Asbestos, a dangerous substance commonly used in buildings, continues to present serious risks in urban areas, because of outdated infrastructure and inappropriate disposal methods. The goal of this study is to help with proactive public health measures by utilizing machine learning algorithms to predict asbestos exposure levels. An IoT-based environmental sensor dataset that tracks temperature humidity and air quality is presented in this study. Random Forest, Support Vector Machines (SVM), and Neural Networks are three machine-learning techniques used to create predictive models that can estimate asbestos concentrations under different conditions. Data preprocessing includes feature extraction and normalization to improve prediction accuracy. Performance metrics such as F1 score, accuracy, sensitivity, and specificity are used to compare the models. Additionally, certain environmental factors that influence asbestos dispersion are identified by the Random Forest feature importance analysis. Moreover, the IoT-based environmental sensor dataset used in this study is derived from real-world deployed sensors installed in high-risk industrial zones. These sensors continuously monitor environmental parameters such as formaldehyde concentration, temperature, humidity, and AQI, ensuring that the data reflects authentic field conditions for reliable model training and evaluation. These findings demonstrate how real-time asbestos exposure prediction using machine learning enables timely interventions. Future studies aim to increase accuracy and computational efficiency, future enhancements may incorporate techniques such as Long Short-Term Memory (LSTM) networks for temporal modeling, CNN pruning for model optimization, and feature selection methods to reduce dimensionality and processing time.

Inhaling formaldehyde a chemical that is widely used in many different industries can have serious health consequences. In order to precisely detect formaldehyde levels in industrial air quality environments, this study makes use of deep learning techniques. Using sensor data gathered from high-risk industrial areas the study focuses on variables like air quality index temperature and humidity. The data is processed by Convolutional Neural Networks (CNNs), which identify trends linked to increases in formaldehyde concentrations. To improve model accuracy preprocessing of the data is done including feature scaling and outlier elimination. The model's performance is assessed using evaluation metrics like Mean Squared Error (MSE), sensitivity, specificity, and prediction accuracy. Results show that when compared to conventional regression models the CNN-based model considerably lowers false positives while achieving a high prediction accuracy. Rapid reaction to hazardous formaldehyde levels is made possible by the deep learning frameworks' real-time monitoring capability which lowers possible health hazards. To improve long-term prediction accuracy and trend identification future research will investigate the use of recurrent neural networks (RNN) for time-series analysis.

The spread of fake news on social media platforms like Facebook threatens societal harmony and undermines the reliability of information. To address this issue, this research employs machine learning techniques to construct a robust and scalable framework for detecting fake news. Using a well-curated dataset of labeled Facebook posts containing both authentic and fake news, the study ensures a balanced representation for effective learning. Textual data was transformed into numerical features through Term Frequency-Inverse Document Frequency (TF-IDF) preprocessing, enabling seamless integration with machine learning algorithms. A variety of classification models, including Support Vector Machines (SVM), Logistic Regression, Gradient Boosting, and Random Forest, were trained and evaluated. Six performance evaluations precision, accuracy, F1 score, recall, Matthews Correlation Coefficient (MCC), and area under the Receiver Operating Characteristic (ROC) curve—were used to measure model effectiveness. The results highlighted Gradient Boosting as the most effective algorithm, achieving superior accuracy and overall performance. This framework demonstrates the capability of machine learning to automate the detection of misinformation, offering a scalable and efficient solution for preserving content credibility on Facebook. The study contributes significantly to the broader effort of combating misinformation, ensuring the dissemination of reliable information, and safeguarding public trust on social media platforms

American scientific publishing group

This study investigates how local self-government institutions can effectively support Electric Vehicle (EV) adoption by integrating advanced governance mechanisms with Machine Learning–based State of Charge (SOC) estimation for Lithium Cobalt Oxide () batteries. A comprehensive dataset was collected from 14 municipal corporations and 22 urban local bodies (ULBs) across South India, comprising 3,200 EV trip logs, 1,150 smart-charging station entries, and 760 battery diagnostic profiles. To enhance SOC prediction accuracy within real municipal operating environments, a Hybrid LSTM–CNN–RNN SOC Estimation Framework (LCR-SOCEF) was developed. The LSTM component models temporal variations in battery behaviour, CNN extracts feature patterns from dynamic charging–discharging cycles, while RNN captures sequential governance-linked operational dependencies, such as traffic density, public-transport routing, and charging-infrastructure accessibility. Results demonstrate that ULBs with stronger institutional coordination, higher infrastructure readiness, and proactive EV governance policies achieved SOC accuracy improvements up to 5.3% for batteries. These findings highlight how ML outputs can serve as decision-support tools for municipal mobility planning, enabling better scheduling of public EV fleets, optimized charging networks, and improved citizen transport services. The study aligns directly offering a governance-centred, technology-integrated framework to strengthen sustainable, decentralized urban mobility transitions.

The inherent volatility and nonlinearity of stock prices make them a crucial challenge in financial markets. This study investigates how well stochastic differential equations (SDEs) with parameter estimation employ the Markov Chain Monte Carlo (MCMC) algorithm to model changes in Indian stock prices. To evaluate this methods predictive accuracy we compare its performance to that of conventional Long Short-Term Memory (LSTM) networks and AutoRegressive Integrated Moving Average (ARIMA) models. A probabilistic estimation of important parameters in the SDE model is made possible by the Bayesian inference framework used in the MCMC algorithm which successfully captures market uncertainties. Our results show each models advantages and disadvantages in predicting stock prices highlighting how well-suited each is for various time horizons and market circumstances. In order to take advantage of both stochastic modeling and deep learning capabilities we also suggest a novel hybrid model that combines SDE-MCMC with LSTM and ARIMA. Results from experiments show that by fusing the advantages of machine learning and statistics the hybrid model increases forecasting accuracy. In addition to offering insights for analysts and investors in making data-driven decisions this research advances stock price prediction techniques.