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

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DDoS Attack Detection Using Explainable AI in Machine Learning 4th

DDoS Attack Detection Using Explainable AI in Machine Learni...

引用

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

作者： Jena, Sudhansu Kumar Ranjan, Ashish Singh, Vibhav Prakash MNNIT Allahabad Prayagraj India IIIT Sonepat Khewra Haryana India

ISBN: (纸本)9783031837951;9783031837968

In network security, Distributed Denial of Service (DDoS) attacks disrupt critical online services and are becoming increasingly sophisticated. Machine learning is essential for detecting and classifying these attacks, using novel datasets featuring modern attack types. As DDoS attacks target crucial platforms like banking and social networks, intrusion detection systems (IDS) integrated with machine learning are vital. Achieving 100% accuracy in attack detection remains challenging due to the lack of comprehensive datasets including newer attack types such as UDP-flood, SIDDoS, HTTP-flood, and Smurf. Machine learning classifies network traffic based on various features, distinguishing normal from DDoS traffic. Explainable AI (XAI) methods, including LIME and SHAP, enhance model interpretability and transparency, offering insights into feature contributions for attack detection. Related works highlight the importance of new datasets and information theory-based techniques, such as information entropy and correlation methods, for DDoS detection. Hybrid approaches and SDN implementations also show promise in improving accuracy. As DDoS attacks evolve and IoT devices increase, this paper aims to enhance network security and protect critical online servies.

关键词： DDos Attack LIME SHAP Explainable AI

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SF-SARnet: Custom SAR Image Generation for Specific Locations 5th

SF-SARnet: Custom SAR Image Generation for Specific Location...

引用

5th China Intelligent Robotics Academic Conference

作者： Ju, Moran Li, Mulin Jin, Sinian Fang, Da Niu, Buniu Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Liaoning Peoples R China State Grid Liaoning Elect Co Ltd Dalian Shahekou Dist Power Supply Branch Dalian Peoples R China

ISBN: (纸本)9789819616138;9789819616145

The scarcity of SAR images and their time-consuming and laborious labeling is a pressing issue in the development of SAR detection algorithms. In addition, the existing generation algorithms do not consider the complex context of real SAR images, resulting in low-quality of the results. To solve these difficulties, we introduce a SAR image generation model consisting of a scene fusion module, generator and discriminator called SF-SARnet. The model accepts target location information as input and generates a mask map, which is then fed to the network for generation. Our main contributions include the following: (1) We design a generator based on the traditional encoder-decoder structure, on top of which we add jump connections at each odd layer. (2) To increase the quality of the fake generation results, we design a loss function that incorporates perceptual loss. (3) To verify the model's performance, comparative verification was performed on the HRSID dataset. The results indicate that our model outperforms the others. Moreover, the generated images can be conveniently used for other related tasks.

关键词： Generative Adversarial Networks Image Generation Synthetic Aperture Radar

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Approximation Algorithms on Linear Equalities and Inequalities Mod p 42nd

Approximation Algorithms on Linear Equalities and Inequaliti...

引用

42nd National Conference on Theoretical computer science

作者： Tang, Zhongzheng Zou, Haoyang Diao, Zhuo Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Cent Univ Finance & Econ Sch Math & Stat Beijing 100081 Peoples R China

ISBN: (纸本)9789819614899;9789819614905

In this paper, we have a set of weighted linear equalities and inequalities of the form A(1)x equivalent to 0(mod p), A2x not equivalent to 0(mod p) where all entries of A(1) and A(2) are in {- 1, 0, 1}. The objective is to assign each x(i) to Z(p) = {0,..., p-1} to maximize the total weight of the satisfied equalities and inequalities. This problem is a generalization of k-Correlation Clustering problem. We design an approximation algorithm with the approximation ratio max{a, (2-p)a+p-1/p}, where a is the weighted proportion of equalities in all equalities and inequalities. As a varies from 0 to 1, the approximation ratio varies from p-1/p to 1 and the minimum value is 1/2 when a is 1/2.

关键词： Linear Equalities and Inequalities mod p Approximation Algorithms Choosing the Better of Two Solutions

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Detection Model for Pulmonary TB on Augmented X-ray Images Enhanced Through Histogram Equalization 4th

Detection Model for Pulmonary TB on Augmented X-ray Images E...

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

作者： Siddiqui, Abdul Karim Garg, Vijay Kumar Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9783031837890;9783031837906

We find deep learning as a subset of machine learning that runs an incurable disease diagnostic system with multi-neural architectures. In recent age a neural model has capabilities to detect more accurately and quickly resulting classified labels. It helps medical practitioners to develop and explain effective detection systems. Tuberculosis is one of the biggest threats that has been remaining a contagious disease since its discovery, posing a significant risk to millions of lives. Many people yield to tuberculosis because of incomplete treatments or the lack of preventive measures. An effective pulmonary TB diagnostic system has remained a big challenge. As it is a contagious disease, it mainly affects the lungs and other vital organs of the human body. Through this paper, an enhanced detection model to classify tuberculosis and non-TB cases using clinical X-ray images has been proposed. The augmented histogram equalized X-rays were applied to top state-of-the-art classifiers, with ResNet50 and ResNet152 achieving the best results, showing accuracies of 99.20% and 99.10%, respectively.

关键词： Pulmonary TB Deep learning Machine learning Chest X-ray images Tuberculosis

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Exploratory Study on Different Transformer Models 4th

Exploratory Study on Different Transformer Models

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

作者： Kumar, Atul Barla, Ciprian Singh, Raman Kumar Kant, Abhishek Kumar, Divya MNNIT Allahabad Dept Comp Sci & Engn Prayagraj India

ISBN: (纸本)9783031837951;9783031837968

Transformers have revolutionized natural language processing (NLP) by surpassing recurrent neural networks (RNNs) in capturing long-range dependencies within sequences. This is primarily due to their self-attention mechanism, which allows the model to attend to all parts of the input simultaneously. However, the complex architecture and numerous hyperparameters of transformers pose challenges in achieving optimal performance. This paper conducts a comprehensive comparison of different transformer architectures and hyperparameters. We explore variations in encoder and decoder design, investigate the impact of hidden layer size, number of attention heads, learning rate, and dropout rate, among others. We evaluate the performance of these configurations on natural language generation (NLG). Our findings shed light on the influence of specific architectural choices and hyperparameter settings on transformer performance, offering valuable insights for researchers and practitioners to optimize transformer models for their specific NLP applications.

关键词： Transformer Models Natural Language Processing Recurrent Neural Networks Natural Language Generation

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An Improved Hybrid Recommendation Algorithm for Vehicle-Cargo Matching in the Logistics 5th

An Improved Hybrid Recommendation Algorithm for Vehicle-Carg...

引用

5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Wang, Yiting Wei, Lifen Xian Univ Technol Sch Econ & Management Xian Peoples R China

ISBN: (纸本)9783031807749;9783031807756

This paper aims to optimize the vehicle-cargo matching of the logistics platform through the hybrid recommendation algorithm to achieve the effective matching between vehicle and cargo sources. This paper constructs a hybrid recommendation model taken into account the content-based recommendation algorithm and the item-based collaborative filtering algorithm. The content-based recommendation algorithm has advantages in matching the inherent attributes of vehicle and cargo sources, whereas the item-based collaborative filtering can deal with the problem of interest drift, and the advantages of these two algorithms are integrated in the hybrid model in this paper. The matching factors are pick out from the data of current logistics platforms, and the feedback competition method is applied for the calculation of the weights, and then the weight similarity is worked out for the recommendation. Multiple aspects are considered in the hybrid model, so as to satisfy the users by recommendation results, thereby achieving vehicle-cargo matching. This paper focuses on the matching of vehicles and cargoes in multiple dimensions, where the personalized and diversified comprehensive recommendation results are acquired to meet the needs of car owners and cargo owners.

关键词： Vehicle-cargo matching Hybrid recommendation algorithm Logistics Content-based recommendation algorithm

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Ordinal and Position Enhance the Framework of the Multimodal Dialogue System 5th

Ordinal and Position Enhance the Framework of the Multimodal...

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5th China Intelligent Robotics Academic Conference

作者： Xu, Longmei Chen, Fei Li, Jinyu Dalian Maritime Univ Dalian Liaoning Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Multimodal dialogue systems aim to process multimodal input information such as text and images simultaneously and then generate coherent and meaningful output dialogue responses. Although current methods have achieved notable progress, they have shortcomings in the following aspects: 1) Insufficient modeling of relationships between multimodal semantic elements, especially ignoring the interaction between the ordinal information in text and the position information of images. 2) Ineffectively integrating information from various modalities in multimodal conversations. To address these limitations, this paper proposes a framework for a multimodal dialogue system. We integrate both ordinal words from text and the position information of images as ordinal information to strengthen the interaction between multimodal semantic elements and to improve the understanding of user intent. In particular, in this framework, we incorporated the self-attention mechanism into the multimodal factorized bilinear pooling (MFB) method, which captures long-range dependencies between modalities, enhancing the effectiveness of multimodal data fusion. Finally, we conducted comprehensive experiments on public multimodal dialogue datasets (MMConv and MMD) and demonstrated that our proposed framework outperforms other methods in the experiments.

关键词： Multimodal Dialogue System Multimodal Fusion Ordinal information Multimodal Factorized Bilinear Pooling

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A Bone Fracture Detection Using CNN Model 4th

A Bone Fracture Detection Using CNN Model

引用

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

作者： Nadia, Afsana Nuruzzaman, Md. Nomani, Khan Md. Shibli Billal, Md. Masum East West Univ Dhaka Bangladesh Hajee Mohammad Danesh Sci & Technol Univ Dinajpur Bangladesh Amer Int Univ Bangladesh Dhaka Bangladesh Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9783031821554;9783031821561

Accurate bone fracture detection can be critical in the medical diagnosis process. This paper proposes a bone fracture detection approach based on deep learning using Convolutional Neural Networks (CNNs) with X-ray images. We have explored three models: one predefined model (MNASNet1) and two custom models (Proposed Model 1 and Proposed Model 2). Model performance is evaluated using metrics such as F1-Score, precision, and accuracy. Results show that the proposed models achieved outstanding performance compared to the MNASNet1 model in most of the metrics. Specifically, Proposed Model 1 achieves the highest overall F1-score, recall, and precision (1.00) and also has the highest validation accuracy (99.89%). Our objective is to develop a custom CNN architecture that demonstrates the potential of our proposed models to enhance fracture detection accuracy and develop a web application to detect bone fractures faster to facilitate more precise clinical assessment and treatment decisions. Doctors can emphasize the benefits and explore collaboration opportunities in a relatable way to integrate our web application into medical practices.

关键词： Bone fracture detection CNN Deep learning X-ray image Image classification Biomedical application Medical image processing

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Efficient Path Planning for Large-Scale Vehicular Networks via Multi-agent Mean Field Reinforcement Learning 5th

Efficient Path Planning for Large-Scale Vehicular Networks v...

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

作者： Chen, Tian Chen, Wenbin Gao, Huifei Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Southern Univ Sci & Technol Inst Future Networks Shenzhen Peoples R China Linkinsense Co Ltd Hefei Peoples R China

ISBN: (纸本)9789819770007;9789819770014

While existing research has made progress in optimizing the route planning performance for a small number of vehicles, it often falls short of adequately considering the dynamic interactions and mutual influence among vehicles in large-scale vehicular networks. This deficiency limits its effectiveness in addressing urban traffic congestion and enhancing the overall efficiency of the transportation system. Therefore, in this paper, we propose a multi-agent mean field reinforcement learning (MAMFRL) framework for large-scale vehicle path planning problems, aiming to improve the efficiency of individual vehicles and the entire transportation system. Specifically, we first utilize mean field (MF) theory to simplify the interactions between agents. Second, MAMFRL employs a convolutional neural network (CNN) layer to extract road information features and obtain spatial correlations of urban traffic. Finally, MAMFRL constrains the rewards to improve the performance of the whole transportation system. Experimental results show that the proposed method can reduce average vehicle travel time by up to 9% and average intersection queue lengths by up to 27.8%.

关键词： traffic congestion intelligent transportation system path planning deep reinforcement learning

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Decoding Student Feedback in Engineering Education: A Latent Dirichlet Allocation and PyLDAvis Approach 4th

Decoding Student Feedback in Engineering Education: A Latent...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Alhassan, Ilyas Rege, Manjeet Univ St Thomas Dept Software Engn & Data Sci St Paul MN 55105 USA

ISBN: (纸本)9783031734762;9783031734779

This study investigates the efficacy of Latent Dirichlet Allocation (LDA) and PyLDAvis in analyzing feedback from final-year engineering students. By dissecting responses to strategically selected questions, we identify key themes that include collaboration, innovation, and technical skills development. This research provides actionable insights for educators to refine teaching strategies and curriculum design to align with industry trends and student experiences. Our findings underscore the unique capabilities of LDA and PyLDAvis in capturing and visualizing students' nuanced perspectives, thereby offering a valuable tool for enhancing responsive engineering education.

关键词： Engineering Education Student Feedback LDA Analysis PyLDAvis Visualization Curriculum Development

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