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Pilot Fatigue Monitoring Using Enhanced YOLOv5 with SKAttention and BiFPN 24

Pilot Fatigue Monitoring Using Enhanced YOLOv5 with SKAttent...

引用

4th international conference on Artificial Intelligence, automation and High Performance Computing, AIAHPC 2024

作者： Liu, Cheng Li, Zongmei School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9798400710049

With the rapid development of computer vision technology, deep learning model is more and more widely used in real-time target detection. This paper presents an improved YOLOv5 model for pilot fatigue detection. By introducing SKAttention mechanism and BiFPN2Concat and BiFPN3Concat modules, the feature extraction and fusion capability of the model are enhanced. A large number of experiments on the self-made dataset show that the improved model outperforms the original model in detection accuracy, recall rate and average accuracy (mAP), especially when dealing with multi-scale targets. The research results of this paper provide an efficient and reliable solution for pilot fatigue detection, which has important practical application value. © 2024 Copyright held by the owner/author(s).

关键词： Feature extraction

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Recommendation of Tourist Attractions and Route Based on Social Network Data Mining

Recommendation of Tourist Attractions and Route Based on Soc...

引用

2023 IEEE international conference on Electrical, automation and computer engineering, ICEACE 2023

作者： Lou, Chao Yang, Xi Jin, Xingchen College of Computer and Information Engineering Guizhou University of Commerce Guiyang Guizhou China Shenzhen Smartcom Business Co. Ltd. Shenzhen China

ISBN: (纸本)9798350309614

Since growing travellers' more and more preferring customisation service, this project indicates that such application is more user-oriented and more particular in personal preferences. Through extracting users' posted geotagged photos and their information from photo sharing website supported by Flickr API. And we design a content filtering algorithm and improve existed TF-IDF algorithm to get user profile before listing travel suggestions via Yelp API and simulated a graphical route for reference by Google API. As a result, a travel recommendation system based on users' travel preferences excavated from social platform photos is achieved. According to the average accuracy rate (MAP) verification, the CFC algorithm used in this study performs better than the LC and CA algorithms in recommending popular scenic spots. © 2023 IEEE.

关键词： CFC Data Mining Text Classification TF-IDF Travel Recommendation

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Blockchain-Based Smart Contract Architecture for Inter-Domain SDN Controller Communication 11

Blockchain-Based Smart Contract Architecture for Inter-Domai...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Ohri, Pulkit Neogi, Subhrendu Guha Sengupta, Somenath Arockiam, Daniel Muttoo, Sunil Kumar Amity University Computer Science and Engineering Department Madhya Pradesh Gwalior India The Neotia University Computer Science and Engineering Department West Bengal Sarisha India Heritage Institute of Technology Computer Science and Engineering Department West Bengal Kolkata India University of Delhi Computer Science and Engineering Department New Delhi India

ISBN: (纸本)9798350350357

In the realm of network management, the integration of Software-Defined Networking (SDN) with blockchain-based smart contracts is an emerging frontier with significant potential to enhance inter-domain communications. This paper presents an in-depth analysis of the application of smart contracts within SDN, particularly focusing on the automation and security of inter-domain interactions. Smart contracts, characterized by their immutable and autonomous nature, offer a novel approach to enforcing network policies and agreements across different SDN domains seamlessly. We explore the inherent benefits of this integration, including enhanced security through the tamper-proof nature of blockchain, and the efficiency gains achieved by automating network policy enforcement. The paper also addresses critical challenges such as scalability, interoperability, and the complexity of smart contract development within the SDN context. Through a combination of theoretical analysis and practical case studies, this research illuminates the transformative potential of smart contracts in SDN, paving the way for more secure, efficient, and self-regulating network environments. The findings and discussions in this paper aim to contribute to the ongoing evolution of SDN, particularly in scenarios where multiple administrative domains necessitate robust, automated, and secure communication frameworks. © 2024 IEEE.

关键词： Smart contract

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Determination of Leaf Degradation Percentage for Banana leaves with Panama Disease Using Image Segmentation of Color Spaces and OpenCV 15

Determination of Leaf Degradation Percentage for Banana leav...

引用

15th international conference on computer and automation engineering, ICCAE 2023

作者： Ibarra, Nionel C. Rivera, Matthew P. Manlises, Cyrel O. Mapúa University School of Electrical Electronics and Computer Engineering Manila Philippines

ISBN: (纸本)9798350396225

Bananas have consistently been the Philippines' third most-sold crop throughout each quarter from the beginning of 2018 until the first quarter of 2020. Banana is a top-traded fruit worldwide. The Philippines is a major banana exporter and producer of the Cavendish variety, the primary variant exported to other countries. Numerous diseases have affected the banana trade, but the disease that has significantly impacted the export trade in the Philippines is the Fusarium oxysporum, Panama Tropical Race 4, and Panama Wilt, more commonly known as the Panama Disease. This paper's primary goal is to obtain the leaf degradation percentage with image segmentation of color spaces while utilizing OpenCV. The t-Statistic had a value of 0.34 and a t-Critical of 2.082. The t-Statistic has a lower value than the t-Critical, meaning that the null hypothesis that the method for determining the presence of Panama Disease is on par with field surveying and testing methods regarding leaf degradation should not be rejected. © 2023 IEEE.

关键词： Image segmentation

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Thermographic Images-Based Lung Cancer Detection Using a Convolutional Neural Network Algorithm

Thermographic Images-Based Lung Cancer Detection Using a Con...

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2023 international conference on IoT, Communication and automation Technology, ICICAT 2023

作者： Saha, Anindita Yadav, Rakesh Kumar Iftm University Department of Computer Science & Engineering UP Moradabad India Maharishi University of Information Technology Msoet Department of Computer Science & Engineering Lucknow India

ISBN: (纸本)9798350302820

The most recent statistics show that lung cancer is the most prevalent type of cancer worldwide, claiming the lives of an estimated untold number of people every year. Accurate diagnosis and early illness identification can increase the likelihood of effective treatment and decrease death. In fact, an early diagnosis can help prevent its spread and protect the prematurely ill from getting it. Machine learning, which uses algorithms capable of locating and identifying patterns in images, enables the identification of lung cancer. In this study, we propose a computer-aided diagnostic (CAD) method for patient identification and diagnosis in an image database. For improved classification outcomes and to identify a greater success rate for the categorization of mammography, we analyses and assess the convolution neural network (CNN) on the three effective cross-folds, k = 2, 3 and 5, and on various epochs (0, 1, 2, 3, 4, and 5). © 2023 IEEE.

关键词： Infrared imaging

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Monocular 3D Object Detection using YOL011 and MiDAS for Autonomous Driving

Monocular 3D Object Detection using YOL011 and MiDAS for Aut...

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2024 international conference on computer and Applications, ICCA 2024

作者： Abdou, Hisham Mohamed Mashaly, Maggie German International University Faculty of Engineering Robotics & Automation Cairo Egypt German University in Cairo Faculty of Information Engineering & Technology Cairo Egypt

ISBN: (纸本)9798350367560

Monocular 3D object detection is a critical component in autonomous driving systems, aiming to infer spatial properties from single-camera inputs. This paper presents a method that integrates YOL011 for 2D object detection and MiDaS for depth estimation to construct accurate 3D bounding boxes. By employing a calibration process and leveraging depth-aware tilting mechanisms, the model achieves precise object localization in real-world settings. The proposed approach demonstrated competitive accuracy on the KITTI dataset, with significant improvements in computational efficiency, making it suitable for real-time applications. Limitations in occlusion handling and depth extent estimation are discussed alongside future enhancement directions. © 2024 IEEE.

关键词： Object recognition

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Joint Optimization of Service Caching, Task Migration and Resource Allocation for Vehicular Edge Computing with Energy Harvesting 4

Joint Optimization of Service Caching, Task Migration and Re...

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4th international conference on computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2024

作者： Zhang, Yi Jiang, Qi Ma, Zhuo Abrokwa, Kofi Kwarteng School of Electrical Engineering and Automation Hefei University of Technology Heifei230009 China

ISBN: (纸本)9798350378542

Effective integration of available resources within edge nodes is essential to improve the performance of vehicular edge computing (VEC) to support various randomly offloaded tasks with limited computing capacity and constrained energy. This paper presents an intelligent adaptive resource integration strategy for VEC with harvesting energy. Service caching, task migration and resource allocation are jointly employed to accommodate the temporally and spatially varying computing demands. The optimization to minimize the long-term average task execution time under energy constraint is formulated as Markov decision processes and solved with a parameterized deep Q-network based learning algorithm. This algorithm employs a centralized training and distributed execution framework, where a parameter network and an action network respectively handle continuous and discrete decisions, effectively tackling the hybrid action space challenges in problem solving. Simulation results demonstrate that the proposed algorithm not only achieves faster convergence but also significantly improves system performance compared to benchmarks. © 2024 IEEE.

关键词： Markov processes

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Inspired Detection Model For Clustering Based Surveillance System: A Non-Conventional Review

Inspired Detection Model For Clustering Based Surveillance S...

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2024 IEEE international conference on automation and Computation, AUTOCOM 2024

作者： Gupta, Anil Kumar Sharma, Rupak Ojha, Rudra Pratap Srm Institute of Science and Technology Department of Computer Science and Engineering NCR Campus Ghaziabad Uttar Pradesh Modinagar India Srm Institute of Science and Technology Department of Computer Applications NCR Campus Ghaziabad Uttar Pradesh Modinagar India Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350382723

in current years, there has been a growing hobby in growing superior surveillance systems for detecting and monitoring anomalous activities. Traditional surveillance systems depend upon handmade policies and features, which are frequently limited of their capacity to accurately become aware of complex anomalies. To conquer this problem, we suggest a better visible anomaly detection version for clustering-based surveillance structures the use of long brief-time period reminiscence (LSTM) Diffusion manner. The proposed version incorporates the temporal context of the CCTV photos with the aid of making use of the LSTM network, which has been proven to deal with sequential data effectively. The LSTM community is used to extract movement features from the video frames, which are then fed into the clustering set of rules. This allows for the identification of comparable styles of sports and grouping them into clusters. Furthermore, we combine the concept of the Diffusion technique into the LSTM version to decorate the clustering method. The Diffusion procedure entails the gradual propagation of information among nodes in a network, which is particularly beneficial in figuring out abrupt changes in activities. This permits the version to locate anomalies with better accuracy and decrease fake alarms. To assess the performance of our proposed version, we behavior experiments on a real-global surveillance dataset. The consequences display that our model outperforms current techniques in terms of accuracy and detection price. © 2024 IEEE.

关键词： Diffusion

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Crowd Density Estimation and Real-time Monitoring using Digital Twins in Transportation Hubs 7

Crowd Density Estimation and Real-time Monitoring using Digi...

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7th IEEE Pune Section international conference, PuneCon 2024

作者： Shah, Malay Goyal, Sayal Salvi, Sanket Dr. Vishwanath Karad Mit World Peace University Department of Computer Engineering and Technology Pune India Symbiosis International University Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331527822

Digital twins transcend static models, offering dynamic virtual replicas of physical entities, thereby revolutionizing data-driven decision-making across industries. This paper proposes a novel digital twin system specifically designed for real-time crowd management, leveraging object detection with YOLOv8s architecture and 3D plotting using a *** library. Our proposed system can be used as an edge analytics tool to minimize latency and enhance real-time decision-making, crucial for applications like autonomous vehicles, industrial automation, and large crowd gatherings. The proposed system was implemented and tested on multiple pre-recorded video feeds and it was evaluated based on time to process frames. For the proposed system an accuracy of 84.21% was achieved by comparing the actual number of people present in the given frame vs the number of people detected. This research demonstrates that digital twin models provide a robust, proactive approach to optimizing operations, enhancing safety, and increasing visitor satisfaction in crowded spaces. © 2024 IEEE.

关键词： Crowdsourcing

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Deep Learning - Driven RPA Bot for Automated Evaluation of Handwritten Examination Papers Using Gen AI

Deep Learning - Driven RPA Bot for Automated Evaluation of H...

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2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Duraimurugan, N. Vaishnavi Sri, S.M. Subhikshaa, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331543617

The incorporation of Robotic Process automation (RPA) and deep learning in the educational evaluation system brings a strong, automated solution for assessing handwritten exam assessments. Educational institutions have exponentially started using RPA systems for monotonous activities like data storage and management, but the capacity extends far beyond these activities. This paper gives an automated solution for evaluating handwritten examination papers in educational institutions by combining advanced deep learning models and RPA (Robotic Process automation). Using CNNs (Convolutional Neural Networks) and BiLSTM (Bidirectional Long Short-Term Memory) networks, the system converts handwritten responses into machine-readable text, trained to various handwriting styles. For grading, a Gen-AI model is used to assess the answers recognized by CNN and BiLSTM. This provides precise grading that aligns closely with human correction. RPA does end-to-end automation, from scanning and processing papers through deep learning models for text recognition to grading and data management thus reducing human effort, enhancing consistency, saving time and improving efficiency in the evaluation process. This technique streamlines examination, allowing teachers and professors to focus more on personalized instruction and academic development. © 2024 IEEE.

关键词： Convolutional neural networks

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