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Phase Lag Index of visual-Memory Processing EEG for Computer-Aided AUD Diagnosis 23

Phase Lag Index of Visual-Memory Processing EEG for Computer...

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9th International conference on Computer Technology Applications, ICCTA 2023

作者： Janah, Nur Zahrati Permanasari, Adhistya Erna Setiawan, Noor Akhmad Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9781450399579

Alcohol use disorder (AUD) causes systemic damage to the human body, including brain structure and function. Computer-aided AUD diagnosis offers fast and reliable detection of AUD to prevent further harm from alcohol consumption. Various machine learning models and feature engineering techniques have been proposed for high system accuracy, reliability, and interpretability. EEG features from brain connectivity are promising due to their interpretability properties. This paper aims to propose a novel feature from task-based EEG signal data for computer-aided AUD diagnosis. The functional connectivity was calculated using the phase lag index (PLI) by choosing eight EEG channels from brain areas processing visual stimuli. The resulting connectivity values were evaluated using discriminant analysis. The proposed feature has yielded a significant discriminant function that proved its differentiation properties. The highest differentiation properties resulted from the gamma band discriminant function with p-value © 2023 ACM.

关键词： Discriminant analysis

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The Multiple Bedding Planes (BPs) Effect on Fracture Height Propagation: Experimental Study and Field data analysis

The Multiple Bedding Planes (BPs) Effect on Fracture Height ...

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2023 SPE Annual Technical conference and Exhibition, ATCE 2023

作者： Li, Shuai Cai, Bo Weng, Dingwei He, Chunming Yi, Xinbin Fu, Haifeng Research Institute of Petroleum Exploration and Development PetroChina Beijing China Oil and Gas and New Energy Company PetroChina Beijing China

ISBN: (纸本)9781613999929

Geological structures such as pre-existence of multiple bedding planes (BPs) significantly influence the hydraulic fracture height propagation in shale gas and shale oil reservoirs. Field engineers have also proved that fracture height is much lower than designed via monitoring such as microseismic and DTS/DAS test. In this paper, hydraulic fracturing simulation is studied by injecting fluids into a 1m×1m×1m rock-block sample with multiple thin BPs developed at the in-situ stress condition, and during the operation process, acoustic emission (AE) monitoring is used to obtain the fracture propagation events. After the fracturing physical simulation, we cut the rock sample from three different perspectives and take visual observation of the fracture geometry via fluorescence irradiate. Secondly, we carried out series field data analysis such as fracturing curve history matching, flow-back data analysis and production data analysis, aiming to find the correlation between fluids leak-off rate, flow-back salt concentration, fracture height, fracture complexity and the number of BPs. Results showed that fracture propagation is a competition controlled by stresses and rock BPs. Fracture propagation is generally perpendicular to minimum principal stress while is locally controlled by the rock BPs. It always initial to be vertical fractures near the wellbore area, while as the vertical fracture penetrates the BPs, fracturing fluids would leak into the BPs and form horizontal fractures, lead to slow propagation in height direction and change the final fracture geometry. Fluorescence irradiate also showed fracture propagation can be divided into three modes, directly penetrate the BPs, arrested by the BPs or bifurcate into BPs, and these are mainly depending on the high-angle natural fractures. The results of this study suggest that a significant portion of fracturing fluids could be retained in BPs, which in turn is the reason of fracture height propagation obstruction. In fi

关键词： Fluorescence

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Comparative analysis of deepfake detection methods using machine learning and deep learning 7

Comparative analysis of deepfake detection methods using mac...

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7th International conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Agrawal, Poorva Pathak, Soham Potey, Shruti Dhengre, Vaishnavi Rakesh, Nitin Parvathi, R. Symbiosis Institute of Technology Nagpur Campus Pune India Dept. of Smart Computing Vellore India

ISBN: (纸本)9798350372816

The paper examines the growing issue of deepfake technology, which uses cutting-edge machine learning to spoof face features in videos. This technology has the potential to be abused in a number of situations, including blackmail, political manipulation, and the staging of terrorist acts. The study's main goal is to present a thorough summary of current studies that use cutting-edge deep learning techniques to identify fake information. The Deepfakes Detection Challenge (DFDC) dataset is introduced, neural network applications are covered, and the effectiveness of current deepfake detection models is assessed. Because deepfake applications potentially pose a hazard, the research emphasizes the need for tools that can automatically detect and evaluate the integrity of digital visual data. Additionally, it discusses numerous algorithms and approaches that have been put out in the literature for producing and identifying deepfakes, with a focus on deep learning-based methods. © 2024 IEEE.

关键词： Contrastive Learning

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2nd World conference on Intelligent and 3D Technologies, WCI3DT 2023

2nd World Conference on Intelligent and 3D Technologies, WCI...

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2nd World conference on Intelligent and 3D Technologies, WCI3DT 2023

ISBN: (纸本)9789819970100

The proceedings contain 31 papers. The special focus in this conference is on Intelligent and 3D Technologies. The topics include: 3D Animation Design and Production Based on Intelligent Algorithm and Virtual Reality;4DFA: Four-Dimensional Full-Anatomy Reconstruction of Individualized Digital Human Models Based on Motion Videos;research on Fault Prediction Technology of Air Compressor Based on Wavelet Neural Network;The Development of Intelligent VR Systems Based on Deep Learning;research and Implementation of Multi Fusion data Model Construction Technology for Distribution Network Digital Twins;research and Implementation of Rules Extraction Technology for Digital Twin Objects in Distribution Network Based on Semantic Understanding;analysis of Urban Landscape Plants’ Configuration Based on Virtual Reality;3D Animation Simulation Based on Computer Virtual Simulation Technology;lightweight Human Pose Estimation Based on Self-Attention Mechanism;new Approach for Soil Moisture Prediction Based on Multiple Influencing Factors;facial Photo-Guided Head Anatomy Modeling Based on Deep Learning and 2D/3D Shape Prior Model Registration;Design of Water Ecological Cleaning Robot Based on Raspberry PI and OpenCV visual Recognition;fragrant Pear Target Identification and Positioning Based on Deep Learning;Online Mini-Game Popularity and Feedback data Prediction Based on Time Series and BP Neural Network Prediction;mileage Pile Detection for Vehicle-Borne Video;exploration of Future Temperature analysis Based on ARIMA Time Series Model and GA-BP Neural Network Prediction Model;research on Feature Extraction Based on Time Series Images;research on Human Eyesight Tracking Algorithm Based on Monocular Vision;Automatic Calibration Method for High Resolution LiDAR and Fisheye Camera.

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4th International conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

4th International Conference on Machine Learning, Advances i...

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4th International conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

ISBN: (纸本)9789819752300

The proceedings contain 33 papers. The special focus in this conference is on Machine Learning, Advances in Computing, Renewable Energy and Communication. The topics include: Enhancing Power Quality in Grid-Tied Solar Photovoltaic Systems;RF-TSVM: Random Forest-Based Transductive Support Vector Machine for Classification and Prediction of Cancer Patterns;resource-Efficient Image Retrieval: A Study of Local Patterns Versus Deep Learning Models;machine Translation of Chinese–Hindi Simple Sentences Using Moses;Innovative Approaches to Reduce Carbon Footprint and Air Pollution: The Role of AI, ML, Cloud Computing, and IoT;investigating Sensor Technology and Benefits of Intelligent Transport Systems;student Attendance System by Quick Responsive Code;analysis of Brain Tumor Detection Using Machine Learning;a Review on Multiple Face Detection Techniques and Challenges;blockchain-Enabled Secure Identity Verification in Agri-Food Supply Chain;Machine Learning Approach for Diagnosis of Schizophrenia Using EEG Signals;Comparative analysis of Web APIS: RESTful and GraphQL;AI-Based Vision Screening Tool for Keratoconus;synergizing Artistry and Technology by Unveiling the Integration of Matte Painting Techniques in Crafting Precise and Immersive visual Effects Backgrounds;Heart Disease Prediction: A Comprehensive exploration of Optimal Predictive AI;dynamic Animation Scaling: Design and Development of Adaptive Character Animations for Varying Sizes;framework for Assessment of Greywater-Assisted Composting Using IoT-Based Sensors;mood-Based Movie Recommendation System Using Sentiment analysis;blockchain-Enabled Consensus Mechanisms for data Integrity and Security in Edge and Cloud Computing Environments;AI-Based Question Paper analysis and Generator with Authentication;pomegranate Leaf Fruit Disease Prediction Using Machine Learning;price Prediction Using Machine Learning Approaches.

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Traffic Sign Classification using Deep Learning Comparative Study 5

Traffic Sign Classification using Deep Learning Comparative ...

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5th International conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Assemlali, Hamza Sael, Nawal Laboratory of Information Technology and Modeling Faculty of Sciences Ben M'Sick Hassan II University Casablanca Morocco

In recent years, the advancement of deep learning technologies has significantly impacted various domains and the field of transportation is no exception. As the demand for robust and accurate traffic sign classification systems continues to rise, this study presents an in-depth exploration and comparison of various techniques employed in the field. Focusing on state-of-the-art methodologies, we assess the effectiveness and performance of multiple classification approaches for traffic sign recognition. The review presents an extensive survey of the literature, encompassing traditional computer vision methods, machine learning algorithms, and the latest advancements in deep learning. The comparative analysis aims to identify the strengths and limitations of each technique, considering factors such as computational efficiency and accuracy. Additionally, the paper implements four models—CNN, ResNet50, VGG19, and EfficientNetB7—for traffic sign classification on the GTSRB dataset, the accuracy results are reported as 99.25%, 99.28%, 98.95%, and 98.24% respectively. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Comparison of Heavy Rain Prediction with 1-hour and 2-hour Prediction Using Himawari-8 data Based on LSTM Algorithm 13

Comparison of Heavy Rain Prediction with 1-hour and 2-hour P...

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13th IEEE International conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Husnul, Nadia Harjupa, Wendi Ahmad, Umar Ali Budiman, Gelar Risyanto Telkom University School of Electrical Engineering Bandung Indonesia Center for Climate and Atmosphere National Research and Innovation Agency Brin Indonesia Kanazawa University Ishikawa Kanazawa Japan

ISBN: (纸本)9798350368086

Weather is one of the most unpredictable things in Indonesia. One of the factors is that Indonesia is an archipelago flanked by two large oceans, the Indian and Pacific Oceans. In addition, the fact that Indonesia is called the 'land of the equator' creates uncertainty and rapid changes in weather movements. So, the Meteorology, Climatology, and Geophysics Agency (BMKG) still have difficulty accurately predicting the weather. This paper uses the Deep Learning method for weather data analysis and processing. The deep learning algorithm is the Long-Short Term Memory (LSTM) algorithm, often used to process time series data. This paper proposes a combination of 13 rain detection parameters with a comparison of 1 -hour and 2-hour heavy rain prediction models using LSTM. The resulting output with the accuracy level of the heavy rain prediction model for 2 hours has better accuracy than the 1-hour prediction model. The 2-hour prediction model is more recommended than the 1 -hour model, with the best prediction model results being a combination of parameters 1,2,4,10, and 11 with an accuracy rate of 87%. Further modeling using 3-hour modeling on parameter combinations 1,2,4,10, and 11 resulted in an accuracy of 88% on the new test data. Modeling for a long time provides promising, accurate results but can provide significant noise. © 2024 IEEE.

关键词： Rain

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Streaming and visualising Neuronal Signals for Understanding Pain

Streaming and Visualising Neuronal Signals for Understanding...

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19th International conference on Service-Oriented Computing (ICSOC)

作者： Espinosa-Oviedo, Javier Alfonso Univ Lumiere Lyon 2 ERIC Lyon France

ISBN: (纸本)9783031141355;9783031141348

This paper presents our stream processing, and visualization system adapted to the requirements of the neuroscience domain. We propose to build a visual stream processing system for supporting the analysis and exploration of data streams in real-time by exploiting human's natural ability for discovering patterns. Our work combines stream processing and data storage techniques with data visualization theory. We study strategies for visualizing different types of data considering constraints related to real-time and data volume.

关键词： data visualization

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MVis4LD: Multimodal visual Interactive System for Lie Detection 16th

MVis4LD: Multimodal Visual Interactive System for Lie Detect...

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16th Asian conference on Intelligent Information and database Systems (ACiiDS)

作者： Sakib, Md Kowsar Hossain Islam, Md Rafiqul Prome, Shanjita Akter Thanh Thao Lam Nguyen Asirvatham, David Ragavan, Neethiahnanthan Ari Wang, Xianzhi Sanin, Cesar Taylors Univ Sch Comp Sci Subang Jaya Malaysia Australian Inst Higher Educ Informat Syst Sydney Australia Vin Univ Coll Business & Management Hanoi Vietnam Taylors Univ Fac Social Sci & Leisure Management Subang Jaya Malaysia Univ Technol Sydney Sch Comp Sci Sydney Australia

ISBN: (纸本)9789819749843;9789819749850

Lying is a prevalent concern impacting our daily lives and social interactions. The pattern to identify lying in text data can be improved by gaining a better understanding of individual behaviour. However, the study of lie detection (LD) is a complicated procedure that typically incorporates many aspects contingent upon individual biases and the characteristics of the data, such as data that lacks legitimate terms or suspicious words. There has been a relatively limited focus on multimodal interactive systems for LD. Thus, this paper introduces a novel approach by presenting a multimodal visual interactive system, referred to as MVis4LD, designed to facilitate users in expressing complex analytical queries seamlessly. It integrates natural language interaction, a promising method for exploration through visualization, and explores the synergies among various modalities to address the limitations of individual modes. The users can provide input as text, click or touch, and voice commands to the system to generate corresponding results, offering a comprehensive chats or plots for LD. Furthermore, we collected an existing dataset by randomly altering the attribute values of 20% of the data in the test set. The findings of the experiment, conducted on 121 individual texts, show that Bidirectional Encoder Representations from Transformers (BERT), a deep learning (DL) model, logistic regression (LR), and K-Nearest Neighbors (KNN) from machine learning (ML) achieve the highest accuracy (84%) compared to other models. The live system with result visualization is available on our GitHub repository (https://amazing- ***/).

关键词： Deep Learning Multimodal visualization Interactive Systems Lie Detection Machine Learning Natural Language Processing BERT

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Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models?

Does Object Grounding Really Reduce Hallucination of Large V...

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2024 conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Geigle, Gregor Timofte, Radu Glavaš, Goran WüNLP Germany Computer Vision Lab CAIDAS University of Würzburg Germany

ISBN: (纸本)9798891761643

Large vision-language models (LVLMs) have recently dramatically pushed the state of the art in image captioning and many image understanding tasks (e.g., visual question answering). LVLMs, however, often hallucinate and produce captions that mention concepts that cannot be found in the image. These hallucinations erode the trustworthiness of LVLMs and are arguably among the main obstacles to their ubiquitous adoption. Recent work suggests that addition of grounding objectives-those that explicitly align image regions or objects to text spans-reduces the amount of LVLM hallucination. Although intuitive, this claim is not empirically justified as the reduction effects have been established, we argue, with flawed evaluation protocols that (i) rely on data (i.e., MSCOCO) that has been extensively used in LVLM training and (ii) measure hallucination via question answering rather than open-ended caption generation. In this work, in contrast, we offer the first systematic analysis of the effect of fine-grained object grounding on LVLM hallucination under an evaluation protocol that more realistically captures LVLM hallucination in open generation. Our extensive experiments over three backbone LLMs reveal that grounding objectives have little to no effect on object hallucination in open caption generation. © 2024 Association for Computational Linguistics.

关键词： visual languages

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