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Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial Vehicles. Multiple UAVs and autopilot systems support it, and it provides bi-directional communication between the UAV and Ground Control Station. The communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to GCS. In order to increase the transfer speed and efficiency, the Mavlink does not encrypt the messages. As a result, the protocol is vulnerable to various security attacks such as Eavesdropping, GPS Spoofing, and DDoS. In this study, we tackle the problem and secure the Mavlink communication protocol. By leveraging the Mavlink packet’s vulnerabilities, this research work introduces an experiment in which, first, the Mavlink packets are compromised in terms of security requirements based on our threat model. The results show that the protocol is insecure and the attacks carried out are successful. To overcome Mavlink security, an additional security layer is added to encrypt and secure the protocol. An encryption technique is proposed that makes the communication between the UAV and GCS secure. The results show that the Mavlink packets are encrypted using our technique without affecting the performance and efficiency. The results are validated in terms of transfer speed, performance, and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink protocol. Our achieved results have significant improvement over the literature and Mavlink in terms of security.

The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road.

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.

The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly categorized into RPL-specific and sensor-network-inherited attacks. Among the most concerning protocol-specific attacks are rank attacks and wormhole attacks in sensor-network-inherited attack types. They target the RPL resources and components including control messages, repair mechanisms, routing topologies, and sensor network resources by consuming. This leads to the collapse of IoT infrastructure. In this paper, a lightweight multiclass classification-based RPL-specific and sensor-network-inherited attack detection model called MC-MLGBM is proposed. A novel dataset was generated through the construction of various network models to address the unavailability of the required dataset, optimal feature selection to improve model performance, and a light gradient boosting machine-based algorithm optimized for a multiclass classification-based attack detection. The results of extensive experiments are demonstrated through several metrics including confusion matrix, accuracy, precision, and recall. For further performance evaluation and to remove any bias, the multiclass-specific metrics were also used to evaluate the model, including cross-entropy, Cohn’s kappa, and Matthews correlation coefficient, and then compared with benchmark research.

On Amazon, buyers can submit reviews on products they have purchased. These reviews contribute to a potential buyer's decision-making process, as buyers read reviews to decide whether to buy a product. Additionally, sellers depend on reviews to improve their product offerings. Amazon's summary of reviews does not clearly indicate if an aspect of a product is mentioned positively or negatively. Buyers can manually read a small number of reviews to understand the overall sentiment towards a product, but reading reviews becomes progressively more difficult as the number of reviews increases, as it can lead to information overload. To address this problem, a hybrid machine learning classification algorithm that employs a branch of natural language processing, specifically aspect-based sentiment analysis, was developed to detect the polarity and key aspects mentioned in Amazon product reviews. Naïve Bayes, SVM, Decision Tree and Random Forest were compared to determine the two best algorithms for this purpose. The hybrid algorithm, named Soft Voting Hybrid Algorithm (SVHA), was implemented by training and testing a voting classifier using soft voting, which produced the final prediction by selecting the class with the highest average sum of probabilities from two base classifiers with the highest accuracies and macro F1-scores. Based on the experiments conducted, SVHA attained higher accuracies and macro F1-scores compared to the other four algorithms, showing its suitability in conducting aspect-based sentiment analysis.

Background The global prevalence of end-stage kidney disease (ESKD) is increasing despite optimal management of traditional risk factors such as hyperglycaemia, hypertension, and dyslipidaemia. This study examines the influence of cardiorenal risk factors, socioeconomic status, and ethnic and cardiovascular comorbidities on ESKD outcomes in the general population. Methods This cross-sectional study analysed data from 502,408 UK Biobank study participants recruited between 2006 and 2010. Multivariable logistic regression models were fitted to assess risk factors for ESKD, with results presented as adjusted odds ratio (aOR) and 95% confidence intervals (95% CI). Results A total of 1191 (0.2%) of the study participants reported ESKD. Diabetes increased ESKD risk by 62% [1.62 (1.36–1.93)], with early-onset diabetes (before age 40) conferring higher odds compared to later-onset (after age 40) [2.26 (1.57–3.24)]. Similarly, early-onset hypertension (before age 40), compared to later onset (after age 40), increased ESKD odds by 73% [1.73 (1.21–2.44)]. Cardiovascular comorbidities, including stroke, hypertension, myocardial infarction and angina, were strongly associated with ESKD [5.97 (3.99–8.72), 5.35 (4.38–6.56), 4.94 (3.56–6.78), and 4.89 (3.47–6.81)], respectively. Males were at 22% higher risk of ESKD than females [1.22 (1.04–1.43)]. Each additional year of diabetes duration increased ESKD odds by 2% [1.02 (1.01–1.03)]. Non-white ethnicity, compared to white and socioeconomically most deprived, compared to the least deprived quintiles, were at 70% and 83% higher odds of ESKD. Each unit of HbA1c rise increased the odds of ESKD by 2%. Compared to microalbuminuria, macroalbuminuria increased the odds of ESKD by almost 10-fold [9.47 (7.95–11.27)] while normoalbuminuria reduced the odds by 73% [0.27 (0.22–0.32)]. Conclusions Early onset of diabetes and hypertension, male sex, non-white ethnicity, deprivation, poor glycaemic control, and prolonged hyperglycaemia are significant risk factors for ESKD. These findings highlight the complexity of ESKD and the need for multifactorial targeted interventions in high-risk populations. Clinical trial number Not applicable.

In this research, we address the problem of accurately predicting lane-change maneuvers on highways. Lane-change maneuvers are a critical aspect of highway safety and traffic flow, and the accurate prediction of these maneuvers can have significant implications for both. However, current methods for lane-change prediction are limited in their ability to handle naturalistic driving scenarios and often require large amounts of labeled data. Our proposed model uses a bidirectional long short-term memory (BiLSTM) network to analyze naturalistic vehicle trajectories recorded from multiple sensors on German highways. To handle the temporal aspect of vehicle behavior, we utilized a sliding window approach, considering both the preceding and following vehicles’ trajectories. To tackle class imbalances in the data, we introduced rolling mean computed weights. Our extensive feature engineering process resulted in a comprehensive feature set to train the model. The proposed model fills the gap in the state-of-the-art lane change prediction methods and can be applied in advanced driver assistance systems (ADAS) and autonomous driving systems. Our results show that the BiLSTM-based approach with the sliding window technique effectively predicts lane changes with 86% test accuracy and a test loss of 0.325 by considering the context of the input data in both the past and future. The F1 score of 0.52, precision of 0.41, recall of 0.75, accuracy of 0.86, and AUC of 0.81 also demonstrate the model’s high ability to distinguish between the two target classes. Furthermore, the model achieved an accuracy of 83.65% with a loss value of 0.3306 on the other half of the data samples, and the validation accuracy was observed to improve over these epochs, reaching the highest validation accuracy of 92.53%. The F1 score of 0.51, precision of 0.36, recall of 0.89, accuracy of 0.82, and AUC of 0.85 on this data sample also demonstrate the model’s strong ability to identify both positive and negative classes. Overall, our proposed approach outperforms existing methods and can significantly contribute to improving highway safety and traffic flow.

Twitter has become a unique platform for social interaction from people all around the world, leading to an extensive amount of knowledge that can be used for various reasons. People share and spread their own ideologies and point of views on unique topics leading to the production of a lot of content. Sentiment analysis is of extreme importance to various businesses as it can directly impact their important decisions. Several challenges related to the research subject of sentiment analysis includes issues such as imbalanced dataset, lexical uniqueness, and processing time complexity. Most machine learning models are sequential: they need a considerable amount of time to complete execution. Therefore, we propose a model sentiment analysis specifically designed for imbalanced datasets that can reduce the time complexity of the task by using various text sequenced preprocessing techniques combined with random majority under-sampling. Our proposed model provides competitive results to other models while simultaneously reducing the time complexity for sentiment analysis. The results obtained after the experimentation corroborate that our model provides great results producing the accuracy of 86.5% and F1 score of 0.874 through XGB.

Background and Aims Use of technological gadgets has rapidly been increasing among adolescents, which may result in health issues and technology addiction. This study focuses on the prevalence of usage of technological gadgets and health‐related complications among secondary school‐going children of Bangladesh. Methods A total of 1803 secondary school students from 21 different districts of Bangladesh participated in the study. The children were asked questions relating to their access to electronic gadgets, time spent on outdoor activities, and whether they experienced any health‐complications as an after‐effect of the usage. A binary logistic regression model was adapted considering time spent on gadgets as an independent variable and health problems (physical and mental) as the dependent variable. Results Among all the gadgets, 67.11% of the participants were reported to use mobile phones on a daily basis. Due to the ongoing COVID‐19 pandemic, 24.48% of respondents used electronic gadgets for attending online classes. The participants were reported to use gadgets significantly more (P < .05) in 2020 as compared to 2019. Children showed less tendency to spend time in outdoor activities. More than 50% of the participants spend time doing outdoor activities for less than 1 hour daily. An association between gadget use and health problems like headache, backache, visual disturbance, and sleeping disturbance has been observed in our study. Conclusion This study demonstrates that different socio‐demographic factors have influence on the use of gadgets by children, and this use has greatly been affecting both the physical and mental health of the secondary school‐going students of Bangladesh.