Similar Functions Research Articles

Three-dimensional Darcy-Forchheimer modelling of MHD hybrid nanofluid over rotating stretching/shrinking surface with Hamilton-Crosser and Yamada-Ota conductivity models

Instead of single nanoparticles, the combined effects of more than one solid nanoparticle have presented wide range of real-word application in several engineering as well as biomedical areas. The present analysis brings out a combined effect of Hamilton-Crosser and Yamada-Ota thermal conductivity models for the magnetohydrodynamic flow of hybridised fluid vi a rotating stretching/shrinking surface. The hybridised fluid comprised of silver and molybdenum tetrasulphide nanoparticle in association with the effect of Joule heating enriches the flow properties. Additionally, the Darcy-Forchheimer inertial drag with the impose of thermal radiation affecting the flow as well as heat transfer properties. The proposed mathematical model equipped with physical assumptions is transmuted into dimensionless form by utilizing similarity functions. Further, the traditional numerical technique is taken care of for the solution of the transmuted model equipped with diversified factors. The important characteristic of several factors are deployed graphically affecting various flow profiles. Finally, the outstanding features explored in the proposed investigation are stated as below; the comparative analysis reveals that, the heat transport properties became advanced in case of Hamilton-Crosser model rather than the Yamada-Ota conductivity model. However, the heat transportation rate is controlled by the increasing Eckert number but thermal radiation enhances it significantly.

A two-stage recommendation optimization algorithm based on item popularity and user features

Financial product recommendation algorithms are mainly product-centered. This article proposes a two-stage recommendation optimization algorithm based on item popularity and user features, named CPCF-TSP, that can make full use of the demographic characteristics of users and mitigate the problem of users being more inclined to choose “hot” financial products. A popularity weight factor is introduced to normalize popularity and modify Pearson's similarity function. The modified Pearson's similarity function is combined with popularity normalization and user features to improve modeling performance. The two-stage recommendation optimization procedure was combined with a collaborative filtering algorithm to improve recommendation precision. CPCF-TSP fully considers user features in building a hybrid recommendation model and solves the problem of user cold-start. It can also mitigate popularity deviations and improve recommendation precision. MovieLens data and Santander Bank client trading data were used in a case study. The results show that the algorithm reduces inaccuracy in the calculation of the weights for recommendation popularity and similarity and is especially suitable for recommending financial products in which user information can be easily collected and the number of users is far greater than the number of products considered.

How ethnic name variations influence data linkage results: A population level study using public voter databases

ObjectivesIt has been shown that record linkage methods can induce bias in resulting datasets since the linkage quality may differ between ethno-racial groups. In this project, we used publicly available voter databases to investigate whether ethno-racial attributes can influence the similarities calculated using approximate string comparison functions on voters' names. ApproachWe used 11 annual snapshots of a publicly available US voter database between 2011 and 2021 with uniquely identifiable voter data. We extracted pairs of the same voter’s first and last name values from two consecutive snapshots where these values differ. We calculated string similarities and created similarity histograms for each of the available ethno-racial categories, and other sociodemographics variables such as gender and age. We characterized common patterns of name disagreements by ethno-racial groups and compared the shapes of these histograms using cumulative density distributions. Preliminary ResultsAcross the 10 snapshot pairs, eligible voters with non-missing first and last names are included (N2011/2012 = 6,193,001, N2020/2021 = 7,852,763), describing 124,009 first name changes and 485,807 last name changes. Results will be presented as a series of density plots. Using Jaro-Winkler similarity scores of 0.7, 0.8 and 0.9 as threshold, we examined whether match rates differ by ethno-racial categories. Conclusions and ImplicationsUsing the same string similarity function on names of individuals of different ethno-racial groups may lead to different distributions of the resulting similarity values. Understanding the patterns of ethno-racial-based name changes in your particular dataset is crucial on selecting linkage parameters that would minimise ethno-racial bias.

Enhancing River Flood Prediction in Early Warning Systems Using Fuzzy Logic-Based Learning

Previous studies show that the fuzzy-based approach predicts incoming floods better than machine learning (ML). However, with numerous observation points, difficulties in manually determining fuzzy rules and membership values increase. This research proposes a novel fuzzy logic-based learning (FLBL) that embeds missing data imputations and a fuzzy rule optimization strategy to enhance ML performance while still benefiting from fuzzy theory. The simple moving average handles sensors’ missing data. The logical mapping is used for fuzzification automation and fuzzy rule generation. The join function between the Szymkiewicz–Simpson coefficient similarity and max function is applied to optimize a fuzzy rules model. The case study uses observation data from three rivers traversing three districts in Semarang City. As a result, FLBL achieves 97.87% accuracy in predicting flood, outperforming the decision tree (96%) and the neural network (73.07%). This work is significant as a part of preventive flood-related disaster plans.

Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305

In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds tested using Organisation for Economic Co-operation and Development Test Guideline 305 for bioaccumulation in fish. Initially, we constructed the q-RASAR model using the partial least squares regression method, yielding promising statistical results for the training set (R2 =0.71, Q2LOO=0.68, mean absolute error [MAE]training=0.54). The model was further validated using the test set (Q2F1=0.77, Q2F2=0.75, MAEtest=0.51). Subsequently, we explored the q-RASAR method using other regression-based supervised machine-learning algorithms, demonstrating favourable results for the training and test sets. All models exhibited R2 and Q2F1 values exceeding 0.7, Q2LOO values greater than 0.6, and low MAE values, indicating high model quality and predictive capability for new, unidentified chemical substances. These findings represent the significance of the RASAR method in enhancing predictivity for new unknown chemicals due to the incorporation of similarity functions in the RASAR descriptors, independent of a specific algorithm.

HDCCT: Hybrid Densely Connected CNN and Transformer for Infrared and Visible Image Fusion

Multi-modal image fusion is a methodology that combines image features from multiple types of sensors, effectively improving the quality and content of fused images. However, most existing deep learning fusion methods need to integrate global or local features, restricting the representation of feature information. To address this issue, a hybrid densely connected CNN and transformer (HDCCT) fusion framework is proposed. In the proposed HDCCT framework, the network of the CNN-based blocks obtain the local structure of the input data, and the transformer-based blocks obtain the global structure of the original data, significantly improving the feature representation. In the fused image, the proposed encoder–decoder architecture is designed for both the CNN and transformer blocks to reduce feature loss while preserving the characterization of all-level features. In addition, the cross-coupled framework facilitates the flow of feature structures, retains the uniqueness of information, and makes the transform model long-range dependencies based on the local features already extracted by the CNN. Meanwhile, to retain the information in the source images, the hybrid structural similarity (SSIM) and mean square error (MSE) loss functions are introduced. The qualitative and quantitative comparisons of grayscale images with infrared and visible image fusion indicate that the suggested method outperforms related works.

How to Measure the Researcher Impact with the Aid of its Impactable Area: A Concrete Approach Using Distance Geometry

AbstractAssuming that the subject of each scientific publication can be identified by one or more classification entities, we address the problem of determining a similarity function (distance) between classification entities based on how often two classification entities are used in the same publication. This similarity function is then used to obtain a representation of the classification entities as points of an Euclidean space of a suitable dimension by means of optimization and dimensionality reduction algorithms. This procedure allows us also to represent the researchers as points in the same Euclidean space and to determine the distance between researchers according to their scientific production. As a case study, we consider as classification entities the codes of the American Mathematical Society Classification System.

Examining the merits of feature-specific similarity functions in the news domain using human judgments

Online news article recommendations are typically of the ‘more like this’ type, generated by similarity functions. Across three studies, we examined the representativeness of different similarity functions for news item retrieval, by comparing them to human judgments of similarity. In Study 1 (N=401\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=401$$\\end{document}), participants assessed the overall similarity of ten randomly paired news articles on politics and compared their judgments to different feature-specific similarity functions (e.g., based on body text or images). In Study 2, we checked for domain differences in a mixed-methods survey (N=45\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=45$$\\end{document}), surfacing evidence that the effectiveness of similarity functions differs across different news categories (‘Recent Events’, ‘Sport’). In Study 3 (N=173\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=173$$\\end{document}), we improved the design of Study 1, by controlling for how news articles were matched, differentiating between dissimilar news articles and articles that were matched on a shared topic, named entities, and/or date of publication, across ‘Recent Events’ and ‘Sport’ categories. Across all studies, we found that users mostly used text-based features (e.g., body text, title) for their similarity judgments, while BodyText:TF-IDF was found to be the most representative for their judgments. Moreover, the strength of similarity judgments by humans and similarity scores by feature-specific functions was strongly affected by how news article pairs were matched. We show that humans and similarity functions are better aligned when two news articles are more alike, such as in a news recommendation scenario.

GCGACNN: A Graph Neural Network and Random Forest for Predicting Microbe-Drug Associations.

The interaction between microbes and drugs encompasses the sourcing of pharmaceutical compounds, microbial drug degradation, the development of drug resistance genes, and the impact of microbial communities on host drug metabolism and immune modulation. These interactions significantly impact drug efficacy and the evolution of drug resistance. In this study, we propose a novel predictive model, termed GCGACNN. We first collected microbe, disease, and drug association data from multiple databases and the relevant literature to construct three association matrices and generate similarity feature matrices using Gaussian similarity functions. These association and similarity feature matrices were then input into a multi-layer Graph Neural Network for feature extraction, followed by a two-dimensional Convolutional Neural Network for feature fusion, ultimately establishing an effective predictive framework. Experimental results demonstrate that GCGACNN outperforms existing methods in predictive performance.

Recognition of domestic <em>Felis Catus</em> vocalisations using a computational associative model

The cat (Felis catus) lives around or with humans and is considered one of the most popular pets in the world. Domestic cats have an extensive and complex vocalisation repertoire, and little is known about the meaning of their vocalisations; these animal sounds have been studied to determine their behavioural characteristics. This work implemented a computational model for cat vocalisation as a signal process to characterise it. Additionally, a recognition model is proposed to identify and associate domestic cat vocalisations with cat intentions. Domestic cat vocalisations were studied by obtaining sounds from audio and video sources stored in the web cloud and processing signals (distress, hiss, howl, meow, fright, purr, sexual call, and trill vocalisations) that belong to some cat situations (soliciting attention, complaining, surprise, threatening, welcoming, fighting, feeling hungry, mating, etc.). In measured signals, environmental noise was removed using a computational algorithm to increase the effectiveness of the recognition process. As a first approach, cat vocalisation patterns were extracted using Welch's method for spectral density estimation, appreciating vocalisation frequency characteristics and statistics. Additionally, a similarity function to compare cat vocalisation signals was developed, excluding useless patterns that could introduce computational problems in the learning phase of the recognition model. Then, an associative memory, the Lernmatrix Thresholded model, was used to recognise the domestic cat vocalisation needed, and pair associations between sounds and moods related to the recognition process result were established, i.e., identifying cat intention through vocalisation by analysing the signals emitted. The experimental results show the performance of the thresholded Lernmatrix among different pattern recognition models, where accurate vocalisation recognition rates are obtained. This computational model emerges as a suitable tool that could be helpful in animal care and ethology science to characterise the behaviour of animals through their vocalisation.

Research on metal surface specular removal algorithm based on unsupervised learning

The highlights on the surface of metal materials can seriously destroy the continuity of the image, produce certain false edges, and cause the texture details in the highlights area to weaken or even disappear, which interferes with the subsequent operations such as surface region segmentation and defect detection. Aiming at the low efficiency, high loss, easy distortion and difficult calibration of metal highlight data, an unsupervised perceptual enhancement network model based on the convolutional neural network (CNN) is proposed. Firstly, the method of generating antagonism is used to generate a large number of metal images with high-light feature information, which is used to increase the number of high-light metal image data sets in the training set. Secondly, a detail enhance model(DEM) and a color enhance model(CEM) are introduced into the context aggregation network to improve the feature detail retention rate in the low resolution weight graphs. Finally, the multi-scale structural similarity function is used to replace the original structural similarity function to solve the insensitive detail when the image size is too large. Experiments show that comparing with other multi-exposure image fusion models, the present model can improve the evaluation index of mutual information and average gradient of fused images by about 10%, and can retain more texture feature information.

Anomaly detection with dual-channel heterogeneous graph based on hypersphere learning

Graph anomaly detection is essential for identifying irregular patterns and outliers within complex network structures in domains like social networks, cybersecurity, finance, and transportation systems. It helps detect security breaches, fraud, and errors, improving decision-making and system reliability. Although current methodologies have advanced the unsupervised detection of graph anomalies, they frequently fail to fully address the nuanced specificities of graph anomalies, such as anomalous nodes, edges, and subgraphs. To overcome this limitation, the study presents MulDualGNN, a unique dual-channel heterogeneous graph anomaly detection framework. MulDualGNN incorporates a global substructure-aware GNN and a local substructure-aware GNN to capture both global and local substructure properties for accurate anomaly detection. Our model incorporates a multi-hypersphere learning target function, which includes macroscopic and mesoscopic hyperspheres. These hyperspheres measure abnormal nodes that deviate from most normal nodes in the entire graph and community structure, respectively. To overcome the model collapse problem in multi-hypersphere learning, our model utilizes the EmbSim similarity function to optimize the training target. The effectiveness and performance advantages of the proposed method are evaluated through extensive experiments on five datasets. The results demonstrate the superior performance of our approach in graph anomaly detection tasks.

Few-shot classification with Fork Attention Adapter

Few-shot learning aims to transfer the knowledge learned from seen categories to unseen categories with a few references. It is also an essential challenge to bridge the gap between humans and deep learning models in real-world applications. Despite extensive previous efforts to tackle this problem by finding an appropriate similarity function, we emphasize that most existing methods have merely considered a single low-resolution representation pair utilized in similarity calculations between support and query samples. Such representational limitations could induce the instability of category predictions. To better achieve metric learning stabilities, we present a novel method dubbed Fork Attention Adapter (FA-adapter), which can seamlessly establish the dense feature similarity with the newly generated nuanced features. The utility of the proposed method is more performant and efficient via the two-stage training phase. Extensive experiments demonstrate consistent and substantial accuracy gains on the fine-grained CUB, Aircraft, non-fine-grained mini-ImageNet, and tiered-ImageNet benchmarks. By comprehensively studying and visualizing the learned knowledge from different source domains, we further present an extension version termed FA-adapter++ to boost the performance in fine-grained scenarios.

An experimental evaluation of Siamese Neural Networks for robot localization using omnidirectional imaging in indoor environments

The objective of this paper is to address the localization problem using omnidirectional images captured by a catadioptric vision system mounted on the robot. For this purpose, we explore the potential of Siamese Neural Networks for modeling indoor environments using panoramic images as the unique source of information. Siamese Neural Networks are characterized by their ability to generate a similarity function between two input data, in this case, between two panoramic images. In this study, Siamese Neural Networks composed of two Convolutional Neural Networks (CNNs) are used. The output of each CNN is a descriptor which is used to characterize each image. The dissimilarity of the images is computed by measuring the distance between these descriptors. This fact makes Siamese Neural Networks particularly suitable to perform image retrieval tasks. First, we evaluate an initial task strongly related to localization that consists in detecting whether two images have been captured in the same or in different rooms. Next, we assess Siamese Neural Networks in the context of a global localization problem. The results outperform previous techniques for solving the localization task using the COLD-Freiburg dataset, in a variety of lighting conditions, specially when using images captured in cloudy and night conditions.

ProxMetrics: modular proxemic similarity toolkit to generate domain-adaptable indicators from social media

In this paper, we introduce ProxMetrics, a novel toolkit designed to evaluate similarity among social media entities through proxemic dimensions. Proxemics is the science that studies the organization of space and the effects of distances on behavior and interactions. It encompasses 5 core dimensions: Distance, Identity, Location, Movement, and Orientation. Adapting the principles of traditional physical proxemics to the digital world of social media, we present a method and a modular similarity function to determine proxemic similarity scores across heterogeneous social media entities (users, groups, places, themes and times) based on these dimensions. The approach used is intended to be modular and generic, ensuring adaptability across various application domains and requirements. The calculated scores act as indicators and offer valuable insights for stakeholders, aligning with distinct domain requirements. Empirical testing in the tourism domain highlights the toolkit’s extensive applicability across a variety of requirements.

Selective policy transfer in multi-agent systems with sparse interactions

Previously trained single-agent strategies, which are considerably easier to acquire than multi-agent strategies, are instructive for multi-agent reinforcement learning, especially when the interactions between agents are sparse. Traditional methods of knowledge transfer, from single-agent source tasks to multi-agent target tasks, typically rely on a pre-designed Markov-decision-process similarity function or metric function for evaluating the task similarity. In this study, we propose a selective policy transfer (SPOT) algorithm that eliminates the need for the manual crafting of a metric function to assess the similarity between source and target tasks. The SPOT algorithm enables agents to autonomously determine when and which policy to transfer using well-trained single-agent policies as options in the training process. We introduced a multi-agent policy-learning option in the option library, thus allowing the SPOT algorithm to leverage the transferred knowledge while concurrently learning new policies. The SPOT algorithm efficiently transfers sequential strategies in a few steps, thereby capturing high-level semantics. Experimental results obtained in both multi-agent arcade and multi-agent particle environments have demonstrated that the proposed algorithm outperforms the state-of-the-art methods in terms of jump-start and convergence speeds. Our evaluation indicators included the jump start, steps to threshold, cumulative reward, and asymptotic performance. Furthermore, visualizations of the strategies and termination functions of the agents aid in elucidating the operational principles of the SPOT algorithm.

A modified Vector Space Model for semantic information retrieval

In this research, we present a modified Vector Space Model which focuses on the semantic relevance of words for retrieving documents. The modified VSM resolves the problem of the classical model performing only lexical matching of query terms to document terms for retrievals. This problem also restricts the classical model from retrieving documents that do not have exact match of query terms even if they are semantically relevant to the query. In the modified model, we introduced a Query Relevance Update technique, which pads the original query set with semantically relevant document terms for optimised semantic retrieval results. The modified model also includes a novel tf−p which replaces the tf−idf technique of the classical VSM, which is used to compute the Term Frequency weights. The replacement of the tf−idf resolves the problem of the classical model penalising terms that occur across documents with the assumption that they are stop words, which in practice, there are usually such words which carry relevant semantic information for documents’ retrieval. We also extended the cosine similarity function with a proportionality weight pqd, which moderates biases for high frequency of terms in longer documents. The pqd ensures that the frequency of query terms including the updated ones are accounted for in proportionality with documents size for the overall ranking of documents. The simulated results reveal that, the modified VSM does achieve semantic retrieval of documents beyond lexical matching of query and document terms.

Numerical simulation of Buongiorno's model on Maxwell nanofluid with heat and mass transfer using Arrhenius energy: a thermal engineering implementation

The thermal features of nanoparticles owing to progressive mechanisms are a fascinating phenomenon due to their applications in energy production, cooling procedures, heat transmission devices. Therefore, in the present study, the magnetohydrodynamic combined convection of Maxwell nanofluid and characteristics of heat transport in the presence of thermal radiation with a nonlinear relationship for modifications in the energy equation have been examined. Moreover, the features of activation energy in the presence of swimming microorganisms are considered. For motivation, the influence of bioconvection, magnetic field, and thermophoresis with convective boundary conditions are part of this investigation. The governing PDEs connected with momentum, energy, concentration, and density are converted into ODEs by using similarity functions. A transformed, dimensionless, nonlinear set of ODEs is tracked via a shooting scheme. The numerical results of prominent parameters have been analyzed in the form of graphs and tables using the computational software MATLAB. A significance improvement in the velocity profile is noted for the increasing value of Maxwell parameter. With rise of mixed convection parameter, both energy and volumetric friction field deteriorated. The determination of Biot number that is associated with the coefficient of heat transfer is more effective for growing the temperature and volumetric friction distribution. These conclusions may be appreciated in improving the efficiency of heat transfer strategies.

On the Extent of Applicability of Various Non-linear Similarity Functions for Computation of Surface Fluxes under Stable Conditions in Numerical Models

On the Extent of Applicability of Various Non-linear Similarity Functions for Computation of Surface Fluxes under Stable Conditions in Numerical Models

Proposed threshold-based and rule-based approaches to detecting duplicates in bibliographic database

Bibliographic databases are used to measure the performance of researchers, universities and research institutions. Thus, high data quality is required and data duplication is avoided. One of the weaknesses of the threshold-based approach in duplication detection is the low accuracy level. Therefore, another approach is required to improve duplication detection. This study proposes a method that combines threshold-based and rule-based approaches to perform duplication detection. These two approaches are implemented in the comparison stage. The cosine similarity function is used to create weight vectors from the features. Then, the comparison operator is used to determine whether the pair of records are grouped as duplication or not. Three research databases: Web of Science (WoS), Scopus, and Google Scholar (GS) on the Science and Technology Index (SINTA) database are investigated. Rule 4 and Rule 5 provide the best performance. For WoS dataset, the accuracy, precision, recall, and F1-measure values were 100.00%. For Scopus dataset, the accuracy and precision values were 100.00%, recall: 98.00%, and the F1-measure value is 98.00%. For GS dataset, the accuracy value was 100.00%, precision: 99.00%, recall: 97.00%, and the F1-measure value is 98.00%. The proposed method is potential tool for accurate detection on duplication records in publication databases.