Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. ...The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.
Recognizing and classifying traffic signs is a challenging task that can significantly improve road safety. Deep neural networks have achieved impressive results in various applications, including ...object identification and automatic recognition of traffic signs. These deep neural network-based traffic sign recognition systems may have limitations in practical applications due to their computational requirements and resource consumption. To address this issue, this paper presents a lightweight neural network for traffic sign recognition that achieves high accuracy and precision with fewer trainable parameters. The proposed model is trained on the German Traffic Sign Recognition Benchmark (GTSRB) and Belgium Traffic Sign (BelgiumTS) datasets. Experimental results demonstrate that the proposed model has achieved 98.41% and 92.06% accuracy on GTSRB and BelgiumTS datasets, respectively, outperforming several state-of-the-art models such as GoogleNet, AlexNet, VGG16, VGG19, MobileNetv2, and ResNetv2. Furthermore, the proposed model outperformed these methods by margins ranging from 0.1 to 4.20 percentage point on the GTSRB dataset and by margins ranging from 9.33 to 33.18 percentage point on the BelgiumTS dataset.
The rapid development in wireless technologies is positioning the Internet of Things (IoT) as an essential part of our daily lives. Localization is one of the most attractive applications related to ...IoT. In the past few years, localization has been gaining attention because of its applicability in safety, health monitoring, environment monitoring, and security. As a result, various localization-based wireless frameworks are being presented to improve such applications’ performances based on specific key performance indicators (KPIs). Therefore, this paper explores the recently proposed localization schemes in IoT. Initially, this paper explains the major KPIs of localization. After that, a thorough comparison of recently proposed localization schemes based on the KPIs is presented. The comparison includes an overview, architecture, network structure, performance parameters, and target KPIs. At the end, possible future directions are presented for the researchers working in this domain.
The early detection of wildfires is a crucial challenge in environmental monitoring, pivotal for effective disaster management and ecological conservation. Traditional detection methods often fail to ...detect fires accurately and in a timely manner, resulting in significant adverse consequences. This paper presents FireXplainNet, a Convolutional Neural Network (CNN) base model, designed specifically to address these limitations through enhanced efficiency and precision in wildfire detection. We optimized data input via specialized preprocessing techniques, significantly improving detection accuracy on both the Wildfire Image and FLAME datasets. A distinctive feature of our approach is the integration of Local Interpretable Model-agnostic Explanations (LIME), which facilitates a deeper understanding of and trust in the model’s predictive capabilities. Additionally, we have delved into optimizing pretrained models through transfer learning, enriching our analysis and offering insights into the comparative effectiveness of FireXplainNet. The model achieved an accuracy of 87.32% on the FLAME dataset and 98.70% on the Wildfire Image dataset, with inference times of 0.221 and 0.168 milliseconds, respectively. These performance metrics are critical for the application of real-time fire detection systems, underscoring the potential of FireXplainNet in environmental monitoring and disaster management strategies.
In the domain of radiological diagnostics, accurately detecting and classifying brain tumors from magnetic resonance imaging (MRI) scans presents significant challenges, primarily due to the complex ...and diverse manifestations of tumors in these scans. In this paper, a convolutional-block-based architecture has been proposed for the detection of multiclass brain tumors using MRI scans. Leveraging the strengths of CNNs, our proposed framework demonstrates robustness and efficiency in distinguishing between different tumor types. Extensive evaluations on three diverse datasets underscore the model’s exceptional diagnostic accuracy, with an average accuracy rate of 97.52%, precision of 97.63%, recall of 97.18%, specificity of 98.32%, and F1-score of 97.36%. These results outperform contemporary methods, including state-of-the-art (SOTA) models such as VGG16, VGG19, MobileNet, EfficientNet, ResNet50, Xception, and DenseNet121. Furthermore, its adaptability across different MRI modalities underlines its potential for broad clinical application, offering a significant advancement in the field of radiological diagnostics and brain tumor detection.
Traffic Sign Recognition (TSR) plays a vital role in intelligent transportation systems (ITS) to improve road safety and optimize traffic management. While existing TSR models perform well in ...challenging scenarios, their lack of transparency and interpretability hinders reliability, trustworthiness, validation, and bias identification. To address this issue, we propose a Convolutional Neural Network (CNN)-based model for TSR and evaluate its performance on three benchmark datasets: German Traffic Sign Recognition Benchmark (GTSRB), Indian Traffic Sign Dataset (ITSD), and Belgian Traffic Sign Dataset (BTSD). The proposed model achieves an accuracy of 98.85% on GTSRB, 94.73% on ITSD, and 92.69% on BTSD, outperforming several state-of-the-art frameworks, such as VGG19, VGG16, ResNet50V2, MobileNetV2, DenseNet121, DenseNet201, NASNetMobile, and EfficientNet, while also providing faster training and response times. We further enhance our model by incorporating explainable AI (XAI) techniques, specifically, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM), providing clear insights of the proposed model decision-making process. This integration allows the extension of our TSR model to various engineering domains, including autonomous vehicles, advanced driver assistance systems (ADAS), and smart traffic control systems. The practical implementation of our model ensures real-time, accurate recognition of traffic signs, thus optimizing traffic flow and minimizing accident risks.
Air pollution has become a global issue due to its widespread impact on the environment, economy, civilization and human health. Owing to this, a lot of research and studies have been done to tackle ...this issue. However, most of the existing methodologies have several issues such as high cost, low deployment, maintenance capabilities and uni-or bi-variate concentration of air pollutants. In this paper, a hybrid CNN-LSTM model is presented to forecast multivariate air pollutant concentration for the Internet of Things (IoT) enabled smart city design. The amalgamation of CNN-LSTM acts as an encoder-decoder which improves the overall accuracy and precision. The performance of the proposed CNN-LSTM is compared with conventional and hybrid machine learning (ML) models on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE). The proposed model outperforms various state-of-the-art ML models by generating an average MAE, MAPE and MSE of 54.80%, 52.78% and 60.02%. Furthermore, the predicted results are cross-validated with the actual concentration of air pollutants and the proposed model achieves a high degree of prediction accuracy to real-time air pollutants concentration. Moreover, a cross-grid cooperative scheme is proposed to tackle the IoT monitoring station malfunction scenario and make the pollutant monitoring more fault resistant and robust. The proposed scheme exploits the correlation between neighbouring monitoring stations and air pollutant concentration. The model generates an average MAPE and MSE of 10.90% and 12.02%, respectively.
The combined effect of ultrasound (US) and pulsed electric field (PEF) was investigated on microbial load and bioactive compounds of grapefruit juice. Grapefruit juice was PEF treated (flow rate: 80 ...ml/min, pulse frequency: 1 kHz, 20 kVcm−1 electric field strength, temperature: 40 °C, time: 600 μs) followed by US treatment in an ultrasonic bath cleaner radiating 600 W at frequency of 28 KHz and 20 °C for 30 min. PEF and US treatment resulted in a significant reduction in microbial load as compared to the control group. Using combined (PEF + US) treatment, carotenoids, lycopene, anthocyanin contents, and total antioxidant activity were increased from 0.84 μg/ml, 0.32 μg/ml, 1.37 mg/L, and 177.48 ascorbic acid equivalent µg/g (control) to 1.26 μg/ml, 0.92 μg/ml, 1.68 mg/L, and 262.32 ascorbic acid equivalent µg/g, respectively. The findings demonstrated that PEF + US could be successfully used for preserving bioactive compounds in grapefruit juice while improving the microbial quality for a better shelf‐life.
Practical applications
In recent years, nonthermal techniques, such as sonication and PEF or combination of these are used for the inactivation of microorganism and preserving the bioactive compounds of different juices. The results of the present study revealed that combined treatment (PEF + US) could improve the antioxidant activity, total phenolics, flavonols, flavonoids, lycopene, and total carotenoids. The findings of this study suggested that PEF + US has a potential to improve the quality of grapefruit juice and may be employed for the processing of grapefruit juice at industrial scale.
In this work, a closed loop control system is developed to optically localize and track micro-robots with high precision. These micro-robots (i.e., micro-conveyors) are in motion simultaneously ...across a smart surface.The developed method’s primary objectives are to optimize their trajectories, avoid collisions between them, and control their position with micrometric resolution. This article presents and characterizes the tracking of a single micro-conveyor, and the method works similarly when multiple micro-robots move over the surface. Our tracking method starts with a scanning phase, where a 2D steering mirror, placed above the smart surface, reflects a laser beam toward the conveying surface seeking for the target. Localization occurs when this light beam reaches the micro-conveyor. By adding a retro-reflective element, that reflects the light in the same direction of the the incident light, onto the surface of the micro-conveyor, the light will be reflected towards a photodetector. Depending on the feedback from the photodetector, the steering mirror rotates to track the trajectory of the micro-conveyor. The tip-tilt angular values of the steering mirror allows the micro-conveyor position to be obtained via calibrated localization system. The aim of this work is to regulate the micro-conveyor, within a closed-loop control system, to reduce the positional error between the actual and desired position. The actual position value is measured in real-time application using our developed optical sensor. Results for tracking in the x-and y-axis have validated the proposed method, with an average tracking error less than 30 µm within a range 150 mm × 150 mm.
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