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共 3137 条记录，以下是121-130 订阅

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Enhancing Security with machine learning-based Finger-Vein Biometric Authentication System 5

Enhancing Security with Machine Learning-based Finger-Vein B...

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5th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Babu, G. Prakash Aswal, Upendra Singh Sindhu, M. Upadhye, Vijay Jagdish Natrayan, L. Patil, Harshal Acharya Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560 107 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601 206 India Parul Institute of Applied Sciences Research and Development Cell Parul University Department of Microbiology Gujarat Waghodia391760 India SIMATS Saveetha School of Engineering Department of Mechanical Engineering Tamil Nadu Chennai602105 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India

ISBN: (纸本)9798350395235

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. the study employs two feature extraction approaches, the Local Binary pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. the simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task. © 2024 IEEE.

关键词： Local binary pattern

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Exploring the Frontiers of Security: Comprehensive Biometric Security Solution with Multiple Modalities Enhanced by machine learning 5

Exploring the Frontiers of Security: Comprehensive Biometric...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Raghavendra, S. Prasanna Kumar, M.J. Shashikala, S.V. Dept. of CSE BGS Institute of Technology Adichunchanagiri University India Dept. of CSE BGS Institute of Technology Adichunchanagiri University India Dept. of CSE BGS Institute of Technology Adichunchanagiri University India

ISBN: (纸本)9798350361155

Individuals' physiological or characteristics of conduct are used by biometric systems to identify and authenticate users. Systems with these attributes fall into two categories: unimodal and multimodal. the former suffers from a few of issues that affect system accuracy, including as noisy data, intra-class variance, inter-class similarity, non-universality, and spoofing. Conversely, multimodal systems, have demonstrated a notable improvement in identification and verification success rates. Multimodal systems detect and process two or more physiological or behavioural features. this study uses fingerprint, iris, and ear data to develop a bimodal biometric sensing technique. DNN Methods of machine learning are applied in the recognition process, and the research explores the design and functioning of bimodal systems. © 2024 IEEE.

关键词： Biometrics

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Optimizing Style recognition Algorithm for Digital Art Images Using Large Language Models (LLMs) 5

Optimizing Style Recognition Algorithm for Digital Art Image...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Cao, Ye Shanghai Art & Design Academy No.851 JIAHANG Rd Jiading District Shanghai201800 China

ISBN: (纸本)9798350379945

In today's era of big data, images have usurped text as the primary content of the Internet, and style analysis of artistic images has emerged as a key area of computer vision research. this study explores the landscape of image style recognition, with a particular focus on digital art images, by harnessing the power of Large Language Models (LLMs). Traditional methods of image style classification, which rely on quantitative standards and image processing techniques, often fall short when faced with complex image content. To address this, personalized classification approaches using unsupervised learning have gained traction, offering flexibility and improved efficiency. In particular, deep learning-based methods have revolutionized fashion style analysis, enabling more accurate recognition and classification through feature extraction. this study proposes a robust style recognition algorithm that utilizes LLMs and ResNet networks to achieve superior performance in digital art image analysis. Experimental results demonstrate the effectiveness of the proposed model, which outperforms traditional approaches such as Generative Adversarial Networks (GANs) and Support Vector machines (SVMs) in terms of accuracy. this highlights the potential of advanced deep learning techniques in capturing complex image style features. © 2024 IEEE.

关键词： Adversarial machine learning

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data Expansion for Named Entity recognition based on migration learning 5

Data Expansion for Named Entity Recognition based on migrati...

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5th international conference on Computer Vision, Image and Deep learning, CVIDL 2024

作者： Li, Qingyu Zhang, Chao Zhou, Gang Zhengzhou University School of Cyber Science and Engineering Henan Zhengzhou450001 China Information Engineering University Henan Zhengzhou450001 China

ISBN: (纸本)9798350373820

the pivotal role of named entity recognition in the field of natural language comprehension significantly influences numerous tasks, including the likes of machine translation, the distillation of events, and the fabrication of knowledge graphs. Confronting the issue of data scarcity, the method initially entails the acquisition of dense semantic insights through the preliminary training of the BERT framework on an expansive textual database. Subsequent refinement of these initially trained models is tailored specifically for nuanced NER assignments, enhancing the detection of sector-specific entities. this refinement is complemented by the integration of character-level feature depiction and the employment of Conditional Random Field layers, augmenting the precision of entity demarcation. A thorough evaluation conducted using datasets from NER that are accessible to the public corroborates the efficacy of this approach, showcasing commendable results in precision and retrieval rates. When juxtaposed with the conventional methodologies predicated on rules or feature engineering, this innovative approach demonstrates notable enhancements in the identification of a diverse array of entity categories, evidenced by an increment of 0.02% in precision and a rise of 3.21% in retrieval. © 2024 IEEE.

关键词： Distillation

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Application of a modified YOLO deep learning algorithm in the identification of silkworm microviruses 5

Application of a modified YOLO deep learning algorithm in th...

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5th international conference on Computer Vision and data mining, ICCVDM 2024

作者： Wang, Xiaoliang Zhang, Liangjing Chen, Xiaomei Chen, Zijuan Guangxi Science & Technology Normal University Laibin545004 China

ISBN: (纸本)9781510682863

Silkworm microparticle virus is a serious silkworm disease in sericulture. It has a direct threat to the production of silkworm cocoon and silkworm species. In the recognition of silkworm microparticle virus, traditional manual microscopy methods have problems such as high labor intensity, unreproducible results, and low recognition accuracy. this article introduces the YOLO deep learning algorithm to identify the silkworm microparticle virus, collect the original image with the microscope, and get the data set after data enhancement. In order to improve the training accuracy the CBAM attention mechanism is introduced based on the Yolov5s network structure. the training results on the silkworm microparticle virus picture data show that the average training accuracy mAp can reach 1.5% following the integration of the attention mechanism. Finally, for different microparticle virus pictures, the YOlOv5s-CBAM structure is better than the Yolo5vs in the identification of microparticles. © 2024 SPIE.

关键词： Invertebrates

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Exploring 2D and 3D Face recognition with FaceNet across Multiple Classifiers 5

Exploring 2D and 3D Face Recognition with FaceNet across Mul...

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5th international conference on Innovative Trends in Information Technology (ICITIIT)

作者： Subudhi, Priyambada Kumar, Gulshan Joshi, Nitigya Gujjari, Bharath Kumar Indian Inst Informat Technol Dept Comp Sci & Engn Sri City AP India

ISBN: (纸本)9798350386813;9798350386820

Face recognition technology has witnessed significant advancements in recent years, with researchers exploring novel approaches to enhance accuracy and robustness. this study investigates the performance of FaceNet, a state-of-the-art deep learning model for face recognition, in both 2D and 3D modalities. Additionally, various classifiers are employed to evaluate their impact on the overall recognition accuracy. Both publicly available as well as self-made datasets have been used in this study. the FaceNet model is employed to extract high-dimensional feature vectors from the facial data, leveraging its ability to map faces into a compact Euclidean space. the 2D face recognition system operates on conventional images, while the 3D counterpart processes depth information for a more comprehensive analysis. To evaluate the recognition accuracy, several classifiers are employed, including Support Vector machines (SVM) and Logistic Regression (LR) model. the classifiers are trained and tested on both 2D and 3D datasets separately. Although, the effectiveness of FaceNet on 2D face recognition has been studied before, this article shows its effectiveness for 3D face recognition as well. Furthermore, the choice of classifier significantly influences the overall recognition accuracy. SVM exhibits robust performance across both modalities, showcasing its versatility in handling complex feature spaces. On the other hand, LR provides a better accuracy in cases where the dataset is equitably distributed.

关键词： Face recognition FaceNet Deep learning Classifiers Support Vector machine Logistic Regression

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Cricket Shot Classification from Video Using Body Pose Landmarks 5

Cricket Shot Classification from Video Using Body Pose Landm...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Rao, Chava Govardhana Varma, N. Sai Harshith Kashish, K.C. Mazumder, Priyangshu Ramasamy, Prema Pes University Dept of Computer Science Bengaluru India

ISBN: (纸本)9798350356236

the primary focus of this study is to use deep learning techniques to classify cricket shots. data acquisition begins by gathering video clips of different cricket shots and manually annotating them. To address imbalance in class, generated synthetic examples are used to ensure each shot type has equal representation. Advanced techniques for handling missing data points using combined object detection and 3D landmark extraction to track the batsman's move-ments are employed for efficiency. To enhance accuracy of the analysis, complex methods are used to deal with missing data. By integrating object detection methods with 3D land-mark extraction, effective tracking of batsman movements is captured. Tests with multiple machine learning models such as simple neural networks and more complicated models which could capture movement sequence over time are tested on the data. the most successful results were achieved with systems that could process video frames in both forward and reverse directions, leading to a more precise identification of cricket shots. this research illustrates the potential of machine learning techniques and how advanced models can be used to analyze and classify sports activities in video data. We contribute to the growing community and knowledge base of automated sports analytics, paving the way for future innovations in real-time action recognition and commentary generation. © 2025 IEEE.

关键词： Deep learning

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Deciphering Feelings: Exploring EEG data for Human Emotion recognition 5

Deciphering Feelings: Exploring EEG Data for Human Emotion R...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Muthunayagom, Prince Subhajini, A.C. Noorul Islam Centre for Higher Education Department of Computer Applications Tamilnadu India

ISBN: (纸本)9798331530013

the ability to decipher human emotions through mind reading techniques has long been a subject of fascination, both in science fiction and scientific research. In recent years, advancements in neuroscience, artificial intelligence, and brain-computer interfaces have brought us closer to understanding and recognizing human emotions from brain activity. Recognizing and understanding human emotions has evolved significant attention in fields like psychology, technology, and healthcare. Electroencephalogram (EEG) signals, reflecting brain activity, offer valuable insights into the neural correlates of emotions. this paper reviews recent advancements in human emotion recognition from EEG signals, focusing on both deep learning and machine learning approaches. Deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promise in automatically extracting features from EEG data and accurately classifying emotions. this study explores methods to recognize human emotions using EEG data, focusing on the use of deep learning models to classify emotional states with high accuracy. Meanwhile, machine learning techniques such as support vector machines (SVMs) and ensemble methods have also been employed for emotion recognition tasks. Various studies utilizing different datasets and methodologies are discussed, highlighting the effectiveness of these approaches in emotion categorization. the review also discusses research gap like minimizing noise, extracting relevant features, and achieving real-time processing in EEG-based emotion recognition. It highlights potential avenues for future research in this area. the proposed approach offers insights into the applicability of EEG for real-time emotion analysis. Moreover, the review emphasizes the significance of EEG in deepening our comprehension of human emotions and its wide-ranging applications in fields such as human-computer interaction, healthcare, and education. © 2024

关键词： Support vector machines

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Efficient English Text Comprehension and pattern Understanding Model with ChatGPT 5

Efficient English Text Comprehension and Pattern Understandi...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Lin, Wei Chengdu Technological University School of Foreign Languages and International Education Sichuan Chengdu611730 China

ISBN: (纸本)9798331530013

In the context of intelligent information system models, in order to further promote the development of natural language processing and explore its application scenarios in different fields, this study studied the English text understanding and pattern recognition model based on ChatGPT middleware from the modeling and analysis level. the model proposed in this study integrates the information of text and audio input as the modeling of multi-modal semantic understanding to improve the accuracy and universality of machine language understanding in complex environments. In order to transform human natural language into structured data and hierarchy that can be understood by machines, this study first explored the significance of natural language processing and the processing challenges in real applications. Subsequently, this study gave an in-depth explanation of the design and methods of the model, focusing on the key role of improving ChatGPT middleware in simplifying the modal fusion process and thus ensuring the accuracy and applicability of the output results. In the experimental verification part, this study used a variety of self-collected data sets to evaluate the model. the experimental results show that the proposed model has an accuracy of 97.5% for text semantic understanding, an accuracy of 98.2% for pattern recognition tasks, and an overall accuracy of 99.2%. Compared with traditional algorithms, proposed model has better comprehensive performance. © 2024 IEEE.

关键词： Middleware

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SMER: A Novel Medical Entity recognition Technique based on ScispaCy(BC5CDR) and Med7 5

SMER: A Novel Medical Entity Recognition Technique based on ...

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5th international conference on Information Management and machine Intelligence, ICIMMI 2023

作者： Gupta, Rashmi Agarwal, Tushar Mehlawat, Aviral Mathur, Deeksha Somwanshi, Devendra Kumar Kumar, Anil Amity School of Engineering Technology Amity University Haryana India Poornima College of Engineering Rajasthan Jaipur India Poornima Institute of Engineering and Technology Rajasthan Jaipur India

ISBN: (纸本)9798400709418

Named Entity recognition (NER) is a stream of Natural language pro-cessing playing a pivotal role in the KDD process of data mining. the Last few years have seen a sudden surge in research about NER models, however, the scope of this research is often restricted to general text and speech. this paper thus talks about a specific stream the Medical NER. In the scope of this paper, a novel technique is proposed which utilizes a combination of two state-of-the-art NER models, Sciscapcy ( BC5CDR) and Med7 to achieve optimal results. Scis-pacy is a machine learning model trained on a sizeable biomedical corpus while Med7 is a rule-based system that specializes in detecting medical concepts by utilizing its large medical pattern vocabulary. the model is evaluated on bench-mark datasets and showcases better susceptibility to medical terms in a text. the model is able to distinguish between a wide range of medical entities including drug name, disease name, drug quantity, clinical procedures, and pathogen name. the paper highlights the potential of combining rule-based systems with machine learning models in developing robust models better suited for domain NER. By fetching strengths from both Scispacy(BC5CDR) and Med7, the paper proposes "SMER - ScispaCy - Med7 Entity Recogniser", an adaptable solution that con-tributes to more accurate and comprehensive medical entity recognition model that can further be used in automatic medical record-keeping, patient medicine orders and facilitate medical practioners. © 2023 ACM.

关键词： data mining

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