Extensive scientific investigation is necessary because every government wants to construct smart cities. This is why examining how researchers approach this area of study is critical. This study investigates global r...
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Knowledge distillation has demonstrated significant potential in addressing the challenge of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher–student (T-S) model pr...
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Due to its decentralized and tamper-proof features, blockchain is frequently employed in the financial, traceability, and distributed storage industries. The agreement algorithm, which is a crucial component of the bl...
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Existing end-to-end quality of service (QoS) prediction methods based on deep learning often use one-hot encodings as features, which are input into neural networks. It is difficult for the networks to learn the infor...
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There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient su...
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The drug traceability model is used for ensuring drug quality and its safety for customers in the medical supply chain. The healthcare supply chain is a complex network, which is susceptible to failures and leakage of...
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Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Light...
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Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.
With the rapid proliferation of the Internet of Things, network traffic prediction has become crucial for intelligent network management, enabling more reliable and flexible services for a vast array of IoT devices an...
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Artificial intelligence has played a significant role in the expansion of the agriculture industry in recent times by evaluating data and making recommendations for better production. An automated method for determini...
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Music genre classification is one of the most interesting topics in digital music. Classifying genres is basically subjective, and different listeners may perceive genres in various ways. Furthermore, it might be diff...
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