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.

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.

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.

Technology is playing a vital role in the improvement of medical field resulting in high life expectancy. The use of wireless networks is one of the modern and efficient ways to monitor health problems remotely. In this context, various wireless monitoring standards are developed to facilities the patient monitoring. Some key standards include IEEE 802.15.4 Low-Rate Wireless Personal Area Network (LR−WPAN), IEEE 802.15.6 Wireless Body Area Network (WBAN) and ETSI smartBAN. These standards consist of multiple sensors that are used to monitor, process and transmit the vitals to the proper destination. Each standard offers some advantages and limitations over the other standard depending on the scenarios. In this paper, all the above-mentioned standards are compared and analyzed on different parameters such as network type, density, functionality, size and energy efficiency.