Pub Date : 2023-01-01DOI: 10.1504/ijica.2023.134184
Purnima Pandit, Shardav Bhatt
In today's world, automatic speech recognition (ASR) is an important task implemented via machine learning (ML) to assist artificial intelligence (AI). It has diverse applications such as human-machine interactions, hands-free computing, voice search, domestic appliance control and many more. Speech recognition in an Indian regional language becomes a very necessary task in order to facilitate people, who can communicate only using their mother tongue and the disabled ones. In this article, we have proposed and performed experiments of speech recognition for Gujarati language, particularly for Gujarati digits. The recorded speech is pre-processed and then speech features are extracted from it using Mel-frequency discrete wavelet coefficient (MFDWC). These features are trained using artificial neural networks (ANN) for classification. Two ANN architectures namely, multi-layer perceptrons (MLP) and radial basis function networks (RBFN) are used for training and recognition. The experimental results obtained in this work are compared with our previous experimental results.
{"title":"Automatic speech recognition of Gujarati digits using wavelet coefficients in machine learning algorithms","authors":"Purnima Pandit, Shardav Bhatt","doi":"10.1504/ijica.2023.134184","DOIUrl":"https://doi.org/10.1504/ijica.2023.134184","url":null,"abstract":"In today's world, automatic speech recognition (ASR) is an important task implemented via machine learning (ML) to assist artificial intelligence (AI). It has diverse applications such as human-machine interactions, hands-free computing, voice search, domestic appliance control and many more. Speech recognition in an Indian regional language becomes a very necessary task in order to facilitate people, who can communicate only using their mother tongue and the disabled ones. In this article, we have proposed and performed experiments of speech recognition for Gujarati language, particularly for Gujarati digits. The recorded speech is pre-processed and then speech features are extracted from it using Mel-frequency discrete wavelet coefficient (MFDWC). These features are trained using artificial neural networks (ANN) for classification. Two ANN architectures namely, multi-layer perceptrons (MLP) and radial basis function networks (RBFN) are used for training and recognition. The experimental results obtained in this work are compared with our previous experimental results.","PeriodicalId":39390,"journal":{"name":"International Journal of Innovative Computing and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136306411","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 : 2023-01-01DOI: 10.1504/ijica.2023.10056700
N. K. Nagwani, Rama Ranjan Panda
{"title":"Fuzzy modelling techniques for improving multi-label classification of software bugs","authors":"N. K. Nagwani, Rama Ranjan Panda","doi":"10.1504/ijica.2023.10056700","DOIUrl":"https://doi.org/10.1504/ijica.2023.10056700","url":null,"abstract":"","PeriodicalId":39390,"journal":{"name":"International Journal of Innovative Computing and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66987058","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 : 2023-01-01DOI: 10.1504/ijica.2023.10054508
J. Marin
{"title":"Performance evaluation of energy reconstruction methods for the ATLAS hadronic calorimeter using collision data","authors":"J. Marin","doi":"10.1504/ijica.2023.10054508","DOIUrl":"https://doi.org/10.1504/ijica.2023.10054508","url":null,"abstract":"","PeriodicalId":39390,"journal":{"name":"International Journal of Innovative Computing and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66987237","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 : 2023-01-01DOI: 10.1504/ijica.2023.10059775
Somya Jain, Adwitiya Sinha
{"title":"Deep learning intelligence for influencer-based topological classification for online social networks","authors":"Somya Jain, Adwitiya Sinha","doi":"10.1504/ijica.2023.10059775","DOIUrl":"https://doi.org/10.1504/ijica.2023.10059775","url":null,"abstract":"","PeriodicalId":39390,"journal":{"name":"International Journal of Innovative Computing and Applications","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136304349","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 : 2023-01-01DOI: 10.1504/ijica.2023.134231
Benjia Hu, Zhiyong Wu, Wen Gao, Ke Meng, Dayin Shi, Xiuwei Hu, Yilong Sun
Aiming at the shortcomings of whale optimisation algorithm, such as easy to fall into local optimisation and slow convergence speed in the later stage, an optimisation method based on three improved strategies is proposed. Firstly, Kent mapping is introduced to initialise the population and enrich the diversity of the population; Secondly, a nonlinear convergence factor strategy is proposed to improve the global search speed and local optimisation accuracy. Finally, inertia weight is added to maintain the balance between global search and local optimisation. Simulation experiments with 13 standard test functions show that the proposed algorithm has remarkable performance in global search, convergence speed and optimisation accuracy. In addition, through its application in path planning, the feasibility and effectiveness of the algorithm proposed in this paper are further verified.
{"title":"Whale optimisation algorithm based on Kent mapping and adaptive parameters","authors":"Benjia Hu, Zhiyong Wu, Wen Gao, Ke Meng, Dayin Shi, Xiuwei Hu, Yilong Sun","doi":"10.1504/ijica.2023.134231","DOIUrl":"https://doi.org/10.1504/ijica.2023.134231","url":null,"abstract":"Aiming at the shortcomings of whale optimisation algorithm, such as easy to fall into local optimisation and slow convergence speed in the later stage, an optimisation method based on three improved strategies is proposed. Firstly, Kent mapping is introduced to initialise the population and enrich the diversity of the population; Secondly, a nonlinear convergence factor strategy is proposed to improve the global search speed and local optimisation accuracy. Finally, inertia weight is added to maintain the balance between global search and local optimisation. Simulation experiments with 13 standard test functions show that the proposed algorithm has remarkable performance in global search, convergence speed and optimisation accuracy. In addition, through its application in path planning, the feasibility and effectiveness of the algorithm proposed in this paper are further verified.","PeriodicalId":39390,"journal":{"name":"International Journal of Innovative Computing and Applications","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136307770","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 : 2023-01-01DOI: 10.1504/ijica.2023.10054513
Rodrigo F. Toso, Eduardo Ogasawara, Fernando P.G. De Sá, R. Coutinho, Diego Brand�ão
{"title":"Wind turbine fault detection: a semi-supervised learning approach with two different dimensionality reduction techniques","authors":"Rodrigo F. Toso, Eduardo Ogasawara, Fernando P.G. De Sá, R. Coutinho, Diego Brand�ão","doi":"10.1504/ijica.2023.10054513","DOIUrl":"https://doi.org/10.1504/ijica.2023.10054513","url":null,"abstract":"","PeriodicalId":39390,"journal":{"name":"International Journal of Innovative Computing and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66986855","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 : 2023-01-01DOI: 10.1504/ijica.2023.10054514
A. Conci, M. Biondi, José M. Riveaux, M. Correia, M. B. Moran, E. C. Vasconcellos, E. W. Gonzalez Clua, J. Cuno
{"title":"Heuristic-based approaches for fracture detection in borehole images","authors":"A. Conci, M. Biondi, José M. Riveaux, M. Correia, M. B. Moran, E. C. Vasconcellos, E. W. Gonzalez Clua, J. Cuno","doi":"10.1504/ijica.2023.10054514","DOIUrl":"https://doi.org/10.1504/ijica.2023.10054514","url":null,"abstract":"","PeriodicalId":39390,"journal":{"name":"International Journal of Innovative Computing and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66986871","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 : 2023-01-01DOI: 10.1504/ijica.2023.10052593
V. Vasudevan, R. Subramanian
{"title":"HARDeep: Design and Evaluation of a Deep Ensemble Model for Human Activity Recognition","authors":"V. Vasudevan, R. Subramanian","doi":"10.1504/ijica.2023.10052593","DOIUrl":"https://doi.org/10.1504/ijica.2023.10052593","url":null,"abstract":"","PeriodicalId":39390,"journal":{"name":"International Journal of Innovative Computing and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66987220","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 : 2023-01-01DOI: 10.1504/ijica.2023.129376
Lydia Nahla Driff, Habiba Drias
In order to refine association rules based on frequent patterns, we advised an improved version of firefly algorithm called IFF. We had to eliminate blind mating from the design of GA and replaced it by mating between mature fireflies, while ensuring balanced convergence. The proposed approach uses advanced methods such as controlled genetic operations to manipulate frequent patterns, and the uses of fuzzy logic to control IFF parameters to assure convergence calibration, based on data size, algorithm iterations and temporary local optimum. Also, we executed IFF under Hadoop to get a MapReduce system and ensure the most optimal execution time. To analyse the quality of our proposal, we made simulations on MEDLINE dataset. Results indicate that the proposed approach is superior to existing algorithms with an accuracy of 10% to 50% and save execution time around 36%, while ensuring a good balance between the quality and variety of knowledge.
{"title":"Fuzzy improved firefly-based MapReduce for association rule mining","authors":"Lydia Nahla Driff, Habiba Drias","doi":"10.1504/ijica.2023.129376","DOIUrl":"https://doi.org/10.1504/ijica.2023.129376","url":null,"abstract":"In order to refine association rules based on frequent patterns, we advised an improved version of firefly algorithm called IFF. We had to eliminate blind mating from the design of GA and replaced it by mating between mature fireflies, while ensuring balanced convergence. The proposed approach uses advanced methods such as controlled genetic operations to manipulate frequent patterns, and the uses of fuzzy logic to control IFF parameters to assure convergence calibration, based on data size, algorithm iterations and temporary local optimum. Also, we executed IFF under Hadoop to get a MapReduce system and ensure the most optimal execution time. To analyse the quality of our proposal, we made simulations on MEDLINE dataset. Results indicate that the proposed approach is superior to existing algorithms with an accuracy of 10% to 50% and save execution time around 36%, while ensuring a good balance between the quality and variety of knowledge.","PeriodicalId":39390,"journal":{"name":"International Journal of Innovative Computing and Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535001","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：