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Improved Classification using Extended Hybrid Feature Selection Approach with Dimensionality Reduction 1

Improved Classification using Extended Hybrid Feature Select...

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1st International visualization, Informatics and Technology conference, IVIT 2022

作者： Xuan, Tan Zi Ming, Lim Tong Boon Hui, Kee Tunku Abdul Rahman University College Faculty of Computing and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9781665486484

Heart Disease has become the major cause of death around the world. The World Health Organization has recorded around 17 million deaths caused by cardiovascular heart disease (CVD). This is popular among the low and middle-income population, where resources and benefits of healthcare programs are lacking and people are not able to pay for the expensive procedures. Hence, having an efficient and effective solution in detecting heart disease occurrence is crucial. In this research, a novel approach of hybrid feature selection with injection of feature dimensionality was studied. Where the proposed method combines the strength of different feature selection techniques and enhanced by dimensionality reduction techniques. This research has used various sources of heart disease dataset from Cleveland, Framingham and Z-Alizadash to study the potential of generalization of the proposed method. In this research, KNN and SVM were applied to tested the proposed feature selection engine. The performance of the feature subsets will be evaluated using various machine learning models. The model performance will be compared and studied using accuracy. © 2022 ieee.

关键词： Feature Selection

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Evolving Autonomous Navigation: A NEAT Approach for Firefighting Rover Operations in Dynamic Environments

Evolving Autonomous Navigation: A NEAT Approach for Firefigh...

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ieee International conference on Electro-information Technology

作者： D. Shrestha D. Valles Department of Computer Science Texas State University Ingram School of Engineering Texas State University

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper introduces a novel approach for automating rover navigation in dynamic environments, explicitly focusing on firefighting applications, using the NeuroEvolution of Augmenting Topologies (NEAT) [1]. The research leverages NEAT's capability to evolve intricate neural networks, aiming to create a sophisticated rover navigation system. This system is designed to adeptly maneuver through complex terrains, avoid obstacles, and locate humans in highrisk fire scenarios. The motivation for this research is rooted in the urgent need for advanced autonomous technologies in lifesaving operations, particularly firefighting, where a rapid, precise response is crucial. The system's performance is rigorously assessed through comprehensive simulation experiments, showcasing its ability to achieve superior navigation efficiency and convergence. Additionally, the research provides an in-depth analysis of the rover's evolved behaviors using various visualization tools such as charts, statistics, and heatmaps. The approach we have used, the NEAT-based solution, demonstrates promising potential, offering a more adaptable and practical approach for real-world firefighting and rescue missions.

关键词： Adaptation models visualization Solid modeling Three-dimensional displays Navigation System performance Transfer learning

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Comparing Human Haptic Perception and Robotic Force/Torque Sensing in a Simulated Surgical Palpation Task

Comparing Human Haptic Perception and Robotic Force/Torque S...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Markert, Timo Matich, Sebastian Hoerner, Elias Pfannes, Jonas Theissler, Andreas Atzmueller, Martin WITTENSTEIN SE Corp Res & Dev Igersheim Germany Osnabruck Univ Semant Informat Syst Grp Osnabruck Germany Aalen Univ Appl Sci Aalen Germany German Res Ctr Artificial Intelligence DFKI Osnabruck Germany

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

In minimally invasive surgery (MIS), the reliable detection of hard inclusions in soft tissue is crucial for the success of the intervention. In robot-assisted surgery (RAS) however, limited technologies are available for intracorporeal tissue stiffness assessment due to the lack of force and tactile feedback from the robot tool tip. This paper investigates both, human haptic perception and robotic F/T sensing in similar experimental setups to draw conclusions about the usage of a haptic sensor for teleoperation in RAS. We use a novel 6-axis F/T sensor compact enough to be moved through trocars during RAS interventions and experimentally analyze its performance in a simulated robotic palpation task. Furthermore, we carry out a comprehensive user study (n=30) and collect fingertip interaction data to investigate human haptic perception. Results show, that both approaches detect larger bead diameters of 19mm and 15mm with high precision and show similar accuracy rates. With regards to interaction forces, subjects on average apply more than 10 times the amount of normal force (F-z =28.8 +/- 4.9 N), which leads to higher accuracy particularly for smaller embedded nodules. The robotic sensing technique, on the contrary, offers distinct advantages by providing more gentle treatments and reducing the risk of tissue damage.

关键词： visualization Tissue damage Instruments Force Biological tissues Robot sensing systems Haptic interfaces

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JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection

JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, ...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ehsanpour, Mahsa Saleh, Fatemeh Savarese, Silvio Reid, Ian Rezatofighi, Hamid Univ Adelaide Adelaide SA Australia Samsung AI Ctr New York NY USA Stanford Univ Stanford CA 94305 USA Monash Univ Melbourne Vic Australia Australian Natl Univ ANU Canberra ACT Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The availability of large-scale video action understanding datasets has facilitated advances in the interpretation of visual scenes containing people. However, learning to recognise human actions and their social interactions in an unconstrained real-world environment comprising numerous people, with potentially highly unbalanced and long-tailed distributed action labels from a stream of sensory data captured from a mobile robot platform remains a significant challenge, not least owing to the lack of a reflective large-scale dataset. In this paper, we introduce JRDB-Act, as an extension of the existing JRDB, which is captured by a social mobile manipulator and reflects a real distribution of human daily-life actions in a university campus environment. JRDB-Act has been densely annotated with atomic actions, comprises over 2.8M action labels, constituting a large-scale spatio-temporal action detection dataset. Each human bounding box is labeled with one pose-based action label and multiple (optional) interaction-based action labels. Moreover JRDB-Act provides social group annotation, conducive to the task of grouping individuals based on their interactions in the scene to infer their social activities (common activities in each social group). Each annotated label in JRDB-Act is tagged with the annotators' confidence level which contributes to the development of reliable evaluation strategies. In order to demonstrate how one can effectively utilise such annotations, we develop an end-to-end trainable pipeline to learn and infer these tasks, i.e. individual action and social group detection.

关键词： visualization Social groups Annotations Pipelines Social robots Robot sensing systems Pattern recognition

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Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders

Point2Cyl: Reverse Engineering 3D Objects from Point Clouds ...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Uy, Mikaela Angelina Chang, Yen-Yu Sung, Minhyuk Goel, Purvi Lambourne, Joseph Birdal, Tolga Guibas, Leonidas Stanford Univ Stanford CA 94305 USA Korea Adv Inst Sci & Technol Daejeon South Korea Autodesk Res San Rafael CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders. Reverse engineering from a raw geometry to a CAD model is an essential task to enable manipulation of the 3D data in shape editing software and thus expand their usages in many downstream applications. Particularly, the form of CAD models having a sequence of extrusion cylinders - a 2D sketch plus an extrusion axis and range - and their boolean combinations is not only widely used in the CAD community/software but also has great expressivity of shapes, compared to having limited types of primitives (e.g., planes, spheres, and cylinders). In this work, we introduce a neural network that solves the extrusion cylinder decomposition problem in a geometry-grounded way by first learning underlying geometric proxies. Precisely, our approach first predicts per-point segmentation, base/barrel labels and normals, then estimates for the underlying extrusion parameters in differentiable and closed-form formulations. Our experiments show that our approach demonstrates the best performance on two recent CAD datasets, Fusion Gallery and DeepCAD, and we further showcase our approach on reverse engineering and editing.

关键词： Point cloud compression Training Solid modeling visualization Three-dimensional displays Shape Reverse engineering

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Unsupervised Domain Generalization by Learning a Bridge Across Domains

Unsupervised Domain Generalization by Learning a Bridge Acro...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Harary, Sivan Schwartz, Eli Arbelle, Assaf Staar, Peter Abu-Hussein, Shady Amrani, Elad Herzig, Roei Alfassy, Amit Giryes, Raja Kuehne, Hilde Katabi, Dina Saenko, Kate Feris, Rogerio Karlinsky, Leonid IBM Res Armonk NY 10504 USA Tel Aviv Univ Tel Aviv Israel Technion Haifa Israel Boston Univ Boston MA 02215 USA MIT Cambridge MA 02139 USA Goethe Univ Frankfurt Germany MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental capacity of the human visual system. In this paper, different from most cross-domain works that utilize some (or full) source domain supervision, we approach a relatively new and very practical Unsupervised Domain Generalization (UDG) setup of having no training supervision in neither source nor target domains. Our approach is based on self-supervised learning of a Bridge Across Domains (BrAD) an auxiliary bridge domain accompanied by a set of semantics preserving visual (image-to-image) mappings to BrAD from each of the training domains. The BrAD and mappings to it are learned jointly (end-to-end) with a contrastive self-supervised representation model that semantically aligns each of the domains to its BrAD-projection, and hence implicitly drives all the domains (seen or unseen) to semantically align to each other. In this work, we show how using an edge-regularized BrAD our approach achieves significant gains across multiple benchmarks and a range of tasks, including UDG, Few-shot UDA, and unsupervised generalization across multi-domain datasets (including generalization to unseen domains and classes).

关键词： Bridges Training Representation learning visualization Semantics Self-supervised learning Visual systems

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Multimodal Contrastive Supervised Learning to Classify Clinical Significance MRI Regions on Prostate Cancer 44

Multimodal Contrastive Supervised Learning to Classify Clini...

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44th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Gutierrez, Yesid Arevalo, John Martinez, Fabio Univ Ind Santander Biomed Imaging Vis & Learning Lab BIVL2ab Bucaramanga Colombia Univ Ind Santander Machine Learning Anal & Comp Vis MLACV Bucaramanga Colombia Univ Ind Santander Dept Syst Engn Bucaramanga Colombia

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Clinically significant regions (CSR), captured over multi-parametric MRI (mp-MRI) images, have emerged as a potential screening test for early prostate cancer detection and characterization. These sequences are able to quantify morphology, micro-circulation, and cellular density patterns that might be related to cancer disease. Nonetheless, this evaluation is mainly carried out by expert radiologists, introducing inter-reader variability in the diagnosis. Therefore, different deep learning models were proposed to support the diagnosis, but a proper representation of prostate lesions remains limited due to the non-alignment among sequences and the dependency of considerable amounts of labeled data for learning. The main limitation of such representation lies in the cross-entropy minimization that only exploits inter-class variation, being insufficient data augmentation and transfer learning strategies. This work introduces a Supervised Contrastive Learning (SCL) strategy that fully exploits the inter and intra-class variability of prostate lesions to robustly represent MRI regions. This strategy extracts lesion sample tuples, with positive and negative labels, regarding a query lesion. Such tuples are involved into an easy-positive, and semi-hard negative mining to project samples that better update the deep representation. The proposed learning strategy achieved an average ROC-AUC of 0.82, to characterize prostate cancer in MRI, using only the 60% of the available annotated data.

关键词： Deep learning visualization Magnetic resonance imaging Supervised learning Transfer learning Morphology Minimization

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PPDL: Predicate Probability Distribution based Loss for Unbiased Scene Graph Generation

PPDL: Predicate Probability Distribution based Loss for Unbi...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Wei Zhang, Haiwei Bai, Qijie Zhao, Guoqing Jiang, Ning Yuan, Xiaojie Nankai Univ Coll Comp Sci Tianjin Peoples R China Nankai Univ Coll Cyber Sci Tianjin Peoples R China Mashang Consumer Finance Co Ltd Tianjin Peoples R China Tianjin Key Lab Network & Data Secur Technol Tianjin Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Scene Graph Generation (SGG) has attracted more and more attention from visual researchers in recent years, since Scene Graph (SG) is valuable in many downstream tasks due to its rich structural-semantic details. However, the application value of SG on downstream tasks is severely limited by the predicate classification bias, which is caused by long-tailed data and presented as semantic bias of predicted relation predicates. Existing methods mainly reduce the prediction bias by better aggregating contexts and integrating external priori knowledge, but rarely take the semantic similarities between predicates into account. In this paper, we propose a Predicate Probability Distribution based Loss (PPDL) to train the biased SGG models and obtain unbiased Scene Graphs ultimately. Firstly, we propose a predicate probability distribution as the semantic representation of a particular predicate class. Afterwards, we re-balance the biased training loss according to the similarity between the predicted probability distribution and the estimated one, and eventually eliminate the long-tailed bias on predicate classification. Notably, the PPDL training method is model-agnostic, and extensive experiments and qualitative analyses on the Visual Genome dataset reveal significant performance improvements of our method on tail classes compared to the state-of-the-art methods.

关键词： Training visualization Analytical models Computational modeling Semantics Optimization methods Tail

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What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics

What's in a Caption? Dataset-Specific Linguistic Diversity a...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chan, David M. Myers, Austin Vijayanarasimhan, Sudheendra Ross, David A. Seybold, Bryan Canny, John F. Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

While there have been significant gains in the field of automated video description, the generalization performance of automated description models to novel domains remains a major barrier to using these systems in the real world. Most visual description methods are known to capture and exploit patterns in the training data leading to evaluation metric increases, but what are those patterns? In this work, we examine several popular visual description datasets, and capture, analyze, and understand the dataset-specific linguistic patterns that models exploit but do not generalize to new domains. At the token level, sample level, and dataset level, we find that caption diversity is a major driving factor behind the generation of generic and uninformative captions. We further show that state-of-the-art models even outperform held-out ground truth captions on modern metrics, and that this effect is an artifact of linguistic diversity in datasets. Understanding this linguistic diversity is key to building strong captioning models, we recommend several methods and approaches for maintaining diversity in the collection of new data, and dealing with the consequences of limited diversity when using current models and metrics.

关键词： Measurement visualization Analytical models Video description Computational modeling Training data Linguistics

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Rice Disease Image Classification using MobileNetV2 Pretrained Model with Attention visualization using Gradient-weighted Class Activation Mapping (Grad-CAM) 6

Rice Disease Image Classification using MobileNetV2 Pretrain...

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6th International conference on information and Communications Technology, ICOIACT 2023

作者： Fiqri, Fahmi Noor Setyaningsih, Sri Saepulrohman, Asep Ipb University Faculty of Science and Mathematics Department of Computer Science Bogor Indonesia Pakuan University Faculty of Science and Mathematics Department of Computer Science Bogor Indonesia

ISBN: (纸本)9798350315639

Rice is one of the staple foods in Asia, particularly in Indonesia. With an ever-increasing demand of food, a stable rice production is mandatory and pest is one of the major challenges faced in sustainable rice production. In this research, we proposed an image classification model based on the MobileNetV2 pretrained model combined with a visual explanation based on the gradient-weighted class activation (Grad-CAM) algorithm to build a robust and accurate classification of rice diseases. The model is based on convolutional neural network (CNN) architecture. First, transfer learning is done from the MobileNetV2 pretrained model to create the classification model, followed by Grad-CAM to produce the visual explanation of the CNN. Finally, the model is trained on 7,077 rice images containing four different diseases (bacterial blight, blast, brown spot, and tungro) with a data augmentation process to increase the dataset's overall variance. This process yields a model with a classification accuracy of up to 99,9%, combined with visual feature explanation making this model a robust and efficient classification model. © 2023 ieee.

关键词： Convolutional neural networks

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