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A novel method for indian vehicle registration number plate detection and recognition using CNN

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8563-8585页

作者： Pandey, Vibha Choubey, Siddhartha Patra, Jyotiprakash Mall, Shachi Choubey, Abha Department of Computer Science Engineering Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science Engineering Shri Shankaracharya Institute of Professional Management and Technology Chhattisgarh Raipur India School of Computer Science and Engineering Galgotias University Greater Noida India

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "preprocessing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. © 2024 – IOS Press. All rights re

关键词： Image classification

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Generating animatable 3D cartoon faces from single portraits

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虚拟现实与智能硬件（中英文） 2024年第4期6卷 292-307页

作者： Chuanyu PAN Guowei YANG Taijiang MU Yu-Kun LAI Department of Electrical Engineering and Computer Sciences University of California BerkeleyBerkeley CA 94704USA Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK

Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D ***,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being *** study presents a novel framework for generating animatable 3D cartoon faces from a single portrait *** First,we transferred an input real-world portrait to a stylized cartoon image using *** then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed *** two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark ***,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation *** Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity ***,we demonstrated the capability of the proposed 3D model for real-time facial animation.

关键词： 3D reconstruction Cartoon face reconstruction Face rigging Stylized reconstruction Virtual reality

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Leveraging the Potential of Large Language Models

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Informatica (Slovenia) 2024年第8期48卷 1-16页

作者： Prasad, Shreya Gupta, Himank Ghosh, Arup Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Information Technology Graceland University LamoniIA United States

This study focuses on enhancing Natural Language Processing (NLP) in generative AI chatbots through the utilization of advanced pre-trained models. We assessed five distinct Large Language Models (LLMs): TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B, and LLAMA-2-13B to identify the most effective one. Our findings revealed that the LLAMA Model excels in comprehending user queries and delivering precise responses during conversations. The article elucidates the methodology employed to evaluate and select various models for our chatbot. Through rigorous testing, we determined that the LLAMA-2-13B model exhibits enhanced response time and accuracy. Additionally, we employed tools such as Facebook Artificial Intelligence Similarity Search (FAISS) and experimented with user interfaces like Streamlit and Chainlit to enhance the chatbot's user-friendliness. The research underscores the significance of selecting the appropriate model for crafting efficient chatbots. Ultimately, the LLAMA-13B model emerged as the standout performer, showcasing superior performance. Benchmark assessments, including HellaSwag and WinoGrande, which gauge common sense reasoning, were employed to evaluate our chatbot's capabilities. The study concludes that LLAMA-based models hold significant promise for the development of innovative and user-friendly chatbots in the future. Povzetek: Študija se osredotoča na izboljšanje obdelave naravnega jezika (NLP) v generativnih AI klepetalnicah z uporabo naprednih vnaprej usposobljenih modelov. Analizirali so pet modelov: TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B in LLAMA-2-13B. Model LLAMA-13B se je izkazal kot najboljši z vrhunskimi rezultati na testih HellaSwag in WinoGrande. Raziskava uporablja orodja, kot je FAISS, in uporabniške vmesnike, kot sta Streamlit in Chainlit za izboljšanje uporabniške izkušnje. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Machine learning

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Enhanced Phishing Website Detection Using Dual-Layer CNN and GRU with Attention Mechanism and Lexical NLP Features

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SN computer Science 2024年第7期5卷 929页

作者： Birthriya, Santosh Kumar Ahlawat, Priyanka Jain, Ankit Kumar Department of Computer Engineering National Institute of Technology Kurukshetra 136119 India

This study develops a cutting-edge framework for detecting phishing attacks, leveraging the synergy between natural language processing and advanced deep learning. It begins with a comprehensive dataset and advances through feature engineering to convert textual data into a structured vector space via word embedding, complemented by principal component analysis for efficient dimensionality reduction. This method ensures the preservation of critical analytical information while optimizing computational resources. The framework utilizes NLP to discern linguistic patterns characteristic of phishing attempts, thus enhancing its detection accuracy. The 2CNN-GRU model for phishing website classification effectively combines spatial feature extraction with sequential processing and attention mechanisms to distinguish between benign and malicious websites. A GRU layer is incorporated to process sequential data effectively, capitalizing on its ability to manage information over different time steps, which is pertinent for sequential patterns within URLs. The model's complexity allows it to capture a wide range of features and patterns, improving its accuracy and robustness in phishing detection. K-Fold cross-validation was performed on both the ISCXURL-2016 dataset and the Ebbu2017 dataset. The model exhibited exceptional performance, achieving a 99.91% accuracy rate with the ISCXURL-2016 dataset and a 99.90% accuracy rate with the Ebbu2017 dataset in distinguishing between benign and phishing web entities. These findings highlight the hybrid model's precision in phishing detection, offering significant potential to enhance cybersecurity measures by accurately identifying and mitigating phishing threats. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks Cyber security Gated recurrent unit networks Natural language processing Phishing URL Principal component analysis

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Dynamic Indian Sign Language Sentence Captioning System Using Machine Learning 5th

Dynamic Indian Sign Language Sentence Captioning System Usin...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Kulkarni, Vedant Dattatray Bajaj, Anushree Shrigopal Marathe, Himanshu Deshmukh, Mandar Sreemathy, R. Jagdale, Jayashree Department of Information Technology Pune Institute of Computer Technology Pune India Department of Electronics and Telecommunication Engineering Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789819791118

Sign language serves as a vital mode of communication for the deaf and hard of hearing community, yet access to sign language content remains limited due to the lack of accurate and timely captioning. In this paper, a comprehensive comparison has been made between Long Short-Term Memory (LSTM)- and Support Vector Machine (SVM)-based neural networks for sign language recognition. Our approach uses LSTM networks to effectively recognize and translate sign language gestures into textual descriptions. We leverage a large dataset of sign language videos paired with English captions for training and evaluation. Through extensive experimentation, we demonstrate the effectiveness of our system in accurately captioning sign language videos, thereby enhancing accessibility for individuals with hearing impairments. Our self-curated dataset includes 28 unique sentences on which our LSTM-based model achieved an accuracy of 90% whereas it was 97% for SVM-based approach. Our research contributes to advancing the field of accessibility technology and holds promise for real-world applications in education, entertainment, and communication for the deaf and hard of hearing community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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ETiSeg-Net: edge-aware self attention to enhance tissue segmentation in histopathological images

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Multimedia Tools and Applications 2024年 1-21页

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Digital pathology employing Whole Slide Images (WSIs) plays a pivotal role in cancer detection. Nevertheless, the manual examination of WSIs for the identification of various tissue regions presents formidable challenges due to its labor-intensive nature and subjective interpretation. Convolutional Neural Network (CNN) based semantic segmentation algorithms have emerged as valuable tools for assisting in this task by automating ROI delineation. The incorporation of attention modules and carefully designed loss functions has shown promise in further augmenting the performance of these algorithms. However, there exists a notable gap in research regarding the utilization of attention modules specifically for tissue segmentation, thereby constraining our comprehension and application of these modules in this context. This study introduces ETiSeg-Net (Edge-aware self attention to enhance Tissue Segmentation), a CNN-based semantic segmentation model that uses a novel edge-based attention module to achieve effective delineation of class boundaries. In addition, an innovative iterative training strategy is devised to efficiently optimize the model parameters. The study also conducts a comprehensive investigation into the impact of attention modules and loss functions on the efficacy of semantic segmentation models. Qualitative and quantitative evaluations of these semantic segmentation models are conducted using publicly available datasets. The findings underscore the potential of attention modules in enhancing the accuracy and effectiveness of tissue semantic segmentation. © The Author(s) 2024.

关键词： Semantic Segmentation

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Automatic emotion recognition using deep neural network

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Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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A hybrid feature weighting and selection-based strategy to classify the high-dimensional and imbalanced medical data

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Neural Computing and Applications 2024年第20期36卷 12299-12316页

作者： Singh, Harpreet Kaur, Manpreet Singh, Birmohan Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Sangrur148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal Sangrur148106 India

Machine learning algorithms generally assume that the data are balanced in nature. However, medical datasets suffer from the curse of dimensionality and class imbalance problems. The medical datasets are obtained from the patient information which creates an imbalance in class distribution as the number of normal persons is more than the number of patients and contains a large number of features to represent a sample. It tends to the machine learning algorithms biased toward the majority class which degrades their classification performance for minority class samples and increases the computation overhead. Therefore, oversampling, feature selection and feature weighting-based four strategies are proposed to deal with the problems of class imbalance and high dimensionality. The key idea behind the proposed strategies is to generate a balanced sample space along with the optimal weighted feature space of the most relevant and discriminative features. The Synthetic Minority Oversampling Technique is utilized to generate the synthetic minority class samples and reduce the bias toward the majority class. An Improved Elephant Herding Optimization algorithm is applied to select the optimal features and weights for reducing the computation overhead and improving the interpretation ability of the learning algorithms by providing weights to relevant features. In addition, thirteen methods are developed from the proposed strategies to deal with the problems of high-dimensionality and imbalanced data. The optimized k-Nearest Neighbor (k-NN) learning algorithm is utilized to perform classification. The performance of the proposed methods is evaluated and compared for sixteen high-dimensional imbalanced medical datasets. Further, Freidman’s mean rank test is applied to show the statistical difference between the proposed methods. Experimental and statistical results show that the proposed Feature Weighting followed by the Feature Selection (FW–FS) method performed significantly b

关键词： Feature Selection

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A Computational Deep Fuzzy Network-Based Neuroimaging Analysis for Brain Hemorrhage Classification

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IEEE Journal of Biomedical and Health informatics 2023年 PP卷 1-8页

作者： Malik, Payal Vidyarthi, Ankit Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

The boundaries and regions between individual classes in biomedical image classification are hazy and overlapping. These overlapping features make predicting the correct classification result for biomedical imaging data a difficult diagnostic task. Thus, in precise classification, it is frequently necessary to obtain all necessary information before making a decision. This paper presents a novel deep-layered design architecture based on Neuro-Fuzzy-Rough intuition to predict hemorrhages using fractured bone images and head CT scans. To deal with data uncertainty, the proposed architecture design employs a parallel pipeline with rough-fuzzy layers. In this case, the rough-fuzzy function functions as a membership function, incorporating the ability to process rough-fuzzy uncertainty information. It not only improves the deep model's overall learning process, but it also reduces feature dimensions. The proposed architecture design improves the model's learning and self-adaptation capabilities. In experiments, the proposed model performed well, with training and testing accuracies of 96.77% and 94.52%, respectively, in detecting hemorrhages using fractured head images. The comparative analysis shows that the model outperforms existing models by an average of 2.6$\pm$0.90% on various performance metrics. IEEE

关键词： Classification (of information)

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