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Moving Vehicle Registration Plate Detection Using Machine Learning

Moving Vehicle Registration Plate Detection Using Machine Le...

引用

Trends in Quantum computing and Emerging Business Technologies (TQCEBT), international conference on

作者： B Sreedhar Gowtham Prasad T V S Sivudu Macherla K. S. Raghuram M. Sunil Kumar Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India Dept of Electronics and Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India Department of IT PACE Institute of Technology and Sciences Ongole AP India Department of Mechanical Engineering Vignan's Institute of Information Technology (A) Visakhapatnam A. P. India Department of Computer Science and Engineering School of Computing Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This research investigates the application of machine learning techniques in the detection of moving vehicle registration plates. The study explores the effectiveness of various algorithms in accurately identifying and localizing license plates in dynamic and real-time scenarios. By utilizing image processing and pattern recognition methods, the research aims to develop a robust and efficient system capable of detecting license plates under challenging conditions, such as varying lighting conditions and different vehicle orientations. The proposed approach offers potential benefits for law enforcement, traffic monitoring, and parking management systems. Through empirical evaluations, the study aims to validate the effectiveness and reliability of the machine learning-based approach for moving vehicle registration plate detection, providing insights for future research and development in this field.

关键词： Quantum computing Heuristic algorithms Machine learning Real-time systems pattern recognition Reliability Vehicle dynamics

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Hybrid Human Interaction recognition Using Feature Extraction and Deep Neural Network

Hybrid Human Interaction Recognition Using Feature Extractio...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Human recognition have become interested in human interactions in images. Interaction recognition is a hot topic in many research fields. In this study, we propose a system for recognizing activities using a Deep Neural Network (DNN). First, we improve the clarity of video frames by transforming them to HSI color. We also use Mean filters to remove any noise. We use Multiple Object Tracking (MOT) and Statistical Region Merging techniques to extract silhouettes. In the feature extraction process, we use Local Binary patterns (LBP), SIFT, and Local Phase Quantization (LPQ). We apply the Particle Swarm Optimization (PSO) algorithm to identify the most important features that best represent the data structure. We then input these selected features into the DNN for classification into different human interactions. We tested this approach on the SBU Interaction dataset, and achieved a recognition rate of $\mathbf{9 2 \%}$.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Biological system modeling Surveillance Artificial neural networks Feature extraction pattern recognition Classification algorithms

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Performance Analysis of Facial Expression recognition Algorithms Based on Machine Learning and Deep Learning

Performance Analysis of Facial Expression Recognition Algori...

引用

Networking, Informatics and computing (ICNETIC), international conference on

作者： Lijie Qin Nengkun Tao Siyi Zheng Xiarui Zheng Beijing National Day School Beijing China No.1 Middle School of Liuyang Hunan Liuyang China Nord Anglia Chinese International School Shanghai Shanghai China University of Wisconsin Madison Wisconsin America

As a form of non-verbal communication, facial expressions contain rich emotional information and some related cognitive behaviors, personality and psychological emotions, and play an important role in people’s daily communication. With the rapid development of pattern recognition technology, facial expression recognition based on computer vision has gradually become a research hotspot. The key to facial expression recognition is to extract different facial expression feature points. Its main technical framework includes traditional recognition methods based on hand-crafted features (apparent features and geometric features) and recognition based on deep learning. In exploration of application boundaries between different methods, in this paper, machine learning algorithms (support vector machines, random forests) and convolutional neural networks (AlexNet, VGG16) were selected to construct recognition models, and quantitatively analyzed the changes of different methods on different emotional results. In addition, the current research problems of facial expression recognition are summarized, and the future development direction of this field is prospected.

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Anastylosis of Frescos - A Web Service in an HPC Environment 14th

Anastylosis of Frescos - A Web Service in an HPC Environment

引用

14th international conference on Large-Scale Scientific Computations (LSSC)

作者： Dimov, Dimo Gurov, Todor Ivanovska, Sofiya Yordanov, Svetlozar Bulgarian Acad Sci BAS Inst Informat & Commun Technol IICT Sofia Bulgaria

ISBN: (纸本)9783031562075;9783031562082

The parallelization in a High Performance computing (HPC) environment of a method, algorithm and software called RINCCAS (Rotation-Invariant NCC for 2D Color Matching of Arbitrary Shaped Fragments of a Fresco) is presented. RINCCAS is designed for virtual restoration of frescoes from their ruins, a known problem of national and world heritage conservation. The method was developed at IICT-BAS for participation in the DAFNE computer competition, Italy, 2019. Prepared in the MATLAB environment, RINCCAS uses a classic NCC approach for positioning of the fragments (rectangular coordinates and accidental rotation angle). It extends NCC to color input images (frescoes and fragments) and to arbitrary shapes of the fragments. RINCCAS is a cubic complexity method. To reduce the execution time, we use parallelization through independent subtasks whose optimal distribution in consecutive sessions we call sequential concatenation within one or more HPC nodes. A description and comparative analysis of the experiments with Avitohol-HPC at IICT-BAS are presented. Upper limits are set for the amount of input data relative to the amount of available RAM, not meeting which significantly slows performance due to system memory swapping. An improvement called parallel concatenation is proposed to significantly alleviate memory constraints using the same theoretical formulation. In conclusion, an invitation is extended to the recently opened website of the RINCCAS-HPC service for Anastylosis of Frescos' enthusiasts and specialists.

关键词： pattern recognition Normalized Cross Correlation (NCC) Digital Anastylosis of Frescos from Ruins Cultural Heritage Preservation on HPC Optimal HPC resource planning

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A MobileNetV2-CBAM-based Model for Forest Fire Classification Using UAV Imagery

A MobileNetV2-CBAM-based Model for Forest Fire Classificatio...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Xinjie Deng Burhan Khan Chee Peng Lim Ming-Yu Liao Chongqing Jianzhu College Institute for Intelligent Systems Research and Innovation Deakin University Chongqing China Institute for Intelligent Systems Research and Innovation Deakin University Waurn Ponds VIC Australia Department of International Business National Kaohsiung University of Science and Technology Taiwan

Forest fires are one of the worldwide severe natural disasters causing human and environmental losses. Early and accurate fire detection is critical to minimize damages caused by forest fires and reduce human risks in firefighting efforts. Recently, Unmanned Aerial Vehicles (UAVs) mounted with cameras and edge computing devices with Artificial Intelligence (AI)-based algorithms have been employed to tackle forest fire detection problems in real time. Considering that edge computing is limited in resources and power consumption, the developed AI algorithms need to be lightweight. This paper proposes lightweight deep learning (DL) algorithms based on MobileNetV2 and attention mechanism to detect forest fires using UAV imagery. Based on several experiments, the proposed model yields an accuracy rate of 86.14% and an F1-score of 0.8719, outperforming other related methods in forest fire detection and classification.

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Global Contextual Learning Network for Multisource Remote Sensing Image Fusion

Global Contextual Learning Network for Multisource Remote Se...

引用

pattern recognition, Machine Vision and Intelligent Algorithms (PRMVIA), international conference on

作者： Puhong Duan Zhuojun Xie Xukun Lu Xudong Kang School of Robotics Hunan University Changsha China College of Electrical and Information Engineering Hunan University Changsha China

ISBN: (数字)9798350389746

ISBN: (纸本)9798350389753

Multisource remote sensing image fusion aims to combine the complementary advantage of different modalities for fine-grained classification. Current multisource image fusion methods are based on a patch-by-patch learning paradigm, which requires high computational burden. In addition, how to combine different attributes is still a challenging issue. In this article, a global contextual learning network (GCLNet) is studied for multisource remote sensing image fusion. In GCLNet, an encoder-decoder convolutional network is proposed to draw global spectral-spatial information, which can significantly reduce the computing cost. Furthermore, a contextual attention feature fusion module is designed to connect coarse-to-fine features between encoder and decoder. Experiments on Augsburg and Yellow River datasets reveal that the proposed GCLNet obtains promising results in the light of classification result and inference speed compared to other advanced fusion methods.

关键词： Machine vision Semantics Radar Radar imaging Inference algorithms Rivers pattern recognition

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Deep Learning Models for recognition of Hand Gestures in Basketball Sports

Deep Learning Models for Recognition of Hand Gestures in Bas...

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Cognitive, Green and Ubiquitous computing (IC-CGU), international conference on

作者： Rinki Gupta Varun Kavanal Anya Joshi Electronics and Communication Engineering Department Amity University Uttar Pradesh Noida India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

In the domain of sports applications, the recognition of hand motions holds significant importance. Leveraging the capabilities of deep learning, this paper is dedicated to devising robust solutions for identifying hand movements of referees in basketball sport. The dataset under consideration encapsulates multiple inputs, consisting of data from surface electromyography (sEMG) sensors and accelerometers. These sensors play a pivotal role in capturing muscle activity and three-dimensional motion information of hand gestures during sports activities. This paper presents a trifecta of Convolutional Neural Network (CNN) and long short-term memory (LSTM) models, each meticulously designed to accommodate specific input data categories. The proposed CNN-LSTM models optimized with just accelerometer, just EMG, as well as multi-channel EMG and accelerometer data yield accuracies of 94.4%, 89.8%, and 94.7%, respectively. Our results unequivocally underline the efficacy of the combined model, which adeptly fuses information from EMG and accelerometer data. This implementation of deep learning methods augments hand motion recognition in the context of basketball refereeing, signifying a significant stride towards streamlined human-motion interaction and more proficient officiating in basketball games.

关键词： Accelerometers Deep learning Analytical models soft sensors Gesture recognition Electromyography Data models

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Research on pattern recognition of Urban and Rural Consumption Behavior Based on Machine Learning and Its Application in Digital Economy

Research on Pattern Recognition of Urban and Rural Consumpti...

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Mechatronics, IoT and Industrial Informatics (ICMIII), international conference on

作者： Xiaowei Mu Yan Liu Zhijun Sheng Jiahui Chen Changchun Humanities and Sciences College Changchun Normal University Changchun China Changchun Humanities and Sciences College Changchun China Changchun University of Science and Technology Changchun China

ISBN: (数字)9798350386639

ISBN: (纸本)9798350386646

This paper introduces an SVM algorithm for studying consumer behavior. This model is a new pattern recognition algorithm based on statistical principle. It has unique advantages in dealing with small samples, nonlinearity and high dimensional pattern recognition. The fuzzy semantic modeling method is constructed by using emotion computing and text mining. At the same time, the internal and external inducement factors are the first part and the purchasing degree of consumers is the second part, and the fuzzy reasoning model is established. The consumption behavior and process of urban and rural residents are constructed by using probability theory. Through the analysis of the relationship between the elements, the corresponding knowledge base is established. The unreasonable purchase degree model of customers is established by Jess method. Finally, taking a tobacco enterprise as an example, an empirical study was made on it. The results show that in urban and rural areas, SVM-based consumer behavior identification and statistical analysis has high accuracy and efficiency.

关键词： Support vector machines Text mining Consumer behavior Accuracy Computational modeling Binary trees Vectors

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Automating Forensic Analysis: Integrating Machine Learning Algorithms for Forensic Evidence and Serial Killer Profiling

Automating Forensic Analysis: Integrating Machine Learning A...

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Mobile, Intelligent, and Ubiquitous computing conference (MIUCC), international

作者： Hana Ahmed Mohamed Hassan Mariam Mohamed Abouelwafa Amira Alaa Saber Ahmed Ehab Ibrahim Nada Shorim Diaa Salama AbdElminaam Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

Serial murder investigations pose significant challenges due to the complexity of criminal patterns and behaviors. This study presents an integrated forensic analysis system using advanced artificial intelligence (AI) techniques to enhance the efficiency and accuracy of criminal investigations, particularly in serial murder cases. By applying various machine learning algorithms, including logistic regression, support vector machines (SVM), K-nearest neighbors (KNN), convolutional neural networks (CNN), and generative adversarial networks (GAN), the system analyzes key forensic evidence such as serial killer patterns, fingerprint recognition, face sketching, bloodstain analysis, and autopsy reports. Each module in the system contributes to narrowing down suspect profiles and linking potential cases using AI-driven pattern recognition and data analysis. The system demonstrated high performance across multiple forensic tasks, with logistic regression and SVM achieving 85% accuracy in serial killer pattern recognition, GAN achieving 99% accuracy in face recognition, and LSTM achieving 99.3% accuracy in bloodstain analysis. The integration of these AI techniques ensures robust, unbiased, and efficient forensic analysis, significantly reducing the time and labor required in traditional investigative methods. The findings of this study underscore the transformative potential of AI in forensic science, providing law enforcement with powerful tools to profile serial killers, analyze crime scenes, and identify critical evidence with precision. This unified AI-based approach holds promise for improving investigative outcomes and enhancing public safety by assisting in the timely resolution of complex murder cases.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Forensics Face recognition Fingerprint recognition Nearest neighbor methods Generative adversarial networks Artificial intelligence

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Dynamic Low-Rank Adaptation Based Pruning Algorithm for Large Language Models

Dynamic Low-Rank Adaptation Based Pruning Algorithm for Larg...

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

作者： Linfeng Li Lei Guo School of Computer Science and Technology Xinjiang University Urumqi China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Deep neural networks have been getting better and better performance while the parameter size has become larger and larger, and have now entered the era of large language models (LLM). However, large language models with huge number of parameters are difficult to be deployed on edge devices with limited computing power and memory resources. Network pruning techniques can reduce the storage and computation requirements of large language models. Although Dynamic Low-Rank Adaptation (DyLoRA) allows efficient fine-tuning of LLM, the deployment of LLM is still hindered by large model sizes and computational costs. In this paper, we propose a DyLoRA-based pruning algorithm that uses gradient updating of DyLoRA module to define the importance of parameters, and then prune the least important parameters based on the resulting importance scores. Through experiments on commonly used evaluation datasets, it is demonstrated that the proposed pruning algorithm is able to achieve 96.60% of the performance of the original model with 20% of the parameters pruned, which outperforms the previous pruning algorithms.

关键词： Performance evaluation Adaptation models Heuristic algorithms Large language models Computational modeling Artificial neural networks pattern recognition Computational efficiency

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