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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 763 条记录，以下是331-340 订阅

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machine-learning-based predictive classifier for sand body types and reservoir properties using geological data

Machine-learning-based predictive classifier for sand body t...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Jinxiang Shao Yujuan Meng Lingyan Zhao Jinzi Liu The Seventh Oil Production Plant Daqing Oilfield Limited Company Daqing China School of Mathematics and Statistics Northeast Petroleum University Daqing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

the classification and identification of reservoir sand body types has important research significance in oil-gas oilfield development plans. the traditional recognition method mainly relies on logging curves and manual recognition, which has the disadvantages of relying on expert experience and longtime consumption. this article is driven by geological data and compares various machine learning algorithms to select the intelligent algorithm suitable for solving this problem, achieving automatic recognition of reservoir sand body types and providing reference for reservoir engineering. the results show that the accuracy of the neural network algorithm reaches 97.4%, which can be used as an effective intelligent algorithm for reservoir sand body type classification and recognition.

关键词： machine learning algorithms Geology Neural networks signal processing algorithms Manuals machine learning signal processing Reservoirs Prediction algorithms Classification algorithms

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A Survey of Industry in Cambodia and Future Prospects Industry 4.0 8

A Survey of Industry in Cambodia and Future Prospects Indust...

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Joint 8th international conference on Digital Arts, Media and Technology and 6th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Chheang, Vuthy Heng, Sokha thourn, Kosorl Yahoui, Hamed Royal University of Phnom Penh Dept. of Telecommunication and Electronic Engineering Phnom Penh Cambodia Royal University of Phnom Penh Faculty of Engineering Phnom Penh Cambodia Institute of Technology of Cambodia Department of Telecommunication and Network Engineering Phnom Penh Cambodia Automation & Processing Center Dept. of Mechanical Engineering Claude Bernard University Lyon 1 Lyon France

ISBN: (纸本)9798350396546

In developed countries, industry 4.0 revolution is growing towards automation and data cloud analysis in manufacturing technologies to create a smart and high-class factory which be able to make higher production and logistics systems with few employees. this paper is based on a project funded by the ERASMUS+ Program of the European Union: ASEAN Factori 4.0, Across South East Asian Nations: From Automation and Control Training to the Overall Roll-out of Industry 4.0, 609854-EPP-1-2019-1-FREPPKA2-CBHE-JP which provided for a great survey of Cambodia industries in order to develop universities programs of automated production system. With these results of surveyed industries contributes to the Telecommunication and Electronic Engineering Department of Royal University of Phnom Penh recognizing where to begin for requirement of future industry 4.0 in Cambodia, such as automated production, applied technologies, and programing skills. In short of future, Cambodia will be extended for Industry 4.0 technologies with 5G, data centers, artificial intelligent (AI), industrial Automation (IA), additive manufacturing, industrial internet of things (IIoT) and smart factories, intelligent management, cyber-security, and machine learning as similar as other developed Asian countries. © 2023 IEEE.

关键词： Industry 4.0

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An overview of cybersecurity based on Network Security Situational Awareness and machine learning

An overview of cybersecurity based on Network Security Situa...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Qiuxiang Li Buyu Liu Pan Chen The First Research Institute of the Ministry of Public Security of P.R.C Beijing China

Network security refers to a series of measures to protect computer systems, network systems and network data from unauthorized access, theft, damage, tampering and interference on the Internet. With the popularity and application of the Internet, the issue of network security has become more and more important, especially in the financial, transportation, medical, government and other industries, security issues cannot be ignored. In this context, it is important to deal with the security issues in a more complex network environment. Network security situational awareness refers to the analysis and monitoring of network data, events and behaviors to keep abreast of security threats and dog overreaching in the network environment so that timely defensive measures can be taken. machine learning techniques use algorithms and models to allow computers to learn and improve on their own. machine learning has a wide range of applications, including image recognition, speech recognition, natural language processing, and other fields. Based on these two important technical concepts, this paper provides an overview of both and their applications in cyberspace security, and concludes with a summary and outlook on their future development.

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Optimized Construction Project Management Using Ai and machine learning 6

Optimized Construction Project Management Using Ai and Machi...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sharma, Kamal Patil, Pravin P Srivastava, Aparna Mayuri, K. Michaelson, Jacob Srivastava, Amit Institute of Engineering and Technology Department of Mechanical Engineering GLA University Uttar Pradesh Mathura India Graphic Era Deemed to Be University Department of Mechanical Engineering Uttarakhand Dehradun India Lloyd Institute of Engineering and Technology Uttar Pradesh Greater Noida India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Lloyd Law College Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

Starting during the 1950s, with the coming of Data Advances, artificial intelligence (AI) strategies have been created. New strong calculations and, specifically, the openness of the web as a monstrous store of unstructured information have rekindled the issue of AI's worth as processing power has expanded. this is empowering for the field of development the board specifically, as development projects are filling in size and intricacy as time and monetary constraints increment the quantity of stakeholders with contending needs. By the day's end, development the executives'utilized to get everything all together with the goal that precise expectations can be made about the end result. the subject of Artificial Intelligence (AI) incorporates AI as a subset and an assortment of related innovations. K-Nearest Neighbour's (KNN) is a famous managed learning strategy, making it one of the most broadly utilized AI calculations. In light of financial information in Vietnam, this article utilized the K-Nearest Neighbour's (KNN) calculation to appraise the development cost file. the structure cost record determining model utilized information from 2016-2019 and was predicated on seven financial factors. As per the discoveries, the K-Nearest Neighbour's (KNN) relapse technique utilized in the development cost list forecast model superior exactness over the customary strategy. © 2023 IEEE.

关键词： Artificial Intelligence Construction Project Management machine learning Optimizing

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6th IFIP TC 5 international conference on Computer, Communication, and signal processing, ICCSP 2022

6th IFIP TC 5 International Conference on Computer, Communic...

引用

6th IFIP TC 5 international conference on Computer, Communication, and signal processing, ICCSP 2022

ISBN: (纸本)9783031116322

the proceedings contain 23 papers. the special focus in this conference is on Computer, Communication, and signal processing. the topics include: Trust Aware Secure Routing Model in MANET: Self-improved Particle Swarm Optimization for Optimal Route Selection;Emulation and Analysis of Software-Defined Networks for the Detection of DDoS Attacks;Collision Elimination for Random Behavior Nodes in Ad Hoc Wireless Network Using Early Backoff Announcement (EBA);Smart License Approval Using AIOT;Automatic Identification of Heart Abnormalities Using PCG signals;predicting Students’ Satisfaction Towards Online Courses Using Aspect-Based Sentiment Analysis;A Survey on DL Based Frameworks for COVID-19 Radiological Diagnosis;impact of Chronic Lung Disease Using Deep learning: A Survey;converting Nebulous Ideas to Reality – A Deep learning Tool for Conditional Synthesis of Character Designs;intelligent System for Diagnosis of Herbs Disease Using Deep learning;performance Analysis of Adaptive Variable Exponent Based Total Variation Image Regularization Algorithm;early Prognosis of Preeclampsia Using machine learning;an Intelligent Intrusion Detection System Using Hybrid Deep learning Approaches in Cloud Environment;self Driving Car in a Constrained Environment;Detection and Classification of Paddy Leaf Diseases Using Deep learning (CNN);Long-Text-to-Video-GAN;preface;novel Hybrid Technique Enhancing Data Privacy and Security;SMADE - Smart Medical Assist Device for Elders;malayalam Language Textual Inference Identification through Attention over Embeddings;fashion Image Classification Using Deep Convolution Neural Network.

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Improving Quantization Matrices for Image Coding by machine learning 22

Improving Quantization Matrices for Image Coding by Machine ...

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6th international conference on Digital signal processing, ICDSP 2022

作者： Ke, Wei Chan, Ka-Hou Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781450395809

We investigate the generation of quantization matrices for image coding in the scenario to balance compression ratio and quality. We make use of machine learning to train and determine those quantization matrices that can achieve the best compression ratio while reaching the quality settings. By introducing the trainable parameters and considering the impact of the quantization module on task performance and compression ratio, the DCT and quantization modules are jointly optimized to minimize the total coding cost. We evaluate the well-trained quantization matrices under various quality settings of JPEG. the results indicate that the proposed scheme can be combined with quality settings to consistently achieve better compression performance. © 2022 ACM.

关键词： Image coding

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A Study on Role of Artificial Intelligence in Education 6

A Study on Role of Artificial Intelligence in Education

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6th international conference on Computing, Electronics and Communications Engineering, iCCECE 2023

作者： Mon, Bisni Fahad Wasfi, Asma Hayajneh, Mohammad Slim, Ahmad Al Ain United Arab Emirates United Arab Emirates University Department of Electrical Engineering Al Ain United Arab Emirates University of New Mexico Department of Electrical and Computer Engineering NM United States

ISBN: (纸本)9798350340921

Artificial intelligence (AI) has revolutionized the field of education, leveraging advancements in computing and information processing technologies. the application of AI in education (AIED) has brought forth a multitude of possibilities, opportunities, and challenges, profoundly impacting educational practices across various domains. Within the realm of learning, instruction, and administration, AI has gained considerable acceptance and utilization in educational institutions. Educators have leveraged the AI tools to enhance the quality of instructional methods and streamline administrative tasks. With AI-powered systems, tasks such as evaluating and grading learner assignments can be accomplished more efficiently and expeditiously. Moreover, the use of cognitive computing and customization has facilitated the individualization and tailoring of syllabus and materials to meet the unique needs of learners. this personalized approach has fostered increased absorption, engagement, and an enriched learning environment for students overall. the impact of AI in education extends beyond traditional boundaries, permeating multiple areas of educational practice. From personalized learning experiences to efficient administrative processes, AI has revolutionized how knowledge is disseminated and acquired. As AI continues to advance, it holds immense potential for further innovation and transformation within the education sector, propelling us into a new era of enhanced educational outcomes and opportunities for students worldwide. this paper provides the information about intelligent education tools, examines the transformative impact of AI in diverse areas of education, and addresses the challenges associated with its implementation. After examining the existing literature on impact of AI in different areas of education, future research directions are also recommended in this paper. © 2023 IEEE.

关键词： machine learning

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Semi-Supervised Cell Instance Segmentation for Multi-Modality Microscope Images 36

Semi-Supervised Cell Instance Segmentation for Multi-Modalit...

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Cell Segmentation Challenge in Multi-Modality High-Resolution Microscopy Images - 36th conference on Neural Information processing Systems, NeurIPS 2022

作者： Wang, Ziyue Fang, Zijie Chen, Yang Yang, Zexi Liu, Xinhao Zhang, Yongbing Shenzhen518055 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China

Many clinical and biological tasks depend on accurate cell instance segmentation. Currently, the rapid development of deep learning realizes the automation of cell segmentation, which significantly decreases the workload of clinicians and researchers. However, most existing cell segmentation frameworks are fully supervised and modality-specific. Towards this end, this paper proposes a semi-supervised cell instance segmentation framework for multi-modality microscope images. Firstly, K-Means clustering is utilized to discriminate the image modality. then, for phase contrast and differential interference contrast images, Cellpose is adopted. For brightfield images, we subdivide them into two sub-categories according to the cell diameter by K-Means and optimize a U-Net for the large diameter group. For fluorescence images, we propose a semi-supervised learning strategy using CDNet. the leaderboard shows that our proposed framework reaches an F1 score of 0.8428 on the tuning set, which ranks 6th among all teams. © 2023 SPIE.

关键词： Cells

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machine learning-Based Stroke Disease Detection System Using Biosignals (ECG and PPG) 6th

Machine Learning-Based Stroke Disease Detection System Using...

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6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Neha Reddy, S. Neha, Adla Subba Rao, S.P.V. Ramaswamy, T. Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India

ISBN: (纸本)9789819984503

Strong vital counteraction and early recognition about prognostic indications are critical for the prevention about stroke disease, which often results in death or severe disability, to treat ischemic or hemorrhagic strokes, thrombolytic or coagulant drugs must be delivered as quickly as feasible. the key to getting competent advice from an impartial commission in the right circumstance is to use a fanlight to look for slowly occurring stroke symptom reactions. According to each individual, these responses vary. However, prior research has mainly focused on the distinct verification about stroke symptoms and a suggestion about correction for stressful situations or detached situation plans after a stroke. Computed tomography (CT) and magnetic resonance imaging (MRI) image review procedures have been widely used in ongoing tests to identify and seek prognostic advice in stroke cases. these methods have limitations, such as extended experiment times and high experiment expenses, despite their constant quest for understanding. In this audit, we mimic an artificial knowledge-based method for predicting the future effects about stroke in the more experienced using many presupposed biography signs about electrocardiogram (ECG) and photoplethysmography (PPG) that were equally received. We designed and executed an assemblage building that combines CNN and LSTM in order to wish stroke persistent while marching as a team. According to the planned approach, which takes into account the modesty surrounding the fact that more experienced objects can wear biography-signal sensors, the biosignals were recorded while moping at a model speed about 1000 Hz each second from the three cathodes about the ECG and the PPG indicator. the senior stroke patients’ real-time predictions’ accuracy was satisfactory. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electrocardiograms

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Android Dynamic Malware Detection Method Based on System Call Sequences

Android Dynamic Malware Detection Method Based on System Cal...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Xiaojian Liu Yifei Zhang Yaoyao Duan Beibei Hou School of Computer Science and Technology Xi'an University of Science and Technology Xi'an Shaanxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the continuous evolution of the types and attack strategies of Android malware, the methods for detecting them must also innovate and evolve. Although existing dynamic detection methods mostly rely on machine learning, they still face numerous challenges in accurately identifying malicious software features. In order to more efficiently detect Android malware, this paper proposes an Android malware detection method based on the CNN-LSTM model. this method uses Strace to capture system call sequence logs and selects certain function parameters from the system calls to construct feature vectors. Finally, a CNN-LSTM model is employed for training and classification. Experimental analysis shows that compared to other machine learning methods and traditional CNN and LSTM networks, the classification performance of the CNN-LSTM model shows the best performance in all evaluation indicators.

关键词： Training Analytical models Computational modeling machine learning signal processing Feature extraction Malware Vectors Long short term memory Faces

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