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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31-40 订阅

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Smart Hackathon Management System with machine learning Integration 3

Smart Hackathon Management System with Machine Learning Inte...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Konkepudi, Bhagya Rekha Peddarapu, Rama Krishna Singam, Vamshi Krishna Katanguri, Sai Shashidhar Reddy Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331515911

In the rapidly growing landscape of hackathons and competitive coding events, managing large-scale participation and evaluating diverse submissions have become increasingly challenging. Traditional methods of handling hackathons are often inefficient, particularly in assessing and summarizing large volumes of participant data, presentations, and project documentation. This paper introduces a Smart Hackathon Management System that integrates advanced machine learning techniques to address these challenges, focusing on text summarization using seq2seq models and image-based text extraction. The proposed system automates the summarization of textual content, such as project proposals and idea presentations, allowing juries to make quick, informed decisions. Additionally, it incorporates a robust image text extraction module, designed to accurately retrieve and analyze text from images, further enhancing the system's capability to handle various forms of data. This integration of machine learning models within a hackathon management platform not only streamlines the evaluation process but also improves accuracy and reduces the manual workload involved in event management. The effectiveness of the proposed system is validated through extensive experiments, highlighting its potential for broader applications in event management and other domains requiring efficient handling of large datasets. © 2025 IEEE.

关键词： Optical character recognition

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Deep learning-based Plant Species Identification using VGG16 and Transfer learning: Enhancing Accuracy in Biodiversity and Agricultural Applications 3

Deep Learning-based Plant Species Identification using VGG16...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kiran, K. Kaur, Arpanpreet School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

In this paper, the method of deep learning is applied for plant species identification, with a special emphasis on Convolutional Neural Network VGG16 for feature extraction and classification. Identification of plants is important in matters concerning biological diversity, agriculture as well as management and utilization of resources but the conventional methods used in identification are time consuming and sometimes inaccurate. machine learning specifically deep learning is a more suitable replacement by offering a fast and accurate identity recognition. The research work works with a Kaggle dataset comprising of 24881 images of four plant species categorized into twenty-two with the objectives of increasing efficiency of the classification. We used transfer learning with weights from the ImageNet data set and obtained accuracy greater than 90% on the test data set, which shows the model’s ability to identify plant species based on leaf structure. To counter overfitting and enhance model generality, data augmentation methods were employed where the input images were rotated by 30, 45 and 60 degrees, and flipped vertically. The performances suggest that deep learning models, such as VGG 16, yield improved recognition for plant classification than conventional methods provide higher reliability. This research makes a valuable input toward a number of applications related to automated systems for agriculture and conservation, discussing possible uses of AI technique for better penetration of plant species, enhancing conservation goals, and future advancements in the agrarian field. Future research will leverage on these discoveries to make improvements on classification frameworks. © 2025 IEEE.

关键词： Plant diseases

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Prediction of Insurance Claims for Health Sector using machine learning Techniques 3

Prediction of Insurance Claims for Health Sector using Machi...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pragatheeswari, E. Kavitha, M.N. Saranya, S.S. Dhanushree, D. Jasmitha, S. Abiradhie, P.V. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science and Design Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's lives. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their medical expenses. The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Each record in the dataset represents an individual’s health insurance charges along with corresponding demographic and health-related characteristics. In this paper, machine learning techniques are used to group the claim amount that has been adopted by the individuals based on the hospitalized expenditure and insurance charges paid by the individuals. In the health sector, machine learning techniques are used to forecast insurance claims by building predictive models from historical data on claims, patient demographics, treatment types, and results. By predicting future claims based on trends seen in the data, these models help insurance firms better manage risk and allocate resources. In case of a fresher to this concept, it feels challenging to understand whereas generating as a pattern can be more flexible to acquire knowledge about how much amount has been claimed. © 2025 IEEE.

关键词： Electronic health record

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Musical Instrument recognition Using machine learning Approaches 3

Musical Instrument Recognition Using Machine Learning Approa...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chethan, B.K. Bharath, K.N. Puttegowda, Kiran Manjula, A.V. Sunil Kumar, D.S. Anitha, K. Yathiraj, G.R. Mysuru India Dayananda Sagar Academy of Technology and Management Department of Ece Bengaluru India Vidyavardhaka College of Engineering Department of Ece Mysuru India The National Institute of Engineering Department of Ece Mysuru India East Point College of Higher Education Department of Computer Applications Bengaluru India Gsss Institute of Engineering Technology for Women Department of Ece Mysuru India Coorg Institute of Technology Department of Cse Ponnampet India

ISBN: (纸本)9798331508456

This work focuses on musical instrument recognition in miscellaneous audio sources, a fundamental issue of the machine auditory system. We contrast traditional machine learning techniques against deep learning strategies in a clinical dataset solved under a supervised multi-label, multi-output classification paradigm. To enhance the performance of the system, we adjust the analysis parameters for window size, hop size, and label threshold levels. Furthermore, we explore the benefits of auxiliary feature sets as alternative data representations, specifically: Auditory features are based on two categories: (1) four-band spectral decomposition of stereo sound data, left, right, mid, and side channels and (2) the harmonic and residual model of the audio signal. Two methods for fusing the models trained on these representations are suggested and we use the best of these methods to increase recognition accuracy. These enhancements lead to a vast improvement;with a 14.25 % increase M-F score for the macro average and a 24.17 % improvement in the exact match ratio overall compared to the baseline. © 2025 IEEE.

关键词： Deep learning

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Face Mask Detection in Real Time using machine learning Algorithms 3

Face Mask Detection in Real Time using Machine Learning Algo...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, R. Sultania, Tanishq Padhmashri, V. Shreya, A. Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798331509675

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet V2 and VGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet V2 yields an accuracy of 96.6% and VGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. VGG16's deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, VGG16 is highly resource intensive and needs extensive computation time. © 2025 IEEE.

关键词： Image segmentation

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Content Recommendations Using machine learning-Based Approaches 3rd

Content Recommendations Using Machine Learning-Based Approac...

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

作者： Indian, Ajay Meena, Gaurav Mohbey, Krishna Kumar Yadav, Bharati Central University of Rajasthan Ajmer India

ISBN: (纸本)9789819624676

In today’s society, recommendation systems are critical. Because there is a huge amount of data around us, whether music, book, or movie data, numerous programs or businesses, including Netflix, Amazon, and Spotify, focus on recommendation systems such as music apps. Systems for recommending content are intended to assist viewers by reducing the need for them to choose from a large variety that may number in the thousands or even millions. With the help of the movie lens dataset, this work aims to conduct a study to analyze the effectiveness of machine learning techniques, like K-NN, XG-Boost algorithms with Pearson Correlation Coefficient, and Count Vectorizer model to develop the Con-tent Recommender System for suggesting the movie. The performance of the proposed systems is evaluated in this study using the RMSE of 1.1062 and 1.1089 for KNN and XG-Boost algorithms, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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machine learning based Heatwave Modeling for Akola, Maharashtra 3

Machine Learning based Heatwave Modeling for Akola, Maharash...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mandal, Nabanita Gajria, Abhinav Rangwala, Hussain Sarode, Tanuja Computer Engineering Department Thadomal Shahani Engineering College Bandra Mumbai India

ISBN: (纸本)9798331527549

Heatwaves are a serious challenge in India, claiming many lives yearly. The heatwaves occur when the temperature rises above the normal continuously for a few days. This paper proposes a Stacked LSTM model that predicts the maximum temperature. The study region is Akola in Maharashtra, India which is prone to heatwaves. It is observed that the Proposed Method gives less Mean Absolute Error as compared to other techniques like Multiple Linear Regression, Random Forest, and Support Vector Regression. After data collection, appropriate data pre-processing technique is applied. Model is built considering the pre-processed data which does not have any anomalies. The reason for improved result of Proposed model is its ability to handle the complex pattern present in the data and learn from it to give better predictions. Maximum temperature is selected as the target variable for prediction and the other features are humidity, dew point etc. are considered here. © 2025 IEEE.

关键词： Multiple linear regression

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

3rd International Conference on Machine Vision and Augmented...

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

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on machine Vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI Images Using Deep learning;software Maintenance Prediction Using Stack Ensemble Deep learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar Signal Processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare data;multimodal Face recognition System Using Hybrid Deep learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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Predicting Course Grades for Students Using machine learning: Determine Early Prediction Time 3rd

Predicting Course Grades for Students Using Machine Learning...

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Nguyen, Thi Hong Bui, Quang Vinh Nguyen, Viet Anh Do, Trung Kien Hanoi National University of Education Hanoi Viet Nam VNU University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9783031809422

Predicting learning outcomes (LO) is an important problem in educational data mining (EDM). Despite extensive research, there remains a gap in determining the optimal timing for predicting early LO with acceptable accuracy. This study proposes a methodology for developing a predictive model using machine learning techniques to identify the appropriate time to forecast students’ course scores in a blended learning environment. The scope of the research includes students participating in the subject ‘Introduction to Computer Science’ at Hanoi National University of Education (HNUE). The experimental data includes data from the learning management system (LMS) and the face-to-face classroom. The research model is built based on machine learning algorithms, including Linear Regression (LN), Support Vector Regression (SVR), and Artificial Neural Networks (ANN). The research results show that the attributes most strongly influencing course scores are average practice score, midterm score, and major. The ANN model provides the highest efficiency with R2 = 0.6033. Early prediction of final exam scores can be made from week 7. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Attack Detection System for Cloud-Based Wireless Sensor Networks Using the Proposed Fast Hyper Deep learning Model 3rd

Attack Detection System for Cloud-Based Wireless Sensor Netw...

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

作者： Saleh, Hadeel M. Marouane, Hend Fakhfakh, Ahmed NTS’COM Laboratory Safax University Sfax Tunisia Continuing Education Center University of Anbar Ramadi Iraq University of Sfax Sfax Tunisia

ISBN: (纸本)9789819624676

Cyber security requires detecting and stopping wireless sensor network attacks. We introduce a fast super deep learning model for cloud-based WSN attack detection in this research. Our offline approach divides data into training and testing sets, preprocesses it, then reduces features using Singular Value Decomposition (SVD) and Principal Component Analysis. We then extract features using our ultrafast deep neural network and put them into a hyper deep-machine learning model. We evaluate our strategy using data mining metrics and preserve the model for further use. We also compare our model to other data mining and machine learning methods using numerous datasets to ensure accuracy and efficiency. We focus on a few key topics. First, we address generalization, complexity, and execution computation time. Second, many datasets increase model generalization. Third, our fast and accurate deep learning approach improves machine learning results. Our Oracle and Java cloud infrastructure lets us design an injection tool to produce and transmit packets to the intrusion detection model. Model predictions stored on an Oracle database offer complete attack information. Convolutional Neural Networks (CNN) and machine learning algorithms like Complement Naive Bayes (CNB) and Stochastic Gradient Descent are among our methods. We also study combining PCA and SVD with machine learning to improve results. Finally, deep feature selection improves the previously reported methods. Overall, our proposed technique for cloud-based WSN attack detection has high generalization, efficiency, and accuracy while addressing major cyber security challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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