In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

Customer reviews on the Internet reflect users’ sentiments about the product, service, and social events. As sentiments can be divided into positive, negative, and neutral forms, sentiment analysis processes identify the polarity of information in the source materials toward an entity. Most studies have focused on document-level sentiment classification. In this study, we apply an unsupervised machine learning approach to discover sentiment polarity not only at the document level but also at the word level. The proposed topic document sentence (TDS) model is based on joint sentiment topic (JST) and latent Dirichlet allocation (LDA) topic modeling techniques. The IMDB dataset, comprising user reviews, was used for data analysis. First, we applied the LDA model to discover topics from the reviews; then, the TDS model was implemented to identify the polarity of the sentiment from topic to document, and from document to word levels. The LDAvis tool was used for data visualization. The experimental results show that the analysis not only obtained good topic partitioning results, but also achieved high sentiment analysis accuracy in document- and word-level sentiment classifications.

Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on convolutional neural networks (CNNs). The device analyses video data recorded from an in-vehicle camera to monitor drivers’ facial expressions and detect fatigue indicators such as yawning and eye states. The system is built on a strong architecture and was trained using a diversified dataset under varying lighting circumstances and facial angles. It uses Haar cascade classifiers for facial area extraction and advanced image processing algorithms for fatigue diagnosis. The results demonstrate that the system obtained a 96.54% testing accuracy, demonstrating the efficiency of using behavioural indicators such as yawning frequency and eye state detection to improve performance. The findings show that CNN-based architectures can address major public safety concerns, such as minimizing accidents caused by drowsy driving. This study not only emphasizes the need of deep learning in establishing dependable and practical driver monitoring systems, but it also lays the groundwork for future improvements, such as the incorporation of new behavioural and physiological measurements. The suggested solution is a big step towards increasing road safety and reducing the risks associated with driver weariness.

Emotion detection holds significant importance in facilitating human–computer interaction, enhancing the depth of engagement. By integrating this capability, we pave the way for forthcoming AI technologies to possess a blend of cognitive and emotional understanding, bridging the divide between machine functionality and human emotional complexity. This progress has the potential to reshape how machines perceive and respond to human emotions, ushering in an era of empathetic and intuitive artificial systems. The primary research challenge involves developing models that can accurately interpret and analyze emotions from both auditory and textual data, whereby auditory data require optimizing CNNs to detect subtle and intense emotional fluctuations in speech, and textual data necessitate access to large, diverse datasets to effectively capture nuanced emotional cues in written language. This paper introduces a novel approach to multimodal emotion recognition, seamlessly integrating speech and text modalities to accurately infer emotional states. Employing CNNs, we meticulously analyze speech using Mel spectrograms, while a BERT-based model processes the textual component, leveraging its bidirectional layers to enable profound semantic comprehension. The outputs from both modalities are combined using an attention-based fusion mechanism that optimally weighs their contributions. The proposed method here undergoes meticulous testing on two distinct datasets: Carnegie Mellon University’s Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset and the Multimodal Emotion Lines Dataset (MELD). The results demonstrate superior efficacy compared to existing frameworks, achieving an accuracy of 88.4% and an F1-score of 87.9% on the CMU-MOSEI dataset, and a notable weighted accuracy (WA) of 67.81% and a weighted F1 (WF1) score of 66.32% on the MELD dataset. This comprehensive system offers precise emotion detection and introduces several significant advancements in the field.

Electric vehicle (EV) charging systems are now integral to smart grids, increasing the need for robust and scalable cyberattack detection. This study presents an online intrusion detection system that leverages an Adaptive Random Forest classifier with Adaptive Windowing drift detection to identify real-time and evolving threats in EV charging infrastructures. The system is evaluated using real-world network traffic from the CICEVSE2024 dataset, ensuring practical applicability. For binary intrusion detection, the model achieves 0.9913 accuracy, 0.9999 precision, 0.9914 recall, and an F1-score of 0.9956, demonstrating highly accurate threat detection. It effectively manages concept drift, maintaining an average accuracy of 0.99 during drift events. In multiclass detection, the system attains 0.9840 accuracy, precision, and recall, with an F1-score of 0.9831 and an average drift event accuracy of 0.96. The system is computationally efficient, processing each instance in just 0.0037 s, making it well-suited for real-time deployment. These results confirm that online machine learning methods can effectively secure EV charging infrastructures. The source code is publicly available on GitHub, ensuring reproducibility and fostering further research. This study provides a scalable and efficient cybersecurity solution for protecting EV charging networks from evolving threats.

Emotion recognition is crucial for enhancing human–machine interactions by establishing a foundation for AI systems that integrate cognitive and emotional understanding, bridging the gap between machine functions and human emotions. Even though deep learning algorithms are actively used in this field, the study of sequence modeling that accounts for the shifts in emotions over time has not been thoroughly explored. In this research, we present a comprehensive speech emotion-recognition framework that amalgamates the ZCR, RMS, and MFCC feature sets. Our approach employs both CNN and LSTM networks, complemented by an attention model, for enhanced emotion prediction. Specifically, the LSTM model addresses the challenges of long-term dependencies, enabling the system to factor in historical emotional experiences alongside current ones. We also incorporate the psychological “peak–end rule”, suggesting that preceding emotional states significantly influence the present emotion. The CNN plays a pivotal role in restructuring input dimensions, facilitating nuanced feature processing. We rigorously evaluated the proposed model utilizing two distinct datasets, namely TESS and RAVDESS. The empirical outcomes highlighted the model’s superior performance, with accuracy rates reaching 99.8% for TESS and 95.7% for RAVDESS. These results are a notable advancement, showcasing our system’s precision and innovative contributions to emotion recognition.