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A family of maximum margin criterion for adaptive learning 25th

A family of maximum margin criterion for adaptive learning

引用

25th International Conference on Neural Information Processing, ICONIP 2018

作者： Cheng, Miao Liu, Zunren Zou, Hongwei Tsoi, Ah Chung Department of Computer Science Qingdao University Qingdao China Division of Information Technology Chongqing Branch of China Merchants Bank Chongqing China Department of Computer Science and Software Engineering University of Wollongong Wollongong Australia

ISBN: (纸本)9783030041816

In recent years, pattern analysis plays an important role in data mining and recognition, and many variants have been proposed to handle complicated scenarios. In the literature, it has been quite familiar with high dimensionality of data samples, but either such characteristics or large data sets have become usual sense in real-world applications. In this work, an improved maximum margin criterion (MMC) method is introduced firstly. With the new definition of MMC, several variants of MMC, including random MMC, layered MMC, 2D2 MMC, are designed to make adaptive learning applicable. Particularly, the MMC network is developed to learn deep features of images in light of simple deep networks. Experimental results on a diversity of data sets demonstrate the discriminant ability of proposed MMC methods are component to be adopted in complicated application scenarios. © Springer Nature Switzerland AG 2018.

关键词： Data mining

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Enhancing Skin Lesion Classification with Ensemble Data Augmentation and Convolutional Neural Networks 2nd

Enhancing Skin Lesion Classification with Ensemble Data Augm...

引用

2nd International Congress of Electrical and computer engineering, ICECENG 2023

作者： Onan, Aytug Bulut, Vahide Ezgi, Ahmet İzmir Katip Celebi University Department of Computer Engineering Izmir Turkey İzmir Katip Celebi University Division of Software Engineering Izmir Turkey İzmir Katip Celebi University Department of Engineering Sciences Izmir Turkey

ISBN: (纸本)9783031527593

Convolutional neural networks (CNNs) have gained considerable recognition due to their achievements in image classification tasks, notably in the domain of medical imaging. However, the scarcity of accurately labeled datasets, especially concerning skin lesion images, poses significant challenges. The manual annotation of medical images, such as those related to skin lesions, is a labor-intensive process requiring expertise. To overcome the limited availability of labeled data, data augmentation emerges as a crucial solution. This technique involves applying various transformations to the original images, enriching the training dataset and improving the model’s generalization. This chapter provides a comprehensive examination of both traditional and ensemble data augmentation methods tailored specifically for skin lesion classification. Traditional methods encompass geometric and color-space transformations, while ensemble methods combine multiple augmentation strategies. Additionally, besides focusing on data augmentation, this study employs four prominent CNN architectures: ResNet50, MobileNetV2, EfficientNet, and DenseNet. Through empirical analysis, this research underscores the significant impact of data augmentation on model accuracy and robustness, especially when dealing with limited annotated data. These findings contribute to the advancement of automated skin lesion identification, offering potential applications in clinical settings by addressing the challenges posed by data scarcity and annotation complexity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image classification

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Application task virtualization platform for spacecraft computer flight software

Application task virtualization platform for spacecraft comp...

引用

DAta Systems In Aerospace, DASIA 2012

作者： Jeong, Hyeona Yoon, Youngil Joe, Hyunwoo Kim, Hyungshin Kang, Sooyeon Choen, Yee-Jin Department of Computer Science and Engineering Chungnam National Universty Deajeon Korea Republic of Satellite Flight Software Division Korea Aerospace Research Institute Deajeon Korea Republic of

ISBN: (纸本)9789290922650

Spacecraft on-board computer is a mission -critical embedded system. Flight software on the computer should be dependable and deterministic so that it has a long development cycle. Due to that, the development and maintenance cost of the flight software is very high. The fully-proven heritage flight software needs to be modified or redeveloped if they are to be executed on a different operating system. Application task dependency to the operating system causes redevelopment or at least re-verification of the heritage software. If the heritage software can be reused with little change, the development cost can be greatly reduced without sacrificing its proven properties. In this paper, we propose a light-weight task virtualization platform to improve the reusability of the flight software. For the platform, we devised a new class of application called "SpaceApp" which is a light weight procedures executed by a virtual machine. An integrated development environment is also developed.

关键词： Virtualization

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Facial animation on an anatomy-based hierarchical face model

Facial animation on an anatomy-based hierarchical face model

引用

Third International Conference on Virtual Reality and Its Application in Industry

作者： Zhang, Yu Prakash, Edmond C. Sung, Eric IMRL Sch. of Elec./Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Division of Software Systems School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore

In this paper we propose a new hierarchical 3D facial model based on anatomical knowledge that provides high fidelity for realistic facial expression animation. Like real human face, the facial model has a hierarchical biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The deformable skin model has multi-layer structure to approximate different types of soft tissue. It takes into account the nonlinear stress-strain relationship of the skin and the fact that soft tissue is almost incompressible. Different types of muscle models have been developed to simulate distribution of the muscle force on the skin due to muscle contraction. By the presence of the skull model, our facial model takes advantage of both more accurate facial deformation and the consideration of facial anatomy during the interactive definition of facial muscles. Under the muscular force, the deformation of the facial skin is evaluated using numerical integration of the governing dynamic equations. The dynamic facial animation algorithm runs at interactive rate with flexible and realistic facial expressions to be generated.

关键词： Virtual reality

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TF-ICF: A new term weighting scheme for clustering dynamic data streams

TF-ICF: A new term weighting scheme for clustering dynamic d...

引用

5th International Conference on Machine Learning and Applications, ICMLA 2006

作者： Reed, Joel W. Yu, Jiao Potok, Thomas E. Klump, Brian A. Elmore, Mark T. Hurson, Ali R. Applied Software Engineering Research Group Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge TN 37831 Computer Science and Engineering Department Pennsylvania State University University Park PA 16801

ISBN: (纸本)0769527353

In this paper, we propose a new term weighting scheme called Term Frequency - Inverse Corpus Frequency (TF-ICF). It does not require term frequency information from other documents within the document collection and thus, it enables us to generate the document vectors of N streaming documents in linear time. In the context of a machine learning application, unsupervised document clustering, we evaluated the effectiveness of the proposed approach in comparison to five widely used term weighting schemes through extensive experimentation. Our results show that TF-ICF can produce document clusters that are of comparable quality as those generated by the widely recognized term weighting schemes and it is significantly faster than those methods. © 2006 IEEE.

关键词： Data structures

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CardioNetFusion: A Deep Learning and Explainable AI Integrated System for Cardiovascular Disease Detection 24

CardioNetFusion: A Deep Learning and Explainable AI Integrat...

引用

24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Raja, Gunasekaran Essaky, Selvam Muthu, Surya Raj Suresh, Babith Sarish Cho, Jaehyuk Yoo, Seohyun Easwaramoorthy, Sathishkumar Veerappampalayam Anna University NGNLab Department of Computer Technology Chennai India Jeonbuk National University Department of Software Engineering Division of Electronics and Information Engineering Jeonju-si Korea Republic of Sunway University School of Engineering and Technology Malaysia

ISBN: (纸本)9798331530631

Early detection of Cardiovascular Diseases (CVD) plays a vital role in effective treatment and management. However, traditional methods for analyzing ECG signals are often limited in both accuracy and interpretability. In this work, we introduce a novel model, CardioNetFusion, designed to address these limitations by incorporating advancements in Deep Learning (DL) and Explainable AI (XAI). The model leverages the strengths of Convolutional Neural Networks (CNN), MobileNetV2, and VGG16 to improve the robustness of ECG signal interpretation. To further enhance transparency and provide healthcare professionals with actionable insights, saliency maps are utilized to visualize model predictions. Additionally, by integrating an Application Programming Interface (API) within an IoT-based sensor fusion framework, the model supports real-time cardiovascular health monitoring. Achieving a classification accuracy of 98.7% in detecting arrhythmias, CardioNetFusion surpasses existing methodologies and offers a practical, reliable solution for early CVD diagnosis and management. Traditional methods of ECG analysis often have limitations in terms of diagnostic accuracy and interpretability. This paper proposes a CardioNetFusion model to address the challenges of conventional methods of ECG data analysis by leveraging advances in Deep Learning (DL) and Explainable AI (XAI). The proposed CardioNetFusion model integrates Convolutional Neural Networks (CNN), MobileNetV2, and VGG16 models to improve the robustness of ECG signal interpretation. Additionally, we utilized saliency maps to enhance model transparency and provide clear, actionable insights for healthcare professionals. Integrating Application Programming Interface (API) in the IoT-based sensor fusion approach to our proposed model facilitates seamless real-time monitoring of cardiovascular health. With an impressive 98.7% accuracy in arrhythmia classification, CardioNetFusion outperformed existing methods and ensured reliab

关键词： Convolutional neural networks

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Optimization Performance Analysis of Different Initialization Methods in Single-Objective and Multi-Objective Chaotic Evolution 13

Optimization Performance Analysis of Different Initializatio...

引用

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Zhang, Xiaoyi Pei, Yan Jiang, Huiyan Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu965-8580 Japan University of Aizu Computer Science Division Aizuwakamatsu965-8580 Japan Northeastern University College of Software Shenyang110819 China

ISBN: (纸本)9798350373332

Chaotic Evolution (CE) is a significantly faster and more robust method for solving single-objective and multi-objective optimization problems. However, there are various factors that can impact the performance of CE, with population initialization being one of them. A well-executed initialization method can contribute to the discovery of better solutions and enhance both convergence rate and accuracy. This paper conducts a comparative experiment on CE algorithms using six different initialization methods. The performance of the CE algorithms utilizing these various initialization methods is analyzed and discussed. To assess convergence rate and accuracy, eight single-objective optimization benchmark functions and five multi-objective optimization benchmark functions are employed in our analysis. The evaluation results reveal significant disparities among the six initialization methods, indicating that different approaches to initialization have a substantial influence on the optimization performance of the CE algorithm. © 2024 IEEE.

关键词： Optimization algorithms

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A handwritten character recognition system based on acceleration

A handwritten character recognition system based on accelera...

引用

7th International Conference on Digital Content, Multimedia Technology and Its Applications, IDCTA 2011

作者： Yuan, Chun Zhang, Shiqi Wang, Zhao Information Science and Technology Division Graduate School at Shenzhen Tsinghua University Shenzhen China Institute of Software School of Electronics Engineering and Computer Science Peking University Beijing China

ISBN: (纸本)9788988678466

In this paper, an handwritten character recognition system based on acceleration is presented. The character recognition system using a 3-dimensional (3D) accelerometer, includes three procedures: original signal detection, signal processing (preprocessing and quantization) and recognition/classification. In quantization procedure, Trajectory Orientation (TO) and Curve Feature (CF) are adopted and compared. In recognition procedure, Fully-connected Hidden Markov Model (HMM) and Left-Right HMM are both implemented and compared. The system, in the recognition of 10 Arabic numerals, achieves the Correct Rate(CR) of 99.05% and the Total Correct Rate (TCR) of 94.76%. © 2011 AICIT.

关键词： Accelerometers

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Optimizing energy using probabilistic routing in Underwater Sensor Network

Optimizing energy using probabilistic routing in Underwater ...

引用

2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2012

作者： Dhurandher, Sanjay K. Obaidat, Mohammad S. Gupta, Abhishek Gupta, Prateek Goel, Siddharth Division of Information Technology Netaji Subhas Institute of Technology University of Delhi New Delhi India Department of Computer Science and Software Engineering Monmouth University NJ United States

ISBN: (纸本)9789898565204

As the Importance of Applications, Such as Ocean Sampling, Environmental Monitoring, Disaster Prevention, and Distributed Tactical Surveillance, Has Recently Grown, the Need for Underwater Communication Has Become More Pronounced. unlike Terrestrial Sensor Networks, Underwater Sensor Networks (Uwsns) Have Different Characteristics Such as a Long Propagation Delay, a Narrow Bandwidth and High Packet *** the Various Challenges Posed by the Underwater Environment, a Routing Algorithm Has Been Proposed in This Paper. the Algorithm Consists of Special Features, including Three Different Types of Nodes in the Architecture Proposed, a Mathematical Formula in Order to Select the next Node to Be Used for Transmission. the Major Aim of the Algorithm Is to Select the next Node to Be Used for Successful Data Delivery, and Ensure Minimum Energy Consumption. the next Node Is Chosen With Utmost Care in Order to Increase the Probability of Successful Data Delivery. the Packet Is Transferred from the Source to the Sub-Destination by Exploiting Minimum Energy of the Nodes. the Simulation Studies for the Protocol Were Conducted using AQUA-GLOMO Network Simulator. the Protocol Was Benchmarked With DSR Routing Protocol. the Matrices That Were Considered for the Simulation Study Were Throughput, PDR, Energy Consumption and Delay and It Was Observed That Our Proposed Model Performed Better in the Underwater Environment.

关键词： Sensor networks

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Increasing resiliency through priority scheduling of asynchronous data replication

Increasing resiliency through priority scheduling of asynchr...

引用

11th International Conference on Parallel and Distributed Systems Workshops, ICPADS 2005

作者： Adams, Kevin P. Gračanin, Denis Hinchey, Michael G. NSWC Dahlgren Division United States Department of Computer Science Virginia Tech United States NASA Software Engineering Lab Goddard Space Flight Center United States

ISBN: (纸本)0769522815

Distributed systems commonly replicate data to enhance system dependability. In such systems, a logical update on a data item results in a physical update on a number of copies. The synchronization and communication required to keep the copies of replicated data consistent introduces a delay when operations are performed. In time-constrained systems or systems distributed over a bandwidth-constrained area, such operational delays generally prove unacceptable. Asynchronous data replication is commonly used to mitigate these delays. We look to develop a general solution for the introduction of an adaptive data replication scheduler to optimize asynchronous replications based on a user-developed priority model in overloaded situations. The solution uses a Multi-Layer Perceptron neural network to mimic the behavior of a historically optimal scheduler through functional approximation with its evaluation through simulation. © 2005 IEEE.

关键词： Distributed computer systems

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