Pub Date : 2023-12-01DOI: 10.1142/s021848852350040x
S. Z. A. Gök, S. Ergün, Baris Bülent Kirlar, Ismail Özcan, Aslı Tayman
In this paper, we mathematically associate crypto-cloud computing with cooperative game theory in the presence of fuzzy uncertainty. For this purpose, we retrieve information from the database of Amazon Web Service. Then we construct a secure crypto-cloud game theoretical model and apply this model to online games under fuzzy uncertainty. Further, we suggest some fuzzy game theoretical solutions by proposing a novel twisted Edwards curve pairing-based scheme over finite fields having the property of indistinguishable under chosen ciphertext attacks. Finally, it is seen that costs of the numerical fuzzy solutions are reduced and their efficiency is increased compared with the numerical crisp ones.
{"title":"Fuzzy Perspective of Online Games by Using Cryptography and Cooperative Game Theory","authors":"S. Z. A. Gök, S. Ergün, Baris Bülent Kirlar, Ismail Özcan, Aslı Tayman","doi":"10.1142/s021848852350040x","DOIUrl":"https://doi.org/10.1142/s021848852350040x","url":null,"abstract":"In this paper, we mathematically associate crypto-cloud computing with cooperative game theory in the presence of fuzzy uncertainty. For this purpose, we retrieve information from the database of Amazon Web Service. Then we construct a secure crypto-cloud game theoretical model and apply this model to online games under fuzzy uncertainty. Further, we suggest some fuzzy game theoretical solutions by proposing a novel twisted Edwards curve pairing-based scheme over finite fields having the property of indistinguishable under chosen ciphertext attacks. Finally, it is seen that costs of the numerical fuzzy solutions are reduced and their efficiency is increased compared with the numerical crisp ones.","PeriodicalId":507871,"journal":{"name":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139188482","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}
Pub Date : 2023-12-01DOI: 10.1142/s0218488523500435
Yingdong Gou, Kexin Wang, Siwen Wei, Changxin Shi
Nowadays, with the rapid expansion of social media as a means of quick communication, real-time disaster information is widely disseminated through these platforms. Determining which real-time and multi-modal disaster information can effectively support humanitarian aid has become a major challenge. In this paper, we propose a novel end-to-end model, named GCN-based Multi-modal Domain Adaptation (GMDA), which consists of three essential modules: the GCN-based feature extraction module, the attention-based fusion module and the MMD domain adaptation module. The GCN-based feature extraction module integrates text and image representations through GCNs, while the attention-based fusion module then merges these multi-modal representations using an attention mechanism. Finally, the MMD domain adaptation module is utilized to alleviate the dependence of GMDA on source domain events by computing the maximum mean discrepancy across domains. Our proposed model has been extensively evaluated and has shown superior performance compared to state-of-the-art multi-modal domain adaptation models in terms of F1 score and variance stability.
{"title":"GMDA: GCN-Based Multi-Modal Domain Adaptation for Real-Time Disaster Detection","authors":"Yingdong Gou, Kexin Wang, Siwen Wei, Changxin Shi","doi":"10.1142/s0218488523500435","DOIUrl":"https://doi.org/10.1142/s0218488523500435","url":null,"abstract":"Nowadays, with the rapid expansion of social media as a means of quick communication, real-time disaster information is widely disseminated through these platforms. Determining which real-time and multi-modal disaster information can effectively support humanitarian aid has become a major challenge. In this paper, we propose a novel end-to-end model, named GCN-based Multi-modal Domain Adaptation (GMDA), which consists of three essential modules: the GCN-based feature extraction module, the attention-based fusion module and the MMD domain adaptation module. The GCN-based feature extraction module integrates text and image representations through GCNs, while the attention-based fusion module then merges these multi-modal representations using an attention mechanism. Finally, the MMD domain adaptation module is utilized to alleviate the dependence of GMDA on source domain events by computing the maximum mean discrepancy across domains. Our proposed model has been extensively evaluated and has shown superior performance compared to state-of-the-art multi-modal domain adaptation models in terms of F1 score and variance stability.","PeriodicalId":507871,"journal":{"name":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139195634","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}
Pub Date : 2023-12-01DOI: 10.1142/s0218488523500411
Vannick Fopa Mawamba, A. S. T. Kammogne, Jacques Kengne, Martin Siewe Siewe
A robust global fuzzy sliding mode controller is designed in this paper for the synchronization of non-linear systems with control input non-linearities (CIN) and uncertainties. A consistent global fuzzy sliding mode control (GFSMC) law is developed, which guarantees the suppression of the reaching phase and the presence of the sliding phase from the initial time. Chattering phenomenon, which is characteristic of customary sliding mode control (SMC), avoided by the on-line fuzzy regulation of the sliding surface in the controller, when the system is subject to disturbances and CIN. Finite-time boundedness (FTB) properties are designed with adequate conditions, which are entrenched in terms of linear matrix inequalities (LMIs) with the help of cost and Lyapunov functions. Numerical simulations for the synchronization problem of the chaotic modified Colpitt’s system and Duffing system clearly show the good performance of the proposed control scheme. The present work provides a regular procedure to design GFSMC for a class of non-linear systems with CIN.
{"title":"Flexible Robust Control Strategy for Synchronization of Uncertain Non-Linear Systems with Control Input Non-Linearity","authors":"Vannick Fopa Mawamba, A. S. T. Kammogne, Jacques Kengne, Martin Siewe Siewe","doi":"10.1142/s0218488523500411","DOIUrl":"https://doi.org/10.1142/s0218488523500411","url":null,"abstract":"A robust global fuzzy sliding mode controller is designed in this paper for the synchronization of non-linear systems with control input non-linearities (CIN) and uncertainties. A consistent global fuzzy sliding mode control (GFSMC) law is developed, which guarantees the suppression of the reaching phase and the presence of the sliding phase from the initial time. Chattering phenomenon, which is characteristic of customary sliding mode control (SMC), avoided by the on-line fuzzy regulation of the sliding surface in the controller, when the system is subject to disturbances and CIN. Finite-time boundedness (FTB) properties are designed with adequate conditions, which are entrenched in terms of linear matrix inequalities (LMIs) with the help of cost and Lyapunov functions. Numerical simulations for the synchronization problem of the chaotic modified Colpitt’s system and Duffing system clearly show the good performance of the proposed control scheme. The present work provides a regular procedure to design GFSMC for a class of non-linear systems with CIN.","PeriodicalId":507871,"journal":{"name":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139189966","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}
Pub Date : 2023-12-01DOI: 10.1142/s0218488523500423
Somayeh Lotfi, Mohammad Ghasemzadeh, M. Mohsenzadeh, M. Mirzarezaee
The Decision tree algorithm is a very popular classifier for reasoning through recursive partitioning of the data space. To choose the best attributes for splitting, the range of each continuous attribute should be split into two or more intervals. Then partitioning criteria are calculated for each value. Fuzzy partitioning can be used to reduce sensitivity to noise and increase tree stability. Also, tree-building algorithms face memory limitations as they need to keep the entire training dataset in the main memory. In this paper, we introduced a fuzzy decision tree approach based on fuzzy sets. To avoid storing the entire training dataset in the main memory and overcome the memory limitations, the algorithm incrementally builds FDTs. Membership functions are automatically generated. The Fuzzy Information Gain (FIG) is then used as the fast split attribute selection criterion, and leaf expansion is performed only on the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm can overcome memory limitations and balance accuracy and complexity while reducing the complexity of the tree.
{"title":"Combining Fuzzy Partitioning and Incremental Methods to Construct a Scalable Decision Tree on Large Datasets","authors":"Somayeh Lotfi, Mohammad Ghasemzadeh, M. Mohsenzadeh, M. Mirzarezaee","doi":"10.1142/s0218488523500423","DOIUrl":"https://doi.org/10.1142/s0218488523500423","url":null,"abstract":"The Decision tree algorithm is a very popular classifier for reasoning through recursive partitioning of the data space. To choose the best attributes for splitting, the range of each continuous attribute should be split into two or more intervals. Then partitioning criteria are calculated for each value. Fuzzy partitioning can be used to reduce sensitivity to noise and increase tree stability. Also, tree-building algorithms face memory limitations as they need to keep the entire training dataset in the main memory. In this paper, we introduced a fuzzy decision tree approach based on fuzzy sets. To avoid storing the entire training dataset in the main memory and overcome the memory limitations, the algorithm incrementally builds FDTs. Membership functions are automatically generated. The Fuzzy Information Gain (FIG) is then used as the fast split attribute selection criterion, and leaf expansion is performed only on the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm can overcome memory limitations and balance accuracy and complexity while reducing the complexity of the tree.","PeriodicalId":507871,"journal":{"name":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139192822","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}
Pub Date : 2023-12-01DOI: 10.1142/s0218488523500447
Sova Pal, P. Dutta, Indadul Khan, Prasenjit Pramanik, A. K. Maiti, M. Maiti
In this study, the features of cyclic crossover process and K-opt are incorporated in the bat algorithm (BA) to solve the Travelling Salesman Problems (TSP) in different environments. Swap operation and swap sequence are applied for the modification of the different operations of the BA to solve the TSPs. The cyclic crossover operation is applied in a regular interval of iterations on the best found solution and each solution of the final population of BA for the enhancement of the exploration as well as exploitation of the search process. K-Opt operation is applied on the population in each iteration of the BA with some probability for the exploitation. The algorithm is tested with a set of benchmark test instances of the TSPLIB. The algorithm produces exact results for a set of significantly large size problems. For the TSPs in fuzzy environment, a fuzzy simulation approach is proposed to deal with the fuzzy data having linear as well as non-linear membership functions. Also, a rough simulation process is proposed to deal with the TSPs in the rough environment where rough estimation can be done following any type of rough measure. The performance of the algorithm is compared with the state-of-the-art algorithms for the TSPs with crisp cost matrices using different statistical tools.
本研究在蝙蝠算法（BA）中加入了循环交叉过程和 K-opt，以解决不同环境下的旅行推销员问题（TSP）。交换操作和交换序列被用于修改 BA 的不同操作，以解决 TSP。循环交叉操作以一定的迭代间隔应用于最佳发现解和 BA 最终群体的每个解，以加强搜索过程的探索和利用。在 BA 的每次迭代中，都会以一定的概率对群体进行 K-Opt 操作，以提高利用率。该算法使用 TSPLIB 的一组基准测试实例进行了测试。该算法对一组规模较大的问题产生了精确的结果。对于模糊环境中的 TSP，提出了一种模糊模拟方法来处理具有线性和非线性成员函数的模糊数据。此外，还提出了一种粗略模拟程序来处理粗略环境中的 TSP，在这种环境中，可以根据任何类型的粗略度量进行粗略估计。利用不同的统计工具，将该算法的性能与具有清晰成本矩阵的 TSP 的最先进算法进行了比较。
{"title":"Coordination of Cyclic crossover and Bat Algorithm for the Travelling Salesman Problems in Different Environments: A Simulation Approach","authors":"Sova Pal, P. Dutta, Indadul Khan, Prasenjit Pramanik, A. K. Maiti, M. Maiti","doi":"10.1142/s0218488523500447","DOIUrl":"https://doi.org/10.1142/s0218488523500447","url":null,"abstract":"In this study, the features of cyclic crossover process and K-opt are incorporated in the bat algorithm (BA) to solve the Travelling Salesman Problems (TSP) in different environments. Swap operation and swap sequence are applied for the modification of the different operations of the BA to solve the TSPs. The cyclic crossover operation is applied in a regular interval of iterations on the best found solution and each solution of the final population of BA for the enhancement of the exploration as well as exploitation of the search process. K-Opt operation is applied on the population in each iteration of the BA with some probability for the exploitation. The algorithm is tested with a set of benchmark test instances of the TSPLIB. The algorithm produces exact results for a set of significantly large size problems. For the TSPs in fuzzy environment, a fuzzy simulation approach is proposed to deal with the fuzzy data having linear as well as non-linear membership functions. Also, a rough simulation process is proposed to deal with the TSPs in the rough environment where rough estimation can be done following any type of rough measure. The performance of the algorithm is compared with the state-of-the-art algorithms for the TSPs with crisp cost matrices using different statistical tools.","PeriodicalId":507871,"journal":{"name":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139192149","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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