Pub Date : 2022-03-28DOI: 10.1142/s1793962322500477
Jyothi Johnson, R. Chitra
Determining and proving that a specific person or several persons may or may not be there at the Crime Scene (CS) in every criminal investigation are vital. Thus, in the law enforcement community, more often the physical evidence is collected, preserved, and analyzed. The accused cannot be predicted by normal people or judge just by looking at the evidence obtained at the analysis phase. So, research studies were undertaken on automated recognition as well as retrieval system aimed at forensic Crime Scene Investigation (CSI). A survey on the investigation of forensic CS evidence is depicted here. The main focus is rendered on the computer-centered automated investigation system. The latest research on the different evidence-centered Forensic Investigation (FI), such as the face, Finger-Print (FP), shoeprint, together with other Foot-Wear (FW) impressions, Machine Learning (ML) algorithm-centered FI, ML-centered pattern recognition, features of disparate evidence in forensic CSI, and various matching technique-centered FI, is surveyed here. Finally, centered on the accuracy and other two metrics, the methods’ performance for CSI is compared. Out of all the other methods, OLBP + LSSVM produced better results for precision and recall followed by CLSTM. In terms of accuracy, CLSTM produced better results than any other method.
{"title":"Survey on the investigation of forensic crime scene evidence","authors":"Jyothi Johnson, R. Chitra","doi":"10.1142/s1793962322500477","DOIUrl":"https://doi.org/10.1142/s1793962322500477","url":null,"abstract":"Determining and proving that a specific person or several persons may or may not be there at the Crime Scene (CS) in every criminal investigation are vital. Thus, in the law enforcement community, more often the physical evidence is collected, preserved, and analyzed. The accused cannot be predicted by normal people or judge just by looking at the evidence obtained at the analysis phase. So, research studies were undertaken on automated recognition as well as retrieval system aimed at forensic Crime Scene Investigation (CSI). A survey on the investigation of forensic CS evidence is depicted here. The main focus is rendered on the computer-centered automated investigation system. The latest research on the different evidence-centered Forensic Investigation (FI), such as the face, Finger-Print (FP), shoeprint, together with other Foot-Wear (FW) impressions, Machine Learning (ML) algorithm-centered FI, ML-centered pattern recognition, features of disparate evidence in forensic CSI, and various matching technique-centered FI, is surveyed here. Finally, centered on the accuracy and other two metrics, the methods’ performance for CSI is compared. Out of all the other methods, OLBP + LSSVM produced better results for precision and recall followed by CLSTM. In terms of accuracy, CLSTM produced better results than any other method.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84944887","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 : 2022-03-18DOI: 10.1142/s1793962322410070
Ming Tie, Hong Fang, Jianlin Wang, Weihua Chen
Online prediction as well as online simulation of surface temperature will play a significant role in flight safety of future near space hypersonic vehicles (HVs). But it still remains a classical scientific problem both in thermodynamics and aerospace science. In view of the complex HV structure and complex heat conduction procedure, three-dimensional numerical simulation is too inefficient for online prediction, while current rapid computation methods cannot meet the requirement of accuracy. Therefore, a hybrid intelligent dynamic modeling approach is proposed to estimate the surface temperature of HV with the combination of mechanism equations, test data and intelligent modeling technology. A simplified model based on a mechanism equation and experimental formulas is presented for predicting or simulating transient heat conduction procedure efficiently, while a case-based reasoning (CBR) algorithm is developed to estimate two uncertain coefficients in the simplified model. Furthermore, a support vector regression (SVR)-based model is developed to compensate the modeling error. With the data both from high-precision finite element computation and from real-world HV thermal protection experiments, a number of comparative simulations demonstrate the effectiveness of the proposed hybrid intelligent modeling approach.
{"title":"Hybrid intelligent modeling approach for online predicting and simulating surface temperature of HVs","authors":"Ming Tie, Hong Fang, Jianlin Wang, Weihua Chen","doi":"10.1142/s1793962322410070","DOIUrl":"https://doi.org/10.1142/s1793962322410070","url":null,"abstract":"Online prediction as well as online simulation of surface temperature will play a significant role in flight safety of future near space hypersonic vehicles (HVs). But it still remains a classical scientific problem both in thermodynamics and aerospace science. In view of the complex HV structure and complex heat conduction procedure, three-dimensional numerical simulation is too inefficient for online prediction, while current rapid computation methods cannot meet the requirement of accuracy. Therefore, a hybrid intelligent dynamic modeling approach is proposed to estimate the surface temperature of HV with the combination of mechanism equations, test data and intelligent modeling technology. A simplified model based on a mechanism equation and experimental formulas is presented for predicting or simulating transient heat conduction procedure efficiently, while a case-based reasoning (CBR) algorithm is developed to estimate two uncertain coefficients in the simplified model. Furthermore, a support vector regression (SVR)-based model is developed to compensate the modeling error. With the data both from high-precision finite element computation and from real-world HV thermal protection experiments, a number of comparative simulations demonstrate the effectiveness of the proposed hybrid intelligent modeling approach.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78428706","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 : 2022-03-17DOI: 10.1142/s179396232341009x
Guo-dong Yi, Lifang Yi, Zaizhao Zhang, Chuihui Li
The factors that affect the performance of the equipment are numerous and complicated, which makes it difficult to establish a performance calculation model. This paper puts forward a data-driven modeling method with reverse process for this problem. Based on the partial least squares (PLS) algorithm and the gray relational analysis (GRA) method, the analysis method of the performance related factors, the extraction method of characteristic variables, and the performance modeling method are studied. The related factors of the energy consumption of an industrial steam turbine are analyzed, and an energy consumption calculation model is established, and the effectiveness of the above-mentioned modeling methods is verified with sample data, which provides a basis for the energy-saving optimization of the steam turbine.
{"title":"Data-driven modeling method with reverse process","authors":"Guo-dong Yi, Lifang Yi, Zaizhao Zhang, Chuihui Li","doi":"10.1142/s179396232341009x","DOIUrl":"https://doi.org/10.1142/s179396232341009x","url":null,"abstract":"The factors that affect the performance of the equipment are numerous and complicated, which makes it difficult to establish a performance calculation model. This paper puts forward a data-driven modeling method with reverse process for this problem. Based on the partial least squares (PLS) algorithm and the gray relational analysis (GRA) method, the analysis method of the performance related factors, the extraction method of characteristic variables, and the performance modeling method are studied. The related factors of the energy consumption of an industrial steam turbine are analyzed, and an energy consumption calculation model is established, and the effectiveness of the above-mentioned modeling methods is verified with sample data, which provides a basis for the energy-saving optimization of the steam turbine.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89201937","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 : 2022-03-12DOI: 10.1142/s1793962322500362
Rui Li, Kaiyu Qin
In this paper, the fixed-wing aircraft asymmetric disturbance effect modeling and closed-loop control system evaluation are considered. The asymmetric disturbance is accurately modeled by a combination of the consideration of inertial parameter variation and realistic aerodynamic characteristics of asymmetric configuration generated by the computational fluid dynamics (CFD) simulation. To analyze the impacts of the asymmetric disturbance on the aircraft, two flight control methodologies are compared. Besides the classic and widely implemented PID controller, an uncertainty and disturbance estimator (UDE)-based controller is additionally designed to deal with the asymmetric disturbance. Comparative simulation results are provided to show that: (1) the performance of PID control degrades significantly under asymmetric disturbances; and (2) the UDE-based controller is capable of dynamically compensating for the disturbance thus delivering better trajectory tracking performance than PID controller.
{"title":"On the modeling of asymmetric disturbance effect and rejection control for fixed-wing aircraft","authors":"Rui Li, Kaiyu Qin","doi":"10.1142/s1793962322500362","DOIUrl":"https://doi.org/10.1142/s1793962322500362","url":null,"abstract":"In this paper, the fixed-wing aircraft asymmetric disturbance effect modeling and closed-loop control system evaluation are considered. The asymmetric disturbance is accurately modeled by a combination of the consideration of inertial parameter variation and realistic aerodynamic characteristics of asymmetric configuration generated by the computational fluid dynamics (CFD) simulation. To analyze the impacts of the asymmetric disturbance on the aircraft, two flight control methodologies are compared. Besides the classic and widely implemented PID controller, an uncertainty and disturbance estimator (UDE)-based controller is additionally designed to deal with the asymmetric disturbance. Comparative simulation results are provided to show that: (1) the performance of PID control degrades significantly under asymmetric disturbances; and (2) the UDE-based controller is capable of dynamically compensating for the disturbance thus delivering better trajectory tracking performance than PID controller.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83583347","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 : 2022-03-12DOI: 10.1142/s1793962322500490
N. Premalatha, S. Sujatha
Data Mining is a process of exploring the huge data in search of reliable patterns and methodical relationship among variables. As a result, the findings may be validated through applying the detected patterns to a novel subset of the data. In simple words, Data Mining is referred as extracting the useful information as large dataset and transforming into reliable structure for future use. Data Mining has shown its incredible performance in various fields to a greater extent, out of which, Educational Data Mining (EDM) is one among them. Many researchers have addressed huge number of problems in EDM and applied various techniques to reveal the useful and hidden information that helped in the process of decision making. Students getting employed during and after graduation are one of the important parts of their life. Students, based on their academic performances, are getting employed in companies they deserve. But still, the probability of getting employed is very less in this competitive world. In this paper, a real-time scenario has been chosen for analyzing various factors for getting employed/unemployed. Various clustering and classification techniques have been implemented and their performances are studied. A hybrid approach is presented in this paper that integrates the benefits of particle swarm optimization (PSO) and fuzzy clustering means (FCMs). The results obtained show that the proposed technique helps in obtaining higher accuracy to other clustering techniques. The proposed clustering algorithm PSO-FCM, accuracy is 34.4%, 36.45% and 28.45% higher than the existing method, time complexity shows 45%, 33% and 49% lower than the existing [Formula: see text]-means clustering, Naïve Bayes clustering and SVM clustering algorithms, respectively.
{"title":"Prediction of students' employability using clustering algorithm: A hybrid approach","authors":"N. Premalatha, S. Sujatha","doi":"10.1142/s1793962322500490","DOIUrl":"https://doi.org/10.1142/s1793962322500490","url":null,"abstract":"Data Mining is a process of exploring the huge data in search of reliable patterns and methodical relationship among variables. As a result, the findings may be validated through applying the detected patterns to a novel subset of the data. In simple words, Data Mining is referred as extracting the useful information as large dataset and transforming into reliable structure for future use. Data Mining has shown its incredible performance in various fields to a greater extent, out of which, Educational Data Mining (EDM) is one among them. Many researchers have addressed huge number of problems in EDM and applied various techniques to reveal the useful and hidden information that helped in the process of decision making. Students getting employed during and after graduation are one of the important parts of their life. Students, based on their academic performances, are getting employed in companies they deserve. But still, the probability of getting employed is very less in this competitive world. In this paper, a real-time scenario has been chosen for analyzing various factors for getting employed/unemployed. Various clustering and classification techniques have been implemented and their performances are studied. A hybrid approach is presented in this paper that integrates the benefits of particle swarm optimization (PSO) and fuzzy clustering means (FCMs). The results obtained show that the proposed technique helps in obtaining higher accuracy to other clustering techniques. The proposed clustering algorithm PSO-FCM, accuracy is 34.4%, 36.45% and 28.45% higher than the existing method, time complexity shows 45%, 33% and 49% lower than the existing [Formula: see text]-means clustering, Naïve Bayes clustering and SVM clustering algorithms, respectively.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74765796","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 : 2022-03-12DOI: 10.1142/s1793962323410088
Huanhuan Wu, Zheng Li, Zhanqi Cui, Jianbin Liu
Deep neural network (DNN) models have been widely used in e-commerce, games, automobiles, manufacturing, and so on. Improper structure, parameters, activation function, or incorrect loss function of the DNN models may cause defects in performance or security. As a result, there are some researches that focus on repairing DNN such as MODE and Apricot. However, the cost of repairing is high or the repair may lead to overfitting. In order to solve this problem, we propose GenMuNN, which is a Mutation-Based Approach to Repair Deep Neural Network Models. First, it analyzes the importance of the weights of the neurons in each layer of the DNN model to the correctness of the final prediction results, and ranks the weights according to the influence on the prediction results of the DNN model. Second, mutation is performed to generate mutants based on the rank of weights, and genetic algorithms are used to select mutants for the next round of mutation until the stop condition is touched. Experiments are carried on a set of DNN models which are trained with the MNIST dataset. The experimental results show that GenMuNN can improve the accuracy of the DNN models.
{"title":"GenMuNN: A mutation-based approach to repair deep neural network models","authors":"Huanhuan Wu, Zheng Li, Zhanqi Cui, Jianbin Liu","doi":"10.1142/s1793962323410088","DOIUrl":"https://doi.org/10.1142/s1793962323410088","url":null,"abstract":"Deep neural network (DNN) models have been widely used in e-commerce, games, automobiles, manufacturing, and so on. Improper structure, parameters, activation function, or incorrect loss function of the DNN models may cause defects in performance or security. As a result, there are some researches that focus on repairing DNN such as MODE and Apricot. However, the cost of repairing is high or the repair may lead to overfitting. In order to solve this problem, we propose GenMuNN, which is a Mutation-Based Approach to Repair Deep Neural Network Models. First, it analyzes the importance of the weights of the neurons in each layer of the DNN model to the correctness of the final prediction results, and ranks the weights according to the influence on the prediction results of the DNN model. Second, mutation is performed to generate mutants based on the rank of weights, and genetic algorithms are used to select mutants for the next round of mutation until the stop condition is touched. Experiments are carried on a set of DNN models which are trained with the MNIST dataset. The experimental results show that GenMuNN can improve the accuracy of the DNN models.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85297624","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 : 2022-03-12DOI: 10.1142/s1793962322500520
Tengbin Zhu, Hao Zhang, Yingjie Xiao
In order to research the rescue path problem in the accident of passenger ships under tilt, this paper establishes a multi-objective rescue path optimization model under tilt effect. By analyzing the fuzzy time and fuzzy risk, the objective functions of this model are optimal satisfaction function and optimal risk function. Related constraints are also described mathematically. The PSO-GA (particle swarm and genetic) hybrid algorithm is used to solve the model when designing the algorithm. Two-level planning is incorporated in the algorithm, the best solution in the lower-level planning is assigned to the upper-level, and the upper-level plan feeds back the result to the lower level, and finally the global optimal Pareto solution is obtained. Decision makers can choose appropriate solutions based on their preference. The simulation experiment compares the multi-objective rescue path optimization model with the traditional time-optimal model. Among the three optimal solution sets, solution 1 decreases by 3.36% in risk and the satisfaction rate increases by 69.44%. Solution 2 rose by 13.96% in risk, but the satisfaction increased by 87.93%, and the risk of solution 3 decreased by 11.41%, while the satisfaction increased by 52.41%. The results show that the established model is reasonable and the algorithm is feasible.
{"title":"Multi-objective rescue path optimization for passenger ship accident under tilt","authors":"Tengbin Zhu, Hao Zhang, Yingjie Xiao","doi":"10.1142/s1793962322500520","DOIUrl":"https://doi.org/10.1142/s1793962322500520","url":null,"abstract":"In order to research the rescue path problem in the accident of passenger ships under tilt, this paper establishes a multi-objective rescue path optimization model under tilt effect. By analyzing the fuzzy time and fuzzy risk, the objective functions of this model are optimal satisfaction function and optimal risk function. Related constraints are also described mathematically. The PSO-GA (particle swarm and genetic) hybrid algorithm is used to solve the model when designing the algorithm. Two-level planning is incorporated in the algorithm, the best solution in the lower-level planning is assigned to the upper-level, and the upper-level plan feeds back the result to the lower level, and finally the global optimal Pareto solution is obtained. Decision makers can choose appropriate solutions based on their preference. The simulation experiment compares the multi-objective rescue path optimization model with the traditional time-optimal model. Among the three optimal solution sets, solution 1 decreases by 3.36% in risk and the satisfaction rate increases by 69.44%. Solution 2 rose by 13.96% in risk, but the satisfaction increased by 87.93%, and the risk of solution 3 decreased by 11.41%, while the satisfaction increased by 52.41%. The results show that the established model is reasonable and the algorithm is feasible.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74932699","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}
Modeling and Simulation of Cyber-Physical Systems (MSCPS) is demanding in terms of immediate response to dynamic and complex changes of CPS. Simulation-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new simulation application, which avoid repeated modeling and thus reduce the redevelopment of submodels. Model composition, one of the important methods, enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application systems. In this paper, a real-time model integration approach for global CPS modeling is proposed, which reuses developed submodels by compositing submodel nodes. Specifically, a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse matching. Submodel properties, including co-simulation distance between submodel nodes, reuse benefit and simulation performance of model nodes, are quantified. Based on the properties, the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the Genetic Algorithm (GA). In the experiment, the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions, among which the Pareto optimal solutions are retrieved. Results show that the effectiveness of the model integration method proposed in this paper is verified.
{"title":"Simulation-oriented model reuse in cyber-physical systems: A method based on constrained directed graph","authors":"Wenzheng Liu, Heming Zhang, Chao Tang, Shuangfei Wu, Hongguang Zhu","doi":"10.1142/s1793962322410057","DOIUrl":"https://doi.org/10.1142/s1793962322410057","url":null,"abstract":"Modeling and Simulation of Cyber-Physical Systems (MSCPS) is demanding in terms of immediate response to dynamic and complex changes of CPS. Simulation-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new simulation application, which avoid repeated modeling and thus reduce the redevelopment of submodels. Model composition, one of the important methods, enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application systems. In this paper, a real-time model integration approach for global CPS modeling is proposed, which reuses developed submodels by compositing submodel nodes. Specifically, a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse matching. Submodel properties, including co-simulation distance between submodel nodes, reuse benefit and simulation performance of model nodes, are quantified. Based on the properties, the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the Genetic Algorithm (GA). In the experiment, the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions, among which the Pareto optimal solutions are retrieved. Results show that the effectiveness of the model integration method proposed in this paper is verified.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74136645","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 : 2022-03-07DOI: 10.1142/s179396232250060x
Xiaodong Wang, Fengju Kang, Hao Gu, Hongtao Liang
{"title":"Regional importance detection of 3D mesh via fusion of local color difference and curvature entropy","authors":"Xiaodong Wang, Fengju Kang, Hao Gu, Hongtao Liang","doi":"10.1142/s179396232250060x","DOIUrl":"https://doi.org/10.1142/s179396232250060x","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74978957","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 : 2022-03-07DOI: 10.1142/s1793962322500581
M. K. Deshmukh, Vinod Vaze, A. Gaikwad
{"title":"Coyote-Wolf optimization-based deep neural network for cancer classification using gene expression profiles","authors":"M. K. Deshmukh, Vinod Vaze, A. Gaikwad","doi":"10.1142/s1793962322500581","DOIUrl":"https://doi.org/10.1142/s1793962322500581","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75584097","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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