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共 7556 条记录，以下是511-520 订阅

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Automation and Optimization of Manual Marks Entry with Smart Scanning App

Automation and Optimization of Manual Marks Entry with Smart...

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computing for Sustainability and Intelligent Future (COMP-SIF), international conference on

作者： C. Bhanuprakash Deepika J Department of MCA Siddaganga Institute of Technology Tumkur India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

The application with a title “Smart Marks Mania” is an innovative solution to automate the student's marks entry in an academic institution. Traditionally, manual entry and handling of marks have been time-consuming, error-prone and inefficient. This application addresses these challenges by adding advanced technologies that include Optical Character recognition and Convolutional Neural Network to automatically extract handwritten marks and student identification numbers(USNs) from scanned answer scripts. It offers an user-friendly interface designed to cater to the requirements of faculty and students. It offers a flexible and scalable structure for system development. Rigorous testing has been conducted to ensure the system's performance, accuracy, and reliability. Overall, this application reduces the traditional manual marks entry process by automating marks extraction, data entry and providing a centralized platform for administrator, faculties and students, thus enhancing efficiency, accuracy, and accessibility in academic assessments. This application showcase how advanced technologies can transform educational processes and enhance the efficiency of assessment methods.

关键词： Accuracy Reviews System performance soft sensors Optical character recognition Manuals Transforms Convolutional neural networks Sustainable development Testing

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022 - Proceedings

4th International Conference on Pattern Analysis and Intelli...

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

ISBN: (纸本)9781665461610

The proceedings contain 57 papers. The topics discussed include: a deep learning-based system for detection at-risk students;an explainable self-labeling grey-box model;development of a biometric authentication platform using voice recognition;binary Archimedes optimization algorithm based feature selection for regression problem;semantic segmentation of breast cancer histopathology images using deep learning;a biased random-key genetic algorithm for extractive single-document summarization;a virtual clustering for data dissemination in vehicular fog computing;a novel method of requirements analysis for big data projects;combination of closed-set and open-set biometric identification;a formal integrated approach for cyber physical systems;and testing cyber-physical production system: test methods categorization and dataset.

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Ubiquitous computing and Intelligent Information Systems (ICUIS), international conference on

作者： Dhruvi Srishwal Shibu PS Harsha Vardan Sajjad Ibrahim. A Gouthaman. P Department Of Networking And Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department Of Networking And Communications Faculty of Engineering and Technology and Technology SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Heart arrhythmias, if not detected early, can lead to severe conditions like stroke or heart failure, making timely and accurate classification critical. Traditional methods of analyzing Electrocardiogram (ECG) data are manual, time-consuming, and prone to errors due to the complexity of ECG signals. This paper presents an automated approach for arrhythmia classification using vector databases and similarity search algorithms. By converting raw ECG time-series data into vectorized representations, the system enables efficient pattern recognition and real-time analysis. Machine learning models applied to the vectorized data enhance classification accuracy, while the use of a vector database allows for quick similarity searches, facilitating real-time comparison of patient data with known arrhythmia patterns. The proposed method significantly reduces analysis time and increases precision, offering a robust framework for real-time monitoring and early detection, thereby improving patient outcomes and enabling timely medical interventions.

关键词： Heart Accuracy Databases Arrhythmia Electrocardiography Ubiquitous computing Vectors Real-time systems pattern recognition Monitoring

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Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer's Disease Using Forestnets 48

Transferring Quantified Emotion Knowledge for the Detection ...

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Pérez-Toro, P.A. Rodríguez-Salas, D. Arias-Vergara, T. Bayerl, S.P. Klumpp, P. Riedhammer, K. Schuster, M. Nöth, E. Maier, A. Orozco-Arroyave, J.R. Friedrich-Alexander Universität Erlangen-Nürnberg Pattern Recognition Lab Erlangen Germany Universidad de Antioquia UdeA Gita Lab Facultad de Ingeniería Medellín Colombia Ludwig-Maximilians University Department of Otorhinolaryngology Head and Neck Surgery Munich Germany Technische Hochschule Georg Simon Ohm Nürnberg Germany

ISBN: (纸本)9781728163277

Progressive loss of memory is the most known symptom of Alzheimer's Disease (AD);however, it also affects other cognitive skills and leads to depression symptoms. This paper presents a transfer learning strategy for automatically detecting AD and depression in AD patients using acoustic information and ForestNet, an artificial neural network that allows computing the contribution of a set of features to a model's decision. The methodology consists of training ForestNet with a dataset commonly used for emotion recognition;then, we fine-tune the pre-trained model to detect AD and depression in AD. We trained the models with several acoustic features commonly used for emotion and AD applications. Unweighted average recalls of up to 0.87 were achieved to classify the disease and up to 0.82 to detect depression in AD. Our results indicate that the information obtained from the Arousal Valence plane may be suitable for discriminating and analyzing depression in AD. © 2023 IEEE.

关键词： Alzheimer's Disease Depression Neural Networks Speech Processing

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Deep Learning Approaches for Effective Human Computer Interaction: A Comprehensive Survey on Single and Multimodal Emotion Detection

Deep Learning Approaches for Effective Human Computer Intera...

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international conference for Convergence of Technology (I2CT)

作者： M. Jagadeesh Shruthi Viswanathan Shruthi Varadarajan Department of Computing Technologies SRMIST Tamil Nadu India Department of CSE Rajalakshmi Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Emotions play an important role in communication, given its complexity, emotion recognition research is difficult because people convey their emotions through a variety of modalities, such as language, voice, and facial expressions. These cues often complement each other. This study explores the recognition of emotions from visual, auditory and also textual information. The proposed system offers flexibility in predicting features and recognizing categorical emotions, even when only one type of data (either audio or video or text) is available. To accomplish this, the system leverages pre-trained models like Efficient-Net, LSTM and Wav2Vec. The study on the evaluation of model performance includes metrics such as accuracy and precision, offering a comprehensive view of predictive capabilities.

关键词： Surveys Emotion recognition Visualization Text recognition Computational modeling soft sensors Speech recognition

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Anatomy-Informed Data Augmentation for Enhanced Prostate Cancer Detection 26th

Anatomy-Informed Data Augmentation for Enhanced Prostate Can...

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26th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Kovacs, Balint Netzer, Nils Baumgartner, Michael Eith, Carolin Bounias, Dimitrios Meinzer, Clara Jaeger, Paul F. Zhang, Kevin S. Floca, Ralf Schrader, Adrian Isensee, Fabian Gnirs, Regula Goertz, Magdalena Schuetz, Viktoria Stenzinger, Albrecht Hohenfellner, Markus Schlemmer, Heinz-Peter Wolf, Ivo Bonekamp, David Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp Heidelberg Germany German Canc Res Ctr Div Radiol Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany Heidelberg Univ Dept Urol Med Ctr Heidelberg Germany German Canc Res Ctr Jr Clin Cooperat Unit Multiparametr Methods Early Heidelberg Germany Heidelberg Univ Inst Pathol Med Ctr Heidelberg Germany Mannheim Univ Appl Sci Mannheim Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany

ISBN: (纸本)9783031439896;9783031439902

Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images. State-of-the-art computer-aided diagnosis systems still rely on simplistic spatial transformations to preserve the pathological label post transformation. However, such augmentations do not substantially increase the organ as well as tumor shape variability in the training set, limiting the model's ability to generalize to unseen cases with more diverse localized soft-tissue deformations. We propose a new anatomy-informed transformation that leverages information from adjacent organs to simulate typical physiological deformations of the prostate and generates unique lesion shapes without altering their label. Due to its lightweight computational requirements, it can be easily integrated into common DA frameworks. We demonstrate the effectiveness of our augmentation on a dataset of 774 biopsy-confirmed examinations, by evaluating a state-of-the-art method for PCa detection with different augmentation settings.

关键词： Data augmentation soft-tissue deformation Prostate cancer detection

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Very Low-Resolution Face recognition Based On Multilinear Side-Information Based Discriminant Analysis

Very Low-Resolution Face Recognition Based On Multilinear Si...

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IEEE international conference on Big Data computing Service and Applications (BigDataService)

作者： Sana Bellili Abdelmalik Ouamane Ammar Chouchane Yassine Himeur Shadi Atalla Wathiq Mansoor Hussain Al Ahmad Department of Electrical Engineering University of Biskra Biskra Algeria University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai UAE

ISBN: (数字)9798350366389

ISBN: (纸本)9798350366396

Facial recognition technology is widely used for biometric authentication, but it struggles with accurately identifying individuals from low-resolution images. This study introduces a new method for improving low-resolution face recognition (LRFR) by combining super-resolution techniques and multilinear subspace learning. Using deep learning VGG-face models for feature extraction and super-resolution methods like SRGAN and SRResNet, the proposed method, Multilinear Side-information-based Discriminant Analysis (MSIDA), enhances facial image quality. This methodology integrates image enhancement, deep feature extraction, and tensor subspace learning for precise face verification. The effectiveness of this approach is demonstrated on the Labeled Faces in the Wild (LFW) dataset, showing superior performance in face verification tasks compared to existing methods. MSIDA achieves accuracies of 91.03%, 91.60%, and 92.57% for different resolutions. This advancement represents significant progress in image processing and pattern recognition, particularly for scenarios involving low-resolution images.

关键词： Image quality Deep learning Accuracy Tensors Face recognition Superresolution Feature extraction Biometric authentication pattern matching Image enhancement

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A Hybrid Method of SVG Parameter Calibration Based on Particle Swarm Optimization and soft Actor-Critic

A Hybrid Method of SVG Parameter Calibration Based on Partic...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Ao Zhao Xuanchen Xiang Ligang Zhao Hongyue Zhen Ruisheng Diao Kaibiao Yan Guangjun Xu ZJU-UIUC Institute Zhejiang University Haining China Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Static var generators (SVGS) are essential for compensating reactive power and maintaining bus voltage levels before and after large disturbances. Accurate modeling of SVG controller parameters is critical to ensure the reliability of dynamic simulation in power system operation. This paper introduces a novel SVG parameter identification method, which integrates the benefits of particle swarm optimization (PSO) and reinforcement learning (RL) to improve the efficiency and accuracy of parameter calibration. We use simulation data generated by PSD BPA software combined with actual power grid data as the environment to optimize the parameter settings of SVG. This study combined PSO with the soft Actor-Critic (SAC) algorithm to optimize and calibrate these parameters. Compared with the simulation results, our method shows significant advantages in calibration accuracy and processing speed and effectively improves the simulation quality and practicability of the SVG model.

关键词： Reactive power Accuracy Computational modeling software algorithms Power system dynamics Reinforcement learning Voltage software Calibration Particle swarm optimization

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Vibe360: Group-Based Deep Emotional Understanding of Immersive Communications

Vibe360: Group-Based Deep Emotional Understanding of Immersi...

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IEEE international conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Maria Sanchez Martinez Ester Gonzalez-Sosa Juan Torres Arjona Pablo Perez Alvaro Villegas Mark Billinghurst XR Lab Nokia Madrid Spain Emphatic Computing Lab University of South Australia Australia

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

Immersive communication systems enable remote attendees to experience events as if physically present. This paper examines group-based emotion recognition from 360-degree video as a potential enhancement for these systems. In a preliminary study, we recorded a 360-degree video with group-level emotion annotations and analyzed it using two advanced deep learning models for face and emotion recognition, providing both categorical and valence-arousal metrics. Results indicate that this approach effectively identifies group-level emotional shifts (e.g., from neutral to positive or negative), though finer distinctions may require additional data sources. This tool could improve immersive communication by supporting audience en-gagement monitoring and enhancing accessibility for individuals with challenges in emotion perception.

关键词： Measurement Deep learning Emotion recognition Solid modeling Communication systems Face recognition soft sensors Prototypes Virtual reality Monitoring

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2nd international conference on Intelligent Systems and pattern recognition, ISPR 2022

2nd International Conference on Intelligent Systems and Patt...

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2nd international conference on Intelligent Systems and pattern recognition, ISPR 2022

ISBN: (纸本)9783031082764

The proceedings contain 32 papers. The special focus in this conference is on Intelligent Systems and pattern recognition. The topics include: Deep Learning-Based Segmentation of Connected Components in Arabic Handwritten Documents;classifying the Human Activities of Sensor Data Using Deep Neural Network;preface;3D Dense & Scaled Reconstruction Pipeline with Smartphone Acquisition;exploratory Analysis of Driver and Vehicle Factors Associated with Traffic Accidents in Morocco;building a Multilingual Corpus of Tweets Relating to Algerian Higher Education;recursive Feature Elimination Technique for Technical Indicators Selection;document-Based Knowledge Discovery with Microservices Architecture;feature Selection for Credit Risk Classification;parameter Identification and Validation of Multi-innovation Least Squares Lithium Battery for Second-Order Battery Model;bat Echolocation Call Detection and Species recognition by Transformers with Self-attention;Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau vs OneCycleLR;an Explainable Predictive Model for the Geolocation of English Tweets;removing Redundancies in Binary Images;a Genetic Model for Medical Images Reproduction;neural Machine Translation of Low Resource Languages: Application to Transcriptions of Tunisian Dialect;SPIRAL: SPellIng eRror Parallel Corpus for Arabic Language;VMs Migration Mechanism for Underloaded Machines in Green Cloud computing;TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect;Road recognition for Autonomous Vehicles Based on Intelligent Tire and SE-CNN;malicious Packet Classification Based on Neural Network Using Kitsune Features;an Hybrid Deep Learning Approach for Prediction and Binary Classification of Student’s Stress;a Novel Deep Convolutional Neural Network Architecture for Customer Counting in the Retail Environment;transfer Learning for the Classification of Small-Cell and Non-small-Cell Lung Cancer;attentional Conditional Generative Adversarial Ne

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