Optimal Clustering Research Articles

Outcome and life expectancy associated with novel subgroups of atrial fibrillation: a data-driven cluster analysis

Abstract Background/Introduction Atrial fibrillation (AF) presents a significant global health burden, affecting millions and associated with elevated risks of mortality, heart failure, and stroke despite current treatment strategies. The current diagnosis of AF relies on episode characteristics, yet its heterogeneity calls for personalized assessment incorporating clinical features, biomarkers, and substrate determination for improved classification and management. Purpose This study aimed to develop a refined classification of AF according to comprehensive risk factors to identify patients at high-risk for poor prognosis and their life expectancy, aiming to facilitate personalized interventions. Methods A total of 7,391 participants aged 40-69 with AF at baseline, from UK Biobank, were recruited in the main analyses. Firstly, LASSO regression with Cox model and factor analysis were adopted for identification and integration of principal risk factors. Second, Consensus clustering analysis was employed to estimate the optimal cluster number and group all 7,391 participants. Difference in the risk of death and major complications, as well as reductions in life expectancy, among clusters were estimated. Replication was done in 2,399 participants with newly diagnosed AF within two years after baseline. Results Risk factors for AF were categorized into seven aspects (metabolic factors, respiratory factors, cardiovascular factors, renal and immunity diseases, mental health, acute illness, age), based on the correlation between them. Five distinct AF clusters were identified according to these seven characteristics: acute illness-related (Cluster 1), mental health-related (Cluster 2), cardiovascular disease-related (Cluster 3), immune-and-renal disease-related (Cluster 4), and respiratory-and-metabolism disease-related AF (Cluster 5). Patients with respiratory-and-metabolism disease-related AF had the highest risk of death, acute myocardial infarction, heart failure, and cerebral ischemic stroke, while those with acute illness-related AF had the lowest corresponding risk. In addition, compared with individuals with acute illness-related AF, those with respiratory-and-metabolism disease-related and mental health-related AF had the top 2 greatest loss of life expectancy. Furthermore, genetic variants for AF had different effect among the five clusters. Replication analysis confirmed the result stability. Conclusion A novel AF classification was developed, which provided insights into varying life expectancy and risks of death and complications among AF subgroups with distinct characteristics. It offers a practical approach for identifying high-risk patients, which may help to tailor precise interventions for AF management.Graphic abstractCluster features

Optimizing Multi-Tier Scheduling and Secure Routing in Edge-Assisted Software-Defined Wireless Sensor Network Environment Using Moving Target Defense and AI Techniques

Software Defined Wireless Sensor Networks (SDWSN) enable flexibility in Wireless Sensor Network (WSN) environments by defining the controllable functions to WSN nodes by the Software Defined Network (SDN) controller. Due to the rapid evolution of SDWSNs, adverse effects also have occurred in terms of interference, energy consumption, and security issues. Several state-of-the-art works lend their utmost best to the SDWSN environment. However, the complete picture (i.e., relatability and security in SDWSN) poses severe challenges. The state-of-the-art issues is addressed in this research by proposing interference-aware Multi-Tier Scheduling for the SDWSN environment (MTS-SDWSN). First, we perform network construction in which the proposed network is constructed in a 2D hexagonal grid structure to resolve the connectivity issue. Upon constructing the network, the SDWSN nodes are clustered and managed to reduce the energy consumption using the Divide Well To Merge Better (DWTMB) algorithm in which the optimal Cluster Leader (CL) is selected based on adequate constraint. The data from the clustered nodes are sent to the Local Base Station (LBS) via CL in which they are scheduled in multi-tier format to diminish the complexity and interference issues. The first tier involved in scheduling among Cluster Members (CMs) and CL using adequate metrics, whereas the successive tiers (i.e., second and third) involved in scheduling among CLs to LBSs and LBSs to Sink Node (SN) are done using the Non-Cooperative Fuzzy Theory (NCFT) method. Last, the scheduled nodes are routed to appropriate destinations using Secure and Optimal Routing Protocol (SORP). The proposed SORP includes the Alibaba and Forty Thieves (AFT) and Multi Criteria Decision Making (MCDM) algorithms for selecting and ranking the optimal routes. Further, the security of the routes is enabled by adopting trust and Moving Target Defense (MTD) mechanisms. The MTD includes route switching among the SDWSN devices and active switch handling using Cycle Generative Adversarial Networks (CGAN) among the switches. The proposed work is implemented using a NS-3.26 simulation tool, and performance of the proposed model and existing works shows that the proposed work outperforms the existing works.

Data-driven Machine Learning Models for Risk Stratification and Prediction of Emergence Delirium in Pediatric Patients Underwent Tonsillectomy/Adenotonsillectomy.

In the pediatric surgical population, Emergence Delirium (ED) poses a significant challenge. This study aims to develop and validate machine learning (ML) models to identify key features associated with ED and predict its occurrence in children undergoing tonsillectomy or adenotonsillectomy. The analysis involved data cleaning, exploratory data analysis (EDA), supervised predictive modeling, and unsupervised learning on a medical dataset (n = 423). After preliminary data cleaning, EDA encompassed plotting histograms, boxplots, pairplots, and correlation heatmaps to understand variable distributions and relationships. Four predictive models were trained including logistic regression (LR), random forest (RF), Support Vector Machine (SVM), and Gradient Boosting (XGBoost). The models were evaluated and compared using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC), precision, recall, and feature importance. The RF model showed better performance and was used for the test (AUC-ROC 0.96, precision 1.00, and recall 0.92 on the validation set). K-means clustering was applied to find groups within the data. Elbow method and silhouette scores were used to determine the optimal number of clusters. The formed clusters were analyzed by aggregating features to understand the characteristics of each cluster. EDA revealed significant positive correlations between age, weight, American Society of Anesthesiologists (ASA) health score, and surgery duration with the risk of developing ED. Among the ML models, RF achieved the highest performance. Key predictive variables, based on the model's feature importance, included delirium screening scales, extubation time, and time to regain consciousness. Unsupervised K-means clustering identified 2-3 optimal clusters, which represented distinct patient subgroups: younger, healthier, low-risk individuals (cluster 0), and older patients with increasing chronic disease burden, higher delirium screening scores, and consequently higher post-operative delirium risk (clusters 1 and 2). ML techniques are valuable tools for extracting insights and making accurate predictions from healthcare data. High-performing algorithm-based models can be implemented for clinical decision support systems, facilitating early identification and intervention for ED in pediatric patients. By investigating various variables, it is possible to assess risk and implement preventive measures effectively. Furthermore, unsupervised clustering reveals distinct patient subgroups, enabling personalized perioperative management strategies and enhancing overall patient care.

Research on a Multi-Scale Clustering Method for Buildings Taking into Account Visual Cognition

Building clustering is a key problem that needs to be solved in the realization of the automatic synthesis of large-scale maps. The selection of different feature and spatial distance calculation methods has a great impact on the clustering results, and the need to manually select appropriate feature and distance metrics leads to the problem of not being able to fully consider the complexity and diversity of buildings. In this paper, we propose a multi-scale clustering method for buildings that takes visual perception into account using the Gestalt principle to simulate how humans classify buildings through visual perception. Moreover, by analyzing the spatial features and texture attributes of buildings, a visual distance is designed to be used as a condition for building classification to assess the similarity between buildings, solving the complexity of manually selecting feature vectors and spatial distances and realizing the adaptive selection of features. Through experimental validation at different scales (macro, meso and micro), the present method is able to achieve the accurate clustering of buildings, and a frequency threshold of 91% is found, which is able to determine the optimal clustering results. The experimental results show that the proposed method can not only fully consider the complexity and diversity of buildings but also effectively support the understanding and analysis of urban spatial structure and provide a scientific decision-making basis for urban planning and management.

EDRP-GTDQN: An adaptive routing protocol for energy and delay optimization in wireless sensor networks using game theory and deep reinforcement learning

Routing protocols, as a crucial component of the internet of things (IoT), play a significant role in data collection and environmental monitoring tasks. However, existing clustering routing protocols suffer from issues such as uneven network energy consumption, high communication delays, and inadequate adaptation to topology changes. To address these issues, this study proposes an adaptive routing algorithm to balance energy consumption and delay using game theory and deep Q-network (DQN) algorithms (EDRP-GTDQN). Specifically, EDRP-GTDQN evaluates the importance of node positions using node centrality and integrates a game-theoretic-based approach to select optimal cluster heads in terms of node centrality and residual energy. Moreover, graph convolutional networks (GCN) and DQN are incorporated to construct transmission paths for cluster heads, adapt to network topology changes, and balance energy consumption and performance. Furthermore, a cluster rotation mechanism is employed to optimize overall network energy consumption and prevent the formation of hotspots. Experimental results demonstrate that EDRP-GTDQN achieves average performance improvements of 19.76%, 30.04%, 44.2%, and 61.42% in average energy consumption, network lifetime, and average end-to-end delay compared to conventional routing protocols such as EECRAIFA, MRP-GTCO, DEEC, and MH-LEACH. Therefore, EDRP-GTDQN is undoubtedly an effective solution to reduce energy consumption and enhance service quality in wireless sensor networks.

A Radial Distribution Function Based Recognition Algorithm of Point Defects in Large-Scale β-Ga2O3 Systems.

The atomic configurations and concentrations of intrinsic defects profoundly influence the electrical and optical properties of the semiconductor materials. This influence is particularly significant in the case of β-Ga2O3, which is a highly promising ultrawide bandgap semiconductor characterized by highly complex intrinsic defect configurations. Despite its importance, there is a notable absence of an accurate method to recognize these defects in large-scale atomistic computational modeling. We design an effective algorithm for the explicit identification of various intrinsic point defects in the β-Ga2O3 lattice, which constitutes the integration of the particle swarm optimization (PSO) and K-means clustering (K-MC) methods. Our algorithm attains the recognition accuracy exceeding 95%. Finally, the algorithm is applied to dynamic simulations, where the feasibility of dynamic real-time detection is explored.

A Method for Evaluating Demand Response Potential of Industrial Loads Based on Fuzzy Control

Demand response (DR) can ensure electricity supply security by shifting or shedding loads, which plays an important role in a power system with a high proportion of renewable energy sources. Industrial loads are vital participants in DR, but it is difficult to assess DR potential because of many complex factors. In this paper, a new method based on fuzzy control is given to assess the DR potential of industrial loads. A complete assessment framework including four steps is presented. Firstly, the industrial load data are preprocessed to mitigate the influence of noisy and transmission losses, and then the K-means algorithm considering the optimal cluster number is used to calculate baseline load of industrial load. Subsequently, an open-loop fuzzy controller is designed to predict the response factor of different industrial loads. Three strongly correlated indicators, namely peak load rate, electricity intensity, and load flexibility, are selected as the input of fuzzy control, which represents response willingness. Finally, the baseline load of diverse clustering scenarios and the response factor are used to calculate the DR potential of different industrial loads. The proposed method takes into account both economic and technical factors comprehensively, and thus, the results better represent the available DR potential in real-world situations. To demonstrate the effectiveness of the proposed method, the case of a medium-sized city in China is studied. The simulation focuses on the top eight industrial types, and the results show they can contribute about 189 MW available DR potential.

Improved identification of network anomalies through optimal CURE clustering

Abstract In this paper, we propose an advanced network anomaly behavior identification framework to overcome the constraints inherent in conventional rule- or signature-based approaches, which often struggle with emerging and previously unknown threats. Central to our framework is an Enhanced CURE (Cluster Updating and REfining) clustering algorithm, meticulously tailored and refined to incorporate a density-based methodology. This enhancement enables the algorithm to discern subtle shifts in network anomaly patterns with heightened precision. The implementation workflow commences with the application of the optimized CURE algorithm to analyze network data, followed by the deployment of a sophisticated anomaly degree ranking mechanism. This mechanism, through meticulous calculation of individual data points' anomaly degrees and subsequent ranking, effectively isolates those deviating significantly from standard behavioral norms, incorporating a strategic threshold to filter out false positives. To validate our methodology's efficacy and its superiority over existing techniques, experiments were conducted utilizing a substantial real-world network dataset. These tests affirm not only a marked increase in the accuracy of abnormal behavior recognition and a reduction in computational intricacy but also demonstrate the adaptability across diverse network ecosystems. Our approach has proven successful in pinpointing a wide array of network anomalies, encompassing malicious cyberattacks, fraudulent activities, unauthorized intrusions, and breaches of security protocols, thereby highlighting its comprehensive capability in bolstering network defense strategies. Despite the notable advancements and successful identification of various network anomalies, our framework currently lacks integration with real-time learning capabilities, limiting its immediate responsiveness to rapidly evolving attack patterns and necessitating ongoing research for dynamic updates and adaptive learning mechanisms.

II-LA-KM: Improved Initialization of a Learning-Augmented Clustering Algorithm for Effective Rock Discontinuity Grouping

Rock mass discontinuities are an excellent information set for reflecting the geometric, spatial, and physical properties of the rock mass. Using clustering algorithms to analyze them is a significant way to select advantageous orientations of structural surfaces and provide a scientific theoretical basis for other rock mass engineering research. Traditional clustering algorithms often suffer from sensitivity to initialization and lack practical applicability, as discontinuity data are typically rough, low-precision, and unlabeled. Confronting these challenges, II-LA-KM, a learning-augmented clustering algorithm with improved initialization for rock discontinuity grouping, is proposed. Our method begins with heuristically selecting initial centers to ensure they are well-separated. Then, optimal transport is used to adjust these centers, minimizing the transport cost between them and other points. To enhance fault tolerance, a learning-augmented algorithm is integrated that iteratively reduces clustering costs, refining the initial results toward optimal clustering. Extensive experiments on a simulated artificial dataset and a real dataset from Woxi, Hunan, China, featuring both orientational and non-orientational attributes, demonstrate the effectiveness of II-LA-KM. The algorithm achieves a 97.5% accuracy on the artificial dataset and successfully differentiates between overlapping groups. Its performance is even more pronounced on the real dataset, underscoring its robustness for handling complex and noisy data. These strengths make our approach highly beneficial for practical rock discontinuity grouping applications.

IMPLEMENTATION OF K-MEANS AND FUZZY C-MEANS CLUSTERING FOR MAPPING TODDLER STUNTING CASES IN GUNUNGKIDUL DISTRICT

Gunungkidul Regency has the highest prevalence of stunted toddlers in the Special Region of Yogyakarta. This study aims to describe the optimal clustering results of toddler stunting cases using the k-means and fuzzy c-means methods and to describe the characteristic of the mapping results of stunting-prone areas for toddlers in Gunungkidul Regency for the years 2020 – 2022. This study maps stunting-prone areas for toddlers across 30 community health centers in Gunungkidul Regency from 2020 to 2022, with variables including the percentage of babies with low birth weight, babies born stunted, babies receiving health services, stunted toddlers, toddlers receiving health services, babies given exclusive breastfeeding, poor couples of reproductive ages, and families with adequate drinking water. The k-means clustering method determines cluster membership using the distance between objects and centroids, while the fuzzy c-means method uses the degree of membership. Cluster evaluation uses the silhouette coefficient, Calinski-Harabasz index, Davies-Bouldin index, and Dunn index to obtain optimal clustering results. The mapping results are presented as a stunting vulnerability map. The findings indicate that the optimal number of clusters is two, with the fuzzy c-means method proving more optimal than the k-means method based on evaluation scores. In 2020, there were 23 community health centers in cluster 0 and 7 in cluster 1. In 2021, there were 21 community health centers in cluster 0 and 9 in cluster 1. In 2022, there were 18 community health centers in cluster 0 and 12 in cluster 1. Generally, community health centers in cluster 0 are less optimal in specific nutrition interventions, such as for infants and toddlers. In contrast, those in cluster 1 are less optimal in sensitive nutrition interventions, such as poverty and water adequacy.

APPLICATION OF K-MEANS++ WITH DUNN INDEX VALIDATION OF GROUPING WEST KALIMANTAN REGION BASED ON CRIME VULNERABILITY

Crime is an unlawful behavior that will be given a punishment or sanctions based on Kitab Undang-Undang Hukum Pidana (KUHP) or other regulations in Indonesia. One of the provinces in Indonesia, namely West Kalimantan reported that criminal cases are increasing in 2021 and 2022. One of the solutions to minimize that case is grouping the district and city in West Kalimantan based on the level of vulnerability so the authority can be more responsive in solving these problems. The grouping can be done by cluster analysis. This analysis aims to group some objects based on the similarity of characteristics. K-Means++ is one of the methods of cluster analysis. K-Means++ is the development of K-Means, in which K-Means++ is smarter than K-Means in selecting the initial centroid because only one initial centroid is chosen randomly, and the initial centroids of the other clusters are done through calculations. This research uses secondary data from BPS of West Kalimantan, consisting of 10 variables. This research aims to form clusters to determine the level of vulnerability of each district and city in West Kalimantan. The selection of the optimal cluster is done by evaluating the cluster. One of these evaluations is the Dunn Index. Based on the analysis results, the optimum number of clusters is with a Dunn Index value of 0.55. The first cluster is categorized as non-vulnerable with ten members, the second cluster as vulnerable with three members, and the third cluster as very vulnerable with one member.

Self-Organizing and Routing Approach for Condition Monitoring of Railway Tunnels Based on Linear Wireless Sensor Network.

Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network's lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads' load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs.

IQL‐OCDA: An intelligent Q‐learning‐based for optimal clustering and data‐aggregation for wireless sensor networks

AbstractWireless sensor networks (WSNs) can suffer from low battery life due to the energy consumption of the routing protocol. Small sensor nodes are often difficult to recharge after deployment. In a WSN, data aggregation is generally used to reduce or eliminate data redundancy between nodes in order to save energy. In the proposed algorithm, sensor nodes are deployed in appropriate clusters and cluster heads are elected using Q‐learning techniques. Nodes are clustered based on the mean values computed during the clustering phase. Lastly, a performance evaluation and comparison of existing clustering algorithms are performed based on Intelligent Q‐learning. The proposed IQL‐OCDA model reduces end‐to‐end delay by 10.11%, increases throughput by 4.15%, and increases network lifetime by 5.1%.

Clinical phenotype of ARDS based on K-means cluster analysis: a study from the eICU database

PurposeTo explore the characteristics of the clinical phenotype of ARDS based on Machine Learning. MethodsThis is a study on Machine Learning. Screened cases of acute respiratory distress syndrome (ARDS) in the eICU database collected basic information in the cases and clinical data on the Day 1, Day 3, and Day 7 after the diagnosis of ARDS, respectively. Using the Calinski-Harabasz criterion, Gap Statistic, and Silhouette Coefficient, we determine the optimal clustering number k value. By the K-means cluster analysis to derive clinical phenotype, we analyzed the data collected within the first 24 hours. We compared it with the survival of cases under the Berlin standard classification, and also examined the phenotypic conversion within the first 24 hours, on day 3, and on day 7 after the diagnosis of ARDS. ResultsWe collected 5,054 cases and derived three clinical phenotypes using K-means cluster analysis. Phenotype-I is characterized by fewer abnormal laboratory indicators, higher oxygen partial pressure, oxygenation index, APACHE IV score, systolic and diastolic blood pressure, and lower respiratory rate and heart rate. Phenotype-II is characterized by elevated white blood cell count, blood glucose, creatinine, temperature, heart rate, and respiratory rate. Phenotype-III is characterized by elevated age, partial pressure of carbon dioxide, bicarbonate, GCS score, albumin. The differences in ICU length of stay and in-hospital mortality were significantly different between the three phenotypes (P<0.05), with phenotype I having the lowest in-hospital mortality (10%) and phenotype II having the highest (31.8%). To compare the survival analysis of ARDS patients classified by phenotype and those classified according to Berlin criteria. The results showed that the differences in survival between phenotypes were statistically significant (P<0.05) under phenotypic classification. ConclusionsThe clinical classification of ARDS based on K-means clustering analysis is beneficial for further identifying ARDS patients with different characteristics. Compared to the Berlin standard, the new clinical classification of ARDS provides a clearer display of the survival status of different types of patients, which helps to predict patient prognosis.

Optimization of the power performance for three hybrid Darrieus-Savonius rotors based on the Taguchi method

Abstract The hybrid vertical-axis wind turbine is a unique design that overcomes the efficiency limitations of Savonius rotors and the start-up challenges of Darrieus rotors. This study uses the Taguchi optimization method to enhance the performance of a cluster of three hybrid Darrieus-Savonius vertical-axis wind turbines. The optimized parameters include distances between adjacent turbine centers, configuration angles, rotational directions, and the pitch angle. The study is the first to investigate the effect of varying the pitch angle of Darrieus airfoil blades on hybrid design performance. The optimal configuration of the three hybrid VAWTs and the isolated rotor is analyzed through flow velocity, pressure, and turbulence kinetic energy contours. The results show that the pitch angles have the greatest effect on the rated power coefficient among other studied influencing parameters. The optimal cluster configuration’s performance improvement is due to the favorable velocity gradient around the blades. Compared to the isolated rotor, the power coefficient enhancement for the optimal case is 46.545% at the rated tip speed ratio of 3.08. The hybrid rotor in the cluster can achieve almost the same power as the isolated rotor up to a tip speed ratio of 4.1 instead of 2.86 for the isolated rotor. However, the decrease in overall efficiency for some cluster configurations is due to wake flow intensity and trapping between the rotors, which causes a stagnation zone.

Sustainable transformation of energy sector: Cluster analysis for the sustainable development strategies of selected European countries

Implementing measures by the European countries within the framework of the ratification of the Paris Climate Agreement should ensure an enlarged share in energy production from alternative energy sources and the introduction of new alternative energy capacities, which would provide an annual decrease in carbon dioxide emissions growth rate, reduce anthropogenic pressure on the natural environment and the use of non-renewable natural resources while preserving biodiversity. This paper aims at forming a system of indicators of sustainable transformation of the energy sector and studying their influence on the policy of environmental-oriented development. The paper applies Ward's method for determining the optimal cluster number and discriminant analysis for checking which variables significantly influence the formation of countries' clusters. The six clusters of countries obtained describe the different features of decarbonisation and climate change mitigation policies. Using discriminant analysis helped to single out the most relevant indicators of the energy sector's transformation in the direction of its decarbonisation and climate change mitigation. The most significant indicators (energy efficiency, electricity production capacities for renewables and wastes, and share of energy from renewable sources) largely determine the pace and features of forming sustainable development strategies for the energy sector. By highlighting the relevant cluster groups in the study, their characteristic features, and ways of transformational processes in the energy sector were discovered. This enabled the authors to form specific directions for strengthening and activating the processes of decarbonisation of the energy sector and climate change mitigation. There outcomes can become the basis for further research and the formation of appropriate strategies for sustainable development and transformation of the energy sector. The results stemming from this study could help scholars and other affiliated persons determine future strategies for research for implementing Sustainable Development Goals 2030.

Energy Efficient Secured PSO Optimized Clustering and Data Aggregation Routing Protocol for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensor nodes are placed to sense and collect data. Due to the energy constraint nature of WSN, optimising the energy during the data dissemination is a major concern. To solve this problem, data aggregation may be used to bring down the redundant transmission of packets in WSN. In most of the previously available techniques, security is also a major concern during data aggregation and routing process with optimized energy. Many data aggregation-based routing systems are subject to security attacks during the data transfer from sensors to source to Clustered Heads (CHs) and then to sink with data aggregation process. Moreover, the existing data aggregation-based routing protocols suffer from data redundancy with less accuracy in aggregated data. For handling such issues order to overcome these issues, an Energy Efficient Secured Clustered Particle Swarm Optimization (PSO) oriented Data Aggregation Routing Protocol (EESCPSO-DARP) that can provide efficient authentication during data aggregation-based routing is introduced in this paper. Moreover, the proposed protocol enhances the rate of the data transmission by efficient prevention of false data injection and other attacks through node authentication and data encryption. This proposed protocol minimizes the energy usage by minimizing the retransmissions by eliminating the possible redundant transmissions of data during data aggregation-based routing. Moreover, the introduced protocol minimizes both communication and computational overhead through optimal clustering and routing with PSO and provides and efficient routing system. This proposed EESCPSO-DARP protocol has been developed by using the NS3 simulator. The results of this protocol showed improved security, higher packet delivery ratio and enhanced network throughput with reduced energy and delay.

Implementation of K-Means Clustering to Human Development Indicators in East Nusa Tenggara

K-Means has been adopted to group various cases related to the quality of humanresources and economic growth. This study aims to apply K-Means to thecharacteristics based on selected Human Development Index (HDI) indicators,namely the average length of schooling, the expectation of length of schooling andlife expectancy in Province of East Nusa Tenggara. The optimal cluster obtainedis 6 clusters. The cluster with the highest average value of all variables is in cluster1 which is Kupang City area. Meanwhile, condition cluster 6 provides the smallestlife expectancy value compared to other clusters. The smallest average variable ofschool length is in cluster 2. The regions that provide the smallest numberexpectation of length of schooling is in cluster 5.

Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number K = 3 was determined. The collected data were clustered into three categories using the K-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7~414.9 m and 750~900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 &gt; D1 &gt; D3 &gt; D4, while, in the other two, it was D1 &gt; D2 &gt; D3 &gt; D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified as deep mining. Based on this boundary, CatBoost prediction models for the depth of coal seam floor failure in deep and shallow parts were constructed and the prediction results of the model test set were compared with the calculation results of the empirical formula. These models exhibited superior accuracy with a lower mean squared error (MSE) and mean absolute error (MAE) and a higher R-squared (R2) compared to the empirical formula. This study helps to enhance the understanding of coal seam floor behavior, guide floor management, and protect groundwater resources by defining deep and shallow mining to accurately predict floor failure depth.

Descriptive Analysis Of K-Means and Apriori Methods To Find Promotion Strategies For University Bhayangkara.

The increasing number of higher education institutions in Indonesia has intensified competition between universities. Universitas Bhayangkara Jakarta Raya (Ubhara Jaya) must develop an effective marketing plan to stand out. This study used segmentation and associative analysis on 2023 student enrolment data. The Apriori algorithm identified patterns in student preferences for study programmes, while the K-Means method categorized students based on demographics and family income. Three income-based clusters were identified: C0 ‘Already stable’ (IDR 1,000,000 - 2,500,000), C1 ‘Focus on promotion strategy’ (IDR 20,000,000), and C2 ‘Maximise promotion again’ (IDR 5,000,000 - 10,000,000). The Davies-Bouldin Index (DBI) indicated k=5 as the optimal cluster number, but k=3 was adequate with a minimal score difference. The most popular programmes were Communication Science and Management, with high support and confidence values. This data helps Ubhara Jaya manage study programme demand and room availability. Combining K-Means and Apriori algorithms is expected to enhance data segmentation[1] and support effective marketing strategies, aiding strategic decisions in higher education marketing.