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IEEE International Conference on Systems, Man and Cybernetics

作者： Sandra-Carina Noble Tomás Ward John V. Ringwood Department of Electronic Engineering Maynooth University Maynooth Ireland Insight SFI Research Centre for Data Analytics Dublin City University Dublin Ireland

The P300 speller is a widely used application in brain-computer interface research. It has been demonstrated that the P300 speller can serve as a neurofeedback training tool for attention enhancement by gradually increasing the difficulty of the spelling task. This adaptive approach makes it harder for users to spell words correctly, encouraging them to improve their attention to counteract the increasing difficulty. Therefore, the adaptive P300 speller has the potential to serve as a treatment option for children with ADHD, elderly patients with dementia, and as a cognitive enhancement tool for healthy adults. However, the training length, including setup time, needs to be quick to ensure user acceptability. This study investigates the effect of different electrode subsets on P300 speller performance, with and without the use of the xDAWN spatial filter. Results indicate that the xDAWN spatial filter can improve performance with many electrodes but can decrease results with fewer than eight electrodes. For scenarios where near-perfect performance is crucial and many electrodes are available, a set of 16 electrodes with the xDAWN spatial filter is recommended. For situations where cost and setup time are a concern and lower performances are acceptable, using six electrodes without the spatial filter were found to be sufficient.

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Point Cloud Registration for LiDAR and Photogrammetric data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms

arXiv

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arXiv 2023年

作者： Xu, Ningli Qin, Rongjun Song, Shuang Geospatial Data Analytics Lab The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

Three-dimensional (3D) point cloud registration is a fundamental step for many 3D modeling and mapping applications. Existing approaches are highly disparate in the data source, scene complexity, and application, therefore the current practices in various point cloud registration tasks are still ad-hoc processes. Recent advances in computer vision and deep learning have shown promising performance in estimating rigid/similarity transformation between unregistered point clouds of complex objects and scenes. However, their performances are mostly evaluated using a limited number of datasets from a single sensor (e.g. Kinect or RealSense cameras), lacking a comprehensive overview of their applicability in photogrammetric 3D mapping scenarios. In this work, we provide a comprehensive review of the state-of-the-art (SOTA) point cloud registration methods, where we analyze and evaluate these methods using a diverse set of point cloud data from indoor to satellite sources. The quantitative analysis allows for exploring the strengths, applicability, challenges, and future trends of these methods. In contrast to existing analysis works that introduce point cloud registration as a holistic process, our experimental analysis is based on its inherent two-step process to better comprehend these approaches including feature/keypoint-based initial coarse registration and dense fine registration through cloud-to-cloud (C2C) optimization. More than ten methods, including classic handcrafted, deep-learning-based feature correspondence, and robust C2C methods were tested. We observed that the success rate of most of the algorithms are fewer than 40% over the datasets we tested and there are still are large margin of improvement upon existing algorithms concerning 3D sparse corresopondence search, and the ability to register point clouds with complex geometry and occlusions. With the evaluated statistics on three datasets, we conclude the best-performing methods for each step and provi

关键词： Iterative methods

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning

Intelligent E-Commerce Chatbot for Personalized Product Reco...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： M.S. Minu Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system’s scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce.

关键词： Text mining Machine learning algorithms Scalability Systems architecture Machine learning Chatbots User experience Real-time systems Electronic commerce Time factors

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A CRITICAL ANALYSIS OF INTERNAL RELIABILITY FOR UNCERTAINTY QUANTIFICATION OF DENSE IMAGE MATCHING IN MULTI-VIEW STEREO

arXiv

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arXiv 2023年

作者： Huang, Debao Qin, Rongjun Geospatial Data Analytics Laboratory The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

Nowadays, photogrammetrically derived point clouds are widely used in many civilian applications due to their low cost and flexibility in acquisition. Typically, photogrammetric point clouds are assessed through reference data such as LiDAR point clouds. However, when reference data are not available, the assessment of photogrammetric point clouds may be challenging. Since these point clouds are algorithmically derived, their accuracies and precisions are highly varying with the camera networks, scene complexity, and dense image matching (DIM) algorithms, and there is no standard error metric to determine per-point errors. The theory of internal reliability of camera networks has been well studied through first-order error estimation of Bundle Adjustment (BA), which is used to understand the errors of 3D points assuming known measurement errors. However, the measurement errors of the DIM algorithms are intricate to an extent that every single point may have its error function determined by factors such as pixel intensity, texture entropy, and surface smoothness. Despite the complexity, there exist a few common metrics that may aid the process of estimating the posterior reliability of the derived points, especially in a multi-view stereo (MVS) setup when redundancies are present. In this paper, by using an aerial oblique photogrammetric block with LiDAR reference data, we analyze several internal matching metrics within a common MVS framework, including statistics in ray convergence, intersection angles, DIM energy, etc. We associate these metrics to the per-point errors evaluated through LiDAR reference data and discuss their potential contributions in estimating internal reliabilities of point clouds derived from DIM algorithms. The experimental results show that ray convergence and DIM energy are relevant indicators for the accuracy of the generated point clouds. Initial investigation shows that these two indicators could be further utilized to infer the measurem

关键词： Photogrammetry

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Big OLAP data Cube Compression Algorithms in Column-Oriented Cloud/Edge data Infrastructures

Big OLAP Data Cube Compression Algorithms in Column-Oriented...

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IEEE International Conference on Multimedia Big data (BigMM)

作者： Alfredo Cuzzocrea The Big Data Engineering and Analytics Laboratory University of Calabria Rende Italy Dept. of Computer Science University of Paris City Paris France

This paper provides a brief overview on the innovative problem of devising and implementing big OLAP data cube compression algorithms in column-oriented Cloud/Edge data infrastructures, an emerging need for next-gener...

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Leveraging Spatial and Temporal Features using CNN-LSTM for Improved Bone Fracture Classification from X-ray Images 6

Leveraging Spatial and Temporal Features using CNN-LSTM for ...

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6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2024

作者： Mewada, Hiren Al-Asad, Jawad F. Patel, Himanshu Mohammed, Nayeemuddin Prince Mohammad Bin Fahd University Electrical Engineering Department P.O. Box Al Khobar31952 Saudi Arabia Saskatchewan Polytechnic School of Information and Communications Technology Artificial Intelligence & Data Analytics Dept. Saskatoon Campus Canada Prince Mohammad Bin Fahd University Civil Engineering Department P.O. Box 1664 Al Khobar31952 Saudi Arabia

ISBN: (纸本)9798331529987

X-ray imaging remains the primary diagnostic tool for identifying bone fractures. Accurate classification of bone fractures from X-ray images is a critical task in the field of orthopedic diagnosis and treatment. However, this task poses several challenges due to the complex and variable nature of fracture patterns, as well as the need to consider the temporal progression of fracture healing. Traditional convolutional neural network (CNN) architectures, while effective in extracting spatial features from X-ray images, may not be sufficient to capture the dynamic and sequential aspects of fracture healing. In this work, we propose an improved Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture to address the limitations of CNN-only models in bone fracture classification. The proposed method aims to leverage the strengths of both CNNs and LSTMs to enhance the classification performance. The CNN component of the network is responsible for extracting relevant visual features from the X-ray images, while the LSTM layers are used to model the temporal relationships between these features, which are crucial for understanding the progression of fracture healing. Experimental results on a dataset of X-ray images of bone fractures demonstrate the superior performance of the CNN-LSTM architecture compared to traditional CNN-based approaches with 97.33% © 2024 IEEE.

关键词： Long short-term memory

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UNIVERSAL APPROXIMATION OF PARAMETRIC OPTIMIZATION VIA NEURAL NETWORKS WITH PIECEWISE LINEAR POLICY APPROXIMATION

arXiv

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arXiv 2023年

作者： Bae, Hyunglip Kim, Jang Ho Kim, Woo Chang Department of Industrial and System Engineering KAIST Korea Republic of Depart of Industrial and Management System Engineering Department of Big Data Analytics Kyung Hee University Korea Republic of

Parametric optimization solves a family of optimization problems as a function of parameters. It is a critical component in situations where optimal decision making is repeatedly performed for updated parameter values, but computation becomes challenging when complex problems need to be solved in real-time. Therefore, in this study, we present theoretical foundations on approximating optimal policy of parametric optimization problem through Neural Networks and derive conditions that allow the Universal Approximation Theorem to be applied to parametric optimization problems by constructing piecewise linear policy approximation explicitly. This study fills the gap on formally analyzing the constructed piecewise linear approximation in terms of feasibility and optimality and show that Neural Networks (with ReLU activations) can be valid approximator for this approximation in terms of generalization and approximation error. Furthermore, based on theoretical results, we propose a strategy to improve feasibility of approximated solution and discuss training with suboptimal solutions. © 2023, CC BY.

关键词： Decision making

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Customizing and Deploying data Science Roadmapping: A Case Study of an Oil and Gas Company

Customizing and Deploying Data Science Roadmapping: A Case S...

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2022 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2022

作者： Kayabay, Kerem Gokalp, Mert Onuralp Gokalp, Ebru Alagoz, Serhat Murat Eren, P. Erhan Kocyigit, Altan Data Informatics Middle East Technical University Network Technologies Department TÜBITAK ULAKBIM Ankara Turkey Data Analytics Center TÜPRAŞ Ankara Turkey Hacettepe University Computer Engineering Department Ankara Turkey Information Systems Middle East Technical University Ankara Turkey

ISBN: (纸本)9781665470278

Many organizations still face challenges leveraging data science in production and need strategic planning for organization-wide data science efforts and assets. data Science Roadmapping (DSR) customizes the widely used Technology Roadmapping to facilitate such planning in organizations, aligning market, business, data, technology, and organization perspectives. This paper employs the action research method to customize and apply DSR in an oil and gas company. The approach mostly met the expectations regarding objectives and outcomes, the first step toward generalizability. The case study provides researchers and practitioners insights into roadmapping customization, remote and agile roadmap development process, and strategic planning for technology adoption in a large organization. Lastly, we identify the future research directions using the recent roadmapping literature. © 2022 IEEE.

关键词： Big data

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Unlabeled data driven cost-sensitive inverse projection sparse representation-based classification with 1/2 regularization

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Science China(Information Sciences) 2022年第8期65卷 39-56页

作者： Xiaohui YANG Zheng WANG Jian SUN Zongben XU Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms School of Mathematics and StatisticsHenan University National Engineering Laboratory for Big Data Analytics School of Mathematics and StatisticsXi'an Jiaotong University

Sparse representation-based classification(SRC) has been widely used because it just relies on simple linear regression ideas to do classification, and it does not need learning. However, the performance of SRC is limited by needing sufficient labeled samples per class and the sensitivity to class imbalance. For tackling these problems, an improved SRC model is constructed in this paper. For alleviating the problem of insufficient labeled samples, an unlabeled data-driven inverse projection sparse representation-based classification model is constructed to achieve effective and stable representation and recognition results. The L1/2 and S1/2 regularizations are introduced to capture the sparsity of 1-D and 2-D, and to make the model have good statistical properties. Moreover, the cost-sensitive strategy is integrated into the model's classification criteria to improve the imbalance of class distribution adaptively, especially for multiclass imbalanced data.A solver utilizing the mixed Gauss-Seidel and Jacobian ADMM algorithm is developed to obtain the approximate solution. Experiments on common public test databases show that the proposed model achieves competitive results compared with the latest published results and some deep-learning algorithms.

关键词： unlabeled-data driven 1/2 regularization cost-sensitive inverse projection sparse representation-based classification

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Melanoma Detection with Transfer Learning

Melanoma Detection with Transfer Learning

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Pragadeshwar Vishnu S Krishvadana C Rani Rajeev Khoodeeram Ahmed Bouridane M Rajesh Kumar School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Emerging Technologies Université Des Mascareignes Mauritius Centre for Data Analytics University of Sharjah Sharjah UAE

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The abnormal growth of skin cells that are exposed to the sun is identified as skin cancer. Even though skin cancer is curable, late diagnosis and improper treatment can lead to severe effects and death. Melanoma has been identified as the deadliest type of skin cancer and it is responsible for $85 \%$ of skin cancer deaths. Even though research on melanoma identification is prominent, still it has not been perfected. Automated Melanoma recognition is a challenging task due to various artifacts such as low-contrast dermoscopic images, and large and similar varieties of other skin cancers. In this study, we take on this challenge using neural networks. We implement an approach in which two trained neural networks are combined to give a higher accuracy output. With the lack of training data for classification, we employ data augmentation techniques. The proposed Model makes use of two types of learning approaches by combining their weights and predicting the output. The combined or ensemble model categorizes the output into 6 different categories with an accuracy of up to $89 \%$. The approach is simple to implement and surpasses other avant-garde techniques in terms of accuracy.

关键词： Accuracy Neural networks Transfer learning Training data Melanoma Learning (artificial intelligence) Predictive models

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