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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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See-Shop System: An Assistive Outdoor Navigation System for the Visual Impaired Based on Deep Learning Methods 3

See-Shop System: An Assistive Outdoor Navigation System for ...

引用

3rd international conference on Advanced Enterprise Information System (AEIS)

作者： Wang, Junjie Zhao, Shanshan Xian Jiaotong Liverpool Univ XJTLU Suzhou Jiangsu Peoples R China

ISBN: (纸本)9798350359268

Visually impaired and blind individuals often encounter challenges in locating stores on the street, requiring guidance to ensure their daily activities are safe and effective. The objective of this study is to develop an assisted navigation system based on real-time semantic segmentation and Chinese text recognition. The proposed system aims to assist visually impaired individuals in obtaining information about the relative location of stores. It is highly user-friendly and can provide coordinate references in unfamiliar environments, aiding them in various activities like navigation and purchasing. In this work, we utilize the BiseNet v2 network, a real-time semantic segmentation model, to identify and detect shop sign objects. Additionally, we employ PP-OCR v3, an ultra-lightweight Optical Character recognition (OCR) network, for Chinese text recognition. Furthermore, the system incorporates a speech broadcast function to convert visual information into auditory feedback. By providing real-time support for visually impaired individuals, this assistive navigation application demonstrates great potential in promoting public welfare.

关键词： Computer Vision Segmantation Shop sign recognition Visually impaired and blind people Assistive Navigation BiseNet v2 PP-OCR v3 morphological operations

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PATRAS: pattern-Aware Generalized Traffic Signal Control Based on Parallel Learning 3

PATRAS: Pattern-Aware Generalized Traffic Signal Control Bas...

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3rd IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2023

作者： Dai, Xingyuan Zhang, Yan Tang, Yiqing Zhao, Chen Sun, Jiaming Cong, Fei Lu, Yanan Lv, Yisheng Chinese Academy of Sciences Institute of Automation Beijing China China Telecom Digital City Technology Co. Ltd Xiongan China

ISBN: (纸本)9798350318470

The data-driven adaptive traffic signal control for real-world scenarios faces two significant challenges: adapting to diverse traffic patterns and addressing the generalization issue from the optimization environment to the deployment environment. To tackle these challenges, this paper proposes a novel approach for pattern-aware generalized traffic signal control (PATRAS) based on parallel learning. It establishes an optimization process comprising descriptive learning, predictive learning, and prescriptive learning, enabling robust and adaptable control for different traffic patterns. First, a lightweight and effective method is employed to characterize various traffic patterns using traffic flow trends. Next, for each specific traffic pattern, an offline reinforcement learning algorithm is applied to optimize the traffic signal control policy directly using collected traffic data instead of simulations. A decision support library is constructed to store and reuse these optimized policies. Lastly, a pattern-matching-based approach is used to recommend appropriate control policies from the library for the traffic pattern, guiding efficient operation of the actual transportation system. Experimental results demonstrate that the offline optimization method employed in PATRAS outperforms real-world demonstrations based on actuated control policies and other baselines in terms of generalization performance. Additionally, the recommendation method effectively improves traffic efficiency by directly applying pre-optimized policies to different traffic patterns. © 2023 IEEE.

关键词： Reinforcement learning

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Ship Ranging Based on Monocular Imaging and Applications 3

Ship Ranging Based on Monocular Imaging and Applications

引用

3rd international conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Li, Boshi Chen, Xianqiao Xie, Kai Chen, Jianfeng School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350359961

Environmental perception, including object recognition and distance estimation, is a crucial aspect in the field of computer vision. Although distance estimation has received relatively less attention compared to object recognition, its accuracy becomes particularly crucial in specific scenarios, such as when ships are entering ports or gates. In such situations, precise distance control is essential, as errors could lead to the risk of collisions between the ship and the dock. Due to significant variations in the sizes of ships, traditional distance estimation methods often exhibit substantial errors. To mitigate these inaccuracies, this paper proposes a self-Adaptive ship size estimation approach. In addition to introducing an adaptive model, we further design a distance estimation model incorporating tilt information to enhance the accuracy of distance estimation, especially for objects closer to the camera. Experimental results demonstrate the effectiveness of this approach in improving the accuracy of distance measurements, particularly in overhead perspectives. © 2023 IEEE.

关键词： component Distance estimation, monocular vision

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Diabetes Prediction Using Nature-Inspired Optimization Algorithms 3

Diabetes Prediction Using Nature-Inspired Optimization Algor...

引用

3rd international conference on Artificial intelligence and Signal Processing, AISP 2023

作者： Vijaya, J. Tirkey, Divyanshu Singh, Priyank Goel, Aditya International Institute of Information Technology Department of DSAI Chhattisgarh Naya Raipur India

ISBN: (纸本)9798350320749

Diabetes is a common genetic disease across the world. It is a chronic disease spurred by either production of insufficient production of insulin by the pancreas or less insulin use by the body. Insulin maintains the blood sugar level of the body. Uncontrolled diabetes can cause hypertension, also known as high blood glucose. Hypertension in the long run can be a cause of heart disease. Therefore, the prediction of diabetes is a major problem in the health sector. Various machine learning methods are already in use for the prediction of diabetes. Traditional methods were first used to address diabetes forecasting, but they take a while to deploy. Therefore, future research has adopted the use of various optimization strategies to solve the issue of diabetes forecasting as well as to boost system selectivity and operating speed. These methods have all proven to be successful in solving different optimization issues and also improve the model significantly. This work proposes a Logistic Regression model by the use of nature-inspired algorithms. We proposed a methodology which uses seven different optimization algorithms instead of gradient descent for linear regression. We implemented a linear regression algorithm from scratch and then used it with different optimization algorithms which improve the overall performance. © 2023 IEEE.

关键词： Blood

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Seeing Through the Lies: A Vision Transformer-Based Solution 3rd

Seeing Through the Lies: A Vision Transformer-Based Solution

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3rd international conference on Artificial-Business Analytics, Quantum and machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Rasool, Aale Katarya, Rahul Big Data Analytics and Web Intelligence Laboratory Department of Computer Science Delhi Technological University New Delhi India

ISBN: (纸本)9789819725076

Deepfake technology has become increasingly sophisticated and poses a growing threat to society, as it can be used to create convincing fake videos for malicious purposes. Therefore, detecting deepfakes has become crucial to ensure the authenticity of visual content. This paper proposes a novel deepfake detection approach that utilizes InceptionResNetV2 for feature extraction and Vision Transformer (ViT) with Nyström Attention mechanism for classification. Our model achieves high accuracy, AUC, precision, and recall on the CelebDFv2 dataset and outperforms state-of-the-art techniques like EfficientNet, XceptionNet, and ResNet. We also perform evaluations on the CelebDFv1 and DFDC datasets. Furthermore, we conduct a comprehensive literature survey to highlight the existing research in deepfake detection. We provide a roadmap for future work in this field, highlighting potential research directions that can further improve the effectiveness and applicability of deepfake detection models. The proposed model shows great potential for real-world applications, and the suggested future work can help to address some of the remaining challenges in this area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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DeFlare-Net: Flare Detection and Removal Network 10th

DeFlare-Net: Flare Detection and Removal Network

引用

10th Biennial international conference on pattern recognition and machine intelligence (PReMI)

作者： Ghodesawar, Allabakash Patil, Vinod Raichur, Ankit Adrashyappanamath, Swaroop Malagi, Sampada Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India

ISBN: (纸本)9783031451690;9783031451706

In this paper, we propose DeFlare-Net to detect, and remove flares. Typically, flares in hand-held devices are inherent due to internal reflection of light and forward scattering of lens material. The distortions due to flares limit the applications in the field of computer vision. Research challenges towards detection and removal of flare persist due to multiple occurrences of flare with varying intensities. The performance of existing flare removal methods are sensitive to the assumption of underlying physics and geometry, leading to artefacts in the deflared image. The current approaches for deflaring involve elimination of light-source implicitly, whilst removal of flare from the image leading to loss of information. Towards this, we propose DeFlare-Net for detection, and removal of flares, while retaining light-source. In this framework, we include Light Source Detection (LSD) module for detection of light-source, and Flare Removal Network (FRN) to remove the flares. Unlike state-of-the-art methods, we propose a novel loss function and call it as DeFlare loss L-DeFlare. The loss L-DeFlare includes flare loss L-flare, light-source loss L-ls, and reconstruction loss L-recon towards removal of flare. We demonstrate the results of proposed methodology on benchmark datasets in comparison with SOTA techniques using appropriate quantitative metrics.

关键词： Flare Detection Flare Removal Lightsource

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A Comprehensive Framework for Data-Driven Management of Chronic Pain with machine Learning and Advanced Analytics 3

A Comprehensive Framework for Data-Driven Management of Chro...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Kaliappan, Seeniappan Socrates, S. Ravi, D. Ranjith Kumar, Gotte Kcg College of Technology Department of Mechatronics Engineering TN Chennai India Velammal Institute of Technology Department of Mechanical Engineering TN Chennai India Bharath Institute of Higher Education and Research. Mechanical Engineering Department TN Chennai India School of Computer Science & Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

Chronic pain management presents a complex challenge in healthcare, demanding innovative approaches for effective intervention. This research introduces a comprehensive framework leveraging machine learning and advanced analytics to address this challenge. In addition, the platform employs cutting-edge analytics to sift through the ocean of healthcare data in search of knowledge applicable to real-world scenarios. By identifying trends in patients' responses to therapies and disclosing associations between various aspects of lifestyle and pain levels, analytics are crucial for determining the most effective method for treating chronic pain. The proposed methodology integrates predictive modeling, forecasting, and treatment optimization algorithms to personalize chronic pain management strategies. By harnessing patient-specific data, including historical pain records, genetic factors, and lifestyle information, the framework aims to enhance accuracy in predicting pain severity, forecasting future pain episodes, and optimizing treatment plans. © 2023 IEEE.

关键词： machine learning

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Review of Big Data Evidence in Criminal Proceedings: Basis of Academic Theory, Practical pattern and Mode Selection 3rd

Review of Big Data Evidence in Criminal Proceedings: Basis o...

引用

3rd BenchCouncil international Symposium on Intelligent Computers, Algorithms, and Applications, IC 2023

作者： Liao, Yicheng Zhejiang University Zhejiang Province Hangzhou China

ISBN: (纸本)9789819700646

Big data materials from emerging technologies such as machine algorithms, robots and advanced artificial intelligence are rushing to the courts. Big data evidence as the product of electronic evidence iteration, mainly in the form of analysis results or reports, the use of big data evidence presents more collaborative advance and the characteristics of stage circulation, but big data evidence in technical dimension present data source and algorithm model level defects, in the legal dimension is beyond a reasonable doubt of proof standard, proof pattern and criminal presumption contradiction. The litigation procedure regulation should be carried out by the path correction of big data evidence transmission, the proof mode should be optimized by the probabilistic analysis of big data evidence and reinforcing evidence, and the illegal exclusion rules of big data evidence should be constructed by the illegal infringement of the digital basic rights as the judgment standard. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Explainable AI for Predictive Analytics on Employee Promotion 3

Explainable AI for Predictive Analytics on Employee Promotio...

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3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Bhattacharya, Abhirup Choudhary, Pradyumn Mukhopadhyay, Sandipan Misra, Bitan Chakraborty, Sayan Dey, Nilanjan Computer Science and Engineering Techno International New Town Kolkata India

ISBN: (纸本)9798350348347

The success of a company depends upon the strategy, technology, finance, and more importantly, the performance and competence of its employees. To efficiently manage the manpower of an organization, the founders, CEO, and managers need to determine which employees must be promoted. Promotion boosts employee morale, improves the resource allocation of the organization, and helps identify talent. In this project, we have explored various machine learning algorithms to predict the promotion of an employee based on multiple employee-related factors such as education, training, rating and length of service. The analysis focuses on creating a predictive model after thorough feature analysis and preprocessing of the training data. We have trained multiple classification models such as logistic regression, decision tree, random forest, and XGBoost. Evaluation metrics, namely, precision, recall, and Fl-score, were used to evaluate the performance of each model, and the XGBoost classifier outperformed the other algorithms. In addition, we have implemented explainable artificial intelligence methods such as local interpretable model agnostic explanations. Additive explanations to derive the reason behind the predictions made by artificial intelligence. The current work managed to predict with an accuracy of 95% and detect the features that have an impact on employee promotion. © 2023 IEEE.

关键词： Lime

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Advanced Lung Cancer Diagnosis and Treatment with Artificial intelligence: Applications, Methods, and Future Directions 3

Advanced Lung Cancer Diagnosis and Treatment with Artificial...

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

作者： Chaudhari, Pawan Dilip Gudadhe, Amit Maharashtra Wardha442001 India Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

AI technology revolutionizes the treatment of lung cancer and other related lung cancer diagnoses, hence becoming the game-changing technology in the diagnosis and treatment of this serious disease. As one of the most prevalent and deadliest cancers globally, early identification, precise diagnosis, and efficient planning for the treatment of lung cancer have always been difficult. The therapy using AI-based methods is introducing another novel way of addressing the problems concerning lung cancer via advanced imaging analysis, predictive modeling, and tailored plans of treatment, the most important among which is of course, imaging analysis. What is certainly most exciting among the applications of AI in lung cancer treatment is in the realm of imaging analysis. AI has been seen to be extremely clever in the interpretation of images rather than using machine learning or deep learning but has been much faster and more accurate than a human being while doing it. Besides, these systems also increase the general accurateness of diagnosis through a reduction of the error in diagnosis and consistent results. Alluring promises are made for AI soon for the treatment of lung cancer. In this paper, we discuss how AI has been transformed into imaging, diagnosis, and treatment planning and how this will improve patient outcomes and completely transform the way healthcare is delivered. We also consider barriers still to be cleared for realizing fully the potential benefits of AI. These include problems such as the quality of available data used in model interpretation and generalizability across different healthcare settings. Therefore, we end and suggest some future research on collaboration by emphasizing the safe, effective, and ethical entry of AI into normal clinical practice. © 2025 IEEE.

关键词： Lung cancer

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