This study improves one of the initialization methods for the k-means clustering algorithm based on a rough set neighbourhood model to ‎enhance performance in noisy datasets. The method involves data normalization, obtaining a neighbourhood threshold based on the 0.25th ‎trimmed mean of pairwise Minkowski distances, calculating cohesion and coupling degrees of the neighbourhoods and between them re-‎spectively, and obtaining the initial cluster centres as the k points having maximum cohesion degrees with minimum coupling degrees among ‎themselves. The approach was evaluated on six datasets using Silhouette, Davies–Bouldin, Calinski–Harabasz, and Dunn–Hubert indices in ‎comparison with an existing method. Results showed that the improved method outperformed the existing method on noisy datasets, achieving higher Silhouette and Dunn–Hubert scores, and lower Davies–Bouldin values, with a slight reduction in Calinski–Harabasz index in ‎one of the datasets. On the non-noisy datasets, the two methods were at par in all four performance indices. With the improved performance, showing that the improved method enhanced the stability and robustness of k-means clustering in the presence of noisy data, it can ‎be recommended for clustering noisy datasets such as gene expression, image, and signal datasets‎.

CODAS with heronian minkowski distance operator: a novel approach for complex fermatean fuzzy multi-criteria group decision analysis

This paper introduces and explores a range of distance measures defined on multi-fuzzy sets, emphasizing both their mathematical foundations and applicability to real-world decision environments. Classical distance metrics such as Minkowski, Hamming, and Euclidean measures are extended to the multi-fuzzy context, and their behaviour is analysed at both the set and element levels. The proposed formulations are rigorously analyzed at both the set level and element level to capture variations in structure and similarity more precisely. The study further examines how these measures are affected when multi-fuzzy sets are transformed via crisp functions or adjusted using fuzzy weight matrices. The Minkowski distance in the original multi-fuzzy sets dominates or bounds the corresponding distance in the multi-fuzzy weighted sets via fuzzy matrix transformation. In addition to these classical extensions, this paper introduces new deviation-based and normalised measures aimed at quantifying unanimity and consensus within group decision-making processes. By extending classical statistical notions such as mean, variance, and standard deviation into the multi-fuzzy domain, the authors develop refined methods for assessing agreement among individual judgments. These are further strengthened through the use of weighted criteria to reflect varying importance. A numerical case study is provided to demonstrate the practical effectiveness of the proposed approach in real-world consensus evaluation. By improving the accuracy of collective decision-making models, the research contributes to transparent, equitable, and evidence-based decision support systems in fields such as education, healthcare, and policy analysis. The study is primarily theoretical and validated through a limited case study; future work may involve empirical validation across larger datasets or multi–fuzzy–neutrosophic extensions.

The rapid advancement of artificial intelligence has enabled practical, data-driven approaches to agricultural quality assessment. However, many existing methods rely on complex sensor systems that are costly and difficult to deploy in the field. This study proposes a lightweight and interpretable K-Nearest Neighbor (KNN) model for non-destructive evaluation of sugarcane milling feasibility using five easily measurable physical attributes: relative distance ratio, internode length, mean diameter, circumference, and weight per centimeter. Samples with Brix less than 16 are categorized as not feasible for milling, while Brix equal to or greater than 16 are classified as possible. A dataset of 1,889 Bululawang samples collected in Malang, East Java, Indonesia, was evaluated across twenty-two scenarios that varied the train-test split, normalization method, distance metric, and neighborhood size. The optimal configuration, consisting of an 80:20 split, Standard normalization, the Minkowski distance metric, and k=75, achieved an accuracy of 78%. The findings confirm that physical measurements can serve as effective predictors of sugarcane quality and support data-driven inspection and sustainable resource utilization in line with SDGs 2, 9, and 12.

ABSTRACT Life testing of engineering systems with dependent components requires robust multivariate methods. Parametric approaches depend on restrictive assumptions, limiting their use in complex or unknown lifetime distribution settings. This study evaluates nonparametric methods for comparing parallel system lifetimes under minimal sample requirements. Three distribution‐free tests are considered: the rank‐energy test, the Wilcoxon‐type rank‐sum precedence test and the Lepage test, the latter two applied to Minkowski distances from lifetime vectors. Results suggest that the Lepage test on Minkowski distances generally outperforms the other two, offering a robust method to compare multi‐component system lifetimes.

Failure mode risk prioritization is crucial in aircraft landing systems, where undetected or misjudged failures can lead to catastrophic outcomes. Effective risk analysis enables proactive maintenance and enhances aviation safety in such critical phases of flight. In this study, a novel hybrid decision-making framework is proposed to prioritize failure modes in aircraft landing systems by integrating the Multi-Attributive Border Approximation Area Comparison (MABAC) method with Prospect Theory under a q-Rung Orthopair Hesitant Fuzzy Set (q-ROHFS) environment. Traditional failure modes and effects analysis (FMEA) approaches often suffer from rigid weighting schemes, lack of sensitivity to expert hesitancy, and an inability to incorporate psychological factors such as risk aversion or subjective evaluations—especially in high-risk domains like aviation. To address these limitations, the proposed model incorporates human psychological behaviour and uncertainty in expert assessments. Prospect Theory is employed to capture decision makers’ risk attitudes and reference-dependent evaluations, while q-ROHFSs allow more flexible and comprehensive representation of hesitant and uncertain information. In this approach, Best-Worst Method (BWM) is used to determine the relative importance of risk factors for each decision maker, and their individual weights are obtained using TOPSIS-based similarity measures. A novel generalized q-ROHF Minkowski distance measure is also introduced and implemented to determine the weights of decision makers in the TOPSIS method, as well as to construct the prospect decision matrix and the distance matrix in the MABAC method, thereby enhancing computational precision. The applicability and effectiveness of the proposed method are demonstrated through a real-world case study on aircraft landing systems, and a sensitivity analysis is conducted to validate the robustness of the results. The findings highlight the method’s capability to reflect expert preferences more realistically and improve risk prioritization decisions in complex safety-critical systems.

. The mixed geographically weighted regression model has been extensively studied both in its estimation and applications. However, in most existing research results of the model, the spatial weights are commonly computed based on the Euclidean distance, which is a straight-line distance metric. Considering the diversity of the sample data and the complexity of the geography, the Euclidean distance may be inappropriate. Thus, it is necessary to define an appropriate distance metric when calibrating a space model. In this article, a class of non Euclidean distance metrics – Minkowski distance, by varying its exponent parameter and the coordinate rotation, is used to compute the spatial weights in calibrating the mixed geographically regression model. Considering the fact that the two-step estimation procedure is less time-consuming and can obtain explicit expressions of the coefficients, we mainly focus on the performance of the two-step estimation method with Minkowski distance. Simulation results demonstrate that the proposed method has more robustness to the sample data and tends to obtain more accurate constant and varying coefficient estimates. A real-world dataset is analyzed to show the application of the proposed estimation method and the article is ended with a conclusion.

The Computer Science Study Program of Universitas Methodist Indonesia is faced with the challenge of verifying the authenticity of student theses, which is still done manually. This study applies the Minkowski Distance method to analyze the level of similarity of thesis abstracts using one hundred samples. The preprocessing stage is carried out through five systematic steps: cleansing to remove non-alphabetic characters, case folding for letter standardization, tokenizing for text splitting, filtering for stopword elimination, and stemming to obtain root words, resulting in word vectors that are analyzed. The Minkowski Distance method is implemented with three parameter variations P = 3, P = 5, and P = 7, where the selection of parameters is based on differences in sensitivity to vector dimensions, the higher the P value, the greater the emphasis on significant differences between dimensions. The test results show that the parameter P = 7 provides the most optimal similarity measurement with the smallest distance of 3.84 for documents with the highest similarity. These findings contribute to the development of a more effective similarity detection system to maintain academic integrity.

Reinforcement knowledge sharing assisted two-archive evolutionary algorithm for many-objective optimization

This study presents an enhanced YOLO-based deep learning model for automatic detection of knee osteoarthritis (KOA) from X-ray images. The integration of Minkowski distance into the loss function improves the model’s sensitivity to spatial variations and noise. Extensive preprocessing and a lightweight YOLO architecture ensure real-time performance with high accuracy. Experimental results demonstrate superior detection rates compared to traditional methods, especially in identifying early-stage KOA.

Salmonella enterica subsp. enterica serovar Enteritidis is a well-known non-typhoidal serovar, commonly associated with foodborne illnesses. Here, we report the draft genome sequence of Salmonella enterica subsp. enterica serovar Enteritidis strain SSTRA25, isolated from a pediatric patient with bacteremia in Mosul, Iraq. The genome was sequenced using the Illumina NovaSeq 6000 platform. The assembled and annotated genome comprised 4733,231 bp with 40 contigs, and a GC content of 52.12%. It contains 4580 coding sequences (CDSs), 69 tRNAs, 9 rRNAs, 14 ncRNAs, 2 CRISPR arrays, and 368 annotated subsystems. The analysis of antimicrobial resistance genes revealed multiple genes associated with various drug classes, including phenicols, penicillin beta-lactams, cephalosporins, carbapenems, and monobactams, with perfect sequence matches. In addition, chromosomal point mutations linked to antimicrobial resistance were identified with significant sequence similarity. Salmonella enterica subsp. enterica serovar Enteritidis SSTRA25 showed a high predicted human pathogenicity score (0.941) and carried multiple virulence factors, including SspH2, SopA, SadA, ShdA, MisL, and several flagellar and outer membrane proteins. In the pathogenic landscape, the closest strain was Salmonella enterica subsp. enterica serovar Holcomb NY_FSL C7–1028, with a Minkowski distance of 0.024804. The genome sequence of Salmonella enterica subsp. enterica serovar Enteritidis SSTRA25 has been deposited in NCBI under the accession number JBNHMR000000000.

Inpainting, for filling missing image regions, is a crucial task in various applications, such as medical imaging and remote sensing. Trending data-driven approaches efficiency, for image inpainting, often requires extensive data preprocessing. In this sense, there is still a need for model-driven approaches in case of application constrained with data availability and quality, especially for those related for time series forecasting using image inpainting techniques. This paper proposes an improved model-driven approach relying on patch-based techniques. Our approach deviates from the standard Sum of Squared Differences (SSD) similarity measure by introducing a Hybrid Similarity (HySim), which combines both strengths of Chebyshev and Minkowski distances. This hybridization enhances patch selection, leading to high-quality inpainting results with reduced mismatch errors. Experimental results proved the effectiveness of our approach against other model-driven techniques, such as diffusion or patch-based approaches, showcasing its effectiveness in achieving visually pleasing restorations. • Experiments investigating the examplar-based approach. • Key differences between similarity and distance measures concepts. • Hybrid Similarity (HySim) measure for patch selection.

Medan City is one of Indonesia's largest cities and faces fundamental challenges in addressing malnourished toddlers.It had a stunting prevalence of 19.9% in 2022.The high rates necessitate a practical approach to identifying and managing high-risk areas.This study aims to map districts in Medan City based on the spatial data of public health center locations and malnutrition data for toddlers, using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm.DBSCAN is a popular clustering algorithm because of its ability to group data based on density and detect outliers as noise.However, using the Euclidean distance function in DBSCAN may not be appropriate for all geospatial cases.The novelty lies in comparing five distance functions (Euclidean, Manhattan, Minkowski, Cosine, Chebyshev) within DBSCAN to determine which produces the most meaningful clustering in a geospatial health context.The study shows that DBSCAN with the Chebyshev distance function cannot effectively map the malnutrition problem in toddlers, as indicated by a Silhouette index (SI) value below 0.25.The clustering quality using Minkowski and Cosine distance functions in DBSCAN is not superior to that of the classical DBSCAN, with all three producing weak and unclear structures.The most effective mapping results come from using the Manhattan distance function in DBSCAN, which yields an SI value of 0.51045, two clusters, and optimal parameters of Minpts = 6-9 and = 6.98-7.8.The first cluster includes two districts (Medan Labuhan and Marelan), while the remaining districts form the second cluster.The analysis of different distance functions provides new insights into how selecting the appropriate distance measure can influence clustering quality in a geospatial context with DBSCAN.The similarity of the clusters is expected to inform decision-making in addressing toddler malnutrition issues in Medan City.

The widespread use of multimedia editing tools has facilitated the creation of realistic video forgeries, jeopardizing the trust in video content. To address frame duplication forgery, a prevalent technique, this paper introduces a novel algorithm leveraging QR decomposition (orthogonal-triangular decomposition) and Minkowski distance. The algorithm extracts frame features using QR decomposition and compares them with a reference frame using Minkowski distance. Candidate duplicates are identified through random block matching. We evaluate the proposed method on standard datasets (TDTVD, LASIESTA, and IVY LAB) and a self-generated dataset. Our method achieves exceptional performance, attaining a perfect -score for video-level detection on both the TDTVD and our self-generated datasets. Notably, for frame-level detection, it achieves an average accuracy of 0.9943, precision of 0.9752, recall of 0.9858, and -score of 0.9803 across all datasets. Our analysis demonstrates the proposed method demonstrates promising performance in detecting multiply-duplicated frames and shows robustness against post-processing, potentially outperforming existing approaches.

A multi-method spatial examination of factors associated with changes in geographic accessibility to buprenorphine providers in HEALing communities study states Kentucky, Massachusetts, and Ohio.

A significant role is played by clustering approaches in data mining processes, which becomes more challenging because of the rising dimension of the available databases. Clustering strategies are employed in various sectors like information retrieval, social network analytics, image processing, and so on. Clustering assists the user to understand dissimilarity and similarity among objects. The big data concept has collected incredible attention from various research areas, like government and industry within a short lifetime. In this paper, a Deep Fractional Calculus-Improved Invasive Weed Optimization fuzzy clustering (Deep FC-IIWO fuzzy clustering) based scheme is developed to cluster big data. The map-reduce architecture is employed to tackle the over-fitting obstacles occurring in the clustering of big data. The features are extracted using the mapping function which is followed by clustering in the reducer stage. Furthermore, the selection of features is essential for further clustering process. Here, the Minkowski distance measure is applied for selecting the most prominent features. The approach of clustering is executed with the help of the devised Deep FC-IIWO fuzzy clustering model. The implemented FC-IIWO framework is newly created by integrating Fractional Calculus (FC) and Improved Invasive Weed Optimizer (IIWO). The proposed Deep FC-IIWO fuzzy clustering scheme performed better than other prevailing models concerning the rand coefficient, Jaccard coefficient, and clustering accuracy of 0.9563, 0.9379, and 0.9522, correspondingly.

Using network theory and data analysis, we study the messages on Twitter (X) about ecological sustainability over the period 2007-2022. With a global view of 70,311,541 messages we examined the sentiment, keywords and hashtags utilised, as well as the correlations between sentiment and both socioeconomic and environmental variables. In addition to the above, we carried out an in-depth analysis of the global interactions network (retweets, replies and quotes), with a special focus on the study of the community network (CNET) (with 4576 supernodes, and 9855 links). The sentiment shown in the text of the tweets was positive over the years in all analysed locations, although close to neutral. Keyword analysis detected terms present in tweets posted from various regions, showing global thinking in the world. The relationships between sentiment and variables examined were continent- and country-specific, identifying a stronger correlation with socioeconomic attributes. Regarding CNET, according to the study performed using adjacency and laplacian embeddings, as well as Chebyshev, Euclidean, Minkowski, and Manhattan distances, pairs of unconnected supernodes appeared to have more similarity in their connection patterns than pairs of connected supernodes, due to the topological structure of CNET which has a large number of peripheral nodes that are not connected to each other, but are connected to nodes with higher centrality. In agreement with the Jaccard coefficient, resource allocation index, Adamic Adar index, and preferential attachment score, there is little possibility of link formation between supernodes. Statistically the supernodes also exhibited high topological similarity. A few specific supernodes host most of the users, showing the highest centralities among those analysed. The basic structure of CNET, which maintained its key properties, was also examined. Strategies that promote communication between supernodes to achieve greater participation and diversity in discussions need to be further investigated.

Context: Epilepsy is a severe chronic neurological disorder with considerable incidence due to recurrent seizures. These seizures can be detected and diagnosed noninvasively using an electroencephalogram. Empirical mode decomposition has shown excellent results in identifying epileptic crises.Method: This study addressed a significant gap by proposing a novel approach for the automated selection of the most relevant intrinsic mode functions (IMFs) using empirical mode decomposition and discrimination metrics such as the Minkowski distance, the mean square error, cross-correlation, and the entropy function. The main objective was to address the challenge of determining the optimal number of IMFs required to accurately reconstruct brain activity signals.Results:The results were promising, as they facilitated the identification of IMFs that contained the most relevant information, marking a significant advancement in the field. To validate these findings, standard methods including the correlation coefficient, the p-value, and the Wasserstein distance were employed. Additionally, an EEGLAB-based brain mapping was conducted, adding robustness and credibility to the results obtained. Conclusions: Our method is a fundamental tool that enhances epileptic seizure identification from EEG signals, with significant clinical implications in the diagnosis and treatment of epilepsy.

Smoking is one of the causes of various diseases in the body. Smoking can also cause abnormal conditions that are pathological and physiological in the oral cavity, one of which is smoker melanosis. The clinical picture of pigmentation smoker melanosis is the presence of scattered brown spots with a diameter of less than 1 cm and is most often located on the gingiva. The data was taken using the oral photograph image capture method using a 12MP resolution camera, provided that the object distance from the camera was 6 cm and the flash was on. This analysis utilized the Gingiva Pigmentation Index (GPI) classification system proposed by Hedin, which compares the pigmented area, and Dummett's Oral Colour Index (DOPI), which assesses the density of pigmentation. In this study, the classification process was carried out with the KNN algorithm using features from digital image processing in the segmentation area, the average value of the red, green, and blue colour levels. The classification process uses the nearest neighbour value of 3 and a p-value of 2 to measure the distance to the nearest neighbor using the Minkowski distance formula. The results of the test data accuracy (1.0) with F1 scores for each class for test data DOPI 0 = 1.0, DOPI 1 = 1.0, DOPI 2 = 1.0, DOPI 3 = 1.0. Meanwhile, the classification process can use more up-to-date methods, such as CNN to improve classification accuracy.

This study aims to optimize the implementation of the K-Nearest Neighbors (K-NN) algorithm for medical image classification by focusing on selecting the optimal KKK parameter and applying dimensionality reduction techniques to improve accuracy and efficiency. The data used was sourced from public medical image repositories such as The Cancer Imaging Archive (TCIA) and Medical Image Analysis datasets, covering various diseases, including brain tumors, lung cancer, and kidney lesions. The research process involves data collection, data preprocessing, dimensionality reduction using Principal Component Analysis (PCA), applying the K-NN algorithm with Euclidean, Minkowski, and Cosine distance metrics, and performance evaluation using accuracy, precision, recall, and F1-score. Experimental results demonstrate that K=5with the Euclidean distance metric provides the best performance, achieving an accuracy of 90%. Additionally, PCA effectively reduces computational time by 30% without significantly compromising accuracy. This study proves that K-NN is an effective method for medical image classification. However, further research is needed to integrate K-NN with deep learning models to enhance performance and feature extraction capabilities.