ERIRMS Evaluation of the Reliability of IoT-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines.

Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line.

Correction: Yuldashev et al. Parking Lot Occupancy Detection with Improved MobileNetV3. 2023, , 7642.

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Data Anomaly Detection for Structural Health Monitoring Based on a Convolutional Neural Network.

Structural health monitoring (SHM) has been extensively utilized in civil infrastructures for several decades. The status of civil constructions is monitored in real time using a wide variety of sensors; however, determining the true state of a structure can be difficult due to the presence of abnormalities in the acquired data. Extreme weather, faulty sensors, and structural damage are common causes of these abnormalities.

An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images.

Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources.

Parking Lot Occupancy Detection with Improved MobileNetV3.

In recent years, parking lot management systems have garnered significant research attention, particularly concerning the application of deep learning techniques. Numerous approaches have emerged for tackling parking lot occupancy challenges using deep learning models. This study contributes to the field by addressing a critical aspect of parking lot management systems: accurate vehicle occupancy determination in specific parking spaces.

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging.

The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool.

An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach.

With an increase in both global warming and the human population, forest fires have become a major global concern. This can lead to climatic shifts and the greenhouse effect, among other adverse outcomes. Surprisingly, human activities have caused a disproportionate number of forest fires.

Development of Language Models for Continuous Uzbek Speech Recognition System.

Automatic speech recognition systems with a large vocabulary and other natural language processing applications cannot operate without a language model. Most studies on pre-trained language models have focused on more popular languages such as English, Chinese, and various European languages, but there is no publicly available Uzbek speech dataset. Therefore, language models of low-resource languages need to be studied and created.

Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People.

Current artificial intelligence systems for determining a person's emotions rely heavily on lip and mouth movement and other facial features such as eyebrows, eyes, and the forehead. Furthermore, low-light images are typically classified incorrectly because of the dark region around the eyes and eyebrows. In this work, we propose a facial emotion recognition method for masked facial images using low-light image enhancement and feature analysis of the upper features of the face with a convolutional neural network.

Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach.

Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, these techniques are unable to correctly synthesize images taken in untamed situations.

Enhanced Classification of Dog Activities with Quaternion-Based Fusion Approach on High-Dimensional Raw Data from Wearable Sensors.

The employment of machine learning algorithms to the data provided by wearable movement sensors is one of the most common methods to detect pets' behaviors and monitor their well-being. However, defining features that lead to highly accurate behavior classification is quite challenging. To address this problem, in this study we aim to classify six main dog activities (standing, walking, running, sitting, lying down, and resting) using high-dimensional sensor raw data.

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5.

Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming.

Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces.

Owing to the availability of a wide range of emotion recognition applications in our lives, such as for mental status calculation, the demand for high-performance emotion recognition approaches remains uncertain. Nevertheless, the wearing of facial masks has been indispensable during the COVID-19 pandemic. In this study, we propose a graph-based emotion recognition method that adopts landmarks on the upper part of the face.

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning.

Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables.

Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People.

Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire detection is a difficult but crucial problem. To prevent injuries and property damage, advanced technology requires appropriate methods for detecting fires as quickly as possible.

Automatic Fire Detection and Notification System Based on Improved YOLOv4 for the Blind and Visually Impaired.

The growing aging population suffers from high levels of vision and cognitive impairment, often resulting in a loss of independence. Such individuals must perform crucial everyday tasks such as cooking and heating with systems and devices designed for visually unimpaired individuals, which do not take into account the needs of persons with visual and cognitive impairment. Thus, the visually impaired persons using them run risks related to smoke and fire.