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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

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A Hybrid ResNet - MobileNet Deep learning Model for Smart Bin Waste Classification 5

A Hybrid ResNet - MobileNet Deep Learning Model for Smart Bi...

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5th international conference on Artificial Intelligence and data Sciences, AiDAS 2024

作者： Azhar, Nur Zulaikha Binti Teo, Noor Hasimah Ibrahim Hamzah, Raseeda Binti Roslan, Rosniza Binti Maskat, Ruhaila Shah Alam Malaysia Melaka Malaysia

ISBN: (纸本)9798331528553

the rapid growth of urban populations and rising consumerism have led to an unprecedented increase in the volume and diversity of municipal waste, now exceeding 2 billion tons annually. A significant portion, at least one-third, is not managed in an environmentally safe manner, posing severe social and environmental challenges. Paper waste, constituting approximately 10% of municipal solid waste, is a notable component due to its high recyclability. Recycling paper can significantly conserve trees and reduce energy and water usage, providing substantial environmental benefits. this study aims to enhance waste management by utilizing image recognition and IoT technologies for the segregation of paper waste at the household level. through the evaluation of two deep learning techniques, this research compare the performance of MobileNet and Restnet50 model for waste classification, assessed using accuracy, precision, recall, and F1-score metrics. Results indicate that the hybrid model combining ResNet50 and MobileNet outperforms standalone models, achieving an average accuracy of 97.90% on the first dataset and 73.49% on the more complex second dataset. these findings demonstrate the potential of integrating advanced machine learning and IoT technologies to improve the efficiency and sustainability of waste management practices. © 2024 IEEE.

关键词： Municipal solid waste

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machine learning Applied to Star-Galaxy-QSO Classification and data Visualization 5

Machine Learning Applied to Star-Galaxy-QSO Classification a...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Ji, Xiang Wu, Yadong Zhang, Yuling Zhang, Han Zhang, Weihan Zhang, Wei Li, Yi Jiang, Liyuan Sichuan University of Science and Engineering School of Computer Science and Engineering Yibin China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

With the continuous development of modern astronomical observation methods, the sky survey data obtained through observation has increased exponentially, and machine learning has gradually replaced traditional scientific computing methods with its powerful computing power in the big data environment. In view of the problems of low classification accuracy, poor classification efficiency and few classification types in the current astronomical spectrum classification work, this study added sub-classification to the original classification benchmark and compared the performance of different machine learning algorithms in the classification problem in detail. Visual presentation of data improves the intuitiveness of data. In this study, one hundred thousand LAMOST DR6 spectral data were used for training, and different machine learning algorithms were used to evaluate the performance of various classifiers, and then correlation analysis was used to greatly improve the classification accuracy of specific categories on the basis of the original research, making the classification accuracy of galaxies and QSO is higher than 98%, which proves the validity of this research. Finally, various attributes of the classified celestial objects are displayed through visualization methods. © 2022 IEEE.

关键词： Classification (of information)

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Towards Deep Supervised Features learning: Item-To-Knowledge Mapping in Educational data Using Convolutional Neural Networks 5

Towards Deep Supervised Features Learning: Item-To-Knowledge...

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5th international conference on pattern Analysis and Intelligent Systems, PAIS 2023

作者： Harbouche, Khadidja Khentout, Chaabane Zenbout, Imene Smaani, Nassima Sinacer, Kawthar University Farhat Abess Setif 1 LRSD Laboratory Faculty of Science Department of Computer Science Setif Algeria University Abdelhamid Merhri Constantine 2 Misc Laboratory Department of Informatics and Its Application Constantine Algeria University Farhat Abess Setif 1 Setif Algeria

ISBN: (纸本)9798350381450

Item to knowledge mapping is a very useful technique in educational data mining that assesses to improve the curriculum system and the order learners should perceive new skills. the wide range of item to knowledge mapping workflows consists of the use of the item-item similarity methods that calculate a certain similarity degree between items, in order to regroup items in highly correlated clusters. Although those methods may skip some information that weaken the performance of clustering phase and eventually assign items to not their right clusters. therefore, in this paper we propose an extra phase in item to knowledge mapping , that learns new features representation from the item-item similarity matrix using supervised feature learning based on deep convolutional neural network and k-means clustering. the proposed model was tested on two different educational data sets, and compared to direct clustering using the item-item similarity matrix. the obtained results shows a very interesting results in terms of higher clustering performance and especially item on clusters distribution in which more than 30% of items changed their initial clusters. © 2023 IEEE.

关键词： data mining

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the Classification of Native and Invasive Species in North America: A Transfer learning and Random Forest Pipeline 5th

The Classification of Native and Invasive Species in North A...

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5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Sarkar, Sayani Chakroborty, Somenath Bellarmine Univ Louisville KY 40205 USA West Virginia Univ Inst Technol Beckley WV 25801 USA

ISBN: (纸本)9783031235986;9783031235993

data Analytic interpretation of plant species is not widely explored. the image variety of plant species can be investigated with the help of machine learning and deep learning techniques. machine learning and deep learning make the plant species investigation automated and almost accurate. In this paper, we have proposed a novel native and invasive plant species classification technique. We use the pre-trained deep convolution neural networks (transfer learning) to extract features from plant species and classify them into Native and Invasive. Here, state-of-the-art deep convolution neural network models like InceptionV 3, MobileNetV 2, ResNetV 2, VGG16, and Xception have been explored to extract the feature vectors from images. Furthermore, the features of the images have been used for image classification using the Random Forest algorithm. the performance of the proposed system is verified on the proposed dataset (Native-Invasive) with aerial view images of native and invasive species. Results were obtained using cross-validation techniques. Our experiments demonstrate the potential of our proposed method for achieving excellent performance with a 95% accuracy using Xception with data augmentation, hyper-parameter optimization, and the Random Forest classification technique. the aerial images were captured and labeled by us which is very novel to our experimental setup. After using deep learning models we got promising results. the dataset used to train the plant species classifier will be made available on request.

关键词： Feature extraction Transfer learning Random Forest Deep learning Image classification Native plant species Invasive plant species

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Research on Big data Automatic mining Method of High-dimensional Text Based on machine learning 5

Research on Big Data Automatic Mining Method of High-dimensi...

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5th international conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2024

作者： Dong, Guofeng Wang, Sihang Wen, Renjun Guo, Yu Sichuan Provincial Big Data Center Sichuan Chengdu610000 China Sichuan Provincial Government Affairs Service Public Resources Exchange Service Center Sichuan Chengdu610000 China China Comservice Enrising Information Technology Co. Ltd. Sichuan Chengdu610041 China

ISBN: (纸本)9798331539818

How to obtain effective and valuable topic information from massive texts has always been a hot topic in text mining. this paper attempts to use the improved algorithm to model massive high-dimensional text data. On the premise of ensuring that the effect reduction of the model is controlled within the effective range, it greatly reduces the time required for the model to run, thus improving the problem of low computational efficiency of the original model when dealing with massive data. In order to solve the problem of improving the running efficiency of LSA algorithm, this paper puts forward an improved method. In addition, this paper proves through experiments that in a big data environment, the improved method greatly reduces the time required to run the LSA method without losing the accuracy of model classification, significantly improves the efficiency of modeling, and provides a theoretical reference for the subsequent automatic mining of high-dimensional text big data. © 2024 IEEE.

关键词： data accuracy

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Research on Construction Scene Cognitive System Based on Deep learning 5

Research on Construction Scene Cognitive System Based on Dee...

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5th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2024

作者： Zhou, Xiaofa Shao, Yinjie Zhang, Zuhong Chen, Xinsheng Zhang, You Wang, Zhen Chen, Shufan State Grid Shanghai Electric Power Company Engineering Construction Consulting Branch Shanghai China Hangzhou Hikvision Digital Technology Corporation Limited Hangzhou China

ISBN: (纸本)9798350355253

With the continuous advancement of artificial intelligence and machine learning technologies, this paper proposes a novel method to enhance safety management on power engineering construction sites. By leveraging object recognition and semantic interpretation technologies from deep learning networks, this study aims to address the complexities and hazards commonly encountered in these environments. the paper delves into the potential of object recognition technology to improve safety management, focusing on YOLOv5-based object recognition algorithms and Transformer-based semantic interpretation algorithms. through a detailed analysis of experimental setup, dataset construction, and deep network training processes, this study demonstrates the effectiveness of these methods in identifying and interpreting safety-related data. the research findings underscore the importance of integrating AI-driven technologies to reduce safety incidents, marking a significant step toward safer and more efficient construction site management. © 2024 IEEE.

关键词： Project management

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conference Committee

Conference Committee

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pattern recognition and machine learning (PRML), IEEE international conference on

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An Experimental Investigation on the Emotion recognition Using Power Spectrum Density and machine learning Algorithms in EEG Signals 5th

An Experimental Investigation on the Emotion Recognition Usi...

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5th EAI international conference on Big data Innovation for Sustainable Cognitive Computing, BDCC 2022

作者： Babu, Nirmal Varghese Kanaga, E. Grace Mary Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

ISBN: (纸本)9783031283239

Emotion recognition is a technique for identifying and classifying human emotions by combining biosensing, face recognition, speech and voice recognition, machine learning, and pattern recognition. the electroencephalogram (EEG), which is based on brain signals, is used in the proposed work to identify the emotions. Here, the DEAP dataset is used to gather information about human emotional states, and machine learning algorithms like SVM, KNN, and MLP were used as the classifiers. In this proposed work, we used Power Spectrum Density as the Feature Extraction Technique and Welch’s Periodogram as the Signal Decomposition Technique. It was observed that gamma signals show more Valence, whereas Alpha signals show Arousal. To Cross validation system has been used to validate the performance of classifiers used in the work. Based on the locations of the electrode, EEG bands, and classifiers, accuracy comparisons were done. Out of the three classifiers, KNN scored more accuracy than other classifiers used. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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Robust Framework for Detecting Heart Stroke using Advanced machine learning Techniques 5

Robust Framework for Detecting Heart Stroke using Advanced M...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Mohanaprakash, T.A. Aladakatti, Shweta S. Kanimozhi, S. Tamilselvi, C. Priscilla, R. Rashika, R.S. Soet Cmr University Department of Cse Bengaluru India R.M.K Engineering College Department of Cse Kavarapettai India Dr. Mgr Educational and Research Institute Department of Information Technology Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science OMR Chennai600119 India Panimalar Engineering College Department of Cse Chennai India

ISBN: (纸本)9798331504403

Strokes of the heart are responsible for an increasing number of deaths that occur on a daily basis, and it is anticipated that this pattern will continue for the foreseeable future. It is unfortunate that determining the presence of such illnesses in human people is a challenging and time-consuming task. the management of tasks that are of such a complex nature might benefit from the utilization of data sets as a valuable tool. the only way to effectively predict the occurrence of heart strokes is through the utilization of automation. this is due to the fact that it is essential to continue monitoring the heart rate. the arrhythmia dataset from the Massachusetts Institute of Technology (MIT) is utilized in this particular piece because it is one of the datasets that is beneficial to us. there are a variety of data mining methodologies that can be utilized in order to accomplish the goal of automating the task that was defined. Within the scope of this article, the approaches that were employed encompass decision trees, naïve bayes, the kNN, and the Random Forest techniques. therefore, the objective of this research is to construct a comparison between the algorithms that have been explained above and to evaluate which one is more accurate in terms of finishing the assignment. We are able to claim that the algorithm Random Forest has achieved a level of accuracy that is 99%, which is the highest level of accuracy among all of the algorithms. this is the conclusion that we are able to reach at the end of the day, after all of the assessments have been completed. © 2024 IEEE.

关键词： Random forests

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An Intelligent Traffic pattern Analysis and Optimization Using machine learning in SDN Enable Network Infrastructure 5

An Intelligent Traffic Pattern Analysis and Optimization Usi...

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5th international conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Pathan, Md. Naimul Fahim, Faizul Islam Akter, Sabrina Ahamed, Shifat Sultana, Abida Green University of Bangladesh Dept of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350394290

Nowadays a large number of traffic passes through the network. However, the network system is unreliable and has safety issues. Different attacking activities may arise on the network traffic. Again the performance, privacy, latency, and control overhead requirements of real-world networks are the fundamental issues in terms of Network performance analysis. Hence analyzing traffic patterns is a very efficient fact that will help to detect anomalies, monitor network availability, and maximize the performance of the network. Recent innovations in networking, such as the revolutionary Software-Defined Networking (SDN), have disrupted the status of network administration. the SDN has the power to decouple its control plan from the data plan which results in more effective observation in terms of traffic analysis. therefore, the pattern analysis of network traffic has been accomplished by a method based on machine learning. In this work, Our mission is to analyze the network traffic using machine learning algorithms for SDN to enable network flows to observe the traffic pattern. We collect SDN-based data and prepossessed it with advanced preprocessing tools and techniques. Afterward, machine learning models i.e. Random Forest (RF) and KNN are applied to find out the traffic pattern. Our experiments conducted in this study demonstrate that machine learning model-based intelligent pattern analysis for network traffic achieves superior outcomes when compared to traditional methods. © 2023 IEEE.

关键词： Anomaly detection

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