Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses

Quantifying External Information in Social Network Analysis: An Application to Comparative Mythology

Social network analysis is a popular tool to understand the relationships between interacting agents by studying the structural properties of their connections. However, this kind of analysis can miss some of the domain-specific knowledge available in the original information domain and its propagation through the associated network. In this work, we develop an extension of classical social network analysis to incorporate external information from the original source of the network. With this extension we propose a new centrality measure, the semantic value, and a new affinity function, the semantic affinity, that establishes fuzzy-like relationships between the different actors in the network. We also propose a new heuristic algorithm based on the shortest capacity problem to compute this new function. As an illustrative case study, we use the novel proposals to analyze and compare the gods and heroes from three different classical mythologies: 1) Greek; 2) Celtic; and 3) Nordic. We study the relationships of each individual mythology and those of the common structure that is formed when we fuse the three of them. We also compare our results with those obtained using other existing centrality measures and embedding approaches. In addition, we test the proposed measures on a classical social network, the Reuters terror news network, as well as in a Twitter network related to the COVID-19 pandemic. We found that the novel method obtains more meaningful comparisons and results than previous existing approaches in every case.

An MAGDM approach with 2-tuple linguistic neutrosophic number for mental health education evaluation of college students

Generally speaking, the evaluation of mental health education (MHE) in colleges is an activity and process of evaluating the elements, processes and effects of MHE in schools by systematically collecting relevant information, following reasonable evaluation principles and applying specialized evaluation methods and techniques according to certain evaluation index systems and value judgment systems. The fundamental goal of MHE evaluation in colleges is to promote and regulate the scientific, healthy and smooth development of MHE in colleges and universities, improve the quality of MHE, promote the reform of MHE, build a good psychological atmosphere in colleges and universities, and effectively improve the psychological quality and mental health of college students. The MHE evaluation of college students is looked as multiple attribute group decision-making (MAGDM). In this paper, the 2-tuple linguistic neutrosophic number cross-entropy (2TLNN-CE) method is defined based on the traditional cross-entropy and 2-tuple linguistic neutrosophic sets (2TLNSs). Then, 2TLNN-CE method is established for MAGDM. Finally, a numerical example for MHE evaluation of college students was given and some comparisons are also conducted to further illustrate advantages of the built method.

Evaluation model of green supplier selection for coal enterprises with similarity measures of double-valued neutrosophic sets based on cosine function

Coal is a vital basic energy source for any economy in the world, and our country is no exception. Our coal resources are abundant, with high production and demand, not comparable to oil and natural gas. The coal supply chain plays an equally important role in economic production, but unfortunately, the current coal supply chain is not focused on greening while creating profits. Unfortunately, the current coal supply chain does not focus on green production and energy conservation and emission reduction while creating profits, which has caused irreversible harm and loss to resources and environment. This has caused irreversible damage and loss to resources and the environment. The green supplier selection for coal enterprises is affirmed as multiple attribute decision making (MADM). In such paper, motivated by the idea of cosine similarity measure (CSM), the CSMs are extended to DVNSs and four CSMs are created under DVNSs. Then, two weighted CSMs are built for MADM under DVNSs. Finally, a numerical example for Green supplier selection for coal enterprises is affirmed and some comparative algorithms are produced to affirm the built method.

GRA method for probabilistic simplified neutrosophic MADM and application to talent training quality evaluation of segmented education

The “3 + 2” segmented training between higher vocational colleges and applied undergraduate courses has opened up the rising channel of vocational education from junior college level to undergraduate level, and promoted the organic connection between higher vocational colleges and Universities of Applied Sciences. It is one of the important ways to establish a modern vocational education system. Exploring the monitoring mechanism of talent training quality is an important measure to ensure the achievement of the segmented training goal, and it is a necessary condition to successfully train high-quality skilled applied talents. The talent training quality evaluation of segmented education is viewed as multiple attribute decision-making (MADM) issue. In this paper, an extended probabilistic simplified neutrosophic number GRA (PSNN-GRA) method is established for talent training quality evaluation of segmented education. The PSNN-GRA method integrated with CRITIC method in probabilistic simplified neutrosophic sets (PSNSs) circumstance is applied to rank the optional alternatives and a numerical example for talent training quality evaluation of segmented education is used to proof the newly proposed method’s practicability along with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute.

An efficient method for ideological and political education quality evaluation of colleges and universities with 2-tuple linguistic neutrosophic sets

Improving college students’ satisfaction with the teaching quality of ideological and political theory courses in colleges and universities is the need to promote college students to consciously fulfill their ideological and political quality, and it is also the need to further form a strong driving force for the teaching reform of ideological and political theory courses in colleges and universities. The requirements of teaching level are also the requirements to further enhance the competitiveness of colleges and universities. The ideological and political education quality (IPEQ) evaluation of college students is looked as multiple attribute group decision-making (MAGDM) problem. In this paper, the 2-tuple linguistic neutrosophic TOPSIS (2TLN-TOPSIS) model is built based on the traditional TOPSIS and 2-tuple linguistic neutrosophic sets (2TLNSs). Firstly, the 2TLNSs is introduced. Then, combine the TOPSIS model with 2TLNSs, the 2TLN-TOPSIS model is established for MAGDM. Finally, a numerical example for IPEQ evaluation of College students have been given and some comparisons are also conducted to further illustrate advantages of the 2TLN-TOPSIS method.

IVNN-Taxonomy method for teaching effect evaluation of “micro-ideological and political” model in medical colleges and universities

How to make good use of new network technology and design the classroom teaching of a course needs to be based on the teaching object, teaching content, and the teacher’s mastery of the technology and teaching platform. In teaching design, scholars also put forward different teaching links based on their own teaching experience. The cooperative learning links should be designed in college teaching. To build a positive and interdependent organizational structure and an equal and democratic learning atmosphere will help students to stimulate their learning motivation and sense of responsibility. The fuzzy evaluation of the teaching effect of the “micro-ideological and political” model in medical colleges and universities is viewed as the multiple attribute group decision making (MAGDM) issue. In such paper, Taxonomy method is designed for solving the MAGDM under interval-valued neutrosophic sets (IVNSs). First, the score function of IVNSs and Criteria Importance Though Intercrieria Correlation (CRITIC) method is used to derive the attribute weights. Second, then, the interval-valued neutrosophic numbers Taxonomy (IVNN-Taxonomy) method is built to deal with MAGDM problem. Finally, a numerical example for teaching effect evaluation of the “micro-ideological and political” model in medical colleges and universities is given to illustrate the built method.

Method for 2-tuple linguistic neutrosophic number MAGDM and applications to physical education teaching quality evaluation in colleges and universities

The quality of physical education (PE) teaching in colleges and universities is the basis for the development of PE disciplines in colleges and universities, so currently thinking about how to effectively improve the quality of PE teaching in colleges and universities has become the first and foremost problem for many college and university PE departments to solve. In order to solve this problem, it is necessary to build a reasonable and scientific evaluation and monitoring system of PE teaching quality, because only by establishing an effective evaluation and monitoring system of teaching quality can we evaluate and supervise all the PE operation properly and scientifically, and then give feedback in the process of evaluation and supervision, such evaluation and monitoring system can greatly promote the continuous improvement of PE teaching quality in colleges and universities. This is also one of the most effective means to improve the quality of PE and achieve the goal of PE in colleges and universities. The PE teaching quality evaluation in Colleges and Universities is frequently viewed as the multiple attribute group decision making (MAGDM) issue. In this paper, the 2-tuple linguistic neutrosophic number grey relational analysis (2TLNN-GRA) method is built based on the traditional grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Then, a numerical example for PE teaching quality evaluation in Colleges and Universities has been given and some comparisons is used to illustrate advantages of 2TLNN-GRA method.

Application of evidence reasoning algorithm and QUALIFLEX with single-valued neutrosophic set for MCDM

Multi-criteria decision-making methods often include attributes with uncertain nature in practical applications, single-valued neutrosophic set is an important approach to solve above problem. QUALIFLEX method is a traditional decision method that makes decision by comparing different permutations of alternatives. In this paper, QUALIFLEX method is developed to solve the MCDM problem with the element of decision matrix is the single-valued neutrosophic number. Besides, since the defects of the original QUALIFLEX method about fusing information of different attributes, this paper uses Dempster-Shafer theory of evidence to integrate the information about weight and alternatives. Finally, by comparing the result with other MCDM methods, we find that the new method can not only obtain reasonable results, but also explain the decision results by probability theory. This paper not only develops the traditional MCDM method, but also a meaningful attempt to apply AI algorithm in MCDM method.

Performance evaluation of family business strategic transition based on the 2-tuple linguistic neutrosophic number multiple attribute group decision making

Since 2010, China’s traditional industry has entered a critical stage of development and enterprise reform and development is imminent. Product homogenization is serious in this market, so that the competition among enterprises is fierce. At the same time, international brands continue to enter the Chinese consumption market, which intensifies the competition and seriously squeezes the market share of Chinese local brands. However, the popularization and development of the Internet and the change of people’s consumption concept and level make the market put forward higher requirements for the development of business operation and many traditional family enterprises have embarked on the road of transformation. It is of great significance and value to clarify the influence of internal factors of family enterprises on strategic transformation. The performance evaluation of family business strategic transition is really a multiple attribute group decision making (MAGDM) problems. In this paper, the 2-tuple linguistic neutrosophic number grey relational analysis (2TLNN-GRA) method is proposed along with on the traditional grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Firstly, the 2TLNNSs is introduced. Then, combine the traditional fuzzy GRA model with 2TLNNSs information, the 2TLNN-GRA method is established and the computing steps for MAGDM are built. Finally, a numerical example for performance evaluation of family business strategic transition has been given and some comparisons is used to illustrate advantages of 2TLNN-GRA method.

An intelligent decision methodology for physical health evaluation of college students based on generalized 2-tuple linguistic neutrosophic geometric HM operators

Physical Health is an important part of health education and health promotion in my country, and the health literacy level of students majoring in physical education in colleges and universities is an important factor in the development of health education in primary and secondary schools, and also directly affects the implementation of school health education in the future. The physical health evaluation of College students is frequently viewed as the multiple attribute decision making (MADM) issue. In our article, we combine the geometric Heronian mean (GHM) operator, generalized weighted Heronian mean (GWHM) operator with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose the generalized 2-tuple linguistic neutrosophic geometric HM (G2TLNGHM) operator and generalized 2-tuple linguistic neutrosophic weighted geometric HM (G2TLNWGHM) operator. Meanwhile, some ideal properties of built operator are studied. Then, the G2TLNWGHM operator is applied to deal with the MADM problems under 2TLNNs. Finally, an example for Physical health evaluation of College students is used to show the proposed methods.

Fabrication of a graphene-based sensor to detect the humidity and the temperature of a metal body with imprecise data analysis

Graphene is a 2D material with remarkable properties. The present study demonstrates the fabrication of a graphene-based sensor for measuring the temperature and humidity of a metal body. The graphene sensor was fabricated by depositing a thin film of graphene nanoparticles between silver electrodes (separated by ∼50 μm) on a glass substrate. The graphene thin film was characterized by XRD, Raman spectroscopy and UV-vis techniques. The capacitance and resistance for both the relative humidity (in the range of 0-100% RH) and temperature (in the range of 230-310 K) were measured using an LCR meter at 1 kHz in a controlled chamber. The graphene-based sensor expressed high sensitivity with fast response and recovery times for both humidity and temperature with long stability and low hysteresis curves. The sensor was also tested on a metal body, which expressed a good response time. Moreover, the measured data of capacitance and resistance was analyzed with classical and neutrosophic analysis as an application of modern material statistics. It was observed that neutrosophic analysis is more flexible for analyzing the capacitance and resistance of the fabricated sensor.

Fabrication of flexible temperature sensors to explore indeterministic data analysis for robots as an application of Internet of Things

The use of flexible electronic devices in different applications of Internet of Things, especially in robot technology, has gained importance to measure different physical factors such as temperature. Moreover, there is a need for a flexible and more informative approach to analyse the data. In this study, we report two flexible temperature sensors based on reduced graphene and multi-walled carbon nanotubes with high sensitivity and quick response and recovery times. The electrical properties of the sensors were studied using an LCR meter associated with a controlled chamber at 1 kHz. We used both classical and neutrosophic methods for analyzing the measured data of temperature sensors and found the more effective method by comparing their methods of analysis.

The use of flexible electronic devices in different applications of Internet of Things, especially in robot technology, has gained importance to measure different physical factors such as temperature.

Single-valued neutrosophic Schweizer-Sklar Hamy mean aggregation operators and their application in multi-attribute decision making

Single-valued neutrosophic sets can efficiently depict a great deal of imprecise, uncertain and discordant information. Hamy mean operator can consider the interrelationships among multiple integrated arguments and Schweizer-Sklar operations express great flexibility in the process of information aggregation. To give full consideration to these advantages, we merge the Hamy mean operator with the Schweizer-Sklar operations in single-valued neutrosophic environment, proposing a single-valued neutrosophic Schweizer-Sklar Hamy mean operator and a single-valued neutrosophic Schweizer-Sklar weighted Hamy mean operator. Besides, we illustrate some specific cases and attributes of the two operators. Moreover, based on the entropy weight method and the single-valued neutrosophic Schweizer-Sklar weighted Hamy mean operator, this paper presents a single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method to tackle multi-attribute decision making problems. At last, the method and other three existing methods are applied to solve a practical multi-attribute decision making problem, which validates the credibility and validity of the single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method by comparing the differences among them.

A Novel Similarity Measure of Single-Valued Neutrosophic Sets Based on Modified Manhattan Distance and Its Applications

A single-valued neutrosophic (SVN) set contains three parameters, which can well describe three aspects of an objective thing. However, most previous similarity measures of SVN sets often encounter some counter-intuitive examples. Manhattan distance is a well-known distance, which has been applied in pattern recognition, image analysis, ad-hoc wireless sensor networks, etc. In order to develop suitable distance measures, a new distance measure of SVN sets based on modified Manhattan distance is constructed, and a new distance-based similarity measure also is put forward. Then some applications of the proposed similarity measure are introduced. First, we introduce a pattern recognition algorithm. Then a multi-attribute decision-making method is proposed, in which a weighting method is developed by building an optimal model based on the proposed similarity measure. Furthermore, a clustering algorithm is also put forward. Some examples are also used to illustrate these methods.

Targets preliminary screening for the fresh natural drug molecule based on Cosine-correlation and similarity-comparison of local network

Background

Chinese herbal medicine is made up of hundreds of natural drug molecules and has played a major role in traditional Chinese medicine (TCM) for several thousand years. Therefore, it is of great significance to study the target of natural drug molecules for exploring the mechanism of treating diseases with TCM. However, it is very difficult to determine the targets of a fresh natural drug molecule due to the complexity of the interaction between drug molecules and targets. Compared with traditional biological experiments, the computational method has the advantages of less time and low cost for targets screening, but it remains many great challenges, especially for the molecules without social ties.

Methods

This study proposed a novel method based on the Cosine-correlation and Similarity-comparison of Local Network (CSLN) to perform the preliminary screening of targets for the fresh natural drug molecules and assign weights to them through a trained parameter.

Results

The performance of CSLN is superior to the popular drug-target-interaction (DTI) prediction model GRGMF on the gold standard data in the condition that is drug molecules are the objects for training and testing. Moreover, CSLN showed excellent ability in checking the targets screening performance for a fresh-natural-drug-molecule (scenario simulation) on the TCMSP (13 positive samples in top20), meanwhile, Western-Blot also further verified the accuracy of CSLN.

Conclusions

In summary, the results suggest that CSLN can be used as an alternative strategy for screening targets of fresh natural drug molecules.

Comparative Analysis of the Simple WISP and Some Prominent MCDM Methods: A Python Approach

This article presents a comparison of the results obtained using the newly proposed Simple Weighted Sum Product method and some prominent multiple criteria decision-making methods. For comparison, several analyses were performed using the Python programming language and its NumPy library. The comparison was also made on a real decision-making problem taken from the literature. The obtained results confirm the high correlation of the results obtained using the Simple Weighted Sum Product method and selected multiple criteria decision-making methods such as TOPSIS, SAW, ARAS, WASPAS, and CoCoSo, which confirms the usability of the Simple Weighted Sum Product method for solving multiple criteria decision-making problems.

Neutrosophic statistical analysis of resistance depending on the temperature variance of conducting material

In this work, we have proposed a neutrosophic statistical approach for the analysis of resistance of conducting material depending on the temperature variance. We have developed a neutrosophic formula and applied it to the resistance data. We also use the classical statistical approach for making a comparison between both approaches. As a result, it is observed that the neutrosophic statistical approach is more flexible and informative. Also, this work suggests that the neutrosophic statistical approach analyzes the resistance of conducting material for big data.

Fabrication of a surface type humidity sensor based on methyl green thin film, with the analysis of capacitance and resistance through neutrosophic statistics

In this work, we propose a humidity sensor based on methyl green thin film. A surface-type humidity sensor Al/MG/Al was fabricated by depositing methyl green thin film between aluminum electrodes. The structural, optical and surface morphological properties of the thin film were characterized by XRD, UV Vis and FESEM. The sensing properties with high sensitivity as well as response time 200 s and recovery 60 s of the humidity sensor were investigated by measuring the capacitance and resistance at 1, 10 and 100 kHz with the humidity varying range from 32 to 85% RH. These measured values were analyzed by classical statistics and neutrosophic statistics. As a result, it was observed that neutrosophic statistics were more informative, flexible and adequate than classical statistics for analyzing the measured values of capacitance and resistance.

An Application of Neutrosophic Set to Relative Importance Assignment in AHP

The paper addresses a new facet of problem regarding the application of AHP in the real world. There are occasions that decision makers are not certain about relative importance assignment in pairwise comparison. The decision makers think the relative importance is among a set of scales, each of which is associated with a different possibility degree. A Discrete Single Valued Neutrosophic Number (DSVNN) with specified degrees of truth, indeterminacy, and falsity is employed to represent each assignment by taking into account all possible scales according to the decision maker’s thought. Each DSVNN assignment is transformed into a crisp value via a deneutrosophication using a similarity-to-absolute-truth measure. The obtained crisp scales are input to a pairwise comparison matrix for further analysis. The proposed neutrosophic set-based relative importance assignment is another additional novelty of the paper, which is different from all prior studies focusing only on the definition of measurement scales. The presented assignment emulates the real-world approach of decision making in human beings which may consider more than one possibility. It is also shown herein that the single and crisp relative importance assignment in the original AHP by Saaty is just a special case of the proposed methodology. The sensitivity analysis informs that when decision makers have neither absolute truth nor falsity about a scale, the proposed methodology is recommended for obtaining reliable relative importance scale. The applicability of the proposed methodology to the real-world problem is shown through the investment in equity market.

Statistical Modeling based Directional Pattern Design (SMDPD) feature extraction for coral reef classification

Coral reefs are a precious ecosystem that supports majority of marine life. The identification of coral species is essential in the conservation and monitoring process. Distinguishing the coral species among the coral reef family is really a challenging task since they have analogous characteristics and have complex spatial borders between the coral classes. This requires experts to identify corals. But due to inconsistency and biasing of manual labelling, the manual annotations of coral reefs are not feasible. The objective of this research work is to identify various types of corals present in the given input video. This work is aimed at identifying thirty-six types of coral by employing a new feature extraction method called Statistical Modeling based Directional Pattern Design (SMDPD) using a new directional pattern. The proposed work outperforms the state-of-art techniques for four coral datasets namely EILAT, EILAT 2 Red Sea, MLC 2010 and RSMAS. Another advantage of this work is the reduction in feature bin size from 255 to just 16 bins.

Multi-attribute Cognitive Decision Making via Convex Combination of Weighted Vector Similarity Measures for Single-Valued Neutrosophic Sets

Similarity measure (SM) proves to be a necessary tool in cognitive decision making processes. A single-valued neutrosophic set (SVNS) is just a particular instance of neutrosophic sets (NSs), which is capable of handling uncertainty and impreciseness/vagueness with a better degree of accuracy. The present article proposes two new weighted vector SMs for SVNSs, by taking the convex combination of vector SMs of Jaccard and Dice and Jaccard and cosine vector SMs. The applications of the proposed measures are validated by solving few multi-attribute decision-making (MADM) problems under neutrosophic environment. Moreover, to prevent the spread of COVID-19 outbreak, we also demonstrate the problem of selecting proper antivirus face mask with the help of our newly constructed measures. The best deserving alternative is calculated based on the highest SM values between the set of alternatives with an ideal alternative. Meticulous comparative analysis is presented to show the effectiveness of the proposed measures with the already established ones in the literature. Finally, illustrative examples are demonstrated to show the reliability, feasibility, and applicability of the proposed decision-making method. The comparison of the results manifests a fair agreement of the outcomes for the best alternative, proving that our proposed measures are effective. Moreover, the presented SMs are assured to have multifarious applications in the field of pattern recognition, image clustering, medical diagnosis, complex decision-making problems, etc. In addition, the newly constructed measures have the potential of being applied to problems of group decision making where the human cognition-based thought processes play a major role.

New similarity measures for single-valued neutrosophic sets with applications in pattern recognition and medical diagnosis problems

The single-valued neutrosophic set (SVNS) is a well-known model for handling uncertain and indeterminate information. Information measures such as distance measures, similarity measures and entropy measures are very useful tools to be used in many applications such as multi-criteria decision making (MCDM), medical diagnosis, pattern recognition and clustering problems. A lot of such information measures have been proposed for the SVNS model. However, many of these measures have inherent problems that prevent them from producing reasonable or consistent results to the decision makers. In this paper, we propose several new distance and similarity measures for the SVNS model. The proposed measures have been verified and proven to comply with the axiomatic definition of the distance and similarity measure for the SVNS model. A detailed and comprehensive comparative analysis between the proposed similarity measures and other well-known existing similarity measures has been done. Based on the comparison results, it is clearly proven that the proposed similarity measures are able to overcome the shortcomings that are inherent in existing similarity measures. Finally, an extensive set of numerical examples, related to pattern recognition and medical diagnosis, is given to demonstrate the practical applicability of the proposed similarity measures. In all numerical examples, it is proven that the proposed similarity measures are able to produce accurate and reasonable results. To further verify the superiority of the suggested similarity measures, the Spearman’s rank correlation coefficient test is performed on the ranking results that were obtained from the numerical examples, and it was again proven that the proposed similarity measures produced the most consistent ranking results compared to other existing similarity measures.

A cosine similarity measure for multi-criteria group decision making under neutrosophic soft environment

In actual life, uncertain and inconsistent information exists widely. How to deal with the information so that it can be better applied is a problem that has to be solved. Neutrosophic soft sets can process uncertain and inconsistent information. Also, Dempster-Shafer evidence theory has the advantage of dealing with uncertain information, and it can synthesize uncertain information and deal with subjective judgments effectively. Therefore, this paper creatively combines the Dempster-Shafer evidence theory with the neutrosophic soft sets, and proposes a cosine similarity measure for multi-criteria group decision making. Different from the previous studies, the proposed similarity measure is utilized to measure the similarity between two objects in the structure of neutrosophic soft set, rather than two neutrosophic soft sets. We also propose the objective degree and credibility degree which reflect the decision makers’ subjective preference based on the similarity measure. Then parameter weights are calculated by the objective degree. Additionally, based on credibility degree and parameter weights, we propose the modified score function, modified accuracy function, and modified certainty function, which can be employed to obtain partial order relation and make decisions. Later, we construct an aggregation algorithm for multi-criteria group decision making based on Dempster’s rule of combination and apply the algorithm to a case of medical diagnosis. Finally, by testing and comparing the algorithm, the results demonstrate that the proposed algorithm can solve the multi-criteria group decision making problems effectively.

Reverse triple I method based on single valued neutrosophic fuzzy inference

The theory of single valued neutrosophic sets, which is a generalization of intuitionistic fuzzy sets, is more capable of dealing with inconsistent information in practice. In this paper, we propose reverse triple I method under single valued neutrosophic environment. Firstly, we give the definitions of single valued neutrosophic t-representation t-norms and single valued neutrosophic residual implications. Secondly, we develop a formula for calculating single valued neutrosophic residual implications. Then we propose reverse triple I method based on left-continuous single valued neutrosophic t-representation t-norms and its solutions. Lastly, we discuss the robustness of reverse triple I method based on the proposed similarity measure.

Some aggregation operators of neutrosophic Z-numbers and their multicriteria decision making method

As the generalization of the classical fuzzy number, the concept of Z-number introduced by Zadeh indicates more ability to depict the human knowledge and judgments of both restraint and reliability as an order pair of fuzzy numbers. In indeterminacy and inconsistent environment, a neutrosophic set is described by the truth, falsity, and indeterminacy degrees, but they lack measures related to reliability. To describe the hybrid information of combining the truth, falsity and indeterminacy degrees with their corresponding reliability degrees, this paper first proposes the concept of a neutrosophic Z-number (NZN) set, which is a new framework of neutrosophic values combined with the neutrosophic measures of reliability, as the generalization of the Z-number and the neutrosophic set. Then, we define the operations of neutrosophic Z-numbers (NZNs) and a score function for ranking NZNs. Next, we present NZN weighted arithmetic averaging (NZNWAA) and NZN weighted geometric averaging (NZNWGA) operators to aggregate NZN information and investigate their properties. Regarding the NZNWAA and NZNWGA operators and the score function, a multicriteria decision making (MDM) approach is developed in the NZN environment. Finally, an illustrative example about the selection problem of business partners is given to demonstrate the applicability and effectiveness of the developed MDM approach in NZN setting.

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

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.

Novel insights into wound age estimation: combined with "up, no change, or down" system and cosine similarity in python environment

Wound age estimation is a complex, multifactorial issue. It is considered to have great practical significance that combining multi-biomarkers and multi-methods for injury time estimation. We optimized our earlier "up, no change, or down" model by adding data on the expression levels of mRNAs encoding ABHD2, MAD2L2, and ARID5A, and we converted the relative quantitative expression levels of seven genes into a vector rather than a color model. We used Python to derive the cosine similarity (CS) between a test set and the vector matrix; the highest similarity most accurately reflected the injury time. For the optimized model, the internal and external verifications were approximately 0.71 and 0.66, respectively. The good double-blinded results indicated that the model was stable and reliable. In summary, we used a vector matrix and cosine similarities derived by Python to mine the levels of genes expressed in contused skeletal muscle. We are the first to combine several biomarkers and methods for wound age estimation.

Skeletal scintigraphy image enhancement based neutrosophic sets and salp swarm algorithm

Recently, several schemes are proposed for enhancing the dark regions of the skeletal scintigraphy image. Nevertheless, most of them are flawed by some performance problems. This paper presents an adaptive scheme based on Salp Swarm algorithm (SSA) and a neutrosophic set (NS) under multi-criteria to enhance the dark regions of the skeletal scintigraphy image efficiently. Enhancing the dark regions is first converted into an optimization problem. The SSA algorithm is used to find the best improvement for each image separately, and then the neutrosophic algorithm is used to find similarity score to each image with adaptive weight coefficients obtained by the SSA algorithm. The proposed algorithm is applied to an Egyptian medical dataset collected from Menoufia University Hospital and it is a no-reference image. The experiments are done using 3 different resolutions 512*512, 256*256, and 128*128 and compared with Gamma Correction, the NS algorithm and the local enhance algorithm. The results demonstrate that the proposed algorithm achieves superior performance in almost criteria fitness function, entropy, eumber of edges, nNaturalness image quality Evaluator, sharpness, sharpness index, and contrast-distorted images using contrast enhancement. The results showed the idea of integration between the falsity membership of the neutrosophic set and the Salp swarm algorithm can be used to Skeletal Scintigraphy enhancement. This paper proved that it can depend on falsity membership of the neutrosophic set in the Image Enhancement field.

A Further Study on Multiperiod Health Diagnostics Methodology under a Single-Valued Neutrosophic Set

Employing the concept and function of tangency with similarity measures and counterpart distances for reliable medical consultations has been extensively studied in the past decades and results in lots of isomorphic measures for application. We compared the majority of such isomorphic measures proposed by various researchers and classified them into (a) maximum norm and (b) one-norm categories. Moreover, we found that previous researchers used monotonic functions to transform an identity function and resulted in complicated expressions. In this study, we provide a theoretical foundation to explain the isomorphic nature of a newer measure proposed by the following research paper against its studied existing one in deriving the same pattern recognition results. Specifically, this study initially proposes two similarity measures using maximum norm, arithmetic mean, and aggregation operators and followed by a detailed discussion on their mathematical characteristics. Subsequently, a simplified version of such measures is presented for easy application. This study completely covers two previous methods to point out that the complex approaches used were unnecessary. The findings will help physicians, patients, and their family members to obtain a proper medical diagnosis during multiple examinations.

Modeling Care Team Structures in the Neonatal Intensive Care Unit through Network Analysis of EHR Audit Logs

BACKGROUND

 In the neonatal intensive care unit (NICU), predefined acuity-based team care models are restricted to core roles and neglect interactions with providers outside of the team, such as interactions that transpire via electronic health record (EHR) systems. These unaccounted interactions may be related to the efficiency of resource allocation, information flow, communication, and thus impact patient outcomes. This study applied network analysis methods to EHR audit logs to model the interactions of providers beyond their core roles to better understand the interaction network patterns of acuity-based teams and relationships of the network structures with postsurgical length of stay (PSLOS).

METHODS

 The study used the EHR log data of surgical neonates from a large academic medical center. The study included 104 surgical neonates, for whom 9,206 unique actions were performed by 457 providers in their EHRs. We applied network analysis methods to model EHR provider interaction networks of acuity-based teams in NICU postoperative care. We partitioned each EHR network into three subnetworks based on interaction types: (1) interactions between known core providers who were documented in scheduling records (core subnetwork); (2) interactions between core and noncore providers (extended subnetwork); and (3) interactions between noncore providers (extended subnetwork). For each core subnetwork, we assessed its capability to replicate predefined core-provider relations as documented in scheduling records. We further compared each EHR network, as well as its subnetworks, using standard network measures to determine its differences in network topologies. We conducted a case study to learn provider interaction networks taking care of 15 neonates who underwent gastrostomy tube placement surgery from EHR log data and measure the effectiveness of the interaction networks on PSLOS by the proportional-odds model.

RESULTS

 The provider networks of four acuity-based teams (two high and two low acuity), along with their subnetworks, were discovered. We found that beyond capturing the predefined core-provider relations, EHR audit logs can also learn a large number of relations between core and noncore providers or among noncore providers. Providers in the core subnetwork exhibited a greater number of connections with each other than with providers in the extended subnetworks. Many more providers in the core subnetwork serve as a hub than those in the other types of subnetworks. We also found that high-acuity teams exhibited more complex network structures than low-acuity teams, with high-acuity team generating 6,416 interactions between 407 providers compared with 931 interactions between 124 providers, respectively. In addition, we discovered that high-acuity and low-acuity teams shared more than 33 and 25% of providers with each other, respectively, but exhibited different collaborative structures demonstrating that NICU providers shift across different acuity teams and exhibit different network characteristics. Results of case study show that providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those who treated higher PSLOS patients (p = 0.03).

CONCLUSION

 Network analysis can be applied to EHR log data to model acuity-based NICU teams capturing interactions between providers within the predesigned core team as well as those outside of the core team. In the NICU, dissemination of information may be linked to reduced PSLOS. EHR log data provide an efficient, accessible, and research-friendly way to study provider interaction networks. Findings should guide improvements in the EHR system design to facilitate effective interactions between providers.

Generalized Ordered Weighted Simplified Neutrosophic Cosine Similarity Measure for Multiple Attribute Group Decision Making

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.

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness.