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A modularly vectorized two dimensional LDA for face recognition

A modularly vectorized two dimensional LDA for face recognit...

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Huxidan Liu, Wan-Quan Lu, Chong Dept. of Computer Science YiLi Normal College Yining 835000 China Dept. of Computing Curtin University of Technology WA 6102 Australia

ISBN: (纸本)9781467314855

In this paper, a modularly vectorized 2DLDA (Mv2DLDA) is proposed for face recognition. First, the original images are divided into modular blocks. Then, each sub-block is transformed into a vector. By using column vector to represent each modular block, we can obtain a two dimensional matrix representation for image. Finally 2DLDA is applied directly on these 2D matrices. Experimental results on ORL, Yale B and PIE databases show that the proposed method can achieve better recognition performance in comparison with RLDA, 2DPCA and 2DLDA. © 2012 IEEE.

关键词： Face recognition

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Dynamic Checkpointing: Fault Tolerance in High-Performance Computing 3

Dynamic Checkpointing: Fault Tolerance in High-Performance C...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bhowmik, Biswajit Verma, Tushar Dineshbhai, Navdeep Dadhania Reddy, Manideep Raya Vsrs Girish, K.K. National Institute of Technology Karnataka Maharshi Sushrut Cas Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad Qc Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331528201

Parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical. © 2024 IEEE.

关键词： Parallel processing systems

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Deep Greedy Network: A Tool for Medical Diagnosis on Exiguous Dataset of COVID-19 1

Deep Greedy Network: A Tool for Medical Diagnosis on Exiguou...

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1st IEEE International Conference for Convergence in Engineering, ICCE 2020

作者： Dey, Sumagna Biswas, Saptarshi Nandi, Srija Nath, Subhrapratim Das, Indrajit Meghnad Saha Institute of Technology Computer Science and Engineering Kolkata India Jadavpur University Computer Science and Engineering Kolkata India Meghnad Saha Institute of Technology Dept. of Information Technology Kolkata India

ISBN: (纸本)9781728173405

The extensive outbreak of COVID-19 has created a worldwide health crisis. Transmission of this disease occurs among people through droplets which causes severe respiratory distress and in turn can also lead to fatal death. At the pinnacle of this pandemic, scientists endeavor to discover the medication for the COVID-19 victims. Artificial Intelligence algorithms, especially, deep learning, on the other hand, is used for the diagnosis of the COVID-19 patients but this requires an enormous radiographic data set to effectively provide an optimized outcome for a particular scenario. This work presents a new technique called 'Deep Greedy Network' which will work efficiently with a finite number of datasets. In spite of peculiarity caused due to limited dataset, the anomaly of overfitting and underfitting could be effectively overcome using the proposed algorithm. This, in turn, is simultaneously going to be both cost-effective and efficient. The proposed architecture ensures the efficacious result after the proper judgement of the trained model on the given X-ray datasets of COVID-19 cases. © 2020 IEEE.

关键词： COVID-19

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Scale Free Fully Informed Particle Swarm Optimization: Parametric Perspective

Scale Free Fully Informed Particle Swarm Optimization: Param...

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2018 IEEE International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2018

作者： Dwivedi, Vasudha Sharma, Ajay Dept. of Computer Science Engineering United College of Engineering and Research AllahabadU.P. India Dept. of Computer Science Engineering Kamla Nehru Institute of Technology SultanpurU.P. India

ISBN: (纸本)9781538641194

The current research work is mainly based on enhancement of a canonical model of the basic Particle Swarm Optimization (PSO) algorithm for achieving maximum optimized results for the real world *** existing topological algorithms like Global PSO (GPSO) and Scale-free fully informed PSO (SFIPSO) performs well but it has low success rate and vulnerable to stuck in local *** this paper scale free topology is modified with the inclusion of a new inertial weight (ω) (WSFIPSO) and results are compared with GPSO, SFIPSO, and inertial weight version of GPSO (WGPSO). These algorithms are tested on eight benchmark functions and WSFIPSO achieves 100% success rate on all benchmark functions, each SFIPSO and GPSO on two-two benchmark functions while WGPSO fails to achieve 100% success rate on any one of the benchmark function. Solution quality of the proposed approach outperforms on seven benchmark functions while GPSO on one. SFIPSO ranked 2nd on five benchmark functions even with low success rates. These findings clearly shows that topology based PSO greatly impact the solution quality. © 2018 IEEE.

关键词： Topology

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Multiview geometry for texture mapping 2D images onto 3D range data

Multiview geometry for texture mapping 2D images onto 3D ran...

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2006 IEEE computer Society Conference on computer Vision and Pattern Recognition, CVPR 2006

作者： Liu, Lingyun Stamos, Ioannis Yu, Gene Wolberg, George Zokai, Siavash Dept. of Computer Science Hunter College CUNY New York NY 10021 Dept. of Computer Science City College CUNY New York NY 10031 Brainstorm Technology New York NY 10011

ISBN: (纸本)0769525970

The photorealistic modeling of large-scale scenes, such as urban structures, requires a fusion of range sensing technology and traditional digital photography. This paper presents a system that integrates multiview geometry and automated 3D registration techniques for texture mapping 2D images onto 3D range data. The 3D range scans and the 2D photographs are respectively used to generate a pair of 3D models of the scene. The first model consists of a dense 3D point cloud, produced by using a 3D-to-3D registration method that matches 3D lines in the range images. The second model consists of a sparse 3D point cloud, produced by applying a multiview geometry (structure-from-motion) algorithm directly on a sequence of 2D photographs. This paper introduces a novel algorithm for automatically recovering the rotation, scale, and translation that best aligns the dense and sparse models. This alignment is necessary to enable the photographs to be optimally texture mapped onto the dense model. The contribution of this work is that it merges the benefits of multiview geometry with automated registration of 3D range scans to produce photorealistic models with minimal human interaction. We present results from experiments in large-scale urban scenes. © 2006 IEEE.

关键词： Computational geometry

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Power in normative systems

Power in normative systems

引用

8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009

作者： Agotnest, Thomas Van Der Hoek, Wiebe Tennenholtz, Moshe Wooldridge, Michael Dept. of Computer Engineering Bergen University College Bergen Norway Dept. of Computer Engineering University of Liverpool Liverpool United Kingdom Microsoft Israel R and D Center Technion-Israel Institute of Technology Israel

ISBN: (纸本)9781615673346

Power indices such as the Banzhaf index were originally developed within voting theory in an attempt to rigorously characterise the influence that a voter is able to wield in a particular voting game. In this paper, we show how such power indices can be applied to understanding the relative importance of agents when we attempt to devise a coordination mechanism using the paradigm of social laws, or normative systems. Understanding how pivotal an agent is with respect to the success of a particular social law is of benefit when designing such social laws: wc might typically aim to ensure that power is distributed evenly amongst the agents in a system, to avoid bottlenecks or single points of failure. After formally defining the framework and illustrating the role of power indices in it. we investigate the complexity of computing these indices, showing that the characteristic complexity result is #P-completeness. We then investigate cases where computing indiccs is computationally easy. Categories and Subject Descriptors 1.2.11 [Distributed Artificial Intelligence]: Multiagent Systems;1.2.4 [Knowledge representation formalisms and methods] General Terms Theory. Copyright © 2009, International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Multi agent systems

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Anomaly Detection from Web Log Data Using Machine Learning Model 7

Anomaly Detection from Web Log Data Using Machine Learning M...

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7th International Conference on computer Applications in Electrical Engineering-Recent Advances, CERA 2023

作者： Mishra, Amit Kumar Bagla, Piyush Sharma, Ravi Pandey, Neeraj Kumar Tripathi, Neha Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India National Institute of Technology Department of Computer Science & Engineering Jalandhar India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305005

The information in the logs produced by the servers, devices, and applications can be utilized to assess the system's health. It's crucial to manually review logs, for instance, during upgrades, to verify whether the update and data movement went smoothly. Manual testing is insufficiently trustworthy, and manual log examination takes much time and effort. In this paper, we propose to search log files for anomalous sequences using the machine learning methods K-Means and DBSCAN. The two data representation approaches examined in this study were feature vector representation and IDF representation. The effectiveness of the deployed machine learning algorithms was examined using evaluation measures like F1 score, recall, and precision. The study found considerable differences in the algorithms' capacities to spot anomalies, with some algorithms being better at seeing various types of abnormal arrangements than their overall prevalence. By using the study's findings, the user might be able to spot strange arrangements after manually sifting through the log file. © 2023 IEEE.

关键词： Anomaly detection

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Automated segmentation and classification of the atherosclerotic carotid plaque in ultrasound videos 24

Automated segmentation and classification of the atheroscler...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Liapi, Georgia D. Gemenaris, Michalis Loizou, Christos P. Kyriacou, Efthyvoulos Constatninou, Kyriacos P. Nicolaides, Andrew Dept. of Electrical Eng. Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus Dept. of Computer Science University of Cyprus Nicosia Cyprus Vascular Screening and Diagnostic Centre Nicosia Cyprus

ISBN: (纸本)9798350339598

The automated and reliable delineation of atherosclerotic carotid plaques in ultrasound (CUS) videos is of significant clinical relevance for management of the disease and the prediction of future stroke events. To facilitate stroke risk assessment, in this study, we propose an integrated software system for the automated segmentation and classification of atherosclerotic carotid plaques in longitudinal CUS videos, which was evaluated using 10 CUS videos, from 10 patients (5 Asymptomatic, AS, and 5 Symptomatic, SY). The proposed methodology involves the following steps: a) CUS video frame (VF) resolution and intensity normalization, b) speckle reduction filtering, c) Motion-mode state-based cardiac cycle (CC) identification, d) deep learning (DL)-based plaque segmentation, e) extraction and selection of plaque region of interest (ROI)-specific textural features, and f) machine learning (ML)-based plaque classification. Initially, one CC (cardiac diastole-systole-diastole) was selected per CUS video, and the CC's consecutive VFs were identified and reduced in number to exclude redundant VFs. All standardized VFs per patient were extracted, cropped and resized to mainly accommodate the ROI and were fed into a priorly trained and evaluated 2-dimensional DL plaque segmentation model. For each VF, the DL-based segmented plaque ROI was projected onto its primary resolution-normalized VF counterpart, from which textural and amplitude modulation-frequency modulation (AM-FM) plaque ROI features were extracted. Statistical analysis on the total AS and SY VFs was used for feature selection. We identified 2 plaque-originating AM-FM features, which exhibited statistically significant differences between the AS and SY standardized VFs (p © 2023 IEEE.

关键词： Frequency modulation

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An evaluation method for toll policy in transportation

An evaluation method for toll policy in transportation

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1st International Conference on E-Business and E-Government, ICEE 2010

作者： Chen, Dafeng He, Xiaozheng Dept. of Computer Science and Technology Nanjing Audit University Nanjing China Dept. of Civil Engineering University of Minnesota MN United States

ISBN: (纸本)9780769539973

Tolling is generally considered to be a method for balancing the supply and demand of social resources. This paper explores the emergence and development of the transportation toll policy. It examines the influence of tolls on travelers' route choices, and even the departure time choices. The modern toll policy is mainly designed to reduce traffic congestion. We list potential motivations for applying the toll policy to balance traffic supply and demand, and then uncover how it works. We discuss the approaches that evaluate the transportation toll pricing policy. Finally, by introducing the study case, California State Route 91, we apply the proposed method to compute the value of time and the value of reliability to evaluate the toll pricing policy on this study site. We also verify the actions and reactions of the toll policy in the practical transportation network. © 2010 IEEE.

关键词： Traffic congestion

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Multi-Agent H-Learning Based Cooperative Spectrum Sensing for Cognitive Radio Networks 32

Multi-Agent H-Learning Based Cooperative Spectrum Sensing fo...

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32nd International Conference on Electrical Engineering, ICEE 2024

作者： Fard, E. Karimpour Nouri, M. Behroozi, H. Sobhi-Givi, S. Sharif University of Technology Dept. of Electrical Engineering Tehran Iran University of Mohaghegh Ardabili Dept. of Electrical and Computer Engineering Ardabil Iran

ISBN: (纸本)9798350376340

Fast and accurate methods for spectrum sensing (SS) are the key elements in cognitive radio networks (CRNs) that achieve high SS. This paper proposes a reinforcement learning (RL) scheme for secondary users (SUs) in a massive multiple-input multiple-output (MIMO) CRN to determine occupation and emptiness of scanned wideband channels. In the proposed H-learning method, the clustered neighboring SUs collect data to check the status of channels that improve reliability of measured sensing. Then, they share information about different frequency channels with other clusters and the status of channel occupation is determined for all SUs. This model-based learning method increases finding free channels and enhances spectral efficiency (SE) in presence of large probability of missing detection. Furthermore, each SU learns the presence pattern of primary user (PU) and gather a dynamic scan priority list to decrease scan overhead and access delays. Simulation results demonstrate the multi-agent reinforcement modified H-learning (MH-learning) method can achieve better performance in terms of PU usage percent, number of attempts, call block rate, and probability of detection in comparison to H-learning and some state-of-art learning methods. © 2024 IEEE.

关键词： Reinforcement learning

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