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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Shubho, Fahimul Hoque Chowdhury, Townim Faisal Cheraghian, Ali Saberi, Morteza Mohammed, Nabeel Rahman, Shafin North South University Dept. of Electrical and Computer Engineering Bangladesh University of Adelaide Australian Institute for Machine Learning Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia University of Technology Sydney School of Computer Science and Dsi Australia

ISBN: (纸本)9798350382204

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics include manual attributes or automatic word vectors from language models (like word2vec). We know attribute annotation is costly, whereas automatic word vectors are relatively noisy. To address this problem, we explore how ChatGPT, a large language model, can enhance class semantics for ZSL tasks. ChatGPT can be a helpful source to obtain text descriptions for each class containing related attributes and semantics. We use the word2vec model to get a word vector using the texts from ChatGPT. Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT. More specifically, we leverage ChatGPT to provide extra supervision for the class description, eventually benefiting ZSL models. We evaluate our approach on various 2D image (CUB and AwA) and 3D point cloud (ModelNet10, ModelNet40, and ScanObjectNN) datasets and show that it improves ZSL performance. Our work contributes to the ZSL literature by applying ChatGPT for class semantics enhancement and proposing a novel word vector fusion method. © 2023 IEEE.

关键词： Zero-shot learning

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Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning

arXiv

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arXiv 2024年

作者： Khaldi, Rohaifa Tabik, Siham Puertas-Ruiz, Sergio de Giles, Julio Peñas Hódar Correa, José Antonio Zamora, Regino Segura, Domingo Alcaraz LifeWatch-ERIC ICT Core Seville41071 Spain Dept. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Dept. of Botany Faculty of Science University of Granada Granada18071 Spain Dept. of Ecology Faculty of Science University of Granada Granada18071 Spain Granada18071 Spain Zaragoza50059 Spain

Monitoring the distribution and size of long-living large shrubs, such as junipers, is crucial for assessing the long-term impacts of global change on high-mountain ecosystems. While deep learning models have shown remarkable success in object segmentation, adapting these models to detect shrub species with polymorphic nature remains challenging. In this research, we release a large dataset of individual shrub delineations on freely available satellite imagery and use an instance segmentation model to map all junipers over the treeline for an entire biosphere reserve (Sierra Nevada, Spain). To optimize performance, we introduced a novel dual data construction approach: using photo-interpreted (PI) data for model development and fieldwork (FW) data for validation. To account for the polymorphic nature of junipers during model evaluation, we developed a soft version of the Intersection over Union metric. Finally, we assessed the uncertainty of the resulting map in terms of canopy cover and density of shrubs per size class. Our model achieved an F1-score in shrub delineation of 87.87% on the PI data and 76.86% on the FW data. The R2 and RMSE of the observed versus predicted relationship were 0.63 and 6.67% for canopy cover, and 0.90 and 20.62 for shrub density. The greater density of larger shrubs in lower altitudes and smaller shrubs in higher altitudes observed in the model outputs was also present in the PI and FW data, suggesting an altitudinal uplift in the optimal performance of the species. This study demonstrates that deep learning applied on freely available high-resolution satellite imagery is useful to detect medium to large shrubs of high ecological value at the regional scale, which could be expanded to other high-mountains worldwide and to historical and forthcoming imagery. © 2024, CC BY-NC-ND.

关键词： Vegetation mapping

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Sentiment Analysis of Movie Reviews: Optimizing Predictions Through TF-IDF Vectorization

Sentiment Analysis of Movie Reviews: Optimizing Predictions ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Mohd Irfan Anne Sreyas Venkatesam Hariharan Shanmugasundaram Mahendra Kumar Gourisaria Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad Telangana India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Sentiment analysis model is aimed to accurately interpret movie reviews, addressing challenges like sarcasm and noisy data. By integrating multiple machine learning classifiers Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression, Decision Tree, and XGBoost—the model enhances sentiment prediction accuracy. The Term Frequency-Inverse Document Frequency (TF-IDF) method is used to extract features from a dataset of 50,000 movie reviews. Performance is evaluated using accuracy, precision, recall, and F1-score, with Logistic Regression achieving the best results, including an highest accuracy of 84.33 % and an F1-score of 0.847. In comparison to traditional methods, this multi-classifier approach offers a deeper understanding of movie reviews and its influence on audience decisions. The study demonstrates the potential of using these machine learning techniques to provide real-time, scalable sentiment analysis, which is crucial for filmmakers and marketers seeking insights into audience satisfaction and box office performance.

关键词： Sentiment analysis Logistic regression Accuracy Reviews Predictive models Motion pictures Feature extraction Real-time systems Noise measurement Random forests

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing: Project-Dissemination Paper

VIR2EM: VIrtualization and Remotization for Resilient and Ef...

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Forum on Specification, Verification and Design Languages, FDL

作者： Alessandro Beghi Nicola Dall'Ora Davide Dalle Pezze Franco Fummi Chiara Masiero Stefano Spellini Gian Antonio Susto Francesco Tosoni Dept. of Information Engineering University of Padova Padova Italy Dept. of Engineering for Innovation Medicine University of Verona Italy Statwolf Data Science s.r.l. Padova Italy

In this paper, we present the project “VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing” by providing details on its research themes and its scientific and technological output. The project, centered on virtualization and remotization in the industrial sector, was promoted by Regione Veneto in Italy, and it has seen the participation and collaboration of 3 universities, 1 public research entity, and 10 companies composed of end users of digital solutions and high knowledge-intensive service providers. The project aims to develop and use tools for the virtualization of processes, systems, resources, and remoting of operations in order to: (1) maximize the efficiency of manufacturing systems under normal operating conditions; (2) maintain operations in case of emergency situations; (3) facilitate the restart of operations downstream of emergency situations by ensuring flexibility and predictive capability. Each theoretical proposal has been validated in distinct industrial facilities by constructing ten different prototypes.

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Visual Navigation for Autonomous Vehicles: An Open-source Hands-on Robotics Course at MIT 12

Visual Navigation for Autonomous Vehicles: An Open-source Ha...

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12th IEEE Integrated STEM Education Conference, ISEC 2022

作者： Carlone, Luca Khosoussi, Kasra Tzoumas, Vasileios Habibi, Golnaz Ryll, Markus Talak, Rajat Shi, Jingnan Antonante, Pasquale Dept. of Aeronautics and Astronautics Massachusetts Institute of Technology United States Senior Research Scientist Data61 Csiro Australia Dept. of Aerospace Engineering University of Michigan United States School of Computer Science University of Oklahoma United States Dept. of Aerospace and Geodesy Technical University of Munich Germany

ISBN: (纸本)9781665484299

This paper reports on the development, execution, and open-sourcing of a new robotics course at MIT. The course is a modern take on 'Visual Navigation for Autonomous Vehicles' (VNAV) and targets first-year graduate students and senior undergraduates with prior exposure to robotics. VNAV has the goal of preparing the students to perform research in robotics and vision-based navigation, with emphasis on drones and self-driving cars. The course spans the entire autonomous navigation pipeline;as such, it covers a broad set of topics, including geometric control and trajectory optimization, 2D and 3D computer vision, visual and visual-inertial odometry, place recognition, simultaneous localization and mapping, and geometric deep learning for perception. VNAV has three key features. First, it bridges traditional computer vision and robotics courses by exposing the challenges that are specific to embodied intelligence, e.g., limited computation and need for just-in-time and robust perception to close the loop over control and decision making. Second, it strikes a balance between dept. and breadth by combining rigorous technical notes (including topics that are less explored in typical robotics courses, e.g., on-manifold optimization) with slides and videos showcasing the latest research results. Third, it provides a compelling approach to hands-on robotics education by leveraging a physical drone platform (mostly suitable for small residential courses) and a photo-realistic Unity-based simulator (open-source and scalable to large online courses). VNAV has been offered at MIT in the Falls of 2018-2021 and is now publicly available on MIT OpenCourseWare (OCW) and at ***/. © 2022 IEEE.

关键词： Computer vision

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Ensemble Learning Using Weighted Convolutional Neural Networks for Parkinson’s Disease

Ensemble Learning Using Weighted Convolutional Neural Networ...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： D. Castillo-Barnes R. Garcia-Diaz J. Merino-Chica F. Segovia C. Jimenez-Mesa J. E. Arco J. Ramírez J. M. Górriz Dept. of Signal Theory Networking and Communications and Andalusian Research Institute on Data Science and Computational Intelligence University of Granada Granada Spain Dept. of Communications Engineering University of Malaga Malaga Spain

In recent years, there has been a growing interest on Computer-Aided-Diagnosis (CAD) systems based on Deep Learning to disentangle how neurodegenerative disorders such as Parkinson’s Disease (PD) behave. Although these models are quite accurate, they are often criticized for their lack of interpretability, high complexity and tendency to overfitting (as a result of the amount of input features –intensity voxels from brain scans– in comparison with the reduced number of input samples). Though there are some proposals that are trying to solve these limitations, such as data augmentation and/or interpretable models, it would be interesting, from the point of view of neuroimaging, to develop a CAD model able to 1) split the problem of analyzing the full brain scans and 2) combine the contribution of smaller Regions of Interest (ROIs). This could help us not only to reduce the complexity of the neural networks focused on the analysis of each ROI, but also as a way to quantify the contribution of each region to CAD decisions. In this context, we have proposed an ensemble learning methodology with penalization of non-reliable input sources focused on the analysis of 7 ROIs (including: accumbens, amygdala, anterior cingulate cortex, cingulate, striatum, substantia nigra and thalamus). This method is able to combine the resulting predictions obtained for each ROI by means of a Weighted Majority Voting decision schema using the scores derived from a two-sample Welch’s test. As shown by our results, this proposal achieves classification rates of 97.5% and is an easily adaptable solution for the analysis of other pathologies.

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Uncertainty Aware Implicit Image Function for Arbitrary-Scale Super-Resolution

Uncertainty Aware Implicit Image Function for Arbitrary-Scal...

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IEEE International Conference on Image Processing

作者： Swastik Jena Saptarshi Panda Bunil Kumar Balabantaray Rajashree Nayak Dept. of Computer Science and Engineering National Institute of Technology Meghalaya India Dept. of Civil Engineering National Institute of Technology Meghalaya India Centre for Data Science JIS Institute of Advanced Studies and Research Kolkata India

The most integral step in computer vision and image processing tasks is the representation of images. Recently, continuous image parameterization using Implicit Neural Representations (INR) has shown a great advantage over discrete representations due to their spatial invariance. This property immediately finds application in the context of single-image super-resolution (SISR) at an arbitrary scale. However, most super-resolution models, including the INR-based Local Implicit Image Function (LIIF), produce only a single output, failing to address the ill-posedness of SISR. Moreover, these models tend to optimize a mean-squared-error (MSE) based loss function which causes blurring and structural distortion in regions exhibiting a high degree of variance (details). Our work proposes a novel uncertainty-aware self-supervised methodology (U-LIIF) that extends on LIIF, to reduce the blurriness and deals with the ill-posedness of SISR. Our U-LIIF does not require any re-training and yields diversified high-resolution images by leveraging model uncertainty. The efficacy of the proposed method is validated by substantial experiments on various benchmark datasets.

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Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT

Time Series Anomaly Detection with CNN for Environmental Sen...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Mirza Akhi Khatun Mangolika Bhattacharya Ciarán Eising Lubna Luxmi Dhirani Dept. Electronic & Computer Engineering SFI CRT Foundations in Data Science University of Limerick Limerick Ireland School of Information Technology Illinois State University Illinois USA Dept. Electronic & Computer Engineering University of Limerick Limerick Ireland

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

This research develops a new method to detect anomalies in time series data using Convolutional Neural Net-works (CNNs) in healthcare-IoT. The proposed method creates a Distributed Denial of Service (DDoS) attack using an loT network simulator, Cooja, which emulates environmental sensors such as temperature and humidity. CNNs detect anomalies in time series data, resulting in a 92 % accuracy in identifying possible attacks.

关键词： Temperature sensors Wireless sensor networks Temperature distribution Accuracy Time series analysis Medical services Humidity

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A Hybrid Machine Learning and Deep Learning Approach for Diabetes Prediction With a Tkinter–Based User Interface

A Hybrid Machine Learning and Deep Learning Approach for Dia...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Sai Bhargav Kasetty Issac Neha Margret K. Sai Madhuri Rajesh Kumar Ojha Nuthalapati Sudha K. Rajakumar SCOPE V.I.T University Vellore Tamilnadu India dept. of CSE Data Science BVRIT Narsapur Hyderabad Telangana India dept. of Computer Science & Engineering Silicon University Bhubaneswar Odisha India School of Engineering Mahindra University Hyderabad Telangana India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Diabetes prediction is a critical task in healthcare that can significantly benefit from advancements in machine learning and deep learning technologies. This paper presents a hybrid approach combining deep learning and traditional machine learning models for diabetes prediction. The proposed system integrates a Tkinter-based Graphical User Interface (GUI) to make it easier for users to interact and make predictions. In our discussion, we will cover the design and implementation of the system, and evaluate the performance of different models using precision, F1 score, recall, and, accuracy to provide insights into their effectiveness. Results demonstrate the efficacy of the hybrid approach, with a focus on model accuracy and user experience.

关键词： Deep learning Technological innovation Accuracy computational modeling Medical services Predictive models User experience Diabetes Informatics Graphical user interfaces

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