Multilayer Fuzzy Cognitive Maps (MFCM) represents an approach for analyzing situations, which allows the simulation, inference, theorem proving of tendencies, and verification of theories, among other things. This paper reviews the promise of this method in a novelty case study of management of professional sports clubs. Professional sports clubs have the possibility of making their management profitable through the proper management of their commercial product portfolio. This possibility is conditioned by the adequate configuration of each component of the commercial product portfolio, namely: Brand licensing, sponsorship, television and media rights, sporting and training rights, and hospitality. Each component of the commercial portfolio must maximize its attractiveness and desirability for each target customer segment. For these purposes, tools that contribute to manage the sports products will be vital to guarantee their commercial success. This article proposes a model based on MFCMs for supporting the management of products in professional sports – an intelligent decision-making system –, conceptually based on the “par conditio” principle. The model considers different aspects related to the “par conditio” principle, such as the socioeconomic environment where the sports activity is carried out, the legal bases of the professional competence, and other sporting aspects (stadium, fans, etc.), which are organized in layers, to analyze the behavior of the different components of the commercial portfolio. It is tested for the two most common sports in Latin America, football and baseball, considering countries of this region where these sports have good economic performance, both to analyze and understand the current situation of the sports products, and infer more appropriate configurations. Several specific case studies are carried out with encouraging results. The paper successfully shows the potentialities of MFCM for diverse purposes, particularly those named above.
{"title":"Multilayer fuzzy cognitive maps for the management of the professional sports product based on the \"par conditio\" principle","authors":"Yolmer Romero, José Aguilar, Oswaldo Terán","doi":"10.3233/kes220011","DOIUrl":"https://doi.org/10.3233/kes220011","url":null,"abstract":"Multilayer Fuzzy Cognitive Maps (MFCM) represents an approach for analyzing situations, which allows the simulation, inference, theorem proving of tendencies, and verification of theories, among other things. This paper reviews the promise of this method in a novelty case study of management of professional sports clubs. Professional sports clubs have the possibility of making their management profitable through the proper management of their commercial product portfolio. This possibility is conditioned by the adequate configuration of each component of the commercial product portfolio, namely: Brand licensing, sponsorship, television and media rights, sporting and training rights, and hospitality. Each component of the commercial portfolio must maximize its attractiveness and desirability for each target customer segment. For these purposes, tools that contribute to manage the sports products will be vital to guarantee their commercial success. This article proposes a model based on MFCMs for supporting the management of products in professional sports – an intelligent decision-making system –, conceptually based on the “par conditio” principle. The model considers different aspects related to the “par conditio” principle, such as the socioeconomic environment where the sports activity is carried out, the legal bases of the professional competence, and other sporting aspects (stadium, fans, etc.), which are organized in layers, to analyze the behavior of the different components of the commercial portfolio. It is tested for the two most common sports in Latin America, football and baseball, considering countries of this region where these sports have good economic performance, both to analyze and understand the current situation of the sports products, and infer more appropriate configurations. Several specific case studies are carried out with encouraging results. The paper successfully shows the potentialities of MFCM for diverse purposes, particularly those named above.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124966318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Collaborative filtering (CF), a representative algorithm of recommendation systems, is a method of using information of the neighbors of active user. The main idea of CF is that users who agreed in the ratings of certain items are likely to agree again in new items. The degree to which the two users’ tendencies in the ratings of the co-rated items are consistent is measured using a similarity measure. Therefore, the similarity measure in CF plays a key role in the extraction of the representative neighbors. Studies on the improvement of similarity indicators for selecting representative neighbors are still ongoing. Recently, a new similarity measure, named OS, was proposed to enhance the recommendation performance by utilizing mathematical equations, such as the integral equation, system of linear differential equations, and non-linear systems. This study aims to understand the limitations of OS and overcome these limitations using the proposed method. In the proposed method, a sigmoid function was used to reflect preferences, such as the positive or negative sentiment of user ratings. In addition, to consider the absolute score difference, some of the formulas were modified, and finally, the performance improvement of the recommendation system was proved through experiments.
{"title":"An improved similarity measure for collaborative filtering-based recommendation system","authors":"Cheongrok Lee, Kyoungok Kim","doi":"10.3233/kes220013","DOIUrl":"https://doi.org/10.3233/kes220013","url":null,"abstract":"Collaborative filtering (CF), a representative algorithm of recommendation systems, is a method of using information of the neighbors of active user. The main idea of CF is that users who agreed in the ratings of certain items are likely to agree again in new items. The degree to which the two users’ tendencies in the ratings of the co-rated items are consistent is measured using a similarity measure. Therefore, the similarity measure in CF plays a key role in the extraction of the representative neighbors. Studies on the improvement of similarity indicators for selecting representative neighbors are still ongoing. Recently, a new similarity measure, named OS, was proposed to enhance the recommendation performance by utilizing mathematical equations, such as the integral equation, system of linear differential equations, and non-linear systems. This study aims to understand the limitations of OS and overcome these limitations using the proposed method. In the proposed method, a sigmoid function was used to reflect preferences, such as the positive or negative sentiment of user ratings. In addition, to consider the absolute score difference, some of the formulas were modified, and finally, the performance improvement of the recommendation system was proved through experiments.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128477019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Classifying haemodialysis sessions, on the basis of the evolution of specific clinical variables over time, allows the physician to identify patients that are being treated inefficiently, and that may need additional monitoring or corrective interventions. In this paper, we propose a deep learning approach to clinical time series classification, in the haemodialysis domain. In particular, we have defined two novel architectures, able to take advantage of the strengths of Convolutional Neural Networks and of Recurrent Networks. The novel architectures we introduced and tested outperformed classical mathematical classification techniques, as well as simpler deep learning approaches. In particular, combining Recurrent Networks with convolutional structures in different ways, allowed us to obtain accuracies above 81%, coupled with high values of the Matthews Correlation Coefficient (MCC), a parameter particularly suitable to assess the quality of classification when dealing with unbalanced classes-as it was our case. In the future we will test an extension of the approach to additional monitoring time series, aiming at an overall optimization of patient care.
{"title":"Novel deep learning architectures for haemodialysis time series classification","authors":"G. Leonardi, S. Montani, Manuel Striani","doi":"10.3233/kes220010","DOIUrl":"https://doi.org/10.3233/kes220010","url":null,"abstract":"Classifying haemodialysis sessions, on the basis of the evolution of specific clinical variables over time, allows the physician to identify patients that are being treated inefficiently, and that may need additional monitoring or corrective interventions. In this paper, we propose a deep learning approach to clinical time series classification, in the haemodialysis domain. In particular, we have defined two novel architectures, able to take advantage of the strengths of Convolutional Neural Networks and of Recurrent Networks. The novel architectures we introduced and tested outperformed classical mathematical classification techniques, as well as simpler deep learning approaches. In particular, combining Recurrent Networks with convolutional structures in different ways, allowed us to obtain accuracies above 81%, coupled with high values of the Matthews Correlation Coefficient (MCC), a parameter particularly suitable to assess the quality of classification when dealing with unbalanced classes-as it was our case. In the future we will test an extension of the approach to additional monitoring time series, aiming at an overall optimization of patient care.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116658658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With global resource waste and environmental pollution becoming increasingly serious, corporate environmental performance (CEP) has received much attention from researchers over the past decade. As an important part of economic development, enterprises also pay increasingly attention to environmental protection and pollution control. CEP is regarded as the result of corporate environmental management. Assessing CEP can not only make enterprises focus on the environmental protection and management, but also promote sustainable social development. And it is frequently viewed as a multi-attribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is needed to tackle it. Depending on the conventional TOPSIS (Technique for Order Preferenceby Similarity to Ideal Solution) method and intuitionistic fuzzy sets (IFSs), this essay design a novel intuitive distance based IF-TOPSIS method to assess CEP. First of all, a related literature review is conducted. What’s more, some necessary theories related to IFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria is decided objectively by utilizing CRITIC method. Afterwards, relying on novel distance measures between IFNs, the conventional TOPSIS method is extended to the intuitionistic fuzzy environment to calculate assessment score of each enterprise. Eventually, an application about CEP evaluation and some comparative analysis have been given to demonstrate the superiority of the designed method. The results illustrate that the designed framework is useful for assessing CEP.
随着全球资源浪费和环境污染的日益严重，企业环境绩效(CEP)在过去的十年中受到了研究者的广泛关注。作为经济发展的重要组成部分，企业也越来越重视环境保护和污染控制。CEP被认为是企业环境管理的结果。评估CEP不仅可以使企业重视环境保护和管理，而且可以促进社会的可持续发展。它经常被看作是一个多属性群体决策问题。因此，需要一种新的MAGDM方法来解决这一问题。本文在传统的TOPSIS (technical for Order Preferenceby Similarity to Ideal Solution)方法和直觉模糊集(ifs)的基础上，设计了一种新的基于直觉距离的IF-TOPSIS方法来评估CEP。首先，对相关文献进行综述。此外，本文还简要介绍了与金融服务相关的一些必要理论。另外，由于标准权重的确定往往存在主观随机性，因此采用CRITIC方法客观地确定标准的权重。然后，依靠ifn之间新颖的距离度量，将传统的TOPSIS方法扩展到直觉模糊环境中，计算各企业的评价分数。最后，通过对CEP评价的应用和对比分析，证明了所设计方法的优越性。结果表明，所设计的框架可用于评估CEP。
{"title":"TOPSIS Model for evaluating the corporate environmental performance under intuitionistic fuzzy environment","authors":"Qing Liu","doi":"10.3233/kes-220014","DOIUrl":"https://doi.org/10.3233/kes-220014","url":null,"abstract":"With global resource waste and environmental pollution becoming increasingly serious, corporate environmental performance (CEP) has received much attention from researchers over the past decade. As an important part of economic development, enterprises also pay increasingly attention to environmental protection and pollution control. CEP is regarded as the result of corporate environmental management. Assessing CEP can not only make enterprises focus on the environmental protection and management, but also promote sustainable social development. And it is frequently viewed as a multi-attribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is needed to tackle it. Depending on the conventional TOPSIS (Technique for Order Preferenceby Similarity to Ideal Solution) method and intuitionistic fuzzy sets (IFSs), this essay design a novel intuitive distance based IF-TOPSIS method to assess CEP. First of all, a related literature review is conducted. What’s more, some necessary theories related to IFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria is decided objectively by utilizing CRITIC method. Afterwards, relying on novel distance measures between IFNs, the conventional TOPSIS method is extended to the intuitionistic fuzzy environment to calculate assessment score of each enterprise. Eventually, an application about CEP evaluation and some comparative analysis have been given to demonstrate the superiority of the designed method. The results illustrate that the designed framework is useful for assessing CEP.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131450108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper compares various significant research techniques concerning the power quality (PQ) events prediction and classification system presented by the authors previously and examines its viability scale as far as the research gap. This paper examines some of the frequently exercised PQ classification techniques named as Feedforward Neural Network (FNN), Sequential Ant Lion Optimizer and Recurrent Neural Network (SALRNN), dual-layer Feedforward network and Short-Time Fourier Transform (STFT) from the most significant literature in order to have more insights of the techniques. The research work has presented a simple framework that retains a balance between higher accuracy in the detection of disturbances as well as also maintains an effective computational performance for a large number of the power signals. The principle aim of the paper is research and comparative analysis of above-mentioned algorithms for searching the best impressive technique to detect and classify the PQ events. The simulation results of this research can be reasoned that the SALRNN technique performed very well to detect and classify the PQ disturbances when compared with the other two techniques such as FNN and STFT. The SALRNN technique is more optimal than the other existing techniques in terms of RMSE, MAPE, MBE, sensitivity, specificity and consumption time is analyzed.
{"title":"Marvellous significance performance analysis of PQ events prediction and classification","authors":"B. Vighneshwari, N. Jayakumar, P. Sandhya","doi":"10.3233/kes-220008","DOIUrl":"https://doi.org/10.3233/kes-220008","url":null,"abstract":"This paper compares various significant research techniques concerning the power quality (PQ) events prediction and classification system presented by the authors previously and examines its viability scale as far as the research gap. This paper examines some of the frequently exercised PQ classification techniques named as Feedforward Neural Network (FNN), Sequential Ant Lion Optimizer and Recurrent Neural Network (SALRNN), dual-layer Feedforward network and Short-Time Fourier Transform (STFT) from the most significant literature in order to have more insights of the techniques. The research work has presented a simple framework that retains a balance between higher accuracy in the detection of disturbances as well as also maintains an effective computational performance for a large number of the power signals. The principle aim of the paper is research and comparative analysis of above-mentioned algorithms for searching the best impressive technique to detect and classify the PQ events. The simulation results of this research can be reasoned that the SALRNN technique performed very well to detect and classify the PQ disturbances when compared with the other two techniques such as FNN and STFT. The SALRNN technique is more optimal than the other existing techniques in terms of RMSE, MAPE, MBE, sensitivity, specificity and consumption time is analyzed.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":"52 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133556116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Optimization algorithms are being widely applied in real-time applications. In recent times, Jaya and Rao algorithms have been prominent. The performance of these algorithms will be analyzed for the objective function. The results thus obtained are more accurate and fast compared to previous algorithms. Also, Jaya and Rao algorithms will be utilized for linear array antenna synthesis for the arrays that are equally spaced. Therefore it is of present interest to evaluate the performance of linear antenna array using these algorithms.
{"title":"Linear Antenna Array synthesis using Rao and Jaya algorithms","authors":"Nagavalli Vegesna, G. Yamuna, T. S. Kumar","doi":"10.3233/kes-220001","DOIUrl":"https://doi.org/10.3233/kes-220001","url":null,"abstract":"Optimization algorithms are being widely applied in real-time applications. In recent times, Jaya and Rao algorithms have been prominent. The performance of these algorithms will be analyzed for the objective function. The results thus obtained are more accurate and fast compared to previous algorithms. Also, Jaya and Rao algorithms will be utilized for linear array antenna synthesis for the arrays that are equally spaced. Therefore it is of present interest to evaluate the performance of linear antenna array using these algorithms.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":"2012 31","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113966257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Identification of eye considering biometric traits is an essential field to recognize persons. Biometrics using iris images seems to be an effective identification of individuals. Various Iris Recognition at-Distance (IAAD) systems are used for extracting features of iris and improve image quality using the biometric model. Even though the quality of the iris is better, accuracy is a challenging question for the research community. Thus, an effective IAAD, namely Chronological Monarch Butterfly Optimization-Deep Belief Network (Chronological MBO-DBN) is devised to detect iris. The detection of iris using DBN is trained with Chronological MBO, which is the integration of Chronological theory and Monarch Butterfly Optimization (MBO). The features of iris are extracted with ScatT-Loop descriptor and Local Gradient Pattern (LGP) and subjected to Chronological MBO-DBN for the recognition of iris which improved accuracy. The implementation of proposed Chronological MBO-based DBN is performed using the dataset, CASIA Iris, and efficiency is evaluated by the accuracy of 96.078%, False Rejection Rate (FRR) of 0.4745% False Acceptance Rate (FAR) of 0.4847%, and F-Measure of 98.658%.
{"title":"Optimization driven deep belief network using chronological monarch butterfly optimization for iris recognition at-a-distance","authors":"Swati D. Shirke, C. Rajabhushnam","doi":"10.3233/kes-220003","DOIUrl":"https://doi.org/10.3233/kes-220003","url":null,"abstract":"Identification of eye considering biometric traits is an essential field to recognize persons. Biometrics using iris images seems to be an effective identification of individuals. Various Iris Recognition at-Distance (IAAD) systems are used for extracting features of iris and improve image quality using the biometric model. Even though the quality of the iris is better, accuracy is a challenging question for the research community. Thus, an effective IAAD, namely Chronological Monarch Butterfly Optimization-Deep Belief Network (Chronological MBO-DBN) is devised to detect iris. The detection of iris using DBN is trained with Chronological MBO, which is the integration of Chronological theory and Monarch Butterfly Optimization (MBO). The features of iris are extracted with ScatT-Loop descriptor and Local Gradient Pattern (LGP) and subjected to Chronological MBO-DBN for the recognition of iris which improved accuracy. The implementation of proposed Chronological MBO-based DBN is performed using the dataset, CASIA Iris, and efficiency is evaluated by the accuracy of 96.078%, False Rejection Rate (FRR) of 0.4745% False Acceptance Rate (FAR) of 0.4847%, and F-Measure of 98.658%.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121680089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, the TODIM has been used to deal with multiple attribute decision making (MADM) problems. The intuitionistic 2-tuple linguistic sets (I2TLSs) are useful tools to depict uncertainty in MADM. This paper use TODIM method to solve MADM with the intuitionistic 2-tuple linguistic numbers (I2TLNs). Firstly, the concepts of I2TLNs are briefly presented, and classical TODIM for MADM are introduced. Then, the classical TODIM method is used to solve MADM with I2TLNs, and its important characteristic is to fully consider the decision makers’ bounded rationality which is a real action in MADM. And a comparative analysis is also given. Finally, a numerical example for service outsourcing providers selection is proposed.
{"title":"Algorithms for multiple attribute group decision making with intuitionistic 2-tuple linguistic information and its application","authors":"Xian-Ping Jiang","doi":"10.3233/kes-220005","DOIUrl":"https://doi.org/10.3233/kes-220005","url":null,"abstract":"Recently, the TODIM has been used to deal with multiple attribute decision making (MADM) problems. The intuitionistic 2-tuple linguistic sets (I2TLSs) are useful tools to depict uncertainty in MADM. This paper use TODIM method to solve MADM with the intuitionistic 2-tuple linguistic numbers (I2TLNs). Firstly, the concepts of I2TLNs are briefly presented, and classical TODIM for MADM are introduced. Then, the classical TODIM method is used to solve MADM with I2TLNs, and its important characteristic is to fully consider the decision makers’ bounded rationality which is a real action in MADM. And a comparative analysis is also given. Finally, a numerical example for service outsourcing providers selection is proposed.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125752802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With respect to intuitionistic fuzzy multiple attribute decision making problems with completely unknown weight information, some operational laws of intuitionistic fuzzy numbers, score function and accuracy function of intuitionistic fuzzy numbers are introduced. To determine the attribute weights, a model based on the information entropy, by which the attribute weights can be determined, is established. We utilize the intuitionistic fuzzy Hamacher weighted averaging (IFHWA) operator to fuse the intuitionistic fuzzy information corresponding to each alternative, and then rank the alternatives and select the most desirable one(s) according to the score function and accuracy function. Finally, an illustrative example for evaluating the logistics efficiency of agricultural products is given to verify the developed approach and to demonstrate its practicality and effectiveness.
{"title":"Evaluation and research on the logistics efficiency of agricultural products with intuitionistic fuzzy information","authors":"Qing Liu","doi":"10.3233/kes-220006","DOIUrl":"https://doi.org/10.3233/kes-220006","url":null,"abstract":"With respect to intuitionistic fuzzy multiple attribute decision making problems with completely unknown weight information, some operational laws of intuitionistic fuzzy numbers, score function and accuracy function of intuitionistic fuzzy numbers are introduced. To determine the attribute weights, a model based on the information entropy, by which the attribute weights can be determined, is established. We utilize the intuitionistic fuzzy Hamacher weighted averaging (IFHWA) operator to fuse the intuitionistic fuzzy information corresponding to each alternative, and then rank the alternatives and select the most desirable one(s) according to the score function and accuracy function. Finally, an illustrative example for evaluating the logistics efficiency of agricultural products is given to verify the developed approach and to demonstrate its practicality and effectiveness.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114621273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Object detection and recognition is a computer vision technology and is considered as one of the challenging tasks in the field of computer vision. Many approaches for detection have been proposed in the past. AIM: This paper is mainly aiming to discuss the existing detection and classification techniques of Deep Convolutional Neural Networks (CNN) with an importance placed on highlighting the training and accuracy of the different CNN models. METHODS: In the proposed work, Faster RCNN, YOLO and SSD are used to detect helmets. OUTCOME: The survey says MobileNets has higher accuracy when compared to VGG16, VGG19 and Inception V3 and is therefore chosen to be used with SSD. The impact of the differences in the amount of training of each algorithm is highlighted which helps understand the advantages and disadvantages of each algorithm and deduce the most suitable.
{"title":"Analysis of deep learning frameworks for object detection in motion","authors":"G. Vaishnavi, Shriya Varada Ramesh, Sanjana Satheesh, Ashwini Kodipalli, Kusuma Thimmaraju","doi":"10.3233/kes-220002","DOIUrl":"https://doi.org/10.3233/kes-220002","url":null,"abstract":"Object detection and recognition is a computer vision technology and is considered as one of the challenging tasks in the field of computer vision. Many approaches for detection have been proposed in the past. AIM: This paper is mainly aiming to discuss the existing detection and classification techniques of Deep Convolutional Neural Networks (CNN) with an importance placed on highlighting the training and accuracy of the different CNN models. METHODS: In the proposed work, Faster RCNN, YOLO and SSD are used to detect helmets. OUTCOME: The survey says MobileNets has higher accuracy when compared to VGG16, VGG19 and Inception V3 and is therefore chosen to be used with SSD. The impact of the differences in the amount of training of each algorithm is highlighted which helps understand the advantages and disadvantages of each algorithm and deduce the most suitable.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127561820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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