Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs. Conventional maintenance methods, such as reactive maintenance (i.e., run to failure) or time-based preventive maintenance (i.e., scheduled servicing), prove ineffective for complex systems with many Internet of Things (IoT) devices and sensors because they fall short in detecting faults at early stages when it is most crucial. This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories, including Normal, Require Maintenance, and Failed. The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification. The FD004 subset of the NASA C-MAPSS dataset, containing multivariate sensor readings from aircraft engines, serves as the training and evaluation data for the model. Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN, achieving an overall average accuracy of 86.66%, precision of 86.00%, recall of 86.33%, and F1-score of 86.33%. Contrary to the previous LSTM-CNN–based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data, our paper provides a three-class maintenance state (i.e., Normal, Require Maintenance, and Failed) along with threshold-based labeling that retains the true nature of the degradation. In addition, our work also provides an IoT-to-cloud-based modular architecture for deployment. It offers Computerized Maintenance Management System (CMMS) integration, making our proposed solution not only technically sound but also practical and innovative. The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.

Fire emergencies have long posed a serious threat to people’s lives, real estate assets, and environmental sustainability in civilized societies, especially when combustible events are detected at late stages of development. Recent advancements in computer vision–based fire detection have enabled automated real-time monitoring; however, most solutions either detect the existence of fire/smoke or employ binary decision-making, which limits visual monitoring systems from being risk-aware. This work introduces a severity-aware fire/smoke detection model that supports intelligent monitoring systems in detecting visual hazards. The goal is to identify varying levels of fire intensity and smoke density and to detect humans in real time. We design a system capable of monitoring environments using components such as sensing devices, network communication buses, cloud data centers, and computer vision–based detectors. The fire/smoke detection model comprises modern deep learning–based object detectors, with the YOLOv12 model serving as the detection backbone. Moreover, our work proposes a two-stage training method that first learns coarse representations of fire, smoke, and humans, and then adapts the detector for fine-grained, severity-aware classification, thereby enhancing severity discrimination and reducing inter-class confusion. We gathered our dataset to comprise approximately 6500 annotated images, split between coarse-grained and severity-aware detection models. The dataset consists of seven classes indicating human presence, three classes indicating varying levels of fire intensity, and three classes indicating varying levels of smoke density. We conducted experiments comparing three baseline object detection architectures (i.e., YOLOv12s, RT-DETR-L, and SSDLite320-MobileNetV3) using identical training/testing configurations. YOLOv12 has outperformed other baseline object detection architectures, achieving 0.929 mAP@50, 0.884 precision, 0.876 recall, 0.880 F1-score, and 2.48 ms per-image latency, providing the best balance between detection performance and real-time processing capability. Our results indicate that severity-aware detection can improve the early-stage detection of intelligent monitoring systems.

People with visual impairments face substantial navigation difficulties in residential and unfamiliar indoor spaces. Neither canes nor verbal navigation systems possess adequate features to deliver real-time spatial awareness to users. This research work represents a feasibility study for the wearable IoT-based indoor object detection assistant system architecture that employs a real-time indoor object detection approach to help visually impaired users recognize indoor objects. The system architecture includes four main layers: Wearable Internet of Things (IoT), Network, Cloud, and Indoor Object Detection Layers. The wearable hardware prototype is assembled using a Raspberry Pi 4, while the indoor object detection approach exploits YOLOv11. YOLOv11 represents the cutting edge of deep learning models optimized for both speed and accuracy in recognizing objects and powers the research prototype. In this work, we used a prototype implementation, comparative experiments, and two datasets compiled from Furniture Detection (i.e., from Roboflow Universe) and Kaggle, which comprises 3000 images evenly distributed across three object categories, including bed, sofa, and table. In the evaluation process, the Raspberry Pi is only used for a feasibility demonstration of real-time inference performance (e.g., latency and memory consumption) on embedded hardware. We also evaluated YOLOv11 by comparing its performance with other current methodologies, which involved a Convolutional Neural Network (CNN) (MobileNet- Single Shot MultiBox Detector (SSD)) model together with the RT-DETR Vision Transformer. The experimental results show that YOLOv11 stands out by reaching an average of 99.07%, 98.51%, 97.96%, and 98.22% for the accuracy, precision, recall, and F1-score, respectively. This feasibility study highlights the effectiveness of Raspberry Pi 4 and YOLOv11 in real-time indoor object detection, paving the way for structured user studies with visually impaired people in the future to evaluate their real-world use and impact.