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Bird Sound Identification System using Deep learning 5

Bird Sound Identification System using Deep Learning

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5th international conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Indumathi, C.P. Diviyalakshmi, K.R. Mahalakshmi, R. Dept.of.CSE Anna University BIT Campus Tiruchirappalli India

this paper identifies the model for classifying the bird sounds using the Convolutional Neural Network (CNN) algorithm. CNN are considered as one of the powerful toolkits developed in the machine learning and that have shown to be more efficient particularly in the field of image processing and sound recognition. the two-step verification process which will participating in the creation of datasets and neural network input. this system uses the collection of labelled and categorized recordings. Spectrograms are generated from the downloaded data which is used as the input for the neural network. the experiments are conducted and it will match with the different configurations, which including the number of bird species and Spectrograms. As a result, we can identify the bird species from its audio recordings. By identifying the bird species on a large scale, it provides a vital information about our ecosystems and biodiversity. the changes in the ecological environment can be reflected by the quantity and composition of bird species. It is one of the major challenges which leads to recognition of many bird species based on their audio. In modern years, the deep learning techniques namely convolutional neural networks (CNN) have played a major role in many areas of science and also successfully provided efficient methodologies, such as pattern recognition, object detection in images, and classification of patterns in audios and speech recognition. © 2024 the Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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2024 5th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

2024 5th International Conference on Artificial Intelligence...

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5th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

ISBN: (纸本)9798331528911

the proceedings contain 212 papers. the topics discussed include: the research and analysis of robust image steganography for arbitrary style transfer;artificial intelligence in standard knowledge services: roles and implementation;efficient plug-and-play module for transformer-based 3D human pose estimation;a new optimization method to real time streaming protocol;bridging the gap using contrastive learning and semantic consistency for unsupervised neural machine translation;survey of semantic-based image-to-image retrieval;Chinese named entity recognition with integrated channel attention;method for multi-hop question answering based on knowledge graph with fusion path;a component-aware Chinese font generation method based on multi-scale features;power grid transient stability assessment method based on big data analysis;and a comparative study of bug triage representation and classification approaches from canonical to large language models.

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Implementation of Customer Segmentation Using machine learning 5

Implementation of Customer Segmentation Using Machine Learni...

引用

5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Kumar, Manish Kumar, Amresh Sabharwal, Munish Galgotias University School Of Computing Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

In recent years, every e-commerce enterprise focuses on Customer Relationship Management (CRM) to provide the better services to the customer as compared to their competitors. Building a better relationship with customer help the enterprises in increasing profit and customer's retention and satisfaction. It is necessary for enterprises to identify the potential customers in the market by mining the customer data to gain profitable insight. One of the efficient ways to identify the different customer characteristics is by applying customer segmentation. In this Paper section 1 is defining the Introduction and then we have used a machine learning algorithm to find the results. Next is the proposed Methodology, methods shown to apply to the result and then the results, conclusion and future work © 2023 IEEE.

关键词： machine learning

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Adv-Cut Paste: Semantic adversarial class specific data augmentation technique for object detection 26

Adv-Cut Paste: Semantic adversarial class specific data augm...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Arun, Kumar S. Pal, Abhijit Mopuri, Konda Reddy Gorthi, Rama Krishna Indian Inst Technol Tirupati Tirupati Andhra Pradesh India Indian Inst Technol Guwahati Gauhati India

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

data augmentation has been a prevalent approach in improving the performance of deep learning models against slight variations in data. Adversarial learning is one such form of data augmentation. In this work, we aim to introduce a framework to generate harder examples for a specific object class and an adversarial attack for the object detection task. We have also presented our study on the effect of training against such generated harder examples and adversarial samples in object detection. We have applied this adversarial learning technique to a YOLOv3 model and due to the nature of the attack, we demonstrated a substantial improvement in average precision (AP) for a single class of the COCO dataset. As per the literature, we are the first to introduce this kind of class-specific data augmentation strategy in object detection. With our approach, we have shown an improvement of 23.34% in AP for Cat class and 3.1% on overall mAP of YOLOv3 model on clean validation data, while 43.5% improvement in AP for the Cat class on the composite images with class-specific adversarial samples.

关键词： Training Deep learning Semantics Object detection data models Adversarial machine learning pattern recognition

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Multi-Class Hypersphere Anomaly Detection 26

Multi-Class Hypersphere Anomaly Detection

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Kirchheim, Konstantin Filax, Marco Ortmeier, Frank Otto von Guericke Univ Dept Comp Sci Magdeburg Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

machine learning-based classification algorithms typically operate under assumptions that assert that the underlying data generating distribution is stationary and draws from a finite set of categories. In some scenarios, these assumptions might not hold, but identifying violating inputs - here referred to as anomalies - is a challenging task. Recent publications propose deep learning-based approaches that perform anomaly detection and classification jointly by (implicitly) learning a mapping that projects data points to a lower-dimensional space, such that the images of points of one class reside inside of a hypersphere, while others are mapped outside of it. In this work, we propose Multi-Class Hypersphere Anomaly Detection (MCHAD), a new hypersphere learning algorithm for anomaly detection in classification settings, as well as a generalization of existing hypersphere learning methods that allows incorporating example anomalies into the training. Extensive experiments on competitive benchmark tasks, as well as theoretical arguments, provide evidence for the effectiveness of our method. Our code is publicly available(1).

关键词： learning systems Training Codes Performance gain Benchmark testing Linear programming Classification algorithms

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A Traffic Sign recognition qnd Speech-BasedFeedback Generation System to Assist Drivers 5

A Traffic Sign Recognition qnd Speech-BasedFeedback Generati...

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5th IEEE international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Sarma, Himangshu Saharia, Navanath IIIT Department of Computer Science & Engineering Andhra PradeshSri City India IIIT Data Engineering Lab CSE Senapati Manipur India

ISBN: (纸本)9798350338287

According to the World Health Organization (WHO), 1.3 million people loss their life annually as a result of road accidents. By alerting or assisting the driver while they are driving, information technology such as computer vision can play a significant part in reducing the accident rate. Keeping that in mind, in this article we presents one traffic sign recognition tool using deep neural network (DNN) models with speech-based feedback for vehicle drivers. the proposed system can give voice-based feedback to driver to direct or inform them to take the appropriate action while driving, such as 'Drive slow, speed breaker ahead', 'Maximum Speed Limit is 80km/hr', 'School Ahead, drive slow', 'Don't take overtake' etc. © 2023 IEEE.

关键词： Deep neural networks

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machine learning Model to Selection of Job Applicant Documents to Increase the Human Resources Department Performance 5

Machine Learning Model to Selection of Job Applicant Documen...

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5th international conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Inayatulloh Mangruwa, Rajiv Dharma Cahya S, Prasetya Putri, Nuril Kusumawardani Soeprapto Bina Nusantara University School of Information System Information Systems Department Jakarta11480 Indonesia Communication and Business-Telkom University Bandung Indonesia University of Technology Malaysia Azman Hashim International Business School Malaysia

ISBN: (纸本)9798350369489

the planning of the company's existing and future human resource needs is included in the process of human resource recruitment. the availability of human resources, in terms of both quality and quantity, will be significantly impacted by the recruitment process, if it is both efficient and successful. the initial stages of the employee recruitment process involve selecting applicant documents according to company needs. Currently, the document selection process is done manually by studying the applicant documents one by one and then grouping them into several categories. this mechanism is inefficient and risks producing the wrong choices. On the other hand, machine learning has text recognition and image processing capabilities to increase the effectiveness and efficiency of the process. So, the purpose of this research is to help companies, especially the human resources division, improve the effectiveness and efficiency of new employee selection, especially the selection and classification of job applicant documents. the research method uses a qualitative approach with a literature review to identify new employee recruitment problems was continued by designing data sets, training data, and testing with machine learning using the LVQ method. the result of this research is that a machine learning model builds a model for selecting job applicant documents using machine learning with the learning Vector Quantization (LVQ) method. © 2023 IEEE.

关键词： Character recognition

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Text Classification Experiments on Contextual Graphs Built by N-Gram Series 4th

Text Classification Experiments on Contextual Graphs Built b...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Sen, Tarik Uveys Yakit, Mehmet Can Gumus, Mehmet Semih Abar, Orhan Bakal, Gokhan Abdullah Gul Univ TR-38080 Kayseri Turkiye Osmaniye Korkut Ata Univ TR-80000 Osmaniye Turkiye

ISBN: (纸本)9783031821493;9783031821509

Traditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. the main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. this research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.

关键词： text-graph transformation graph convolution network deep learning text mining

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A Deep learning Method for Home Health Monitoring 5th

A Deep Learning Method for Home Health Monitoring

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5th international conference on data Science, machine learning and Applications, ICDSMLA 2023

作者： Reddy, I. Ravi Prakash Khan, Munazza Autonomous Hyderabad India

ISBN: (纸本)9789819780426

In today’s world, health condition recognition is still a challenge. Many neglect their early symptoms of various health diseases and do not maintain any personal digital health records. Moreover, in the recent pandemic we have seen that the shortage and Due to the lack of medical care, early COVID-19 identification at home was necessary to stop the virus's spread and provide the patient with prompt care. the continuous development of machine learning models, deep learning has also been widely used and tt has aided in the development of image processing and computer vision. Keeping this in mind, this paper proposes a web application for image processing technology and applies deep learning-based image processing to the detection of medical problems using the CNN VGG-16 model. For monitoring health conditions from home. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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EEITE 2024 - Proceedings of 2024 5th international conference in Electronic Engineering, Information Technology and Education

EEITE 2024 - Proceedings of 2024 5th International Conferenc...

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5th international conference in Electronic Engineering, Information Technology and Education, EEITE 2024

ISBN: (纸本)9798350372878

the proceedings contain 52 papers. the topics discussed include: evaluating the performance of a MIMO channel under tree shadowing for low-altitude UAVs;eco-design applied to FDM 3D printing technology;a tethered unmanned aircraft system for crowd dynamics and unusual pattern extraction;the future of privacy: a review on AI’s role in shaping data security;enhancing industry 4.0 cybersecurity training through cyber range platform;the selectively integrated returns intelligent optimization system – SΕIRIOS;segment anything model for scan-to-structural analysis in cultural heritage;a naive bayes classifier for fault detection and classification using dimension reduction technique;bias detection and correction methods for machine learning algorithms;and perceptions of international students in a higher education institute in Sweden.

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