Similarity Algorithm Research Articles

Mcan-YOLO: An Improved Forest Fire and Smoke Detection Model Based on YOLOv7

Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed to effectively capture multi-scale information, replacing the traditional module. Additionally, the content-aware reassembly of features (CARAFE) replaced the conventional upsampling method, further reducing the number of parameters. The normalization-based attention module (NAM) was integrated after the ELAN-T module to enhance the recognition of various fire smoke features, and the Mish activation function was used to optimize model convergence. A real fire smoke dataset was constructed using the mean structural similarity (MSSIM) algorithm for model training and validation. The experimental results showed that, compared to YOLOv7-tiny, Mcan-YOLO improved precision by 4.6%, recall by 6.5%, and mAP50 by 4.7%, while reducing the number of parameters by 5%. Compared with other mainstream algorithms, Mcan-YOLO achieved better precision with fewer parameters.

A multi-entity reinforced main path analysis: Heterogeneous network embedding considering knowledge proximity

Main path analysis (MPA) is an important approach in detecting the trajectory of knowledge diffusion in a specific research domain. Previous studies always focus on citation-based relationships, overlooking other structural forms in citation network. This study introduces a multi-entity reinforced MPA model by constructing a knowledge graph from paper metadata, including citations, authors, journals, and keywords. We construct heterogeneous network to reveal relationships among various entities. Different knowledge graph embedding models are employed to train the network, thereby obtaining entity and relation embeddings. The cosine similarity algorithm is adopted to measure the knowledge proximity between these embeddings. We take the Internet of Thing domain as an example to verify the performance of the multi-entity reinforced MPA through both quantitative and qualitative analysis. Our findings indicate that the adjusted MPA exhibits stronger topic relevance, demonstrating the effectiveness of the method in capturing complex knowledge relationships.

Comparison of text information from information sources based on the cosine similarity algorithm

This article presents research aimed at developing an optimal concept of analysis and comparison of information sources based on large volumes of textual information using natural language processing (NLP) methods. The object of the study was Telegram news channels, which are used as sources of text data. Texts were pre-processed, including cleaning, tokenization, and lemmatization, to form a global dictionary consisting of unique words from all information sources. For each source, a vector representation of the texts was built, the dimension of which corresponds to the number of unique words in the global dictionary. The frequency of use of each word in the channel's texts was displayed in the corresponding positions of the vector. By applying the cosine similarity algorithm to pairs of vectors, a square matrix was obtained that demonstrates the degree of similarity between different sources. The results of the study show the effectiveness of the proposed approach for quantitative assessment of the similarity of textual data from different sources. The need for further optimization of the algorithm was identified, in particular by parameterization to achieve a balance between accuracy and computational cost, as well as the separation of words with excessive weight, such as specific terms or channel names. The proposed method can be applied to the analysis of information flows, the identification of relationships between sources and the study of the socio-cultural influence of media content in the conditions of the modern information environment.

Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education.

Objective, valid, and reliable evaluations are needed in order to develop haptic skills in dental education. The aim of this study is to investigate the validity and reliability of the machine learning method in evaluating the haptic skills of dentistry students. One-hundred fifty 6th semester dental students have performed Class II amalgam (C2A) and composite resin restorations (C2CR), in which all stages were evaluated with Direct Observation Practical Skills forms. The final phase was graded by three trainers and supervisors separately. Standard photographs of the restorations in the final stage were taken from different angles in a special setup and transferred to the Python program which utilized the Structural Similarity algorithm to calculate both the quantitative (numerical) and qualitative (visual) differences of each restoration. The validity and reliability analyses of inter-examiner evaluation were tested by Cronbach's Alpha and Kappa statistics (p=0.05). The intra-examiner reliability between Structural Similarity Index (SSIM) and examiners was found highly reliable in both C2A (α=0.961) and C2CR (α=0.856). The compatibility of final grades given by SSIM (53.07) and examiners (56.85) was statistically insignificant (p>0.05). A significant difference was found between the examiners and SSIM when grading the occlusal surfaces in C2A and on the palatal surfaces of C2CR (p<0.05). The concordance of observer assessments was found almost perfect in C2A (κ=0.806), and acceptable in C2CR (κ=0.769). Although deep machine learning is a promising tool in the evaluation of haptic skills, further improvement and alignments are required for fully objective and reliable validation in all cases of dental training in restorative dentistry.

Variable frequency selective structure (CFRP/Cu/CFRP) micro blind hole laser high-quality drilling method guided by acoustic emission technology

The laminated structure of carbon fiber reinforced plastic (CFRP)-Cu-CFRP is an important structure for aerospace radome. The blind holes manufacturing is core process, but there are technological challenges in achieving the exposure of a 10 μm thick copper foil at the bottom of the blind hole. The exact position of the 10 μm thick copper foil within the material’s internal space is unknown, and the uneven heat accumulation and conduction can easily cause burning and damage to the ultra-thin copper foil. To date, there is no publicly accessible literature that documents the execution of this specific process. In this paper, novel variable inner diameter spiral scanning guided by Cu-CFRP interface accurately extracted using cosine similarity (CS) algorithm is proposed to obtain a blind hole with near-zero ablation damage. The similarity of acoustic emission signals generated by laser interaction with Cu and CFRP, as well as thermal accumulation and conduction behavior under different scanning radius, are described. The high-quality blind holes have been successfully drilled, with a large area copper foil exposed at the bottom, while the surface of the copper foil remaining intact without any damage or heat affected zone. Compared with traditional drilling method, the average exposed copper foil area at the bottom of the blind holes is increased by 238.4 % (from 0.464 mm2 to 1.570 mm2). And the average taper of blind hole is reduced by 41.7 % (from 0.470 to 0.274). This method can effectively increase the area of exposed copper foil and reduces the taper of blind holes, improve the welding strength and conductivity of wires in aerospace radomes, thereby enhancing the stealth performance and operational reliability of fighter jets.

The Sharing Model of Kindergarten Art Enlightenment Education Curriculum Resources Based on Deep Learning Algorithm

In order to meet the autonomous needs of children’s art learning, this paper studies the sharing model of kindergarten art enlightenment education curriculum resources under the deep learning algorithm. Build a kindergarten art enlightenment education curriculum resource-sharing model that includes the classification and storage layer of education curriculum resources, the metadata annotation layer of education curriculum resources and the shared business service layer. The deep learning algorithm is used in the classification and storage layer of education curriculum resources. The education curriculum resources are divided into courseware resources, case resources and media materials resources through unsupervised restricted Boltzmann machine (RBM) coding and supervised training process. The ontology model of various educational curriculum resources is constructed, and the semantic information of educational curriculum resources is described using ontology. The metadata annotation layer of education curriculum resources annotates the metadata semantics of education curriculum resources through the constructed ontology, which improves the consistency and sharing of education curriculum resources management. The implementation of resource sharing business service is based on semantic analysis and query of educational curriculum resources. Therefore, the shared business service layer adopts a semantic query method based on improved similarity algorithm, which combines semantic distance, node density, node depth and relationship type to build a semantic similarity calculation method to achieve semantic analysis and query of educational curriculum resources. The experimental results show that the model is suitable for multi-classification of shared resources in the kindergarten art enlightenment education curriculum, and the semantic query accuracy is high in the process of sharing. When the threshold is [Formula: see text] 0.5, the query effect of the sharing model in this paper is ideal, which can meet the needs of users’ autonomous learning.

A method for assessing and locating protection measurement loop errors based on an improved similarity algorithm

A method for assessing and locating protection measurement loop errors based on an improved similarity algorithm

Research on the railway multi-source homonymous geographical entity matching algorithm based on dynamic time warping

This study aims to explore an efficient technique for matching multisource homonymous geographical entities in railways to address the identification issues of homonymous geographical entities. Focusing on railway line vector spatial data, this research investigates the matching problem of multisource homonymous geographical entities. Building on statistical feature matching of attribute data, a curve similarity calculation method based on the DTW algorithm is designed to achieve better local elastic matching, overcoming the limitations of the Fréchet algorithm. The empirical study utilizes railway line layer data from two data sources within Beijing’s jurisdiction, fusing 6237 segment lines from source 2 with 105 long lines from source 1. The structural comparison between the two data sources is conducted through statistical methods, applying cosine similarity and the maximum similarity value of TF-IDF for text similarity calculation. Finally, Python is used to implement the DTW algorithm for curve similarity. The experimental results show an average DTW distance of 3.92, a standard deviation of 4.63, and a mode of 0.005. Similarity measurement results indicate that 95.53% of records are within the predetermined threshold, demonstrating the effectiveness and applicability of the method. The findings significantly enhance the accuracy of railway data matching, promoting the informatization of the railway industry, and hold substantial significance for improving railway operational efficiency and system performance.

Improved Discrimination of Mass Spectral Isomers Using the High-Dimensional Consensus Mass Spectral Similarity Algorithm.

This study employs a high-dimensional consensus mass spectral (HDCMS) similarity scoring technique to discriminate isomers collected using an electron ionization mass spectrometer. The HDCMS method was previously introduced and applied to the discrimination of mass spectra of constitutional isomers, methamphetamine and phentermine, collected with direct analysis real-time mass spectrometry (DART-MS). The method formulates the problem of discriminating mass spectra in a mathematical Hilbert space and is hence called "high dimensional." It requires replicate mass spectra to build a Gaussian model and evaluate the inner products between these functions. The resulting measurement variability is used as a signature by which to discriminate spectra. In this work, HDCMS is tested on electron impact ionization (EI) mass spectra of 7 terpene and terpene-related (C10H16 and C10H14) isomers with experimental retention indices that differ by less than 30 and with traditional cosine similarity scores greater than 0.9, on a scale of 0 to 1, when compared with at least one other compound in the test set. Using identical instrument parameters, 15 replicate gas chromatography-electron ionization-mass spectrometry (GC-EI-MS) spectra of each isomer were collected and separated into distinct library and query sets. The HDCMS algorithm discriminated each isomer, indicating the method's potential. Because the method requires replicate measurements, observations from a simple heuristic study of the number of replicates required to discriminate these isomers is presented. The paper concludes with a discussion of compound discrimination using HDCMS and the benefits and drawbacks of applying the method to EI-MS data.

Using Polars to Improve String Similarity Performance in Python

IntroductionString similarity is central to textual record linkage, and is often calculated with Levenshtein distance or Jaro-Winkler distance. In polars-strsim, we leverage the Polars DataFrame interface to surpass all existing Python libraries in computing these distances. Polars-strsim is especially useful for larger-than-memory datasets. Objectives and ApproachPolars is a library built to analyse and manipulate tabular data. It is written in Rust, but can be called from Python for ease of use. Polars-strsim implements the Levenshtein and Jaro-Winkler algorithms as a Polars extension. We compare performance against nine other Python libraries on three million pairs of first names. ResultsWhen computing Levenshtein/Jaro-Winkler distance for this benchmark, polars-strsim is respectively 5x/4x faster than the current fastest Python alternative and 47x/35x faster than the median. The Polars streaming engine can be seamlessly used to manipulate larger than memory datasets. One practical application of this in record linkage is to find all pairs of records in a dataset where the Levenshtein distance computed across one (or more) fields is below a certain threshold (e.g., during blocking). With polars-strsim, this calculation can be performed in Python with CPU parallelism even if the input and/or output doesn’t fit in memory. Conclusions/ImplicationsThis work introduces a powerful library for record linkage practitioners. Polars-strsim provides the convenience of Python, the performance of a strongly/statically typed compiled language, and the full power of the Polars streaming engine when computing string similarity, and can be updated to implement additional similarity algorithms.

Mesocorticolimbic and Cardiometabolic Diseases-Two Faces of the Same Coin?

The risk behaviors underlying the most prevalent chronic noncommunicable diseases (NCDs) encompass alcohol misuse, unhealthy diets, smoking and sedentary lifestyle behaviors. These are all linked to the altered function of the mesocorticolimbic (MCL) system. As the mesocorticolimbic circuit is central to the reward pathway and is involved in risk behaviors and mental disorders, we set out to test the hypothesis that these pathologies may be approached therapeutically as a group. To address these questions, the identification of novel targets by exploiting knowledge-based, network-based and disease similarity algorithms in two major Thomson Reuters databases (MetaBase™, a database of manually annotated protein interactions and biological pathways, and IntegritySM, a unique knowledge solution integrating biological, chemical and pharmacological data) was performed. Each approach scored proteins from a particular approach-specific standpoint, followed by integration of the scores by machine learning techniques yielding an integrated score for final target prioritization. Machine learning identified characteristic patterns of the already known targets (control targets) with high accuracy (area under curve of the receiver operator curve was ~93%). The analysis resulted in a prioritized list of 250 targets for MCL disorders, many of which are well established targets for the mesocorticolimbic circuit e.g., dopamine receptors, monoamino oxidases and serotonin receptors, whereas emerging targets included DPP4, PPARG, NOS1, ACE, ARB1, CREB1, POMC and diverse voltage-gated Ca2+ channels. Our findings support the hypothesis that disorders involving the mesocorticolimbic circuit may share key molecular pathology aspects and may be causally linked to NCDs, yielding novel targets for drug repurposing and personalized medicine.

A best-match approach for gene set analyses in embedding spaces.

Embedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine-learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose an Algorithm for Network Data Embedding and Similarity (ANDES), a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein-protein interactions, can be used as a novel overrepresentation- and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multiorganism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.

Constructing a Hospital Department Development-Level Assessment Model: Machine Learning and Expert Consultation Approach in Complex Hospital Data Environments.

Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data. This study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management. Data related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department's development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm. Deep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital's training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective. This study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions.

Exploring Natural Compounds as Potential CDK4 Inhibitors for Therapeutic Intervention in Neurodegenerative Diseases through Computational Analysis.

CDK4 is a member of the serine-threonine kinase family, which has been found to be overexpressed in a plethora of studies related to neurodegenerative diseases. CDK4 is one of the most validated therapeutic targets for neurodegenerative diseases. Hence, the discovery of potent inhibitors of CDK4 is a promising candidate in the drug discovery field. Firstly, the reference drug Palbociclib was identified from the available literature as a potential candidate against target CDK4. In the present study, the Collection of Open Natural Products (COCONUT) database was accessed for determining potential CDK4 inhibitors using computational approaches based on the Tanimoto algorithm for similarity with the target drug, i.e., Palbociclib. The potential candidates were analyzed using SWISSADME, and the best candidates were filtered based on Lipinski's Rule of 5, Brenk, blood-brain barrier permeability, and Pains parameter. Further, the molecular docking protocol was accessed for the filtered compounds to anticipate the CDK4-ligand binding score, which was validated by the fastDRH web-based server. Based on the best docking score so obtained, the best four natural compounds were chosen for further molecular dynamic simulation to assess their stability with CDK4. In this study, two natural products, with COCONUT Database compound ID-CNP0396493 and CNP0070947, have been identified as the most suitable candidates for neuroprotection.

PEMBUATAN SISTEM REKOMENDASI KARYA TULIS ILMIAH DENGAN ALGORITMA COSINE SIMILARITY

This study aims to make the journal recommendation system using the cosine similarity algorithm. With the growing abundance of online scholarly journals, researchers face challenges in finding relevant publications. The cosine similarity algorithm calculates similarity between journals based on various features such as pengarangs, Juduls, and abstracts. By applying this algorithm, the research aims to make a recommendation system. The system will assist researchers in discovering relevant journals aligned with their interests. The study involves data collection, feature analysis, and implementing the cosine similarity algorithm. The improved system is expected to benefit researchers in the field of computer science and informatics

Bird song comparison using deep learning trained from avian perceptual judgments.

Our understanding of bird song, a model system for animal communication and the neurobiology of learning, depends critically on making reliable, validated comparisons between the complex multidimensional syllables that are used in songs. However, most assessments of song similarity are based on human inspection of spectrograms, or computational methods developed from human intuitions. Using a novel automated operant conditioning system, we collected a large corpus of zebra finches' (Taeniopygia guttata) decisions about song syllable similarity. We use this dataset to compare and externally validate similarity algorithms in widely-used publicly available software (Raven, Sound Analysis Pro, Luscinia). Although these methods all perform better than chance, they do not closely emulate the avian assessments. We then introduce a novel deep learning method that can produce perceptual similarity judgements trained on such avian decisions. We find that this new method outperforms the established methods in accuracy and more closely approaches the avian assessments. Inconsistent (hence ambiguous) decisions are a common occurrence in animal behavioural data; we show that a modification of the deep learning training that accommodates these leads to the strongest performance. We argue this approach is the best way to validate methods to compare song similarity, that our dataset can be used to validate novel methods, and that the general approach can easily be extended to other species.

DeepSketch: A Query Sketching Interface for Deep Time Series Similarity Search

By empowering domain experts to perform interactive exploration of large time series datasets, sketch-based query interfaces have revitalized interest in the well-studied problem of time series similarity search. In this new interaction paradigm, recent similarity algorithms (e.g., Qetch, Peax, LineNet) that attempt to capture perceptually relevant features have supplanted older, more straightforward distance measures (e.g., Euclidean, DTW). However, the downside of these algorithms is the resulting difficulty in designing corresponding index structures to support efficient similarity search over large datasets, thus necessitating brute-force search. This demo will showcase Deep Time Series Similarity Search (DTS3), our pluggable indexing pipeline for arbitrary distance measures. DTS3 can automatically train a foundation model for any custom, user-supplied distance measure with no strict constraints (e.g., differentiability), thus enabling fast retrieval via an off-the-shelf vector DBMS. Using our DeepSketch web interface, participants can compare DTS3 to the baseline brute-force versions of several similarity algorithms to see that our approach can achieve much lower latency without sacrificing accuracy when searching over large, real-world time series datasets.

IMPLEMENTATION OF THE BEST TRAINING PRODUCT SALES RECOMMENDATION SYSTEM USING COSENI SIMILARITY ALGORITHM

Introduction: Sales of training products often encounter difficulties in providing the right recommendations according to customer needs and preferences. This research aims to develop a Content-Based Filtering-based recommendation system to address this challenge, especially for PT Menara Indonesia. Method: This research focuses on the Cosine Similarity algorithm, this research designs and implements the model through Django's REST API and Streamlit's Dashboard Website. The data used consists of tabular data that includes company profiles and product models. The Content-Based Filtering method is used to improve sales efficiency and support marketing strategies. Results and Discussion: The results show that the implementation of the Cosine Similarity algorithm provides training product recommendations that are suitable and relevant to customer needs. The suitability and relevance of the recommendation results are marked by the similarity between the two items that have been combined to produce a value of 10% to 100% which has been sorted according to the recommendation results. 10% is a recommendation result that is not very similar to the input needs and 100% is the most appropriate and accurate recommendation with the input needs. Conclusion: With the use of Natural Language Processing technology, this system can produce more accurate and relevant recommendations. This research is expected to help PT Menara Indonesia in increasing sales

Impact assessment methods for teaching activities on sustainable development goals in higher education institutions: A case study from a Bosnian university

This study explores various approaches for assessing the impact of teaching activities on Sustainable Development Goals (SDGs). In addition to the well-known keyword search method, the study also introduces innovative methods utilizing text similarity algorithms, specifically Jaccard and Cosine algorithms. The performances of the traditional keyword search method and the proposed text similarity algorithms are then compared with the results obtained from a self-identification study conducted among the academic staff within the case study context. The strengths and weaknesses of each method are also discussed, aiming to contribute to a comprehensive understanding of impact assessment methodologies in the context of SDGs in education.

RetrieveDroid: k-NN Performance Evaluation of Distance Measurement Schemes for Android Malware Detection Using Case-Based Retrieval

One of the big challenges in cybersecurity is the detection of Android attacks since Android is the most popular mobile operating system. Within this system, applications require certain permissions to access critical resources. Investigation of the use of permissions is a concern to check whether an application is not mislead to divulge sensitive information. This work aims to determine whose distance schemes offers the best malware detection based on permission similarities. Case based reasoning (CBR) is a concept which aims to find a solution based on historical experiences. CBR performance relies on finding similarities between actual cases and stored cases and then to deduce solutions. This paper proposes to transform app data as vector of appearance of dangerous permissions and to store such vectors based on CBR structure. Then we investigate k-NN classification performance related to five distance-based metrics such as Euclidean, Cosine, Manhattan, Minkowski, and Mahalanobis. Experiments were carried out with a set of 419 applications, including 203 malicious and 216 benign samples. The whole dataset has been split in training set of 291 samples with 162 benign and 129 malicious, and the testing set of 128 samples with 54 benign and 74 malicious samples. k-Nearest Neighbor (k-NN) are used as the similarity algorithm in which the distance model is varied in each of the five distance models. Results reveal that Minkowski and Manhattan models provide the best overall performance to detect Android malware based on permissions, in terms of accuracy (99.21%) and precision (97%). This work is a good start to recommend to researchers distance metrics exploitable when performing permission similarity-based detection.