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Global trade depends on long-haul transportation, yet comfort for drivers on lengthy trips is sometimes neglected. Rough roads have a major negative influence on driver comfort and increase the risk of weariness, distracted driving, and accidents. Using Random Forest regression, a machine learning technique well-suited to examining big datasets and nonlinear relationships, this study examines the relationship between road roughness and driver comfort. Using the MIRANDA mobile application, data were gathered from 1,048,576 rows, including vehicle acceleration and values for the International Roughness Index (IRI). The Support Vector Regression (SVR) and XGBoost models were used for comparative analysis. Random Forest was preferred because of its ability to be deployed in real time and use less memory, even if XGBoost performed better in terms of training time and prediction accuracy. The findings showed a significant relationship between driver discomfort and road roughness, with rougher roads resulting in increased vertical acceleration and lower comfort levels (Road Roughness: SD—0.73; Driver's Comfort: Mean—10.01, SD—0.64). This study highlights how crucial it is to provide smooth surfaces and road maintenance in order to increase road safety, lessen driver weariness, and promote long-haul driver welfare. These results offer information to transportation authorities and policymakers to help them make data-driven decisions that enhance the efficiency of transportation and road conditions. [ABSTRACT FROM AUTHOR]

The dynamic expressions of emotion convey both the emotional and functional states of an individual's interactions. Recognizing the emotional states helps us understand human feelings and thoughts. Systems and frameworks designed to recognize human emotional states automatically can use various affective signals as inputs, such as visual, vocal and physiological signals. However, emotion recognition via a single modality can be affected by various sources of noise that are specific to that modality and the fact that different emotion states may be indistinguishable. This review examines the current state of multimodal emotion recognition methods that integrate visual, vocal or physiological modalities for practical emotion computing. Recent empirical evidence on deep learning methods used for fine-grained recognition is reviewed, with discussions on the robustness issues of such methods. This review elaborates on the profound learning challenges and solutions required for a high-quality emotion recognition system, emphasizing the benefits of dynamic expression analysis, which aids in detecting subtle micro-expressions, and the importance of multimodal fusion for improving emotion recognition accuracy. The literature was comprehensively searched via databases with records covering the topic of affective computing, followed by rigorous screening and selection of relevant studies. The results show that the effectiveness of current multimodal emotion recognition methods is affected by the limited availability of training data, insufficient context awareness, and challenges posed by real-world cases of noisy or missing modalities. The findings suggest that improving emotion recognition requires better representation of input data, refined feature extraction, and optimized aggregation of modalities within a multimodal framework, along with incorporating state-of-the-art methods for recognizing dynamic expressions. [ABSTRACT FROM AUTHOR]

Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the Industrial Revolutions, which have continued expansion and progress into the fourth Industrial Revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. In order to achieve a better understanding of the impact of these technologies, in this study, we methodically reviewed the literature on the subject of the data collection and processing mechanisms of 4IR technologies in the context of transport. Gaps in present practices are identified in the study, especially with regard to the integration and scalability of these technologies in transportation networks. In order to fully reap the rewards of 4IR technologies, it is also necessary to apply standardized methods for data gathering and processing. In this report, we offer insights into current obstacles and make recommendations for future research to solve these concerns through a comprehensive evaluation of the literature, with the goal of promoting the development of intelligent and sustainable transportation systems. [ABSTRACT FROM AUTHOR]

Human–Machine Interaction (HMI) systems are integral to various domains and rely on human operators for effective performance. The sense of agency (SoA) is crucial in these systems, as it influences the operator's concentration and overall efficiency. This review explores the SoA in HMI systems, analyzing its definition, key influencing factors, and methods for enhancement. We provide a comprehensive examination of SoA-related research and suggest strategies for measuring and improving the SoA. Two key research directions are highlighted: the impact of user experience on the SoA, and the role of the SoA in enabling unconscious communication between humans and machines. We propose a development route for HMI systems, outlining a progressive structure across three stages: machine-centric, human-centric, and human–machine integration. Finally, we discuss the potential of gaming platforms as tools for advancing SoA research in HMI systems. Our findings aim to enhance the design and functionality of HMI systems, ensuring improved operator engagement and system performance. [ABSTRACT FROM AUTHOR]

Genuine leather manufacturing is a multibillion-dollar industry that processes animal hides from varying types of animals such as sheep, alligator, goat, ostrich, crocodile, and cow. Due to the industry's immense scale, there may be numerous unavoidable causes of damages, leading to surface defects that occur during both the manufacturing process and the bovine's own lifespan. Owing to the heterogenous and manifold nature of leather surface characteristics, great difficulties can arise during the visual inspection of raw materials by human inspectors. To mitigate the industry's challenges in the quality control process, this paper proposes the application of a modern vision transformer (ViT) architecture for the purposes of low-resolution image-based anomaly detection for defect localisation as a means of leather surface defect classification. Utilising the low-resolution defective and non-defective images found in the opensource Leather Defect detection and Classification dataset and higher-resolution MVTec AD anomaly benchmarking dataset, three configurations of the vision transformer and three deep learning (DL) knowledge transfer methods are compared in terms of performance metrics as well as in leather defect classification and anomaly localisation. Experiments show the proposed ViT method outperforms the light-weight state-of-the-art methods in the field in the aspect of classification accuracy. Besides the classification, the low computation load and low requirements for image resolution and size of training samples are also advantages of the proposed method. [ABSTRACT FROM AUTHOR]

In recent times, distributed denial of service (DDoS) has been one of the most prevalent security threats in internet-enabled networks, with many internet of things (IoT) devices having been exploited to carry out attacks. Due to their inherent security flaws, the attacks seek to deplete the resources of the target network by flooding it with numerous spoofed requests from a distributed system. Research studies have demonstrated that a DDoS attack has a considerable impact on the target network resources and can result in an extended operational outage if not detected. The detection of DDoS attacks has been approached using a variety of methods. In this paper, a comprehensive survey of the methods used for DDoS attack detection on selected internet-enabled networks is presented. This survey aimed to provide a concise introductory reference for early researchers in the development and application of attack detection methodologies in IoT-based applications. Unlike other studies, a wide variety of methods, ranging from the traditional methods to machine and deep learning methods, were covered. These methods were classified based on their nature of operation, investigated as to their strengths and weaknesses, and then examined via several research studies which made use of each approach. In addition, attack scenarios and detection studies in emerging networks such as the internet of drones, routing protocol based IoT, and named data networking were also covered. Furthermore, technical challenges in each research study were identified. Finally, some remarks for enhancing the research studies were provided, and potential directions for future research were highlighted. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) connect millions of devices in diverse areas such as smart cities, e‐health, transportation and defense to meet a wide range of human needs. To provide these services, a large amount of data needs to be transmitted to the IoT network servers. However, the IoT networks suffer from limited resources such as buffer size, node processing capabilities, and server capacities adversely affecting throughputs, latency, and energy consumption. Additionally, the ensuing heavy network traffic due to large amount of data transmitted results in congestion which degrades IoT network performance. Therefore, innovative congestion control techniques, e.g., queue management approach needs to be developed to overcome congestion problems in IoT networks. In this paper, a novel priority queuing technique (Npqt++) is developed to control congestion in IoT networks. The Npqt++ implements a preemptive/nonpreemptive discipline with a discretion rule to classify network traffic based on their real‐time requirement into priority groups. If the discretion rule for low priority packets is satisfied, high priority packets are pushed to the front of the queue; otherwise, they wait in the queue. Our approach significantly outperforms existing techniques in terms of throughput, delay, and energy consumption. [ABSTRACT FROM AUTHOR]