Euclidean Distance Similarity Research Articles

Threshold Study for Hanzi Image Recognition: Defining Character and Component Limits in Chinese, Japanese, and Korean Script Processing

Chinese, Japanese, and Korean (CJK) Hanzi image recognition still faces significant challenges, particularly with pixel resolution, stroke count, character frequency and structure. This paper introduces the concepts of character and component thresholds to establish foundational parameters for Hanzi recognition, considering Unevenly Distributed Composite Character (UDCC), which account for up to 88% of Chinese characters. We compiled corpora of Simplified, Traditional characters, and Japanese Kanji, analyzing 3395 images with stroke counts ranging from 1 to 64, and developed the ZH-TC-IM965858 database, containing 33,950 images across 10 pixel resolutions. Using Euclidean distance and ResNet50 similarity analysis, we identified a character threshold of 26 strokes for 24[Formula: see text]×[Formula: see text]24 pixel images, a component threshold of 14 strokes, and a comprehensive threshold of 16 strokes. We found that 7.61% of 8105 common Chinese characters, 27.41% of 96,585 Traditional characters, and 5.99% of 2163 Japanese Kanji exceed this comprehensive threshold. Character Length and Character Frequency (CLCF) models were employed to explore relationships between stroke count, frequency, and thresholds. Additionally, Scale-invariant Feature Transform (SIFT) was applied to match radicals and components, providing insights for improving recognition accuracy. This research advances Hanzi image recognition and enhances multimodal Large Language Models (LLMs) for ideographic languages.

Measuring Distances in High Dimensional Spaces Why Average Group Vector Comparisons Exhibit Bias, And What to Do about it

Abstract Analysts often seek to compare representations in high-dimensional space, e.g., embedding vectors of the same word across groups. We show that the distance measures calculated in such cases can exhibit considerable statistical bias, that stems from uncertainty in the estimation of the elements of those vectors. This problem applies to Euclidean distance, cosine similarity, and other similar measures. After illustrating the severity of this problem for text-as-data applications, we provide and validate a bias correction for the squared Euclidean distance. This same correction also substantially reduces bias in ordinary Euclidean distance and cosine similarity estimates, but corrections for these measures are not quite unbiased and are (non-intuitively) bimodal when distances are close to zero. The estimators require obtaining the variance of the latent positions. We (will) implement the estimator in free software, and we offer recommendations for related work.

Penerapan Fuzzy Logic dalam Sistem Rekomendasi Film: Studi Kasus Justice League (2017)

Enhancing the user experience on social networks and e-commerce sites is often supported by accurate recommendation systems. This recommendation task is challenging as it requires a high degree of accuracy. This paper discusses the implementation of fuzzy logic in a movie recommendation system. Two similarity measurement methods are used: the first by considering the choices of similar users, and the second by matching genres of movies that have been rated by the user. To calculate similarity, four different techniques are used, namely Euclidean Distance, Manhattan Distance, Pearson Coefficient, and Cosine Similarity. Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are applied in decision-making. Experiments are conducted on the Movie Lens dataset, and a comparative performance analysis is reported. Experimental results show that ANFIS outperforms FIS in most cases when the Pearson correlation metric is used for similarity calculation.

Improving Satellite-Based Retrieval of Maize Leaf Chlorophyll Content by Joint Observation with UAV Hyperspectral Data

While satellite-based remote sensing offers a promising avenue for large-scale LCC estimations, the accuracy of evaluations is often decreased by mixed pixels, attributable to distinct farming practices and diverse soil conditions. To overcome these challenges and to account for maize intercropping with soybeans at different growth stages combined with varying soil backgrounds, a hyperspectral database for maize was set up using a random linear mixed model applied to hyperspectral data recorded by an unmanned aerial vehicle (UAV). Four methods, namely, Euclidean distance, Minkowski distance, Manhattan distance, and Cosine similarity, were used to compare vegetation spectra from Sentinel-2A with the newly constructed database. In a next step, widely used vegetation indices such as NDVI, NAOC, and CAI were tested to find the optimum method for LCC retrieval, validated by field measurements. The results show that the NAOC had the strongest correlation with ground sampling information (R2 = 0.83, RMSE = 0.94 μg/cm2, and MAE = 0.67 μg/cm2). Additional field measurements sampled at other farming areas were applied to validate the method’s transferability and generalization. Here too, validation results showed a highly precise LCC estimation (R2 = 0.93, RMSE = 1.10 μg/cm2, and MAE = 1.09 μg/cm2), demonstrating that integrating UAV hyperspectral data with a random linear mixed model significantly improves satellite-based LCC retrievals.

Optimizing site selection for construction and demolition waste resource treatment plants using a hesitant neutrosophic set: a case study in Xiamen, China

The site selection of construction and demolition waste resource treatment plants is a complicated multi-criteria decision-making (MCDM) issue of ‘not in my back yard’ (NIMBY) facilities, which is affected by spatial, economic, societal, environmental and other factors. Traditional site selection MCDM methods expose the shortcomings of evaluation information representation and spatial factor analysis. This study develops an optimized MCDM site selection method in a hesitant neutrosophic set (HNS) environment, capable of effectively handling uncertainty and fully incorporating spatial considerations. The innovation of this research lies in integrating HNS into MCDM, combined with geographic information system technology, revised DEMATEL-based analytic network process method, cosine similarity and Euclidean distance similarity measures. A case study in Xiamen, China, validates this method’s effectiveness, and sensitivity analysis and comparative studies demonstrate its superior stability, robustness and resolution. The main contribution of this study is the development of an MCDM optimization method for siting large-scale NIMBY facilities.

Robust Spherical Laplacian Embedding.

Laplacian embedding (LE) aims to project high-dimensional input data samples, which often contain nonlinear structures, into a low-dimensional space. However, existing distance functions used in the embedding space fail to provide discriminative representations for real-world datasets, especially those related to text analysis or image processing. Cosine similarity measurements are advantageous in dealing with sparse data but are fragile to the impact of outliers and noise from samples or data features. In this work, we propose robust spherical LE (RS-LE), a powerful method that builds the LE on a novel metric that unifies the Euclidean distance and cosine similarity in a spherical space using a robust lp,p -norm. We introduce an efficient iterative algorithm, the proximal alternating linearized minimization (ALM) method, to solve the difficult optimization problem that arises from the new metric, which is neither convex nor smooth. This algorithm allows the solution to achieve both global and objective convergence. Our findings are presented in rigorous theoretical analysis and validated in experiments.

Deep convolutional generative adversarial network for generation of computed tomography images of discontinuously carbon fiber reinforced polymer microstructures

Computed tomography images are of utmost importance when characterizing the heterogeneous and complex microstructure of discontinuously fiber reinforced polymers. However, the devices are expensive and the scans are time- and energy-intensive. Through recent advances in generative adversarial networks, the instantaneous generation of endless numbers of images that are representative of the input images and hold physical significance becomes possible. Hence, this work presents a deep convolutional generative adversarial network trained on approximately 30,000 input images from carbon fiber reinforced polyamide 6 computed tomography scans. The challenge lies in the low contrast between the two constituents caused by the close proximity of the density of polyamide 6 and carbon fibers as well as the small fiber diameter compared to the necessary resolution of the images. In addition, the stochastic, heterogeneous microstructure does not follow any logical or predictable rules exacerbating their generation. The quality of the images generated by the trained network of 256 pixel ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 256 pixel was investigated through the Fréchet inception distance and nearest neighbor considerations based on Euclidean distance and structural similarity index measure. Additional visual qualitative assessment ensured the realistic depiction of the complex mixed single fiber and fiber bundle structure alongside flow-related physically feasible positioning of the fibers in the polymer. The authors foresee additionally huge potential in creating three-dimensional representative volume elements typically used in composites homogenization.

Cluster analysis applied to obtaining reference models for building thermal performance studies

Cluster analysis has often been used to obtain reference models. However, using different methods in each analysis step may yield different results. Furthermore, the validation of the results is often neglected. This study aims to develop a cluster analysis method to obtain reference models for use in thermal performance studies of buildings from different clustering configurations. The procedure proposed is composed of six steps: (1) initial database composition, (2) data matrices formation, (3) cluster analysis, (4) determination of reference models, (5) methods validation and (6) results interpretation. Different data treatments, similarity measures and partitioning algorithms were combined in the cluster analysis to define the method with the best cluster formation. The weighting factor was the data treatment that most contributed to obtaining suitable clustering solutions. City-block, Euclidean Distance and Squared Euclidean Distance similarity measures resulted in suitable formations as well as Complete Linkage, Ward and K-means algorithms. Two clusters were obtained from the cluster methodology, for which two reference models were determined. Hypothesis tests showed that clusters differ for most performance indicators. It was concluded that the procedure proposed could identify the combination of methods that results in the best application of cluster analysis. The main contribution of this paper is to introduce a procedure for validating clusters. This procedure is an applicable technique to obtain reference models.

Use of recommendation models to provide support to dyslexic students

Dyslexia is the most widespread specific learning disorder and significantly impair different cognitive domains. This, in turn, negatively affects dyslexic students during their learning path. Therefore, specific support must be given to these students. In addition, such a support must be highly personalized, since the problems generated by the disorder can be very different from one to another. In this work, we explored the possibility of using AI to suggest the most suitable supporting tools for dyslexic students, so as to provide a targeted help that can be of real utility. To do this, we relied on recommendation algorithms, which are a branch of machine learning, that aim to detect personal preferences and provide the most suitable suggestions. We hence implemented and trained three collaborative-filtering recommendation models, namely an item-based, a user-based and a weighted-hybrid model, and studied their performance on a large database of 1237 students’ information, collected with a self-evaluating questionnaire regarding all the most used supporting strategies and digital tools. Each recommendation model was tested with three different similarity metrics, namely Pearson correlation, Euclidean distance and Cosine similarity. The obtained results showed that a recommendation system is highly effective in suggesting the optimal help tools/strategies for everyone, with an error less then 12%. As a further evidence of the effectiveness of the implemented system, its precision was 0.85 and its recall was 0.83. The best performing filter was the hybrid one, when Pearson’s correlation is used to measure the distance among users and/or items. In addition, in a final testing performed on 50 students, dyslexic students who used the recommendation algorithm increased their academic scores of almost 1 point in a 1 to 10 scale, showing higher learning performance compared to students who did not use it. This demonstrates that the proposed approach is successful and can be used as a new and effective methodology to support students with dyslexia.

Three-Dimensional View Relationship-Based Context-Aware Emotion Recognition.

Context-aware emotion recognition (CAER) leverages comprehensive scene information, including facial expressions, body postures, and contextual background. However, current studies predominantly rely on facial expressions, body postures, and global contextual features; the interaction between the agents (target individuals) and other objects in the scene is usually absent or incomplete. In this article, a three-dimensional view relationship-based CAER (TDRCer) method is proposed, which comprises two branches: the personal emotional branch (PEB) and the contextual emotional branch (CEB). First, PEB is designed for the extraction of facial expression features and body posture features from the agent. A vision transformer (ViT), pretrained by contrastive learning with a novel loss function combining Euclidean distance and cosine similarity, is applied to enhance the robustness of facial expression features. Meanwhile, the human body contour images extracted by semantic segmentation are fed into another ViT to extract body posture features. Second, CEB is constructed for the extraction of global contextual features and interactive relationships among objects in the scene. The images masked by the agents' bodies are fed into a ViT to extract global contextual features. By leveraging both the gaze angle and depth map, a three-dimensional view graph (3DVG) is constructed to represent the interactive relationships between agents and objects in the scene. Then, a graph convolutional network is employed to extract interactive relationship features from the 3DVG. Finally, the multiplicative fusion strategy is applied to fuse the features of two branches, and the fused features are utilized to classify the emotions. TDRCer achieves an accuracy of 89.90% on the CAER-S dataset and a mean average precision (mAP) of 36.02% on the EMOTIons in context (EMOTIC) dataset. The code can be accessed at https://github.com/mengTender/TDRCer.

MLRR-ATV: A Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization for scRNA-seq Data Clustering.

Since genomics was proposed, the exploration of genes has been the focus of research. The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to explore gene expression at the single-cell level. Due to the limitations of sequencing technology, the data contains a lot of noise. At the same time, it also has the characteristics of highdimensional and sparse. Clustering is a common method of analyzing scRNA-seq data. This paper proposes a novel singlecell clustering method called Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization (MLRR-ATV). The Adaptive Total-Variation (ATV) regularization is introduced into Low-Rank Representation (LRR) model to reduce the influence of noise through gradient learning. Then, the linear and nonlinear manifold structures in the data are learned through Euclidean distance and cosine similarity, and more valuable information is retained. Because the model is non-convex, we use the Alternating Direction Method of Multipliers (ADMM) to optimize the model. We tested the performance of the MLRRATV model on eight real scRNA-seq datasets and selected nine state-of-the-art methods as comparison methods. The experimental results show that the performance of the MLRRATV model is better than the other nine methods.

Supervised Manifold Learning Based on Multi-Feature Information Discriminative Fusion within an Adaptive Nearest Neighbor Strategy Applied to Rolling Bearing Fault Diagnosis.

Rolling bearings are a key component for ensuring the safe and smooth operation of rotating machinery and are very prone to failure. Therefore, intelligent fault diagnosis research on rolling bearings has become a crucial task in the field of mechanical fault diagnosis. This paper proposes research on the fault diagnosis of rolling bearings based on an adaptive nearest neighbor strategy and the discriminative fusion of multi-feature information using supervised manifold learning (AN-MFIDFS-Isomap). Firstly, an adaptive nearest neighbor strategy is proposed using the Euclidean distance and cosine similarity to optimize the selection of neighboring points. Secondly, three feature space transformation and feature information extraction methods are proposed, among which an innovative exponential linear kernel function is introduced to provide new feature information descriptions for the data, enhancing feature sensitivity. Finally, under the adaptive nearest neighbor strategy, a novel AN-MFIDFS-Isomap algorithm is proposed for rolling bearing fault diagnosis by fusing various feature information and classifiers through discriminative fusion with label information. The proposed AN-MFIDFS-Isomap algorithm is validated on the CWRU open dataset and our experimental dataset. The experiments show that the proposed method outperforms other traditional manifold learning methods in terms of data clustering and fault diagnosis.

Lost item identification model development using similarity prediction method with CNN ResNet algorithm

<p><strong>Background:</strong> Incidents of personal belongings being lost often occur due to our negligence as human beings or criminal acts such as theft. The methods used to address such situations are still manual and ineffective. The manual process of reporting lost items requires significant time and effort. Additionally, matching the information of lost items with the found ones becomes increasingly difficult, and finding the original owners can be time-consuming. <strong>Objectives and Methods:</strong> This research aims to develop an approach that aids the community in the management of lost items by incorporating a process of item identification. It proposes the creation of an iOS-based prototype model that implements image comparison and string matching. The ResNet-50 architecture extracts features from images, and the Euclidean Distance method measures similarity between these features. Natural language processing used for text pre-processing and employs the cosine similarity metric to assess textual similarity in item descriptions. <strong>Result and Conclusion:</strong> By combining Euclidean distance and cosine similarity values, the model predicts similar lost item reports. Image comparison provides an accuracy result of 29.96% correctness, while string matching with 97.92% correctness. Thorough testing and validation confirm the model’s success across different reports.</p>

CASE BASED REASONING UNTUK PENENTUAN JENIS BEASISWA INTERNAL (BSM DAN PPS) UNIVERSITAS ‘AISYIYAH YOGYAKARTA

Education is the most important thing to educate the nation's life, but there are still many students who fail because of constrained tuition fees, in order to overcome this, the University of 'Aisyiyah Yogyakarta provides internal scholarship assistance to students in the form of Student Study Assistance (BSM) scholarships and Scholarship Improvement Scholarships (PPS). Many students apply for scholarships each year. The criteria for submitting scholarships tend to be similar every year, resulting in data being used to assess repetitions or having similarities between old data and new data. Case Based Reasoning (CBR) is a system that aims to solve a new case by adapting solutions found in previous similar cases. To simplify the process of determining the type of scholarship obtained by students, Case Based Reasoning is used. Similarity calculation for the case of determining the type of PPS and BSM scholarship using Euclidean Distance Similarity. The results of the K05 case similarity calculation are similar to the K02 case, the similarity value obtained is 0.86727, so the K05 case will get a PPS scholarship

A method for stitching remote sensing images with Delaunay triangle feature constraints

The process of synthesizing multiple images into a seamless panoramic image is referred to as remote sensing image stitching. Existing studies focus less on the influence of topography on the appearance and texture of images and the perturbation of image spectra by topographic changes. This paper presents a remote sensing image stitching method that considers the impact of topography and geomorphology. First, the feature matching was optimized using the Euclidean distance similarity of texture features and the nearest neighbor distance ratio of feature points in remote sensing images as constraints. Then, the Delaunay triangle mesh of feature points in the image overlapping region was constructed, the geometric features of Delaunay triangles were used to optimize the triangle matching and reduce the matching redundancy, and the affine transformation matrix was solved based on the comprehensive consideration of the geometric features of Delaunay triangles and the texture features of the remote sensing images. Finally, the weighted fusion algorithm was applied to stitch and fuse the images. Three image datasets were selected for the experiments, one in which there were large terrain undulations in the imaging regions, one in which the main body of the imaging regions was water, and one in which the overall terrain of the imaging regions had relatively gentle slopes but obscuring features were also present. The results showed that the average correct rates of method feature matching were 89.78%, 94.99%, and 96.17%, which were the best for each algorithm, and the average feature matching times were 4.13, 8.27 and 7.19 s. These times are much lower than those obtained with the APAP and AANAP algorithms and basically the same as those achieved with the SPHP and SURF algorithms. In terms of visual effect, the AG, and indices of the proposed method were all significantly improved compared to the APAP, SPHP, AANAP, and SURF algorithms, the advantages were most obvious when dealing with datasets with large topographic relief in the imaging regions, with the maximum improvement of the AG, and indices were 85.61%, 95.68%, and 93.12%, respectively. Therefore, it is possible to conclude that the proposed method is more suitable for remote sensing image stitching and fusion of different topographic and geomorphological conditions.

Stress tensor similarity index based on Euclidean distance for numerical back analysis of in situ stress fields

Evaluating the similarity between measured and predicted stress states is critical for the numerical back analysis of three-dimensional in situ stress fields. However, conventional nontensor approaches process stress components or the magnitude and orientation of principal stresses separately, which may yield contradictory results among multiple indices. To address this issue, we propose the Euclidean distance similarity (EDS), which is a dimensionless single index based on spatial distance. As a case study, EDS was applied to a site with complex geological conditions for verification. EDS can be derived in the form of stress component or principal stress. EDS exhibited a positive correlation with the initial difference and magnitude error of principal stresses and changed periodically with the Euler angle of rotation around the principal stress. Moreover, three magnitude errors and three Euler angles had different effects on EDS. Finally, EDS can be classified into five levels (i.e., extremely high, high, normal, low, and very low) according to the magnitude errors of principal stresses and Euler angles, where the “normal” level is recommended as a general error standard. The thresholds of EDS differ according to the initial stress level. These results are a valuable contribution to the similarity evaluation of stress tensors.

Feature Extraction and Classification of Canopy Gaps Using GLCM- and MLBP-Based Rotation-Invariant Feature Descriptors Derived from WorldView-3 Imagery

Accurate mapping of selective logging (SL) serves as the foundation for additional research on forest restoration and regeneration, species diversification and distribution, and ecosystem dynamics, among other applications. This study aimed to model canopy gaps created by illegal logging of Ocotea usambarensis in Mt. Kenya Forest Reserve (MKFR). A texture-spectral analysis approach was applied to exploit the potential of WorldView-3 (WV-3) multispectral imagery. First, texture properties were explored in the sub-band images using fused grey-level co-occurrence matrix (GLCM)- and local binary pattern (LBP)-based texture feature extraction. Second, the texture features were fused with colour using the multivariate local binary pattern (MLBP) model. The G-statistic and Euclidean distance similarity measures were applied to increase accuracy. The random forest (RF) and support vector machine (SVM) were used to identify and classify distinctive features in the texture and spectral domains of the WV-3 dataset. The variable importance measurement in RF ranked the relative influence of sets of variables in the classification models. Overall accuracy (OA) scores for the respective MLBP models were in the range of 80–95.1%. The respective user’s accuracy (UA) and producer’s accuracy (PA) for the univariate LBP and MLBP models were in the range of 67–75% and 77–100%, respectively.

CASE BASED REASONING (CBR) FOR OBESITY LEVEL ESTIMATION USING K-MEANS INDEXING METHOD

As many as 600 million of the 1.9 billion adults who are overweight are obese. Obesity that is not treated immediately will be a risk factor for increasing cardiovascular, metabolic, degenerative diseases, and even death at a young age. Case Based Reasoning (CBR) can be used to estimate a person's obesity level using previous cases. The old case with the highest similarity will be the solution for the new case. Indexing methods such as the K-Means Algorithm are needed so that the search for similar cases does not involve all cases on a case base so that it can shorten the computation time at the retrieve stage and still produce optimal solutions. Cosine similarity is used to find relevant clusters of new cases and Euclidean distance similarity is used to calculate similarity between cases. Random subsampling method was used to validate the CBR system. The test results with K=2 indicate that the CBR is better than the CBR-K-Means, each of which produces an average accuracy of 88.365% and 88.270% at a threshold of 0.8. CBR-K-Means produces an average computation time at the retrieve stage of 33.55 seconds and is faster than the CBR of 35.5 seconds.

Crystallographic variant mapping using precession electron diffraction data

In this work, we developed three methods to map crystallographic variants of samples at the nanoscale by analyzing precession electron diffraction data using a high-temperature shape memory alloy and a VO2 thin film on sapphire as the model systems. The three methods are (I) a user-selecting-reference pattern approach, (II) an algorithm-selecting-reference-pattern approach, and (III) a k-means approach. In the first two approaches, Euclidean distance, Cosine, and Structural Similarity (SSIM) algorithms were assessed for the diffraction pattern similarity quantification. We demonstrated that the Euclidean distance and SSIM methods outperform the Cosine algorithm. We further revealed that the random noise in the diffraction data can dramatically affect similarity quantification. Denoising processes could improve the crystallographic mapping quality. With the three methods mentioned above, we were able to map the crystallographic variants in different materials systems, thus enabling fast variant number quantification and clear variant distribution visualization. The advantages and disadvantages of each approach are also discussed. We expect these methods to benefit researchers who work on martensitic materials, in which the variant information is critical to understand their properties and functionalities.

Visual Assessment of Cluster Tendency with Variations of Distance Measures

Finding the cluster structure is essential for analyzing self-organized networking structures, such as social networks. In such problems, a wide variety of distance measures can be used. Common clustering methods often require the number of clusters to be explicitly indicated before starting the process of clustering. A preliminary step to clustering is deciding, firstly, whether the data contain any clusters and, secondly, how many clusters the dataset contains. To highlight the internal structure of data, several methods for visual assessment of clustering tendency (VAT family of methods) have been developed. The vast majority of these methods use the Euclidean distance or cosine similarity measure. In our study, we modified the VAT and iVAT algorithms for visual assessment of the clustering tendency with a wide variety of distance measures. We compared the results of our algorithms obtained from both samples from repositories and data from applied problems.