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检索条件"任意字段=Proceedings of the 7th International Conference on Algorithms, Computing and Systems"

共 3437 条记录，以下是741-750 订阅

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CESMP: Chinese-English Segment-aligned Multi-field Patent Data 7

CESMP: Chinese-English Segment-aligned Multi-field Patent Da...

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7th IEEE international conference on Cloud computing and Intelligence systems, CCIS 2021

作者： Liu, Wuying Wang, Lin Hu, Fumao Guangdong University of Foreign Studies Laboratory of Language Engineering and Computing Guangzhou510420 China Shanghai International Studies University Xianda College of Economics and Humanities Shanghai200083 China Luoyang Institute of Science and Technology School of Foreign Languages Luoyang471023 China

ISBN: (纸本)9781665441490

Patent data from various countries in the world implies the essence of scientific discovery and technological innovation of all human beings, but language differences have become a huge obstacle to patent data retrieval and communication. We hope to build a bridge from Chinese to English in the patent domain, so that English speakers can make better use of Chinese patent data. With the help of natural language processing technologies such as optical character recognition, Chinese text processing, machine translation and English text processing, we construct digital Chinese-English segment-aligned multi-field patent (CESMP) data from scanned Chinese patents. the current CESMP data consists of 610,310 patent documents in XML format. Each patent document contains six required fields (date, publication, ipc, title, abstract, and claim) and four optional fields (cpc, wipo, originalapplicant, and currentowner), among which the wipo, title, abstract, and claim fields are aligned with Chinese and English segments. Supported by well-structured bilingual patent data, on the one hand, the resource construction algorithms can efficiently build a bilingual patent dictionary and a parallel patent segment bank;on the other hand, the deep natural language processing algorithms can be effectively implemented into many practical intelligent applications such as cross-language patent retrieval, patent spam filtering, patent network analysis, patent machine translation, etc. © 2021 IEEE.

关键词： Optical character recognition

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A Contribution-factor Based Under-Frequency Load Shedding Scheme in a Regional Grid 7

A Contribution-factor Based Under-Frequency Load Shedding Sc...

引用

7th Asia conference on Power and Electrical Engineering, ACPEE 2022

作者： Zeng, Pijiang Liu, Sicheng Shi, Libao Yang, Mingliang Zhang, Dan Yan, Wenlin Yunnan Power Grid Co. Ltd. Yunnan Power Dispatching Control Center Kunming China Tsinghua University National Key Laboratory of Power Systems in Shenzhen Shenzhen International Graduate School Shenzhen China

ISBN: (纸本)9781665418195

Under-frequency load shedding (UFLS) is one of the most principal measures to maintain frequency stability and prevent successive frequency drop or even blackouts when there exists a large unbalanced power of the power grid. In this paper, a contribution-factor based UFLS strategy is proposed to achieve rapid and effective frequency recovery under a large system disturbance. Taking into account the frequency drop caused by the supply-demand imbalance due to the power plant failure in a regional grid, a contribution factor is derived to implement effective load shedding, and a load shedding priority index considering fault severity and probability is further defined to better adapt to the simultaneous failure of multiple power plants. In addition, in order to improve the efficiency of simulation, the solution process is divided into two steps: seeking approximate solution based on regulation power and seeking accurate solution based on steady-state frequency. Finally, taking an actual power grid in China as a case, the numerical simulations and corresponding comparative analysis are performed to verify the validity of the proposed UFLS strategy. © 2022 IEEE.

关键词： Frequency stability

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Validation of machine learning for Clinical Assessment of lumbar spine pathologies Using Graphic User Interface 7

Validation of machine learning for Clinical Assessment of lu...

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7th international conference on Image and Signal Processing and their Applications, ISPA 2022

作者： Boudjella, Aissa Arab, Sarah Bellebna, Bachir Guerbouz, Rabeh Bircham International University Dept. Electrical Engineering Miami United States Etablissement Hospitalo Universitaire Service Neurochirurgie Oran Algeria

ISBN: (纸本)9781665480420

the vertebral column degenerative disc clinical disease repressent an important part of the activity in neurospine surgery, in particular, the lumbar pathologies. Humans have certain limitations in terms of accuracy and effective time to perform the diagnostic prediction. the diagnosis by the radiologist is not always as accurate as the use of computer-aided technology (CAT). Nowadays, CAT becomes necessary to overcome these limitations in diagnosing diseases. It will help a neurosurgeon to make rapid and effective decisions. the purpose of our work is to introduce machine learning computer-aided technology methods to perform the diagnostic of pathology on the column vertebral based on the K-Nearest Neighbor classifier. Several patients with lumbar spine disease will be presented to evaluate and validate the performance of the proposed technique. It can help to diagnose a patient effectively based on the statistics performance metrics: Accuracy, precision score, sensitivity, F1-score, and specificity. the simulation results show that the proposed technique can correctly predict the pathologies with a significant higher recognition rate (100%), with the training accuracy and the test accuracy in the range of (82.6%~100.0%), (70.2%~95.2%), respectively. To conclude, the proposed model works better, indicating a pretty good prediction and a high recognition rate in detecting a new patient disease effectively. this will result in reducing the heavy physician workloads and diagnostic time. the proposed technique can be recommended to be integrated for clinical support decision systems and will serve as a primary screening and diagnosis guidline. © 2022 IEEE.

关键词： Pathology

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It Takes three to Tango: Understanding the World of Online Gossips through Netnography, Big Data Topic Modelling and Sentiment Analysis. 7

It Takes Three to Tango: Understanding the World of Online G...

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7th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2022

作者： Drus, Zulfadzli Khalid, Haliyana Universiti Teknologi Malaysia Azman Hashim International Business School Kuala Lumpur Malaysia

ISBN: (数字)9781665465823

ISBN: (纸本)9781665465823

Gossips can be dangerous for business, but some of the content helps businesses identify customers' perceptions, thus permitting opportunities to grow. To date, there has been an increase participation in online gossip, in which people utilise platforms like Facebook, blogs and online forums to snoop on and discuss their favourite celebrities. In this study, we propose three approaches for exploring the context and comprehending the content and sentiment users discussed in online gossip threads: Netnography, topic modelling, and sentiment analysis. We retrieved 6055 conversation threads, including three social media celebrity threads, from an online forum called Cari Forum. Using netnography, we were able to identify seven topics in the thread. the thread was then further analysed using the Latent Dirichlet Allocation (LDA) topic modelling approach. We discovered that seven is the optimal number of topics detected in the thread with no overlap. We used the Logistic Regression Model to evaluate each conversation thread's sentiment. We identify each topic's positive and negative sentiment, obtained through our sentiment analysis approach. the three methods helped us to understand consumers' opinions and stories about the lives and businesses of social media celebrities. the implications, research limitations, recommendations for future research and conclusion have been elaborated on in this paper. © 2022 IEEE.

关键词： Sentiment analysis

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15th international Symposium on Advanced Parallel Processing Technologies, APPT 2023

15th International Symposium on Advanced Parallel Processing...

引用

15th international Symposium on Advanced Parallel Processing Technologies, APPT 2023

ISBN: (纸本)9789819978717

the proceedings contain 24 papers. the special focus in this conference is on Advanced Parallel Processing Technologies. the topics include: the Optimization of IVSHMEM Based on Jailhouse;multi-agent Cooperative computing Resource Scheduling Algorithm for Periodic Task Scenarios;CLMS: Configurable and Lightweight Metadata Service for Parallel File systems on NVMe SSDs;Combining Cache and Refresh to Optimize SSD Read Performance Scheme;CCS: A Motif-Based Storage Format for Micro-execution Dependence Graph;Hydis: A Hybrid Consistent KVS with Effective Sync Among Replicas;an Automatic Deployment Method for Hybrid Cloud Simulation Platform;reliability Optimization Scheduling and Energy Balancing for Real-Time Application in Fog computing Environment;federated Classification for Multiple Blockchain systems;towards Privacy-Preserving Decentralized Reputation Management for Vehicular Crowdsensing;delay Optimization for Consensus Communication in Blockchain-Based End-Edge-Cloud Network;A Low-Latency Hardware Accelerator for YOLO Object Detection algorithms;SCFM: A Statistical Coarse-to-Fine Method to Select Cross-Microarchitecture Reliable Simulation Points;on-Demand Triggered Memory Management Unit in Dynamic Binary Translator;MFHBT: Hybrid Binary Translation System with Multi-stage Feedback Powered by LLVM;step and Save: A Wearable Technology Based Incentive Mechanism for Health Insurance;spear-Phishing Detection Method Based on Few-Shot Learning;time Series Classification Based on Data-Augmented Contrastive Learning;from Ledger to P2P Network: De-anonymization on Bitcoin Using Cross-Layer Analysis;robust Online Crowdsourcing with Strategic Workers;New Filter2D Accelerator on the Versal Platform Powered by the AI Engine;preface.

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Building Disease Prediction Model Using Machine Learning algorithms on Electronic Health Records' Logs 7

Building Disease Prediction Model Using Machine Learning Alg...

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7th international conference on Digital Technologies in Education, Science and Industry, DTESI 2022

作者： Rakhmetulayeva, Sabina Kulbayeva, Aliya International Information Technology University Manas St. 34/1 Almaty Kazakhstan

the number of tasks devoted to predicting the incidence of infectious diseases is growing rapidly due to the availability of statistical data supporting the analysis. this article describes the main solutions currently available for creating short and long-term disease forecasts. their limitations and practical applications are shown. Much attention is given to the Naïve Bayes classification, logistic regression, artificial neural network algorithm and k-means artificial neural networks as methods of model analysis based on machine learning. this article provides an overview of two popular machine learning algorithms used to predict diseases. the standard datasetis used for a wide range of diseases including fungal infection, allergy, GERD, chronic cholestasis, peptic ulcer disease, diabetes, bronchial asthma, migraine, paralysis (brain hemorrhage) and more. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Neural networks

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A Model for Predicting Crime Risk 7th

A Model for Predicting Crime Risk

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7th international conference on Artificial Intelligence and Virtual Reality, AIVR 2023

作者： Mehdipour, Farhad Hewage, U.H.W.A. Boonrat, Wisanu Naviza, April Love Vidhya, Vimita Aharari, Ari Department of Information Technology Otago Polytechnic—Auckland International Campus Auckland New Zealand Department of Computer and Information Science SOJO University Kumamoto Japan

ISBN: (纸本)9789819990177

Efficient resource allocation and effective risk management are paramount for governments and police forces. this article presents a comprehensive model designed to predict crime risk levels, with the goal of optimising resource allocation and reducing the associated time, cost, and effort in risk management. By considering crucial factors such as location, time, and crime type, our model endeavours to provide accurate and actionable insights. To ensure the reliability of our predictions, we implemented various data wrangling techniques, including feature selection, data validation, and the creation of new measures. these steps were instrumental in preparing the data for analysis and generating reasonably accurate results. Additionally, we explored a range of machine learning algorithms, namely Logistic Regression, Gaussian Naive Bayes, Decision Tree, XG Boost, and Random Forest, to predict crime risk levels. the models were meticulously validated using cross-validation techniques and evaluated based on diverse performance metrics. In a bid to further advance our predictive capabilities, we leveraged deep learning techniques with TensorFlow, enabling a performance comparison against traditional machine learning models. Notably, the Random Forest algorithm has emerged as the most effective, yielding an impressive accuracy of 90%. the culmination of our efforts is a successful software application, complete with a user interface integrated with our cutting-edge prediction model. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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A Deep Learning Approach for Recognizing Malayalam Sign Language Gestures

A Deep Learning Approach for Recognizing Malayalam Sign Lang...

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international conference on ent computing and Control systems (ICICCS)

作者： Kevin Issac Esther Daniel Bright Gee Varghese Dept. of Computer Science & Engineering Karunya Institute of Technology and Sciences Coimbatore India Dept. of Computer Science Maharishi International University Fairfield USA

Communication involves the sharing or exchanging of information, ideas, and emotions, which requires a common language between individuals. For those with speech impairments, sign language serves as a means of communication. the lack of understanding of sign language can create difficulties in communication between individuals with speech impairments and those without, posing a significant challenge. One solution to this challenge is to create a machine learning-based model that recognizes various sign language motions and converts them into a shared language. this model can be trained to benefit those seeking to interact and communicate with individuals with speech impairments. Although Sign Language Recognition systems exist, they are limited in the number of languages that can be recognized. this article introduces a real-time Sign Language Recognition system that employs transfer learning with TensorFlow and utilizes a webcam for the purpose of detecting and interpreting sign language gestures specific to the Malayalam language.

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Application of AI Technology in Internet Finance and Analysis of Security Risks 13th

Application of AI Technology in Internet Finance and Analysi...

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13th international conference on Frontier computing, FC 2023

作者： Wang, Ou Ye, Huan Li, Runfa Guangzhou Huali Science and Technology Vocational College Zengcheng Guangzhou511325 China Guangzhou International Economics College Guangzhou510540 China Guangzhou College of Technology and Business Guangzhou510850 China

ISBN: (纸本)9789819995370

Internet finance is a new financial market formed by the interaction of traditional financial markets and emerging financial markets. In this article, we will focus on how to use AI technology to analyze security risks. Artificial intelligence (AI) is an intelligent technology that can make decisions based on certain knowledge or rules without human intervention. AI can help us reduce human error. the application of artificial intelligence technology in internet finance is becoming increasingly widespread, including risk control, customer service, intelligent investment advisory, and other fields. Among them, artificial intelligence algorithms can help financial institutions determine whether there are risks based on users’ personal information and behavior patterns, improve risk management level, and improve service quality. At the same time, artificial intelligence technology can also provide personalized investment advice to customers, helping them achieve better returns. However, the application of artificial intelligence technology in internet finance also brings some security risks. For example, hackers can exploit algorithmic vulnerabilities, attack artificial intelligence systems, and steal users’ personal information and funds. In addition, artificial intelligence systems are susceptible to human manipulation and erroneous indications, resulting in misjudgment and bias. this requires financial institutions to adopt strict security measures, improve their technological level, ensure the safety and privacy of customers, and ensure the sustainable and healthy development of artificial intelligence technology. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Artificial intelligence

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Apla+: a Practical Service-oriented Programming Language for Service Orchestration 7

Apla+: a Practical Service-oriented Programming Language for...

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7th international conference on Computer and Communication systems, ICCCS 2022

作者： Xie, Wuping Wan, Songsong Jiang, Dongming Jiangxi Normal University Nss International S&t Cooperation Base of China Nanchang China Jiangxi S&t Normal University School of Foreign Languages Nanchang China Jiangxi S&t Normal University School of Mathematical&Computer Nanchang China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

Service-oriented paradigm utilizes simple small-scale services as fundamental elements for developing applications which are complex large-scale services. efficiently orchestrating services in a platform-independent and dynamic way is still desired as the service-oriented computing depicting. We concentrate on modeling the characteristics of services under the view of clearly distinguishing between service and resource providing service, then propose a practical and effective service-oriented programming language Apla+ based on PAR. the language emphasizes on not only combining services provided by others, but also developing atomic services. Meanwhile, a fascinating architecture is raised to invoke various resources which remain unchanged on account of establishing the relationship between the resources and the services. then both the service provider and the service consumer pay attention on their concerns via separating service orchestration and atomic services development, and services can be modeled conveniently through providing a higher level of abstraction. Finally we also give a case study to illustrate the feasibility of this language. © 2022 IEEE.

关键词： Modeling languages

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