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Journal articles on the topic 'Generalized cosine similarity'
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1
Ye, Jun. "Generalized Ordered Weighted Simplified Neutrosophic Cosine Similarity Measure for Multiple Attribute Group Decision Making." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 1 (January 2020): 51–62. http://dx.doi.org/10.4018/ijcini.2020010104.
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The paper proposes a generalized ordered weighted simplified neutrosophic cosine similarity (GOWSNCS) measure by combining the cosine similarity measure of simplified neutrosophic sets (SNSs) with the generalized ordered weighted averaging (GOWA) operator and investigates its properties and special cases. Then, the author develops a simplified neutrosophic group decision-making method based on the GOWSNCS measure to handle multiple attribute group decision-making problems with simplified neutrosophic information. The prominent characteristics of the GOWSNCS measure are that it not only is a generalization of the cosine similarity measure but also considers the associated weights for attributes and decision makers in the aggregation of the cosine similarity measures of SNSs to alleviate the influence of unduly large or small similarities in the process of information aggregation. Finally, an illustrative example of investment alternatives is provided to demonstrate the application and effectiveness of the developed approach.
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Liu, Donghai, Xiaohong Chen, and Dan Peng. "Interval-Valued Intuitionistic Fuzzy Ordered Weighted Cosine Similarity Measure and Its Application in Investment Decision-Making." Complexity 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1891923.
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We present the interval-valued intuitionistic fuzzy ordered weighted cosine similarity (IVIFOWCS) measure in this paper, which combines the interval-valued intuitionistic fuzzy cosine similarity measure with the generalized ordered weighted averaging operator. The main advantage of the IVIFOWCS measure provides a parameterized family of similarity measures, and the decision maker can use the IVIFOWCS measure to consider a lot of possibilities and select the aggregation operator in accordance with his interests. We have studied some of its main properties and particular cases such as the interval-valued intuitionistic fuzzy ordered weighted arithmetic cosine similarity (IVIFOWACS) measure and the interval-valued intuitionistic fuzzy maximum cosine similarity (IVIFMAXCS) measure. The IVIFOWCS measure not only is a generalization of some similarity measure, but also it can deal with the correlation of different decision matrices for interval-valued intuitionistic fuzzy values. Furthermore, we present an application of IVIFOWCS measure to the group decision-making problem. Finally the existing similarity measures are compared with the IVIFOWCS measure by an illustrative example.
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Ye, Jun, Shigui Du, and Rui Yong. "Similarity Measures between Intuitionistic Fuzzy Credibility Sets and Their Multicriteria Decision-Making Method for the Performance Evaluation of Industrial Robots." Mathematical Problems in Engineering 2021 (January 19, 2021): 1–10. http://dx.doi.org/10.1155/2021/6630898.
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To enhance the credibility level/measure of an intuitionistic fuzzy set (IFS), this study proposes the notion of an intuitionistic fuzzy credibility set (IFCS) to express the hybrid information of a pair of a membership degree and a credibility degree and a pair of a nonmembership degree and a credibility degree. Next, we propose generalized distance and similarity measures between IFCSs and then further generalize the weighted generalized distance measure of IFCSs to the trigonometric function-based similarity measures of IFCSs, including the cosine, sine, tangent, and cotangent similarity measures based on the weighted generalized distance measure of IFCSs. Then, a multicriteria decision making (MCDM) method using the proposed similarity measures is developed in the environment of IFCSs. An illustrative example about the performance evaluation of industrial robots and comparative analysis are presented to indicate the applicability and efficiency of the developed method in the setting of IFCSs.
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Liu, Donghai, Xiaohong Chen, and Dan Peng. "Cosine Distance Measure between Neutrosophic Hesitant Fuzzy Linguistic Sets and Its Application in Multiple Criteria Decision Making." Symmetry 10, no. 11 (November 7, 2018): 602. http://dx.doi.org/10.3390/sym10110602.
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This paper proposes a neutrosophic hesitant fuzzy linguistic term set (NHFLTS) based on hesitant fuzzy linguistic term set (HFLTS) and neutrosophic set (NS), which can express the inconsistent and uncertainty information flexibly in multiple criteria decision making problems. The basic operational laws of NHFLTS based on linguistic scale function are also discussed. Then we propose the generalized neutrosophic hesitant fuzzy linguistic distance measure and discuss its properties. Furthermore, a new similarity measure of NHFLTS combines the generalized neutrosophic hesitant fuzzy linguistic distance measure and the cosine function is given. A corresponding cosine distance measure between NHFLTSs is proposed according to the relationship between the similarity measure and the distance measure, and we develop the technique for order preference by similarity to an ideal solution (TOPSIS) method to the obtained cosine distance measure. The main advantages of the proposed NHFLTS is defined on linguistic scale function, the decision makers can flexibly convert the linguistic information to semantic values, and the proposed cosine distance measure between NHFLTSs with TOPSIS method can deal with the related decision information not only from the point of view of algebra, but also from the point of view of geometry. Finally, the reasonableness and effectiveness of the proposed method is demonstrated by the illustrative example, which is also compared to the other existing methods.
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Gulistan, Muhammad, Mutaz Mohammad, Faruk Karaaslan, Seifedine Kadry, Salma Khan, and Hafiz Abdul Wahab. "Neutrosophic cubic Heronian mean operators with applications in multiple attribute group decision-making using cosine similarity functions." International Journal of Distributed Sensor Networks 15, no. 9 (September 2019): 155014771987761. http://dx.doi.org/10.1177/1550147719877613.
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This article introduces the concept of Heronian mean operators, geometric Heronian mean operators, neutrosophic cubic number–improved generalized weighted Heronian mean operators, neutrosophic cubic number–improved generalized weighted geometric Heronian mean operators. These operators actually generalize the operators of fuzzy sets, cubic sets, and neutrosophic sets. We investigate the average weighted operator on neutrosophic cubic sets and weighted geometric operator on neutrosophic cubic sets to aggregate the neutrosophic cubic information. After this, based on average weighted and geometric weighted and cosine similarity function in neutrosophic cubic sets, we developed a multiple attribute group decision-making method. Finally, we give a mathematical example to illustrate the usefulness and application of the proposed method.
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Kate, Rohit J. "Normalizing clinical terms using learned edit distance patterns." Journal of the American Medical Informatics Association 23, no. 2 (July 31, 2015): 380–86. http://dx.doi.org/10.1093/jamia/ocv108.
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Abstract Background Variations of clinical terms are very commonly encountered in clinical texts. Normalization methods that use similarity measures or hand-coded approximation rules for matching clinical terms to standard terminologies have limited accuracy and coverage. Materials and Methods In this paper, a novel method is presented that automatically learns patterns of variations of clinical terms from known variations from a resource such as the Unified Medical Language System (UMLS). The patterns are first learned by computing edit distances between the known variations, which are then appropriately generalized for normalizing previously unseen terms. The method was applied and evaluated on the disease and disorder mention normalization task using the dataset of SemEval 2014 and compared with the normalization ability of the MetaMap system and a method based on cosine similarity. Results Excluding the mentions that already exactly match in UMLS and the training dataset, the proposed method obtained 64.7% accuracy on the rest of the test dataset. The accuracy was calculated as the number of mentions that correctly matched the gold-standard concept unique identifiers (CUIs) or correctly matched to be without a CUI. In comparison, MetaMap’s accuracy was 41.9% and cosine similarity’s accuracy was 44.6%. When only the output CUIs were evaluated, the proposed method obtained 54.4% best F -measure (at 92.1% precision and 38.6% recall) while MetaMap obtained 19.4% best F -measure (at 38.0% precision and 13.0% recall) and cosine similarity obtained 38.1% best F -measure (at 70.3% precision and 26.1% recall). Conclusions The novel method was found to perform much better than the MetaMap system and the cosine similarity based method in normalizing disease mentions in clinical text that did not exactly match in UMLS. The method is also general and can be used for normalizing clinical terms of other semantic types as well.
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Arafah, Muhammad. "IMPLEMENTATION OF GENERALIZED VECTOR SPACE MODEL METHOD AT AUTOMATIC ASSESSMENT OF ONLINE ESSAY EXAM." Journal of Information Technology and Its Utilization 1, no. 2 (December 17, 2018): 43. http://dx.doi.org/10.30818/jitu.1.2.1893.
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The aim of the study was to design and implement automatic testing of online essay examinations using the Generalized Vector Space Model (GVSM) method. This data is obtained through (1) Literature Study (2) Observation (3) Documentation. The results of this study indicate that the automatic scoring system with the GVSM weighting method and the cosine similarity similarity calculation method have the accuracy of the assessment with an average of 66%.
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Kar, Arindam, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, and Mahantapas Kundu. "A Gabor-Block-Based Kernel Discriminative Common Vector Approach Using Cosine Kernels for Human Face Recognition." Computational Intelligence and Neuroscience 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/421032.
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In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low-energized discriminating feature vectors to obtain the real component of a complex quantity for face recognition. In order to derive positive kernel discriminative vectors, we apply only those kernel discriminative eigenvectors that are associated with nonzero eigenvalues. The feasibility of the low-energized Gabor-block-based generalized KDCV method with cosine kernel function models has been successfully tested for classification using theL1, L2distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach.
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Liu, Peide, Muhammad Munir, Tahir Mahmood, and Kifayat Ullah. "Some Similarity Measures for Interval-Valued Picture Fuzzy Sets and Their Applications in Decision Making." Information 10, no. 12 (November 25, 2019): 369. http://dx.doi.org/10.3390/info10120369.
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Similarity measures, distance measures and entropy measures are some common tools considered to be applied to some interesting real-life phenomena including pattern recognition, decision making, medical diagnosis and clustering. Further, interval-valued picture fuzzy sets (IVPFSs) are effective and useful to describe the fuzzy information. Therefore, this manuscript aims to develop some similarity measures for IVPFSs due to the significance of describing the membership grades of picture fuzzy set in terms of intervals. Several types cosine similarity measures, cotangent similarity measures, set-theoretic and grey similarity measures, four types of dice similarity measures and generalized dice similarity measures are developed. All the developed similarity measures are validated, and their properties are demonstrated. Two well-known problems, including mineral field recognition problems and multi-attribute decision making problems, are solved using the newly developed similarity measures. The superiorities of developed similarity measures over the similarity measures of picture fuzzy sets, interval-valued intuitionistic fuzzy sets and intuitionistic fuzzy sets are demonstrated through a comparison and numerical examples.
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Song, Wanqing, Wujin Deng, Dongdong Chen, Rong Jin, and Aleksey Kudreyko. "Hybrid Approach of Fractional Generalized Pareto Motion and Cosine Similarity Hidden Markov Model for Solar Radiation Forecasting." Fractal and Fractional 7, no. 1 (January 13, 2023): 93. http://dx.doi.org/10.3390/fractalfract7010093.
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Power from solar energy is not reliable, due to weather-related factors, which diminishes the power system’s reliability. Therefore, this study suggests a way to predict the intensity of solar irradiance using various statistical algorithms and artificial intelligence. In particular, we suggest the use of a hybrid predictive model, combining statistical properties and historical data training. In order to evaluate the maximum prediction steps of solar irradiance, the maximum Lyapunov exponent was applied. Then, we used the cosine similarity algorithm in the hidden Markov model for the initial prediction. The combination of the Hurst exponent and tail parameter revealed the self-similarity and long-range dependence of the fractional generalized Pareto motion, which enabled us to consider the iterative predictive model. The initial prediction was substituted into a stochastic differential equation to achieve the final prediction, which prevents error propagation. The effectiveness of the hybrid model was demonstrated in the case study.
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Naeem, Muhammad, Muhammad Qiyas, Mohammed M. Al-Shomrani, and Saleem Abdullah. "Similarity Measures for Fractional Orthotriple Fuzzy Sets Using Cosine and Cotangent Functions and Their Application in Accident Emergency Response." Mathematics 8, no. 10 (September 24, 2020): 1653. http://dx.doi.org/10.3390/math8101653.
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The fractional orthotriple fuzzy set (FOFS) is more generalized than the spherical fuzzy set (SFS) and picture fuzzy set (PFS) to cope with awkward and complex information in fuzzy set (FS) theory. The FOFS is a more powerful technique with respect to the existing drawbacks because of its conditions, i.e., the sum of the f powers of positive, neutral, and negative grades is bounded to [0,1]. With the advantages of the FOFS, in this paper, we study the basic definitions and some existing similarity measures (SMs) of intuitionistic fuzzy sets (IFSs), PFSs, Pythagorean fuzzy sets (PyFSs) and SFSs. The existing approaches have certain limitations and cannot be applied to problems that are in the form of FOFSs. The goal of this paper is to propose the idea of some new SMs including cosine SMs for FOFSs, SMs for FOFSs based on the cosine function, and SMs for FOFSs based on the cotangent function. Further, some weighted SMs (WSMs) are also proposed for which the weight of the attributes is considered. Then, we apply these SMs and WSMs to the pattern recognition problem. Finally, the comparative study of the new SMs for FOFSs is established with existing SMs, and also, some advantages of the proposed work are discussed.
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Martuza, Muhammad, and Khan A. Wahid. "Low Cost Design of a Hybrid Architecture of Integer Inverse DCT for H.264, VC-1, AVS, and HEVC." VLSI Design 2012 (May 13, 2012): 1–10. http://dx.doi.org/10.1155/2012/242989.
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The paper presents a unified hybrid architecture to compute the 8×8 integer inverse discrete cosine transform (IDCT) of multiple modern video codecs—AVS, H.264/AVC, VC-1, and HEVC (under development). Based on the symmetric structure of the matrices and the similarity in matrix operation, we develop a generalized “decompose and share” algorithm to compute the 8×8 IDCT. The algorithm is later applied to four video standards. The hardware-share approach ensures the maximum circuit reuse during the computation. The architecture is designed with only adders and shifters to reduce the hardware cost significantly. The design is implemented on FPGA and later synthesized in CMOS 0.18 um technology. The results meet the requirements of advanced video coding applications.
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Luo, Hongwei, Yijie Shen, Feng Lin, and Guoai Xu. "Spoofing Speaker Verification System by Adversarial Examples Leveraging the Generalized Speaker Difference." Security and Communication Networks 2021 (February 9, 2021): 1–10. http://dx.doi.org/10.1155/2021/6664578.
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Speaker verification system has gained great popularity in recent years, especially with the development of deep neural networks and Internet of Things. However, the security of speaker verification system based on deep neural networks has not been well investigated. In this paper, we propose an attack to spoof the state-of-the-art speaker verification system based on generalized end-to-end (GE2E) loss function for misclassifying illegal users into the authentic user. Specifically, we design a novel loss function to deploy a generator for generating effective adversarial examples with slight perturbation and then spoof the system with these adversarial examples to achieve our goals. The success rate of our attack can reach 82% when cosine similarity is adopted to deploy the deep-learning-based speaker verification system. Beyond that, our experiments also reported the signal-to-noise ratio at 76 dB, which proves that our attack has higher imperceptibility than previous works. In summary, the results show that our attack not only can spoof the state-of-the-art neural-network-based speaker verification system but also more importantly has the ability to hide from human hearing or machine discrimination.
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Kung, Fan-Jie, and Mingsian R. Bai. "A nested generalized sidelobe canceller for source counting, localization, and signal separation in reverberant fields." Journal of the Acoustical Society of America 154, no. 6 (December 1, 2023): 3769–81. http://dx.doi.org/10.1121/10.0023963.
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This paper proposes a nested generalized sidelobe canceller (NGSC) for typical array signal processing tasks, including source counting, localization, and signal separation. Multiple blocking matrices are arranged in a nested structure to successively eliminate dominant sources until the remaining signal is predominantly incoherent noise. The microphone signals are dereverberated using a multichannel weighted prediction error algorithm. In source counting, the number of sound sources is determined by tracking the average power of the blocked signal. In source localization, the direction-of-arrival (DOA) is estimated via cosine similarity in conjunction with the golden section search. In signal separation, the estimated DOA enables speech separation in a linearly constrained minimum variance beamformer with postfiltering (LCMV-PF). Monte Carlo simulations are performed to compare the proposed NGSC approach with four baselines, minimum description length, second order statistic of the eigenvalue, multistage Wiener filtering, and multiple signal classification. The results show that NGSC achieves at least 28.80% higher source counting accuracy with a 1.04° lower root mean square degree error than the baselines. The signal-to-distortion ratio achieved by LCMV-PF is 1.52 dB higher than that achieved by the linearly constrained minimum power beamformer and the multichannel Wiener filter.
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Vakhromova, E. Yu, I. V. Beketova, A. A. Gerasimenko, V. I. Goremychkin, and V. P. Krivoshlyapov. "ALGORITHM OF CROSS LANGUAGE FUZZY SEARCH BASED ON HASH-VECTORS FOR AUTOMATIC COMPARISON OF PERSONAL NAMES." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 189 (March 2020): 29–36. http://dx.doi.org/10.14489/vkit.2020.03.pp.029-036.
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The algorithm of cross language fuzzy search based on hash vectors for automatic matching of personal names is proposed. In the response mode for an input request, names in Latin spelling and a given value for the similarity measure, the algorithm determines the set of output Cyrillic names contained in the database of the information search system. The principal feature of the proposed algorithm is the rejection of the direct translation of personal names. Instead, the hashing mechanism of personal names is used, followed by mapping them into the same hidden vector space where the computational procedures of the decision-making system are built. In the process of research, it was solved a number of actual intermediate tasks. Thus, the decomposition algorithms of the explored database, the generation and clustering of the dictionary of basic morphemes are an instrument that is of independent value in solving the problem of automatically translating names from a foreign language, the translation rules of which are unknown – the socalled generalized transcription. After mapping names into a vector space, the matching operation is reduced to assessing the similarity between vectors. As a measure of similarity, several quantities were considered in the study. The most convenient measure of similarity is the cosine similarity, the critical value of which was obtained by plotting the FMR (False Match Rate) and FNMR (False Non-Match Rate) graphs. The developed algorithm is universal with respect to the languages used, that is, it does not depend on a specific alphabet. In the practical implementation of the developed algorithm, a series of experimental studies was carried out using a database containing more than 2.5 million names.
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Zhang, Yuanpeng, Jingye Guan, Haobo Wang, Kaiming Li, Ying Luo, and Qun Zhang. "Generalized Zero-Shot Space Target Recognition Based on Global-Local Visual Feature Embedding Network." Remote Sensing 15, no. 21 (October 28, 2023): 5156. http://dx.doi.org/10.3390/rs15215156.
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Existing deep learning-based space target recognition methods rely on abundantly labeled samples and are not capable of recognizing samples from unseen classes without training. In this article, based on generalized zero-shot learning (GZSL), we propose a space target recognition framework to simultaneously recognize space targets from both seen and unseen classes. First, we defined semantic attributes to describe the characteristics of different categories of space targets. Second, we constructed a dual-branch neural network, termed the global-local visual feature embedding network (GLVFENet), which jointly learns global and local visual features to obtain discriminative feature representations, thereby achieving GZSL for space targets with higher accuracy. Specifically, the global visual feature embedding subnetwork (GVFE-Subnet) calculates the compatibility score by measuring the cosine similarity between the projection of global visual features in the semantic space and various semantic vectors, thereby obtaining global visual embeddings. The local visual feature embedding subnetwork (LVFE-Subnet) introduces soft space attention, and an encoder discovers the semantic-guided local regions in the image to then generate local visual embeddings. Finally, the visual embeddings from both branches were combined and matched with semantics. The calibrated stacking method is introduced to achieve GZSL recognition of space targets. Extensive experiments were conducted on an electromagnetic simulation dataset of nine categories of space targets, and the effectiveness of our GLVFENet is confirmed.
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Alrashdi, Ayed M., Meshari Alazmi, and Masad A. Alrasheedi. "Generalized Penalized Constrained Regression: Sharp Guarantees in High Dimensions with Noisy Features." Mathematics 11, no. 17 (August 28, 2023): 3706. http://dx.doi.org/10.3390/math11173706.
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The generalized penalized constrained regression (G-PCR) is a penalized model for high-dimensional linear inverse problems with structured features. This paper presents a sharp error performance analysis of the G-PCR in the over-parameterized high-dimensional setting. The analysis is carried out under the assumption of a noisy or erroneous Gaussian features matrix. To assess the performance of the G-PCR problem, the study employs multiple metrics such as prediction risk, cosine similarity, and the probabilities of misdetection and false alarm. These metrics offer valuable insights into the accuracy and reliability of the G-PCR model under different circumstances. Furthermore, the derived results are specialized and applied to well-known instances of G-PCR, including l1-norm penalized regression for sparse signal recovery and l2-norm (ridge) penalization. These specific instances are widely utilized in regression analysis for purposes such as feature selection and model regularization. To validate the obtained results, the paper provides numerical simulations conducted on both real-world and synthetic datasets. Using extensive simulations, we show the universality and robustness of the results of this work to the assumed Gaussian distribution of the features matrix. We empirically investigate the so-called double descent phenomenon and show how optimal selection of the hyper-parameters of the G-PCR can help mitigate this phenomenon. The derived expressions and insights from this study can be utilized to optimally select the hyper-parameters of the G-PCR. By leveraging these findings, one can make well-informed decisions regarding the configuration and fine-tuning of the G-PCR model, taking into consideration the specific problem at hand as well as the presence of noisy features in the high-dimensional setting.
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Liu, Donghai, Yuanyuan Liu, and Xiaohong Chen. "The New Similarity Measure and Distance Measure of a Hesitant Fuzzy Linguistic Term Set Based on a Linguistic Scale Function." Symmetry 10, no. 9 (August 29, 2018): 367. http://dx.doi.org/10.3390/sym10090367.
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The existing cosine similarity measure for hesitant fuzzy linguistic term sets (HFLTSs) has an impediment as it does not satisfy the axiom of similarity measure. Due to this disadvantage, a new similarity measure combining the existing cosine similarity measure and the Euclidean distance measure of HFLTSs is proposed, which is constructed based on a linguistic scale function; the related properties are also given. According to the relationship between the distance measure and the similarity measure, a corresponding distance measure between HFLTSs is obtained. Furthermore, we generalize the technique for order preference by similarity to an ideal solution (TOPSIS) method to the obtained distance measure of the HFLTSs. The principal advantages of the proposed method are that it cannot only effectively transform linguistic information in different semantic environments, but it can also avoid the shortcomings of existing the cosine similarity measure. Finally, a case study is conducted to illustrate the feasibility and effectiveness of the proposed method, which is compared to the existing methods.
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Manikantan, Aditya. "A Hybrid Recommendation System for Video Games: Combining Content-based & Collaborative Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1647–53. http://dx.doi.org/10.22214/ijraset.2021.38246.
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Abstract: Recommending video games can be trickier than movies. When it comes to selecting a video game, many factors are involved such as its genre, platform on which it’s played, duration of main and side quests, and more. However, recommending games based on just these features won’t suffice as a person who, for example, enjoys a certain genre of game can equally enjoy a vastly different genre. Therefore, a scoring mechanism is required which takes into account both, features of a game (contentbased filtering) and also studies the buying patterns of people playing a particular game (collaborative filtering). In this paper I have proposed a way to take into account both content-based and collaborative filtering into the final recommendation. I have used cosine similarity to quantify the similarity between the features of games. Along with this, I have employed a Deep fullyconnected AutoEncoder (DAE) to generalize the implicit data representation of an user’s buying patterns. Finally, I present a novel approach to combine the scores of these filtering techniques in such a way that it gives equal weightage to both. In other words, they both have equal influence over the final list of the top 10 games recommended to the user. Keywords: Hybrid Recommender, Collaborative filtering, Content-based filtering, Cosine similarity, AutoEncoder.
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Carbó-Dorca, Ramon, and Emili Besalú. "Shells, point cloud huts, generalized scalar products, cosines and similarity tensor representations in vector semispaces." Journal of Mathematical Chemistry 50, no. 1 (September 4, 2011): 210–19. http://dx.doi.org/10.1007/s10910-011-9906-4.
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Zhang, Li, and Xican Li. "Grey angle cosine relational degree model based on generalized greyness of interval grey number." Grey Systems: Theory and Application, December 26, 2023. http://dx.doi.org/10.1108/gs-08-2023-0081.
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PurposeAim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.Design/methodology/approachFirstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.FindingsThe results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.Practical implicationsThe research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.Originality/valueThe paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.
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"Movie Recommendation System using Cosine Similarity and KNN." International Journal of Engineering and Advanced Technology 9, no. 5 (June 30, 2020): 556–59. http://dx.doi.org/10.35940/ijeat.e9666.069520.
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Over the past years, the internet has broadened the horizon of various domains to interact and share meaningful information. As it is said that everything has its pros and cons therefore, along with the expansion of domain comes information overload and difficulty in extraction of data. To overcome this problem the recommendation system plays a vital role. It is used to enhance the user experience by giving fast and coherent suggestions. This paper describes an approach which offers generalized recommendations to every user, based on movie popularity and/or genre. Content-Based Recommender System is implemented using various deep learning approaches. This paper also gives an insight into problems which are faced in content-based recommendation system and we have made an effort to rectify them.
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Goltaji, Marzieh, Javad Abbaspour, Abdolrasool Jowkar, and Seyed Mostafa Fakhrahmad. "Comparison of text-based and linked-based metrics in terms of estimating the similarity of articles." Journal of Librarianship and Information Science, April 29, 2023, 096100062311657. http://dx.doi.org/10.1177/09610006231165759.
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The aim of this study is to identify the power of text-based metrics (Cosine and Lucene similarity) and linked-based (Co-citation, bibliographic coupling, Amsler, PageRank, and HITS) and their combination in estimating the similarity of articles with each other. The experiments were conducted on a test collection of 26,262 articles in the PubMed Central Open Access Subset (PMC OAS) of CITREC that, in addition to having linked-based metrics, their full text was available for calculating text-based metrics. Thirty articles were selected as primary articles, and articles related to each of them were retrieved based on the mesh similarity metric. Then, the similarity of the retrieved documents based on text-based and linked-based metrics was also extracted. In the next stage, text-based, linked-based, and hybrid metrics were entered into the generalized regression model to estimate the similarity of the articles to determine their power; finally, the performance of the models was compared based on the mean squared error and correlation. The results showed that the model included Cosine and Lucene similarity metrics in text-based metrics. In linked-based metrics, HITS (Hub), HITS (authority), PageRank, and co-citation had the highest power, respectively; but the bibliographic coupling and Amsler could not enter the model. In general, a comparison of text-based, linked-based, and hybrid metrics performance indicated that the linked-based model estimates similarity between articles better than the text-based model, and the combination of text-based and linked-based metrics makes little change in improving the power of the articles. Despite the importance and application of text-based and linked-based metrics to measure the similarity of articles, a study that examines the power of these metrics alone and in comparison with each other in estimating the similarity of articles was not observed.
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Bakhshi, Fatemeh, and Mehrdad Ashtiani. "An approach for reaching consensus in large-scale group decision-making focusing on dimension reduction." Complex & Intelligent Systems, March 4, 2024. http://dx.doi.org/10.1007/s40747-024-01377-4.
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AbstractGroup decision-making and consensus modeling have always been important research topics. With the widespread use of the Internet, group decisions can be made online, in which a large number of decision-makers participate. Most of the existing studies on large-scale group decision-making consider 20–50 decision-makers. Therefore, there is a need for a framework that focuses on situations where thousands of decision-makers exist. As dimension reduction is one of the five primary challenges in large-scale group decision-making, in this study, after reviewing the existing approaches, a new model is presented using a statistical approach along with complex network analysis techniques. The opinions are generalized first, and then the network of opinions is built. This new method reduces the dimensions of the problem by considering a hierarchy of opinions. Different scenarios were designed for the evaluation. The results show that the effect of this generalization on dimension reduction depends on the parameters of the problem. We have shown that in a group decision scenario with 3000 decision-makers and 6 alternatives, 99% of the data was reduced. As dimension reduction is the main focus of the current research, the effect of consistency on the diversity of opinions has also been investigated, and the results show that opinion consistency affects opinion generalization, which in turn affects dimension reduction. In addition, in the performed simulations, three types of functions were used to calculate similarity. The aim was to determine the best similarity function for the decision problems whose purpose was to rank the available alternatives. The results show that Euclidean similarity is a strict criterion compared with Cosine similarity.
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Chen Xing-Yu, Zhou Xin, Bai Xing, Yu Zhan, Wang Yu-Jie, Li Xin-Jia, Liu Yang, and Sun Ming-Ze. "Equivalence analysis of Fourier ghost imaging and sinusoidal ghost imaging." Acta Physica Sinica, 2023, 0. http://dx.doi.org/10.7498/aps.72.20222317.
Full textAbstract:
Ghost imaging, also known as correlated imaging, is one of the research hotspots in the imaging field. In recent years, ghost imaging technology has been developing continuously, gradually showing a diversified trend, but the correlation between each other is weak, and the research progress is slower than before. Studying the essence of ghost imaging theory is a feasible direction to explore the unknown field of ghost imaging. Through research, we find that Fourier ghost imaging and sinusoidal ghost imaging are based on the same type of orthogonal sine and cosine speckle, which has a very high similarity. At the same time, sinusoidal ghost imaging method is a complete spatial description and spatial imaging process, so we guessed that these two imaging methods can reveal the relationship between spatial imaging and Fourier domain imaging. On this basis, it is proved that Fourier ghost imaging and sinusoidal ghost imaging are equivalent in principle. The former can be realized by n-step phase shift, while the latter can be equivalent to two-step phase shift. Considering that both of these methods use sine and cosine fringes as structural speckle, combined with the spatial decomposition characteristics of sine speckle imaging, the relationship between structural speckle imaging and traditional ghost imaging is analyzed, and the principles of some methods constructed on the basis of these two methods are explained. The simulation results applied to edge detection show that the combination of the two methods can simultaneously obtain the better anti-noise performance of Fourier ghost imaging and the higher imaging efficiency of sinusoidal ghost imaging. Since sinusoidal ghost imaging relates the characteristics of image spatial decomposition with traditional ghost imaging, and is equivalent to the expression of orthogonal Fourier transform domain of Fourier ghost imaging, the association of ghost imaging methods in Fourier domain and even in the whole orthogonal transform domain and spatial domain can be generalized. This conclusion may provide a way for associating different kinds of ghost imaging, and it can be hoped that more and more new types of ghost imaging will be developed.
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Kark, Sarah M., Matthew T. Birnie, Tallie Z. Baram, and Michael A. Yassa. "Functional Connectivity of the Human Paraventricular Thalamic Nucleus: Insights From High Field Functional MRI." Frontiers in Integrative Neuroscience 15 (April 21, 2021). http://dx.doi.org/10.3389/fnint.2021.662293.
Full textAbstract:
The paraventricular thalamic nucleus (PVT) is a small but highly connected nucleus of the dorsal midline thalamus. The PVT has garnered recent attention as a context-sensitive node within the thalamocortical arousal system that modulates state-dependent motivated behaviors. Once considered related to generalized arousal responses with non-specific impacts on behavior, accumulating evidence bolsters the contemporary view that discrete midline thalamic subnuclei belong to specialized corticolimbic and corticostriatal circuits related to attention, emotions, and cognition. However, the functional connectivity patterns of the human PVT have yet to be mapped. Here, we combined high-quality, high-resolution 7T and 3T resting state MRI data from 121 young adult participants from the Human Connectome Project (HCP) and thalamic subnuclei atlas masks to investigate resting state functional connectivity of the human PVT. The 7T results demonstrated extensive positive functional connectivity with the brainstem, midbrain, ventral and dorsal medial prefrontal cortex (mPFC), anterior and posterior cingulate, ventral striatum, hippocampus, and amygdala. These connections persist upon controlling for functional connectivity of the rest of the thalamus. Whole-brain contrasts provided further evidence that, compared to three nearby midline thalamic subnuclei, functional connectivity of the PVT is strong with the hippocampus, amygdala, ventral and dorsal mPFC, and middle temporal gyrus. These findings suggest that, even during rest, the human PVT is functionally coupled with many regions known to be structurally connected to rodent and non-human primate PVT. Further, cosine similarity analysis results suggested the PVT is integrated into the default mode network (DMN), an intrinsic connectivity network associated with episodic memory and self-referential thought. The current work provides a much-needed foundation for ongoing and future work examining the functional roles of the PVT in humans.
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Sui Yu-Mei, He Zhao-Jian, Bi Ren-Gui, Kong Peng, Wu Ji-Eng, Zhao He-Ping, and Deng Ke. "Ultrathin acoustic metasurface carpet cloaking based on Helmholtz resonances." Acta Physica Sinica, 2024, 0. http://dx.doi.org/10.7498/aps.73.20231706.
Full textAbstract:
As the development of metamaterials, the acoustic cloaking has attracted extensive attention due to its novel physics and potential application. In recent years, based on the phase reimbursement modulation from Generalized Snell’s law and coordinate transformation, the acoustic cloakings underwater and in the air have both received broad and in-depth researches. However, there is still an urgent need to design acoustic cloaks that are thinner and less affected by the incident angle of acoustic waves. Further, the designed cloaks should have a relative wider operating band and be more suitable for irregular objects.<br/>In this paper, an ultrathin curved acoustic metasurface carpet cloaking is studied by using of phase reimbursement modulation. The phase modulation is based on Helmholtz resonances (HR). The metasurface carpet is immersed in air, since the vibration mode of acoustic waves in the air is relatively single, thus the physical essence can be elucidated more clearly. The carpet cloak is constructed by 52 Helmholtz resonant units, and the size of resonant unit is less than 0.2 of working wavelength.<br/>The phase change of HR unit is solved analytically by using of the Generalized Snell’s law, and confirmed by the Multiphysics COMSOL software. The parameter effects of HR unit on the phase change is studied. It demonstrates that, the phase change of HR unit is sensitive for the change of height and radii of HR unit, while the change of width of HR cavity neck can make the phase of HR unit smoothly change. Therefore, the width of HR cavity neck is designed in constructing the 52 HR units, and the height and radii of HR unit stay fixed.<br/>The simulating results demonstrate that the designed cloak works well in the frequency ranging from 5850Hz to 7550Hz. Also, we study the cloaking effect for oblique incidence, showing that the carpet cloak works well for incident angle is less than 30°. To quantitative analysis of the bandwidth of the cloaking, we calculate the Cosine Similarity (CSI) value. It elucidates that, the value of the cloak is great close to that of the flat ground in the corresponding working frequency range. The cloak designed in this paper are made up by the ultrathin Helmholtz Resonant structures, which is simple and easy to realize and is useful for potential applications.