Pub Date : 2021-01-01DOI: 10.1504/ijsi.2021.10040179
S. K. Jena, Aditya Dev Mishra, A. Husain
{"title":"A robust approach for digital watermarking of satellite imagery dataset","authors":"S. K. Jena, Aditya Dev Mishra, A. Husain","doi":"10.1504/ijsi.2021.10040179","DOIUrl":"https://doi.org/10.1504/ijsi.2021.10040179","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"22 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85656192","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 : 2021-01-01DOI: 10.1504/ijsi.2021.10041290
R. Joshi, Vinod Kumar, S. Vyas, Abrar Ahmed Chhipa
{"title":"MPPT optimisation techniques and power electronics for renewable energy systems: wind and solar energy systems","authors":"R. Joshi, Vinod Kumar, S. Vyas, Abrar Ahmed Chhipa","doi":"10.1504/ijsi.2021.10041290","DOIUrl":"https://doi.org/10.1504/ijsi.2021.10041290","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"51 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88728034","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 : 2021-01-01DOI: 10.1504/IJSI.2021.10036458
Nishant Kumar, Lokesh Yadav, D. Tomar
{"title":"Analysis of malware by integrating API extracted from dynamic and memory analysis","authors":"Nishant Kumar, Lokesh Yadav, D. Tomar","doi":"10.1504/IJSI.2021.10036458","DOIUrl":"https://doi.org/10.1504/IJSI.2021.10036458","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"103 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73637917","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 : 2021-01-01DOI: 10.1504/IJSI.2021.10037055
M. Dua, Aakshi Mittal
{"title":"Automatic speaker verification system using three dimensional static and contextual variation-based features with two dimensional convolutional neural network","authors":"M. Dua, Aakshi Mittal","doi":"10.1504/IJSI.2021.10037055","DOIUrl":"https://doi.org/10.1504/IJSI.2021.10037055","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"14 10 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78309956","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 : 2021-01-01DOI: 10.1504/ijsi.2021.10041193
Philemon Daniel, Umesh Pant, S. Chauhan, Pankaj Pant
{"title":"Unsupervised word translation for English-Hindi with different retrieval techniques","authors":"Philemon Daniel, Umesh Pant, S. Chauhan, Pankaj Pant","doi":"10.1504/ijsi.2021.10041193","DOIUrl":"https://doi.org/10.1504/ijsi.2021.10041193","url":null,"abstract":"","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"21 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87183482","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 : 2020-03-20DOI: 10.1504/ijsi.2020.106411
W. Pemarathne, T. Fernando
Ant colony optimisation algorithms have been applied to solve wide range of difficult combinatorial optimisation problems like routing problems, assigning problems, scheduling problems and revealed remarkable solutions. In this paper we present a novel approach of ant colony optimisation algorithm to solve the electrical cable routing problem. The study focuses on optimising wire lengths, number of bends and angles of bends. We have studied these objectives in cable routing and modified the ant colony system algorithm to get better solutions. Ants are directed to search for the optimal path between the starting and the ending points by avoiding the obstacles. While ants are navigating, they travel the paths with less number of bends and consider angles of the bends towards 90, 180, and 270 degrees. Normal walls are presented as a grid and doors, windows and other obstacles are represented as rectangles. The possible points to follow by ants are designed according to the BS 7671 (IET Wiring Regulations) standards. The results of the simulation prove with comparisons that this method is feasible and effective for optimising the electrical wire routing.
{"title":"Multi objective ant colony algorithm for electrical wire routing","authors":"W. Pemarathne, T. Fernando","doi":"10.1504/ijsi.2020.106411","DOIUrl":"https://doi.org/10.1504/ijsi.2020.106411","url":null,"abstract":"Ant colony optimisation algorithms have been applied to solve wide range of difficult combinatorial optimisation problems like routing problems, assigning problems, scheduling problems and revealed remarkable solutions. In this paper we present a novel approach of ant colony optimisation algorithm to solve the electrical cable routing problem. The study focuses on optimising wire lengths, number of bends and angles of bends. We have studied these objectives in cable routing and modified the ant colony system algorithm to get better solutions. Ants are directed to search for the optimal path between the starting and the ending points by avoiding the obstacles. While ants are navigating, they travel the paths with less number of bends and consider angles of the bends towards 90, 180, and 270 degrees. Normal walls are presented as a grid and doors, windows and other obstacles are represented as rectangles. The possible points to follow by ants are designed according to the BS 7671 (IET Wiring Regulations) standards. The results of the simulation prove with comparisons that this method is feasible and effective for optimising the electrical wire routing.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"46 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83743187","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 : 2020-03-20DOI: 10.1504/ijsi.2020.106396
G. Kakandikar, Omkar Kulkarni, Sujata L. Patekar, Trupti Bhoskar
Deep drawing is a manufacturing process in which sheet metal is progressively formed into a three-dimensional shape through the mechanical action of a punch forming the metal inside die. The flow of metal is complex mechanism. Pots, pans for cooking, containers, sinks, automobile body parts such as panels and gas tanks are among a few of the items manufactured by deep drawing. Uniform strain distribution in forming results in quality components. The predominant failure modes in sheet metal parts are springback, wrinkling and fracture. Fracture or necking occurs in a drawn part, which is under excessive tensile loading. The prediction and prevention of fracture depends on the design of tooling and selection of process parameters. Firefly algorithm is one of the nature inspired optimisation algorithms and is inspired by firefly's behaviour in nature. The proposed research work presents novel approach to optimise fracture in automotive component-tail cap. The optimisation problem has been defined to optimise fracture within the constraints of radius on die, radius on punch and blank holding force. Fire fly algorithm has been applied to find optimum process parameters. Numerical experimentation has been conducted to validate the results.
{"title":"Optimising fracture in automotive tail cap by firefly algorithm","authors":"G. Kakandikar, Omkar Kulkarni, Sujata L. Patekar, Trupti Bhoskar","doi":"10.1504/ijsi.2020.106396","DOIUrl":"https://doi.org/10.1504/ijsi.2020.106396","url":null,"abstract":"Deep drawing is a manufacturing process in which sheet metal is progressively formed into a three-dimensional shape through the mechanical action of a punch forming the metal inside die. The flow of metal is complex mechanism. Pots, pans for cooking, containers, sinks, automobile body parts such as panels and gas tanks are among a few of the items manufactured by deep drawing. Uniform strain distribution in forming results in quality components. The predominant failure modes in sheet metal parts are springback, wrinkling and fracture. Fracture or necking occurs in a drawn part, which is under excessive tensile loading. The prediction and prevention of fracture depends on the design of tooling and selection of process parameters. Firefly algorithm is one of the nature inspired optimisation algorithms and is inspired by firefly's behaviour in nature. The proposed research work presents novel approach to optimise fracture in automotive component-tail cap. The optimisation problem has been defined to optimise fracture within the constraints of radius on die, radius on punch and blank holding force. Fire fly algorithm has been applied to find optimum process parameters. Numerical experimentation has been conducted to validate the results.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"104 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79225571","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 : 2020-03-20DOI: 10.1504/ijsi.2020.106388
M. Neshat, A. Pourahmad, Z. Rohani
Problem solving is a very important subject in the world of AI. In fact, a problem can be considered one or more goals along with a set of available interactions for reaching those goals. One of the best ways of solving AI problems is to use search methods. The simplified memory bounded A* (SMA*) is one of the best methods of informed search. In this research, a hybrid method was proposed to increase the performance of SMA* search. The combining fuzzy logic with this search method and improving it with PSO algorithm brought satisfactory results. The use of fuzzy logic leads to increase the search flexibility especially when a robot dealing with lots of barriers and path changes. Furthermore, combining PSO saves the search from being trapped into local optimums and provides for search some correct and accurate suggestions. In the proposed algorithm, the results indicate that the cost of search and branching factor are decreased in comparison with other methods.
{"title":"Improving the cooperation of fuzzy simplified memory A* search and particle swarm optimisation for path planning","authors":"M. Neshat, A. Pourahmad, Z. Rohani","doi":"10.1504/ijsi.2020.106388","DOIUrl":"https://doi.org/10.1504/ijsi.2020.106388","url":null,"abstract":"Problem solving is a very important subject in the world of AI. In fact, a problem can be considered one or more goals along with a set of available interactions for reaching those goals. One of the best ways of solving AI problems is to use search methods. The simplified memory bounded A* (SMA*) is one of the best methods of informed search. In this research, a hybrid method was proposed to increase the performance of SMA* search. The combining fuzzy logic with this search method and improving it with PSO algorithm brought satisfactory results. The use of fuzzy logic leads to increase the search flexibility especially when a robot dealing with lots of barriers and path changes. Furthermore, combining PSO saves the search from being trapped into local optimums and provides for search some correct and accurate suggestions. In the proposed algorithm, the results indicate that the cost of search and branching factor are decreased in comparison with other methods.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"8 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86822722","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 : 2020-03-20DOI: 10.1504/ijsi.2020.106404
S. Gupta, Kusum Deep, S. Mirjalili
Grey wolf optimiser (GWO) is a relatively simple and efficient nature-inspired optimisation algorithm which has shown its competitive performance compared to other population-based meta-heuristics. This algorithm drives the solutions towards some of the best solutions obtained so far using a unique mathematical model, which is inspired from leadership behaviour of grey wolves in nature. To combat the issue of premature convergence and local optima stagnation, an enhanced version of GWO is proposed in this paper. The proposed algorithm is named accelerated grey wolf optimiser (A-GWO). In A-GWO, novel modified search equations are developed that enhances the exploratory behaviour of wolves at later generations, and the exploitation of search space is also improved in the whole search process. To validate the performance of the proposed algorithm, set of 23 well-known classical benchmark problems are used. The results and comparison through various metrics show the reliability and efficiency of the A-GWO.
{"title":"Accelerated grey wolf optimiser for continuous optimisation problems","authors":"S. Gupta, Kusum Deep, S. Mirjalili","doi":"10.1504/ijsi.2020.106404","DOIUrl":"https://doi.org/10.1504/ijsi.2020.106404","url":null,"abstract":"Grey wolf optimiser (GWO) is a relatively simple and efficient nature-inspired optimisation algorithm which has shown its competitive performance compared to other population-based meta-heuristics. This algorithm drives the solutions towards some of the best solutions obtained so far using a unique mathematical model, which is inspired from leadership behaviour of grey wolves in nature. To combat the issue of premature convergence and local optima stagnation, an enhanced version of GWO is proposed in this paper. The proposed algorithm is named accelerated grey wolf optimiser (A-GWO). In A-GWO, novel modified search equations are developed that enhances the exploratory behaviour of wolves at later generations, and the exploitation of search space is also improved in the whole search process. To validate the performance of the proposed algorithm, set of 23 well-known classical benchmark problems are used. The results and comparison through various metrics show the reliability and efficiency of the A-GWO.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"28 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76996752","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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