检索结果-内蒙古大学图书馆

AI: the future of humanity

学校读者我要写书评

暂无评论

Discover Artificial Intelligence 2024年第1期4卷 1-14页

作者： Rawas, Soha Faculty of Science Department of Mathematics and Computer Science Beirut Arab University Beirut Lebanon

Artificial intelligence (AI) is reshaping humanity's future, and this manuscript provides a comprehensive exploration of its implications, applications, challenges, and opportunities. The revolutionary potential of AI is investigated across numerous sectors, with a focus on addressing global concerns. The influence of AI on areas such as healthcare, transportation, banking, and education is revealed through historical insights and conversations on different AI systems. Ethical considerations and the significance of responsible AI development are addressed. Furthermore, this study investigates AI's involvement in addressing global issues such as climate change, public health, and social justice. This paper serves as a resource for policymakers, researchers, and practitioners understanding the complex link between AI and humans. © The Author(s) 2024.

关键词： Artificial intelligence

Robust subspace clustering via two-way manifold representation

学校读者我要写书评

暂无评论

Multimedia Tools and Applications 2025年第16期84卷 16339-16356页

作者： Ezeora, Nnamdi Johnson Anichebe, Gregory Emeka Nzeh, Royransom Chiemela Uzo, Izuchukwu Uchenna Department of Computer Science University of Nigeria Nsukka Nigeria School of Computer Science and Communication Engineering JiangSu University JiangSu 212013 China

Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overlapping subspaces. To tackle this problem and ensure that each sample is only assigned to a single subspace, a new method is proposed in this paper. Specifically, a two-way learning technique is introduced by inducing data manifold via two representative structures. The first is a low-rank structure learned directly from original data. The second structure is an affinity matrix obtained via a k symmetric nearest neighbor graph. By further introducing a dual regularization term, both structures are allowed to guild themselves adaptively to find robust clustering directly without spectral post-processing. In order to evaluate the effectiveness of the proposed method, several experiments are conducted on multiple benchmark datasets. The experimental results, evaluated using six standard metrics, clearly demonstrate that the proposed method outperforms state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clustering algorithms

Improved Infrared Road Object Detection Algorithm Based on Attention Mechanism in YOLOv8

学校读者我要写书评

暂无评论

IAENG International Journal of computer science 2024年第6期51卷 673-680页

作者： Luo, Zilong Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In Currently, research in the field of infrared road object detection is primarily focused on enhancing model performance and robustness to address the challenges posed by complex real-world driving scenarios. In response to these challenges, this paper proposes an infrared road object detection algorithm based on an attention mechanism. By incorporating the CPCA module, which utilizes attention mechanisms, into the YOLOv8s model, the algorithm enhances the model’s focus on unobstructed areas and highly illuminated sections, extracting crucial feature information to improve both accuracy and robustness. Additionally, the original model’s downsampling layer is replaced with the Context Grided Network Block Downsampling (CGBD) module, which not only preserves feature edge information but also effectively handles local and contextual features, thereby enhancing the overall feature capturing capabilities of the model. To address the issue of equal aspect ratios in the model’s original loss function, the proposed algorithm adopts the superior Weighted Intersection over Union (WIoU). This not only addresses the shortcomings of the original loss function (CIoU) but also demonstrates increased sensitivity in classification tasks. Experimental results show that the improved algorithm, compared to YOLOv8s, achieves a 1.4% increase in mean average precision (mAP), along with notable improvements in precision and recall. Furthermore, when compared to mainstream model algorithms, the enhanced model significantly outperforms in infrared road object detection tasks, providing validation of its effectiveness. © (2024), (International Association of Engineers). All rights reserved.

关键词： Object detection

Sustainable Forest Fire detection: A Mapping through Bibliometrics and Scientific Knowledge Research 4

学校读者我要写书评

暂无评论

Sustainable Forest Fire detection: A Mapping through Bibliom...

4th International Conference on Electrical engineering and computer science, ICECOS 2024

作者： Exaudi, Kemahyanto Stiawan, Deris Suprapto, Bhakti Yudho Universitas Sriwijaya Faculty of Computer Science & Faculty of Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Departement of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Engineering Departement of Electrical Engineering Palembang Indonesia

ISBN: (纸本)9798350368253

The increasing incidence of forest and land fires has become an urgent global concern in recent decades. In an effort to improve detection and understanding of forest fires, information technology-based approaches have become a major focus. This study explores the sustainability of forest and land fire detection through the perspective of scientific knowledge and bibliometric analysis to determine current developments and the latest insights in research. This study integrates open data with network-based visualization to present the latest developments in the scientific literature about forest fire detection. This article identified research trends, collaboration patterns, and contributions to relevant knowledge. The results highlight the evolution and technological trends in forest fire detection approaches as well as a focal point for future research. This approach provides valuable insights for researchers, practitioners and decision makers in efforts to mitigate and handle forest fires earlier and more effectively. © 2024 IEEE.

关键词： Forest ecology

Surface Defect Detection Algorithm for Strip Steel Based on Improved YOLOv7 Model

学校读者我要写书评

暂无评论

IAENG International Journal of computer science 2024年第3期51卷 308-316页

作者： Wang, Zhu Liu, Weisheng the College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China the College of Computer Science and Software Engineering the University of Science and Technology Liaoning CO Anshan114051 China

This research proposes a refined deep learning framework aimed at boosting the precision and efficacy of detecting surface imperfections in strip steel. This method integrates enhancement and simplification techniques inspired by the You Only Look Once version 7 (YOLOv7) detection method, resulting in significant enhancements in the model's accuracy, speed, and flexibility. The substitution of ELAN with Bottleneck Transformer 3 (BoT3) leads to improved accuracy and mean Average Precision (mAP) values, while also introducing a more lightweight network architecture. The incorporation of the Involution mechanism enhances the model's feature extraction capabilities, thereby improving its ability to recognize small targets through the utilization of local perceptual fields. The ASPP_CA architecture leverages a multi-scale feature fusion technique along with an attention mechanism to reduce model parameters and enhance inference speed. Furthermore, it extends the model's receptive field, allowing it to capture additional visual information. The enhanced algorithm, denoted as YOLOv7-IBA, demonstrates empirical results that underscore its superiority over the three current state-of-the-art detection techniques in identifying surface flaws on strip steel. The accuracy has been improved to 82.9%, representing a significant increase of 7.2% compared to the previous performance. Furthermore, the mean mAP value has experienced a 3.2% increase, reaching a total of 79.9%. Moreover, there has been a remarkable 8.8% improvement in efficiency. The adoption of this approach holds the potential to enhance both the precision and productivity of strip surface flaw detection, while also providing valuable methodological support for the advancement of other related disciplines. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Object detection

Small Object Detection in Aerial Drone Imagery based on YOLOv8

学校读者我要写书评

暂无评论

IAENG International Journal of computer science 2024年第9期51卷 1346-1354页

作者： Pan, Junyu Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In recent years, the utilization of unmanned aerial vehicles (UAVs) for aerial target detection has gained significant attention due to their high-altitude perspective and maneuverability, which offer novel opportunities and tremendous potential in this field. However, detecting targets in UAV aerial images remains highly challenging due to the presence of numerous small targets with limited feature information, as well as issues like target occlusion and complex backgrounds that severely impact detection accuracy. To address these challenges, we propose a detection model called BDC-YOLOv8 that aims to enhance accuracy for small targets while minimizing computational complexity. Specifically, we augment the YOLOv8 architecture by incorporating a dedicated detection head tailored for small targets to improve performance when encountering such objects. Additionally, we restructure the neck network of the model to better extract and fuse feature information from targets with significant scale variations. Furthermore, we introduce the concept of DynamicHead to enhance the detection head by incorporating various attention mechanisms suitable for our task ahead of the original detection head, thereby enhancing the model’s capability to detect objects of different scales and complex backgrounds. Moreover, we introduce Convolutional Block Attention Module (CBAM) to identify regions of interest in densely populated areas. Extensive experiments conducted on the VisDrone2019 dataset yield promising results where our model achieves a mean Average Precision (mAP) score of 38% and an AP50 score of 59.6%. Compared to the original YOLOV8 model, improvements are observed with increases in mAP by 2.5% and AP50 by 3.7%, respectively. Notably, our model demonstrates a significant enhancement in detecting small targets with an increase in APs evaluation metric by 4.1%. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Aerial photography

Underwater Biological Target Detection Algorithm and Research Based on YOLOv7 Algorithm

学校读者我要写书评

暂无评论

IAENG International Journal of computer science 2024年第6期51卷 594-601页

作者： Zhuang, Hongwei Liu, Weisheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China

Underwater target detection is an important method for detecting marine organisms. However, due to the image occlusion of underwater targets, blurred water quality, poor lighting conditions, small targets, and complex backgrounds, the detection of underwater biological targets has posed significant challenges. In the intricate underwater environment, the conventional feature extraction method has a few drawbacks, including imprecise feature extraction, sluggish detection speed, and inadequate robustness. Consequently, an underwater target detection method based on the enhanced You Only Look Once 7 (YOLOv7) is proposed in this study. The network architecture is reconstructed, and the Deformable Convolutional Network (DCN) modules replace some 3×3 convolutional blocks in the ELAN structure to offset sampling points and reduce background interference. Skip connections and 1× 1 convolutional architecture are added to the DCN module to improve the model’s perception of image details. In addition, Contextual Transformer 3 (COT3) is also incorporated to improve visual performance. Finally, to improve the detection efficiency of small objects, the CIoU loss function is finally replaced by the Normalized Wasserstein Distance (NWD) algorithm. The mAP of DCCN-YOLOv7 on the URPC dataset is 80.4%, according to the experimental results, 2.8% higher than the YOLOv7 network model that is used as a baseline. Furthermore, in contrast to the original YOLOv7 algorithm, the detection speed and accuracy are higher, making it more appropriate for target recognition underwater. © (2024), (International Association of Engineers). All rights reserved.

关键词： Feature extraction

Underwater Target Detection Based on Improved YOLOv7

学校读者我要写书评

暂无评论

IAENG International Journal of computer science 2024年第4期51卷 422-429页

作者： Fu, Junshang Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning 114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan 114051 China

Underwater target detection is an important part of marine exploration. However, in complex underwater environments due to factors like light absorption and scattering, as well as variations in water quality and clarity. These challenges result in inaccurate target feature extraction, sluggish detection speeds, and insufficient robustness in the detection methods. In order to address these issues, an enhanced YOLOv7 network (YOLOv7-SPNW-D) is proposed for underwater target detection in this study. The SPD-MP module structure replaces the MP module in the neck network to capture small targets and enhance detection accuracy. A novel NWD loss function is employed to facilitate smoother extraction of small target features. This enhances feature extraction and improves network inference speed. Additionally, incorporating a small target detection module enables the providing of more comprehensive small target information within a deep feature map. This, in turn, improves the capture of small target features in complex backgrounds, and avoids feature loss and enhancing model exactness. Through ablation experiments on the URPC dataset, it is shown that the improved YOLOv7-SPNW-D algorithm performs better than the original YOLOv7 algorithm, with the mAP50 value increased to 87.0%, proving the effectiveness of this method. In conclusion, the improved YOLOv7-SPNW-D model is more suitable for underwater marine organism target detection. © (2024) International Association of Engineers.

关键词： Water quality

Transforming healthcare delivery: next-generation medication management in smart hospitals through IoMT and ML

学校读者我要写书评

暂无评论

Discover Artificial Intelligence 2024年第1期4卷 1-9页

作者： Rawas, Soha Faculty of Science Department of Mathematics and Computer Science Beirut Arab University Beirut Lebanon

The management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management. © The Author(s) 2024.

关键词： Machine learning