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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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SERTUS dataset Collection from Spontaneous Environments 3rd

SERTUS Dataset Collection from Spontaneous Environments

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3rd international conference on Language Processing and Knowledge Management-LPKM

作者： Nasr, Latifa Iben Masmoudi, Abir Belguith, Lamia Hadrich Sfax Univ MIRACL Lab ANLP RG FSEGS Sfax Tunisia

ISBN: (纸本)9783031850660;9783031850677

This paper introduces SERTUS (Speech Emotion recognition TUnisian Spontaneous), an extensive dataset collection intended to propel research in Speech Emotion recognition (SER), particularly within the realm of Tunisian Dialect (TD). SERTUS encompasses both registers of the Tunisian Dialect: the Popular (familiar) register and the intellectual register, capturing a diverse range of emotions in spontaneous environments and natural interactions across different regions of Tunisia. This work delineates the methodology utilized in crafting SERTUS, highlighting the challenges and strategies involved in capturing spontaneous interactions. Moreover, we underscore the importance of including TD and the multidomain nature of the dataset, illustrating its potential applications in various domains such as sports, politics, and culture. It's imperative to note that this corpus collection adhered to a rigorous protocol to ensure corpus quality, as it will undergo annotation in future research endeavors.

关键词： data collection Spontaneous emotion speech Tunisian Dialect

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Deep-learning-based research on detection algorithms for marine fish 3

Deep-learning-based research on detection algorithms for mar...

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3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Li, Mengwei Wuhan Polytechnic University China

ISBN: (纸本)9781510661363

The identification of marine fish species is significant for marine fish research and conservation and the development and utilization of marine fish resources. To increase the speed and accuracy of detection of marine fish species in natural environments, this document proposes a fish recognition method based on the improved YOLOv5 model. First, the efficient channel attention mechanism module is incorporated into the backbone network to enhance the learning of local and global features of the feature map;then, the GIoU loss function is modified into the Focal-EIoU loss function to reduce the impact of low-quality samples on the detection effect. Under the same training conditions, the experimental results show that the improved YOLOv5s model achieves 94.6% detection precision, 93.8% recall rate, and 92.4% mean average precision. The enhanced fish detection method based on YOLOv5 proposed in this study has good accuracy and effectiveness and can meet the detection of marine fish species in a natural environment. © COPYRIGHT SPIE.

关键词： Object detection

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Analysis and prediction of student achievement in online teaching based on KNN 3

Analysis and prediction of student achievement in online tea...

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3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Li, Hongmei Zhang, Haihang Anhui Xinhua University China Hefei No. 11 Middle School China

ISBN: (纸本)9781510661301

Using learning analytics technology to mine online learning features can optimize the teaching process. On the basis of collecting students' online learning information, the similarity between features is firstly analyzed;secondly, machine learning algorithm is used to construct a student's learning performance prediction model, and the accuracy and K value of the model are analyzed;The teacher's teaching process reform puts forward the corresponding direction. © 2023 SPIE.

关键词： data mining

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Probability of Heart Disease using various machine learning Algorithms 3

Probability of Heart Disease using various Machine Learning ...

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3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Pillai, Suryanarayan Mogra, Saachi Shriman, Nakul Bakade, Kanchan SVKM's Nmims Mukesh Patel School of Technology Management and Engineering Department of Electronics and Telecommunication Mumbai India

ISBN: (纸本)9798350348347

Heart disease is the leading cause of death globally, requiring advanced predictive knowledge and skills. Healthcare systems lack effective analysis tools to uncover hidden patterns. Using Kaggle's dataset, an automated method aims to predict disease occurrences using data mining techniques, reducing costs and waiting times. Heart disease risk demands a sizable dataset that is excessively large and complex toprocess and analyze using traditional methods. Our goal is to identify an effective and precise method for estimating the probability of heart illness. The experimental results show the highest accuracy, Area under curve is best achieved by the random forest classifier which is about 83.96%, 92.35%, and the highest F1 score is achieved by SVM which is about 80.7%. © 2023 IEEE.

关键词： Heart

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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...

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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

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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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