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OAM pattern recognition Method Based on ResNet-18 Migration Learning

OAM Pattern Recognition Method Based on ResNet-18 Migration ...

引用

software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing (SNPD-Winter), ACIS international Winter conference on

作者： Zhong Li Xiaoxia Yang Zhenning Gao Zehui Cheng North University of China school of software Taiyuan China

Aiming at the application of Orbital Angular Momentum (OAM) in the field of optical communication, this paper proposes an OAM pattern recognition method based on ResNet-18 transfer learning. The OAM mode is a light wave with a spiral phase structure, which has the characteristics of high degree of freedom and large capacity, and has broad application prospects in the field of optical communication. However, detection and classification of OAM modes is difficult due to their complex wavefront structures. To address this issue, we use a pretrained ResNet-18 model to classify OAM patterns. This method uses the pre-trained ResNet-18 model for feature extraction and fine-tuning, which can effectively improve the classification performance of the model. Through experimental verification, the method has high accuracy and stability in the classification task of OAM mode. The research in this paper provides strong support for the application of OAM technology, and has certain reference value for image classification problems in other fields.

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HCMA'22: 3rd international Workshop on Human-centric Multimedia Analysis 22

HCMA'22: 3rd International Workshop on Human-centric Multime...

引用

30th ACM international conference on Multimedia (MM)

作者： Zhang, Dingwen Fang, Chaowei Liu, Wu Liu, Xinchen Song, Jingkuan Zhu, Hongyuan Huang, Wenbing Smith, John Northwestern Polytech Univ Xian Peoples R China Xidian Univ Xian Peoples R China JD AI Res Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China ASTAR Singapore Singapore Tsinghua Univ Beijing Peoples R China IBM Res Yorktown Hts NY 10598 USA

ISBN: (纸本)9781450392037

The Third international Workshop on Human-Centric Multimedia Analysis concentrates on the tasks of human-centric analysis with multimedia and multimodal information. It involves multiple tasks such as face detection and recognition, human body pattern analysis, person re-identification, human action detection, etc. Today, multiple multimedia sensing technologies and large-scale computing infrastructures are emerging at a rapid velocity a wide variety of big multi-modality data for human-centric analysis, which provides rich knowledge to help tackle these challenges. Researchers have strived to push the limits of human-centric multimedia analysis in a wide variety of applications, such as intelligent surveillance, retailing, fashion design, and services. Therefore, this workshop aims to provide a platform to bridge the gap between the communities of human analysis and multimedia.

关键词： Human Analysis Multi-media and Multi-modal Analysis

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Deep Neural Network Accelerator: Exploring the Potential of 3D Chiplet Integration

Deep Neural Network Accelerator: Exploring the Potential of ...

引用

pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Shu Wang Tianxing Wang Siying Bi Feng Jiao Xinrong Long Zhong Ma Third Technical Department Xi'an Microelectronics Technology Institute (of CASC) Xi'an China Sencond Technical Department Xi'an Microelectronics Technology Institute (of CASC) Xi'an China First Technical Department Xi'an Microelectronics Technology Institute (of CASC) Xi'an China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

The development of deep neuro networks brings a sharp rise in demands for expeditious convolutional computing operations, but the “memory wall” originated from the intense data transfer, has greatly hindered the performance of deep neural network accelerators. As the integrated circuits have entered the post-Moore's law era, evolving the neural network accelerator by advance semiconductor fabrication technology is no longer available. This paper presents an in-depth analysis of data flow within a fundamental neural processing unit system, examining the impact of 3D integration and chiplet architecture on overcoming the “memory wall” challenge. A versatile neural processing unit architecture, featuring adjustable processing elements and buffer sizes, is developed to evaluate performance improvements induced by 3D chiplet integration. The results prove the feasibility of 3D chiplet integration in performance advancement of neural processing unit from both memory and processing element perspectives, which provides a complete guidance for potential solutions for reducing memory bottlenecks.

关键词： Fabrication Three-dimensional displays Chiplets Computer architecture Artificial neural networks Data transfer System-on-chip pattern recognition Through-silicon vias Three-dimensional integrated circuits

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Resume Parsing and Skill Extraction using Custom pattern Matching algorithm and Gemini API

Resume Parsing and Skill Extraction using Custom Pattern Mat...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Atharva Joshi Shailaja Pede Prathmesh Kolekar Aditya Metkar Department of Computer Science Engineering (Artificial Intelligence and Machine Learning) PCCOE SPPU India Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

In the realm of contemporary recruitment, efficient resume parsing is essential for talent acquisition. This paper investigates resume parsing using advanced OCR technology and NLP methods. We begin with preprocessing, enhancing scanned resumes for legibility. Leveraging the Tesseract OCR engine, we convert images to machine-readable text. Through NLP techniques with spaCy, we extract key information like personal details, education, experience, and *** approach integrates the Gemini Pro Vision API for semantic analysis and question-answering. This enriches our framework, enabling deeper insights and implicit attribute inference. Empirical validation across diverse datasets demonstrates the robustness of our methodology. This work sheds light on performance metrics and challenges in automated resume parsing, paving the way for future enhancements.

关键词： Visualization Technological innovation Machine learning algorithms Resumes Optical character recognition Semantics Robustness Data mining pattern matching Recruitment

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Potentialfinder - Fostering Network Innovation by Connecting Data Owners Using Scaled Business-Relevant pattern recognition and Clustering 55

Potentialfinder - Fostering Network Innovation by Connecting...

引用

55th Annual Hawaii international conference on System Sciences (HICSS)

作者： Riechert, Mathias Bhandari, Roshan Amle, Abhijeet Abhinav, Abhimanyu Hubig, Nina Clemson Univ Clemson SC 29634 USA

ISBN: (纸本)9780998133157

The advancement in collection, computing and storage technologies has led to an exponential growth of available data in multiple disciplines. However, the human capacity of analyzing this data does not grow at the same rate, leaving a vast amount of potential disparate, invisible and unused. We want to enhance the capability of humans to automatically find relevant patterns in data to leverage potential in this increasing sea of data. We present an innovation network creation framework and Python library that detects exponential growth patterns from publicly available tabular data. It works as a magnifying glass to reveal the most relevant parts of the data and the processes represented by it. The extracted exponential patterns can be useful for topic or disease detection as well as for organisations such as venture capital and consulting firms to improve investment decisions. Additionally, startups and innovation units in corporates can leverage these information to base their business models on insights into sectors, markets or customer segments with exponential growth. To foster the innovation based on the revealed patterns, we connect the respective data owners that uploaded similar patterns. This paper proposes a framework for networked innovation creation including a) an algorithm to automatically detect exponential, b) approaches to scale its application to public tabular data in different sizes and formats, c) a similarity network connecting the found patterns to innovation networks, d) a clustering to group the data owners and enable co- and crowd innovation. We run experiments on large scale data for all steps to provide evidence for cost-efficiency, scalability and feasibility of the contributions.

关键词： Python

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pattern recognition and Prediction in Time Series Data Through Retrieval-Augmented Techniques

Pattern Recognition and Prediction in Time Series Data Throu...

引用

Electrical Electronics and computing Technologies (ICEECT), international conference on

作者： Nitya Singh Aleena Swetapadma School of Computer Engineering Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Time series forecasting is an essential technique used in various domains, with finance being one of the primary users. There are several techniques for time series forecasting, with machine learning being one of the most common practices. Machine learning models rely on numerical input with time encoded in the input for prediction. However, visual pattern-based time series forecasting is also a popular method practiced by professionals in various fields. Traditional numerical input-based models succeed in recognizing patterns but often fail when the patterns are skewed or stretched compared to those in the training data. The aim of the work is to address this issue using a visual approach. In this work, OpenAI’s CLIP model has been used to perform a visual similarity search among visual time series patterns to achieve forecasting using the top k most similar patterns. The performance of the model shows it can be used effectively for time series analysis.

关键词： Visualization Databases Time series analysis Machine learning Metadata Predictive models Market research Vectors Numerical models Forecasting

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Cloud computing pattern Identification with Machine Learning: A Study

Cloud Computing Pattern Identification with Machine Learning...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), international conference on

作者： Pardeep Kumar Jindal Manbir Kaur Durgesh Srivastava Preeti Sharma Mirgender Kumar Chandigarh Engineering College Mohali Jhanjeri Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Delivering needed computer resources over the Internet has made cloud computing an increasingly important requirement. Automated methods to find broad trends in cloud systems' usage and behaviour are becoming increasingly necessary as they get more complicated. The artificial intelligence subfield of machine learning provides an intriguing solution to this issue. The methods for detecting performance abnormalities, forecasting resource usage, and optimizing cloud system configurations are the main topics of this paper's review of recent work on machine learning for general pattern recognition in cloud computing. We also talk about the shortcomings and difficulties of the current methods and offer ideas for further research in this field.

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Classification of Audio Signals Based on Deep Learning Methods for Google Net and Alex Net

Classification of Audio Signals Based on Deep Learning Metho...

引用

Advances in Engineering Science and Technology (AEST), international conference on

作者： Noor S. Ali Ehab Abdulrazaaq Hussein Laith Ali Abdul-Rahaim Department of Electrical Engineering College of Engineering University of Babylon Babylon Iraq

ISBN: (数字)9798331502690

ISBN: (纸本)9798331502706

Audio signals have a significant impact on every element of human life. Audio classification systems can be developed to solve the efficiency problems of conventional methods using the learning capabilities of cloud computing and deep learning architectures. This research proposes deep learning techniques to classify environmental audio signals based on the spectrograms generated for these sounds while using deep learning techniques. Spectrogram images of environmental audio signals train deep-learning networks such as Google Net and Alex Net on cloud computing. Both networks are trained on a large number of datasets. The achieved accuracy can reach 92.5% on Google Net, and 87.5% on Alex Net. This paper provides that the proposed technique for audio signal classification can be efficient for sound classification and recognition systems.

关键词： Deep learning Cloud computing Tensors Accuracy Convolution pattern classification Spectrogram

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A pattern recognition Method for Scene Sketch Images Using Deep Learning and Its Evaluation 11

A Pattern Recognition Method for Scene Sketch Images Using D...

引用

Joint 11th international conference on soft computing and Intelligent Systems and 21st international Symposium on Advanced Intelligent Systems (SCIS-ISIS)

作者： Oshima, Kengo Takagi, Noboru Sawai, Kei Masuta, Hiroyuki Motoyoshi, Tatsuo Toyama Prefecture Univ Dept Intelligent Design Engn Toyama Japan

ISBN: (纸本)9781728197326

Many studies on handwritten figure recognition have been published so far. For handwritten graphics in the fields of science and engineering, clear drawing rules are often clear, that is the shapes of the elements are predetermined. On the other hand, there is no drawing rule for sketches such as scene sketches;this makes it difficult to design a feature extractor. Based on this background, in recent years, research on recognition models using deep learning for sketch images has been reported. However, the recognition rate of all the previous research results are about 70% or less. Therefore, in this paper, we propose a new CNN model for recognizing landscape sketch images, and verify its effectiveness by computer experiments. Since there is no benchmark data for landscape sketch images, it cannot be compared with the results of previous studies, but the recognition rate of the CNN model proposed here is about 80%, which is higher than that of previous studies.

关键词： Sketch Understanding Deep Neural Network

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A CGAN-based DDoS Attack Detection Method in SDN 17

A CGAN-based DDoS Attack Detection Method in SDN

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Liu Luo Jiang Wang Li Jia China Acad Informat & Commun Technol Informat & Commun Off Beijing Peoples R China North China Elect Power Univ Dept Elect & Commun Engn Baoding Peoples R China

ISBN: (纸本)9781728186160

Distributed denial of service (DDoS) attack is a common way of network attack. It has the characteristics of wide distribution, low cost and difficult defense. The traditional algorithms of machine learning (ML) have such shortcomings as excessive systemic overhead and low accuracy in detection of DDoS. In this paper, a CGAN (conditional generative adversarial networks, conditional GAN) -based method is proposed to detect the attack of DDoS. On off-line training, five features are extracted in order to adapt the input of neural network. On the online recognition, CGAN model is adopted to recognize the packets of DDoS attack. The experimental results demonstrate that our proposed method obtains the better performance than the random forest-based method.

关键词： soft defined network (SDN) Distributed denial of service (DDoS) Conditional generative adversarial network (CGAN) Attack Detection

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