-
Sensors (Basel, Switzerland) Jun 2024Rapid advancements in connected and autonomous vehicles (CAVs) are fueled by breakthroughs in machine learning, yet they encounter significant risks from adversarial...
Rapid advancements in connected and autonomous vehicles (CAVs) are fueled by breakthroughs in machine learning, yet they encounter significant risks from adversarial attacks. This study explores the vulnerabilities of machine learning-based intrusion detection systems (IDSs) within in-vehicle networks (IVNs) to adversarial attacks, shifting focus from the common research on manipulating CAV perception models. Considering the relatively simple nature of IVN data, we assess the susceptibility of IVN-based IDSs to manipulation-a crucial examination, as adversarial attacks typically exploit complexity. We propose an adversarial attack method using a substitute IDS trained with data from the onboard diagnostic port. In conducting these attacks under black-box conditions while adhering to realistic IVN traffic constraints, our method seeks to deceive the IDS into misclassifying both normal-to-malicious and malicious-to-normal cases. Evaluations on two IDS models-a baseline IDS and a state-of-the-art model, MTH-IDS-demonstrated substantial vulnerability, decreasing the F1 scores from 95% to 38% and from 97% to 79%, respectively. Notably, inducing false alarms proved particularly effective as an adversarial strategy, undermining user trust in the defense mechanism. Despite the simplicity of IVN-based IDSs, our findings reveal critical vulnerabilities that could threaten vehicle safety and necessitate careful consideration in the development of IVN-based IDSs and in formulating responses to the IDSs' alarms.
PubMed: 38931632
DOI: 10.3390/s24123848 -
Sensors (Basel, Switzerland) Jun 2024Varroa mites, scientifically identified as , pose a significant threat to beekeeping and cause one of the most destructive diseases affecting honey bee populations....
Varroa mites, scientifically identified as , pose a significant threat to beekeeping and cause one of the most destructive diseases affecting honey bee populations. These parasites attach to bees, feeding on their fat tissue, weakening their immune systems, reducing their lifespans, and even causing colony collapse. They also feed during the pre-imaginal stages of the honey bee in brood cells. Given the critical role of honey bees in pollination and the global food supply, controlling Varroa mites is imperative. One of the most common methods used to evaluate the level of Varroa mite infestation in a bee colony is to count all the mites that fall onto sticky boards placed at the bottom of a colony. However, this is usually a manual process that takes a considerable amount of time. This work proposes a deep learning approach for locating and counting Varroa mites using images of the sticky boards taken by smartphone cameras. To this end, a new realistic dataset has been built: it includes images containing numerous artifacts and blurred parts, which makes the task challenging. After testing various architectures (mainly based on two-stage detectors with feature pyramid networks), combination of hyperparameters and some image enhancement techniques, we have obtained a system that achieves a mean average precision (mAP) metric of 0.9073 on the validation set.
Topics: Animals; Varroidae; Bees; Deep Learning; Software; Mite Infestations; Beekeeping; Image Processing, Computer-Assisted
PubMed: 38931612
DOI: 10.3390/s24123828 -
Sensors (Basel, Switzerland) Jun 2024Human pose estimation (HPE) is a technique used in computer vision and artificial intelligence to detect and track human body parts and poses using images or videos....
Human pose estimation (HPE) is a technique used in computer vision and artificial intelligence to detect and track human body parts and poses using images or videos. Widely used in augmented reality, animation, fitness applications, and surveillance, HPE methods that employ monocular cameras are highly versatile and applicable to standard videos and CCTV footage. These methods have evolved from two-dimensional (2D) to three-dimensional (3D) pose estimation. However, in real-world environments, current 3D HPE methods trained on laboratory-based motion capture data encounter challenges, such as limited training data, depth ambiguity, left/right switching, and issues with occlusions. In this study, four 3D HPE methods were compared based on their strengths and weaknesses using real-world videos. Joint position correction techniques were proposed to eliminate and correct anomalies such as left/right inversion and false detections of joint positions in daily life motions. Joint angle trajectories were obtained for intuitive and informative human activity recognition using an optimization method based on a 3D humanoid simulator, with the joint position corrected by the proposed technique as the input. The efficacy of the proposed method was verified by applying it to three types of freehand gymnastic exercises and comparing the joint angle trajectories during motion.
Topics: Humans; Deep Learning; Posture; Joints; Imaging, Three-Dimensional; Algorithms; Movement; Video Recording
PubMed: 38931606
DOI: 10.3390/s24123823 -
Sensors (Basel, Switzerland) Jun 2024The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of...
The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such pedestrian-friendly walking environments. Identifying locations and routes with high pedestrian concentration is critical for improving pedestrian-friendly walking environments. This paper presents a quantitative method to map pedestrian walking behavior by utilizing real-time data from mobile phone sensors, focusing on the University of Moratuwa, Sri Lanka, as a case study. This holistic method integrates new urban data, such as location-based service (LBS) positioning data, and data clustering with unsupervised machine learning techniques. This study focused on the following three criteria for quantifying walking behavior: walking speed, walking time, and walking direction inside the experimental research context. A novel signal processing method has been used to evaluate speed signals, resulting in the identification of 622 speed clusters using K-means clustering techniques during specific morning and evening hours. This project uses mobile GPS signals and machine learning algorithms to track and classify pedestrian walking activity in crucial sites and routes, potentially improving urban walking through mapping.
Topics: Walking; Humans; Pedestrians; Machine Learning; Sri Lanka; Algorithms; Universities; Geographic Information Systems; Cell Phone; Cluster Analysis
PubMed: 38931604
DOI: 10.3390/s24123822 -
Sensors (Basel, Switzerland) Jun 2024Bathymetry estimation is essential for various applications in port management, navigation safety, marine engineering, and environmental monitoring. Satellite remote...
Bathymetry estimation is essential for various applications in port management, navigation safety, marine engineering, and environmental monitoring. Satellite remote sensing data can rapidly acquire the bathymetry of the target shallow waters, and researchers have developed various models to invert the water depth from the satellite data. Geographically weighted regression (GWR) is a common method for satellite-based bathymetry estimation. However, in sediment-laden water environments, especially ports, the suspended materials significantly affect the performance of GWR for depth inversion. This study proposes a novel approach that integrates GWR with Random Forest (RF) techniques, using longitude, latitude, and multispectral remote sensing reflectance as input variables. This approach effectively addresses the challenge of estimating bathymetry in turbid waters by considering the strong correlation between water depth and geographical location. The proposed method not only overcomes the limitations of turbid waters but also improves the accuracy of depth inversion results in such complex aquatic settings. This breakthrough in modeling has significant implications for turbid waters, enhancing port management, navigational safety, and environmental monitoring in sediment-laden maritime zones.
PubMed: 38931586
DOI: 10.3390/s24123802 -
Sensors (Basel, Switzerland) Jun 2024Mapping soil properties in sub-watersheds is critical for agricultural productivity, land management, and ecological security. Machine learning has been widely applied...
Mapping soil properties in sub-watersheds is critical for agricultural productivity, land management, and ecological security. Machine learning has been widely applied to digital soil mapping due to a rapidly increasing number of environmental covariates. However, the inclusion of many environmental covariates in machine learning models leads to the problem of multicollinearity, with poorly understood consequences for prediction performance. Here, we explored the effects of variable selection on the prediction performance of two machine learning models for multiple soil properties in the Haihun River sub-watershed, Jiangxi Province, China. Surface soils (0-20 cm) were collected from a total of 180 sample points in 2022. The optimal covariates were selected from 40 environmental covariates using a recursive feature elimination algorithm. Compared to all-variable models, the random forest (RF) and extreme gradient boosting (XGBoost) models with variable selection improved in prediction accuracy. The values of the RF and XGBoost models increased by 0.34 and 0.47 for the soil organic carbon, by 0.67 and 0.62 for the total phosphorus, and by 0.43 and 0.62 for the available phosphorus, respectively. The models with variable selection presented reduced global uncertainty, and the overall uncertainty of the RF model was lower than that of the XGBoost model. The soil properties showed high spatial heterogeneity based on the models with variable selection. Remote sensing covariates (particularly principal component 2) were the major factors controlling the distribution of the soil organic carbon. Human activity covariates (mainly land use) and organism covariates (mainly potential evapotranspiration) played a predominant role in driving the distribution of the soil total and soil available phosphorus, respectively. This study indicates the importance of variable selection for predicting multiple soil properties and mapping their spatial distribution in sub-watersheds.
PubMed: 38931566
DOI: 10.3390/s24123784 -
Sensors (Basel, Switzerland) Jun 2024Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibile methods used to...
Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibile methods used to determine tumor malignancy. Regarding image analysis, the application of deep learning has become increasingly prevalent in recent years. However, a significant issue is the unbalanced nature of available datasets, with some classes having more images than others, which may impact the performance of the models due to poorer generalizability. A possible strategy to avoid this problem is downsampling the class with the most images to create a balanced dataset. Nevertheless, this approach is not recommended for small datasets as it can lead to poor model performance. Instead, techniques such as data augmentation are traditionally used to address this issue. These techniques apply simple transformations such as translation or rotation to the images to increase variability in the dataset. Another possibility is using generative adversarial networks (GANs), which can generate images from a relatively small training set. This work aims to enhance model performance in classifying histopathological images by applying data augmentation using GANs instead of traditional techniques.
Topics: Humans; Neural Networks, Computer; Breast Neoplasms; Image Processing, Computer-Assisted; Deep Learning; Female; Algorithms; Image Interpretation, Computer-Assisted
PubMed: 38931561
DOI: 10.3390/s24123777 -
Sensors (Basel, Switzerland) Jun 2024This paper introduces a cutting-edge data architecture designed for a smart advertising context, prioritizing efficient data flow and performance, robust security, while...
This paper introduces a cutting-edge data architecture designed for a smart advertising context, prioritizing efficient data flow and performance, robust security, while guaranteeing data privacy and integrity. At the core of this study lies the application of federated learning (FL) as the primary methodology, which emphasizes the authenticity and privacy of data while promptly discarding irrelevant or fraudulent information. Our innovative data model employs a semi-random role assignment strategy based on a variety of criteria to efficiently collect and amalgamate data. The architecture is composed of model nodes, data nodes, and validator nodes, where the role of each node is determined by factors such as computational capability, interconnection quality, and historical performance records. A key feature of our proposed system is the selective engagement of a subset of nodes for modeling and validation, optimizing resource use and minimizing data loss. The AROUND social network platform serves as a real-world case study, illustrating the efficacy of our data architecture in a practical setting. Both simulated and real implementations of our architecture showcase its potential to dramatically curtail network traffic and average CPU usage, while preserving the accuracy of the FL model. Remarkably, the system is capable of achieving over a 50% reduction in both network traffic and average CPU usage even when the user count escalates by twenty-fold. The click rate, user engagement, and other parameters have also been evaluated, proving that the proposed architecture's advantages do not affect the smart advertising accuracy. These findings highlight the proposed architecture's capacity to scale efficiently and maintain high performance in smart advertising environments, making it a valuable contribution to the evolving landscape of digital marketing and FL.
PubMed: 38931549
DOI: 10.3390/s24123765 -
Sensors (Basel, Switzerland) Jun 2024In an era marked by escalating concerns about digital security, biometric identification methods have gained paramount importance. Despite the increasing adoption of...
In an era marked by escalating concerns about digital security, biometric identification methods have gained paramount importance. Despite the increasing adoption of biometric techniques, keystroke dynamics analysis remains a less explored yet promising avenue. This study highlights the untapped potential of keystroke dynamics, emphasizing its non-intrusive nature and distinctiveness. While keystroke dynamics analysis has not achieved widespread usage, ongoing research indicates its viability as a reliable biometric identifier. This research builds upon the existing foundation by proposing an innovative deep-learning methodology for keystroke dynamics-based identification. Leveraging open research datasets, our approach surpasses previously reported results, showcasing the effectiveness of deep learning in extracting intricate patterns from typing behaviors. This article contributes to the advancement of biometric identification, shedding light on the untapped potential of keystroke dynamics and demonstrating the efficacy of deep learning in enhancing the precision and reliability of identification systems.
Topics: Deep Learning; Humans; Biometric Identification; Algorithms; Biometry; Handwriting
PubMed: 38931547
DOI: 10.3390/s24123763 -
Sensors (Basel, Switzerland) Jun 2024Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma,...
Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.
Topics: Humans; Radar; Artificial Intelligence; Neural Networks, Computer; Automobile Driving; Support Vector Machine; Algorithms; Machine Learning
PubMed: 38931541
DOI: 10.3390/s24123754