Recently, the concept of e-commerce product review evaluation has become a research topic of sign... more Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed through tokenization, lemmatization, and stemming operations. The Harris hawk optimization (HHO) algorithm selects two datasets’ corresponding features. Finally, the sentiments from online reviews are classified into positive and negative categories regarding the proposed ERF-XGB approach. Hyperparameter tuning is used to find the optimal parameter values that improve the performance of the proposed ERF-XGB algorithm. The performance of the proposed ERF-XGB approach is analyzed using evaluation indicators, namely accuracy, recall, precision, and F1-score, for different existing approaches. Compared with the existing method, the proposed ERF-XGB approach effectively predicts sentiments of online product reviews with an accuracy rate of about 98.7% for the ChnSentiCorp dataset and 98.2% for the IMDB dataset.
Recently, the concept of e-commerce product review evaluation has become a research topic of sign... more Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed thro...
Recently, the concept of e-commerce product review evaluation has become a research topic of sign... more Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed thro...
Recently, cloud computing resources have become one of the trending technologies that permit the ... more Recently, cloud computing resources have become one of the trending technologies that permit the user to manage diverse resources and a huge amount of data in the cloud. Task scheduling is considered one of the most significant challenges and ineffective management results in performance degradation. It is necessary to schedule the task effectively with maximum resource utilization and minimum execution time. Therefore, this paper proposes a novel technique for effective task scheduling with enhanced security in the cloud computing environment. A novel convolutional neural network optimized modified butterfly optimization (CNN-MBO) algorithm is proposed for scheduling the tasks, thereby maximizing the throughput and minimizing the makespan. Secondly, a modified RSA algorithm is employed to encrypt the data, thereby providing secure data transmission. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its perfor...
Recently, cloud computing resources have become one of the trending technologies that permit the ... more Recently, cloud computing resources have become one of the trending technologies that permit the user to manage diverse resources and a huge amount of data in the cloud. Task scheduling is considered one of the most significant challenges and ineffective management results in perfor- mance degradation. It is necessary to schedule the task effectively with maximum resource utilization and minimum execution time. Therefore, this paper proposes a novel technique for effective task scheduling with enhanced security in the cloud computing environment. A novel convolutional neural network optimized modified butterfly optimization (CNN-MBO) algorithm is proposed for scheduling the tasks, thereby maximizing the throughput and minimizing the makespan. Secondly, a modified RSA algorithm is employed to encrypt the data, thereby providing secure data transmission. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its performance. In addition to this, the proposed approach is compared with various other task scheduling-based approaches for various performance metrics, namely, resource utilization, response time, as well as energy consumption. The experimental results revealed that the proposed approach achieved minimum energy consumption of 180 kWh, a minimum response time of the 20 s, a minimum execution time of 0.43 s, and maximum utilization of 98% for task size 100.
Recently, cloud computing resources have become one of the trending technologies that permit the ... more Recently, cloud computing resources have become one of the trending technologies that permit the user to manage diverse resources and a huge amount of data in the cloud. Task scheduling is considered one of the most significant challenges and ineffective management results in performance degradation. It is necessary to schedule the task effectively with maximum resource utilization and minimum execution time. Therefore, this paper proposes a novel technique for effective task scheduling with enhanced security in the cloud computing environment. A novel convolutional neural network optimized modified butterfly optimization (CNN-MBO) algorithm is proposed for scheduling the tasks, thereby maximizing the throughput and minimizing the makespan. Secondly, a modified RSA algorithm is employed to encrypt the data, thereby providing secure data transmission. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its perfor...
Recently, the concept of e-commerce product review evaluation has become a research topic of sign... more Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed through tokenization, lemmatization, and stemming operations. The Harris hawk optimization (HHO) algorithm selects two datasets’ corresponding features. Finally, the sentiments from online reviews are classified into positive and negative categories regarding the proposed ERF-XGB approach. Hyperparameter tuning is used to find the optimal parameter values that improve the performance of the proposed ERF-XGB algorithm. The performance of the proposed ERF-XGB approach is analyzed using evaluation indicators, namely accuracy, recall, precision, and F1-score, for different existing approaches. Compared with the existing method, the proposed ERF-XGB approach effectively predicts sentiments of online product reviews with an accuracy rate of about 98.7% for the ChnSentiCorp dataset and 98.2% for the IMDB dataset.
Recently, the concept of e-commerce product review evaluation has become a research topic of sign... more Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed thro...
Recently, the concept of e-commerce product review evaluation has become a research topic of sign... more Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed thro...
Recently, cloud computing resources have become one of the trending technologies that permit the ... more Recently, cloud computing resources have become one of the trending technologies that permit the user to manage diverse resources and a huge amount of data in the cloud. Task scheduling is considered one of the most significant challenges and ineffective management results in performance degradation. It is necessary to schedule the task effectively with maximum resource utilization and minimum execution time. Therefore, this paper proposes a novel technique for effective task scheduling with enhanced security in the cloud computing environment. A novel convolutional neural network optimized modified butterfly optimization (CNN-MBO) algorithm is proposed for scheduling the tasks, thereby maximizing the throughput and minimizing the makespan. Secondly, a modified RSA algorithm is employed to encrypt the data, thereby providing secure data transmission. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its perfor...
Recently, cloud computing resources have become one of the trending technologies that permit the ... more Recently, cloud computing resources have become one of the trending technologies that permit the user to manage diverse resources and a huge amount of data in the cloud. Task scheduling is considered one of the most significant challenges and ineffective management results in perfor- mance degradation. It is necessary to schedule the task effectively with maximum resource utilization and minimum execution time. Therefore, this paper proposes a novel technique for effective task scheduling with enhanced security in the cloud computing environment. A novel convolutional neural network optimized modified butterfly optimization (CNN-MBO) algorithm is proposed for scheduling the tasks, thereby maximizing the throughput and minimizing the makespan. Secondly, a modified RSA algorithm is employed to encrypt the data, thereby providing secure data transmission. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its performance. In addition to this, the proposed approach is compared with various other task scheduling-based approaches for various performance metrics, namely, resource utilization, response time, as well as energy consumption. The experimental results revealed that the proposed approach achieved minimum energy consumption of 180 kWh, a minimum response time of the 20 s, a minimum execution time of 0.43 s, and maximum utilization of 98% for task size 100.
Recently, cloud computing resources have become one of the trending technologies that permit the ... more Recently, cloud computing resources have become one of the trending technologies that permit the user to manage diverse resources and a huge amount of data in the cloud. Task scheduling is considered one of the most significant challenges and ineffective management results in performance degradation. It is necessary to schedule the task effectively with maximum resource utilization and minimum execution time. Therefore, this paper proposes a novel technique for effective task scheduling with enhanced security in the cloud computing environment. A novel convolutional neural network optimized modified butterfly optimization (CNN-MBO) algorithm is proposed for scheduling the tasks, thereby maximizing the throughput and minimizing the makespan. Secondly, a modified RSA algorithm is employed to encrypt the data, thereby providing secure data transmission. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its perfor...
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