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检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

共 558 条记录，以下是321-330 订阅

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Multidisciplinary data Integration for Artificial-Intelligence-Assisted Well Placement and Trajectory Design Optimization Under Uncertainty

Multidisciplinary Data Integration for Artificial-Intelligen...

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Abu Dhabi international Petroleum Exhibition and conference 2022, ADIPEC 2022

作者： Su, Shi Tahir, Sofiane Ghorayeb, Kassem Ramatullayev, Samat Garcia-Teijeiro, Xavier Hussein, Assef Mohamad Kloucha, Chakib Kada Mustapha, Hussein Schlumberger ADNOC Upstream United Arab Emirates

ISBN: (纸本)9781613998724

Infill well placement performed as part of field-development planning is traditionally performed by identifying areas of high remaining mobile hydrocarbons and good reservoir rock quality to be targeted. The identification of hotspots was also largely performed on single-model realizations and, therefore, not robust considering the reservoir characterization uncertainties. Increasing efforts were put into incorporating the uncertainties as a key element of the infill well placement workflow by computing probability maps to identify the hotspots with higher chances of success for infill production. The maps were still generated solely based on dynamic reservoir-simulation model results. In this paper we present an intelligent workflow that integrates the opportunity index probability maps concept derived exclusively from dynamic reservoir simulation models, with drilling risk maps derived from drilling data analysis and completions quality maps derived from geomechanical studies, and artificial-intelligence-driven reservoir target classification. The integration provides more depth in the hotspot selection by identifying the most productive and feasible locations for infill drilling. The locations are then used for well placement and trajectory design optimization. The well trajectories optimize factors in the hotspot locations, locations of existing drilling centers, surface topology for new drilling centers to be designed, numbers of available slots on each drilling center, and capital costs such as drilling economics and drilling center cost. Infill injection wells are placed in conjunction with the infill production wells either following a pattern-type of design or peripheral injection. The designed wells are evaluated via an automated pipeline using reservoir simulation where the set of wells will be tested against the ensemble of realizations under uncertainty. A probabilistic approach is taken for the infill well performance and the economics evaluation for c

关键词： Infill drilling

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Influence of "Corpus data driven Learning + Learning driven data" Mode on ESP Writing Under the Background of Artificial Intelligence

Influence of "Corpus Data Driven Learning + Learning Driven ...

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international conference on Application of intelligent systems in Multi-modal Information Analytics, MMIA 2020

作者： Su, Zheqian Liu, Miao Jiang, Man Shang, Yongwei Foreign Language Department Changchun University of Chinese Medicine Changchun China Planning and Finance Division Changchun University of Chinese Medicine Changchun China Health Management Department Changchun University of Chinese Medicine Changchun China

ISBN: (纸本)9783030514303

Objective: at present, in the actual process of foreign language teaching, there are still some deficiencies in the language materials and the knowledge of language analysis. The research aims to better stimulate the academic paper writing of college students of Traditional Chinese Medicine (TCM) by constructing the model of "Corpus data driven Learning (C-DDL) + Learning driven data (LDD)". Methods: The junior students (n = 80) in one university of TCM were divided into experimental group (n = 40) and control group (n = 40). After the no difference comparison, the educational experiment was carried out 12 times in 3 months. In the experimental group, the Chinese-English Parallel Corpus of TCM created by Hyconc software and the artificial intelligence network evaluation system were used, while the control group was taught by traditional style. The research tool is the ESP Writing Ability Scale of TCM. Results: in the five dimensions of word accuracy, sentence structure, text structure, writing efficacy and writing motivation, the post test of the experimental group and the control group were significantly better than the pre test (p 0.05). Conclusion: through the "Corpus data driven Learning (C-DDL) + Learning driven data (LDD)" mode, students gradually adapt to the beginning of independent language mining and exploration. In the process of academic language processing, construction, transformation and experiment of TCM English, it can make language knowledge more systematic and academic. © 2021, Springer Nature Switzerland AG.

关键词： Students

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An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices 35th

An Improved Subject-Independent Stress Detection Model Appli...

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35th international conference on Industrial, Engineering and Other Applications of Applied intelligent systems (IEA/AIE)

作者： Van-Tu Ninh Manh-Duy Nguyen Smyth, Sinead Minh-Triet Tran Healy, Graham Nguyen, Binh T. Gurrin, Cathal Dublin City Univ Dublin Ireland Univ Sci VNU HCM Ho Chi Minh City Vietnam

ISBN: (纸本)9783031085307;9783031085291

Stress is a complex issue with wide ranging physical and psychological impacts on human daily performance. Specifically, acute stress detection is becoming a valuable application in contextual human understanding. Two common approaches to training a stress detection model are subject-dependent and subject-independent training method. Although subject-dependent training method is proven to be the most accurate approach to build stress detection models, subject-independent one is a more practical and cost-efficient method, as it facilitates the deployment of stress level detection and management systems in consumer-grade wearable devices without requiring additional data for training from end-users. To improve the performance of subject-independent stress detection models, in this paper, we introduce a stress-related bio-signal processing pipeline with a simple neural network architecture using statistical features extracted from multimodal contextual sensing sources including Electrodermal Activity (EDA), Blood Volume Pulse (BVP), and Skin Temperature (ST) captured from a consumer-grade wearable device. Using our proposed model architecture, we compare the accuracy of stress detection models that use measures from each individual signal source with the one employing the fusion of multiple sensor sources. Extensive experiments on the publicly available WESAD dataset demonstrate that our proposed model outperforms conventional methods as well as providing 1.63% higher mean accuracy score compared to the state-of-the-art model while maintaining a low standard deviation. Our experiments also show that combining features from multiple sources produces more accurate predictions than using only one sensor source individually.

关键词： Affective computing Stress detection model Human context Multimodal sensing

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An Efficient Deep Transfer Learning Network for Characterization of Stroke Patients’ Motor Execution from Multi-Channel EEG-Recordings

An Efficient Deep Transfer Learning Network for Characteriza...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Oluwarotimi Williams Samuel Mojisola Grace Asogbon Frank Kulwa Alistair A. McEwan Sunday Timothy Aboyeji Rami Khushaba Peng Fang Guanglin Li School of Computing and Data Science Research Centre University of Derby Derby United Kingdom Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS Shenzhen China Australian Centre for Field Robotics Sydney University Chippendale NSW Australia

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Recent advances in stroke rehabilitation technology have been focused on developing intelligent Rehabilitation Robots (IRR) that can effectively engage post-stroke patients (PSP) in intuitive motor training for full function recovery. Most existing rehabilitation robots incorporate functionalities that are passive in nature, constraining PSP to predetermined trajectories that often deviate from patients’ limb movement intentions, consequently hindering recovery. To resolve this issue, a robust deep-transfer learning driven network (DTLN) is developed to adequately characterize PSP’s motion intention signatures from neural oscillations towards achieving intuitive and active training. Thus, we investigated and proposed the utilization of mu-frequency spectrum (muFS) based CWT approach for Scalograms construction, which serves as inputs to the DTLN model that characterizes multiple classes of PSP’s motor execution signatures from multi-channel electroencephalography (EEG) recordings. Then, we evaluated the proposed method using EEG data from six PSP and compared the decoding results to those of related approaches under similar experimental settings. The proposed method resulted in a significant increment of 10.84 % - 13.19% decoding accuracy across stroke patients and better convergence in comparison to other methods. Additionally, the method exhibited distinct task separability for individual motor execution signature across patients. In conclusion, our method offers a consistently accurate decoding of motor tasks that could enable intuitively active robotic training in PSPs with impaired motor function.

关键词： Training Accuracy Transfer learning Stroke (medical condition) Motors Assistive robots Electroencephalography Decoding Trajectory Robots

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Modeling of An Feedback System for Interpretation of Emotion Using AI

Modeling of An Feedback System for Interpretation of Emotion...

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Advanced Technologies in intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Pradeep Kumar Shah Manjunath C. R Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India Department of Computer Science and Engineering Jain University Bangalore India

ISBN: (纸本)9781665493970

Studies in mental neuroscience and brain research are gradually demonstrating how feelings play a crucial role in thought processes. This information is gradually being used to the reproduction and mental cycle demonstrations in the Counterfeit Canny and Fake Life areas. However, there aren't many comparisons between projects and very little research is done on possible components of sensation that might be used in computational systems initiatives. It is crucial for system improvement to comprehend the emotions underlying these supplied opinions at a finer granularity. Such crucial information cannot be fully ascertained through AI-based big data feeling analysis; as a result, text-based emotion identification incorporating AI in gaming big data has emerged as an urgent topic of normal language processing study. [1] The subjective audit takes into account various inclination models, datasets, calculations, and application fields of text-based feeling discovery, despite the fact that the examination work in this sector is ongoing. Additionally, SA aids in comprehending genuine comments made on a variety of platforms, like Amazon, Excursion Counsel, and others. This thorough review's primary goals were to summarise key findings from earlier studies and to provide an overview of SA models in the context of AI-driven SA. Additionally, this study provides an overview of SAs that are lexicon- and ontology-based as well as AI models that are used to analyze the ambience of a given environment.

关键词： Support vector machines Solid modeling Analytical models Philosophical considerations Neuroscience Virtual reality Big data

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An intelligent Robust One Dimensional HAR-CNN Model for Human Activity Recognition using Wearable Sensor data

An Intelligent Robust One Dimensional HAR-CNN Model for Huma...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： P. Deepan R. Santhosh Kumar B. Rajalingam P. Santosh Kumar Patra S. Ponnuthurai St. Martins Engineering College Roever Engineering College

One of the biggest new trends in artificial intelligence is the ability to recognise people's movements and take their actions into account. It can be used in a variety of ways, including for surveillance, security, human-computer interaction, and content-based video retrieval. There have been a number of researchers that have presented vision-based techniques to human activity recognition. Several challenges need to be addressed in the creation of a vision-based human activity recognition system, including illumination variations in human activity recognition, interclass similarity between scenes, the environment and recording setting, and temporal variation. To overcome the above mentioned problem, by capturing or sensing human actions with help of wearable sensors, wearable devices, or IoT devices. Sensor data, particularly one-dimensional time series data, are used in the work of human activity recognition. Using 1D-Convolutional Neural Network (CNN) models, this works aims to propose a new approach for identifying human activities. The Wireless Sensor data Mining (WISDM) dataset is utilised to train and test the 1D-CNN model in this dissertation. The proposed HAR-CNN model has a 95.2%of accuracy, which is far higher than that of conventional methods.

关键词： Wireless communication Wireless sensor networks Wearable computers Surveillance Time series analysis data models Real-time systems

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(NMRNN-LSTM) - Novel Modified RNN with Long and Short-Term Memory Unit in Healthcare and Big data Applications

(NMRNN-LSTM) - Novel Modified RNN with Long and Short-Term M...

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Advancements in Smart, Secure and intelligent computing (ASSIC), international conference on

作者： N. Deepa S. Prabakeran Devi. T Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Department of Networking and Communications School of Computing - SRM Institute of Science & Technology (SRMIST) Kattankulathur Chengalpattu District

In the modern world, people's expectations and needs are automatically supportive and easy to use such as voice messages, playing music, or movies automatically which may reduce the manual operations mostly. In past decades technological advances such as machine learning and its application over many data like structured and unstructured are very much tedious. Whereas the operations based on non-categorical data, and categorical data are working rapidly using Natural Language Processing (NLP) comparatively, existing ones were not very productive. Each process on the internet is carrying an enormous amount of information which can lag in storage as well as performance. When any CRUD operations such as create, modify, update and delete are being analyzed one at a time, complex data such as unstructured and structured data are used in any field. In such a way the location analysis, social media data, health organization information, etc are categorized in natural language processing (NLP). The proposed work is organized as i) managing the huge amount of data in healthcare and log files created due to electronic health record management(EHR), ii) Unstructured data that are generated from all electronic equipment such as monitoring heartbeat, brain waves, etc that can be interpreted to classify using machine learning algorithms. To overcome the complications and medical records access inefficiency due to the complex structure of the dataset, Natural language processing uses the recurrent neural network along with the novel modified long and short-term memory unit (NMRNN-LSTM). Using the big data types such as structured, unstructured, and reinforcement kind of databases which handle images such as CTs, X-rays, MRI, raw texts, video streaming medical history to have effective systems and clinical records for enhancing the technological Medical care.

关键词： Temperature distribution Machine learning algorithms Recurrent neural networks databases Social networking (online) Memory management X-rays

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Towards a Model-driven Framework for data and Application Portability in PaaS Clouds 1st

Towards a Model-Driven Framework for Data and Application Po...

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1st international conference on Sustainable Technologies for Computational Intelligence (ICTSCI)

作者： Kaur, Kiranbir Sharma, Sandeep Kahlon, Karanjeet Singh Guru Nanak Dev Univ Amritsar 143001 Punjab India

ISBN: (纸本)9789811500299;9789811500282

Cloud computing paradigm has gained momentum during last decade owing to the numerous benefits (Cost, Scalability, etc.) it offers. Several developers and organizations have steadily got interested in PaaS (Platform as a Service) clouds since it allows deploying applications without any requirement for a consecrate infrastructure, installations of dependencies or server configurations. However, there still exist some issues that need to be resolved to make Cloud computing and PaaS widely adopted. Portability of applications along with the portability of databases (DBs) among heterogeneous cloud platforms is one of the issues. To address this, a middleware platform has been proposed in this paper in which the application layer and the DB layer of that application shave been decoupled from each other. Both the layers can be ported from one platform (PaaS) to another PaaS independent of each other. Model-driven Engineering (MDE) approach has been leveraged to make the middleware flexible, further making application layer and DBlayer portable, productive and reusable. MDE has been proved to be a better alternative to standardization in the literature.

关键词： Cloud computing Application portability PaaS clouds Model-driven engineering

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Collaborative Learning in data Science Education: A data Expedition as a Formative Assessment Tool 21st

Collaborative Learning in Data Science Education: A Data Exp...

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21st international conference on Interactive Collaborative Learning (ICL) / 47th IGIP international conference on Engineering Pedagogy - Teaching and Learning in a Digital World

作者： Maksimenkova, Olga Neznanov, Alexey Radchenko, Irina Natl Res Univ Higher Sch Econ Moscow Russia ITMO Univ St Petersburg Russia

ISBN: (纸本)9783030119324;9783030119317

The paper addresses the questions of data science education of current importance. It aims to introduce and justify the framework that allows flexibly evaluate the processes of a data expedition and a digital media created during it. For these purposes, the authors explore features of digital media artefacts which are specific to data expeditions and are essential to accurate evaluation. The rubrics as a power but hardly formalizable evaluation method in application to digital media artefacts are also discussed. Moreover, the paper documents the experience of rubrics creation according to the suggested framework. The rubrics were successfully adopted to two data-driven journalism courses. The authors also formulate recommendations on data expedition evaluation which should take into consideration structural features of a data expedition, distinctive features of digital media, etc.

关键词： data expedition data science Collaborative technologies Education

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Intelligence Graphs for Threat Intelligence and Security Policy Validation of Cyber systems

Intelligence Graphs for Threat Intelligence and Security Pol...

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international conference on Artificial Intelligence and Applications, ICAIA 2020

作者： Vassilev, Vassil Sowinski-Mydlarz, Viktor Gasiorowski, Pawel Ouazzane, Karim Phipps, Anthony Cyber Security Research Centre London Metropolitan University London United Kingdom School of Computing and Digital Media London Metropolitan University London United Kingdom

ISBN: (纸本)9789811549915

While the recent advances in data science and machine learning attract lots of attention in cyber security because of their promise for effective security analytics, vulnerability analysis, risk assessment, and security policy validation remain slightly aside. This is mainly due to the relatively slow progress in the theoretical formulation and the technological foundation of the cyber security concepts such as logical vulnerability, threats, and risks. In this article, we are proposing a framework for logical analysis, threat intelligence, and validation of security policies in cyber systems. It is based on multi-level model, consisting of ontology of situations and actions under security threats, security policies governing the security-related activities, and graph of the transactions. The framework is validated using a set of scenarios describing the most common security threats in digital banking, and a prototype of an event-driven engine for navigation through the intelligence graphs has been implemented. Although the framework was developed specifically for application in digital banking, the authors believe that it has much wider applicability to security policy analysis, threat intelligence, and security by design of cyber systems for financial, commercial, and business operations. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cybersecurity

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