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.

Amid growing concerns over global climate change and the need for sustainable infrastructure development, remote communities such as Rigolet in Newfoundland and Labrador (NL), which primarily rely on diesel generators, face unique challenges and opportunities. This study proposes a transition to a hybrid energy system (HES) that integrates wind and solar energy with battery storage and diesel generator backups. The feasibility and implications of this transformation in Rigolet were assessed using HOMER Pro software, contrasting it with the current diesel-centric model. The feasibility, environmental impact, and economic implications of implementing a HES in Rigolet were thoroughly examined. The methodology employed includes a detailed simulation and optimization of the HES configuration suitable for 125 households with a population of 327. The findings reveal that integrating wind and solar electricity with the existing diesel infrastructure, coupled with battery storage, reduced diesel consumption by 352 tons per year and Carbon Dioxide (CO₂) emissions by 929 tons per year. Additionally, other pollutants such as Carbon Monoxide (CO), Particulate Matter (PM), Sulfur Dioxide (SO₂), and Nitrogen Oxide (NO) were significantly reduced. The proposed system demonstrates a reasonable Net Present Cost (NPC) of \$5.17 million with a Levelized Cost of Energy (LCoE) of \$0.22/kWh. This shift towards a HES not only illustrates significant environmental advantages and an increase in the percentage of renewable energy but also provides economic benefits through cost reductions over the long term compared to the existing diesel-dependent configuration. The proposed system provides a reliable and sustainable energy solution for Rigolet, presenting a replicable and innovative model for other similar remote locations aiming for a greener future.

This comprehensive review investigates the ethical implications of artificial intelligence (AI)-driven predictive analytics in healthcare, with a focus on patient privacy, algorithmic bias, equitable access, and transparency. The study further explores the integration of these ethical considerations into educational frameworks to enhance the training and preparedness of healthcare professionals in the responsible use of AI technologies. A systematic literature review was conducted using databases such as PubMed, Scopus, and Google Scholar, employing keywords related to AI, predictive analytics, healthcare, education, and ethics. Articles published from 2017 onwards, discussing the ethical challenges and applications of AI in healthcare and educational settings, were included. Thematic analysis of selected articles revealed significant ethical concerns, including patient privacy, algorithmic bias, and equitable access to AI technologies. Findings underscored the necessity for robust data protection mechanisms, transparent algorithm development, and equitable access policies. The study also highlighted the importance of incorporating AI literacy and ethical training in medical education. An ethical framework was proposed, outlining strategies to address these challenges in both healthcare practice and educational curricula. This framework aims to ensure the responsible use of AI technologies, promote transparency, and mitigate biases in healthcare settings. By addressing a critical gap in understanding the ethical implications of AI-driven predictive analytics in healthcare and its integration into education, the study contributes to the development of guidelines and policies for the equitable and transparent deployment of AI. The proposed ethical framework provides actionable recommendations for stakeholders, aiming to enhance medical education and improve patient outcomes while upholding essential ethical principles.

Rainfall is crucial for agricultural practices, and climate change has significantly altered rainfall patterns. Understanding the dynamic nature of rainfall in the context of climate change through Machine Learning (ML) and Deep Learning (DL) algorithms is essential for ensuring food security. ML techniques provide tools for processing large-scale data to extract meaningful insights. This study aims to compare the performance of a ML algorithm, Random Forest (RF), with a DL algorithm, Long Short-Term Memory (LSTM), in predicting rainfall in six state capitals in Southwest Nigeria: Osogbo, Ikeja, Ibadan, Akure, Ado-Ekiti, and Abeokuta. The dataset for this study was sourced from the HelioClim website archive, which offers high-quality solar radiation and meteorological data derived from satellite measurements. This archive is known for its accuracy and reliability, providing extensive and consistent historical datasets for various applications. The monthly rainfall data spanning from January 1, 1980, to December 31, 2022, were used, with 80% allocated for training and 20% for validation. As the data are time series, each model was constructed using a look-back period of five months, meaning the past five months' rainfall data served as input features. The performance of these algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results indicated that the RF algorithm yielded the lowest MSE, RMSE, and MAE across all selected cities in Southwest Nigeria. This study demonstrated the superiority of RF regression over LSTM in predicting rainfall in these regions, providing a valuable tool for agricultural planning and climate adaptation strategies.

The Jaintiapur-Jaflong region, strategically positioned between the subsiding Surma Basin to the south and the uplifting Shillong Massif to the north, presents a unique geological setting. This study employed geological clinometers and other field methods to ascertain the geological characteristics of the area. The regional strike was determined to be N66˚W, with a dip direction of S24˚W and a dip angle of 42.25˚. Through extensive field investigations, including geological mapping, stratigraphic logging, rock sampling, fossil analysis, and structural analysis, complemented by Global Positioning System (GPS), photography, remote sensing, and Geographic Information System (GIS) technologies, seven lithostratigraphic units were identified. These include the variegated color sandstone, mottled clay, yellowish to reddish-grey sandstone, sandy shale with intercalated silty shale, pinkish sandstone, bluish to blackish-grey shale, and limestone units, corresponding sequentially to Dupi Tila, Girujan Clay, Tipam Sandstone, Surma Group, Jenum Shale Fm, Kopili Shale, and Sylhet Limestone Fm, respectively. Five critical geological contact boundaries were delineated, with notable boundaries identified at the Dupigaon-Sari River Section, the Lalakhal-Tetulghat Section, the Nayagang-Gourishankar Section, and between the Barail and Jaintia groups at the Tamabil-Jaflong Highway Road Cut Section. These findings elucidate the geological contacts and stratigraphic units, providing significant implications for paleoenvironmental reconstruction, resource potential assessment, and stratigraphic correlation, thus enhancing the understanding of regional geological history and laying a foundation for future research endeavors.

This study investigates perceptions of greenwashing within Indonesia's burgeoning fintech sector from the viewpoints of consumers and industry professionals. The research employs a stratified purposive sampling technique to ensure representation across diverse demographics familiar with fintech services. Purposive sampling identified and selected 18 consumers and 24 industry professionals with specific expertise relevant to fintech. Both groups participated in Likert-scale surveys designed to gauge their perceptions of greenwashing across various dimensions: product transparency, social responsibility, environmental impact, ethical investment options, and green marketing practices. Findings reveal generally positive consumer views towards product transparency (4.0), social responsibility (4.2), and green marketing practices (4.5), with more tempered ratings for environmental impact (3.5) and ethical investment options (3.8). Similarly, industry professionals rated product transparency (4.2), social responsibility (4.1), and green marketing practices (4.3) positively, with slightly higher ratings for environmental impact (3.9) and comparable ratings for ethical investment options (3.7). Hypothesis testing indicates significant disparities between consumer and professional perceptions, particularly concerning trust in fintech claims and perceived sustainability impacts. The study underscores the need for fintech firms to enhance transparency and ethical standards to bolster consumer trust and align with industry expectations. Ultimately, this research contributes to a deeper understanding of greenwashing within fintech, offering insights for industry stakeholders and policymakers to foster sustainable practices.