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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

ISBN: (纸本)9783031837890

The proceedings contain 109 papers. The special focus in this conference is on Advanced network Technologies and Intelligent computing. The topics include: Exploratory Study on Different Transformer models;visionLingua: Empowering Multimodal Understanding with Cutting-Edge Language models;human Action Recognition Using Mediapipe Holistic Keypoints: A Deep Learning Approach;machine Learning for Waste-to-Energy: Optimization and Predictive Analytics;enhanced Traffic Forecasting for Urban Planning Using DeepWalk Embeddings and Spatio-Temporal Graph Ordinary Differential Equation Model;fault Detection in Semiconductor Manufacturing Using Advanced Machine Learning Techniques with Interpretability;fine-Tuning RetinaNet for Few-Shot Fruit Detection;Smart Street: AI-Powered Traffic Flow Enhancement with Adaptive Signal Control;a Hybrid Machine Learning Approach for Accurate Milk Quality Classification;Designing VoiceGPT Chatterbox Concierge: A Personal AI Assistant for Daily Services Using Supervised Machine Learning Based Speech Recognition Techniques;improved Classification Techniques Using Convolutional neural networks for Rice Plant Disease Detection;Enhancing Customer Segmentation and Behavior Prediction Through Machine Learning and Medium Gaussian SVM;Grapevine SDLC Model for Real-Time Fake News Classification;Detecting AI-Generated Text Using Fine-Tuned Transformers: A Study on Academic Integrity;MoReco: AI Model Recommendation and Optimization for B5G networks;file Prefetching Accuracy Enhancement Using Deep Learning Approaches;Mamba Based Conditional GAN for optical Cloud Removal of Satellite Images;a Hybrid Quantum Machine Learning Model for Multi-class Classifier;adaptive Resource Management in Massive Machine-Type Communications Using Reinforcement Learning;Advanced Machine Learning and Features Based Data Analytics of Electricity Consumption in USA and India;cross-Domain Adversarial Attacks and Robust Defense Mechanisms for Multimodal neural networks;deep Featu

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A Novel Approach to Recognize optical Characters of Number Plate Using Object Detection 4th

A Novel Approach to Recognize Optical Characters of Number P...

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4th International Conference on Futuristic Trends in networks and computing Technologies, FTNCT 2021

作者： Bhardwaj, Arnav Srivastava, Kaivalya Sourav Kar, Antara Gupta, Sumita Department of Computer Science & Engineering ASET Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789811950360

Today, the world has enhanced in terms of security but still there are occasions where people do not change even with the changing times. In the recent scenario, cars are replete and with amelioration in the number of cars, the susceptibility to threats also has increased dramatically. A complex, precise and high performing software is needed to detect the number plate to keep a check on the security and the traffic. For such surveillance work, it is paramount that recent computer domains like object detection, object recognition, image segmentation, optical character recognition and other domains are used to develop a solution that gives high accuracy based on the given available conditions. Detecting and recognizing the number plates and enhancing the accuracy in which the license numbers of cars are detected are the need of the hour. In this paper, two approaches to detect number plates are compared, which are, performing optical character recognition (OCR) on original images, and performing object detection on the images to locate cars and then performing OCR on the cropped images of the cars. The considered OCR models are EasyOCR, Keras-OCR, WPOD-Net as well as convolution neural network (CNN) and You Only Look Once (YOLOv3) model for object detection. The results of the OCR models were compared with the actual texts using Levenshtein distance. On the basis of the performance, EasyOCR performs best, providing a similarity ratio of 71.8% on original images, which further improves to 81.2%, when combined with YOLOv3 object detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： optical character recognition

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

ISBN: (纸本)9783031837821

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

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4th International Conference on Advanced network Technologies and Intelligent computing, ANTIC 2024

ISBN: (纸本)9783031837920

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Research and Practice of Browser Electronic Data Forensics Based on SVM 2

Research and Practice of Browser Electronic Data Forensics B...

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2nd International Conference on Digital Signal and Computer Communications, DSCC 2022

作者： Wu, Zhaoxu Luo, Haibo Pang, Haomin Yi, Biwu School of Computing Neusoft Institute Guangdong Guangdong Foshan China

ISBN: (纸本)9781510656734

Firstly, this paper introduces the background and significance of browser automatic forensics, and analyzes the current situation and development trend of browser forensics at home and abroad. Cuting into the automatic analysis of browser history, bag-of-words model and TF-IDF model are established as feature extraction, and naive Bayes, support vector machine and convolutional neural network are used as classification algorithm models. A simulation experiment is carried out in the browser history file data, and through the analysis and comparison of the results, a better combination result is obtained from multiple feature extraction and classification algorithms. © 2022 SPIE.

关键词： Extraction

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Analysis and Research of Teaching Management System Based on Emotion Detection 2

Analysis and Research of Teaching Management System Based on...

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2nd International Conference on Electronic Information Technology, EIT 2023

作者： Gao, Xia Fu, Wanying Xu, Fangqin Department of Information and Intelligent Engineering Shanghai Publishing and Printing College Shanghai China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

With the diversification and popularization of intelligent technology, how to integrate education into contemporary technology to get improvement in teaching always is the research direction people are crazy in. The traditional teaching mode is updating and evolving round after round. The significance and effect of them not only stay in technological innovation, but also reflect and achievements. Otherwise, all the evolution just stay on the surface without meaning. This research will focus on the connection between the emotion-detection and learning efficiency. The specific meaning of implementation includes two points. The first point is about assisting the class management through emotion-detection technology. According to a number of research reports, there is a huge correlation between emotion and learning efficiency. In addition, teaching forms have become more and more diversified. From online to offline, diversification usually be accompanied with problems that is how to inspect Learning effectiveness because of the traditional test and task point just have little limited effect. The second point is actually the detection of the content of courses. Through emotion recognition, it can accurately reflect whether knowledge points in the classroom is reasonable or not. The feedback from students can be detected through real-time data. The system is based on the keras deep learning framework and open CV source library to achieve emotion detection. Finally data visualization displays learning efficiency. © 2023 SPIE.

关键词： neural network models

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Classifying spectra of emission-line regions with neural networks -An application to integral field spectroscopic data of M33

arXiv

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arXiv 2025年

作者： Bracci, Caterina Belfiore, Francesco Ginolfi, Michele Feltre, Anna Mannucci, Filippo Marconi, Alessandro Cresci, Giovanni Bertola, Elena Bombini, Alessandro Ceci, Matteo Marconcini, Cosimo Moreschini, Bianca Scialpi, Martina Tozzi, Giulia Ulivi, Lorenzo Venturi, Giacomo Dipartimento di Fisica e Astronomia Università di Firenze Via G. Sansone 1 Sesto F.no FirenzeI-50019 Italy INAF - Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 FlorenceI-50125 Italy 50019 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data & Quantum Computing Via Magnanelli 2 BO Casalecchio di Reno40033 Italy University of Trento Via Sommarive 14 TrentoI-38123 Italy Gießenbachstraße 1 Garching85748 Germany Scuola Normale Superiore Piazza dei Cavalieri 7 PisaI-56126 Italy

Emission-line regions are key to understanding the properties and evolution of galaxies, as they trace the exchange of matter and energy between stars and the interstellar medium (ISM). In nearby galaxies, individual nebulae can be resolved from the background, and classified through their optical spectral properties. Traditionally, the classification of these emission-line regions in H ii regions, planetary nebulae (PNe), supernova remnants (SNRs), and diffuse ionised gas (DIG) relies on criteria based on single or multiple emission-line ratios. However, these methods face limitations due to the rigidity of the classification boundaries, the narrow scope of information they are based upon, and the inability to take line-of-sight superpositions of emission-line regions into account. In this work, we explore the use of artificial neural networks to classify emission-line regions using their optical spectra. Our training set consists of simulated optical spectra, obtained from photoionisation and shock models, and processed to match observations obtained with the MUSE integral field spectrograph at the ESO/VLT. We evaluate the performance of the network on simulated spectra exploring a range of signal-to-noise (S/N) levels, different values for dust extinction, and the superposition of different nebulae along the line of sight. At infinite S/N the network achieves perfect predictive performance, while, as the S/N decreases, the classification accuracy declines, reaching an average of ∼ 80% at S/N(Hα)=20. We then apply our model to real spectra from MUSE observations of the Local Group galaxy M33. The network provides a robust classification of individual spaxels, even at low S/N, identifying H ii regions and PNe and distinguishing them from SNRs and diffuse ionised gas, while identifying overlapping nebulae. Moreover, we compare the network’s classification with traditional diagnostics, finding a satisfactory level of agreement between the two approaches. We identify

关键词： Taxonomies

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The Hintons in your neural network: a Quantum Field Theory View of Deep Learning 38

The Hintons in your Neural Network: a Quantum Field Theory V...

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International Conference on Machine Learning (ICML)

作者： Bondesan, Roberto Welling, Max Initiat Qualcomm Technol Inc Qualcomm AI Res Qualcomm Technol Netherlands BV San Diego CA 92121 USA

ISBN: (纸本)9781713845065

In this work we develop a quantum field theory formalism for deep learning, where input signals are encoded in Gaussian states, a generalization of Gaussian processes which encode the agent's uncertainty about the input signal. We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles, dubbed "Hintons". On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing, and provides quantum deformations of neural networks that can be run efficiently on those devices. Finally, we discuss a semi-classical limit of the quantum deformed models which is amenable to classical simulation.

关键词： Deep learning

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Improving Transfer Time Prediction of ML models via Auto-correcting Dynamical Systems Modeling

Improving Transfer Time Prediction of ML Models via Auto-cor...

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IEEE Conference on network Softwarization (NetSoft)

作者： Venkat Sai Suman Lamba Karanam Byrav Ramamurthy School of Computing University of Nebraska-Lincoln Lincoln NE USA

ISBN: (数字)9798350369588

ISBN: (纸本)9798350369595

Machine Learning (ML) is extensively used for predicting transfer times for general purpose Wide Area networks (WANs) or public Internet applications, but for Research and Education networks (RENs) two major gaps exist in literature. First, RENs i.e. networks carrying large data flows have received limited attention by the networking community. RENs behave differently compared to the general purpose Internet applications and other network types. Hence, ML models from other network types cannot be used interchangeably for large data transfers. Second, the ML models are used as blackboxes to train on measured network values and then used to predict transfer times or other runtime network parameters. In this paper, we present a dynamical systems model of the large data transfers typical of RENs in the form of a system of Ordinary Differential Equations (ODEs) inspired by the Lotka-Volterra competition model. We present a transfer time prediction component called Dynamic Transfer Time Predictor (DTTP) which solves the ODEs and predicts the future transfer times. Second we formulate a loss function based on Lyapunov function called Lyapunov Drift Correction (LDC) that self-corrects the transfer time prediction errors *** design and develop our model, we studied real-world datasets consisting of over 100 million transfer records collected from platforms such as Open Science Grid (OSG), Large Hadron Collider optical Private network (LHCOPN), Worldwide LHC Grid (WLCG), as well as the RENs of Internet2 and ESNet. We integrate our model into well-known neural network models and regressors and present evaluation results.

关键词： Wide area networks Runtime Large Hadron Collider Predictive models Ordinary differential equations Data transfer Mathematical models

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AI surrogate models for error analysis in optical systems

AI surrogate models for error analysis in optical systems

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Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Peter Meszmer Neha Mundada Majid Tavakolibasti Bernhard Wunderle Faculty for electrical engineering and information technologies Chair materials and reliability of microsystems Chemnitz University of Technology Chemnitz Germany

This study proposes an artificial intelligence-based model that utilizes irradiance data acquired with the Digital Twin of a modern flash Light Detection and Ranging (LiDAR) sensor to predict the relative position and orientation of a 4-receiver lens stack model in the receiver path of a flash LiDAR system. Therefore, the artificial intelligence is able to analyze potential errors in the alignment of the lens stack. The data used to train and validate the described convolutional neural network is a result of optical simulations generated with Zemax Opticstudio, utilizing a Monte Carlo approach. The success of the proposed method is effectively proven by showing superior error estimation results.

关键词： Laser radar Error analysis optical computing Predictive models optical fiber networks optical receivers Data models

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