Regarding the ubiquity of digitalization and electronic processing, an automated review processing system, also known as sentiment analysis, is crucial. There were many architectures and word embeddings employed for effective sentiment analysis. Deep learning is now-a-days becoming prominent for solving these problems as huge amounts of data get generated per second. In deep learning, word embedding acts as a feature representative and plays an important role. This paper proposed a novel deep learning architecture which represents hybrid embedding techniques that address polysemy, semantic and syntactic issues of a language model, along with justifying the model prediction. The model is evaluated on sentiment identification tasks, obtaining the result as F1-score 0.9254 and F1-score 0.88, for MR and Kindle dataset respectively. The proposed model outperforms many current techniques for both tasks in experiments, suggesting that combining context-free and context-dependent text representations potentially capture complementary features of word meaning. The model decisions justified with the help of visualization techniques such as t-SNE.
{"title":"Amalgamation of Embeddings With Model Explainability for Sentiment Analysis","authors":"Shila Jawale, S.D. Sawarker","doi":"10.4018/ijaec.315629","DOIUrl":"https://doi.org/10.4018/ijaec.315629","url":null,"abstract":"Regarding the ubiquity of digitalization and electronic processing, an automated review processing system, also known as sentiment analysis, is crucial. There were many architectures and word embeddings employed for effective sentiment analysis. Deep learning is now-a-days becoming prominent for solving these problems as huge amounts of data get generated per second. In deep learning, word embedding acts as a feature representative and plays an important role. This paper proposed a novel deep learning architecture which represents hybrid embedding techniques that address polysemy, semantic and syntactic issues of a language model, along with justifying the model prediction. The model is evaluated on sentiment identification tasks, obtaining the result as F1-score 0.9254 and F1-score 0.88, for MR and Kindle dataset respectively. The proposed model outperforms many current techniques for both tasks in experiments, suggesting that combining context-free and context-dependent text representations potentially capture complementary features of word meaning. The model decisions justified with the help of visualization techniques such as t-SNE.","PeriodicalId":251628,"journal":{"name":"International Journal of Applied Evolutionary Computation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122000687","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}
This paper proposes to enhance the Artificial Bee Colony (ABC) metaheuristic with a Tabu adaptive memory to optimize the multilevel thresholding for Image Segmentation. This novel method is named Tabu-Adaptive Artificial Bee Colony (TA-ABC). To find the optimal thresholds, two novel versions of the proposed technique named TA-ABC-BCV and TA-ABC-ET are developed using respectively the thresholding functions namely the Between-Class Variance (BCV) and the Entropy Thresholding (ET). To prove the robustness and performance of the proposed methods TA-ABC-BCV and TA-ABC-ET, several benchmark images taken from the USC-SIPI Image Database are used. The experimental results show that TA-ABC-BCV and TA-ABC-ET outperform other existing optimization algorithms in the literature. Besides, compared to TA-ABC-ET and other methods from the literature all experimental results prove the superiority of TA-ABC-BCV.
{"title":"TABU-ADAPTIVE ARTIFICIAL BEE COLONY METAHEURISTIC FOR IMAGE SEGMENTATION","authors":"","doi":"10.4018/ijaec.302015","DOIUrl":"https://doi.org/10.4018/ijaec.302015","url":null,"abstract":"This paper proposes to enhance the Artificial Bee Colony (ABC) metaheuristic with a Tabu adaptive memory to optimize the multilevel thresholding for Image Segmentation. This novel method is named Tabu-Adaptive Artificial Bee Colony (TA-ABC). To find the optimal thresholds, two novel versions of the proposed technique named TA-ABC-BCV and TA-ABC-ET are developed using respectively the thresholding functions namely the Between-Class Variance (BCV) and the Entropy Thresholding (ET). To prove the robustness and performance of the proposed methods TA-ABC-BCV and TA-ABC-ET, several benchmark images taken from the USC-SIPI Image Database are used. The experimental results show that TA-ABC-BCV and TA-ABC-ET outperform other existing optimization algorithms in the literature. Besides, compared to TA-ABC-ET and other methods from the literature all experimental results prove the superiority of TA-ABC-BCV.","PeriodicalId":251628,"journal":{"name":"International Journal of Applied Evolutionary Computation","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115528461","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}
The utilization of Le`vy flight to create new candidate solutions is one of the most powerful elements of CS. Candidate solutions are modified using this method by making a lot of minor modifications and a few big jumps. As a result, CS will be able to significantly increase the link between exploration and exploitation while also improving its search capabilities. The cuckoo search optimization (CSO) algorithm is applied to interval type-2 fuzzy logic controller (IT2FLC) in this research to determine the optimal parameters of membership functions (MFs) of interval type-2 fuzzy logic systems (IT2FLSs). The study takes into account two forms of MFs: triangular and trapezoidal. When perturbations are applied during the execution of each control issue, the CSO algorithm's performance and efficiency improve significantly. The proposed approach is tested using two benchmark control problems: water tank controller and inverted pendulum controller.
{"title":"Metaheuristic Optimization Algorithm for Optimal Design of Type-2 Fuzzy Controller","authors":"H. Patel","doi":"10.4018/ijaec.315637","DOIUrl":"https://doi.org/10.4018/ijaec.315637","url":null,"abstract":"The utilization of Le`vy flight to create new candidate solutions is one of the most powerful elements of CS. Candidate solutions are modified using this method by making a lot of minor modifications and a few big jumps. As a result, CS will be able to significantly increase the link between exploration and exploitation while also improving its search capabilities. The cuckoo search optimization (CSO) algorithm is applied to interval type-2 fuzzy logic controller (IT2FLC) in this research to determine the optimal parameters of membership functions (MFs) of interval type-2 fuzzy logic systems (IT2FLSs). The study takes into account two forms of MFs: triangular and trapezoidal. When perturbations are applied during the execution of each control issue, the CSO algorithm's performance and efficiency improve significantly. The proposed approach is tested using two benchmark control problems: water tank controller and inverted pendulum controller.","PeriodicalId":251628,"journal":{"name":"International Journal of Applied Evolutionary Computation","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132458165","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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