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Sentiment analysis is a critical component in understanding customer opinions and reactions. This study explores the application of sentiment analysis using Python on the Amazon Fine Food Reviews dataset to classify customer reviews as positive or negative, enabling businesses to gain valuable insight into customer sentiments. This study used and compared the efficiency of Logistic Regression, Support Vector Machines, Random Forest, XGBoost, LSTM, and ALBERT. The comparison results showed that the LSTM and ALBERT classifiers stand out with remarkable accuracy (96%) and substantial support for positive and negative reviews. On the other hand, although the Random Forest classifier had similar accuracy (96%), it exhibited lower support for positive and negative sentiments. [ABSTRACT FROM AUTHOR]

Traffic management and control applications require comprehensive knowledge of traffic flow data. Typically, such information is gathered using traffic sensors, which have two basic challenges: First, it is impractical or impossible to install sensors on every arc in a network. Second, sensors do not provide direct information on origin-to-destination (O–D) demand flows. Consequently, it is essential to identify the optimal locations for deploying traffic sensors and then enhance the knowledge gained from this link flow sample to forecast the network's traffic flow. This article presents residual neural networks—a very deep set of neural networks—to the problem for the first time. The suggested architecture reliably predicts the whole network's O–D flows utilizing link flows, hence inverting the standard traffic assignment problem. It deduces a relevant correlation between traffic flow statistics and network topology from traffic flow characteristics. To train the proposed deep learning architecture, random synthetic flow data was generated from the historical demand data of the network. A large-scale network was used to test and confirm the model's performance. Then, the Sioux Falls network was used to compare the results with the literature. The robustness of applying the proposed framework to this particular combined traffic flow problem was determined by maintaining superior prediction accuracy over the literature with a moderate number of traffic sensors. [ABSTRACT FROM AUTHOR]

Grid computing technology is a technique to share various resources like memory, storage, processor that are idle, to solve very huge and complex problems. These resources are shared among various heterogeneous computers, which form a virtual organization. The computers involved in communication for sharing resources might be from the same domain or from different domains. The risk is more in communication among computers from different domains since they are unknown to each other and there are more chances to misuse the resources. To avoid the above mentioned problems, various trust models are prepared to assess the computers for their trustworthiness in communication. This paper proposes a trust model, which uses a regression model to predict the behavior of client and service provider for further communication in a more secure manner. This regression model is developed based on the data collected through a survey; also, a reliability test was conducted to check the consistency of collected data, which was found to be good. This paper also shows the usage of randomized algorithm for trust model in grid computing proposed by us, based on the various parameter values collected through the survey. [ABSTRACT FROM AUTHOR]