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arXiv 2021年

作者： Brown, Rachel Dutell, Vasha Walter, Bruce Rosenholtz, Ruth Shirley, Peter McGuire, Morgan Luebke, David NVIDIA Research Vision Science UC Berkeley Computer Graphics Cornell University Brain and Cognitive Sciences CSAIL Massachusetts Institute of Technology NVIDIA Research

computer graphics seeks to deliver compelling images, generated within a computing budget, targeted at a specific display device, and ultimately viewed by an individual user. The foveated nature of human vision offers an opportunity to efficiently allocate computation and compression to appropriate areas of the viewer's visual field, especially with the rise of high resolution and wide field-of-view display devices. However, while the ongoing study of foveal vision is advanced, much less is known about how humans process imagery in the periphery of their vision-which comprises, at any given moment, the vast majority of the pixels in the image. We advance computational models for peripheral vision aimed toward their eventual use in computer graphics. In particular, we present a dataflow computational model of peripheral encoding that is more efficient than prior pooling-based methods and more compact than contrast sensitivity-based methods. Further, we account for the explicit encoding of "end stopped" features in the image, which was missing from previous methods. Finally, we evaluate our model in the context of perception of textures in the periphery. Our improved peripheral encoding may simplify development and testing of more sophisticated, complete models in more robust and realistic settings relevant to computer graphics. © 2021, CC BY.

关键词： Budget control

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Sentiment Analysis on Drug Reviews Using Diverse Classification Techniques

Sentiment Analysis on Drug Reviews Using Diverse Classificat...

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Communication, Computing and Industry 4.0 (C2I4), International Conference on

作者： Premkumar Duraisamy Yuvaraj Natarajan K.R.Sri Preethaa K. Mouthami Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Research Professor Intelligent Construction Automation Center Kyungpook National University Deagu South Korea

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely “Azithromycin” in conjugation with Buprenorphine has Type ‘A’ side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions.

关键词： Support vector machines Drugs Analytical models Terminology Bit error rate Support vector machine classification Transformers

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Blockchain effect on E-commerce: A framework for key research areas

Blockchain effect on E-commerce: A framework for key researc...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Nethravathi K Jeidy Panduro-Ramirez Anita Gehlot Amit Barve Sanjay C. P R. Ponnusamy Department of BMS Jain University Bangalore India Universidad Tecnológica del Perú Uttaranchal Institute of Technology Uttaranchal University India Department of Associate Professor & HOD Parul University Department of IT& Business Analytics Sri Balaji University Pune (SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

Several business applications and procedures are expected to be significantly disrupted by Blockchain-based technologies, which has significant consequences for ecommerce. In light of the potential blockchain based technology are popularly known as "trust building systems" that can be used in multiple business models and standard guidelines which evolved placed above a white period to achieve believe, serviceability, and enforcement in various domains such as cunsumers related business, business related connections have to be questioned and possibly adjusted. By enabling trustless trade interactions that work without specialized intermediaries or even central authority in the case of permission lessblock chains, Blockchain has the power to change how electronic commerce operates. Furthermore, giving universal access to immutable data across the whole supply chain may significantly alter the information and value exchange between businesses and consumers. The methodology presented in this paper are intended to motivate further study of the potential effects of Blockchain on e-commerce. technology, law, organizational and quality challenges, as well as consumer difficulties, are the four key categories. This paper demonstrates how Blockchain may affect many Electronic commercial components in these relevant fields.

关键词： Technological innovation Law Biological system modeling Supply chains Trustless services Blockchains Cryptocurrency

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Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences

Minimal Cases for Computing the Generalized Relative Pose us...

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International Conference on computer Vision (ICCV)

作者： Banglei Guan Ji Zhao Daniel Barath Friedrich Fraundorfer College of Aerospace Science and Engineering National University of Defense Technology China Computer Vision and Geometry Group ETH Zürich Institute for Computer Graphics and Vision Graz University of Technology Austria Remote Sensing Technology Institute German Aerospace Center Germany

ISBN: (纸本)9781665428132

We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs). A new constraint is derived interpreting the relationship of ACs and the generalized camera model. Using the constraint, we demonstrate efficient solvers for two types of motions assumed. Considering that the cameras undergo planar motion, we propose a minimal solution using a single AC and a solver with two ACs to overcome the degenerate case. Also, we propose a minimal solution using two ACs with known vertical direction, e.g., from an IMU. Since the proposed methods require significantly fewer correspondences than state-of-the-art algorithms, they can be efficiently used within RANSAC for outlier removal and initial motion estimation. The solvers are tested both on synthetic data and on real-world scenes from the KITTI odometry benchmark. It is shown that the accuracy of the estimated poses is superior to the state-of-the-art techniques.

关键词： computer vision Motion estimation Computational modeling Pose estimation Benchmark testing Cameras Image sequences

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A Visual Real-time Fire Detection using Single Shot MultiBox Detector for UAV-based Fire Surveillance

A Visual Real-time Fire Detection using Single Shot MultiBox...

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International Conference on Communications and Electronics, ICCE

作者： A. Q. Nguyen H. T. Nguyen V. C. Tran Huy X Pham J. Pestana School of Electronics and Telecommunications Hanoi University of Science and Technology Vietnam Aarhus University Denmark Institute for Computer Graphics and Vision (ICG) Graz University of Technology (TU Graz) Austria

ISBN: (数字)9781728154718

ISBN: (纸本)9781728154725

Early fire detection and alarm are significantly important to reduce the losses caused by fire. Conventional methods in fire detection using smoke and heat detectors have disadvantages in accuracy, latency as well as the detection area. In this paper, we propose and implement a real-time fire detection solution for large area surveillance using the unmanned aerial vehicle with an integrated visual detection and alarm system. The system includes a low-cost camera, a light weight companion computer, a flight controller as well as localization and telemetry modules. To achieve real-time detection, Single Shot MultiBox Detector (SSD) algorithm is implemented as the heart of the system. We used MobileNets base model, which more efficient for mobile and embedded vision applications, instead of conventional VGG-16/ResNet model to achieve the mean average precision of 92.7% with the detection speed of 26 FPS.

关键词： Visualization Surveillance Computational modeling Detectors Visual systems Real-time systems Telemetry

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The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias*

The Unconstrained Ear Recognition Challenge 2023: Maximizing...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ž. Emeršić T. Ohki M. Akasaka T. Arakawa S. Maeda M. Okano Y. Sato A. George S. Marcel I. I. Ganapathi S. S. Ali S. Javed N. Werghi S. G. Işık E. Sarıtaş H. K. Ekenel V. Hudovernik J. N. Kolf F. Boutros N. Damer G. Sharma A. Kamboj A. Nigam D. K. Jain G. Cámara-Chávez P. Peer V. Štruc University of Ljubljana (UL SI) Shizuoka University (SU JP) Idiap Research Institute (IDIAP CH) Electrical Engineering and Computer Science Department Khalifa University (KU UAE) Department of Computer Engineering (ITU TR) Istanbul Technical University Microelectronic Guidance and Electro-Optical Group ASELSAN Fraunhofer Institute for Computer Graphics Research IGD (IGD DE) Technische Universität Darmstadt (TUD DE) Indian Institute of Technology Mandi (IIT IN) HCL Imaging and Robotics R&D Lab (HCL IN) School of Artificial Intelligence Dalian University of Technology (DLUT CN) Federal University of Ouro Preto (UFOP BR)

The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://***/

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SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data

arXiv

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arXiv 2022年

作者： Huber, Marco Boutros, Fadi Luu, Anh Thi Raja, Kiran Ramachandra, Raghavendra Damer, Naser Neto, Pedro C. Gonçalves, Tiago Sequeira, Ana F. Cardoso, Jaime S. Tremoço, João Lourenço, Miguel Serra, Sergio Cermeño, Eduardo Ivanovska, Marija Batagelj, Borut Kronovšek, Andrej Peer, Peter Štruc, Vitomir Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Norwegian University of Science and Technology Norway INESC TEC Portugal University of Porto Portugal Yoonik Portugal Vaelsys R&D Spain University of Ljubljana Slovenia

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and attract solutions that deal with detecting face morphing attacks while protecting people’s privacy for ethical and legal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to outperforming the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://***/marcohuber/SYN-MAD-2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Competition

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Cloud-Powered Healthcare Appointment Optimization with Reinforcement Learning for Efficiency

Cloud-Powered Healthcare Appointment Optimization with Reinf...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： S. Mohan Kumar John Benito Jesudasan Peter S. Kolangiammal Azath Mubarakali S. Karthik S. Sujatha Professor and Director Research and Innovations CMR University Bangalore India Cyber | Data Science & Engineering Capital One Services LLC Richmond Virginia USA Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India College of Computer Science King Khalid University Saudi Arabia Department of Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Improving efficiency and patient satisfaction through better appointment scheduling is a problem for healthcare systems across the globe. To improve healthcare appointment scheduling procedures, this research proposes a new method that makes use of cloud computing and reinforcement learning (RL) algorithms. to maximize healthcare provider efficiency while minimizing patient wait times, resource usage, and operational costs by dynamically learning and adapting scheduling rules using RL, taking use of the scalability and computing capacity of cloud infrastructure. To train RL agents, create a simulation environment that mimics real-world healthcare conditions. Our findings show that RL-based scheduling strategies are more efficient at scheduling appointments than conventional techniques, and we prove it via rigorous testing and assessment. Additionally, we demonstrate how our strategy can handle dynamic healthcare contexts with different patient numbers and resource restrictions, demonstrating its flexibility and resilience. The results show that RL approaches run by the cloud can change healthcare appointment scheduling for the better, leading to healthcare systems that are more responsive and flexible. Utilizing the complementary strengths of cloud computing and RL, our method provides a data-driven, scalable solution to the hard problem of healthcare schedule optimization, which improves both patient care and operational efficiency

关键词： Cloud computing Technological innovation Schedules Job shop scheduling Processor scheduling Medical services Optimal scheduling Reinforcement learning Dynamic scheduling Testing

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A novel prediction of remaining useful life time of rolling bearings using convolutional neural network with bidirectional long short term memory

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Balamurugan, R. Takale, Dattatray G. Parvez, M. Muzammil Gnanamurugan, S. Associate Professor Department of Mechanical Engineering M.Kumarasamy College of Engineering Karur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Assistant Professor/ ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Avadi Chennai India

Detection of bearing problems increases the importance of the service life of rotating machinery. Convolutional neural networks (CNNs) are often used in current research, and databases built on deep learning (DL) models have improved capabilities in the field of defect diagnosis. We use the publicly available Case Western Reserve University (CWRU) dataset to compare the classification accuracy and gain more adaptive knowledge and insights about the proposed approach. Extensive tests and evaluations are performed on the dataset to verify the diagnostic effectiveness of the recommended method in different situations. To demonstrate the superiority of the proposed method, we compare multiple views of the same dataset with similar tasks. CNN supports degraded index sequences to reduce noise and stop temporal oscillations. A new CNN-BiLSTM model is used to capture current and historical inspection data and predict the RUL's service life and supported power levels. Regarding production, we follow health rates. The proposed method was evaluated by accelerating the bearing motion to failure, and the results demonstrated its advantages in terms of more accurate RUL prediction. According to the experimental results, the proposed center distance measurement method is a new and valuable means for intelligent bearing diagnosis. Experimental results using 48 K and 12 K CWRU datasets show that the overall accuracy of the BiLSTM method is 99.80% and 98.3%, respectively, which is better in diagnosis than some popular models. © 2024 The Authors

关键词： BiLSTM CNN CWRU dataset Rolling bearings life time

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A Feasible Dashboard to predict Patent Mining Using Classification Algorithms

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Procedia computer science 2020年 167卷 2011-2021页

作者： Darshana A Naik Brunda C J Seema S Assistant Professor Department of Computer Science Ramaiah Institute Of Technology Bangalore India Student Department of Computer Science Ramaiah Institute Of Technology Bangalore India

The number of patents that are being filed across the world is increasing day by day. With the increase in patents being filed the process of segregating the patents based on their class becomes even more difficult. There are a set of features that are extracted from the dataset that is previously present. The features that are being extracted will vary for each document. Patents documents are issued as legal rights by the government for protecting the owner of invention. This exclusive right helps in protecting the invention to be used by others from using, selling, developing another, etc for some period of time. After the feature extraction is done there are two steps that need to be carried out, namely: Classification and prediction. For this purpose, decision tree algorithm is used which makes use of the most prominent feature and classification is done using those features. Therefore, for classification a hierarchical decision tree algorithm is used along with the probability of patent conversion. Based on the classification that is done a model will be created and whenever a new entity is brought it is compared with the model file that was created using the available datasets and is predicted as a particular class. Thus, both classification of existing dataset and the prediction for any new dataset based on previous inputs can be achieved thereby facilitating the patent mining process In proposed system this paper mainly concentrating on the prediction of the patent using data mining techniques. Decision tree algorithm for the classification of the patent mining is applied. And also classification of the data using some attributes is done, namely; association frequency, grant or applicant, patent country, patent status, grant patent using these attributes prediction of patent mining using Naive Bayes algorithm is done. The proposed algorithm gives best result for patent mining. The patent status is checked whether he got grant access of the patent in predicti

关键词： patent mining prediction classification decision tree data mining naive bayse association frequency grant patent applicant

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