Understanding and predicting air quality is pivotal for public health and environmental management, especially in urban areas like Delhi. This study utilizes a comprehensive dataset from the Central Pollution Control ...
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This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)*** to the presence of an eavesdropper(Eve),the system’s com-munication...
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This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)*** to the presence of an eavesdropper(Eve),the system’s com-munication links may be *** paper proposes deploying an intelligent reflecting surface(IRS)on the UAV to enhance the communication performance of mobile vehicles,improve system flexibility,and alleviate eavesdropping on communication *** links for uploading task data from vehicles to a base station(BS)are protected by IRS-assisted physical layer security(PLS).Upon receiving task data,the computing resources provided by the edge computing servers(MEC)are allocated to vehicles for task *** blockchain-based computation offloading schemes typically focus on improving network performance,such as minimizing energy consumption or latency,while neglecting the Gas fees for computation offloading and the costs required for MEC computation,leading to an imbalance between service fees and resource *** paper uses a utility-oriented computation offloading scheme to balance costs and *** paper proposes alternating phase optimization and power optimization to optimize the energy consumption,latency,and communication secrecy rate,thereby maximizing the weighted total utility of the *** results demonstrate a notable enhancement in the weighted total system utility and resource utilization,thereby corroborating the viability of our approach for practical applications.
The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in *** malicious applications have become a serious concern to the...
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The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in *** malicious applications have become a serious concern to the security of Android *** address this problem,researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android ***,most existing studies have focused on the classification task and overlooked the feature selection process,which is crucial to reduce the training time and maintain or improve the classification *** current paper proposes a new Android malware detection and classification approach that identifies the most important features to improve classification performance and reduce training *** proposed approach consists of two main ***,a feature selection method based on the Attention mechanism is used to select the most important ***,an optimized Light Gradient Boosting Machine(LightGBM)classifier is applied to classify the Android samples and identify the *** feature selection method proposed in this paper is to integrate an Attention layer into a multilayer perceptron neural *** role of the Attention layer is to compute the weighted values of each feature based on its importance for the classification *** evaluation of the approach has shown that combining the Attention-based technique with an optimized classification algorithm for Android malware detection has improved the accuracy from 98.64%to 98.71%while reducing the training time from 80 to 28 s.
Message queuing telemetry transport (MQTT) has emerged as the widely adopted application layer protocol for IoT environments because of its lightweight header, minimal power, and bandwidth requirements. Despite its po...
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Message queuing telemetry transport (MQTT) has emerged as the widely adopted application layer protocol for IoT environments because of its lightweight header, minimal power, and bandwidth requirements. Despite its popularity, the earlier version of the protocol, MQTT v3.1.1, encounters performance issues in large-scale implementations and required an update to handle the growing requirements of modern IoT applications. In response to these concerns, MQTT v5.0 was released with several significant features designed to enhance the reliability, user experience, and performance of IoT systems. While the MQTT protocol features were intended to facilitate robust and efficient communications, adversaries could exploit these features to mount various types of attacks in IoT deployments. More specifically, the Denial-of-Service (DoS) attacks toward the MQTT protocol have recently gained a lot of attention from the research community. However, the existing works primarily focus only on exploring the possibilities of misusing the MQTT v3.1.1 protocol features to generate DoS attacks in IoT realms. In this work, we attempt to extensively investigate the advanced protocol features of MQTT v5.0 that can be exploited to launch DDoS attacks impacting the IoT paradigm. We present the first critical evaluation of Distributed DoS (DDoS) attacks on the MQTT v5.0 protocol by analyzing three significant features: 1) CONNECT properties;2) user properties;and 3) flow control. Moreover, we systematically propose attack scenarios based on the adversary’s capabilities, thus illustrating the practicality of proposed attacks in real-world scenarios. Furthermore, we built a real-world testbed for IoT healthcare application to evaluate the severity of the identified attacks. The experimental results demonstrate the effectiveness of these attacks in impacting the availability of guaranteed IoT services to legitimate users, even in times of need. Additionally, we disclose the insightful findings o
This paper centers on leveraging Convolution-Augmented Transformer Models originally designed for Automatic Speech Recognition (ASR) in the realm of Sign Language—specifically, American Sign Language Fingerspelling R...
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Many Internet of Things(IoT)systems are based on the intercommunication among different devices and centralized ***,there are several commercial and research platforms available to simplify the creation of such IoT **...
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Many Internet of Things(IoT)systems are based on the intercommunication among different devices and centralized ***,there are several commercial and research platforms available to simplify the creation of such IoT ***,developing these systems can often be a tedious *** address this challenge,a proposed solution involves the implementation of a unified program or script that encompasses the entire system,including IoT devices *** approach is based on an abstraction,integrating the control of the devices in a single program through a programmable ***,the proposal processes the unified script to generate the centralized system code and a controller for each *** adopting this approach,developers will be able to create IoT systems with significantly reduced implementation costs,surpassing current platforms by more than 10%.The results demonstrate that the single program approach can significantly accelerate the development of IoT systems relying on device communication.
With the rise of Arabic digital content, effective summarization methods are essential. Current Arabic text summarization systems face challenges such as language complexity and vocabulary limitations. We introduce an...
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Chronic Kidney Cancer (CKC) is a disease that hindrances the blood-filtering mechanism of the kidney and is increasing at an alarming rate in the recent few years. As CKC does not show any earlier symptoms, the earlie...
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Deep Learning (DL) is currently transforming health services by significantly improving early cancer diagnosis, drug discovery, protein–protein interaction analysis, and gene editing. The main purpose of this review ...
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Road extraction from high-resolution remote sensing images can provide vital data support for applications in urban and rural planning, traffic control, and environmental protection. However, roads in many remote sens...
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Road extraction from high-resolution remote sensing images can provide vital data support for applications in urban and rural planning, traffic control, and environmental protection. However, roads in many remote sensing images are densely distributed with a very small proportion of road information against a complex background, significantly impacting the integrity and connectivity of the extracted road network structure. To address this issue, we propose a method named StripUnet for dense road extraction from remote sensing images. The designed Strip Attention Learning Module (SALM) enables the model to focus on strip-shaped roads;the designed Multi-Scale Feature Fusion Module (MSFF) is used for extracting global and contextual information from deep feature maps;the designed Strip Feature Enhancement Module (SFEM) enhances the strip features in feature maps transmitted through skip connections;and the designed Multi-Scale Snake Decoder (MSSD) utilizes dynamic snake convolution to aid the model in better reconstructing roads. The designed model is tested on the public datasets DeepGlobe and Massachusetts, achieving F1 scores of 83.75% and 80.65%, and IoUs of 73.04% and 67.96%, respectively. Compared to the latest state-of-the-art models, F1 scores improve by 1.07% and 1.11%, and IoUs increase by 1.28% and 1.07%, respectively. Experiments demonstrate that StripUnet is highly effective in dense road network extraction. IEEE
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