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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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Multi-Modal Representation Learning with Text-Driven soft Masks

Multi-Modal Representation Learning with Text-Driven Soft Ma...

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conference on Computer Vision and pattern recognition (CVPR)

作者： Jaeyoo Park Bohyung Han Computer Vision Laboratory ECE IPAI Seoul National University

We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy. First, we generate diverse features for the image-text matching (ITM) task via soft-masking the regions in an image, which are most relevant to a certain word in the cor-responding caption, instead of completely removing them. Since our framework relies only on image-caption pairs with no fine-grained annotations, we identify the relevant regions to each word by computing the word-conditional vi-sual attention using multi-modal encoder. Second, we encourage the model to focus more on hard but diverse examples by proposing a focal loss for the image-text contrastive learning (ITC) objective, which alleviates the inherent limitations of overfitting and bias issues. Last, we perform multi-modal data augmentations for self-supervised learning via mining various examples by masking texts and rendering distortions on images. We show that the combination of these three innovations is effective for learning a pretrained model, leading to outstanding performance on multiple vision-language downstream tasks.

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2nd international conference on Big Data, IoT and Machine Learning, BIM 2023

2nd International Conference on Big Data, IoT and Machine Le...

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2nd international conference on Big Data, IoT and Machine Learning, BIM 2023

ISBN: (纸本)9789819989362

The proceedings contain 69 papers. The special focus in this conference is on Big Data, IoT and Machine Learning. The topics include: Phylogeny Reconstruction Using k-mer Derived Transition Features;developing an Interpretable Machine Learning Model for Divorce Prediction;riot Perception and Safety Navigation of Autonomous Vehicles Using Deep Learning;An Explainable AI Enable Approach to Reveal Feature Influences on Social Media Customer Purchase Decisions;Field Programmable Gate Array in DNA computing;XAI-Driven Model Explainability and Prediction of P2P Bank Loan Default Network;design Implication of a Compact-Sized, Low-Fidelity Rover for Tough Terrain Exploration;VioNet: An Enhanced Violence Detection Approach for Videos Using a Fusion Model of Vision Transformer with Bi-LSTM and 3D Convolutional Neural Networks;rank Your Summaries: Enhancing Bengali Text Summarization Via Ranking-Based Approach;an Efficient Machine Learning Classification Model for Rainfall Prediction in Bangladesh;study on the Analysis and Prediction of Drug Addiction Among University Students of Bangladesh Using Machine Learning;A Deep CNN-Based Approach for Revolutionizing Bengali Handwritten Numeral recognition;performance Analysis of Multiple Deep Learning Models for Image Retrieval Problems;Advancing Lung Cancer Diagnosis Through Deep Learning and Grad-CAM-Based Visualization Techniques;a Novel Approach to Detect Stroke from 2D Images Using Deep Learning;Enhancing Pneumonia Diagnosis: An Ensemble of Deep CNN Architectures for Accurate Chest X-Ray Image Analysis;dataset for Road Roughness Assessment Using Image Classification Techniques and Deep Learning Models: A Case Study on Bangladeshi National Highways;Noise-Aware-Based Texture Descriptor, Evaluation Adjacent Distance Local Ternary pattern EAdLTP for Image Classification.

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Robust Kernel Sparse Subspace Clustering

Robust Kernel Sparse Subspace Clustering

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ivica Kopriva Division of Computing and Data Science Rudjer Boskovich Institute Zagreb Croatia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Kernel methods are widely used in pattern recognition, including subspace clustering (SC). They transform nonlinear problems in the input data space into linear ones in a high-dimensional feature space. This transformation, achieved through the kernel trick, makes computationally tractable nonlinear algorithms possible. However, kernelizing linear algorithms through the kernel trick is infeasible in case of gross sparse corruptions that are modeled by the ℓ 1 -norm of the error term. To address this, we propose, for the first time, a robust kernel sparse SC (RKSSC) algorithm for data with gross sparse corruptions. We validated the proposed approach on two well-known datasets, using the linear robust SSC algorithm and nonlinear (kernel-based) SSC algorithm as baseline models. According to the Wilcoxon test, the RKSSC's algorithm clustering performance is statistically significantly better than baselines.

关键词： Computational modeling Clustering algorithms Signal processing algorithms Transforms Signal processing pattern recognition Sparse matrices Kernel Speech processing Optimization

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An End-to-End Combinatorial Optimization Method for R-band Chromosome recognition with Grouping Guided Attention 25th

An End-to-End Combinatorial Optimization Method for R-band C...

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25th international conference on Medical Image computing and Computer Assisted Intervention (MICCAI)

作者： Xia, Chao Wang, Jiyue Qin, Yulei Gu, Yun Chen, Bing Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Med Shanghai Inst Hematol Ruijin Hosp Shanghai Peoples R China

ISBN: (纸本)9783031164408;9783031164392

Chromosome recognition is a critical and time-consuming process in karyotyping, especially for R-band chromosomes with poor visualization quality. Existing computer-aided chromosome recognition methods mainly focus on better feature representation of individual chromosomes while neglecting the fact that chromosomes from the same karyotype share some common distribution and are more related compared to chromosomes across different patients. In the light of such observation, we start from a global perspective and propose an end-to-end differential combinatorial optimization method for R-band chromosome recognition. To achieve this, a grouping guided feature interaction module (GFIM) is built for feature aggregation between similar chromosome instances. Specially, a mask matrix is built for self-attention computation according to chromosome length grouping information. Furthermore, a deep assignment module (DAM) is designed for flexible and differentiable label assignment. It exploits the aggregated features to infer class probability distributions between all chromosomes in a cell. Experimental results on both normal and numerically abnormal karyotypes confirmed that our method outperforms state-of-the-art chromosome recognition and label assignment methods. The code is available at: https://***/xiabc612/R-band-chromosome-recognition.

关键词： R-band chromosome Label assignment Transformer

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2nd international conference on Emerging Electronics and Automation, E2A 2022

2nd International Conference on Emerging Electronics and Aut...

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2nd international conference on Emerging Electronics and Automation, E2A 2022

ISBN: (纸本)9789819968541

The proceedings contain 50 papers. The special focus in this conference is on Emerging Electronics and Automation. The topics include: Effect of Shallow Learning Techniques and Feature Selection for Breast Cancer Detection;A Data Analytics-Based Study in SARS-CoV-2 Genome Revealed a Commonality in the Infection pattern;pain-Level Detection Using Heart Rate Variability;automated Segmentation of Macula in Retinal Images Using Deep Learning Methodology;Skin Lesion Segmentation Using Wavelet Transform on MATLAB;optimization of Compact Zoom Lens Design for Optical Imaging Systems;brain Tumor Detection Using Image Classifier;unfalsified Adaptive Control of a Robotic System;a Study on Significant Progress in Face recognition and Its Related Techniques Toward Better Achievement for Various Applications;a Survey on Dynamic Load Balancing Techniques in Cloud computing;blockchain Enabled Secured Carpooling Platform with Proof of Concept;multi-stage and Multi-objective Dynamic Optimization-Based Scheduling of Uncertain Hybrid Renewable Energy Systems with Real-Time Energy Compensation;design of Regenerative Snubber Circuit for Motor Drivers;clustering-Based Multi-objective Stochastic Optimization Approach in Scheduling Optimal Energy Dispatch of Multiple Energy Sources in the Presence of Uncertainty;drone Charging System Using Wireless Power Transfer by Inductive Coupling;Performance Evaluation and Assessment of 100 kW Grid-Tied SPV System in Subtropical Climatic Conditions;vibration Energy Harvester Using Piezoelectric with Truncated Cone Rectangular Beam Structure for Low-Power Applications;switched-Boost Modified Z-Source Inverter for Renewable Energy Application;semi-active Control for Seismic Vibration Mitigation of Building System with Magnetorheological Damping Device;Performance Analysis of Fuzzy Logic with P&O MPPT for Grid-Connected Solar PV System Under Varying Solar Irradiance;simulation and Analysis of 24-h V2G System.

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Enhancing Classification Power: Tree Strength-Infused Enriched Random Forest

Enhancing Classification Power: Tree Strength-Infused Enrich...

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international conference on computing and Networking Technology (ICCNT)

作者： Vikas Jain Tej Bahadur Chandra Atul Kumar Srivastava SCSET Bennett University Gr. Noida India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Over the last two decades, Random forest (RF) has been one of the well-known and most exploited ensemble-based machine learning approaches. It has been used for computer vision, pattern recognition, medical imaging, and many other fields. However, it has always been a challenging task to come up with an approach that will lead to constructing a more reliable RF. In this study, we used a concept of tree strength to construct the RF, called as Enriched Random Forest (ERF). As a feature extraction, Bag of Visual Words (BoVW) is used, and Grey Wolf Optimization (GWO) as a feature selection during the construction of the ERF. The suggested method has been evaluated using the MNSIT, Caltech-101, Caltech-256, and UCI repositories—all of which are well-known and openly accessible. The outcomes of the experiments indicate that the ERF has achieved better results than other RF-based approaches.

关键词： Radio frequency Visualization Computer vision Forestry Feature extraction pattern recognition Reliability Random forests Optimization Biomedical imaging

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Computer vision for enhancing security and surveillance in a public space

Computer vision for enhancing security and surveillance in a...

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computing and Intelligent Reality Technologies (ICCIRT), international conference on

作者： Deepak Dilip Jadhav Saswati Debnath Revanuru Harinath Reddy K Ramalakshmi Kabildev Chellamuthu AU Center of Excellence-Computer Vision Alliance University Bengaluru Karnataka India Proglint Software Solutions Chennai Tamil Nadu India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

One of the main issues in computer vision is object and action recognition. Object detection is to find anything in the scene, and action recognition tracks the subject’s movements. This paper proposes a method for improving real-time surveillance systems with OpenCV library and an upgraded YOLO v8 (You Only Look Once-version 8). The primary goal of the paper is surveillance and security. This study examines the training of computer systems to recognize digital patterns for supporting surveillance activities such as monitoring anomalous activity and criminal activity. It draws attention to the possibility of enhancing current object detection frameworks with cuttingedge artificial intelligence features to produce surveillance systems that are more perceptive and adaptable.

关键词： YOLO Training Computer vision Tracking Surveillance Feature extraction Real-time systems Libraries pattern recognition Computer security

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2022 IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2022

2022 IEEE International Conference on Electronics, Computing...

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2022 IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2022

ISBN: (纸本)9781665497817

The proceedings contain 197 papers. The topics discussed include: analysis of effect of non-idealities of active devices in tunable grounded positive and negative impedance multiplier;sign language recognition and translation to speech for mine workers using deep learning technologies;designing a D-flipflop using novel sleep transistor technique;English to Marathi text translation using deep learning;modulation and signal class labelling with active learning and classification using machine learning;comb-shaped patch antenna design study with shifted arms and asymmetric architecture enabling controlled resonance change and radiation pattern;a proactive connection setup mechanism for large quantum networks;comparative analysis of pi and fuzzy based speed controllers for indirect field-oriented control of induction motor drives;a simple monopole ISM band antenna for on-body wireless communication in the millimeter-wave range;automatic building footprint extraction using random forest algorithm from high resolution google earth images: a feature-based approach;and attention-based battery parameter augmentation technique to alleviate limited data challenges.

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Enhancing Typing Dynamics Emotion recognition: A Multi-Class XGBoost Approach for Accurate Sentiment Detection

Enhancing Typing Dynamics Emotion Recognition: A Multi-Class...

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Advancements in computing (ICAC), international conference on

作者： Sudesh Bandara Tharindu Dasath Senula Nanayakkara Samadhi Rathnayake Thusithanjana Thilakarthna Faculty of Computing Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This research paper aims to enhance the accuracy and efficiency of emotion recognition through keystroke dynamics by utilizing a multi-class XGBoost model. The study addresses the limitations observed in previous models, such as Support Vector Machines and Logistic Regression, which struggled with the complex, high-dimensional nature of keystroke data. The primary objective was to develop a robust model capable of capturing the subtle variations in typing behavior associated with different emotional states. To achieve this, a comprehensive approach was adopted, involving the collection of detailed keystroke datasets, advanced feature engineering, and systematic hyperparameter tuning. The XGBoost model was chosen for its superior ability to manage complex interactions within large datasets. Through rigorous evaluation, the XGBoost model outperformed traditional models in accuracy, precision, and recall, demonstrating significant improvements in emotion classification tasks. The findings of this study reveal that XGBoost, when combined with meticulous feature engineering and hyperparameter optimization, offers a substantial advancement in the field of emotion recognition via keystroke dynamics. The research not only confirms the effectiveness of XGBoost in this domain but also provides new insights into optimizing machine learning models for emotion detection. These results have broad implications for the development of more emotionally aware computing systems, potentially improving user interaction across various applications.

关键词： Support vector machines Emotion recognition Logistic regression Adaptation models Accuracy Systematics Computational modeling Keystroke dynamics pattern recognition Tuning

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