Enhanced situation space mining for data streams

Early-life experiences and neuromodulatory mechanisms shape discontinuous behavioral plasticity across developmental stages and modify the spectrum of long-term individuality patterns within isogenic populations.

Several theories are proposed for unsupervised learning in one layer nonlinear network. It has been shown that all the learning rules developed under the theories merge at performing principal component analysis (PCA) type tasks when the network reduces into linear one. However, for nonlinear networks the performances of these rules become different, which indicates many possibilities for nonlinear extensions of PCA. These theories provide a number of potential guidelines for further explorations on nonlinear PCA type learning. Moreover, the relations between these proposed theories as well as to some existing theories have also been discussed. >

Vortices in type-II superconductors driven over random disorder are known to exhibit a remarkable variety of distinct nonequilibrium dynamical phases that arise owing to the competition between vortex-vortex interactions, the quenched disorder, and the drive. These include pinned states, elastic flows, plastic or disordered flows, and dynamically reordered moving crystal or moving smectic states. The plastic flow phases can be particularly difficult to characterize since the flows are strongly disordered. Here, we perform principal component analysis (PCA) on the positions and velocities of vortex matter moving over random disorder for different disorder strengths and drives. We find that PCA can distinguish the known dynamic phases as well as or better than previous measures based on transport signatures or topological defect densities. In addition, PCA recognizes distinct plastic flow regimes, a slowly changing channel flow and a moving amorphous fluid flow, that do not produce distinct signatures in the standard measurements. Our results suggest that this position and velocity-based PCA approach could be used to characterize dynamic phases in a broader class of systems that exhibit depinning and nonequilibrium phase transitions.

Ginger (Zingiber officinale) is a herbaceous perennial plant widely recognized as a popular spice across the world. India ranks as the largest producer and consumer of ginger globally. However, ginger cultivation faces several production constraints, primarily due to variability in weather conditions. The uncertainty associated with weather patterns poses significant challenges for both farmers and policymakers, hindering timely decision-making at the field and market levels. Crop weather models serve as a reliable statistical tool that represents complex relationship between crop and weather parameters. A field experiment conducted at Kerala Agricultural University, Vellanikkara, Thrissur district using Maran variety in 2021-2022. The experiment was conducted using a split plot design. The main plot treatments consisted of four planting dates: 1st June (D1), 15th June (D2), 1st July (D3), and 15th July (D4). The subplot treatments comprised three types of organic mulches: green leaves (M1), paddy straw (M2), and dry coconut leaves (M3). The June 1st planted crop yielded more fresh ginger (19957.81 kg ha-1) than the July 15th planted crop. A consistent decline in observed yield was noted from D1 to D4 across all mulch types. Early planting (D1) showed the highest yields, (15,276.74 kg ha⁻¹), whereas the latest planting (D4) yielded the lowest (1,589.5 kg ha⁻¹). Paddy straw mulch produced high yield (fresh yield of 16941.36 kg ha-1) which was on par with and green leaves mulch (fresh yield of 15798.1 kg ha-1). PCA is an adaptive data analysis technique for reducing the dimensionality of large data sets like weather parameters hence increasing the interpretability with minimum information loss. Regression equations were fitted estimating yield for green leaves; paddy straw and dry coconut leaves mulch by performing Principal Component Analysis (PCA). From analysis both the observed and estimated yields were comparable. This study investigates the effects of planting dates and mulching types on ginger yield in the face of fluctuating weather patterns, as well as the usefulness of statistical modelling like PCA and regression in yield prediction. This yield prediction models can be utilised by farmers and policy makers to predict yield in advance.

Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time.

Kernel Principal Component Analysis (KPCA) is a widely used technique in the dimension reduction, de-noising and discovering nonlinear intrinsic dimensions of data set. In this paper we describe a reweighing kernel-based classification method for improving recognition problem. Firstly, we map the training samples to the feature space by non-linear transformation, and then perform principal component analysis(PCA) using the selected kernel function in the feature space, and get the linear representation of testing samples in the feature space. Secondly, by using the idea of reweighting, we select the similarity between testing sample and each training sample as the weight of reweighting, then take the final weight as the criteria of classification. The experimental results demonstrate that our method is more accurate than Support Vector Machine (SVM) classification method and Linear Discriminant Analysis (LDA) classification. In addition, the number of training samples that our method need is much smaller than some other methods.

Data stream mining is a research area that has grown enormously in recent years. The main challenge is to extract knowledge in real-time from a possibly unbounded stream of data. Clustering, a process in which groupings within the data are to be identified, data streams is an useful technique to extract and identify underlying structures of the data. An open question in the field of stream clustering is how to evaluate the proposed algorithms. In this survey, we review the literature in the domain to identify the common methodologies, datasets, and evaluation measures, used to evaluate the algorithms. We provide a short summary of the stream clustering algorithms in the literature, but our primary focus lies in the survey of cluster validation relevant to the evaluation of data stream clustering algorithms. We begin our literature review with the inception of clustering incrementally, namely with the introduction of the balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm.We identify that the evaluation methodologies primarily focus on performance, and that aspects such as cluster quality are rarely visited. Performance has been the focal point of all evaluation, both in terms of computational performance and accuracy, since the inception of clustering data streams. We also identify that issues that exist in the conventional clustering domain are also present in the data stream clustering. However, minor additions to the evaluation methods can improve both the applicability and usefulness of the algorithms.

Principal component analysis of routine blood test results with Parkinson's disease: A case-control study.

We describe an algorithm to perform Principal Component Analysis (PCA) over encrypted data using homomorphic encryption. PCA is a fundamental tool for exploratory data analysis and dimensionality reduction, and thus a useful application for privacy-preserving computation in the cloud.

FRI0133 Understanding Anemia in Rheumatoid Arthritis: The Association of Hemoglobin and Hepcidin Levels with Clinical Disease Activity and Acute Phase Response

We propose a set of kinematic features that are derived from the optical flow for human action recognition in videos. The set of kinematic features includes divergence, vorticity, symmetric and antisymmetric flow fields, second and third principal invariants of flow gradient and rate of strain tensor, and third principal invariant of rate of rotation tensor. Each kinematic feature, when computed from the optical flow of a sequence of images, gives rise to a spatiotemporal pattern. It is then assumed that the representative dynamics of the optical flow are captured by these spatiotemporal patterns in the form of dominant kinematic trends or kinematic modes. These kinematic modes are computed by performing Principal Component Analysis (PCA) on the spatiotemporal volumes of the kinematic features. For classification, we propose the use of multiple instance learning (MIL) in which each action video is represented by a bag of kinematic modes. Each video is then embedded into a kinematic-mode-based feature space and the coordinates of the video in that space are used for classification using the nearest neighbor algorithm. The qualitative and quantitative results are reported on the benchmark data sets.

Stream data applications have become more and more prominent recently and the requirements for stream clustering algorithms have increased drastically. Due to continuously evolving nature of the stream, it is crucial that the algorithm autonomously detects clusters of arbitrary shape, with different densities, and varying number of clusters. Although available density-based stream clustering are able to detect clusters with arbitrary shapes and varying numbers, they fail to adapt their thresholds to detect clusters with different densities. In this paper we propose a stream clustering algorithm called HASTREAM, which is based on a hierarchical density-based clustering model that automatically detects clusters of different densities. The density thresholds are independently adapted to the existing data without the need of any user intervention. To reduce the high computational cost of the presented approach, techniques from the graph theory domain are utilized to devise an incremental update of the underlying model. To show the effectiveness of HASTREAM and hierarchical density-based approaches in general, several synthetic and real world data sets are evaluated using various quality measures. The results showed that the hierarchical property of the model was able to improve the quality of density-based stream clusterings and enabled HASTREAM to detect streaming clusters of different densities.

As the world's older population grows dramatically, the needs of continuing care retirement communities increases. Studies show that privacy can be a major concern for adopting technologies, while the older population prefers smart homes [1]. In order to minimize the number of sensors to be installed in each house, we performed Principal Component Analysis (PCA) to filter out the relatively unimportant sensors. We applied a machine learning model to classify residents' activity types, using a different set of sensors chosen by PCA. Then, we validated the trade-off between the classification model accuracy and the number of sensors used in classification. Our experiment shows that feature engineering helps reduce accuracy degradation for activity type classification when using fewer sensors in smart homes.

As a key tool for real-time monitoring of air pollutant concentrations, the chemical sensor, the core component of the low-cost Air Quality Monitor (AQM), is susceptible to a variety of factors during the measurement process, leading to errors in the measurement data. To enhance the measurement accuracy of chemical sensors, this paper presents a calibration method based on the PCR-GPR model. This method not only effectively enhances the measurement accuracy of chemical sensors, but also combines the interpretability of traditional statistical models with the high-precision characteristics of Gaussian Process Regression (GPR) models. First, we perform Principal Component Analysis (PCA) on the measurement data of the AQM to solve the multicollinearity problem. Through PCA, we successfully extracted 8 principal components, which not only contained 95% of the information in the original data, but also effectively eliminated the correlation between the variables, providing a more robust data base for subsequent modeling. Subsequently, we established a Principal Component Regression (PCR) model using the concentration of pollutants measured by the national monitoring station as the dependent variable and the 8 principal components extracted above as the independent variables. The PCR model can effectively extract the linear relationship between the independent and dependent variables, providing a linear part of the explanation for the calibration process. However, there are often complex nonlinear relationships between pollutant concentrations and AQM measurements. To capture these nonlinear relationships, we further established a GPR model with the residuals of the PCR model as the dependent variable and the measurement data of the AQM as the independent variable. By combining the PCR model and the GPR model, we obtained the final PCR-GPR calibration model. It is worth mentioning that this study adopted the time series cross-validation method for data grouping, an innovative approach that is more aligned with real-world scenarios and adequately captures the seasonal variations in pollutant concentrations. The experimental results show that the model exhibits excellent performance on several evaluation metrics and can calibrate the chemical sensor well, improving its measurement accuracy by 16.94% ~ 82.01%.

Multivariate statistical characterization of the tolerance of argon inductively coupled plasmas to organic solvents