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Infrared Image Analysis for Human Face recognition 5

Infrared Image Analysis for Human Face Recognition

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5th international conference on computing and Informatics (IEEE ICCI)

作者： Aji, Muhammad Eka Setio Alfanz, Rocky Prakasa, Esa Univ Sultan Ageng Tirtayasa Fac Engn Dept Elect Engn Cilegon Indonesia Natl Res & Innovat Agcy BRIN Res Ctr Informat Comp Vis Res Grp Bandung Indonesia

ISBN: (纸本)9781665499736;9781665499729

Thermal infrared images have potential to captured the images without being affected by illumination. In this paper, we proposed a face recognition method using infrared images. The Convolution Neural Network are used to extract feature from the images with the combination of Haar Cascade, and Local Binary pattern to indicate the face area. At the same time, images were acquired by using infrared camera and webcam. Compared between infrared and visible images, the experimental result from infrared images by using Convolutional Neural Network in combined with Haar Cascade show superiority in the measurement result of several parameters used with the highest score in accuracy up to 98% which outperforms the other experiment in this paper.

关键词： Infrared images Convolution Neural Network Haar Cascade Local Binary pattern Visible Images

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6th international conference on Innovative computing and Communication, ICICC 2023

6th International Conference on Innovative Computing and Com...

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6th international conference on Innovative computing and Communication, ICICC 2023

ISBN: (纸本)9789819940707

The proceedings contain 128 papers. The special focus in this conference is on Innovative computing and Communication. The topics include: Wearable Sensor Based Cloud Data Analytics Using Federated Learning Integrated with Classification by Deep Learning Technique;comparison of Decision Tree and Random Forest for Default Risk Prediction;trusted Cloud Service Framework for Cloud computing Security;HITR-ECG: Human Identification and Classification Simulation System Using Multichannel ECG Signals: Biometric Systems Era;brain Disorder Classification Using Deep Belief Networks;Diabetic Retinopathy Detection Using Deep CNN Architecture and Medical Prescription;an Innovative software Engineering Approach to Machine Learning for Increasing the Effectiveness of Health Systems;region of Interest and Feature-based Analysis to Detect Breast Cancer from a Mammogram Image;vehicular Ad Hoc Network: A Review;Securing the Networks Against DDoS Attacks Using Blockchain Technology;the Proposed Deep Learning Combined with Knowledge Graphs for Fake News Detections on Social Networks;algorithm-Driven Predictive Analysis of Blue-Chip Stocks in the Murky Indian Environment;survey on Crime Analysis, Forecasting, and Prediction Using Machine Learning;soft computing and Data Mining Techniques for Dengue Detection: A Review;Extended HD-MAABE Scheme Supporting Delegation and Revocation for Cloud-based Organizational Data Sharing;detection of Open Metal Sites in Metal–Organic Frameworks Using Machine Learning;PCA-Based Feature Selection and Hybrid Classification Model for Speech Emotion recognition;ANFIS and Kernel Extreme Learning Machine to the Assessment and Identification of Seismic b-value as Precursor;payroll Management Using Blockchain;centric Management of Resources in Modern Distributed computing;enhancing Security of IoT Data Transmission in Social Network Applications.

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Detecting Cyber Bullying in the Social Media Using Deep Learning Algorithms 15th

Detecting Cyber Bullying in the Social Media Using Deep Lear...

引用

15th international conference on soft computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Mridul, Aunik Hasan Ahsan, Nowreen Sultana, Zakia Asif, Asifuzzaman Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Data Science Clarkson University New York United States Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (纸本)9783031810824

Cyberbullying is a pressing issue in the digital age, especially in non-English-speaking communities. This study focuses on using deep learning techniques to detect cyberbullying in the Bangla language. The research employs a Bangla dataset that includes both cyberbullying and non-cyberbullying text, which undergoes preprocessing, tokenization, and sequence transformation for input into a LSTM recurrent neural network. The LSTM model incorporates word embedding and dropout techniques to enhance its performance. Evaluation metrics like accuracy and AUC-ROC score are employed to assess the model's effectiveness. The study investigates many methods, such as vernacular-specific preparation and data augmenting, that are specifically designed for the identification of cyberbullying in the Bangla language. The findings demonstrate deep learning's capacity to identify cyberbullying in Bangla, which makes a significant contribution to resolving this problem in non-English settings. The research reveals impressive accuracy rates, with LSTM achieving 99.99% accuracy, and a different approach, Convolutional Neural Network (CNN), yielding 99.022%. Ensemble models, including Gradient Boosting, XGBoost, and Random Forest, are also employed and optimized using hyperparameter tuning. Notably, LSTM stands out as the most reliable classifier for cyberbullying detection, as confirmed by the experimental findings and recent research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Random forests

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Local Binary pattern Induced Optimized CNN for Brain Tumor Diagnosis from MRI Data 10th

Local Binary Pattern Induced Optimized CNN for Brain Tumor D...

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10th Biennial international conference on pattern recognition and Machine Intelligence (PReMI)

作者： Nath, Pranay Mondal, Surajit Ghosh, Lidia Maulana Abul Kalam Azad Univ Technol Kolkata West Bengal India Univ Engn & Management Inst Engn & Management Kolkata West Bengal India

ISBN: (纸本)9783031451690;9783031451706

Advancements in medical science have led to new approaches for preventing, diagnosing, and treating brain tumors, studied by researchers across different fields. The accurate identification of tumors in MRI scans can assist in disease identification, treatment evaluation, and radiation-based therapies. Currently, humans manually perform this task, but research has explored the integration of computer processing in MRI analysis. While MRIs provide a comprehensive view of the brain to identify tumors, they lack accuracy in pinpointing their location and size. To address this, an improved version of Local Binary pattern (LBP) encoded optimized Convolutional neural network is proposed to improve diagnostic accuracy. LBP captures texture information in a local neighborhood of each pixel, providing additional features to learn from and enhancing the accuracy of texture-based image classification. The model is evaluated on the Figshare dataset through multiple experiments.

关键词： Optimized Convolutional Neural Network Local Binary pattern Brain tumor Figshare dataset MRI analysis

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Advanced Student Monitoring System for Physical Classes Using pattern recognition and Behavioral Analysis 5

Advanced Student Monitoring System for Physical Classes Usin...

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5th international conference on Advancements in computing, ICAC 2023

作者： Rathnayaka, R.K.A.R. Mallawaraarachchi, S.M.A. Wijesiriwardana, H.G.N.D. Perera, S.S.A. Perera, Jeewaka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research aims to develop an intelligent classroom system that utilizes Computer Vision and Deep Learning to measure students' concentration levels and performance, enabling teachers to gain insight into overall performance and adjust their teaching accordingly. The main goal of this research is to develop a precise way of tracking student concentration level and automate attendance marking in physical classroom. The proposed system consists of four components. To take attendance, it utilizes OpenCV and dlib for precise face recognition and eye gaze tracking to measure student focus levels. In the emotion analysis component, it employs DeepFace, dlib, and MediaPipe to identify students' emotional states and behaviors. For improving low-resolution video footage, the low-resolution enhancement component combines dlib and ESRGANG. In the concentration level component, Convolutional Neural Networks (CNN) are used to build a precise model for assessing students' concentration levels. The implemented system offers real-time attendance tracking, emotional and behavioral insights, and updates on students' concentration levels. It improves the detection of students at the back of the classroom using low-resolution footage. These features support educators in identifying students who need assistance, adjusting teaching methods, and creating a more engaging learning environment, ultimately enhancing classroom performance. © 2023 IEEE.

关键词： Face recognition

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Visualization of Covid 19 pattern using machine learning

Visualization of Covid 19 pattern using machine learning

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Thanjaivadivel, M. Pious, Ignatious K. Jagannathang, J. School of Computing and Information Technology Reva University Bengaluru India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India School of Information Technology and Engineering Vellore Institute of Technology Vellore Dt Tamil Nadu Katpadi India

ISBN: (纸本)9781032669663

The Wuhan market in China is the source of a terrible and undetectable threat to the entire planet. The time between the outbreak and the epidemic was barely a few months. Visual data analysis makes it easier to get information from numerous data sources in a better method because it helps to understand how the world combats this disease. In this essay, we’ll demonstrate how one might visualise a map of their own nation, along with the regions where this dreadful sickness has spread. This essay discusses the COVID-19 epidemic’s data identification and analysis. In a smart, smart, and global nation, data recognition aids in the integration of all data sources, including the number of instances discovered, deaths, and current cases. Data is disseminated by putting it into visual aids like graphs and maps. This offers current and trustworthy information that enables us to monitor the number of persons admitted to hospitals in every region of India as well as in other nations around the world. This clarifies how you can handle the threat that the Coronavirus poses. In this essay, we’ll show how our theory can be used to analyse and forecast the spread of COVID-19. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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A Vision-Based Counter System for Children’s Dribble 23

A Vision-Based Counter System for Children’s Dribble

引用

9th international conference on computing and Artificial Intelligence, ICCAI 2023

作者： Jingxin, Liang Chengqi, Liang Xufeng, Ling Jie, Yang Shanghai Normal University Tianhua College AI School No. 1661 North Sheng Xin Road *** China Gaozhou Middle School Guangdong Province Gaozhou China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University 800 Dongchuan Road 200240 China

ISBN: (纸本)9781450399029

In this paper, a vision-based counter system for children’s dribble based on Google MediaPipe’s human posture recognition algorithm and YOLOv5 object recognition algorithm is introduced. Firstly, the hands’ coordinates of the child is located using MediaPipe, and the coordinates of the ball is found using YOLOv5;by the relative positions of the hands and the ball in the multi-frame images of the video, an action sequence of ball holding, touching and ball bouncing is recognized and used to count the number of dribble. The experimental results show that the system can count a single child’s dribble accurately. © 2023 Copyright held by the owner/author(s).

关键词： Computer vision

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Semantics Fusion of Hierarchical Transformers for Multimodal Named Entity recognition 20th

Semantics Fusion of Hierarchical Transformers for Multimodal...

引用

20th international conference on Intelligent computing (ICIC)

作者： Tong, Zhao Liu, Qiang Shi, Haichao Xia, Yuwei Wu, Shu Zhang, Xiao-Yu Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyberspace Secur Beijing Peoples R China Chinese Acad Sci Inst Automat Ctr Res Intelligent Percept & Comp CRIPAC Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9789819756681;9789819756698

Multimodal Named Entity recognition (MNER) is crucial for identifying named entities by utilizing information across different modalities. However, traditional approaches face two major challenges: (1) They struggle to efficiently learn from both textual and visual features to enhance the performance of recognition;(2) Most of these methods fail to consider the relevance between text and images, this could lead to learning that is unrelated to the texts, potentially exerting uncertain or even negative effects on the learning process of multimodal models. To address these issues, we introduce a novel method employing connection-based transformers for MNER, which establishes a direct linkage between uni-modal and multi-modal encoders. This approach facilitates enhanced communication between the top layers of uni-modal encoders and every layer of the multi-modal encoders, thereby improving representation learning through the sophisticated semantics of hierarchical Transformers. Additionally, our method features a gating mechanism that selectively integrates visual clues, effectively filtering out irrelevant visual data and enhancing multi-modal learning. Comparative evaluations on the Twitter-2015 and Twitter 2017 datasets demonstrate our method's superiority, showing an average performance increase of 2.71%-10.76% and 1.16%-13.79%, respectively. These improvements underscore our model's effectiveness and its advancement over current state-of-the-art models.

关键词： Multimodal Named Entity recognition Multimodal Alignment Semantics Fusion

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Security System using Open CV based Facial recognition 2

Security System using Open CV based Facial Recognition

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2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Mitra, Dipra Gupta, Shikha Goyal, Ankur AIT-CSE Chandigarh University Punjab Mohali India

ISBN: (纸本)9781665437899

In this article, an ongoing face-acknowledgement-based security framework for homes and workplaces has been introduced. It has been created utilizing a Local Binary pattern Histogram (LBPH) calculation. Facial recognition has been adequately used to foster frameworks with the end goal of safety in workplaces and homes, just as monitoring crimes through ID by these reconnaissance frameworks. Variations in posture and points of human faces, like light, have been a significant limiting factor for most face recognition calculations;however, the LBPH calculation accounts for both of these variations. This paper features the benefit of this calculation over the others and furthermore shares the reasons behind leaning toward its OpenCV execution over MATLAB. © 2022 IEEE.

关键词： Local binary pattern

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Detecting Vehicles on the Edge: Knowledge Distillation to Improve Performance in Heterogeneous Road Traffic

Detecting Vehicles on the Edge: Knowledge Distillation to Im...

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Bharadhwaj, Manoj Ramadurai, Gitakrishnan Ravindran, Balaraman Indian Inst Technol Robert Bosch Ctr Data Sci & Artificial Intelligen Dept Comp Sci & Engn Madras Tamil Nadu India Indian Inst Technol Robert Bosch Ctr Data Sci & Artificial Intelligen Dept Civil Engn Madras Tamil Nadu India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The drastic growth in the number of vehicles in the last few decades has necessitated significantly better traffic management and planning. To manage the traffic efficiently, traffic volume is an essential parameter. Most methods solve the vehicle counting problem under the assumption of state-of-the-art computation power. With the recent growth in cost-effective Internet of Things (IoT) devices and edge computing, several machine learning models are being tailored for such devices. Solving the traffic count problem on these devices will enable us to create a real-time dashboard of network-wide live traffic analytics. This paper proposes a Detect-Track-Count (DTC) framework to count vehicles efficiently on edge devices. The proposed solution aims at improving the performance of tiny vehicle detection models using an ensemble knowledge distillation technique. Experimental results on multiple datasets show that the custom knowledge distillation setup helps generalize a tiny object detector better.

关键词： Performance evaluation Image edge detection Computational modeling soft sensors Roads Detectors Real-time systems

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