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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3244 条记录，以下是401-410 订阅

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Impact of Self-supervised learning in Feature Representation learning Using Rotation Pre-text Task 3rd

Impact of Self-supervised Learning in Feature Representation...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Khokhariya, Avani Thakkar, Amit Bhatt, Nikita Chandubhai S. Patel Institute of Technology CHARUSAT Changa India

ISBN: (纸本)9789819783540

In the recent past, promising performance has been achieved by supervised deep learning networks in various domains like computer vision, machine translation, speech recognition, natural language processing (NLP), etc. However current deep-learning approaches require a large amount of manual data to achieve promising performance. However, manual annotation of data is time-consuming and needs human expertise, which is always not possible in a domain like healthcare. Recently an alternative method called Self Supervised learning (SSL), uses unlabeled data and does not require manual annotation. With the help of a pretext task, SSL extracts and learns features from the unlabeled data, enabling models trained for these tasks to acquire latent representations that enhance subsequent tasks like object detection and classification. In this work, the unlabeled data is fed to the convolution neural network (CNN), which learns features and transfers them to the downstream task to generate the labeled data. The experiments are conducted on the fashion MNIST dataset in a self-supervised learning environment and a comparison is made with a supervised learning environment. The experiment concludes that self-supervised learning achieves similar performance on unlabeled data which saves the high cost of data labeling. The self-supervised learning environment is preferable in various domains where human annotation for labeling datasets is very costly or not feasible. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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Technique for Forecasting Future Market Movement Using machine learning and Deep learning Algorithms 3

Technique for Forecasting Future Market Movement Using Machi...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Bhatt, Rahul Kumari, Alka Rajesh, A. Rajasekaran, Suresh Babu Deshmukh, Vishal Pandurang Srivastava, Anil Dev Bhoomi Uttarakhand University Department of Cse Uttarakhand Dehradun India Arka Jain University Department of Computer Science Jharkhand Jamshedpur India Department of Cse Bangalore India Nvidia Corporation Group Product Manager United States Yashwantrao Mohite Institute of Management Department of Business Administration Karad India Sppu College of Commerce Nande Pune India

ISBN: (纸本)9798350399264

Because of the advent of vast realities, the practise of deep reading to foresee financial charges and patterns has gained even more traction. We structured limited stock market data sets, provided fully customizable capacity designing, and thorough dominating-based recreation to estimate the value change of stock company sectors. The suggested method integrates a customised deep learning-based device for stock commercial centre rate pattern anticipation with several functions designing approaches and pre-processing the dataset of the stock market marketplace. We performed indepth evolutions with widely used models based on machine learning, and we proved that the offered solution is superior because of the meticulous function-design work we put into it. For predicting trends in the stock market, the gadget achieves a very high level of accuracy. By carefully preparing for and evaluating projected time, developing features, and re-processing methods of data, the proposed work adds to stock inspection and focuses on local region within the financial and specialist industries. © 2023 IEEE.

关键词： Forecasting

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Literature Survey on Application of Deep learning in Face recognition 3

Literature Survey on Application of Deep Learning in Face Re...

引用

3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Anil, J. Padma Suresh, L. Krishna Veni, S.H. Noorul Islam Centre for Higher Education Kumaracoil Department of Electrical & Electronics Engineering Tamil Nadu Kanyakumari District Thuckalay India Baselios Mathews Ii College of Engineering Sasthamcotta Department of Electrical & Electronics Engineering Kerala Kollam India Baselios Mathews Ii College of Engineering Department of Computer Science & Engineering Kerala Sasthamcotta Kollam India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Deep learning is an emerging technique in artificial intelligence in which a machine tries to copy the learning capability of human brain. As deep learning tries to mimic human brain it has the capability of overcoming many challenges in face recognition like change in expressions, pose, illumination, age etc. to an extent. This research explores some of the most advanced deep learning methods for face recognition. The algorithms that are being reviewed in this article are based on various techniques such as data augmentation, generation of synthetic training data, batch normalisation, linear binary pattern network, and others, which are used in conjunction with conventional Convolutional Neural Network to improve the performance of the algorithms. When comparing the approaches presented in this article to traditional face recognition algorithms, it can be concluded that the performance of face recognition is significantly enhanced. © 2022 IEEE.

关键词： Face recognition

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Facial recognition and machine learning-based Student Attendance Monitoring System 3

Facial Recognition and Machine Learning-based Student Attend...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Narkhede, Namrata Menon, Anupama Mathane, Itisha Nikam, Sayali Dange, Suchita A.P. Shah Institute of Technology Department of Computer Engineering Thane India

ISBN: (纸本)9798350338607

AI-based student monitoring system provides an innovative approach that enables schools and institutions to monitor student attendance performance and behavior. By utilizing cutting-edge technologies like face recognition, the system makes it easier for teachers to keep track of students' attendance while also giving administrators useful information. The technology automatically logs student attendance, doing away with the necessity for manual human attendance taking. Each student's attendance rate is determined, and the attendance analysis is made available. Administrators can more easily identify students with strong or poor attendance records thanks to the visualization module of the system, which presents attendance data as a graphical representation. Administrators can use this function to ensure that students attend classes on a regular basis. Additionally, the system notifies students and their parents through email when a particular level of attendance is not met, encouraging students to improve their attendance by taking appropriate action and fostering accountability. The AI-based student monitoring system is a useful tool for teachers and students. It helps to raise academic attainment by promoting an accountability and attendance culture in organizations and institutions. The system also provides administrators with useful data and minimizes the effort for educators. © 2023 IEEE.

关键词： Face recognition

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The Scientific Analysis on Effective Yoga Posture recognition Techniques 3rd

The Scientific Analysis on Effective Yoga Posture Recognitio...

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

作者： Rajgure, E.G. Patidar, Raghavendra School of Engineering ECE University of Technology Jaipur India

ISBN: (纸本)9789819743582

Today’s globalized scenario of competition has increased beyond expectations and to fulfill it, everyone has to face many competitive challenges and stressful work culture. In addition to prescribing the proper medication, many medical professionals advise optimal food, pranayama, and yoga to maintain this environment. Due to this physical health awareness number of professional yoga practitioners has been increased. Yoga exercises with proper postures are very important to get the benefits of increasing flexibility, muscle strength and also improve the functionality of the respiratory system. Along with spirituality, yoga has a number of impacts on the human body which ultimately integrates the coordination between mind and body. Due to which regular practitioners can maintain self-discipline and self-awareness. Due to improper methods of practice, maximum beginners do not get benefited from the routine yoga practice. Our main goal is to review the literature on yoga posture recognition systems based on scientific analysis. It also presents our methodical approach to posture recognition and classification based on AI and machine learning techniques, which we hope will help validate the accuracy of the postures performed during yoga practice. In order to collect time series data from sensors for various postures, our ongoing work will create an embedded wearable sensor-based system. Along with this, we have also enlisted measurable benefits of practicing yoga in the improvements of human health parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence machine learning Posture recognition Yoga

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Application of data mining Technology in Exam Score Analysis 3

Application of Data Mining Technology in Exam Score Analysis

引用

3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Yang, Yang Jilin Agricultural Science and Technology University Jilin China

In order to solve the shortage of the traditional educational administration system in the application of student achievement data in Colleges and universities, and on the basis of traditional data query and statistical work, this paper proposes a examination score analysis and application system based on data mining algorithm to deeply explore the internal relationship between examination scores and various factors. Through the construction of performance analysis algorithm model to predict and analyze students' performance more accurately, it is found through model comparison that the improved model has improved the accuracy of performance prediction by 1.89%, the accuracy by 1.1%, and the recall rate of the improved model algorithm by 0.5% compared with the original algorithm, which verifies the availability and value of the model. © 2023 The Authors. Published by Elsevier B.V.

关键词： data mining

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Malicious Traffic Classification Algorithm Based on Multimodal Fusion 3rd

Malicious Traffic Classification Algorithm Based on Multimod...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Li, Xinhang He, Mingshu International School Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819616930

The application of deep learning in classifying malicious network traffic is prevalent. However, our paper highlights two frequently neglected issues in current methodologies: first, network traffic data need to be truncated and zero-filled to adapt to the training process of deep learning, which brings a certain degree of information loss;second, individual packets of network traffic will show different importance in the classification task due to their own data quality and location. To solve these two problems, a multimodal attention network is proposed to mitigate the information loss by fusing data from two different modalities in the network traffic and solving the imbalance of data from different modalities in the training process using a regularization method. Additionally, we incorporate a dual attention module to assess the significance of each packet, enabling the model to prioritize and emphasize the most crucial ones. To evaluate the model’s effectiveness, we test it on three openly accessible datasets: UNSW-NB15, ISCXIDS2012, and CIC-DoS2017. Our findings reveal that the model presented herein surpasses several conventional deep learning approaches in terms of classification outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： deep learning malicious behavior multimodal network traffic classification

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A Convolutional Neural Network-Based Web Prototype to Support Melanoma Skin Cancer Detection 3

A Convolutional Neural Network-Based Web Prototype to Suppor...

引用

3rd international conference on Information Systems and Software Technologies (ICI2ST)

作者： Rosas-Lara, Mauro Mendoza-Tello, Julio C. Flores, Aldrin Zumba-Acosta, Gema Univ Cent Ecuador Fac Ingn & Ciencias Aplicadas Quito Ecuador

ISBN: (纸本)9781665455176

The skin is the organ that protects the human body. However, factors such as solar radiation damage the texture and skin cells. Sometimes, the lack of timely diagnosis leads to skin cancer. In this line, melanoma is the most dangerous type of cancer and has caused the greatest number of deaths related to skin diseases. With this problem, collaborative efforts between different research areas are necessary to support early detection of the disease. In this way, the evolution of algorithms based on neural networks plays an important role for image processing, which is an essential activity for pattern detection and recognition in medical diagnosis. Faced with this challenge, this research proposes a web prototype based on convolutional neural networks to support melanoma detection. In this context, the reference framework suggested by the Cross Industry Standard Process for data mining (CRISP-DM) was used. For this, 18,000 high-quality images were compiled from the data science community. In addition, two learning models (based on convolutional neural network and Res-Net50) were created and evaluated. With these premises, a web application was developed using the waterfall model. Finally, conclusions and future work are suggested at the end of the document.

关键词： machine learning neural network melanoma data mining skin cancer

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A machine learning Based Melanoma Skin Cancer using Hybrid Texture Features 3

A Machine Learning Based Melanoma Skin Cancer using Hybrid T...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Meenakshi, K. Adepu, Abhiram Nagandla, Venkata Vignesh Teja Agarwal, Siddhanth GRIET Dept. of ECE Hyderabad India

ISBN: (纸本)9798350338607

There is a great rise in patients afflicted with the disease Melanoma, the most unpredictable and potentially fatal cancer. If treated in time, the fatality rate can be reduced significantly in most cases. Active Melanoma discovery in the incipient stage is difficult to recognize. As the availability of qualified dermatologists is scarce, a computerized automated method of finding Melanoma accurately is urgently needed. The aforementioned research aims to accomplish this by using supervised machine learning algorithms that are capable of accurately identifying and classifying melanoma to separate skin lesions from the images collected from the combined dataset of the ISIC archive and the PH2 dermoscopic images. For segmenting the tumor portion, the Otsu Thresholding algorithm is employed. The segmented images are applied to a preprocessing network to extract features of ABCD, GLCM, LBP and Histogram of Gradients (HG). Later, these features are trained with different supervised classifiers to generate the model. Later the test data is fed to the model to obtain the predictive labels. The results of the proposed skin melanoma detection prove that skin melanoma detection with XGBoost provides superior results compared to the existing works in the literature. © 2023 IEEE.

关键词： Local binary pattern

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The spatiotemporal evolution of carbon emissions in China using machine learning methods: evidence from remote sensing data 3

The spatiotemporal evolution of carbon emissions in China us...

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3rd international conference on Remote Sensing, Surveying, and Mapping, RSSM 2024

作者： Zhang, Yuxin Sun, Chenghao Ming, Yitong Wang, Yuhao School of Management Shandong University of Traditional Chinese Medicine Jinan250355 China School of Economics and Trade Shandong Management University Jinan250357 China School of Energy and Power Engineering Qilu University of Technology Jinan250357 China

ISBN: (数字)9781510680081

ISBN: (纸本)9781510680074

The spatial-temporal distribution and heterogeneity of carbon emissions are important issues in ecological environmental protection and climate change monitoring. Based on DMSP/OLS night light remote sensing data and socioeconomic data, this paper uses machine learning methods to study the carbon dioxide emissions of 281 prefecture-level cities in China from 2005 to 2020, and reveals the characteristics of spatial-temporal distribution, growth trend and agglomeration of carbon emissions. The results show that in recent years, Chinese carbon emissions show an increasing trend, and regional carbon emissions have obvious characteristics of spatial agglomeration and unbalanced distribution. From the spatial dimension, the pattern of carbon emissions gradually evolves to "high in the east and low in the west" and "high in the north and low in the south". This study can provide scientific reference for carbon emission estimation and prediction, energy conservation and ecological environmental protection. © 2024 SPIE.

关键词： machine learning

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