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Industrial environments face increasing challenges in achieving accurate environmental monitoring and maintaining data privacy. This paper presents the SecureIoT-FL framework, an innovative integration of federated learning and multi-sensor fusion for real-time, privacy-preserving prediction of environmental pollutants in industrial settings. A normalized Federated Averaging (N-FedAvg) as an enhanced version of the conventional FedAvg algorithm, is introduced for global model aggregation to balance the contributions from all clients with varying data volumes, thus stabilizing the learning process. This study contributes a scalable and secure solution for environmental monitoring and risk management in industrial IoT applications. The framework leverages IoT nodes equipped with Raspberry Pi 5 and sensors to detect pollutants (e.g., PM2.5, PM10, CO2, VOCs, CH₂O, CO, O₃, NO₂, H₂S, O2, CH₄, SO₂) across three distinct environments: a PCBA factory, a CNC machining factory, and a plastic injection factory. Over five months, the system collected over 516,000 data points, enabling a multi-source data fusion approach to enhance predictive accuracy. The key outcomes of the proposed work include significant improvements in the model performance over 10 biweekly training rounds, with increasing the accuracy from 70 % to 92 % and 68–89 % for LSTM and CNN models, respectively. The framework effectively predicts the pollutant concentrations over multiple timeframes (10, 30, and 60 minutes), supporting timely interventions to reduce worker exposure to harmful pollutants. The SecureIoT-FL framework employs federated learning with TLS encryption, ensuring data privacy while supporting secure local model training and weight sharing via MQTT over AWS.

This review discusses the progress made in the traffic-sign detection and recognition methods and algorithms over the last decade with analyzing the strengths and drawbacks of each algorithm. The recent development of traffic sign recognition on the roads highlights the necessity for precise detection of road's traffic signs in various driving scenarios. In addition, the connections between the detection algorithms before and after the advent of deep learning are revealed. The Traffic sign recognition has been developed to identify various shapes, sizes, orientations, and appearances of signs in diverse conditions. Researchers have proposed numerous algorithms to address these challenges. The traffic recognition methods have been categorized in this paper into three main techniques, namely, conventional, deep learning, and hybrid based methods. The algorithms are compared with each others via regression, segmentation, and hybrid techniques, specifically SSD, YOLO, Faster R-CNN, Pixel Aggregation Network, and Mask R-CNN. The results demonstrate that the hybrid based detection algorithms outperform others in true-positive rates, false-positive rates, the number of test images, accuracy, and processing time. Such outcomes illustrate the potential of hybrid methods in the creation of accurate and effective TSD systems, thereby paving the way for further research in this field.

The increasing demand for high-quality vegetable oils has amplified the need for efficient, and accurate, oil recognition systems. This paper introduces the Portable Intelligent Oil Recognition System (PIORS), a pioneering advancement in the field. PIORS stands as the first-ever portable, AI-powered device tailored for rapid and accurate oil type identification, capable of delivering results within a mere five seconds. Using advanced machine learning, PIORS can distinguish between various oil types such as sesame, black seed, flaxseed, and almond oils, ensuring the quality and safety of the final product. The device's innovative design integrates an advanced sensor array with seamless Bluetooth connectivity, offering real-time data synchronization with mobile applications. Several Machine learning models, including Support Vector Machines (SVM), AdaBoost, Random Forest, K-Nearest Neighbors (K-NN), Gradient-Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) were implemented and thoroughly validated to obtain correct categorization. The results show that the XGBoost instantaneous classification algorithm continuously beat the competition, obtaining an astounding success rate of 97 percent in predicting and differentiating between the four oil classes. PIORS not only sets a new standard for speed and efficiency in oil quality assessment but also paves the way for groundbreaking applications in food safety, environmental monitoring, and quality control processes. This paper details the development, implementation, and validation of PIORS, showcasing its potential to revolutionize the agri-food industry with its cutting-edge, AI-driven approach to oil recognition.

Illegal logging poses a significant threat to environmental conservation, contributing to deforestation, ecosystem disruption, and economic losses. Traditional detection methods, such as ground patrols and satellite imagery, are often ineffective, expensive, and time-consuming. This paper introduces a novel Unmanned Aerial Vehicles (UAV)-based IoT system for chainsaw noise detection and prevention. The system uses low-cost IoT nodes with microphones and microcontrollers to detect chainsaw sounds via a Convolutional Neural Network (CNN) model. These nodes reduce power consumption, allowing the battery to last at least 8 years. Detected chainsaw sounds are transmitted to a LoRaWAN gateway and forwarded to a cloud server. The cloud server processes the data and dispatches a drone equipped with a camera to the site. The drone streams live video to the cloud, where AI algorithms analyze the footage for human presence. If detected, the drone activates sound and light alarms to deter illegal logging. Experimental results show that IoT nodes can detect chainsaw sounds up to 100 m, with the CNN model achieving an accuracy of 99.37 % and a loss of 0.0185. Comparisons with Logistic Regression, Decision Tree, Random Forest, and SVM demonstrated the superior performance of CNN. This UAV-IoT system offers a fast, reliable, scalable, and real-time solution for detecting and preventing illegal logging. Leveraging advanced AI and IoT technologies, this system enhances environmental monitoring and conservation efforts. It represents a significant step forward in the fight against illegal logging, providing a practical, effective, and sustainable solution.

Soft robotic hands have gained significant attention in recent years due to their simplicity in control, ease of fabrication, and remarkable compliance. However, traditional designs often suffer from limited contact area and insufficient grasping force. In this study, we propose a novel four-finger pneumatic soft robotic hand with wave-shaped contour actuators to address these issues. Inspired by natural grasping mechanisms, the proposed design integrates multiple actuation modes to enhance both adaptability and reliability. An analytical model based on constant curvature bending theory and the Yeoh constitutive model is developed to guide the design and predict the performance of the actuators. The model is validated through extensive Abaqus finite element simulations, demonstrating excellent agreement with experimental results. Our findings confirm that the actuators achieve consistent and reliable bending motion with optimal structural parameters. Experimental evaluation of the soft robotic hand includes bending trajectory analysis and grasping tests with various irregular objects. The results show that the actuators provide a maximum bending angle of 180° under 26 kPa pressure and maintain stable grasps for objects of different shapes and sizes. The soft robotic hand demonstrates superior grasping adaptability and reliability due to its high compliance and optimized design. This study paves the way for practical applications in industrial automation, medical devices, and service robotics, highlighting the potential of wave-shaped contour actuators in enhancing the functionality of soft robotic hands.

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.