Recent Advances in Computer Science and Communications - Online First

With the rapid development of society, motor vehicles have become one of the main means of transportation. However, as the number of motor vehicles continues to increase, traffic safety accidents also continue to appear, bringing serious threats to people's lives, and property safety. Fatigue driving is one of the important causes of traffic safety accidents.

To address this problem, a target detection algorithm called VA-YOLO is designed to improve the speed and accuracy of facial recognition for fatigue checking. The algorithm employs a lightweight backbone network, VanillaNet, instead of the traditional backbone network, which reduces the computational and parametric quantities of the model. The SE attention mechanism is also introduced to enhance the model's attention to the target features, which further improves the accuracy of target detection. Finally, in terms of the bounding box regression loss function, the SIoU loss function is used to reduce the error.

The experimental results show that, compared toYolov8n, the VA-YOLO algorithm improves the accuracy by 1.3% while the number of parameters decreases by 30%.

This shows that the VA-YOLO algorithm has a significant advantage in realizing the balance between the number of parameters and accuracy, which is important for improving the speed and accuracy of fatigue driving detection.

Pest detection plays a crucial role in smart agriculture; it is one of the primary factors that significantly impact crop yield and quality. Objective: In actual field environments, pests often appear as dense and small objects, which pose a great challenge to field pest detection. Therefore, this paper addresses the problem of dense small pest detection.

We combine a pyramid vision transformer and prime sample attention (named PVT-PSA) to design an effective pest detection model. Firstly, a pyramid vision transformer is adopted to extract pest feature information. Pyramid vision transformer fuses multi-scale pest features through pyramid structure and can capture context information of small pests, which is conducive to the feature expression of small pests. Then, we design prime sample attention to guide the selection of pest samples in the model training process to alleviate the occlusion effect between dense pests and enhance the overall pest detection accuracy.

The effectiveness of each module is verified by the ablation experiment. According to the comparison experiment, the detection and inference performance of the PVT-PSA is better than the other eleven detectors in field pest detection. Finally, we deploy the PVT- PSA model on a terrestrial robot based on the Jetson TX2 motherboard for field pest detection.

The pyramid vision transformer is utilized to extract relevant features of pests. Additionally, prime sample attention is employed to identify key samples that aid in effectively training the pest detection models. The model deployment further demonstrates the practicality and effectiveness of our proposed approach in smart agriculture applications.

Cyber-Physical Systems (CPSs) integrate computing, control, and communication technologies, bridging cyberspace and the physical world to enhance critical infrastructure and safety-critical systems. Existing surveys often address CPS security from a single perspective, necessitating a more comprehensive approach.

This paper presents a thorough review of CPS security from three perspectives: the physical domain, the cyber domain, and the cyber-physical domain. We examine attacks on physical components like sensors, cyber-attacks targeting CPSs, and integrated cyber-physical attacks. For each domain, we analyse corresponding detection and defence mechanisms.

Our review reveals that CPSs face significant security threats across all domains. In the physical domain, attacks on sensors can disrupt system operations, but various defences are available. In the cyber domain, CPSs are vulnerable to malware, hacking, and denial-of-service attacks, with several detection and defence strategies in place. The cyber-physical domain highlights complex threats where cyber-attacks cause physical damage, requiring comprehensive security approaches.

By examining CPS security from multiple perspectives, this review provides a holistic understanding of current threats and defence mechanisms. It identifies future research directions to enhance CPS security, aiming to better protect critical infrastructure against evolving cyber threats.

To address the challenges of low visibility, object recognition difficulties, and low detection accuracy in foggy weather, this paper introduces the WViT-YOLO real-time fog detection model, built on the YOLOv5 framework. The NVIT-Net backbone network, incorporating NTB and NCB modules, enhances the model's ability to extract both global and local features from images.

An efficient convolutional C3_DSConv module is designed and integrated with channel attention mechanisms and ShuffleAttention at each upsampling stage, improving the model's computational speed and its ability to detect small and blurry objects. The Wise-IOU loss function is utilized during the prediction stage to enhance the model's convergence efficiency.

Experimental results on the publicly available RTTS dataset for vehicle detection in foggy conditions demonstrate that the WViT-YOLO model achieves a 3.2% increase in precision, a 9.5% rise in recall, and an 8.6% improvement in mAP50 compared to the baseline model. Furthermore, WViT-YOLO shows a 9.5% and 8.6% mAP50 improvement over YOLOv7 and YOLOv8, respectively. For detecting small and blurry objects in foggy conditions, the model demonstrates approximately a 5% improvement over the benchmark, significantly enhancing the detection network's generalization ability under foggy conditions.

This advancement is crucial for improving vehicle safety in such weather. The code is available at https://github.com/QinghuaZhang1/mode.

Wireless networks are essential communication technologies that prevent cable installation prices and burdens. Because of this technology's pervasive usage, wireless network safety is a significant problem. Owing to distributed and open wireless medium aspects, attackers might use different jamming methods to exploit physical and MAC layer protocol vulnerabilities. In addition, jamming attacks require to be accurately grouped so that suitable countermeasures can be considered. Given the potential severity of such attacks, precisely identifying and classifying them is critical for implementing effective responses. The motivation for this paper is the need to improve the detection and categorization of jamming signals using modern machine learning algorithms, consequently enhancing wireless network security and reliability.

In this paper, we compare some machine learning models' efficiency for diagnosing jamming signals.

Such algorithms refer to support vector machine (SVM) and k-nearest neighbors (KNN). We checked the signal features that recognize jamming signals. After the jamming attack model, the developed grey wolf optimizer version known as IGWO (improved grey wolf optimizer) has been discussed for feature extraction of software usability. Four separate metrics were employed as features to detect jamming attacks in order to evaluate the machine learning models. This novel feature extraction method is crucial for improving the accuracy of jamming detection.

The measurements of these parameters were gathered through a simulation of a real setting. And generated a large dataset using these parameters.

The simulation results illustrate that the KNN algorithm based on jamming detection could diagnose jammers having a minimal likelihood of false alarms and a high level of accuracy.

Mobile devices have become an integral part of our digital lives, facilitating various tasks and storing a treasure trove of sensitive information. However, as more people utilize mobile devices, sophisticated cyber threats are emerging to elude traditional security measures.

The use of evasion techniques by malicious actors presents a significant challenge to mobile security, necessitating creative solutions. In this work, we investigate the potential critical role that the aspect-oriented programming paradigm AspectJ can play in strengthening mobile security against evasion attempts. Evasion techniques cover a wide range of tactics, including runtime manipulation, code obfuscation, and unauthorized data access.

These tactics usually aim to bypass detection and avoid security measures. In order to address the aforementioned issues, this paper uses AspectJ to give developers a flexible and dynamic way to add aspects to their coding structures so they can monitor, intercept, and respond to evasive actions. It illustrates how AspectJ can improve mobile security and counteract the long-lasting risks that evasion techniques present in a dynamic threat landscape.

Consequently, this work proposes a novel defense mechanism incorporating AspectJ into a significant paradigm of security against evasion with 91.33% accuracy and demonstrates the successful detection of evasive attacks.

The use of 20% blended biofuels to fossil fuels is one of the important targets of the Government of India to address the impacts of Climate Change, energy-related environmental pollution, and illnesses due to air pollution.

The National Policy on Biofuels 2018 (NPB 2018) is in place to boost the emerging production of biofuels and, therefore, respond to different international agreements, including the Sustainable Development Goals (SDGs) and the Paris Agreement. Hence, this article examined the production of biofuels in India in line with Agenda 2030 to project the share to be taken by biofuels as its contribution to the country’s energy needs.

The results were compromising; it was observed that the data from 2000 up to 2017 were not on the side of realizing the targets of production and consumption of biofuels in India, whereas the data from 2018 up to now showed a hope of achieving 2030 set goal of E20 petrol in 2025-26, and E5 diesel in 2030. It was clear that the production of bioethanol was boosting compared to its sibling biodiesel, and renewable energy will continue to have a hard take a good share in the total annual energy used in India.

It is recommended to share data between different stakeholders to promote more research, as the low performance in achieving the targets was due to poor communication and missing technology, rather than the lack of feedstock or unavailability of production facilities.

The aim of this work is to succinctly communicate the key aspects of a research study on breast cancer. This includes highlighting the global impact and prevalence of breast cancer, emphasizing the challenges of early diagnosis, discussing the potential of technological advancements, and showcasing the role of machine learning algorithms in the context of liver cancer diagnosis.

Cancer, notably breast cancer, represents a global health challenge, claiming a significant toll with 12.5% of new cancer cases annually. The prevalence of breast cancer among women worldwide is alarming, resulting in 2.26 million incidents and the unfortunate loss of 685,000 lives.

This article focuses on the critical aspect of early breast cancer diagnosis, acknowledging its heightened difficulty in developing nations compared to developed counterparts. The potential for advancements in technology to serve as a beacon of hope lies in early identification and timely treatment, offering salvation to numerous women and significantly elevating survival chances.

In this intricate landscape, machine learning algorithms, particularly in diagnosing liver cancer at its nascent stages, emerge as instrumental tools. The study employs the latest Coimbra dataset, encompassing nine key attributes and a binary classification attribute, with values 1 and 2 signifying benign and malignant cases, respectively.

Supervised machine learning algorithms, including Bayes net, multilayer perceptron, IBK, random committee, and random tree, are meticulously applied. Certain models exhibit superior accuracy, precision, recall, and performance, positioning them as promising cornerstones for breast cancer analysis.

This structured abstract highlights the urgent need for effective screening and prevention strategies, emphasizing the potential of advanced technology and machine learning algorithms to play a pivotal role in the early detection and analysis of breast cancer, offering hope for improved outcomes and survival rates.

In order to realize seamless indoor and outdoor positioning, the positioning results of multiple positioning methods are taken into consideration, and a seamless indoor and outdoor positioning method that ignores the differences in indoor and outdoor environments is required now.

The implementation of Pseudo satellite/GNSS combined indoor and outdoor fusion positioning for seamless indoor and outdoor environment positioning.

An adaptive federated filter is needed for this environment, which can dynamically adjust the information allocation parameters and measurement noise of the sub-filters in the federated filters based on positioning data. It adopts multi-sensor fusion filter to design a seamless indoor and outdoor positioning method. Different positioning data is fused through federated filtering, ultimately seamless indoor and outdoor positioning is realized.

This algorithm achieves a fixed ambiguity pseudo satellite/GNSS accuracy of better than 0.15 meters in low-density buildings where there are more than 7 GNSS satellites. When there are fewer than 4 GNSS satellites and the positions is severely obstructed, GNSS alone cannot realize the position, but with the support of pseudo satellites, the accuracy of position can be better than 0.3m. Even without GNSS and only 4 pseudo satellites, the accuracy of position can still be better than 0.5 m.

The relevant experimental results indicate that the method proposed can be used for practical applications of indoor and outdoor fusion positioning.