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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

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ML Based Multi-Faceted Health Monitoring Devices: Enhancing Elderly Well-Being 2

ML Based Multi-Faceted Health Monitoring Devices: Enhancing ...

引用

2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Chaudhry, Sneha Priya Malode, Aayushi Swathika, O.v. Gnana School of Electrical Engineering Vellore Institute of Technology Chennai India Centre for Smart Grid Technologies Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350366570

This research paper explores the application, implementation, and assessment of health parameters in the elderly healthcare sector using Random Forest and Support vector Machine algorithms in Machine Learning. These algorithms increase the accuracy and predictive capabilities of health monitoring devices that analyze heart rate, fall risks, stress levels, and hypertension in the elderly in real time. The integration of IoT technology into the system ensures continuous and efficient monitoring with reliable communication and connectivity. The sensors are located to pick up essential health data, which provides the baseline for a deep analysis of health trends. This paper elaborates on how machine learning predicts and monitors health parameters in the elderly population. © 2024 IEEE.

关键词： Electronic health record

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Text Filtering Insignificant Word (TFIW) Readability Framework Model to Explain NLP in Sentiment analysis Using Reinforcement Machine Learning 2

Text Filtering Insignificant Word (TFIW) Readability Framewo...

引用

2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kandakatla, Rajesh Kumar, v Dhilip Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Dept. Computer Science and Engineering Chennai India BVRIT HYDERABAD College of Engineering for Women Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350366570

Reading of a text in a given sentence have lot of parameters to consider for expressing an opinion on a given text to conclude nature of the text. The data which need to be formed in the form of data sets based on specific constrains. The Natural Language Processing (NLP) will always presume that Text readability made as the considerable measure for better round spread of any newspaper. The data related to these documents must be collected and store them in data sets. During this process of measuring text readability at the granular level of the text reading is a cumbersome task. This will always implicate a problem of machine readability of the text and explainability of the text taken from the documents mentioned above. In this context human readability will act an important role in predicting the text reading at granular level where the text-based sentiment analysis have been predicted through a machine learning model. The text reading has been evaluated in order to improve the readability through explainable system. The explainable systems will capture and explain the predictions of text reading. In this technical work an explainable system Text Filtering Insignificant Word (TFIW) is introduced where it improves the textual reading at the granule level based on sentiments by prediction the words which need to be filtered. This will improve the explainability of text and text reading and improve the fast text and explainability of the document. © 2024 IEEE.

关键词： Reinforcement learning

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data mining for the global multiplex weekly average income analysis

Data mining for the global multiplex weekly average income a...

引用

2022 International conference on ENTERprise Information Systems, CENTERIS 2022 - International conference on Project MANagement, ProjMAN 2022 and International conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Dominic, Nicholas Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

The box office (BO) income had significantly declined up to 80% in 2020, as the COvID-19 pandemic emerged. To minimize further financial risks, multiplex (multiple cinema complexes) owners need to analyze their potential income for each movie, each week. Therefore, we developed a proper data mining strategy that allows multiplex owners to analyze and discover insights on how successfully produced movies could be. The methodology comprises (1) data loading and exploration, (2) data cleaning, (3) data selection, integration, and transformation using Pentaho, (4) data mining in which the results were stored in the MySQL database, and (5) pattern evaluation and presentation using Qlik Sense as the Business Intelligence (BI) dashboard. Based on our data mining methodology, we revealed that drama, comedy, action, and thriller are favorite genres. We also found that DreamWorks Animation and Pixar Animation Studios are both the most popular production houses, even Apatow Productions and Escape Artists still have the biggest revenue on average. © 2022 The Authors. Published by Elsevier B.v.

关键词： Animation

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PraK Tool v3: Enhancing video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

引用

31st International conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, vojtěch verner, Benjamin vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： video analysis

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EEG Signal analysis Using the NeuroSky Chip of the Mindflex Device in Trained and Untrained Dogs 41

EEG Signal Analysis Using the NeuroSky Chip of the Mindflex ...

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41st IEEE Central America and Panama Student conference, CONESCAPAN 2024

作者： Carranza, Kristel Nicole Gamero-v, Manuel Adalid Universidad Tecnológica Centroamericana UNITEC Facultad de Ingeniería San Pedro Sula Honduras

ISBN: (纸本)9798350377590

This research project addresses the need for a tool to visualize and analyze EEG signals in dogs during training, with the goal of enhancing our understanding of canine brain responses and advancing the development of more effective and humane training techniques. To explore how dogs process and categorize verbal and visual stimuli, including human facial expressions and commands, this study references various authors who have conducted direct work with animals, analyzing different factors and contributing to the relevance of this research. The study employs a quantitative approach and an experimental design to systematically and objectively investigate related as-pects. Four canine subjects were used to ensure a detailed focus on animal behavior. This cross-sectional study allowed for data acquisition at a specific point in time, facilitating precise analysis. EEG signals from the dogs were successfully captured and analyzed, leading to a better understanding of their brain responses during training sessions. Relevant data on EEG waves were obtained, and comparisons among the subjects helped identify patterns and differences in brain responses. Significant differences in frequency bands and magnitudes of brain waves were observed between trained and untrained dogs during training. These disparities suggest distinct brain responses to presented stimuli and cognitive tasks. Expanding the study to include a broader range of dogs can help ensure that the results are more representative of the canine population in general. © 2024 IEEE.

关键词： Electroencephalography

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Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an analysis of the CoDraw dataset 17

Instruction Clarification Requests in Multimodal Collaborati...

引用

17th conference of the European-Chapter of the Association-for-Computational-Linguistics (EACL)

作者： Madureira, Brielen Schlangen, David Univ Potsdam Computat Linguist Dept Linguist Potsdam Germany German Res Ctr Artificial Intelligence DFKI Berlin Germany

ISBN: (纸本)9781959429449

In visual instruction-following dialogue games, players can engage in repair mechanisms in face of an ambiguous or underspecified instruction that cannot be fully mapped to actions in the world. In this work, we annotate Instruction Clarification Requests (iCRs) in CoDraw, an existing dataset of interactions in a multimodal collaborative dialogue game. We show that it contains lexically and semantically diverse iCRs being produced self-motivatedly by players deciding to clarify in order to solve the task successfully. With 8.8k iCRs found in 9.9k dialogues, CoDraw-iCR (v1) is a large spontaneous iCR corpus, making it a valuable resource for data-driven research on clarification in dialogue. We then formalise and provide baseline models for two tasks: Determining when to make an iCR and how to recognise them, in order to investigate to what extent these tasks are learnable from data.

关键词： Clarifiers

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Design and Performance analysis of a Depth Map and Location Prediction Algorithm Based on Recurrent Neural Network Learning

Design and Performance Analysis of a Depth Map and Location ...

引用

2024 International conference on Electronics, Information, and Communication, ICEIC 2024

作者： Lee, Jae-Won Lim, Kitaeg Jang, Seonghyun An, Byoung-Man Lin, Chi-Ho Korea Electronics Technology Institute Korea Republic of Semyung University Korea Republic of

ISBN: (纸本)9798350371888

In this paper, we propose the design and performance analysis of a depth mapping and location prediction algorithm optimized for road driving environments with complex geometric features. The proposed algorithm uses a convolutional recurrent cell (ConvLSTM), which combines Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), to predict the depth map and location of the next frame from consecutive image frames. By integrating an Autoencoder for encoding spatial information and an RNN for handling temporal continuity, the algorithm can utilize both spatial and temporal information from visual data for more accurate predictions. The performance of the algorithm was validated using the KITTI vision Benchmark dataset, and the results confirmed that our approach is more efficient than location estimation methods based on complex sensors. These findings indicate that the proposed algorithm can significantly improve performance in understanding the complex spatial structure of road driving environments and estimating location. © 2024 IEEE.

关键词： Recurrent neural networks

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Fossa Optimization Algorithm with Convolution Neural Network Based Brain Tumor Segmentation and Classification in MRI Images 3

Fossa Optimization Algorithm with Convolution Neural Network...

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： veeresh Shivaprakash Hossain, Ramy Read Aruna, T.M. Kanth, Tatiraju.v.Rajani Sir M Visvesvaraya College of Engineering Raichur India Visvesvaraya Technological University Belagavi India Government Engineering College Raichur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Tvr Consulting Services Private Limited Gajularamaram Hyderabad India

ISBN: (纸本)9798331508456

Magnetic Resonance Image (MRI) brain tumor segmentation is critical and significant in a clinical field that assist in diagnosis, complete growth prediction and tumor mass rates required for patients. The complexity in segmenting brain tumors are challenging due to the various structures, shapes, locations, visual characteristics, frequency, texture and contrast. To overcome this issue, this research proposes a Fossa Optimization Algorithm (FOA) with Convolution Neural Network (CNN) for brain tumor classification. The Geodesic Active Contour (GAC) is used for segmentation which identifies tumor boundaries precisely in noisy and intricate data. FOA utilizes dynamic search strategy by its hunting behavior, balancing exploration and exploitation effectively. Its adaptive step size enables better convergence when avoiding local optima. Furthermore, its ability to capture edges and patterns makes it effective for classification. CNN are better to diverse in scale, noise and rotation because of its shared weights and local connectivity reduce the complexity thereby leading to higher accuracy and quick processing in classification. The FOA-CNN obtains 99.92% accuracy, 99.20% f1-score, 99.07% sensitivity and 99.34% precision for BRATS2020 dataset which is superior than Spatial Transformer Network and Non-local Attention Mechanism (STN-NAM). © 2025 IEEE.

关键词： Convolutional neural networks

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Establishing Design Consensus toward Next-Generation Retail: data-Enabled Design exploration and Participatory analysis 22

Establishing Design Consensus toward Next-Generation Retail:...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Yao, Yuan Cai, Junai Du, Kexin Hou, Yuxuan Mi, Haipeng Tsinghua Univ Future Lab Acad Arts & Design Beijing Peoples R China Tsinghua Univ Future Lab Beijing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Chinese Acad Social Sci Beijing Peoples R China Sch Visual Arts New York NY USA

ISBN: (纸本)9781450391573

Integration of online and offline retail faces challenges in technology adoption, interaction style evolution and customer behavior shifts, while also being complicated by diverse perspectives from different stakeholders of consumers, retail staf, and retail business unit people. To explore how we can tackle the aforementioned challenges, this work applied the data-enabled design method and participatory data analysis to a case study, where 400 student consumers' shopping behavior data was collected, cross-analyzed, and visualized in a campus chain store. We then invited 13 stakeholders to join a co-creation workshop for a further participatory data analysis. In the workshop, the different stakeholders came to a design consensuses which we summarized into a series of practical design recommendations for improving the current store. Finally, we generalized the case study process as a contextual-informed-aware model, which can contribute to professional design practice for the retail industry.

关键词： data-enabled Design Participatory data analysis Customer Behavior Understanding Design in Retail

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Revolutionizing Stock Price Prediction with Automated Facebook Prophet analysis 7

Revolutionizing Stock Price Prediction with Automated Facebo...

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7th International conference on Inventive Computation Technologies, ICICT 2024

作者： Annapoorna, E. Sujil, Sreya v. Sreepriya, S. Abhishek, S. Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350359299

Making predictions, about stock prices has always been a challenge in research even though the Efficient Market Hypothesis suggests otherwise. While it's widely believed that it's impossible to predict stock prices with precision there have been studies in the literature showing that accurate predictions can be made by using predictor models and relevant variables. The complexity of predicting stock market volatility adds another layer of difficulty as stock prices are influenced by factors such as conditions, psychological factors, and logical considerations. In this study, the Facebook Prophet model is utilized to forecast stock prices by analyzing data from Yahoo Finance. The experimental results indicate that Facebook Prophet can be effectively used to predict stock prices overtime periods. The research work's emphasis on Facebook Prophet provides not only accurate forecasts but also seamless visualization, allowing users to gain confidence in analyzing, visualizing, and forecasting stock prices. This comprehensive effort provides users with the knowledge to navigate the difficulties of stock price analysis, employing automation to quickly acquire solid forecasts and visual insights, regardless of the selected stock. © 2024 IEEE.

关键词： Forecasting

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