Fuzzy-based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

Wang, Yun; Subhan, Fazlı; Shamshirband, Shahaboddin; Asghar, Muhammad Zubair; Ullah, Ikram; Habib, Ammara

doi:10.32604/cmc.2020.07920

Cited by 21 publications

(14 citation statements)

References 7 publications

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“…For example, petroleum industries are one of the largest industrial plants worldwide that require water for their operations [19,20]. As the produced water contains hazardous materials and can pose significant environmental problems, it cannot be reused without retreatment [21][22][23][24][25][26][27][28][29][30]. Therefore, the use of treated water to continue the operations should be strictly promoted by the World Health Organization to [31][32][33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Current Challenges and Advancements on the Management of Water Retreatment in Different Production Operations of Shale Reservoirs

Syah

Heidary

Rajabi

et al. 2021

Water

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Nowadays, water savings on industrial plants have become a significant concern for various plants and sections. It is vitally essential to propose applicable and efficient techniques to retreat produced water from onshore and offshore production units. This paper aimed to implement the PFF (Photo Fenton Flotation) method to optimize the water treatment procedure, as it is a two-stage separation technique. The measurements were recorded for the HF (hydraulic fracturing) and CEOR (chemically enhanced oil recovery) methods separately to compare the results appropriately. To assure the efficiency of this method, we first recorded the measurements for five sequential days. As a result, the total volume of 2372.5 MM m3/year of water can be saved in the HF process during the PFF treatment procedure, and only 20% of this required fresh water should be provided from other resources. On the other hand, the total volume of 7482.5 MM m3/year of water can be saved in CEOR processes during the PFF treatment procedure, and only 38% of this required fresh water should be provided from other resources. Therefore, the total water volume of 9855 MM m3 can be saved each year, indicating the efficiency of this method in supplying and saving the water volume during the production operations from oilfield units.

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Section: Introductionmentioning

confidence: 99%

Current Challenges and Advancements on the Management of Water Retreatment in Different Production Operations of Shale Reservoirs

Syah

Heidary

Rajabi

et al. 2021

Water

View full text Add to dashboard Cite

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“…ere has been extensive work carried out in the area of text-based analysis of feeling [8], construction of lexicons [4], cognition and analysis of aspects of feeling [6], and analysis of visual feelings [1,9]. However, further research is required in the area of cognitive-based social media analysis, with a focus on extracting and categorizing emotions from social media content.…”

Section: Research Motivationmentioning

confidence: 99%

“…Computational linguistic experts have performed many studies to detect and identify emotions at various levels, including words, expressions, sentences, and analysis [4][5][6][7]. Many studies, on the other hand, focus on emotion-related bearing terms, with little attention paid to textual clues to emotions, which, if included, may improve the output of cognitive-based sentiment classification for social media data.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Model for the Detection and Classification of Emotions from Textual Content

et al. 2022

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Emotion-based sentimental analysis has recently received a lot of interest, with an emphasis on automated identification of user behavior, such as emotional expressions, based on online social media texts. However, the majority of the prior attempts are based on traditional procedures that are insufficient to provide promising outcomes. In this study, we categorize emotional sentiments by recognizing them in the text. For that purpose, we present a deep learning model, bidirectional long-term short-term memory (BiLSMT), for emotion recognition that takes into account five main emotions (Joy, Sadness, Fear, Shame, Guilt). We use our experimental assessments on the emotion dataset to accomplish the emotion categorization job. The datasets were evaluated and the findings revealed that, when compared to state-of-the-art methodologies, the proposed model can successfully categorize user emotions into several classifications. Finally, we assess the efficacy of our strategy using statistical analysis. This research’s findings help firms to apply best practices in the selection, management, and optimization of policies, services, and product information.

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“…The development of sophisticated tools and rapid computer-assisted techniques have made it easier to scan and automatically detect anomalies in a crop in real time [4]. Conventional machine learning techniques have had considerable success in recognizing and diagnosing plant disease, but they are limited to the following sequential image processing tasks: image segmentation using clustering and other methods [5,6], feature extraction [7], and pattern recognition using support vector machines (SVM) [8], k-nearest neighbor method [9], and Artificial Neural Network [10]. It is difficult to pick and extract the best observable pathological characteristics, necessitating the use of highly qualified engineers and experienced specialists, which is not only arbitrary but also inefficient of manpower and financial capital.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

et al. 2021

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Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. The proposed CNN model extracts complementary discriminative features by integrating multiple layers. The CNN model was checked against many state-of-the-art deep learning models on the Citrus and PlantVillage datasets. The experimental results indicate that the CNN Model outperforms the competitors on a number of measurement metrics. The CNN Model has a test accuracy of 94.55 percent, making it a valuable decision support tool for farmers looking to classify citrus fruit/leaf diseases.

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Fuzzy-based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

Cited by 21 publications

References 7 publications

Current Challenges and Advancements on the Management of Water Retreatment in Different Production Operations of Shale Reservoirs

Current Challenges and Advancements on the Management of Water Retreatment in Different Production Operations of Shale Reservoirs

A Deep Neural Network Model for the Detection and Classification of Emotions from Textual Content

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

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