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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

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Unveiling Multilingual Text Summarization: Harnessing Generative AI for Multi-Modeled Image and Text Synthesis 1

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8th international conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Malpure, Tanvi Dande, Ishwari Parwekar, Chinmayee Ambarish Parwekar, Pritee MKSSS’s Cummins College of Engineering for Women Maharashtra Pune India School of Computer Engineering KIIT University Odisha Bhubaneswar India Delhi-Meerut Road Ghaziabad India

ISBN: (数字)9789819786664

ISBN: (纸本)9789819786657

In today’s digital and information-driven world, the volume of data being generated is enormous and with the explosion of online content, news articles, reports, etc. the capability to extract key information quickly and efficiently is becoming essential. With the technological advancements in the fields of Natural language processing and Generative AI techniques, multilingual summarization is gaining prominence. Furthermore, the development of summarization techniques specifically for Indian languages is immensely important as India being a linguistically diverse country has a myriad of languages spoken across various regions. With most of the online content and global knowledge being available in English, a substantial amount of people finds it difficult to consume the content. Hence, in this paper, we propose VistaSamiksha, a multifaceted, multilingual chatbot that carries out the task of multilingual abstractive summary generation designed to process input both in textual and image format. The proposed system can take input from any three languages, namely, English, and popularly spoken Indian languages Marathi and Hindi, and can provide an abstractive summary output in the chosen language. We have experimented with different pre-trained sequence-to-sequence models to find out the most suitable models for building our system and have presented a detailed overview of the models and our approach in this paper. The ultimate objective is to offer abstractive summaries in any of the user selected languages from the three available options, when the input is presented in any of the three languages. This addresses the challenge of consuming large volumes of data, thereby aiding users in accessing information efficiently. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Natural language processing systems

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SEA4DQ 2022 - Proceedings of the 2nd international Workshop on Software engineering and AI for data Quality in Cyber-Physical systems/Internet of Things, co-located with ESEC/FSE 2022

SEA4DQ 2022 - Proceedings of the 2nd International Workshop ...

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2nd international Workshop on Software engineering and AI for data Quality in Cyber-Physical systems/Internet of Things, SEA4DQ 2022, co-located with the 30th ACM Joint European Software engineering conference and Symposium on the Foundations of Software engineering, ESEC/FSE 2022

ISBN: (纸本)9781450394598

The proceedings contain 6 papers. The topics discussed include: data quality issues in online reinforcement learning for self-adaptive systems;data quality as a microservice: an ontology and rule based approach for quality assurance of sensor data in manufacturing machines;effect of time patterns in mining process invariants for industrial control systems: an experimental study;preliminary findings on the occurrence and causes of data smells in a real-world business travel data processing pipeline;data quality issues for vibration sensors: a case study in ferrosilicon production;and data quality issues in solar panels installations: a case study.

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Machine and Deep Learning-based Techniques for Precision Agriculture with Comparative Analysis 2

Machine and Deep Learning-based Techniques for Precision Agr...

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2nd international conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Bhavana, Munugapati Rao, Koppula Srinivas Department of Computer Science & Engineering MLR Institute of Technology Affiliated to JNTUH Hyderabad India

ISBN: (纸本)9798350340235

Water and soil resources are crucial to agricultural growth and must be utilized more effectively. Precision Agriculture (PA), an innovative approach in modern farming, integrates advanced technologies and data-driven methods to enhance agricultural practices and increase crop productivity while minimizing resource inputs and environmental impact. This review explores the many facets of precision agriculture, covering its methods, difficulties, and potential. This study attempts to provide the most recent data on PA technology and techniques for determining the most precise answer to diverse scenarios in agricultural operations. This study discusses research that has been done over the past five years (2018-2023) on approaches based on machine learning (ML) and deep learning (DL) for very accurate crop and weed yield predictions, as well as soil and weather forecasting. The analysis finishes with a discussion of precision agriculture's challenges and potential solutions. In PA, existing authors perform on the limited data and are more time-consuming. Issues with data quality, such as missing or inconsistent data, affect the reliability of analysis and predictions. And it affects the prediction accuracy. Unpredictable weather patterns can impact the effectiveness of specific PA evaluations. To overcome these issues, eliminate missing values and normalize the data using the min-max normalization method. And employ novel deep learning techniques such as Bi -directional convolutional long short-term memory and some optimization algorithms such as Northern Goshawk Optimization algorithm and Crayfish Optimization Algorithm to optimize the prediction parameters to achieve higher prediction accuracy with less computational time. This article describes how knowledge-based agriculture may raise output and product quality over the long run. © 2023 IEEE.

关键词： Comparative Analysis Crop Yield Prediction IoT-Based Techniques Precision Agriculture Soil Properties

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PTA Application in Carbon Capture and Storage - Challenges, Diagnostics, Modeling

PTA Application in Carbon Capture and Storage - Challenges, ...

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2024 international Petroleum Technology conference, IPTC 2024

作者： Idorenyin, E.H. Al-Abdulwahab, I.M. Petroleum Engineering & Development Saudi Aramco Eastern Province Dhahran Saudi Arabia

ISBN: (纸本)9781959025184

This paper documents pressure transient analysis (PTA) aspects of carbon capture and storage (CCS), from design considerations through modeling while also highlighting attendant challenges. CO2 injection in porous media has expanded in utility and increased in popularity following global consensus on decarbonization. To this end, CCS is unarguably a viable decarbonization option for the petroleum industry. However, it requires a reliable understanding of the subsurface in terms of quality, integrity, and capacity. In this study, we expatiate on the place of PTA analysis in long-term CO2 storage in porous media. This work focuses on CO2 injection in saline aquifers;nonetheless, findings here can be easily adapted to depleted oil and gas systems. We first discuss CO2 injection-falloff test design vis-à-vis expectations from PTA-driven formation description. This is followed by an overview of analysis techniques that guarantee reliable estimate of injectivity and robust characterization. We then demonstrate the use of transient modeling to obtain a first-level evaluation of storage capacity in addition to improved characterization. The challenges encountered are also discussed. Our findings show that knowledge of potential storage sites is indispensable in successful CO2 sequestration. A properly designed and carefully implemented well test remains a viable window to acquiring representative dynamic data which, upon analysis, provides valuable information about the storage media. Diagnostics and modeling must be supported with accurate description of the injection fluid as well as the in-situ formation fluid, otherwise the results may be in serious error. This paper provides guidance toward best practices on the design, diagnostics and modeling of CO2 injection-falloff tests for the purpose of robust formation description and long-term CO2 sequestration. Copyright © 2024, international Petroleum Technology conference.

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An ontology-based multi-source heterogeneous data integration framework for cutting tool lifecycle management*

An ontology-based multi-source heterogeneous data integratio...

引用

IEEE international conference on Automation Science and engineering (CASE)

作者： Mingyuan Xia Xianwen Zhao Xiaofeng Hu School of Mechanical Engineering Shanghai Jiao Tong University P. R. China Shanghai Changxing Marine Laboratory P. R. China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Tool lifecycle management is critical to improving productivity and reducing costs in manufacturing systems. To make efficient and accurate tool management decisions, it is necessary to have access to multiple related data across software systems. However, the interpretation, integration, and analysis of multi-source data is challenged by structural and semantic heterogeneity. To address this challenge, this study proposes an ontology-based method for integrating multi-source heterogeneous data. The first step involves developing an ontology model and SysML model to create a standardized and semantically consistent description of material, information, processes, and their interactions within the manufacturing system. Next, an automatic extraction tool is developed for multi-source data instances based on the constructed models. The tool converts data instances into a structurally and semantically consistent format and stores them, along with the relationships, in a graph database. A case study is presented using an aero-engine fuel nozzle production line as an example to demonstrate data integration. Additionally, a decision aid application is developed for tool selection, a typical scenario in the tool lifecycle, to validate the proposed methodology's contribution to tool lifecycle management.

关键词： Accuracy Semantics data integration Ontologies data models data mining Aircraft propulsion Time-domain analysis Time-varying systems manufacturing systems

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Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks 17th

Stochastic Collapsed Variational Inference for Structured Ga...

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17th conference of the international Federation of Classification Societies, IFCS 2022

作者： Meng, Rui Lee, Herbert K. H. Bouchard, Kristofer Biological Systems and Engineering Division Lawrence Berkeley National Laboratory Berkeley United States University of California Santa Cruz United States

ISBN: (纸本)9783031090332

This paper presents an efficient variational inference framework for a family of structured Gaussian process regression network (SGPRN) models. We incorporate auxiliary inducing variables in latent functions and jointly treat both the distributions of the inducing variables and hyper-parameters as variational parameters. Then we take advantage of the collapsed representation of the model and propose structured variational distributions, which enables the decomposability of a tractable variational lower bound and leads to stochastic optimization. Our inference approach is able to model data in which outputs do not share a common input set, and with a computational complexity independent of the size of the inputs and outputs to easily handle datasets with missing values. Finally, we illustrate our approach on both synthetic and real data. © 2023, The Author(s).

关键词： Optimization

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data engineering and AI-Powered Skin Cancer Identification for Healthcare Applications 28th

Data Engineering and AI-Powered Skin Cancer Identification f...

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28th international conference on knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Animal and Agriculture Department Hartpury University Gloucester United Kingdom Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model's parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model's effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN's potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis © 2024 The Authors.

关键词： Convolutional neural networks

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Hotel Recommendation System Based on Customer's Reviews Content Based Filtering Approach 4

Hotel Recommendation System Based on Customer's Reviews Cont...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Shah, Hill Jacob, Lija Christ University Lavasa Pune Department of Data Science Pune India

ISBN: (纸本)9781665474368

Recommendation systems are fantastic tools for remembering people's ideas in order to gain knowledge more efficiently and selectively. Recently, booking and searching for hotels online has become more common. As it takes more time, online hotel research is growing more quickly. In addition, the amount of knowledge accessible online is continuously expanding. User preferences have a big impact on hotel recommendations. The most effective recommendations may be made by recommendation systems by utilising historical user preference data. To solve this problem, recommender systems have suggested content-based filtering methods. Product recommendations, recommendations for websites, news articles, restaurants, and TV series are all examples of applications for content-based recommender systems. The dataset for this project includes client evaluations of the offered Kaggle profile. Word embedding, word2vec, and TF-IDF natural language processing methods were used for feature extraction. The algorithm shows the user the top 10 suggested hotels based on the user's past knowledge of the hotel's location. © 2022 IEEE.

关键词： Recommender systems

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Analyzing Simulink Modeling Capabilities of Real Technological Disruption in Power System Section

Analyzing Simulink Modeling Capabilities of Real Technologic...

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international conference on Industrial engineering, Applications and manufacturing (ICIEAM)

作者： Pavel Sorochenko Violetta Zatsepina Yevgeny Zatsenin Institute of pfysics and power engineering Lipetsk State Technical University Lipetsk Russia

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

Modern PSP systems can record precise instrument transformer readings during failures. This helps analyze the operation of algorithms and the correct trigger settings for specific protection systems and automations. However, if the settings are incorrect, the equipment can undergo electromechanical and thermal impacts causing its damage and failure. Using the simulation modeling method can help prevent such situations by the better adjustment of settings. In this research, the authors analyzed the applicability of the developed simulation model for a transit 110 kV power line (powered from 220 kV substation buses) with a set of protection systems installed. The applicability was analyzed in comparison with the AURA data at the moment of the emergency shutdown of a 110 kV power line located in the Voronezh Oblast.

关键词： Analytical models Instrument transformers Substations Automation Software packages Industrial engineering manufacturing

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Sentiment Analysis on Text data: Methods, Applications, Challenges and Future Directions

Sentiment Analysis on Text data: Methods, Applications, Chal...

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2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Sudha, K. Muthumarilakshmi, S. Kavitha, G. Hashini, S. Kumar, V. Nivas Subramanian. R, Siva R.M.D Engineering College Dept of Csbs Tamil Nadu India S.A Engineering College Dept of Cse Tamil Nadu India Rajalakshmi Institute of Technology Dept of Cse Chembarambakkam India R&d Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Dept of Cse Tamil Nadu India Jawahar Engineering College Dept of Ece Chennai India Rmk College of Engg and Tech Dept of Cse Tamil Nadu India

ISBN: (纸本)9798350313413

Sentiment analysis, or opinion mining, is the computer examination of text data thoughts, opinions, and emotions. Emotions analysis has gained popularity because to the growing volume of user-generated content on social media and the need to understand public mood and client feedback. This review article summarizes sentiment analysis's knowledge, methods, challenges, and advancements. Introduce sentiment analysis and its usefulness in many sectors before discussing how sentiment analysis approaches have grown from rule-based systems to machine learning and deep learning. Next, discuss how sentiment analysis improves politics, business, marketing, social media monitoring, and recommendation engines. This survey research examines the latest sentiment analysis methods, including supervised learning models, unsupervised methods, lexicon-based methods, and hybrid models. Examine each approach's pros and cons and applicability for different scenarios. Sarcasm and irony identification, context-dependent sentiment, noisy and unstructured text data, and multilingual sentiment analysis are all discussed. The methods, challenges, and advances of sentiment analysis are covered in this comprehensive study. It encourages innovation and cooperation in text data sentiment analysis by providing a comprehensive resource. © 2023 IEEE.

关键词： computational analysis opinion mining public opinion Sentiment analysis text data user-generated content

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