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A collection of sets displays a proximity gap with respect to some property if for every set in the collection, either (i) all members are δ-close to the property in relative Hamming distance or (ii) only a tiny fraction of members are δ-close to the property. In particular, no set in the collection has roughly half of its members δ-close to the property and the others δ-far from it. We show that the collection of affine spaces displays a proximity gap with respect to Reed--Solomon (RS) codes, even over small fields, of size polynomial in the dimension of the code, and the gap applies to any δ smaller than the Johnson/Guruswami--Sudan list-decoding bound of the RS code.We also show near-optimal gap results, over fields of (at least) linear size in the RS code dimension, for δ smaller than the unique decoding radius. Concretely, if δ is smaller than half the minimal distance of an RS code V ⊂ Fnq, then every affine space is either entirely δ-close to the code or, alternatively, at most an (n/q)-fraction of it is δ-close to the code. Finally, we discuss several applications of our proximity gap results to distributed storage, multi-party cryptographic protocols, and concretely efficient proof systems. We prove the proximity gap results by analyzing the execution of classical algebraic decoding algorithms for Reed--Solomon codes (due to Berlekamp--Welch and Guruswami--Sudan) on a formal element of an affine space. This involves working with Reed--Solomon codes whose base field is an (infinite) rational function field. Our proofs are obtained by developing an extension (to function fields) of a strategy of Arora and Sudan for analyzing low-degree tests. [ABSTRACT FROM AUTHOR]

The traveling salesman problem (TSP) is a frequently studied problem by researchers today and belongs to the class of combinatorial optimization problems. It can be used to solve many current world problems such as scheduling, circuit design, layout design of plants in factories, route planning and printed circuit design. Therefore, researchers working in the field of optimization methods use it as a realistic test environment and evaluate the performance of new algorithms on it. In this study, a discrete version of the orca predation algorithm (OPA) was developed, called DOPA. DOPA is a novel discrete and permutation-coded optimization algorithm. As in OPA, it consists of two phases: chasing and attacking. However, these phases in DOPA were arranged to be able to solve a combinatorial optimization problem such as TSP. In the chasing phase, the distances between the orcas were calculated with the Hamming distance, and the speed values were obtained by using these distances. The positions of the orcas were updated using the speed values and 2-opt algorithm. In the attacking phase, the positions of the orcas were calculated by order crossover (OX1) operator. In the position adjustment procedure, swap local search operator was used. Thus, the convergence of the algorithm was accelerated. DOPA has much less parameters than OPA. Therefore, it is a simple and effective algorithm with few parameters. The parameters of DOPA were optimized by the Taguchi statistical method. It was tested on 67 well-known TSP instances. The results of different performance measures and tests showed that DOPA is a highly competitive and alternative method compared to other methods. [ABSTRACT FROM AUTHOR]

This paper introduces a novel similarity function that evaluates both the quantitative and qualitative similarities between data instances, named QQ-Means (Qualitative and Quantitative-Means). The values are naturally scaled to fall within the range of − 1 to 1. The magnitude signifies the extent of quantitative similarity, while the sign denotes qualitative similarity. The effectiveness of the QQ-Means for cluster analysis is tested by incorporating it into the K-means clustering algorithm. We compare the results of the proposed distance measure with commonly used distance or similarity measures such as Euclidean distance, Hamming distance, Mutual Information, Manhattan distance, and Chebyshev distance. These measures are also applied to the classic K-means algorithm or its variations to ensure consistency in the experimental procedure and conditions. The QQ-Means similarity metric was evaluated on gene-expression datasets and real-world complex datasets. The experimental findings demonstrate the effectiveness of the novel similarity measurement method in extracting valuable information from the data. [ABSTRACT FROM AUTHOR]

With the ever-increasing volume of digital images available online, it has become important to identify similar images quickly and accurately across a variety of domains. Perceptual hashing is known to be the most widely used method for such near-duplicate image retrieval. While content-based features provide superior accuracy in detecting similar images, using hash codes derived from these features reduces storage requirements and improves time efficiency. However, as the image volume increases, the computational complexity of perceptual hashing poses a challenge. Another significant challenge is the robustness of perceptual hash functions against adversarial manipulations. To deal with these issues and to improve the accuracy of near duplicate image retrieval, this paper proposes a double perceptual hashing approach. Here, the primary hash performs a coarse matching and retrieves all the relevant images to the query image. Subsequently, a secondary hash performs fine matching by eliminating false positive images identified by the primary hash. While dual hash functions enhance robustness, another novel strategy of partitioning the primary hash into equal-sized segments boosts storage efficiency and accelerates the search speed by over tenfold compared to the naive approach. Experimental results using Copydays dataset augmented with 30,000 random images show average mAP and response time of 0.89 and 0.101sec respectively verifying its efficiency on large datasets. [ABSTRACT FROM AUTHOR]

AbstractOrthogonal arrays (OAs) are applied in the statistical design of experiments, coding theory, cryptography, various types of software testing and quality control and have many connections to other combinatorial designs. The Hamming distances of OAs play a critical role in many areas such as algebraic combinatorics, quantum information theory, and wireless sensor networks. However, the calculation method of Hamming distances is difficult, especially for mixed orthogonal arrays (MOAs), since they rely on constructions or structures of OAs in many cases. In this article, by using the number of coincidences, we find some OAs with high strength and MOAs with strength 2 and 3, whose Hamming distances do not depend on their structures. We also calculate Hamming distances of a series of unsaturated asymmetrical OAs via their construction methods such as deleting columns, the expansive replacement method, and difference schemes. [ABSTRACT FROM AUTHOR]

A large number of perceptual image hashing schemes have been developed in recent decades, and they usually calculated the hash distances using Euclidean distance, correlation coefficient, normalized Hamming distance, etc. These hash distances are not customized measurements for hash sequences, which are all single values. To this end, a new distance measurement method, i.e. a distance vector instead of a single value, is proposed in this paper. Since a hash sequence can be regarded as the compressed representation of image perceptual content, the correlation between images is maintained between hash sequences. In our method, the correlation between hash sequences is captured by residuals and co-occurrence matrix to form a distance vector. The correlation differences between perceptually similar and distinct images can be captured in the obtained distance vectors. Using a binary classifier, it is easy to classify the distance vectors of perceptually similar image pairs and perceptually distinct image pairs. As a result, content authentication accuracy of perceptual image hashing can be effectively increased, which is verified by experimental results. [ABSTRACT FROM AUTHOR]

Linear Diophantine fuzzy soft set (LDFSS) is a powerful tool to handle uncertain information more flexibly and comprehensively. The aim of this paper is to propose some information measures (similarity measure, distance measure and entropy) for the LDFSS environment. For this aim, we first redefine the notion of LDFSS from a more general perspective. Then, we extend some well-known classical distances such as Hamming distance, Euclidean distance, Minkowski distance and etc. for the LDFSS environment. Also, we give some similarity measures where some of which are constructed from the presented distance measures and introduce the entropy measure for the LDFSSs. After, we construct two different decision-making methods, one based on the presented similarity measures and the other based on the TOPSIS method with entropy. Finally, we demonstrate a numerical example related to the medical diagnosis to illustrate that the proposed methods are more objective and feasible in the applications. [ABSTRACT FROM AUTHOR]

In 2020, Cao et al. proved that any repeated-root constacyclic code is monomially equivalent to a matrix product code of simple-root constacyclic codes. In this paper, we study a family of matrix product codes with wonderful properties, which is a generalization of linear codes obtained from the [ u + v | u - v ] -construction and [ u + v | λ - 1 u - λ - 1 v ] -construction. Then we show that any λ -constacyclic code (not necessary repeated-root λ -constacyclic code) of length N over the finite field F q with gcd (q - 1 ord (λ) , N) ≥ 2 , where ord (λ) is the order of λ in the cyclic group F q ∗ = F q \ { 0 } , is a matrix product code of some constacyclic codes. It is a highly interesting question that the existence of sequences { C 1 , C 2 , C 3 ,... } of Euclidean (or Hermitian) self-dual codes with square-root-like minimum Hamming distances, i.e., C i is an [ n (C i) , k (C i) , d (C i) ] q -linear code such that lim i → + ∞ n (C i) = + ∞ and lim i → + ∞ d (C i) n (C i) > 0. Based on the [ u + v | λ - 1 u - λ - 1 v ] -construction, we construct several families of Euclidean (or Hermitian) self-dual codes with square-root-like minimum Hamming distances by using Reed-Muller codes, projective Reed-Muller codes. And we construct some new Euclidean isodual λ -constacyclic codes with square-root-like minimum Hamming distances from Euclidean self-dual cyclic codes and Euclidean self-dual negacyclic codes by monomial equivalences. [ABSTRACT FROM AUTHOR]

The notion of Fréchet mean (also known as “barycenter”) network is the workhorse of most machine learning algorithms that require the estimation of a “location” parameter to analyse network-valued data. In this context, it is critical that the network barycenter inherits the topological structure of the networks in the training dataset. The metric-which measures the proximity between networks-controls the structural properties of the barycenter. This work is significant because it provides for the first time analytical estimates of the sample Fréchet mean for the stochastic blockmodel, which is at the cutting edge of rigorous probabilistic analysis of random networks. We show that the mean network computed with the Hamming distance is unable to capture the topology of the networks in the training sample, whereas the mean network computed using the effective resistance distance recovers the correct partitions and associated edge density. From a practical standpoint, our work informs the choice of metrics in the context where the sample Fréchet mean network is used to characterize the topology of networks for network-valued machine learning. [ABSTRACT FROM AUTHOR]

This paper considers the notion of nonbinary (linear) codes and the notion of hypergraphs and makes a relation between two mathematical tools for real-world applications. We consider a nonbinary (linear) code as an underline set and construct a type of hypergraphs called N.C-hypergraph (N.L.C-hypergraph) and establish any given hypergraph is isomorphic to an N.C-hypergraph (N.L.C-hypergraph). This study introduces an equivalence relation on any given code as a positive equivalence relation based on the weight of codewords, generates quotient N.C-hypergraph, and computes the cardinal of its hyperedges and Hamming distance of any given two code words of quotient N.C-hypergraphs. Also, we introduce a binary relation on quotient N.C-hypergraphs, convert them to simple graphs as code-based graphs, and prove that all graphs are positive code-based graphs. We characterize the minimum distance and maximum distance of codewords in hyperedges of code-based (hyper)graphs concerning linear codes. Finally, we design real problems (such as wireless sensor (hyper)networks and American standard code for information interchange) as complex hypernetworks (such that are based on the code words) as a quotient N.C-hypergraph (N.L.C-hypergraph) and convert them to the code-based graphs (such as wireless sensor networks). [ABSTRACT FROM AUTHOR]

ABSTRACT As a fundamental component that supports business connectivity and enables intelligent decision‐making, vector spatial data encounter increasing copyright and security challenges throughout their lifecycle. There is an imminent need for a comprehensive lifecycle protection solution to ensure data security and adherence to copyright laws. This study introduces an integrated protection scheme designed to address copyright and security issues across the entire lifecycle of vector spatial data. First, DNA encryption is applied to the vector spatial data, with the key subsequently encrypted using the RSA public key. Concurrently, GD‐PBIBD fingerprint encoding is used to generate a fingerprint sequence. Thereafter, this sequence is scrambled using logistic mapping, and a quantization mechanism embeds the scrambled fingerprint sequence into the coordinates. Subsequently, the RSA private key is used to decrypt the key and extract the fingerprint information. Finally, logistic mapping is employed to restore the fingerprint sequence, and Hamming distance calculations are used to identify colluding users and trace potential data leaks. Experimental studies indicate that the proposed DNA encryption algorithm is effective for various types of vector spatial data, including point, polyline, and polygon data. It features a large key space and high key sensitivity. Furthermore, the GD‐PBIBD fingerprint encoding is simple to construct, offers excellent imperceptibility, and provides robust resistance to both single‐ and multi‐user attacks. This research addresses the gap in full lifecycle copyright and security protection for vector spatial data and offers a holistic solution for secure data circulation, thereby demonstrating significant practical value. [ABSTRACT FROM AUTHOR]

Background: A better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated. Methods: The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD). Results: Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 =.44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 =.33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes. Conclusion: These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression. Trial registration: Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018). [ABSTRACT FROM AUTHOR]

The rapid progress in the transfer of information and its availability are the reasons behind the widespread use of the Internet. Document images are the most complicated and challenging category among various forms of digital data to which watermarking can be applied for its security and authentication. Watermarking of the document image is a very difficult task, since these images have very limited redundancy. Because of this factor there has been very little research in this area. A comprehensive research should be carried out to ensure the effective assessment, review as well as implementation of document image watermarking techniques. This paper reviews the existing document image watermarking by considering different evaluation parameters. Based on this review, a variety of challenges and directions have been prescribed for the development of effective watermarking techniques for document images. [ABSTRACT FROM AUTHOR]

As the proliferation of video content continues, and many video archives lack suitable metadata, therefore, video retrieval, particularly through example‐based search, has become increasingly crucial. Existing metadata often fails to meet the needs of specific types of searches, especially when videos contain elements from different modalities, such as visual and audio. Consequently, developing video retrieval methods that can handle multi‐modal content is essential. An innovative Multi‐modal Video Search by Examples (MVSE) framework is introduced, employing state‐of‐the‐art techniques in its various components. In designing MVSE, the authors focused on accuracy, efficiency, interactivity, and extensibility, with key components including advanced data processing and a user‐friendly interface aimed at enhancing search effectiveness and user experience. Furthermore, the framework was comprehensively evaluated, assessing individual components, data quality issues, and overall retrieval performance using high‐quality and low‐quality BBC archive videos. The evaluation reveals that: (1) multi‐modal search yields better results than single‐modal search; (2) the quality of video, both visual and audio, has an impact on the query precision. Compared with image query results, audio quality has a greater impact on the query precision (3) a two‐stage search process (i.e. searching by Hamming distance based on hashing, followed by searching by Cosine similarity based on embedding); is effective but increases time overhead; (4) large‐scale video retrieval is not only feasible but also expected to emerge shortly. [ABSTRACT FROM AUTHOR]

An elastic-degenerate (ED) string is a sequence of n finite sets of strings of total length N, introduced to represent a set of related DNA sequences, also known as a pangenome. The ED string matching (EDSM) problem consists in reporting all occurrences of a pattern of length m in an ED text. The EDSM problem has recently received some attention by the combinatorial pattern matching community, culminating in an O ~ (n m ω - 1) + O (N) -time algorithm [Bernardini et al., SIAM J. Comput. 2022], where ω denotes the matrix multiplication exponent and the O ~ (·) notation suppresses polylog factors. In the k-EDSM problem, the approximate version of EDSM, we are asked to report all pattern occurrences with at most k errors. k-EDSM can be solved in O (k 2 m G + k N) time, under edit distance, or O (k m G + k N) time, under Hamming distance, where G denotes the total number of strings in the ED text [Bernardini et al., Theor. Comput. Sci. 2020]. Unfortunately, G is only bounded by N, and so even for k = 1 , the existing algorithms run in Ω (m N) time in the worst case. In this paper we make progress in this direction. We show that 1-EDSM can be solved in O ((n m 2 + N) log m) or O (n m 3 + N) time under edit distance. For the decision version of the problem, we present a faster O (n m 2 log m + N log log m) -time algorithm. We also show that 1-EDSM can be solved in O (n m 2 + N log m) time under Hamming distance. Our algorithms for edit distance rely on non-trivial reductions from 1-EDSM to special instances of classic computational geometry problems (2d rectangle stabbing or 2d range emptiness), which we show how to solve efficiently. In order to obtain an even faster algorithm for Hamming distance, we rely on employing and adapting the k-errata trees for indexing with errors [Cole et al., STOC 2004]. This is an extended version of a paper presented at LATIN 2022. [ABSTRACT FROM AUTHOR]

Leveraging the simplicity of nucleotide mismatch distributions, we provide an intuitive window into the evolution of the human influenza A 'nonstructural' (NS) gene segment. In an analysis suggested by the eminent Danish biologist Freddy B. Christiansen, we illustrate the existence of a continuous genetic "backbone" of influenza A NS sequences, steadily increasing in nucleotide distance to the 1918 root over more than a century. The 2009 influenza A/H1N1 pandemic represents a clear departure from this enduring genetic backbone. Utilizing nucleotide distance maps and phylogenetic analyses, we illustrate remaining uncertainties regarding the origin of the 2009 pandemic, highlighting the complexity of influenza evolution. The NS segment is interesting precisely because it experiences less pervasive positive selection, and departs less strongly from neutral evolution than e.g. the HA antigen. Consequently, sudden deviations from neutral diversification can indicate changes in other genes via the hitchhiking effect. Our approach employs two measures based on nucleotide mismatch counts to analyze the evolutionary dynamics of the NS gene segment. The rooted Hamming map of distances between a reference sequence and all other sequences over time, and the unrooted temporal Hamming distribution which captures the distribution of genotypic distances between simultaneously circulating viruses, thereby revealing patterns of nucleotide diversity and epi-evolutionary dynamics. [ABSTRACT FROM AUTHOR]

In the field of fuzzy sets, distance measures can effectively quantify the relevant uncertainty. Regarding hesitant fuzzy sets (HFSs), improved hesitant fuzzy distance measures have recently been proposed by fusing classical distance measures with hesitation degrees, and the corresponding information enrichment can be probabilistically advanced to pursue new distance measures of probabilistic hesitant fuzzy sets (PHFSs). Aiming at PHFSs, the improved distance measures of HFSs are simulated and extended in this paper, and thus improved distance measures of PHFSs are proposed; the new PHFSs distances are utilized to construct a new method of probabilistic hesitant fuzzy decision-making, called MEREC-TODIM. Firstly, the new probabilistic hesitant fuzzy Hamming distance and Euclidean distance are directly and parametrically established by incorporating hesitation degrees; accordingly, the improved distance measures exhibit a 2 × 2 system on (non-parameter, parameter) and (Hamming, Euclidean), and their distance property, measure size, parameter monotonicity, and promotion degeneration are investigated and acquired. Furthermore, a modified score function is proposed for MEREC to determine attribute weights, and thus a corresponding decision method with TODIM (i.e., MEREC-TODIM) is established for PHFSs applications on evaluation sorting and optimization selection. Finally, MEREC-TODIM is validated through parameter analyses and decision comparisons, and it is effectively applied to two practical examples: Carbon Capture Utilization Storage and PhD Admission Interviews. Highlights: PHF Hamming and Euclidean distances fusing hesitation degrees provide extensions on HFSs and improvements on PHFSs. An improved score function considering deviation degrees emerges to induce MEREC for attribute weight determination. Improved measures-based TODIM and MEREC constitute MEREC-TODIM decision making with validity and performance. [ABSTRACT FROM AUTHOR]

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Glaucoma is a significant eye disease that can lead to irreversible vision loss if not detected and treated early. This research focuses on developing an automated glaucoma detection system using a combination of a convolutional neural network (CNN) with the residual network 18 (ResNet18) architecture, locality sensitive hashing (LSH), and Hamming distance calculation. The CNN model is trained to extract meaningful features from retinal images, while LSH enables efficient indexing and retrieval of similar images. Hamming distance calculations are utilized to measure the dissimilarity between binary codes obtained from LSH. A dataset of 506 retinal images, consisting of 117 glaucoma images, 19 glaucoma suspect images, and 370 healthy images. The proposed glaucoma detection system achieved an average accuracy of 99.96%, sensitivity of 99.97%, and specificity of 99.94% during training, and 82.37% accuracy, 86.78% sensitivity, and 73.55% specificity during testing. Comparative analysis demonstrated its superiority over traditional methods. Further research should focus on larger datasets and explore multi-class classification for different glaucoma stages. The proposed system has potential for early glaucoma detection, facilitating timely intervention, and preventing vision loss. [ABSTRACT FROM AUTHOR]

In this paper, we have determined the generators and rank of a 2D cyclic code of length mn over a finite chain ring ℛ with residue field 𝔽q, where m is arbitrary and q ≡ 1(mod n). Further, we have obtained a necessary and sufficient condition for a 2D cyclic code over a finite chain ring ℛ to be MHDR. Some examples of 2D cyclic codes have been constructed and ranks of these 2D cyclic codes have been calculated by us. We have also given a few examples of 2D cyclic codes over some finite chain rings, which are MHDR.

Choosing building materials suppliers is crucial as it directly affects the quality, cost, and timeline of a project. Quality suppliers ensure the reliability and consistency of materials, reducing construction risks. The right suppliers offer competitive pricing and flexible delivery schedules, helping control budgets and optimize resource use. Additionally, suppliers' professional support and technical services can enhance construction efficiency and quality. By carefully selecting suppliers, project teams can establish long-term partnerships, improving overall construction standards and market competitiveness. Thus, supplier selection is a key factor in the success of construction projects. The process of selecting and applying building material suppliers (BMSs) is a multiple-attribute group decision-making (MAGDM) method. This study constructs the 2-tuple linguistic neutrosophic number combined grey relational analysis (2TLNN-CGRA) approach, which integrates the grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). To validate the method, a numerical example for building material supplier selection is provided, accompanied by comparative studies to highlight the effectiveness of the 2TLNN-CGRA approach. The key contributions of this research include (1) the utilization of the 2TLNN-CGRA approach to address MAGDM under 2TLNSs conditions; (2) the incorporation of both 2TLNN Hamming distance (2TLNNHD) and 2TLNN Euclidean distance (2TLNNED) within the 2TLNSs framework in the 2TLNN-CGRA approach; (3) the demonstration of the method through a numerical example for supplier selection; and (4) a series of efficient comparisons with other existing decision-making approaches. [ABSTRACT FROM AUTHOR]

The significance of evaluating the design quality of Maximum Power Point Tracking (MPPT) controllers in photovoltaic systems lies in ensuring efficient and stable operation under various environmental conditions. By assessing the controller's tracking efficiency, dynamic response speed, steady-state error, and disturbance rejection capability, the system's energy output can be optimized, equipment lifespan extended, operating costs reduced, and overall system reliability and performance improved, thereby maximizing solar energy utilization efficiency. The quality evaluation of MPPT controller design for photovoltaic systems is framed as a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM (ExpTODIM) approach has been introduced to address this type of decision-making. Single-valued neutrosophic sets (SVNSs) are employed as a decision-making tool to represent fuzzy data during the quality evaluation process of MPPT controller design in photovoltaic systems. In this study, a method combining the single- valued neutrosophic number and Exponential TODIM (SVNN-Com-ExpTODIM), based on the SVNN Hamming distance (SVNNHD) and SVNN Euclidean distance (SVNNED), is proposed to solve the MAGDM problem under SVNSs. Finally, a numerical study is conducted to demonstrate the effectiveness of the SVNN-Com-ExpTODIM approach for the quality evaluation of MPPT controller design in photovoltaic systems, supported by comparative analysis. [ABSTRACT FROM AUTHOR]

The evaluation of public opinion management capability for public crises in universities assesses the ability of universities to guide and handle public opinion during crises. The evaluation covers areas such as opinion monitoring, crisis response, information dissemination, and media communication. Through a systematic assessment, universities can improve their crisis management capabilities, reduce the spread of negative public opinion, and maintain campus stability and reputation. The public opinion management capability evaluation for public crises in universities is multiple-attribute group decision-making (MAGDM). Recently, the Logarithmic TODIM (LogTODIM) technique was interpreted to cope with MAGDM. The single-valued neutrosophic sets (SVNSs) are interpreted as decision tools for characterizing fuzzy data during the public opinion management capability evaluation for public crises in universities. In this study, the single-valued neutrosophic number combined Logarithmic TODIM (SVNN-Com-LogTODIM) technique based on the SVNN Hamming distance (SVNNHD) and SVNN Euclidean distance (SVNNED) is interpreted to solve the MAGDM under SVNSs. Conclusively, numerical study for public opinion management capability evaluation for public crises in universities is interpreted to elucidate the SVNN-Com-LogTODIM technique through comparative analysis. [ABSTRACT FROM AUTHOR]

Due to today's competitive environment and information boom, companies are concerned about production planning in uncertain conditions. This paper integrates decision-making method with production scheduling model by considering limited resources. In this paper, a new mathematical model is extended for production project scheduling with multiple execution modes. The main aim of the formulation is to plan and schedule real production projects in uncertain environments. A new mixed-integer linear formulation is presented by considering trade-off of cost, time, as well as quality. Cost–time-quality trade-off is extended with the interval information. In the presented model, activity quality could be enhanced by reworking. The interval forms of some parameters, including duration, quality of activities, cost, and total available resources, are obtained by determining weights of experts and aggregating them. The presented group decision-making method is based on a bi-directional projection measure to deal with interval information. Since the mathematical model is multi-objective and some data are interval, a new modified solution method is developed for solving the model. The presented method for both decision-making and mathematical models is investigated by a real-world production project and two datasets to ascertain the accuracy of the model. Finally, an appropriate sensitivity analysis is proposed. [ABSTRACT FROM AUTHOR]

The task of constructing test sequences difference characteristics was studied. Its relevance for generating controlled random tests and complexity in finding difference measures for the case of symbolic tests were substantiated. The limitations of using traditional distance characteristics to obtain a measure of the difference between test sets are shown. For the binary case, a new measure of the difference MH(Ti, Tk) of two character test sets Ti and Tk is defined based on the classical Hamming distance. This measure represents n components, each of which is determined by the Hamming distance between the binary set Ti and the pattern Tk cyclically shifted by v bits. The main properties of the proposed dissimilarity measure are reviewed and its effectiveness for classifying test candidates when generating controlled random tests is shown. Experimental results are presented that confirm the effectiveness of the proposed difference measure.

Introduction. The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health. Materials and methods. This study uses statistical methods and machine learning techniques from simple to complex: logistic regression model, random forest method, and gradient boosting. The calculations used the AAindex matrices in parallel to the Hamming distance. The calculations were carried out with different types and values of antigenic escape thresholds, on four data sets. The results were compared using common binary classification metrics. Results. Significant differentiation is shown depending on the data sets used. The best results were demonstrated by all three models for the forecast autumn season of 2022, which were preliminary trained on the February season of the same year (Auroc 0.934; 0.958; 0.956, respectively). The lowest results were obtained for the entire forecast year 2023, they were set up on data from two seasons of 2022 (Aucroc 0.614; 0.658; 0.775). The dependence of the results on the types of thresholds used and their values turned out to be insignificant. The additional use of AAindex matrices did not significantly improve the results of the models without introducing significant deterioration. Conclusion. More complex models show better results. When developing cross-immunity models, testing on a variety of data sets is important to make strong claims about their prognostic robustness.

Let [Formula: see text], where p is an odd prime and r is a positive integer. Consider a ring [Formula: see text], where [Formula: see text] with [Formula: see text] and [Formula: see text] with [Formula: see text]. In this paper, we examine the algebraic structure of constacyclic codes over the ring [Formula: see text] of block length [Formula: see text]. Further, a construction of quantum error-correcting codes (QECCs) from constacyclic codes over [Formula: see text] is also given. Moreover, we derive a number of new QECCs.

Abstract This paper describes a generalized Diophantine fuzzy sets, which can be seen as a generalization of both Diophantine fuzzy sets and Pythagorean fuzzy sets. We define the basic properties of generalized Diophantine fuzzy set, as well as their relationships and distances. We compare Diophantine fuzzy sets with other Diophantine Pythagorean fuzzy sets to demonstrate their importance in the literature. We introduce new operators including necessity, possibility, accuracy function and score function. Furthermore, we discuss the new distance between normalized Euclidean distance and normalized Hamming distance. For a generalized Diophantine fuzzy relation, image and inverse image functions are defined. Numerous real-world applications can be found for the prevalent ideas of intuitionistic fuzzy sets, Pythagorean fuzzy sets, Diophantine fuzzy sets and q-rung orthopair fuzzy sets. Regretfully, these theories about the membership and non-membership grades have their own limits. We provide a new idea the generalized Diophantine fuzzy set that eliminates these limitations by including reference parameters. Compared to other kinds of fuzzy sets, there are more applications for generalized Diophantine fuzzy sets. We offer practical examples that show how different enhanced distances might be used in everyday situations. Additionally, to demonstrate the effectiveness of the suggested approach, flowchart based multi-criteria decision-making is provided and used to a numerical example. The outcomes are assessed for various parameter values. Furthermore, a comparative analysis developed to demonstrate the superiority of the suggested technique over current methodologies.

In this paper, we propose a new class of error-detecting codes based on quasigroups using automorphisms of the finite field 픽pn and the additive group (픽p, +). We demonstrate that these codes are effective in detecting burst errors caused by hardware damage or noisy transmission. We also explore the codes ability to detect various types of double errors, including transposition errors, twin errors, and phonetic errors. Our analysis extends beyond bit-level errors to include block-level errors. Finally, we provide a comparative analysis and demonstrate the real-world applications of our code. [ABSTRACT FROM AUTHOR]

The article titled "A Criterion of Properness for a Family of Functions" explores the concept of proper families of functions and their applications in different areas, such as cryptography. It introduces the idea of properness and investigates the actions and transformations that maintain properness. The article also discusses the isometry group of the space and its connection to weak isometries. It concludes by stating that properness-preserving transformations can be represented as compositions of reencodings and consistent renumberings of families. The text acknowledges that there are other types of transformations not covered in the paper. [Extracted from the article]

We address the problem of modifying vertex weights of a tree in such an optimal way that a given facility (vertex) becomes a 1-median in the modified tree. Here, each modifying cost receive any value within an interval. As the costs ar.e not exactly known, we apply the concept of absolute robust and minmax regret criteria to measure the cost functions. We first consider the absolute robust inverse 1-median problem with sum objective function. The duality of the problem helps to know the convexity of the induced univariate minimization problem. Based on the convexity, an O (n log 2 n) time algorithm is developed, where n is the number of vertices on the underlying tree. We also apply the minmax regret criteria to the uncertain inverse 1-median problem with Chebyshev norm and bottleneck Hamming distance. It is shown that in the optimal solution there exists exactly one cost coefficient attaining the upper bound and the others attaining their lower bounds. Hence, we develop strongly polynomial-time algorithms for the problems based on this special property. [ABSTRACT FROM AUTHOR]

AbstractA model-based approach is developed for clustering categorical data with no natural ordering. The proposed method exploits the Hamming distance to define a family of probability mass functions to model the data. The elements of this family are then considered as kernels of a finite mixture model with an unknown number of components. Conjugate Bayesian inference has been derived for the parameters of the Hamming distribution model. The mixture is framed in a Bayesian nonparametric setting, and a transdimensional blocked Gibbs sampler is developed to provide full Bayesian inference on the number of clusters, their structure, and the group-specific parameters, facilitating the computation with respect to customary reversible jump algorithms. The proposed model encompasses a parsimonious latent class model as a special case when the number of components is fixed. Model performances are assessed via a simulation study and reference datasets, showing improvements in clustering recovery over existing approaches. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

AbstractOne of the most common strategies to achieve optimum designs for physical and computer high-dimensional experiments with no model pre-specification and limited budget is to uniformly spread the design points over the experimental domain, which are called uniform designs. A uniform design provides good design space coverage (space-filling), resulting in more accurate estimates using fewer points. Algorithmic search is commonly used for finding such designs, but this approach becomes ineffective for large problems. Though the construction of uniform designs theoretically is challenging, intriguing and encouraging findings have recently been accomplished for highly specialized cases. This paper gives a new theoretical construction method for high-dimensional experiments with a mixture of four-level and sixteen-level factors via an adjusted version of the multiple quadrupling algorithm (Elsawah, Communication in Mathematics and Statistics 10:623-652, 2022). The new technique (called AMQA) is simple to use since it only needs small suitable four-level designs, which may be easily generated theoretically or by algorithmic search. The quality of the small designs ensures the quality of the large designs. The effectiveness of the AMQA is investigated both theoretically and computationally. The main findings demonstrate that, compared with other widely used techniques, the AMQA provides more accurate results for high-dimensional screening using a variety of models, including polynomial, spline, and kriging models, as well as variable selection techniques, including AIC, BIC, RIC, SCAD, and LASSO. To further improve performance, a hybrid algorithm that combines the AMQA with the iterative threshold accepting algorithm is proposed. [ABSTRACT FROM AUTHOR]

In today's digital world, the extensive use of devices, including smartphones, tablets, IoT devices, and the internet, emphasizes the necessity for strong security measures. These measures are essential to safeguard both user data and sensitive government information. As technology advances, the enhancement of cryptographic methods becomes imperative, ensuring alignment with this rapid progression. This paper introduces a novel approach to encrypting classified data using the modified AES cipher system. It employs a dynamic and secure substitution byte operation with a 4x4 matrix, replacing the static and public 16x16 S-box found in the original version of AES. To construct the presented S-box, the system makes a secret key of 56 bytes (4x14 Rounds), where each round of the AES-256 utilizes 4 bytes to generate a 4x4 Hexadecimal Playfair matrix. By altering the S-boxes in each round and block (where each block utilizes AES key expansion to generate a 56-byte secret key for the Playfair matrix), it becomes feasible to produce distinct encrypted blocks even when the original blocks remain identical. In terms of security, the effectiveness of the provided method has been verified by experimental and analytical studies including the Avalanche effect, Balanced output, Hamming distance, and Time complexity. The result shows that the Avalanche effect of the proposed method exceeds 50% and increase the time of brute force attack. Moreover, the proposed algorithm exhibits impressive execution speed, handling approximately 200 KB in just one second. [ABSTRACT FROM AUTHOR]

Recent studies have exposed the vulnerabilities of deep neural networks to some carefully perturbed input data. We propose a novel untargeted white box adversarial attack, the dynamic programming-based sub-pixel score method (SPSM) attack (DPSPSM), which is a variation of the traditional gradient-based white box adversarial approach that is limited by a fixed hamming distance using a dynamic programming-based structure. It is stimulated using a pixel score metric technique, the SPSM, which is introduced in this paper. In contrast to the conventional gradient-based adversarial attacks, which alter entire images almost imperceptibly, the DPSPSM is swift and offers the robustness of manipulating only a small number of input pixels. The presented algorithm quantizes the gradient update with a score generated for each pixel, incorporating contributions from each channel. The results show that the DPSPSM deceives the model with a success rate of 30.45% in the CIFAR-10 test set and 29.30% in the CIFAR-100 test set. [ABSTRACT FROM AUTHOR]

Orthogonal arrays (OAs) are widely applied in the statistical design of experiments. The Hamming distances of OAs play an increasingly important role in various areas; however, it is difficult to use Hamming distances, especially for tight and mixed orthogonal arrays (MOAs). For example, two OAs with the same parameters may have distinct Hamming distances due to their different structures and constructions. In addition, studies on Hamming distances of tight OAs are scant, with the exception of a study indicating that a tight symmetrical OA of strength 2 has a constant Hamming distance. In this article, we calculate Hamming distances for high-strength OAs, including all tight symmetrical OAs of strength 3, 4, and 5. Interestingly, the Hamming distances do not depend on the constructions or structures of the arrays. Additionally, we study the Hamming distances of several classes of MOAs of strength 2 and 3, some of which are not related to the structures of the arrays. [ABSTRACT FROM AUTHOR]

In this paper, we present a heuristic for the non‐unicost set covering problem using local branching. Local branching eliminates the need to define a problem specific search neighbourhood for any particular (zero‐one) optimisation problem. It does this by incorporating a generalised Hamming distance neighbourhood into the problem, and this leads naturally to an appropriate neighbourhood search procedure. We apply our approach to the non‐unicost set covering problem. Computational results are presented for 65 test problems that have been widely considered in the literature. Our results indicate that our heuristic is better than six of the eight other heuristics we examined, slightly worse than that of one heuristic, but that there is a single heuristic that outperforms all others. We believe that the work described here illustrates that the potential for using local branching, operating as a stand‐alone matheuristic, has not been fully exploited in the literature. [ABSTRACT FROM AUTHOR]

The recognition of telugu hand written text is been one of the problems in many applications. To overcome the problem a deep learning technique is proposed in this work i.e. a Dense convolutional neural network (DCNN) model. A telugu dataset which is taken form IIIT-HW-Telugu is utilized to perform the proposed model. In this paper a four stage telugu word retrieval is performed, initially thinning of image is performed using morphological operation, secondly Densenet-CNN is applied for thinning image, thirdly perform OCR based image segmentation, finally two models like HARRIS and BRISK features to extract the features and evaluate information from the given input HWT images. The parameters evaluated are hamming distance, PSNR, MSE, Noise Sensitivity and rate of thinning. The proposed model outperformed well compared to other methods. The PSNR obtained using proposed model is 54.74, hamming distance is 1.2. [ABSTRACT FROM AUTHOR]

In this paper we will analyze one linear code from the theoretical point of view. Namely, the code definition is based on linear quasigroups. In the previous work we classified the quasigroups of order 4 according to their probability of undetected errors. Now, in this paper we will conclude whether the linear quasigroups that are in the same class in this classification obtain equal number of surely detected incorrectly transmitted bits. Also, we will classify the linear quasigroups of order 4 according to the number of errors that the code surely detects when they are used for coding. At the end we will make conclusion which quasigroups of order 4 are overall best for coding having in mind both important parameters for every code for error detection: the number of errors that the code surely detects and the probability of undetected errors. [ABSTRACT FROM AUTHOR]

V. Levenshtein first proposed the sequence reconstruction problem in 2001. This problem studies the same sequence from some set is transmitted over multiple channels, and the decoder receives the different outputs. Assume that the transmitted sequence is at distance d from some code and there are at most r errors in every channel. Then the sequence reconstruction problem is to find the minimum number of channels required to recover exactly the transmitted sequence that has to be greater than the maximum intersection between two metric balls of radius r, where the distance between their centers is at least d. In this paper, we study the sequence reconstruction problem of permutations under the Hamming distance. In this model we define a Cayley graph over the symmetric group, study its properties and find the exact value of the largest intersection of its two metric balls for d = 2 r . Moreover, we give a lower bound on the largest intersection of two metric balls for d = 2 r - 1 . [ABSTRACT FROM AUTHOR]

This paper describes a generalized Diophantine fuzzy sets, which can be seen as a generalization of both Diophantine fuzzy sets and Pythagorean fuzzy sets. We define the basic properties of generalized Diophantine fuzzy set, as well as their relationships and distances. We compare Diophantine fuzzy sets with other Diophantine Pythagorean fuzzy sets to demonstrate their importance in the literature. We introduce new operators including necessity, possibility, accuracy function and score function. Furthermore, we discuss the new distance between normalized Euclidean distance and normalized Hamming distance. For a generalized Diophantine fuzzy relation, image and inverse image functions are defined. Numerous real-world applications can be found for the prevalent ideas of intuitionistic fuzzy sets, Pythagorean fuzzy sets, Diophantine fuzzy sets and q-rung orthopair fuzzy sets. Regretfully, these theories about the membership and non-membership grades have their own limits. We provide a new idea the generalized Diophantine fuzzy set that eliminates these limitations by including reference parameters. Compared to other kinds of fuzzy sets, there are more applications for generalized Diophantine fuzzy sets. We offer practical examples that show how different enhanced distances might be used in everyday situations. Additionally, to demonstrate the effectiveness of the suggested approach, flowchart based multi-criteria decision-making is provided and used to a numerical example. The outcomes are assessed for various parameter values. Furthermore, a comparative analysis developed to demonstrate the superiority of the suggested technique over current methodologies. [ABSTRACT FROM AUTHOR]

Let λ = λ0 + uλ1 + ⋯ + uk−1λ k−1 be a Type 1 unit in ℜk = 픽pm + u픽pm + ⋯ + uk−1픽 pm(uk = 0), where p is an odd prime, m is a positive integer and λ0,λ1,…,λk−1 ∈ 픽pm,λ0≠0,λ1≠0. In this paper, we give the complete structure of all Type 1 λ-constacyclic codes and their duals of length 8ps over the finite commutative chain ring ℜk in terms of their generator polynomials. Using this structure, we determine the Hamming distance and the Rosenbloom–Tsfasman (RT) distance of all Type 1 λ-constacyclic codes. For pm ≡ 1(mod 4) and a unit λ ∈ ℜ2, we determine the b -symbol distances of all λ-constacyclic codes of length 8ps over ℜ2, where b ≤ 8. As illustrations, we provide several λ-constacyclic codes with new parameters with respect to Hamming, RT and b-symbol metrics. MDS codes are widely recognized for their optimal error-correction capability, and MDS b-symbol codes are generalization of MDS codes. We found some MDS b-symbol constacyclic codes of length 8ps over ℜ2. Additionally, for pm ≡ 1(mod 4), we provide a decoding algorithm for Type 1 constacyclic codes of length 8ps over ℜk with respect to the Hamming, RT and b-symbol metrics. [ABSTRACT FROM AUTHOR]

Dopingless (DLFET) provides better reliability against any physically doped devices. Hence, this paper aims to provide a fair comparison between conventional junctionless (JLFET) and DLFET based ring oscillator (RO) physical unclonable function (PUF) that would lead to a better security solution against any aging constraints. To include aging challenges in our simulation, we stressed conventional JLFET and DLFET against channel hot carrier (CHC) and bias temperature instability (BTI) for 2000 secs. The maximum drain current deviation obtained in JLFET is 20.7 % and that of DLFET is 16 %. Hence, DLFET has more resistance against aging rollbacks than JLFET. Further, 256 staged DL-RO-PUF and JL-RO-PUF are implemented and it is observed that a DL-RO has 60 % better oscillating frequency as compared to a JL-RO. Also, we found that the DL-RO-PUF produce more unique keys than JL-RO-PUF as the inter hamming distance (HD) is 46.9 % for former and 44.6 % for later during normal working conditions. Also, we found that DL-RO-PUF is more reliable than JL-RO-PUF as the maximum intra-HD of former is 3.23 % and of later is 3.66 %. Hence, the novelty of this work is to introduce a highly unique and reliable security solution that helps to provide sustainable electronic systems. [ABSTRACT FROM AUTHOR]

We consider a setting in which the alternatives are binary vectors and the preferences of the agents are determined by the Hamming distance from their most preferred alternatives. We consider only rules that are unanimous, anonymous, and component-neutral, and focus on strategy-proofness, weak group strategy-proofness, and strong group strategy-proofness. We show that component-wise majority rules are strategy-proof, and for three agents or two components also weakly group strategy-proof, but not otherwise. These rules are even strongly group strategy-proof if there are two or three agents. Our main result is an impossibility result: if there are at least four agents and at least three components, then no rule is strongly group strategy-proof. [ABSTRACT FROM AUTHOR]

Elementary cellular automata are the simplest form of cellular automata, studied extensively by Wolfram in the 1980s. He discovered complex behavior in some of these automata and developed a classification for all cellular automata based on their phenomenology. In this paper, we present an algorithm to classify them more effectively by measuring difference patterns using the Hamming distance. Our classification aligns with Wolfram's and further categorizes them into additional subclasses. Finally, we have found a heuristic reasoning providing and explanation about why some rules evolve into fractal patterns. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to enhance spectral efficiency in communication systems by dynamically adjusting the mapping between cyclic permutation coding (CPC) and its injected form. By monitoring channel conditions such as interference levels and impulsive noise strength, the system optimises the coding scheme to maximise data transmission reliability and efficiency. The CPC method employed in this work maps information bits onto non-binary symbols in a cyclic manner, aiming to improve the Hamming distance between mapped symbols. To address challenges such as low data rates inherent in permutation coding, injection techniques are introduced by removing δ column(s) from the CPC codebook. Comparative analyses demonstrate that the proposed dynamic adaptation scheme outperforms conventional permutation coding and injection schemes. Additionally, we present a generalised mathematical expression to describe the relationship between the spectral efficiencies of both coding schemes. This dynamic approach ensures efficient and reliable communication in environments with varying levels of interference and impulsive noise, highlighting its potential applicability to systems like power line communications. [ABSTRACT FROM AUTHOR]

Human interactions have led to the emergence of a higher complexity of urban metabolic networks; hence, traditional natural- or agriculture-oriented biogeochemical models might not be transferred well to urban environments. Increasingly serious environmental problems require the development of new concepts and models. Here, we propose a basic paradigm for urban–rural complex nitrogen (N) metabolic network reconstruction (NMNR) by introducing new concepts and methodologies from systems biology at the molecular scale, analyzing both local and global structural properties and exploring optimization and regulation methods. Using the Great Hangzhou Areas System (GHA) as a case study, we revealed that pathway fluxes follow a power law distribution, which indicates that human-dominated pathways constitute the principal part of the functions of the whole network. However, only 1.16% of the effective cycling pathways and an average hamming distance of only 5.23 between the main pathways indicate that the network lacks diverse pathways and feedback loops, which could lead to low robustness. Furthermore, more than half of the N fluxes did not pass through core metabolism, causing waste and pollution. We also provided strategies to design network structures and regulate system function: improving robustness and reducing pollution by referring to the characteristics of biochemical metabolic networks (e.g., the bow-tie structure). This method can be used to replace the trial-and-error method in system regulation and design. By decomposing the GHA N metabolic network into 4398 metabolic pathways and the corresponding fluxes with a power law distribution, NMNR helps us quantify the vulnerability in the current urban nitrogen cycle. The basic ideas and methodology in NMNR can be applied to coupled human and natural systems to advance global sustainable development studies, and they can also extend systems biology from the molecule to complex ecosystems and lead to the development of multi-scale unified theory in systems biology. [ABSTRACT FROM AUTHOR]

In this era of demanding invulnerable security systems against the threat of hacking, physically unclonable functions (PUFs), especially in which can spawn multiple security keys within a single device, has gained attention. This investigation explores the multi‐key generable PUF devices employing organic small molecules, specifically C8‐BTBT and PTCDI‐C13. The variation stems from the formation of irregular PN junctions, haphazardly configured grain boundaries of C8‐BTBT. A comprehensive analysis including scanning electron microscopy (SEM), atomic force microscopy (AFM), kelvin probe force microscopy (KPFM), impedance spectroscopy (IS), and optical simulation, has been substantiated the underlying mechanisms. Exploiting the photo‐responsive characteristics within the light wavelength spectrum of 660 and 530 nm, alongside the electrical characteristics, the capability to generate a total of 30 distinct multi‐security keys in a single device is been successfully. These keys, distinguished by variable parameters such as voltage, light wavelength, and the calculated photo‐to‐dark current ratio (PDCR), manifest novel quantitative and qualitative dimensions in security protocol customization. Inter‐Hamming distance and entropy of these cryptographic keys exhibit commendable averages of 51.9–53.1%, and 0.81, respectively. Moreover, a noteworthy average bit‐aliasing mean value of 51.9%, derived from four distinct batches, underscores the pragmatic feasibility of the proposed conceptual framework for PUF devices. [ABSTRACT FROM AUTHOR]

This work introduces a quantum subroutine for computing the distance between two patterns and integrates it into two quantum versions of the kNN classifier algorithm: one proposed by Schuld et al. and the other proposed by Quezada et al. Notably, our proposed subroutine is tailored to be memory-efficient, requiring fewer qubits for data encoding, while maintaining the overall complexity for both QkNN versions. This research focuses on comparing the performance of the two quantum kNN algorithms using the original Hamming distance with qubit-encoded features and our proposed subroutine, which computes the distance using amplitude-encoded features. Results obtained from analyzing thirteen different datasets (Iris, Seeds, Raisin, Mine, Cryotherapy, Data Bank Authentication, Caesarian, Wine, Haberman, Transfusion, Immunotherapy, Balance Scale, and Glass) show that both algorithms benefit from the proposed subroutine, achieving at least a 50% reduction in the number of required qubits, while maintaining a similar overall performance. For Shuld's algorithm, the performance improved in Cryotherapy (68.89% accuracy compared to 64.44%) and Balance Scale (85.33% F1 score compared to 78.89%), was worse in Iris (86.0% accuracy compared to 95.33%) and Raisin (77.67% accuracy compared to 81.56%), and remained similar in the remaining nine datasets. While for Quezada's algorithm, the performance improved in Caesarian (68.89% F1 score compared to 58.22%), Haberman (69.94% F1 score compared to 62.31%) and Immunotherapy (76.88% F1 score compared to 69.67%), was worse in Iris (82.67% accuracy compared to 95.33%), Balance Scale (77.97% F1 score compared to 69.21%) and Glass (40.04% F1 score compared to 28.79%), and remained similar in the remaining seven datasets. [ABSTRACT FROM AUTHOR]