scholarly journals Recognition of Handwritten Characters Using Cosine Measure

2013 ◽  
Vol 6 (2) ◽  
pp. 111
Author(s):  
Sumit Saha ◽  
Tanmoy Som
Keyword(s):  
Cosine Measure

scholarly journals Positive bases with maximal cosine measure

2018 ◽  
Vol 13 (6) ◽  
pp. 1381-1388 ◽  
Author(s):  
Geir Nævdal
Keyword(s):  
Cosine Measure ◽  
Positive Bases

scholarly journals Multiple-attribute Decision-Making Method under a Single-Valued Neutrosophic Hesitant Fuzzy Environment

2015 ◽  
Vol 24 (1) ◽  
pp. 23-36 ◽  
Author(s):  
Jun Ye
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Multiple Attribute Decision Making ◽  
Weighted Averaging ◽  
Hesitant Fuzzy Set ◽  
Neutrosophic Set ◽  
Fuzzy Environment ◽  
Cosine Measure ◽  
Multiple Attribute

AbstractOn the basis of the combination of single-valued neutrosophic sets and hesitant fuzzy sets, this article proposes a single-valued neutrosophic hesitant fuzzy set (SVNHFS) as a further generalization of the concepts of fuzzy set, intuitionistic fuzzy set, single-valued neutrosophic set, hesitant fuzzy set, and dual hesitant fuzzy set. Then, we introduce the basic operational relations and cosine measure function of SVNHFSs. Also, we develop a single-valued neutrosophic hesitant fuzzy weighted averaging (SVNHFWA) operator and a single-valued neutrosophic hesitant fuzzy weighted geometric (SVNHFWG) operator and investigate their properties. Furthermore, a multiple-attribute decision-making method is established on the basis of the SVNHFWA and SVNHFWG operators and the cosine measure under a single-valued neutrosophic hesitant fuzzy environment. Finally, an illustrative example of investment alternatives is given to demonstrate the application and effectiveness of the developed approach.

Similarity Learning Algorithm Selection for Chronic Renal Failure Patients Treatment Strategy Optimization

2020 ◽  
Vol 26 (12) ◽  
pp. 701-705
Author(s):  
I. Lakman ◽  
◽  
R. Akhmetvaleev ◽  
D. Enikeev ◽  
R. Khaziakhmetov ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Rank Order ◽  
Calcium Balance ◽  
Similarity Learning ◽  
Algorithm Selection ◽  
Specific Patient ◽  
Cosine Measure ◽  
Measure Of Similarity ◽  
Personalized Approach

One of the main methods on which the personalized approach in medicine is based is finding a pair of patients who are similar in the properties of the disease. The objective of the study is to select the most effective similarity learning instrument amongst three options anaemia treatment and phosphorus-calcium balance recovery in dialysis patients, ranked according to the highest similarity to the particular patient. As soon as methods for comparing instruments will achieve the goal, the algorithm of weight tagging is used, modified by the authors by adding more weights values to important features — the cosine measure, the soft cosine measure, considering the similarity of drug alternative and their bioavailability. As a metric that evaluates the quality of algorithms, a combined metric is used that takes into account the quality of treatment classification as effective and the rank order of the greatest correspondence of therapy to a specific patient. As a result, using the opinions of nephrologists as experts, it was shown that the best measure of similarity is the soft cosine measure.

scholarly journals A Novel Kernel for RBF Based Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Wasim Aftab ◽  
Muhammad Moinuddin ◽  
Muhammad Shafique Shaikh
Keyword(s):  
Basis Function ◽  
Distance Measures ◽  
Gaussian Kernel ◽  
Phase Shift Keying ◽  
Multiple Phase ◽  
Cosine Measure ◽  
Symbol Detection ◽  
Shift Keying ◽  
Rbf Kernel ◽  
Artificial Neural Network Ann

Radial basis function (RBF) is well known to provide excellent performance in function approximation and pattern classification. The conventional RBF uses basis functions which rely on distance measures such as Gaussian kernel of Euclidean distance (ED) between feature vector and neuron’s center, and so forth. In this work, we introduce a novel RBF artificial neural network (ANN) where the basis function utilizes a linear combination of ED based Gaussian kernel and a cosine kernel where the cosine kernel computes the angle between feature and center vectors. Novelty of the proposed work relies on the fact that we have shown that there may be scenarios where the two feature vectors (FV) are more prominently distinguishable via the proposed cosine measure as compared to the conventional ED measure. We discuss adaptive symbol detection for multiple phase shift keying (MPSK) signals as a practical example to show where the angle information can be pivotal which in turn justifies our proposed RBF kernel. To corroborate our theoretical developments, we investigate the performance of the proposed RBF for the problems pertaining to three different domains. Our results show that the proposed RBF outperforms the conventional RBF by a remarkable margin.

Single-value neutrosophic cosine measure for evaluation of port logistics competitiveness

2020 ◽  
Vol 39 (3) ◽  
pp. 4667-4675
Author(s):  
Changxing Fan ◽  
En Fan ◽  
Jihong Chen ◽  
Jun Ye ◽  
Kang Zhou
Keyword(s):  
Comprehensive Evaluation ◽  
Great Influence ◽  
Value Added ◽  
Evaluation Index System ◽  
Logistics System ◽  
Logistics Service ◽  
Supply Chain Logistics ◽  
Actual Application ◽  
Cosine Measure ◽  
Port Logistics

Port as an irreplaceable important node in the process of logistics is a special form of the integrated logistics system, which completes the basic logistics service and value-added services in the global supply chain logistics system. At present, the port logistics service has become an important breakthrough in the competition of ports, the improvement of port logistics competitiveness has great influence on the development of port and port city and even the area economic development. Analyzing from the port logistics competitiveness, this paper establishes a comprehensive evaluation index system and proposes a single-value neutrosophic cosine measure method to evaluate the port logistics competitiveness of five sample ports, and gets the score sorting of the logistics competitiveness of these five ports. This method as a helpful tool is clear and easy for port logistics competitiveness evaluation during actual application.

scholarly journals THE RIGHT THRESHOLD VALUE: WHAT IS THE RIGHT THRESHOLD OF COSINE MEASURE WHEN USING LATENT SEMANTIC ANALYSIS FOR EVALUATING STUDENT ANSWERS?

2006 ◽  
Vol 15 (05) ◽  
pp. 767-777 ◽  
Author(s):  
PHANNI PENUMATSA ◽  
MATTHEW VENTURA ◽  
ARTHUR C. GRAESSER ◽  
MAX LOUWERSE ◽  
XIANGEN HU ◽  
...  
Keyword(s):  
Natural Language ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Intelligent Tutoring System ◽  
Threshold Value ◽  
Subject Matter Experts ◽  
Language Understanding ◽  
Discriminative Ability ◽  
Cosine Measure ◽  
The Right

AutoTutor is an intelligent tutoring system that holds conversations with learners in natural language. AutoTutor uses Latent Semantic Analysis (LSA) to match student answers to a set of expected answers that would appear in a complete and correct response or which reflect common but incorrect understandings of the material. The correctness of student contributions is decided using a threshold value of the LSA cosine between the student answer and the expectations. In previous work LSA has shown to be effective in detecting good answers of students. The results indicate that the best agreement between LSA matches and the evaluations of subject matter experts can be obtained if the cosine threshold is allowed to be a function of the lengths of both student answer and the expectation being considered. Based on some of our experiences with LSA and AutoTutor, we are developing a new mathematical model to improve the precision of AutoTutor's natural language understanding and discriminative ability.

scholarly journals Applying Machine Learning to the Task of Generating Search Queries

2021 ◽  
Vol 24 (2) ◽  
pp. 272-293
Author(s):  
Александр Михайлович Гусенков ◽  
Алина Рафисовна Ситтикова
Keyword(s):  
Neural Networks ◽  
Semantic Analysis ◽  
Short Term Memory ◽  
Complete Evaluation ◽  
Cosine Measure ◽  
Expert Analysis ◽  
Transformer Model ◽  
Value Decomposition ◽  
Gated Recurrent Unit ◽  
User Queries

In this paper we research two modifications of recurrent neural networks – Long Short-Term Memory networks and networks with Gated Recurrent Unit with the addition of an attention mechanism to both networks, as well as the Transformer model in the task of generating queries to search engines. GPT-2 by OpenAI was used as the Transformer, which was trained on user queries. Latent-semantic analysis was carried out to identify semantic similarities between the corpus of user queries and queries generated by neural networks. The corpus was convert-ed into a bag of words format, the TFIDF model was applied to it, and a singular value decomposition was performed. Semantic similarity was calculated based on the cosine measure. Also, for a more complete evaluation of the applicability of the models to the task, an expert analysis was carried out to assess the coherence of words in artificially created queries.

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