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检索条件"丛书名=Communications in Computer and Information Science"

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Unmanned Combat Aerial Vehicle Air Combat Decision-Making Method Based on Trust Region-Based Proximal Policy Optimization with Rollback 5th

Unmanned Combat Aerial Vehicle Air Combat Decision-Making Me...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Li, Peng Zhu, Jing Zhai, Xiangping Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

The manuscript introduces an autonomous air combat maneuver selection approach for unmanned combat aerial vehicles (UCAVs), grounded in deep reinforcement learning techniques., that is the trust region-based proximal policy optimization with rollback (TR-PPO-RB). The traditional proximal policy optimization (PPO) algorithm has been enhanced through the introduction of a novel clipping function that facilitates the accommodation of rollback actions. Additionally, new trigger conditions for clipping have been formulated, grounded in the principles of trust region theory. Through the constructed multi-agent simulation environment, this paper verifies the effectiveness and practicability of the proposed method in simulating red-blue confrontation. Experimental results show that the TR-PPO-RB method can effectively guide the UCAV to make air combat decisions and improve the air combat rate significantly.

关键词： UCAV Confrontation Air Combat Decision-making Deep Reinforcement Learning TR-PPO-RB Algorithm

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xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision Long Short-Term Memory Network 7th

xLSTM-FER: Enhancing Student Expression Recognition with Ext...

引用

8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Huang, Qionghao Chen, Jili Zhejiang Normal Univ Zhejiang Key Lab Intelligent Educ Technol & Appli Jinhua Zhejiang Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Student expression recognition has become an essential tool for assessing learning experiences and emotional states. This paper introduces xLSTM-FER, a novel architecture derived from the Extended Long Short-Term Memory (xLSTM), designed to enhance the accuracy and efficiency of expression recognition through advanced sequence processing capabilities for student facial expression recognition. xLSTM-FER processes input images by segmenting them into a series of patches and leveraging a stack of xLSTM blocks to handle these patches. xLSTMFER can capture subtle changes in real-world students' facial expressions and improve recognition accuracy by learning spatial-temporal relationships within the sequence. Experiments on CK+, RAF-DF, and FERplus demonstrate the potential of xLSTM-FER in expression recognition tasks, showing better performance compared to state-of-the-art methods on standard datasets. The linear computational and memory complexity of xLSTM-FER make it particularly suitable for handling high-resolution images. Moreover, the design of xLSTM-FER allows for efficient processing of non-sequential inputs such as images without additional computation.

关键词： Facial Expression Recognition Student Academic Performance Memory Network Vision xLSTM

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Integrating Machine Learning and Deep Learning Techniques for Enhanced Historical Script Classification 4th

Integrating Machine Learning and Deep Learning Techniques fo...

引用

4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Boudraa, Merouane Bennour, Akram Al-Sarem, Mohammed Shukla, Piyush Kumar Echahid Cheikh Larbi Tebessi Univ LAMIS Lab Tebessa Algeria Taibah Univ Coll Comp Sci & Engn Medina Saudi Arabia Technol Univ Madhya Pradesh Bhopal Madhya Pradesh India

ISBN: (纸本)9783031821554;9783031821561

The accurate identification of script styles in historical manuscripts is crucial for gaining insights into their historical context and significance. This study proposes an integrated system that combines manual and machine learning features to effectively identify script styles within manuscripts, utilizing the ClAMM dataset. The system comprises three primary steps: preprocessing the dataset using denoising and binarization techniques, extracting manual features using the Harris detector, and performing script classification using pretrained CNN models. By merging manual feature engineering with advanced deep learning techniques, our system showcases its ability to accurately recognize script styles competing with state-of-the-art methods with accuracy of 89.2%. These results not only validate the effectiveness of our approach but also contribute significantly to the broader advancement of script classification in historical manuscript analysis.

关键词： Script Classification historical manuscripts feature extraction deep learning transfer-learning

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A Design of Experiments to Analyze Crop Growth Based on Seed Position in Sowing 4th

A Design of Experiments to Analyze Crop Growth Based on Seed...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Torres-Mercado, Alina Eustate-Daza, Karen Moreno-Cruz, Jeffrey Pais Cerna, Gabriel Troncoso-Palacio, Alexander Univ Costa Especializac Gest Integral Calidad Barranquilla Colombia Univ Andres Bello Fac Engn Antonio Varas 880 Providencia Santiago 7500000 Chile

ISBN: (纸本)9783031832093;9783031832109

The methods of sowing and the specific conditions of the seeds used have a significant impact on the complex process of plant growth. The choice of seed location not only reflects the genetic and physiological characteristics of the plant but also helps to make it healthier. This, in turn, leads to improved growth and higher yields in various agricultural environments. In this study, an experimental design was implemented to analyze different methods of planting cacao seeds to determine which method is most effective in minimizing root and hypocotyl malformations. For the seeding process, it was ensured that all samples were from the same source. Two types of substrates and three different levels of seed placement were selected to serve as the basis for cultivation in soil contained in polyethylene bags. The experimental design was randomized with two factors and three levels, resulting in a total of 18 samples. The results were analyzed using the Design of Experiments (DOE) methodology in Minitab (R) software, along with photographic documentation of seed growth after 15 days. The analysis revealed that the seeds sown in the vertical position had fewer malformations. Specifically, 83% of the total samples fell into this category, which is consistent with a normal distribution. It can therefore be concluded that vertical seed positioning is the most effective planting method for promoting healthy growth and minimizing malformations.

关键词： Cocoa crops Design of experiments Planting methods Seed position

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An Analysis of Malicious Behaviors of Open-Source Packages Using Dynamic Analysis 37th

An Analysis of Malicious Behaviors of Open-Source Packages U...

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37th International Conference on computer Applications in Industry and Engineering

作者： Thanh-Cong Nguyen Duc-Ly Vu Debnath, Narayan C. Univ Informat Technol Ho Chi Minh City Vietnam Eastern Int Univ Sch Comp & Informat Technol Binh Duong Vietnam

ISBN: (纸本)9783031762727;9783031762734

There has been an increasing number of malicious open-source packages in recent years. A recent backdoor attack on the Linux xz utility has highlighted the importance of security checks on open-source packages, especially popular ones. While major security scanners focus on identifying vulnerabilities (CVEs) in open-source packages, there are very few studies on malware analysis techniques for them. Similar to traditional malware analysis, there are two types of analysis for open-source packages: static and dynamic analysis. Static analysis techniques mainly focus on analyzing the source code of a package while dynamic analysis techniques execute the code in an isolated environment. Dynamic analysis techniques seem more promising than static analysis techniques, as they can expose packages' behaviors at runtime. However, current dynamic analysis tools (e.g., package-analysis) make minimal effort to provide insight into the behaviors of open-source packages. In this paper, we attempt to analyze the dynamic behaviors of open-source packages on popular package repositories, including npm, PyPI, RubyGems, Packagist, and ***. We also analyze the discrepancies in behaviors between benign and malicious packages at runtime, which is helpful in building rules for malware detection. Our study finds that malicious packages perform a significantly higher number of domain communications and command executions. Malicious packages use simple techniques for malicious operations such as base64 or curl.

关键词： Dynamic malware analysis Open-source malicious packages Open-source software security Software supply chain Security Software supply chain attacks

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Photovoltaic Cell Defect Classification Using Attention U-Net Image Segmentation 4th

Photovoltaic Cell Defect Classification Using Attention U-Ne...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Alzu'bi, Ahmad Saleh, Zaid Jordan Univ Sci & Technol Dept Comp Sci Irbid 22110 Jordan Jordan Univ Sci & Technol Dept Elect Engn Irbid 22110 Jordan

ISBN: (纸本)9783031821523;9783031821530

The need to automate solar cell quality monitoring techniques is growing as solar power utilization gets significant momentum. However, the detection of internal defects in photovoltaic (PV) cells is still quite a challenge, highlighting the demand for a reliable method to reduce the requirement for manual inspections. This paper presents a deep learning model to automatically detect and classify defective PV cells in Electroluminescence images using attention-based U-Net image segmentation. The proposed deep learning architecture uses ResNet50 and attention U-Net networks to formulate a set of discriminating image features, providing an effective procedure of deep transfer learning. The extracted image descriptors are encoded and learned from image masks and annotations that were reprocessed to improve the generalization capability of the photovoltaic cell classifier. The semantic deep learning model with image augmentation and mask processing provides a baseline to detect and classify any possible faults in photovoltaic cells. The experimental results demonstrate the effectiveness of semantic attention U-Net-Resnet50 deep architecture in classifying PV cells, achieving a precision of 95% and an F1-score of 94%. It also achieved an IoU of 90% for PV image segmentation.

关键词： Photovoltaic Cell Deep Learning Attention U-Net Image Semantic Segmentation

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Exploring Efficacy of Cryptographic Hash Functions SHA-256 and BLAKE2s Against HashBoost 4th

Exploring Efficacy of Cryptographic Hash Functions SHA-256 a...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Yadav, Jyoti Yashwant Shevkar, Ranjana Sanjay Savitribai Phule Pune Univ Dept Comp Sci Pune Maharashtra India

ISBN: (纸本)9783031837821;9783031837838

Cryptographic hash functions are the backbone of data integrity and security, playing a pivotal role in safeguarding digital information. The study begins with a detailed comparison of conventional hash functions, examining key parameters such as block size, word size, output size, number of rounds, logical operations as well as their compression function. The novel HashBoost function, designed to enhance both computational efficiency and security, is introduced. This paper presents a comprehensive analysis of three cryptographic hash functions: SHA-256, BLAKE2s, and HashBoost, with a focus on their performance across varying message sizes. The results indicate that while SHA-256 and BLAKE2s offer robust security features, HashBoost significantly enhances in terms of computational speed, particularly with larger data volumes. HashBoost maintains essential cryptographic properties, including determinism, pre-image resistance, collision resistance, and the avalanche effect, making it a strong candidate for time-sensitive applications like blockchain. Future work includes the development of a dedicated blockchain simulator to test and validate these functions in real-world scenarios, ensuring that HashBoost can meet the demanding requirements of modern cryptographic applications. The findings highlight the importance of balancing security and efficiency in the design of cryptographic hash functions.

关键词： Cryptographic Hash Function HashBoost BLAKE2s SHA-256 Efficiency

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Brain Tumor Classification from MRI Scans Using a Novel CNN Architecture and Optimization Techniques 4th

Brain Tumor Classification from MRI Scans Using a Novel CNN ...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Hussain, Shaik Jaffar Devi, B. Rupa Mahalakshmi, V. Harikala Parveen, S. Z. Athinarayanan, S. Sri Venkateswara Inst Sci & Technol Dept CSE Kadapa 516003 India Annamacharya Inst Technol & Sci Dept CSE Tirupati Andhra Pradesh India Jazan Univ Coll Engn & Comp Sci Dept Comp Sci Jazan Saudi Arabia Annamacharya Univ Dept ECE Rajampet India Annamacharya Inst Technol & Sci UGC AUT ONOMOUS Dept CSE AI & ML Kadapa 516003 AP India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Sch Comp Dept CSE Chennai Tamil Nadu India

ISBN: (纸本)9783031734762;9783031734779

Classification of brain tumours is crucial for computer-aided diagnostics (CAD) in health assessments. In light of the extensive procedures involved, manually identifying brain tumors using magnetic resonance imaging (MRI) is frequently labor-intensive and difficult, with the possibility of errors in detection and classification. Healthcare has significantly benefited from current developments in Deep Learning (DL), which have greatly enhanced the automation of medical image processing and diagnoses. One subclass of DL techniques, Convolutional Neural Networks (CNNs), is particularly good at visual learning and image categorization. To categorize brain tumours into three groups: gliomas, meningiomas, and pituitary tumours, we introduced the CNN method. We assessed the algorithm's performance using a benchmark dataset and contrasted it with pre-trained models already in use, including VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3. According to the experimental findings, our suggested model had a high classification accuracy of 98.5%, with 99% precision, recall, and f1-score. These outcomes suggest that our approach accurately classifies the most prevalent brain tumours. The algorithm is a valuable tool to help clinicians identify brain tumours quickly and accurately because of its excellent generalization ability and speed of execution.

关键词： Brain Tumor MRI CNN Deep Learning Transfer Learning

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Enhancing Sustainability in the Hospital Supply Chain: An Analysis of Advancements, Challenges, and Future Directions 4th

Enhancing Sustainability in the Hospital Supply Chain: An An...

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Ruiz-Orjuela, Erika Tatiana Gatica, Gustavo Adarme-Jaimes, Wilson Univ Nacl Colombia Bogota Colombia Univ Andres Bello Santiago Chile

ISBN: (纸本)9783031840777;9783031840784

Hospital supply chain management (HSCM) is under pressure to adopt sustainable practices while balancing economic, social, and environmental factors. This systematic literature review explores the advancements, challenges, and future perspectives of sustainability in HSCM. By drawing on articles published between 2013 and 2023, the review identifies key themes such as the adoption of emerging technologies, the impact of socioeconomic changes, the need for resilience and risk management, and the potential of the circular economy. The analysis reveals a growing emphasis on social sustainability, which considers stakeholder perspectives and ethical considerations in supply chain decisions. Challenges include integrating sustainability into existing practices, balancing competing objectives, and measuring the impact of sustainable initiatives. Future research in HSCM should explore emerging technologies, social sustainability, post-pandemic resilience, and circular economy principles. By addressing these challenges and opportunities, stakeholders can contribute to a more sustainable and resilient healthcare system.

关键词： Hospital supply chain review sustainability trends

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Key Dimensions for Human-Robot Interaction in XR for Rescue Robots 1

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15th Indian Conference on Human-computer Interaction Design and Research

作者： Rajashekhar, V. S. Indian Inst Technol Dept Design Kanpur Uttar Pradesh India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

The post-earthquake search operation requires faster response by the rescue team since several lives need to be saved quickly. Designing the Human-Robot Interaction (HRI) for Extended Reality (XR) requires knowledge of virtual and remote environments. While interacting with the virtual and remote environments, the user's perspective needs to be addressed based on the scenario. This work focuses on selecting the six key dimensions for XR-based remote HRI. Then, we arrive at the conditions in the local environment and map the key dimensions for each of them. This will help the rescue team face post-earthquake search operations with the available XR technologies.

关键词： Human Robot Interaction Extended Reality Rigid bodied robots Soft robots

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