Sentiment analysis, a crucial component of natural language processing (NLP), involves the classification of subjective information by extracting emotional content from textual data. This technique plays a significant role in the movie industry by analyzing public opinions about films. The present research addresses a gap in the literature by conducting a comparative analysis of various machine learning algorithms for sentiment analysis in film reviews, utilizing a dataset from Kaggle comprising 50,000 reviews. Classifiers such as Logistic Regression, Multinomial Naive Bayes, Linear Support Vector Classification (LinearSVC), and Gradient Boosting were employed to categorize the reviews into positive and negative sentiments. The emphasis was placed on specifying and comparing these classifiers in the context of film review sentiment analysis, highlighting their respective advantages and disadvantages. The dataset underwent thorough preprocessing, including data cleaning and the application of stemming techniques to enhance processing efficiency. The performance of the classifiers was rigorously evaluated using metrics such as accuracy, precision, recall, and F1-score. Among the classifiers, LinearSVC demonstrated the highest accuracy at 90.98%. This comprehensive evaluation not only identified the most effective classifier but also elucidated the contextual efficiencies of various algorithms. The findings indicate that LinearSVC excels at accurately classifying sentiments in film reviews, thereby offering new insights into public opinions on films. Furthermore, the extended comparison provides a step-by-step guide for selecting the most suitable classifier based on dataset characteristics and context, contributing valuable knowledge to the existing literature on the impact of different machine learning approaches on sentiment analysis outcomes in the movie industry.

In the realm of urban public affairs management, the necessity for accurate and intelligent distribution of resources has become increasingly imperative for effective social governance. This study, drawing on crime data from Chicago in 2022, introduces a novel approach to public affairs distribution by employing Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and their integration. By extensively preprocessing textual, numerical, boolean, temporal, and geographical data, the proposed models were engineered to discern complex interrelations among multidimensional features, thereby enhancing their capability to classify and predict public affairs events. Comparative analysis reveals that the hybrid LSTM-MLP model exhibits superior prediction accuracy over the individual LSTM or MLP models, evidencing enhanced proficiency in capturing intricate event patterns and trends. The effectiveness of the model was further corroborated through a detailed examination of training and validation accuracies, loss trajectories, and confusion matrices. This study contributes a robust methodology to the field of intelligent public affairs prediction and resource allocation, demonstrating significant practical applicability and potential for widespread implementation.

Rice is a staple food for a significant portion of the global population, particularly in countries where it constitutes the primary source of sustenance. Accurate classification of rice varieties is critical for enhancing both agricultural yield and economic outcomes. Traditional classification methods are often inefficient, leading to increased costs, higher misclassification rates, and time loss. To address these limitations, automated classification systems employing machine learning (ML) algorithms have gained attention. However, when raw data is inadequately organized or scattered, classification accuracy can decline. To improve data organization, normalization processes are often employed. Despite its widespread use, the specific contribution of normalization to classification performance requires further validation. In this study, a dataset comprising two rice varieties Osmancik and Cammeo produced in Turkey was utilized to evaluate the impact of normalization on classification outcomes. The k-Nearest Neighbor (KNN) algorithm was applied to both normalized and non-normalized datasets, and their respective performances were compared across various training and testing ratios. The normalized dataset achieved a classification accuracy of 0.950, compared to 0.921 for the non-normalized dataset. This approximately 3% improvement demonstrates the positive effect of data normalization on classification accuracy. These findings underscore the importance of incorporating normalization in ML models for rice classification to optimize performance and accuracy.

Accurately predicting whether bank users will opt for time deposit products is critical for optimizing marketing strategies and enhancing user engagement, ultimately improving a bank’s profitability. Traditional predictive models, such as linear regression and Logistic Regression (LR), are often limited in their ability to capture the complex, time-dependent patterns in user behavior. In this study, a hybrid approach that combines Long Short-Term Memory (LSTM) neural networks and a stacked ensemble learning framework is proposed to address these limitations. Initially, LSTM models were employed to extract temporal features from two distinct bank marketing datasets, thereby capturing the sequential nature of user interactions. These extracted features were subsequently input into several base classifiers, including Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbour (KNN), to conduct initial classifications. The outputs of these classifiers were then integrated using a LR model for final decision-making through a stacking ensemble method. The experimental evaluation demonstrates that the proposed LSTM-stacked model outperforms traditional models in predicting user time deposits on both datasets, providing robust predictive performance. The results suggest that leveraging temporal feature extraction with LSTM and combining it with ensemble techniques yields superior prediction accuracy, thereby offering a more sophisticated solution for banks aiming to enhance their marketing efficiency.

Leaf diseases pose a significant threat to global agricultural productivity, impacting both crop yields and quality. Traditional detection methods often rely on expert knowledge, are labor-intensive, and can be time-consuming. To address these limitations, a novel framework was developed for the segmentation and detection of leaf diseases, incorporating complex fuzzy set (CFS) theory and advanced spatial averaging and difference techniques. This approach leverages the Hue, Saturation, and Value (HSV) color model, which offers superior contrast and visual clarity, to effectively distinguish between healthy and diseased regions in leaf images. The HSV space was utilized due to its ability to enhance the visibility of subtle disease patterns. CFSs were introduced to manage the inherent uncertainty and imprecision associated with disease characteristics, enabling a more accurate delineation of affected areas. Spatial techniques further refine the segmentation, improving detection precision and robustness. Experimental validation on diverse datasets demonstrates the proposed method’s high accuracy across a variety of plant diseases, highlighting its reliability and adaptability to real-world agricultural conditions. Moreover, the framework enhances interpretability by offering insights into the progression of disease, thus supporting informed decision-making for crop protection and management. The proposed model shows considerable potential in practical agricultural applications, where it can assist farmers and agronomists in timely and accurate disease identification, ultimately improving crop management practices.