SRide: An Online System for Multi-Hop Ridesharing

Shah, I.; ElAffendi, Mohammed; Qureshi, Basit

doi:10.3390/su12229633

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…The field of attacking may cause serious issues that users face in the information-hidden world [12]. Due to the exponential growth of information, the IR can play a progressively imperative role in the future [11].…”

Section: Introductionmentioning

confidence: 99%

Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

et al. 2021

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The epidemic increase in online reviews' growth made the sentiment classification a fascinating domain in academic and industrial research. The reviews assist several domains, which is complicated to gather annotated training data. Several sentiment classification methodologies are devised for performing the sentiment analysis, but retrieval of information is not accurately performed, less effective, and less convergence speed. In this paper, we propose a sentiment paper proposes a sentiment classification model, namely Spider Monkey Crow Optimization algorithm (SMCA), for training the deep recurrent neural network (DeepRNN). In this method, the telecom review is employed to remove stop words and stemming to eliminate inappropriate data to minimize user's seeking time. Meanwhile, the feature extraction is performed using SentiWordNet to derive the sentiments from the reviews. The extracted SentiWordNet features and other features, like elongated words, punctuation, hashtag, and numerical values, are employed in the DeepRNN for classifying sentiments. To retrieve the required review, the Fuzzy K-Nearest neighbor (Fuzzy-KNN) is employed to retrieve the review based on a distance measure. With rigorous assessments and experimentation, it is observed that the proposed SMCA-based DeepRNN performs better in terms of accuracy of 97.7%, precision of 95.5%, recall of 94.6%, and F1-score 96.7%, respectively.

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Section: Introductionmentioning

confidence: 99%

Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

et al. 2021

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A Survey of Machine Learning-Based Ride-Hailing Planning

Wen,

Li,

Lau

2024

IEEE Trans. Intell. Transport. Syst.

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Performance evolution for sentiment classification using machine learning algorithm

Hassan,

Qureshi,

Khan

et al. 2023

J APPL RES TECH ENG

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Machine Learning (ML) is an Artificial Intelligence (AI) approach that allows systems to adapt to their environment based on past experiences. Machine Learning (ML) and Natural Language Processing (NLP) techniques are commonly used in sentiment analysis and Information Retrieval Techniques (IRT). This study supports the use of ML approaches, such as K-Means, to produce accurate outcomes in clustering and classification approaches. The main objective of this research is to explore the methods for sentiment classification and Information Retrieval Techniques (IRT). So, a combination of different machine learning algorithms is used with a dataset from amazon unlocked mobile reviews and telecom tweets to achieve better accuracy as it is crucial to consider the previous predictions related to sentiment classification and IRT. The datasets consist of user reviews ratings and algorithms utilized consist of K-Means Clustering algorithm, Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) algorithms. The amalgamation of each algorithm with the K-Means resulted in high levels of accuracy. Specifically, the K-Means combined with Logistic Regression (LR) yielded an accuracy rate of 99.98%. Similarly, the K-Means integrated with Random Forest (RF) resulted in an accuracy of 99.906%. Lastly, when the K-Means was merged with the Decision Tree (DT) Algorithm, the accuracy obtained was 99.83%.We exhibited that we could foresee efficient, effective, and accurate outcomes.

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SRide: An Online System for Multi-Hop Ridesharing

Cited by 3 publications

References 31 publications

Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

A Survey of Machine Learning-Based Ride-Hailing Planning

Performance evolution for sentiment classification using machine learning algorithm

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