Database Approach Research Articles

On the Problem Related to Reductive Attributes in the Incomplete Decision Tables

Attribute reduction is a key problem in the process of data mining and knowledge discovery. Up to now, many attribute reduction algorithms in incomplete decision tables have been proposed. However, the research results related to conditional attributes and reduct of incomplete decision tables are still limited. By relational database approach, this paper investigates some properties of conditional attributes and proposes an algorithm to determine all reductive attributes of consistent incomplete decision tables in polynomial time. The proposed algorithm is an effective tool to eliminate all redundant attributes in data pre-processing in order to improve the efficiency of data mining models.

Subtypes in Patients Taking Prescribed Opioid Analgesics and Their Characteristics: A Latent Class Analysis.

BackgroundOwing to their pharmacological properties the use of opioid analgesics carries a risk of abuse and dependence, which are associated with a wide range of personal, social, and medical problems. Data-based approaches for identifying distinct patient subtypes at risk for prescription opioid use disorder in Germany are lacking.ObjectiveThis study aimed to identify distinct subgroups of patients using prescribed opioid analgesics at risk for prescription opioid use disorder.MethodsLatent class analysis was applied to pooled data from the 2015 and 2021 Epidemiological Survey of Substance Abuse. Participants were aged 18–64 years and self-reported the use of prescribed opioid analgesics in the last year (n = 503). Seven class-defining variables based on behavioral, mental, and physical health characteristics commonly associated with problematic opioid use were used to identify participant subtypes. Statistical tests were performed to examine differences between the participant subtypes on sociodemographic variables and prescription opioid use disorder.ResultsThree classes were extracted, which were labeled as poor mental health group (43.0%, n = 203), polysubstance group (10.4%, n = 50), and relatively healthy group (46.6%, n = 250). Individuals within the poor mental health group (23.2%, n = 43) and the polysubstance group (31.1%, n = 13) showed a higher prevalence of prescription opioid use disorder compared to those of the relatively healthy group.ConclusionThe results add further evidence to the knowledge that patients using prescribed opioid analgesics are not a homogeneous group of individuals whose needs lie in pain management alone. Rather, it becomes clear that these patients differ in their individual risk of a prescription opioid use disorder, and therefore identification of specific risks plays an important role in early prevention.

Decoupled Data-Based Approach for Learning to Control Nonlinear Dynamical Systems

This article addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical. This problem is subject to the “curse of dimensionality” associated with the dynamic programming method. This article proposes a novel decoupled data-based control (D2C) algorithm that addresses this problem using a decoupled, “open-loop–closed-loop,” approach. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system. Then, closed-loop control is developed around this open-loop trajectory by linearization of the dynamics about this nominal trajectory. By virtue of linearization, a linear quadratic regulator based algorithm can be used for this closed-loop control. We show that the performance of D2C algorithm is approximately optimal. Moreover, simulation performance suggests a significant reduction in training time compared to other state-of-the-art algorithms.

Mutual Nearest Neighbor Contrast and Hybrid Prototype Self-Training for Universal Domain Adaptation

Universal domain adaptation (UniDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain under domain shift and category shift. Without prior category overlap information, it is challenging to simultaneously align the common categories between two domains and separate their respective private categories. Additionally, previous studies utilize the source classifier's prediction to obtain various known labels and one generic "unknown" label of target samples. However, over-reliance on learned classifier knowledge is inevitably biased to source data, ignoring the intrinsic structure of target domain. Therefore, in this paper, we propose a novel two-stage UniDA framework called MATHS based on the principle of mutual nearest neighbor contrast and hybrid prototype discrimination. In the first stage, we design an efficient mutual nearest neighbor contrastive learning scheme to achieve feature alignment, which exploits the instance-level affinity relationship to uncover the intrinsic structure of two domains. We introduce a bimodality hypothesis for the maximum discriminative probability distribution to detect the possible target private samples, and present a data-based statistical approach to separate the common and private categories. In the second stage, to obtain more reliable label predictions, we propose an incremental pseudo-classifier for target data only, which is driven by the hybrid representative prototypes. A confidence-guided prototype contrastive loss is designed to optimize the category allocation uncertainty via a self-training mechanism. Extensive experiments on three benchmarks demonstrate that MATHS outperforms previous state-of-the-arts on most UniDA settings.

Legal Provisions Along with Implementation Status of Occupational Safety and Health in Nepalese Legislation

Introduction: Cloud computing is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications. Objectives: This study aimed to evaluate the acceptance of employees in the medical records and E-health departments to cloud computing in King Khalid General, Hafar Al-Batin, Saudi Arabia. Further, it determined their perceived level of understanding and knowledge to this technology especially on its utilization in safely accessing and securing personal health record of patients. Method: A total of 53 participants filled-up and completed the survey questionnaire that measures their level of knowledge and understanding to cloud technology. A descriptive cross-sectional design was utilized for this study. Results and Discussion: From the results of the survey, a general mean of 3.72 and standard deviation of 1 having a verbal intepretation of ‘agree’ were obtained. These results translates to an acceptable level of utilizing cloud computing in the hospitals. Conclusion: Cloud computing creates an efficient management of patient health records making it more accessible and secured. It is an innovative management approach of record databases in a Saudi hospital with a promising future to be adopted by other hospitals in Saudi Arabia.

Evolving Gaussian on-line clustering in social network analysis

In this paper, we present an evolving data-based approach to automatically cluster Twitter users according to their behavior. The clustering method is based on the Gaussian probability density distribution combined with a Takagi–Sugeno fuzzy consequent part of order zero (eGauss0). This means that this method can be used as a classifier that is actually a mapping from the feature space to the class label space. The eGauss method is very flexible, is computed recursively, and the most important thing is that it starts learning “from scratch”. The structure adapts to the new data using adding and merging mechanisms. The most important feature of the evolving method is that it can process data from thousands of Twitter profiles in real time, which can be characterized as a Big Data problem. The final clusters yield classes of Twitter profiles, which are represented as different activity levels of each profile. In this way, we could classify each member as ordinary, very active, influential and unusual user. The proposed method was also tested on the Iris and Breast Cancer Wisconsin datasets and compared with other methods. In both cases, the proposed method achieves high classification rates and shows competitive results.

Machine learning hybrid approach for the prediction of surface tension profiles of hydrocarbon surfactants in aqueous solution

HypothesisPredicting the surface tension (SFT)-log(c) profiles of hydrocarbon surfactants in aqueous solution is computationally non-trivial, and empirically challenging due to the diverse and complex architecture and interactions of surfactant molecules. Machine learning (ML), combining a data-based and knowledge-based approach, can provide a powerful means to relate molecular descriptors to SFT profiles. ExperimentsA dataset of SFT for 154 model hydrocarbon surfactants at 20–30 °C is fitted to the Szyszkowski equation to extract three characteristic parameters (Γmax,KL and critical micelle concentration (CMC)) which are correlated to a series of 2D and 3D molecular descriptors. Key (∼10) descriptors were selected by removing co-correlation, and employing a gradient-boosted regressor model to rank feature importance and carry out recursive feature elimination (RFE). The hyperparameters of each target-variable model were fine-tuned using a randomised cross-validated grid search, to improve predictive ability and reduce overfitting. FindingsThe ML models correlate favourably with test experimental data, with R2= 0.69–0.87, and the merits and limitations of the approach are discussed based on ‘unseen’ hydrocarbon surfactants. The incorporation of a knowledge-based framework provides an appropriate smoothing of the experimental data which simplifies the data-driven approach and enhances its generality. Open-source codes and a brief tutorial are provided.

Sampled-data extremum-seeking framework for constrained optimization of nonlinear dynamical systems

Most extremum-seeking control (ESC) approaches focus solely on the problem of finding the extremum of some unknown, steady-state input–output map, providing parameter settings that lead to optimal steady-state system performance. However, many industrial applications also have to deal with constraints on operating conditions due to, e.g., actuator limitations, limitations on tunable system parameters, or constraints on measurable variables. In particular, constraints on measurable variables are typically unknown in terms of their relationship with the tunable system parameters. In addition, the constraints on system inputs as a result of the constraints on measurable variables may conflict with the otherwise optimal operational condition, and hence should be taken into account in the data-based optimization approach. In this work, we propose a sampled-data extremum-seeking framework for the constrained optimization of a class of nonlinear dynamical systems with measurable constrained variables. In this framework, barrier function methods are employed, exploiting both the objective function and constraint functions which are available through output measurement only. We show, under the assumption that the parametric initialization yield operating conditions that do not violate the constraints, that (1) the resulting closed-loop dynamics is stable, (2) constraint satisfaction of the inputs is guaranteed for all iterations of the optimization process, and (3) constrained optimization is achieved. We illustrate the working principle of the proposed framework by means of an industrial case study of the constrained optimization of extreme ultraviolet light generation in a laser-produced plasma source within a state-of-the-art lithography system.

Inertial Data-Based AI Approaches for ADL and Fall Recognition

The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, which appears to be a suitable option in uncontrolled environments. Several authors have addressed ADL recognition using Artificial Intelligence (AI)-based algorithms, obtaining encouraging results. However, the number of ADL recognized by these algorithms is still limited, rarely focusing on transitional activities, and without addressing falls. Furthermore, the small amount of data used and the lack of information regarding validation processes are other drawbacks found in the literature. To overcome these drawbacks, a total of nine public and private datasets were merged in order to gather a large amount of data to improve the robustness of several ADL recognition algorithms. Furthermore, an AI-based framework was developed in this manuscript to perform a comparative analysis of several ADL Machine Learning (ML)-based classifiers. Feature selection algorithms were used to extract only the relevant features from the dataset’s lower trunk inertial data. For the recognition of 20 different ADL and falls, results have shown that the best performance was obtained with the K-NN classifier with the first 85 features ranked by Relief-F (98.22% accuracy). However, Ensemble Learning classifier with the first 65 features ranked by Principal Component Analysis (PCA) presented 96.53% overall accuracy while maintaining a lower classification time per window (0.039 ms), showing a higher potential for its usage in real-time scenarios in the future. Deep Learning algorithms were also tested. Despite its outcomes not being as good as in the prior procedure, their potential was also demonstrated (overall accuracy of 92.55% for Bidirectional Long Short-Term Memory (LSTM) Neural Network), indicating that they could be a valid option in the future.

Feature of metallic oxide nanoparticles in the thermal efficiency and flow structure of non-Newtonian homogeneous nanofluid: experimental data-based mathematical approach

Nanofluids are innovativeheat transfer fluids with superior thermophysical properties and have numerous engineering applications in several necessary fields. Keeping this in mind, the flow characteristics of non-Newtonian nanofluids over moving flat surfaces are discussed. We use nanofluids that are synthessed by dispersing the nanoparticles SiO2, TiO2, and MgO with (20–30 nm, 60–70 nm), (30, 50 nm) and (20 nm, 40 nm) particle diameters, respectively, in ethylene glycol-based fluid, including 5%, 10%, 15%, and 20% nanoparticle volume fraction. Mathematical modeling is demonstrated by continuity, momentum, heat equations, and parameters of the rheological model are associated with the viscosity-shear rate relation that follows the experimental results. We consequently explore velocity and temperature profiles through graphs under the impact of distinct nanoparticle volume fractions and diameters. The heat transfer rate and shear stress at the wall are investigated under different nanoparticle volume fractions. Furthermore, we have investigated and understood the behavior of mass flow rate and momentum flux through displacement and momentum thickness parameters. The results revealed that velocity is decreased due to increased viscosity and deficiency in mass and momentum flow rates with the enhancement in nanoparticle concentration.

Data predictive control of nonlinear process feature dynamics through latent variable behaviours

The paper presents a new big data-based approach to control of feature dynamics of continuous nonlinear chemical/industrial processes, based on the behavioural systems theory and deep learning tools. From time-series process data, the feature dynamics of a nonlinear process are extracted using an Autoencoder (AE), a type of artificial neural network. The feature dynamics are embedded in, and can be constructed from, a linear dynamic behaviour of latent variables. The latent variable dynamic space is described by a kernel representation and linearly maps the feature variable space. A Data Predictive Control (DPC) approach is developed to optimise the feature variables by controlling the latent variable dynamics using a system behaviour framework. Behaviour-based dissipativity conditions are adopted to deal with errors that arise in the latent and feature variable spaces during neural network training. A case study is presented to illustrate the proposed approach.

A stock time series forecasting approach incorporating candlestick patterns and sequence similarity

This article aims to implement trend forecasting of stock time series based on candlestick patterns and sequence similarity. Financial time series forecasting plays a central role in hedging market risks and optimizing investment portfolios. This is a challenging task, as financial engineering requires the proposed approach to be interpretable, robust, and compatible. It is noted that many published research studies are based on multi-modal data, which makes the prediction approaches increasingly complex, difficult to interpret, and does not allow the migration across different data. Given this situation, it is believed that a candlestick data-based approach is promising. It is already recognized by the technical analyses, prevalent in financial markets, more readily available, and has better interpretability. In this paper, the forecasting approach is divided into two steps. In the first step, sequential pattern mining is used to obtain candlestick patterns from multidimensional candlestick data, and the correlation between different patterns and the corresponding future trends are calculated. In the second step, a new sequence similarity is proposed to match the diverse candlestick sequences with the existing patterns. The method is validated on real data from 800 stocks in the Chinese stock market, which are divided into two groups of experiments, and the average accuracy achieved by the proposed method is 56.04% and 55.56%, which is higher than the SVM model (50.83% and 51.32%) and the LSTM model (50.71% and 50.68%) used for comparison, proving that our work is more stable and accurate. This work is instructive for further research around candlestick data to follow.

Through-focus EUV multilayer defect compensation considering optical proximity correction.

Extreme ultraviolet (EUV) multilayer defects result in the degradation of through-focus imaging quality. The optical proximity effect is another crucial factor that degrades the imaging quality. Both the impacts of the defects and the optical proximity effects could be mitigated by modifying the original mask patterns. A heuristic-based defect compensation method considering optical proximity correction and through-focus optimization is proposed in this paper. The edge of the mask pattern and the insertion of sub-resolution assist features (SRAFs) are optimized by covariance matrix adaptation evolution strategy (CMA-ES) to compensate for the degradation of the imaging quality with a certain defocus range. New encoding strategies for the edge pixels of the mask pattern and the SRAFs are proposed and utilized in this paper to ensure the manufacturability of the mask and the efficiency of the optimization at the same time. The rigorous database approach based on the scattering matrix is adopted to simulate the mask diffraction spectrum efficiently. Simulations verify that the through-focus imaging quality of both the defective masks with bump defects and pit defects could be obviously improved by the proposed defect compensation method.

Towards Fast and Accurate Federated Learning with Non-IID Data for Cloud-Based IoT Applications

As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL) is becoming popular in the Internet of Things (IoT) design. However, when the data collected by IoT devices are highly skewed in a non-independent and identically distributed (non-IID) manner, the accuracy of the vanilla FL method cannot be guaranteed. Although there exist various solutions that try to address the bottleneck of FL with non-IID data, most of them suffer from extra intolerable communication overhead and low model accuracy. To enable fast and accurate FL, this paper proposes a novel data-based device grouping approach that can effectively reduce the disadvantages of weight divergence during the training of non-IID data. However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure. To solve this problem, we propose an improved version by exploiting similarity information using the Locality-Sensitive Hashing (LSH) algorithm without exposing extracted feature maps. Comprehensive experimental results on well-known benchmarks show that our approach can not only accelerate the convergence rate, but also improve the prediction accuracy for FL with non-IID data.

A proposal for future data organization in enterprise systems—an analysis of established database approaches

The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future.

CFD modelling of a secondary settling tanks: generalization based on database relations

The modelling of a secondary settling tank area using numerical methods has been quite extensively explored and researched by numerous authors and papers. These developed models utilize different approaches, from efforts to create a solely deterministic models to attempts to generalize calibrated empirical models. Nevertheless, the processes are not easy to simulate due to the high complexity of the physics involving multiple phases, bio-chemical reactions and non-Newtonian fluids. Therefore, an additional research effort should be focused on improving these models and to validate and verify them against experimental measurements. This article is focused on creating a numerical model for settling tank optimization, which builds on the previous works and is then extended with newly obtained relations from vast experimental measuring using the database approach. The objective is to create a model, which is based on an obtained relation and could easily be adjusted to different flow scenarios and sludge types at different wastewater treatment plants.

An enhanced privacy-preserving record linkage approach for multiple databases

An enhanced privacy-preserving record linkage approach for multiple databases

A Method of Monitoring and Evaluating the Quality of English Classroom Teaching Based on Big Data

To improve the reliability and accuracy of quality of English instruction in the classroom monitoring and assessment, as well as to reduce the time spent monitoring and evaluating the quality of English instruction in the classroom, a big data-based approach to monitor and evaluate the quality of English instruction in the classroom is proposed. A frequent itemset mining technique for quality of English instruction in the classroom monitoring data is built based on a study of the theoretical basis of big data and the features of quality of English instruction in the classroom monitoring data. To complete the data transformation of quality of English instruction in the classroom monitoring, the multivalued continuous attribute is quantified and turned into a two-dimensional Boolean data matrix. To mine the frequent itemsets of quality of English instruction in the classroom monitoring data, a frequent itemset mining algorithm based on the compressed matrix is applied. This work creates a quality of English instruction in the classroom evaluation model using the gray correlation method of multiobjective decision-making and weighted gray correlation analysis to realize the monitoring and evaluation of quality of English instruction in the classroom. The experimental results suggest that the proposed method is highly reliable and accurate and that it can significantly reduce the time spent monitoring and evaluating the quality of English classroom instruction.

‘Constructing a Critical Situation’: A Data-Based Approach to the Study of Cultural Periodicals and Art Criticism

The article proposes a three-tier model for the analysis of cultural dynamics of the visual arts field, based on data extracted from cultural periodicals and art criticism. Originally developed on the case study of the visual arts scene in Croatia at the turn of the millennium, the model is flexible and can be applied to analyse the complexity of the art field in different (trans)national spaces and time periods. The article outlines the theoretical foundations of the model, developed at the intersection of (digital) art history, sociology of art, social network analysis and relational sociology, and gives a detailed description of the criteria that were used to define the network boundaries in the original research. The three-tier model foregrounds some of the central actors of the art field, such as cultural periodicals, art critics, artists and cultural institutions, who are approached using both quantitative and qualitative methods. It conceptualizes cultural periodicals and art critics that form part of their networks as sharing aesthetic and ideological values, and as producing specific cultural narratives within the structure of the scene. The analytic potential of the model is focused on determining the cohesiveness of different social circles present in an art scene, on identifying the changing cultural trends and narratives that permeate the field, and on revealing the contribution of different social circles to these network and cultural structures.

A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar

In recent decades, substantial efforts have been devoted in flood monitoring, prediction, and risk analysis for aiding flood event preparedness plans and mitigation measures. Introducing an initial framework of spatially probabilistic analysis of flood research, this study highlights an integrated statistical copula and satellite data-based approach to modelling the complex dependence structures between flood event characteristics, i.e., duration (D), volume (V) and peak (Q). The study uses Global daily satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (spatial resolution of ∼5 km) during 1981–2019 to derive a Standardized Antecedence Precipitation Index (SAPI) and its characteristics through a time-dependent reduction function for Myanmar. An advanced vine copula model was applied to model joint distributions between flood characteristics for each grid cell. The southwest (Rakhine, Bago, Yangon, and Ayeyarwady) and south (Kayin, Mon, and Tanintharyi) regions are found to be at high risk, with a probability of up to 40% of flood occurrence in August and September in the south (Kayin, Mon, and Tanintharyi) and southwest regions (Rakhine, Bago, Yangon, and Ayeyarwady). The results indicate a strong correlation among flood characteristics; however, their mean and standard deviation are spatially different. The findings reveal significant differences in the spatial patterns of the joint exceedance probability of flood event characteristics in different combined scenarios. The probability that duration, volume, and peak concurrently exceed 50th-quantile (median) values are about 60–70% in the regions along the administrative borders of Chin, Sagaing, Mandalay, Shan, Nay Pyi Taw, and Keyan. In the worst case and highest risk areas, the probability that duration, volume, and peak exceed the extreme values, i.e., the 90th-quantile, about 10–15% in the southwest of Sagaing, southeast of Chin, Nay Pyi Taw, Mon and areas around these states and up to 30% in the southeast of Dekkhinathiri township (Nay Pyi Taw). The proposed approach could improve the evaluation of exceedance probabilities used for flood early warning and risk assessment and management. The proposed framework is also applicable at larger scales (e.g., regions, continents and globally) and in different hydrological design events and for risk assessments (e.g., insurance).