Artificial Clustering Research Articles

Unsupervised Clustering of Adult Spinal Deformity Patterns Predicts Surgical and Patient-Reported Outcomes.

Retrospective cohort study. To evaluate whether different radiographic clusters of adult spinal deformity identified using artificial intelligence-based clustering are associated with distinct surgical outcomes. Patients were classified based on the results of a previously conducted analysis that examined clusters of deformity, including Moderate Sagittal (Mod Sag), Severe Sagittal (Sev Sag), Coronal, and Hyper-Thoracic Kyphosis (Hyper-TK). The surgical data, HRQOL, and complication outcomes of these clusters were then compared. The final analysis included 1062 patients. Similar to published results on a different patient sample, Mod Sag and Sev Sag patients were older, more likely to have a history of previous spine surgery, and more disabled. By 2-year, all clusters improved in HRQOL and reached a similar rate of minimal clinically important difference (MCID).The Sev Sag cluster had the highest rate major complications (53% vs 34-40%), and complications leading to reoperation (29% vs 17-23%), implant failures (20% vs 8-11%), and operative complications (27% vs 10-17%). Coronal patients had the highest rate of pulmonary complications (9% vs 3-6%) but the lowest rate of X-ray imbalance (10% vs 19-21%). No significant differences were found in neurological complications, infection rate, gastrointestinal, or cardiac events (all P > .1). Kaplan-Meier survival curves demonstrated a lower time to first complications for the Sev Sag cluster. All clusters of adult spinal deformity benefit similarly from surgery as they all achieved similar rates of MCID. Although the rates of complications varied among the clusters, the types of complications were not significantly different.

Assessing the Relationships between Physical Activity, a Healthy Life, and Personal Happiness in European Union Countries.

Background: Maintaining a physically active lifestyle is a determinant factor of a healthy life and personal happiness. Meanwhile, physical inactivity remains a significant issue, resulting in negative consequences for public health. Objectives: This paper investigates the relationships between physical activity, physical inactivity, a healthy life, life expectancy, and personal happiness in European Union (EU) countries. Methods: This empirical study uses an artificial neural network and cluster analysis to analyze and interpret data from 27 EU countries. Artificial neural network analysis enables the assessment of the relationships between physical activity and inactivity, a healthy life, and personal happiness, while cluster analysis identifies groups of EU countries based on physical activity, healthy life, and personal happiness indicators. Results: The results show significant positive links between physical activity and improvements in healthy living and personal happiness. Conclusions: This study highlights considerable variations among EU countries regarding the levels of physical activity, healthy living, and personal happiness, emphasizing the importance of promoting physical activity to enhance public health and overall well-being. The findings suggest the need to develop customized policies that address country-specific factors and promote an active lifestyle.

Leveraging Large-scale AI Models for Transforming China's Marine Industry: A TOE Framework Analysis

As an important driving force leading a new round of scientific and technological revolution and industrial change, artificial intelligence is becoming a hot spot. Promoting the deep integration of "artificial intelligence + ocean" will help build a world-class Marine artificial intelligence industry cluster with global competitiveness. How to effectively apply artificial intelligence to the Marine industry to solve the problems of resource redundancy and information island caused by industrial agglomeration is urgent for innovation and breakthrough. In this paper, TOE and modified T-TOE framework are used to analyze the above problems, and the following conclusions are drawn: 1) From the perspective of technical factors, intelligent information technology plays an important role in promoting the construction of large-scale models at the level of hardware and software; From the perspective of organizational factors, enterprise digital transformation and open sharing platform play an important role in supporting organization construction. From the perspective of environmental factors, policy support and market orientation play an important role in encouraging the construction of large models. 2) From the perspective of T-T, the breakthrough and application of technology need to balance the contradiction between development and conflict; From the perspective of T-O, intelligent supply-demand matching and transition resistance have both positive and negative effects; From the perspective of T-E, there is a problem of actively promoting multidimensional interactive innovation and achieving a balance. Relevant studies provide new ideas for the comprehensive development scenario of Marine industry and inject new impetus into the development of Marine industry.

Feature Selection and Dimensionality Reduction for Large Data set Clustering using Self-Organizing Maps

Data mining is a form of analyzing the data which relates the newline techniques from fields like statistics machine learning databases artificial newline intelligence etc Clustering is one of the most important data mining newline techniques in which cluster of objects are grouped based on their similarity newline "Clustering is the process of accumulating the data records into considerable newline subclasses clusters in a way which enhances the relationship within clusters newline and reduces the similarity among two different clusters. The activity occurs every new academic year and schools with plenty of new students registered may feel a bit overwhelmed with this grouping assignment. A decision support system which can automatically perform grouping on a list of students may be able to help the school’s staffs with this repetitive task. A self-organizing map (SOM) is an example of unsupervised learning algorithm using an artificial neural network structure to produce a low dimensional representation from a given input.

Metabolic pathway engineering of high-salinity-induced overproduction of L-proline improves high-salinity stress tolerance of an ectoine-deficient Halomonas elongata.

Halophilic bacteria have adapted to survive in high-salinity environments by accumulating amino acids and their derivatives as organic osmolytes. L-Proline (Pro) is one such osmolyte that is also being used as a feed stimulant in the aquaculture industry. Halomonas elongata OUT30018 is a moderately halophilic bacterium that accumulates ectoine (Ect), but not Pro, as an osmolyte. Due to its ability to utilize diverse biomass-derived carbon and nitrogen sources for growth, H. elongata OUT30018 is used in this work to create a strain that overproduces Pro, which could be used as a sustainable Pro-rich feed additive. To achieve this, we replaced the coding region of H. elongata OUT30018's Ect biosynthetic operon with the artificial self-cloned proBm1AC gene cluster that encodes the Pro biosynthetic enzymes: feedback-inhibition insensitive mutant γ-glutamate kinase (γ-GKD118N/D119N), γ-glutamyl phosphate reductase, and pyrroline-5-carboxylate reductase. Additionally, the putA gene, which encodes the key enzyme of Pro catabolism, was deleted from the genome to generate H. elongata HN6. While the Ect-deficient H. elongata KA1 could not grow in minimal media containing more than 4% NaCl, H. elongata HN6 thrived in the medium containing 8% NaCl by accumulating Pro in the cell instead of Ect, reaching a concentration of 353.1 ± 40.5 µmol/g cell fresh weight, comparable to the Ect accumulated in H. elongata OUT30018 in response to salt stress. With its genetic background, H. elongata HN6 has the potential to be developed into a Pro-rich cell factory for upcycling biomass waste into single-cell feed additives, contributing to a more sustainable aquaculture industry.IMPORTANCEWe report here the evidence for de novo biosynthesis of Pro to be used as a major osmolyte in an ectoine-deficient Halomonas elongata. Remarkably, the concentration of Pro accumulated in H. elongata HN6 (∆ectABC::mCherry-proBm1AC ∆putA) is comparable to that of ectoine accumulated in H. elongata OUT30018 in response to high-salinity stress. We also found that among the two γ-glutamate kinase mutants (γ-GKD118N/D119N and γ-GKD154A/E155A) designed to resemble the two known Escherichia coli feedback-inhibition insensitive γ-GKD107N and γ-GKE143A, the γ-GKD118N/D119N mutant is the only one that became insensitive to feedback inhibition by Pro in H. elongata. As Pro is one of the essential feed additives for the poultry and aquaculture industries, the genetic makeup of the engineered H. elongata HN6 would allow for the sustainable upcycling of high-salinity waste biomass into a Pro-rich single-cell eco-feed.

Artificial neural network-based clustering in Wireless sensor Networks to balance energy consumption

Artificial neural network-based clustering in Wireless sensor Networks to balance energy consumption

Прогнозирование социальных изменений с помощью алгоритмов машинного обучения

This study is devoted to analyzing the capabilities of machine learning algorithms in the context of predicting social change. The main objective of the work is to evaluate the effectiveness and accuracy of various machine learning models, including artificial neural networks, decision trees and clustering methods, for analyzing so-cial data and predicting relevant trends. The research methodology includes data collection and preprocessing, training models based on selected algorithms, and evaluating their performance using standard metrics such as accuracy, completeness, and F1-measure. The results demonstrate that the application of machine learning can not only identify current social trends, but also predict future social changes with reasonable probability. The paper also discusses potential limitations related to data availability and quality, as well as ethical consid-erations for the use of algorithmic methods in the social sciences. It concludes by suggesting directions for fur-ther research, including improving the interpretability of models and enhancing multidisciplinary collaboration to better understand social processes.

Using unsupervised learning to classify inlet water for more stable design of water reuse in industrial parks.

The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of 'water quality distance' was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies. Of the six cases, three were employed to illustrate the feasibility of the unsupervised learning clustering algorithm. The results indicated that the clustering algorithm exhibited greater stability and excellence compared to both artificial clustering and ChatGPT-based clustering. The remaining three cases were utilized to showcase the reliability of the three clustering algorithms. The findings revealed that the AGNES algorithm demonstrated superior potential application ability. The average purity in six cases of K-means, DBSCAN, and AGNES were 0.947, 0.852, and 0.955, respectively.

Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods

Electrofacies analysis conducted the distribution effects throughout the reservoir despite the difficulty of characterizing stratigraphic relationships. Clustering methods quantitatively define the reservoir zone from non-reservoir considering electrofacies. Asmari Formation is the most significant reservoir of the Mansouri oilfield in SW Iran, generally composed of carbonate and sandstone layers. The stratigraphical study is determined by employing 250 core samples from one exploratory well in the studied field. Five zones with the best reservoir quality in zones 3 and 5 containing sandstone/shale are determined. Moreover, multi-resolution graph-based and artificial neural network clustering involving six logs are employed. Utilizing Geolog software, an optimal model with eight clusters with better rock separation is obtained. Eventually, five electrofacies with different lithological compositions and reservoir conditions are identified and based on lithofacies describing thin sections, sandstone, and shale in zones 3 and 5 show high reservoir quality. According to the depth related to these zones, most of the facies that exist in these depths include sandstone and dolomite facies, and this is affected by the two factors of the primary sedimentary texture and the effect of the diagenesis process on them. Results can compared to the clustering zone determination in other nearby sandstone reservoirs without cores.

Emissions Reporting Maturity Model: supporting cities to leverage emissions-related processes through performance indicators and artificial intelligence

Climate change and global warming have been trending topics worldwide since the Eco-92 conference. However, little progress has been made in reducing greenhouse gases (GHGs). The problems and challenges related to emissions are complex and require a concerted and comprehensive effort to address them. Emissions reporting is a critical component of GHG reduction policy and is therefore the focus of this work. It is crucial to improve the process efficiency of emissions reporting in order to achieve better emissions reduction results, as there is a direct link between effective emissions policies implemented by cities and emissions reduction (or increase) due to the effectiveness of these policies. Hence, to achieve this goal, this work proposes a series of steps to investigate, search and develop performance indicators (PIs) for emissions reporting. These performance indicators are based on the data provided by cities on the processes they go through to address emission problems. PIs can be used to guide and optimize the policies responsible for implementing emission reduction measures at the city level. Therefore, the main goal of this work is two-fold: (i) to propose an emission reporting evaluation model to leverage emissions reporting overall quality and (ii) to use artificial intelligence (AI) to support the initiatives that improve emissions reporting. Thus, this work presents an Emissions Reporting Maturity Model (ERMM) for examining, clustering, and analysing data from emissions reporting initiatives to help the cities to deal with climate change and global warming challenges. The model is built using Capability Maturity Model (CMM) concepts and uses artificial intelligence clustering technologies, performance indicator candidates and a qualitative analysis approach to find the data flow along the emissions-related processes implemented by cities. The Performance Indicator Development Process (PIDP) proposed in this work provides ways to leverage the quality of the available data necessary for the execution of the evaluations identified by the ERMM. Hence, the PIDP supports the preparation of the data from emissions-related databases, the classification of the data according to similarities highlighted by different clustering techniques, and the identification of performance indicator candidates, which are strengthened by a qualitative analysis of selected data samples. Thus, the main goal of ERRM is to evaluate and classify the cities regarding the emission reporting processes, pointing out the drawbacks and challenges faced by other cities from different contexts, and at the end to help them to leverage the underlying emissions-related processes and emissions mitigation initiatives.

Rational design patterns for artificial ice cluster construction at bottoms of mined-out kimberlite pits by means of layer-by-layer water filling

Rational design patterns for artificial ice cluster construction at bottoms of mined-out kimberlite pits by means of layer-by-layer water filling

Effect of Temperature on Ultrasonic Degradation of Sodium Poly(Styrene Sulfonate): Analysis of Online Viscometric Data with Theoretical Models and Machine Learning Approaches

The effect of temperature (15, 20, 25, 30, and 35 °C) on the ultrasonic degradation of sodium poly(styrene sulfonate) polyelectrolyte (5.10−3 g/ml) in aqueous NaCl solutions (2.75 m) was investigated. Using the thousands of data obtained by an online viscometer monitoring technique, the degradation kinetics, and the effect of temperature on this process were analyzed in detail with 12 theoretical models reported in the literature. The Giz, OHM, and Tang-Goto models were found to give good results, and these models were selected to evaluate the effect of temperature on the ultrasonic polymer degradation. The degradation constant and the limiting molecular weight at which no polymer chain scission occurs were found to decrease with increasing temperature for all models. Furthermore, modeling was performed using popular machine learning algorithms, such as Multilayer Perceptron, Adaptive Neuro-Fuzzy Inference System, Long Short-Term Memory, Gradient Boosting Regression, Light Gradient Boosting Machine, eXtreme Gradient Boosting Regressor, CatBoost, and Random Forest. The best results were obtained with the Long Short-Term Memory, a deep learning approach. In addition, optimization techniques, such as Particle Swarm Optimization, Genetic Algorithm, Artificial Bee Colony, Clustering, and Global Optimization Method based on a parabolic approach, were used to determine the optimum molecular weight evolution and degradation temperature.

In Vitro Module Editing Of NRPS Enables Production Of Highly Potent Gq -Signaling Inhibitor FR900359 Derived From Unculturable Plant Symbiont.

Heterotrimeric G proteins are key mediators in the signaling of G protein-coupled receptors (GPCR) that are involved in a plethora of important physiological processes and thus major targets of pharmaceutical drugs. The cyclic depsipeptides YM-254890 and FR900359 are strong and selective inhibitors of the Gq subfamily of G proteins. FR900359 was first reported to be produced by unculturable plant symbiont, however, a culturable FR900359 producer was discovered recently by the standard strategy, screening of the producing strain from the environment. As another strategy, we introduce herein the different way to supply natural compounds of unculturable microorganism origin. We therefore embarked on constructing an artificial biosynthetic gene cluster (BGC) for FR900359 with YM-254890 BGC as a template using "in vitro module editing" technology, first developed for the modification of type-I PKS BGCs, to edit YM-254890 BGC. The resulting artificial BGCs coding FR900359 were heterologously expressed in the Pseudomonas putida KT2440 host strain.

In Vitro Module Editing Of NRPS Enables Production Of Highly Potent Gq‐Signaling Inhibitor FR900359 Derived From Unculturable Plant Symbiont

AbstractHeterotrimeric G proteins are key mediators in the signaling of G protein‐coupled receptors (GPCR) that are involved in a plethora of important physiological processes and thus major targets of pharmaceutical drugs. The cyclic depsipeptides YM‐254890 and FR900359 are strong and selective inhibitors of the Gq subfamily of G proteins. FR900359 was first reported to be produced by unculturable plant symbiont, however, a culturable FR900359 producer was discovered recently by the standard strategy, screening of the producing strain from the environment. As another strategy, we introduce herein the different way to supply natural compounds of unculturable microorganism origin. We therefore embarked on constructing an artificial biosynthetic gene cluster (BGC) for FR900359 with YM‐254890 BGC as a template using “in vitro module editing” technology, first developed for the modification of type‐I PKS BGCs, to edit YM‐254890 BGC. The resulting artificial BGCs coding FR900359 were heterologously expressed in the Pseudomonas putida KT2440 host strain.

Improving Wind Power Generation Forecasts: A Hybrid ANN-Clustering-PSO Approach

This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson’s analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model.

Digital Revolution, Sustainability, and Government Revenues: A Transversal Analysis of How Digital Transformation and Sustainable Practices Impact Sustainable Government Revenues

In recent decades, digital transformation and sustainability have become two crucial aspects of economic and social development in the European Union (EU), improving government tax collection and other government incomes. This paper investigates the interplay between digital transformation and sustainability and their impact on total general government revenue (TGGR) in the European Union (EU). The study uses quantitative research methods to analyze relationships among variables, including artificial neural networks and cluster analyses. The variables considered in this research are the Digital Economy and Society Index (DESI), the Sustainable Development Goals (SDG) index, and TGGR. Our findings reveal intricate connections between digital transformation, sustainability, and TGGR across EU member states. The DESI score measures the level of digitalization within each country, while the SDG Index assesses sustainability performance. The study demonstrates that countries with higher DESI scores tend to experience increased TGGR, indicating that a more robust digital economy contributes positively to government revenue, among other elements. The research identifies clusters of EU member states with similar patterns of digital transformation, sustainability, and TGGR. These clusters highlight challenges and opportunities, guiding policymakers in tailoring strategies to boost digitalization and government revenue sustainably.

A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering.

An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel "explanation-weighted" clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one.

Retracted: Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images.

[This retracts the article DOI: 10.1155/2021/5573010.].

Improving plastic manufacturing processes with the integration of Six Sigma and machine learning techniques: a case study

ABSTRACT This research integrates machine learning (ML) and Six Sigma’s Define, Measure, Analyze, Improve, and Control (DMAIC) methodology to address these issues. The study details the selection and utilization of ML techniques, including Linear Regression (LR), Artificial Neural Network (ANN), Decision Tree (DT), K-nearest neighbors (KNN), and Cluster Analysis (CA). Implemented at the Innovative Plastic Manufacturing Company in Egypt, this research enhances the consistency of plastic bottle production by addressing issues such as surface marks, flashes, bubbles, and variations in liter capacity. Integrating Six Sigma with ML techniques reduces the average defect rate from approximately 67.8%. It elevates the Sigma level from 3.14 to 4.30, reducing material over-consumption costs from 5% to 1.7% of total manufacturing expenses. Notably, the KNN model achieves the best results for defect testing, with an R-squared value of 98.8%. These methodologies lead to cost reduction, increased competitiveness, and improved product quality when implemented.

Application of Fuzzy and Conventional Forecasting Techniques to Predict Energy Consumption in Buildings

This paper presents the implementation and analysis of two approaches (fuzzy and conventional). Using hourly data from buildings at the University of Granada, we have examined their electricity demand and designed a model to predict energy consumption. Our proposal was conducted with the aid of time series techniques as well as the combination of artificial neural networks and clustering algorithms. Both approaches proved to be suitable for energy modelling although nonfuzzy models provided more variability and less robustness than fuzzy ones. Despite the relatively small difference between fuzzy and nonfuzzy estimates, the results reported in this study show that the fuzzy solution may be useful to enhance and enrich energy predictions.