Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585913
Hama Soltani, M. Amroune, Issam Bendib, M. Haouam
Breast cancer is an obsession that haunts all women. but early detection for it increases the cure rate, for attain this objective It is very important to create a system to diagnose suspicious masses. On the other hand, is a difficult task due to the fact that breast lumps vary in size and appearance. In this paper, we propose an automatic breast mass segmentation method based on the Mask RCNN model of deep learning using detectron2.our model is trained and testing using the public dataset INbreast. The proposed method achieved results with precision and F1 score 95.87 and 81.05 on INbreast dataset, respectively.
{"title":"Breast Cancer Lesion Detection and Segmentation Based On Mask R-CNN","authors":"Hama Soltani, M. Amroune, Issam Bendib, M. Haouam","doi":"10.1109/ICRAMI52622.2021.9585913","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585913","url":null,"abstract":"Breast cancer is an obsession that haunts all women. but early detection for it increases the cure rate, for attain this objective It is very important to create a system to diagnose suspicious masses. On the other hand, is a difficult task due to the fact that breast lumps vary in size and appearance. In this paper, we propose an automatic breast mass segmentation method based on the Mask RCNN model of deep learning using detectron2.our model is trained and testing using the public dataset INbreast. The proposed method achieved results with precision and F1 score 95.87 and 81.05 on INbreast dataset, respectively.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126004694","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}
Traffic forecasting is one of the most difficult challenges in the area of ITS (intelligent transportation systems) because of complex spatial correlations on road networks and non-linear temporal dynamics of changing road conditions. To address these issues, researchers proposed models that combine GCNs (Graph Convolution Networks) and RNNs (Recurrent Neural Networks), in order to inherit the advantages of both of them and become capable of extracting spatiotemporal correlations. Restricting the efficiency of the models by their precision without concern for their structure made the models become more complex, although simple models sometimes produce better results. In this research, we introduce a simple model, called Long Short-Term Memory network for Traffic Forecasting (LSTM-TF), which uses the LSTM for extracting spatial-temporal dependencies. Experiments show that the LSTM-TF outperforms state-of-the-art baselines on real-world traffic datasets, proving our hypothesis that simple models as the LSTM-TF produce sometimes better results than more complex ones.
{"title":"Is Classical LSTM more Efficient than Modern GCN Approaches in the Context of Traffic Forecasting?","authors":"Haroun Bouchemoukha, Mohamed Nadjib, Zennir Atidel Lahoulou","doi":"10.1109/ICRAMI52622.2021.9585940","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585940","url":null,"abstract":"Traffic forecasting is one of the most difficult challenges in the area of ITS (intelligent transportation systems) because of complex spatial correlations on road networks and non-linear temporal dynamics of changing road conditions. To address these issues, researchers proposed models that combine GCNs (Graph Convolution Networks) and RNNs (Recurrent Neural Networks), in order to inherit the advantages of both of them and become capable of extracting spatiotemporal correlations. Restricting the efficiency of the models by their precision without concern for their structure made the models become more complex, although simple models sometimes produce better results. In this research, we introduce a simple model, called Long Short-Term Memory network for Traffic Forecasting (LSTM-TF), which uses the LSTM for extracting spatial-temporal dependencies. Experiments show that the LSTM-TF outperforms state-of-the-art baselines on real-world traffic datasets, proving our hypothesis that simple models as the LSTM-TF produce sometimes better results than more complex ones.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128249074","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585931
B. Selma, Boustia Narhimene, Rezoug Nachida
During the last few years, deep learning revolutionized several fields including: image analysis, speech recognition and language processing. Deep learning has also become pervasive and demonstrated effectiveness in the field of recommender systems and information retrieval. Unlike the conventional recommendation systems, deep learning have the unique ability to successfully capture non-trivial and non-linear interactions between user and item, allowing for the codification of more complicated abstractions. We begin by providing a brief overview of recommender systems and deep learning. Second, we present a complete overview of the current state of the art in deep learning-based RS. Then, we describe a possible future research direction of the field. Finally, we conclude the review.
{"title":"Deep Learning for Recommender Systems: Literature Review and Perspectives","authors":"B. Selma, Boustia Narhimene, Rezoug Nachida","doi":"10.1109/ICRAMI52622.2021.9585931","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585931","url":null,"abstract":"During the last few years, deep learning revolutionized several fields including: image analysis, speech recognition and language processing. Deep learning has also become pervasive and demonstrated effectiveness in the field of recommender systems and information retrieval. Unlike the conventional recommendation systems, deep learning have the unique ability to successfully capture non-trivial and non-linear interactions between user and item, allowing for the codification of more complicated abstractions. We begin by providing a brief overview of recommender systems and deep learning. Second, we present a complete overview of the current state of the art in deep learning-based RS. Then, we describe a possible future research direction of the field. Finally, we conclude the review.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127741402","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585923
Billel Nebili, Atmane Khellal, A. Nemra
Several algorithms for target recognition in infrared images were proposed by reasearchers to develop an efficient advanced driver assistance systems. In this paper, an approach based on bag of features framework, SIFT and SVM, is evaluated for target recognition problem. First, SIFT extractor is applied to all the training set. Then, features were clustered by K-means; the cluster centers are regarded as visual words to form a visual vocabulary. For each image, a histogram of quantized local descriptors is computed according to the frequency of visual words in each sub-region, which are obtained by the spatial pyramid matching technique. The generated feature vector will be mapped for later use as an input to SVM. Extensive experiments are carried out in FLIR dataset. Our experimental results show that the proposed method exceeds the-state-of-art in target recognition on two class FLIR dataset with 3% improvement in accuracy classification.
{"title":"Histogram Encoding of SIFT Based Visual Words for Target Recognition in Infrared Images","authors":"Billel Nebili, Atmane Khellal, A. Nemra","doi":"10.1109/ICRAMI52622.2021.9585923","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585923","url":null,"abstract":"Several algorithms for target recognition in infrared images were proposed by reasearchers to develop an efficient advanced driver assistance systems. In this paper, an approach based on bag of features framework, SIFT and SVM, is evaluated for target recognition problem. First, SIFT extractor is applied to all the training set. Then, features were clustered by K-means; the cluster centers are regarded as visual words to form a visual vocabulary. For each image, a histogram of quantized local descriptors is computed according to the frequency of visual words in each sub-region, which are obtained by the spatial pyramid matching technique. The generated feature vector will be mapped for later use as an input to SVM. Extensive experiments are carried out in FLIR dataset. Our experimental results show that the proposed method exceeds the-state-of-art in target recognition on two class FLIR dataset with 3% improvement in accuracy classification.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131800972","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585905
Ehsan Gord, R. Dashti, M. Najafi, M. Tahavori, H. Shaker
In designing engineering systems, definitive solutions can hardly be applied to actual scenarios. This issue is mainly originated from production constraints and the environmental conditions of the actual systems under exploitation. Therefore, a small change in the design variables vector may lead to a significant change in the optimal design that minimizes the objective functions. Hence, it is important to develop methods that provide optimal (or even sub-optimal) solutions with less sensitivity to the uncertainty of the design variables. This is the focus of this paper. We present a robust Non-dominated-Sorting Genetic Algorithm II (NSGA-II)-based multi-objective constrained optimization algorithm. To further illustrate the method, the proposed algorithm is used in the robust and constrained optimal design of a sample engineering system. Evaluation of the obtained results shows that multi-objective engineering problems can be solved by the multi-objective robust optimization (MORO) through finding Pareto solutions, so that by changing the problem parameters, the changes of the solutions will be within an acceptable range.
{"title":"A Multi-Objective Constrained Robust Optimization Based on NSGA-II Algorithm","authors":"Ehsan Gord, R. Dashti, M. Najafi, M. Tahavori, H. Shaker","doi":"10.1109/ICRAMI52622.2021.9585905","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585905","url":null,"abstract":"In designing engineering systems, definitive solutions can hardly be applied to actual scenarios. This issue is mainly originated from production constraints and the environmental conditions of the actual systems under exploitation. Therefore, a small change in the design variables vector may lead to a significant change in the optimal design that minimizes the objective functions. Hence, it is important to develop methods that provide optimal (or even sub-optimal) solutions with less sensitivity to the uncertainty of the design variables. This is the focus of this paper. We present a robust Non-dominated-Sorting Genetic Algorithm II (NSGA-II)-based multi-objective constrained optimization algorithm. To further illustrate the method, the proposed algorithm is used in the robust and constrained optimal design of a sample engineering system. Evaluation of the obtained results shows that multi-objective engineering problems can be solved by the multi-objective robust optimization (MORO) through finding Pareto solutions, so that by changing the problem parameters, the changes of the solutions will be within an acceptable range.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127615911","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585980
M. Gasmi, M. Derdour, Abdellatif Gahmousse, M. Amroune, H. Bendjenna, Brahim Sahraoui
Molecular classification in pathological anatomy is an important task as it is extremely convenient for the diagnosis of cancer and its subtypes for adequate therapeutic choice. With the development of computer vision, cancer classification has become an interdisciplinary subject in both medicine and computer vision.A multi-input convolutional neural network is designed for the molecular classification of cancer based on a collected dataset, which contains four tissues treated with four antibodies; each one of them is composed of 33 images. The proposed model achieves a satisfactory accuracy of 90.43% after data augmentation. Even though the data augmentation contributes to the model, the accuracy is still limited by the lack of sample diversity.
{"title":"Multi-Input CNN for molecular classification in breast cancer","authors":"M. Gasmi, M. Derdour, Abdellatif Gahmousse, M. Amroune, H. Bendjenna, Brahim Sahraoui","doi":"10.1109/ICRAMI52622.2021.9585980","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585980","url":null,"abstract":"Molecular classification in pathological anatomy is an important task as it is extremely convenient for the diagnosis of cancer and its subtypes for adequate therapeutic choice. With the development of computer vision, cancer classification has become an interdisciplinary subject in both medicine and computer vision.A multi-input convolutional neural network is designed for the molecular classification of cancer based on a collected dataset, which contains four tissues treated with four antibodies; each one of them is composed of 33 images. The proposed model achieves a satisfactory accuracy of 90.43% after data augmentation. Even though the data augmentation contributes to the model, the accuracy is still limited by the lack of sample diversity.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114368553","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585942
Bilal Saoud
Routing is very important to ensure the transmission of data between nodes in wireless sensor network (WSN). Many routing protocols have been proposed. WSN has many common features with Ad Hoc network. In this paper we have evaluated two Ad Hoc routing protocols in WSN topology. The results showed that we can use Ad Hoc routing protocols to ensure the transmission of data in WSN topology. The impact of mobility has been studied on Directed Diffusion protocol in WSN topology.
{"title":"Simulation and Analysis of Routing Protocols in Wireless Sensor Network","authors":"Bilal Saoud","doi":"10.1109/ICRAMI52622.2021.9585942","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585942","url":null,"abstract":"Routing is very important to ensure the transmission of data between nodes in wireless sensor network (WSN). Many routing protocols have been proposed. WSN has many common features with Ad Hoc network. In this paper we have evaluated two Ad Hoc routing protocols in WSN topology. The results showed that we can use Ad Hoc routing protocols to ensure the transmission of data in WSN topology. The impact of mobility has been studied on Directed Diffusion protocol in WSN topology.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116439214","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585984
Yazid Merah, Tayeb Kenaza
Exploiting Cyber Threat Intelligence (CTI) as a valuable, updated, and structured source of information on threats and vulnerabilities can be a strong support for providing effective cybersecurity solutions. CTIs are shared across dedicated online platforms via a machine-readable format, such as Structured Threat Information eXpression (STIX). Meanwhile, ontology-based semantic knowledge modeling has become a promising solution that provides a machine-readable language for downstream work to address cybersecurity issues. Hence, by incorporating STIX concepts we propose in this paper an ontological-based CTI analysis that provides valuable threats information according to the security alerts reported by an analyzer. To test our ontology, we developed a set of reasoning rules to infer new knowledge on cyber threats. The experimental results show that such knowledge can be inferred by applying our approach for an ongoing and effective monitoring of cyber threats.
{"title":"Proactive Ontology-based Cyber Threat Intelligence Analytic","authors":"Yazid Merah, Tayeb Kenaza","doi":"10.1109/ICRAMI52622.2021.9585984","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585984","url":null,"abstract":"Exploiting Cyber Threat Intelligence (CTI) as a valuable, updated, and structured source of information on threats and vulnerabilities can be a strong support for providing effective cybersecurity solutions. CTIs are shared across dedicated online platforms via a machine-readable format, such as Structured Threat Information eXpression (STIX). Meanwhile, ontology-based semantic knowledge modeling has become a promising solution that provides a machine-readable language for downstream work to address cybersecurity issues. Hence, by incorporating STIX concepts we propose in this paper an ontological-based CTI analysis that provides valuable threats information according to the security alerts reported by an analyzer. To test our ontology, we developed a set of reasoning rules to infer new knowledge on cyber threats. The experimental results show that such knowledge can be inferred by applying our approach for an ongoing and effective monitoring of cyber threats.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125715981","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585926
Narveen Kumar, N. Choudhary
Free vibrations of liquid in the annular region of a rigid circular cylindrical container with a rigid baffle on the free surface are considered. The liquid inside the container is considered; ideal and incompressible, and the fluid motion is irrotational. In the above-considered geometry, along with assumptions made, the velocity potential is introduced, which satisfies Laplace’s equation inside the liquid domain. The boundary value problem (BVP) is formulated using the linear water wave theory. The analytical solution of BVP is obtained in terms of velocity potential with unknown frequency. The velocity potential is used in free surface conditions, which results in a system of homogeneous algebraic equations. The necessary condition for a non-trivial solution of this homogenous system is used to compute the frequencies. Mode shapes of the container in the presence of a rigid baffle are reported using ANSYS software.
{"title":"Simulation and Semi-Analytical Approach on Sloshing Mitigation","authors":"Narveen Kumar, N. Choudhary","doi":"10.1109/ICRAMI52622.2021.9585926","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585926","url":null,"abstract":"Free vibrations of liquid in the annular region of a rigid circular cylindrical container with a rigid baffle on the free surface are considered. The liquid inside the container is considered; ideal and incompressible, and the fluid motion is irrotational. In the above-considered geometry, along with assumptions made, the velocity potential is introduced, which satisfies Laplace’s equation inside the liquid domain. The boundary value problem (BVP) is formulated using the linear water wave theory. The analytical solution of BVP is obtained in terms of velocity potential with unknown frequency. The velocity potential is used in free surface conditions, which results in a system of homogeneous algebraic equations. The necessary condition for a non-trivial solution of this homogenous system is used to compute the frequencies. Mode shapes of the container in the presence of a rigid baffle are reported using ANSYS software.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129624895","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585908
Ouaar Fatima
The operation of demand and supply in a market is known as the market mechanism. The market will be in equilibrium at price (P), when quantity (Q) will be bought and sold. In equilibrium the quantity of a good supplied by producers equals the quantity demanded by consumers. Due to the fact that traditional deterministic methods or algorithms do not cope well to solve a large number of problems in practice; The aim of this paper is to solve approximately the demand and supply equilibrium equation which has an imperative role to describe the relation between consumers/ producers and price/quantity by means of the Salp Swarm Algorithm (SSA), inspired by the swarming behavior of salps when searching foods in deep oceans as well as the Genetic Algorithm (GA) inspired by the process of natural selection. The demand and supply equilibrium equation as an Initial Value Problem (IVP) is considered as an optimization problem, since it can almost be solved by classical mathematical tools with less precision. The effectiveness of the proposed method is tested via a simulation study between the exact results, the SSA and GA results. The comparison between these performances after many replications shows that SSA is a very powerful and can produce robust solutions on low dimensional problems with minimal error.
{"title":"Optimization of Demand and Supply Equilibrium equation by using Salp Swarm Algorithm","authors":"Ouaar Fatima","doi":"10.1109/ICRAMI52622.2021.9585908","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585908","url":null,"abstract":"The operation of demand and supply in a market is known as the market mechanism. The market will be in equilibrium at price (P), when quantity (Q) will be bought and sold. In equilibrium the quantity of a good supplied by producers equals the quantity demanded by consumers. Due to the fact that traditional deterministic methods or algorithms do not cope well to solve a large number of problems in practice; The aim of this paper is to solve approximately the demand and supply equilibrium equation which has an imperative role to describe the relation between consumers/ producers and price/quantity by means of the Salp Swarm Algorithm (SSA), inspired by the swarming behavior of salps when searching foods in deep oceans as well as the Genetic Algorithm (GA) inspired by the process of natural selection. The demand and supply equilibrium equation as an Initial Value Problem (IVP) is considered as an optimization problem, since it can almost be solved by classical mathematical tools with less precision. The effectiveness of the proposed method is tested via a simulation study between the exact results, the SSA and GA results. The comparison between these performances after many replications shows that SSA is a very powerful and can produce robust solutions on low dimensional problems with minimal error.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128996674","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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