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Low Resolution Face recognition Based on Contrast Representa Distillation 3

Low Resolution Face Recognition Based on Contrast Representa...

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3rd international conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Xu, Shenghua Tang, Shulei Anhui University AHU-IAI Ai Joint Laboratory China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

ISBN: (纸本)9798350372052

Face recognition is a challenging problem in computer vision, but most mature face recognition algorithms focus on the high resolution (HR) case and suffer from severe performance degradation at Low-resolution (LR), owing to the difficulty for getting information. In this paper, we propose a training method based on contrast representation distillation (Crd). The proposed method firstly feeds the features extracted from the HR and LR into two linear layers, allowing both of them to be mapped into a uniform space. Based on the idea of contrast learning, the cosine similarity is used as a loss to increase the similarity of the same person at different resolutions and decrease the similarity of different people. Experiments on real and simulated datasets with different resolutions have been used to verify the effectiveness of the method, all experimental results achieved SOTA. © 2023 IEEE.

关键词： Distillation

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Automated Duplicate Question Analysis with NLP 3

Automated Duplicate Question Analysis with NLP

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohmmad, Sallauddin Chittimalla, Rishika Cholleti, Alekhya Gunda, Sanjana ChandanaMirupuri, Siri Namani, Vaishnavi School of Computer Science and Artificaial Intelligence SR University Telangana Warangal India SR University Telangana Warangal India

ISBN: (纸本)9798331527549

As the capacity of user-generated content endures to upsurge, question duplication has emerged as a must-have for increasing efficiency in question-answering systems. The aim of this paper is to explore how to look for duplicate questions using machine and deep learning methods, reducing duplication in large sets of question data with great accuracy and thus improving user experience at knowledge platforms. This exploration includes models of Logistic Regression, Naive Bayes, Decision Trees, LSTM networks, BERT embeddings, and advanced ensemble methods, such as XGBoost. From above, it is seen that the performance metrics vary across different models, and its accuracy is shown as follows - accuracy of Logistic Regression at 78.9%, 76.4% accuracy of Naive Bayes, 72.3% of Decision Trees, and 82.6% in the LSTM model;the best accuracy has been obtained with the model of XGBoostoptimised by Optuna at a rate of 91.5%, even higher than some deep learning models such as BERT. The results depicted above indicate the outcome of hyperparameter tuning via Optuna on model performance. Our results highlight better effectiveness from optimized ensemble learning techniques in question deduplication, very useful in the implementation of efficient question-answering and knowledge management solutions. © 2025 IEEE.

关键词： Question answering

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Image-point cloud registration method based on image matching 3

Image-point cloud registration method based on image matchin...

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3rd international conference on machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Ai, Yang Yang, Xi School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MoE China Key Laboratory of Ancient Chinese Script Culture Relics and Artificial Intelligence Jilin University China

ISBN: (纸本)9781510681880

Image-to-point cloud registration is the task of fusing 2D images with 3D point clouds, typically combining the rich texture information from images with the spatial information from point clouds for a more comprehensive scene representation. However, the feature discrepancy between different modalities often makes it challenging to accurately match. Addressing this issue, this paper proposes an image-matching-based algorithm for image-to-point cloud registration. Initially, for cross-modal issues, we use 3D visualization tools to screenshot the 3D models, reading the camera information of the screenshots to establish a correspondence between the 3D models and 2D images. By employing a pretrained image matching model, the algorithm extracts keypoints and matches images of the screenshots and images, establishing image keypoints pairing relationships. Then, using the camera projection model, it maps the 3D coordinates to image keypoints, obtaining image-to-point cloud keypoint pairing relationships and calculating the transformation matrix between the image and 3D point cloud using PnP-RANSAC. The proposed method, compared with the manual annotations method, proves to significantly improve efficiency and stability in real-world scenarios. © 2024 SPIE.

关键词： Image matching

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A Hybrid Deep Learning Approach for Early Detection of Diabetic Retinopathy 3

A Hybrid Deep Learning Approach for Early Detection of Diabe...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mary Vespa, M. Shajeena, J. Shiny, R.M. Gayathri Devi, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Diabetes is a malnutrition that results from elevated glucose levels. Diabetes eventually results in diabetic retinopathy (DR), a retinal condition that significantly impairs vision. It is an insulin-dependent difficulty that impacts ocular health. The receptive to cells at the back of the individual retina's veins gets injured, which is the cause of it. A favorable prognosis for this condition depends on its early identification. In this paper, the application of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) on color fundus images is used for diabetic retinopathy is applied for understanding the task. The goal of the proposed DR identification method is to use deep learning to automatically identify the problem. An hybrid model that was trained and tested to distinguish between healthy and DR-affected fundi was able to achieve an accuracy of roughly 99.07% using fundus images from diabetic patients that were available in MESSIDOR. © 2025 IEEE.

关键词： Convolutional neural networks

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Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of machines in Manufacturing Industry 3

Systematic Literature Review on Industry Revolution 4.0 to P...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Sowmya, P. Ravichandran, Sathish Kumar Rakshitha Department of Robotics and Artificial Intelligence Karnataka Nitte India Christ University School of Engineering and Technology Department of Computer Science Karnataka Bangalore India Department of Artificial Intelligence and Machine Learning Karnataka Nitte India

ISBN: (纸本)9781665462167

Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance. © 2023 IEEE.

关键词： Industry 4.0

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Design Thinking in Electrical Engineering: A Pathway to Human-Centered Innovations 3

Design Thinking in Electrical Engineering: A Pathway to Huma...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, D. Siva Ramkumar, M. Arun, M. Baig, Rahmath Ulla Sivaramkrishnan, M. India Sns College of Technology Dept of Ece India Department of Mechanical Engineering Chennai602105 India Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Karpagam COllege of Engineering Dept of Eee India

ISBN: (纸本)9798331509675

Design thinking is a useful technique in many technical domains since it is a novel methodology that emphasizes comprehending and meeting customer demands. Using design thinking in electrical engineering provides a fresh approach to creating user-focused solutions in a field where technological complexity is constantly changing. In order to foster creativity and useful problem-solving, this study explores how applying design thinking concepts might transform electrical engineering's conventional procedures. Engineers may produce more efficient and user-friendly designs by utilizing crucial phases like empathy, problem identification, ideation, prototyping, and iterative testing. In order to demonstrate how this strategy results in more flexible and sustainable solutions, the discussion cites particular instances, such as developments in consumer electronics, electric power systems, and renewable energy. The study also emphasizes how teamwork and ongoing iteration foster creativity while maintaining designs' alignment with user requirements and societal impact. © 2025 IEEE.

关键词： Systems thinking

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Mitigating IoT Security Vulnerabilities Using Artificial intelligence Approaches 3

Mitigating IoT Security Vulnerabilities Using Artificial Int...

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3rd international conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Chandni, Razia Ismail Parvin, Mst Shahnaj Central Women's University Department of Computer Science And Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

The system proposed in this paper focuses on providing an improved security solution to mitigate threats in IoT (Internet Of Things) systems. IoT works on connecting digital devices assigned with unique identifiers to exchange data over a network without real-time human-to-human or human-to-computer interaction. However, IoT devices pose severe security risks for their ease of use. Thus, the paper proposes a two-stage-based Intrusion Detection System (IDS) that allows users to protect their IoT devices from vulnerabilities at network levels. The improvement from the existing system on security monitoring is that this prototype system is a lightweight, simpler architecture that can be implemented within in-house hardware and in cloud solutions. For the first stage, Signature-based IDS (SIDS), the system outperformed the previous system with an accuracy of 99.4 \% with the XGBOOST algorithm on the UNSW15_NB15 Dataset. It is trained to save known malicious patterns. If the packet header matches any pattern, SIDS classifies it and blocks the attack. The second stage, Anomaly-based IDS (AIDS), uses Artificial intelligence algorithms, LSTM, and 1D CNN to form a hybrid model. It was tested with two datasets, UNSW15_NB15 Dataset and BotIoT Dataset, which had 100\% and 86.4\% accuracy, respectively. If SIDS gets an unknown pattern, it will be passed to AIDS. This will count the deviation between the incoming packet and the normal packet. If the deviation exceeds the normal threshold, it will flag the packet as a vulnerability. The research also focuses on classifying IoT security threats into two main categories: Network-based and Hardware-based. © 2024 IEEE.

关键词： Network security

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Data Analytics on Blitz E-sports: Enhancing Gaming Experience through Personalization and Prediction 3

Data Analytics on Blitz E-sports: Enhancing Gaming Experienc...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vaneeta, M. Kusuma, T. Shruthi, G. Avanish Rao, B.S. Anand, Nikhil Abhishek, M. Ramaiah Institute of Technology Department of Artificial Intelligence & Data Science Bangalore India Ramaiah Institute of Technology Department of Information Science Bangalore India

ISBN: (纸本)9798331515911

This paper delves into the comprehensive data analytics of Blitz E-sports, a dynamic gaming cafe, to enhance operational efficiency and customer satisfaction. The study focuses on three pivotal objectives: revenue optimization, game recommendations, and churn prediction. Through meticulous analysis of customer data, gaming trends, and operational metrics, this proposal equips Blitz E-sports with action able insights to enhance profitability, customer engagement, and long-term sustainability. The potential of data-driven decision-making in the competitive e-sports industry is demonstrated by leveraging advanced machine learning techniques and big data technologies. This paper aims to reveal significant improvements in personalized user experiences, optimized revenue strategies, and effective customer retention measures. © 2025 IEEE.

关键词： Sales

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SVM-based Detection of Bone Fracture using Weighed Local Binary pattern Features 3

SVM-based Detection of Bone Fracture using Weighed Local Bin...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Balaji, N. Sathish Hemachandran, M. Pawan Kumar, B. Sriram Kumar, S. Jansi, R. SRM Institute of Science and Technology Department of Electronics and Communication Engineering SRM Nagar Kattankulathur603203 India SRM Institute of Science and Technology Faculty of Engineering and Technology SRM Nagar Kattankulathur603203 India

ISBN: (纸本)9798331523893

The work presented in this research outlines a new method for detecting bone fractures, which is based on a machine learning model. To achieve this goal, Support Vector machine (SVM) classifiers are trained on a dataset of images depicting bone fractures and normal bones enabling them to categorize bone images into two classes - bone fracture and non-bone fracture images. The results have shown that the system attains a great performance of 98.42% accuracy, suggesting high correctness of fracture detection cases. Moreover, the articulated system also comprises state-of- the- art image preprocessing methods such as removal of noise, any adaptive histogram equalization, additional sharpening and edge detection of images for the purpose of improving features. Among key developments, is the introduction of a unique feature which is termed as Weighed Local Binary pattern (WLBP) for the inquisitive information containing textures and patterns in the images. Therefore, the present study tends to provide a very strong as well as an accurate model for fracture assessment, which is likely to improve the quality of the diagnosis and the patients' care. © 2025 IEEE.

关键词： Local binary pattern

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3rd international conference on machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021

3rd International Conference on Machine Learning, Image Proc...

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3rd international conference on machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021

ISBN: (纸本)9789811958670

The proceedings contain 68 papers. The special focus in this conference is on machine Learning, Image Processing, Network Security and Data Sciences. The topics include: Analyzing Wearable Data for Diagnosing COVID-19 Using machine Learning Model;comparative Analysis of Classification Methods to Predict Diabetes Mellitus on Noisy Data;a Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print;region Classification for Air Quality Estimation Using Deep Learning and machine Learning Approach;neuroevolution-Based Earthquake Intensity Classification for Onsite Earthquake Early Warning;detection of Credit Card Fraud by Applying Genetic Algorithm and Particle Swarm Optimization;traditional Indian Textile Designs Classification Using Transfer Learning;classification of Electrocardiogram Signal Using Hybrid Deep Learning Techniques;automated Detection of Type 2 Diabetes with Imbalanced and machine Learning Methods;fault Diagnosis in Wind Turbine Blades Using machine Learning Techniques;real-Time Detection of Vehicles on South Asian Roads;stock Market Prediction Using Ensemble Learning and Sentimental Analysis;Multiple Feature-Based Tomato Plant Leaf Disease Classification Using SVM Classifier;a Methodological Review of Time Series Forecasting with Deep Learning Model: A Case Study on Electricity Load and Price Prediction;unexpected Alliance of Cardiovascular Diseases and Artificial intelligence in Health Care;a Novel Smartphone-Based Human Activity recognition Using Deep Learning in Health care;An Enhanced Deep Learning Approach for Smartphone-Based Human Activity recognition in IoHT;classification of Indoor–Outdoor Scene Using Deep Learning Techniques;prediction of the Reference Evapotranspiration Data from Raipur Weather Station in Chhattisgarh using Decision Tree-Based machine Learning Techniques;deep Transfer Learning and Intelligent Item Packing in Retail Management;preface.

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