in personalized treatment planning.

Detection of Major Depressive Disorder Using Genetic Algorithm for Features from EEG Signals

Introduction: Depression is a widespread mental disorder that significantly impacts individuals' well-being and quality of life. In the previous few years, researchers have looked into the potential of electroencephalogram signals in detecting and diagnosing depression. The present study investigates an approach for depression detection using a genetic algorithm to optimize the selection of discrete wavelet transform features from EEG signals. The proposed method involves decomposing EEG data into seven sub-bands using the DWT, extracting relevant statistical, geometric, and physiological features, and then employing a genetic algorithm to identify the most informative features for depression recognition. The SVM classifier achieved the highest overall accuracy of 91.73%, a sensitivity of 91.01%, a recall of 92.18%, and an F1-score of 91.45%, outperforming the other models. Experimental results indicate that the proposed approach outperforms hand-engineered methods and highlighting its potential as a complementary tool for depression diagnosis.