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Multi-CNN Model for Multi-Classification of Cultural Heritage Monuments

Multi-CNN Model for Multi-Classification of Cultural Heritag...

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international conference on pattern Analysis and Intelligent Systems (PAIS)

作者： Toufik Djelliout Hassina Aliane Department of Computer Science Faculty of Exact Sciences University of Bejaia Algeria Research Center for Scientific and Technical Information Algiers Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

the use of convolutional neural networks (CNN) in the preservation of cultural heritage monuments, especially in conflict-affected regions such as Gaza, Ukraine, Iraq and others, represents a significant advancement in heritage conservation efforts. this paper presents an approach that uses a Multi-CNN model to classify images of cultural heritage monuments into various categories, encompassing period, monument type and location. By leveraging the capabilities of CNNs, this model demonstrates a high level of accuracy in categorizing heritage monuments based on multiple attributes. the study highlights the superior performance of the Multi-CNN model compared to other popular models such as DenseNet169, GoogleNet and MnasNet, highlighting its effectiveness in accurately classifying images of cultural heritage monuments in various dimensions. According to the evaluation results, the top-performing multi-CNN model achieves a classification accuracy of 94.52%, outperforming the single CNN models. the DenseNet196 model achieves 93.70% accuracy, the MnasNet model achieves 92.80% accuracy, and the GoogleNet model achieves 88.18% accuracy.

关键词： Analytical models pattern recognition Cultural differences Convolutional neural networks pattern analysis Intelligent systems

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A feature selection-based framework for human activity recognition using wearable multimodal sensors 11

A feature selection-based framework for human activity recog...

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Proceedings of the 6th international conference on Body Area Networks

作者： Mi Zhang Alexander A. Sawchuk University of Southern California Los Angeles CA

ISBN: (纸本)9781936968299

Human activity recognition is important for many applications. this paper describes a human activity recognition framework based on feature selection techniques. the objective is to identify the most important features to recognize human activities. We first design a set of new features (called physical features) based on the physical parameters of human motion to augment the commonly used statistical features. To systematically analyze the impact of the physical features on the performance of the recognition system, a single-layer feature selection framework is developed. Experimental results indicate that physical features are always among the top features selected by different feature selection methods and the recognition accuracy is generally improved to 90%, or 8% better than when only statistical features are used. Moreover, we show that the performance is further improved by 3.8% by extending the single-layer framework to a multi-layer framework which takes advantage of the inherent structure of human activities and performs feature selection and classification in a hierarchical manner.

关键词： human activity recognition wearable multimodal sensors feature selection feature design pattern recognition

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Optimizing Cache Locality for Irregular Data Accesses on Many-Core Intel Xeon Phi Accelerator Chip

Optimizing Cache Locality for Irregular Data Accesses on Man...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Nhat Phuong Tran Dong Hoon Choi Myungho Lee Department of Computer Science and Engineering Myongji University Cheo-In Gu Yong In Kyung Ki Do Korea Korea Institute of Science and Technology Information (KISTI) Yuseong-Gu Daejeon Korea

ISBN: (纸本)9781479961245

Many-core accelerator chips are becoming increasingly popular these days for its high performance floating-point performance exceeding 1 Tflops per chip. Aho-Corasick (AC) is a multiple patterns string matching algorithm commonly used in computer and network security, bioinformatics, among others. In order to simultaneously match a number of string patterns with respect to the input text data, a Deterministic Finite Automata (DFA) is constructed from a given set of pattern strings. the DFA is referenced almost randomly, whereas the input data is sequentially accessed. As the number of pattern strings increases, the irregular DFA accesses lead to poor cache locality and low overall performance. In this paper, we present a cache locality optimizing parallelization on the many-core accelerator chip, the Intel Xeon Phi. A given set of pattern strings is partitioned into into multiple sets of a smaller number of patterns so that multiple small DFAs are constructed instead of single large DFA. the accesses to multiple small DFAs lead to significantly smaller cache footprints in each core's cache and result in impressive performance improvements. Experimental results on the Intel Xeon Phi 5110P show that our approach delivers up to 2.00 × speedup compared with the previous approach using single large DFA.

关键词： pattern matching Graphics processing units Coprocessors Automata Computer architecture Partitioning algorithms

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Online Discriminative Semantic-Preserving Hashing for Large-Scale Cross-Modal Retrieval 18th

Online Discriminative Semantic-Preserving Hashing for Large-...

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18th Pacific Rim international conference on Artificial Intelligence, PRICAI 2021

作者： Yi, Jinhan He, Yi Liu, Xin Department of Computer Science and Technology Huaqiao University Xiamen361021 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Xiamen China

ISBN: (纸本)9783030891879

Cross-modal hashing has drawn increasing attentions for efficient retrieval across different modalities, and existing methods primarily learn the hash functions in a batch based mode, i.e., offline methods. Nevertheless, the multimedia data often comes in a streaming fashion, which makes the batch based learning methods uncompetitive for large-scale streaming data due to the large memory consumption and calculation. To address this problem, we present an Online Discriminative Semantic-Preserving Hashing (ODSPH) method for large-scale cross-modal retrieval, featuring on fast training speed, low memory consumption and high retrieval accuracy. Within the proposed ODSPH framework, we utilize the newly coming data points to learn the hash codes and update hash functions in a stream manner. When new data comes, the corresponding hash codes are obtained by regressing the class label of the training examples. For hash function, we update it with the accumulated information from each round. Besides, we design a novel momentum updating method to adaptively update the hash function and reduce quantization loss, which can produce discriminative hash codes for high retrieval precision. Extensive experiments on three benchmark datasets show that the proposed ODSPH method improves the retrieval performance over the state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Modification of polyp size and shape from two endoscope images using RBF neural network 4th

Modification of polyp size and shape from two endoscope imag...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Iwahori, Yuji Tsuda, Seiya Woodham, Robert J. Bhuyan, M.K. Kasugai, Kunio Chubu University Matsumoto-cho 1200 Kasugai Aichi487-8501 Japan Department of Computer Science University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati Guwahati781039 India Department of Gastroenterology Aichi Medical University Nagakute-cho Aichi-gun Aichi480-1195 Japan

ISBN: (纸本)9783319276762

In the medical imaging applications, endoscope image is used to observe the human body. the VBW (Vogel-Breus-Weickert) model is proposed as a method to recover 3-D shape under point light source illumination and perspective projection. However, the VBW model recovers relative, not absolute, shape. Here, shape modification is introduced to recover the exact shape. Modification is applied to the output of the VBW model. First, a local brightest point is used to estimate the reflectance parameter from two images obtained with movement of the endoscope camera in depth. A Lambertian sphere image is generated using the estimated reflectance parameter and VBW model is applied for a sphere. then Radial Basis Function Neural Network (RBF-NN) learning is applied. the NN implements the shape modification. NN input is the gradient parameters produced by the VBW model for the generated sphere. NN output is the true gradient parameters for the true values of the generated sphere. Here, regression analysis is introduced in comparison with the performance by NN. Depth can then be recovered using the modified gradient parameters. Performance of shape modification by NN and regression analysis was evaluated via computer simulation and real experiment. the result suggests that NN gives better performance than the regression analysis to improve the absolute size and shape of polyp. © Springer international Publishing Switzerland 2015.

关键词： Regression analysis

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Practical applications of intelligent systems :Porceedings of the sixth international conference on intelligent systems and knowledge engineering, Shanghai, China, Dec 2011 (ISKE2011) /

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丛书名： Advances in Intelligent and Soft Computing

2011年

作者： international conference on intelligent systems and knowledge engineering(6th :2011 :Shanghai China)editors Yinglin Wang and Tianrui Li. Wang Yinglin. Li Tianrui.

ISBN: (纸本)9783642256578

Proceedings oftheSixthinternational conference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. this proceedings doesnt only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. the book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. the proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific research fields. Dr. Yinglin Wang is a professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; Dr. Tianrui Li is a professor at the School of Information Science and Technology, Southwest Jiaotong University, China.

关键词： Intellegent Systems Knowledge engineering

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Technical Aspects of Using IoE (Internet of Energy) Metadata in Smart Cities

Technical Aspects of Using IoE (Internet of Energy) Metadata...

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international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Islambek Rustambekov Balkibayeva Zhanagul Shahida Karakhodjaeva Aziz Ashurov Mukhammad Ali Turdialiev Akmal Akromov Jahongir Yuldashev Tashkent State University of Law Tashkent Uzbekistan Judge of the Constitutional Court of the Republic of Uzbekistan Tashkent Uzbekistan Department of International Private Law Tashkent State University of Law Tashkent Uzbekistan

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

this paper examines the technical aspects of utilizing Internet of Energy (IoE) metadata in smart city environments. through comparative and inductive analysis of existing literature and technical reports, we explore how advanced metadata management techniques can address inefficiencies in processing and analyzing large volumes of energy data in urban systems. Key findings include the potential for standardized metadata ontologies, edge computing for data preprocessing, and machine learning algorithms for pattern recognition in energy consumption. We propose strategies for developing unified IoE metadata management systems that integrate with broader smart city infrastructure. While metadata-driven approaches show promise for enhancing energy efficiency and sustainability in urban environments, challenges in data privacy and security remain. this research highlights IoE metadata as a critical tool for optimizing energy systems in smart cities through advanced data analytics and management techniques.

关键词： Machine learning algorithms Data analysis Smart cities Energy Internet Metadata Ontologies Energy efficiency pattern recognition Sustainable development Edge computing

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Neural and behavioral substrates of emotions as actions to reduce embodied dissonance

Neural and behavioral substrates of emotions as actions to r...

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IEEE international conference on Development and Learning, ICDL

作者： Shogo Yonekura Yasuo Kuniyoshi Yoichiro Kawaguchi Interfaculty Initiative in Information Studies University of Tokyo Bunkyo Tokyo Japan Department of Mechano-Informatics Graduate School of Information Science and Technology University of Tokyo Bunkyo Tokyo Japan

Toward better understandings of emotions, we argue how emotions should be synthesized, from phenomenological, evolutionary-psychological, and embodied perspectives. In particular, based on the assumption that an emotion consist of appraisal process and reaction/expression process, we propose a novel model of primitive emotions, in which an appraisal mechanism exploits informations of "embodied dissonance" existing as conflicts among desired and actual states. the model consists of action modules representing motivation, and evaluation modules representing actual states, both of the modules are coordinated by a constraint satisfaction neural network which behaves by a minimization principle of its neural energy. By using a physically simulated bipedal creature, we show that the interplay of neural and body-environment dynamics leads to syncope/fainting-similar states which emerge as adaptive responses to embodied dissonance, that "drastic and random movements" coupled with dissonance leads to the transitions of stimulus-response patterns from approach to avoidance, and that the transitions let the creature to cope with high-order problems, the causal structure of which is not perceived nor recognized by the creature. Taking these results into account, we discuss three things; (1) a principle to discriminate emotional and non-emotional cold behaviors of artificial agents; (2) a prospective architecture of appraisal processes of animal's emotions; and (3) a prospective account for the difference among fear, anxiety, hysteria, anger, and sadness. this study may shed light on a link of emotions and intelligence.

关键词： Robots Appraisal Information science Neural networks Animal structures pattern recognition Artificial intelligence Protection Humans Birds

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Hypervelocity damage pattern recognition on Aluminum alloy based on acoustic emission

Hypervelocity damage pattern recognition on Aluminum alloy b...

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international conference on Information Science and Control Engineering (ICISCE)

作者： Kai Zhang Baojun Pang Runqiang Chi Hongyu Jiao Department of Mechanical Engineering Changshu institute of technology Changshu China Department of Space Engineering Mechanics Harbin institute of technology China

ISBN: (数字)9781728157122

ISBN: (纸本)9781728157139

Space debris is a threat to manned spacecraft due to its high velocity, a method based on BP neural network is presented to estimate the degree of hypervelocity impact damage. In this research, projectiles were fired by two stage light gas gun to impact single aluminum plate, the damage formed on target plate was a crater or a perforating hole. the impact signals were obtained by ultrasonic sensors. According to the signal characteristics of different damage mode, signal amplitude, S2 modal energy, proportion of high-frequency energy and signal propagation distance were chosen as the input characteristic parameters to build a neural network. Experimental results showed that the trained network can be used to identify damage patterns, the correct rate was higher than 85%. It can also predict the depth of crater and hole diameter.

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Analysis of Complex Network Topological Features for EEG Signals Classification

Analysis of Complex Network Topological Features for EEG Sig...

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Data-driven Optimization of Complex Systems (DOCS), international conference on

作者： Ping Zhen Yuanyuan Yue Qingshan Liu School of Mathematics Southeast University Nanjing Jiangsu China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Electroencephalogram (EEG) signals provide direct insights into the physiological activities of brain, making EEG analysis valuable for understanding cognitive processes and neurological disorders. the main objective of this study is to investigate the effectiveness of extracting combined topological features for epilepsy recognition based on EEG data. We utilize visibility graphs and persistent diagrams to extract both graph features and homological features. these features are then combined to enhance the classification performance, surpassing the results achieved by using graph features alone. Notably, our approach achieves high accuracy rates of 100% for classifying Z and S, 99.75% for N and S, and 97.88% for F and S. these findings significantly contribute to the field of EEG-based epilepsy classification and provide valuable insights for further research and investigation.

关键词： Neurological diseases Time series analysis Epilepsy pattern classification Feature extraction Electroencephalography Physiology Data mining Signal analysis Optimization

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