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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

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2022 IEEE 5th international conference on Electronics and Communication Engineering, ICECE 2022

2022 IEEE 5th International Conference on Electronics and Co...

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5th IEEE international conference on Electronics and Communication Engineering, ICECE 2022

ISBN: (纸本)9781665487894

the proceedings contain 48 papers. the topics discussed include: dynamic resource allocation for all-optical multicast in space multiplexed elastic optical networks;research on packet pilot multiplexing and encryption strategy in 5G MIMO system;an empirical model for 1-D molecular drift-diffusion communication channels under reflecting and absorbing boundary conditions;design of intelligent frequency hopping anti continuous change jamming system based on migration learning;configuration mechanism of software defined chip applied in signal processing;research on key technologies of satellite mobile communication system;design of a digital twin for spacecraft division network system;applications of capacity enlargement and traffic management control in hybrid satellite-terrestrial 5g networks;a coarse-grained reconfigurable array based ethernet reliable transmission design;mobile networks classification based on time-series clustering;and studies documenting the components associated with Instagram influence: a review of the scientific literature.

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Better Qualitative Searching for Effecting the Performance of machine Translation 1

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5th international conference on Applied machine learning and Data Analytics, AMLDA 2022

作者： Banerjee, Anasua Kumar, Vinay Banik, Debajyoty National Institute of Technology Jharkhand Jamshedpur India Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

By improving a better comprehension of the human language for linguistically based human-computer communication, natural language processing (NLP) contributes to the empowerment of intelligent machines. Instead of hand-coding massive sets of rules, NLP can use machine learning to learn these rules automatically by examining a set of rules. With a view to better and clearer results in the endeavor to achieve success in the field of language translation, we have gone through some of the techniques like machine Translation, Punctuation Restoration, Name Entity Recognition, and Sentiment Analysis. Here we mentioned different state-of-the-art methods for different techniques of NLP. this paper offers an abstract analysis of the current state of language translation techniques. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Image processing for Future machine learning Algorithm Applications on Infrared thermography of Building Envelope Systems 5th

Image Processing for Future Machine Learning Algorithm Appli...

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5th international conference on Building Energy and Environment, COBEE 2022

作者： Gertsvolf, David Horvat, Miljana Khademi, April Aslam, Danesh Berardi, Umberto Department of Architectural Science Toronto Metropolitan University TorontoON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9789811998218

this paper presents and discusses the development of a procedure required for the future application of machine learning (ML) in the review and investigation of infrared (IR) thermography images for building envelopes. A background on ML and existing applications within the building science industry is presented to capture an understanding of the possibilities available within this innovative method of evaluating deficiencies of building envelopes. then, a breakdown of the groundwork and image processing required for the application of ML algorithms is reported. A procedure developed using a python coding environment is presented, demonstrating the process of taking an RGB IR image, grey scaling it and normalizing for the application of unsupervised ML algorithm. Similarly, for the purpose of supervised ML, the same approach is presented, followed by a procedure for the creation of segmentation masks using two types of approaches: temperature threshold and manually identified segments. A discussion of possible development of these new techniques concludes this paper. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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5th international Workshop on New Approaches for Multidimensional signal processing, NAMSP 2024

5th International Workshop on New Approaches for Multidimens...

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5th international Workshop on New Approaches for Multidimensional signal processing, NAMSP 2024

ISBN: (纸本)9789819624812

the proceedings contain 28 papers. the special focus in this conference is on New Approaches for Multidimensional signal processing. the topics include: Jamming Image Recognition Problem;Performance Evaluation of Surface Defect Segmentation with an Novel Battery Surface RGB Dataset;dynamic Tensor Least Angle Regression Using L1 Homotopy;multidimensional signal Analysis of Colorectal Cancer Using Bioinformatic Techniques;performance Evaluation of Workpiece Counting Neural Network with a Novel Sketch Dataset for Reducing Background Noise;entity Resolution Using Transformers for Synthetic Datasets;sound-Driven Identification of South African Birds;application of Deep learning Models for Multimodal Information Fusion in Intelligent Information Classification;an Introductory Glimpse of machine learning Applications in Multidimensional signal Analysis;electromyography-Based Hand Movement Detection Using machine learning Algorithm;research on the Application of Reinforcement learning in Film Script Generation and Narrative Innovation;design of Publicity Animation Sample Based on Dynamic Simulation Technology;research on Image Enhancement Technology Based on Semi-Supervised learning for Oil Painting Image;performance Analysis of Digital Control Oscillator Using Ant Colony Optimization;a Study on the Application of Different Types of Electric Motors as Electric Vehicles’ Drives and on the Necessity of 3D Image Analysis;a Deep learning-Based Study of Adolescents’ Self-Esteem Developmental Patterns: Exploring the Intersection of Psychology and Data Science;research on Interactive Experience of Virtual Reality Technology in Oral English Teaching;A SOM Algorithm for Chronic Hepatitis B Drug Expenditure Forecast;construction of Knowledge Map and Design of Decision Support System for Diagnosis of Tumor Disease;intelligent Hotel Management System Combined with Facial Recognition Technology.

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5th international conference on Inventive Computation Technologies, ICICT 2022 - Proceedings

5th International Conference on Inventive Computation Techno...

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5th international conference on Inventive Computation Technologies, ICICT 2022

ISBN: (纸本)9781665408370

the proceedings contain 243 papers. the topics discussed include: provably secure pseudonyms based authentication protocol for wearable ubiquitous computing environment;analysis of dc motor speed control and interfacing it with drowsiness detection;height and weight estimation of an individual from virtual visuals;projective synchronization of uncertain chaotic systems with different dimensions and external noise disturbance;design and implementation of a rescue and surveillance robot using cross-platform application;design of voice controlled multifunctional computer numerical control (CNC) machine;Bangla font recognition using transfer learning method;electroencephalography (EEG) based home automation for physically challenged people using brain computer interface (BCI);optimization of redirection algorithm based on cache technology in programming implementation of college students' mental health education information platform;a resistance detection device for microelectronic semiconductors: from signal detection to fusion analysis;and research on positioning accuracy of welding robot based on visual data mining and pattern fusion analysis algorithm.

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Dense Convolution Siamese Network for Hyperspectral Image Target Detection 5

Dense Convolution Siamese Network for Hyperspectral Image Ta...

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5th international conference on Information Communication and signal processing, ICICSP 2022

作者： Shen, Kun Xie, Weixin Tang, Haojin Li, Yanshan Shenzhen University Atr National Key Laboratory of Defense Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9781665485890

Compared with grayscale and RGB images, hyperspectral image (HSI) can provide both spatial and spectral information of ground targets, which makes it possible to improve the efficiency and accuracy of target detection. therefore, the research of HSI target detection algorithms has attracted widespread concern in recent years. With the development of hardware devices and the arrival of big data era, deep learning algorithms have been successfully applied to image processing, text recognition and other fields. However, due to the complex gathering environment of HSI, it is so difficult to obtain a large number of labeled samples, which limits the application of deep learning algorithms in HSI target detection. therefore, a dense convolution Siamese network (DCSN) is proposed for HSI target detection, which improves the accuracy in the scenery of small-scale training samples. the main contributions of this paper include the following three points. First, we design a target sample generation method based on improved autoencoder to enhance target training data. then, a background selection method based on density estimation is presented, which can acquire typical background samples effectively. Finally, a spectral feature extraction method based on dense convolution is proposed to extract the more discriminative spectral features. the experimental results of HSI target detection on Muufl Gulfport and San Diego datasets indicate that our proposed DCSN is able to achieve superior performance than the existing target detectors. © 2022 IEEE.

关键词： Convolution

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Ghosting Effect Removal for Multi-Frame Super-Resolution on CCTV Videos with Moving Objects 2022

Ghosting Effect Removal for Multi-Frame Super-Resolution on ...

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5th international conference on machine Vision and Applications (ICMVA)

作者： Singian, Jarrett Ethan Tan, Jade Nicole Tierro, Martin Angelo Del Gallego, Neil Patrick Ilao, Joel Antioquia, Arren Matthew De La Salle Univ Manila Philippines De Le Salle Univ Graph Animat Multimedia & Entertainment GAME Lab Manila Philippines De Le Salle Univ Ctr Automat Res CAR Manila Philippines

ISBN: (纸本)9781450395670

With the increased use of closed-circuit television (CCTV) footage for security and surveillance purposes as well as for object or person recognition and efficiency monitoring, high-quality CCTV videos are necessary. In this paper, we propose Corgi Eye, a moving object removal + super-resolution framework for enhancing CCTV footages to remove ghosting artifacts caused by performing multiframe super-resolution (MISR) on moving objects. Our method extends the framework of Eagle Eye, which is an existing MISR framework tailored for mobile devices. Our results demonstrate that the system can completely remove ghosting effects caused by moving objects while performing MISR on CCTV footage. Our proposed method demonstrates competitive performance when compared to Eagle Eye, achieving a 16% increase in terms of PSNR metric. Additionally, our method can produce clear images, on par with deep learning approaches such as ESPCN and SOF-VSR.

关键词： ghosting effect removal image segmentation video segmentation image processing image enhancement super-resolution background subtraction moving object detection

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A Comparison of machine learning Algorithms for Emotional Intelligence Measure 5

A Comparison of Machine Learning Algorithms for Emotional In...

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5th IEEE international conference of Women in Data Science (WiDS-PSU)

作者： Farhat, Nilofer Fatima, Ruhi Kazi, Sabeena Naaz, Humera Majmaah Univ Coll Comp & Informat Sci Almajmaah Saudi Arabia

ISBN: (纸本)9781665408127

Emotional Intelligence (EI) refers to a person's ability to recognize, express, understand, manage, and use his or her own and others' emotions. EI is currently regarded as a distinct course of action, capable of excelling in a variety of disciplines. It is now used to make fascinating and significant forecasts about a person's life outcomes. Automated emotional information processing has become a trend in recent research approaches, with machine learning (ML) development. ML can assist EI in light of the success it has experienced in several other fields. ML and the deep learning approach can be used for the prediction of certain human cognition aspects like intelligence, working memory, attention, or focus. this study is based on a comparison of different machine learning Algorithms (MLA) for regression analysis of PEC (Profile of Emotional Competence) data. the regression algorithms SVM (Support Vector machine - Regression), k-NN (k- Nearest Neighbor), and Ridge Regression are applied to the dataset. these MLAs were implemented in Python, trained using 70% of the PEC dataset and tested with 30% data. A comparative analysis evaluates the performance of MLAs using error metrics like ME, RMSE, MAE, EVS, coefficient of determinant and model accuracy, on the PEC measure of EI. the result identified the performance of SVM and k-NN in different EI domains and Ridge Regression as the best performing method on the given PEC data. this work contributes to a better understanding of the ML approach to the PEC EI measure and opens up the possibility of using the same approach for other EI measures.

关键词： Emotional Intelligence k-NN machine learning PEC Ridge Regression SVM

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processing and analysis of phosphoric acid production data using machine learning methods

Processing and analysis of phosphoric acid production data u...

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13th international conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th international conference on Emerging Data and Industry 4.0, EDI40 2022

作者： Bichri, Ahmed Abderafi, Souad MOSE2PI Mohammadia Engineering School Mohammed v University in Rabat Av. IbnSina B.P. 765 Rabat Agdal10090 Morocco OCP SA Jorf Lasfar Morocco

the attack and filtration unit is the main section of the wet process phosphoric acid production. It must ensure maximum production of phosphoric acid while reducing P2O5losses. the main purpose of this work is to search for a machine learning model able to predict the production of phosphoric acid. Industrial data collected during the operation of the process was used to find the appropriate machine learning model for the prediction of phosphoric acid production. the collected data are first prepared and cleaned, then we elaborated the correlation matrix for all the variables Xj and the output of filter A and filter B and look for the relations between the inputs and outputs. then we identified the variation ranges of the parameters of each filter for a best production of phosphoric acid at the two filters. Finally, three models were analyzed and tested. these are the "K Neighbors", "Random Forest", and "Decision Tree" models. We conclude that the last two models present the best results with an MSE of 4%. © 2022 Elsevier B.V.. All rights reserved.

关键词： Big data Data analysis machine learning Operating parameters Phosphoric acid

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Cloud Computing, machine learning, and Secure Data Sharing enabled through Blockchain 5

Cloud Computing, Machine Learning, and Secure Data Sharing e...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Singh, Rajesh Chandra, Akkaraju Sailesh Bbhagat, Babeetta Panduro-Ramirez, Jeidy Gaikwad, Anil Pandurang Pant, Bhasker Uttaranchal Institute of Technology Uttaranchal University Uttarakhand India University Doctoral Scholar School of Business and Management Karnataka Bangalore India Chhatrapati Shivaji Maharaj University Department of Computer Science & Information Technology Navi Mumbai India Universidad Tecnológica Del Perú Department of Business Peru Savitribai Phule Pune University JSPM'S JSCOE-MCA Department Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network's perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries' uses due to its decentralized, accessible, & safe structure. this system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations. © 2022 IEEE.

关键词： Blockchain

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