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检索条件"任意字段=4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023"

共 108 条记录，以下是61-70 订阅

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Performance Evaluation Of machine learning Algorithms For Rainfall Prediction Using Dimensionality Reduction Techniques

Performance Evaluation Of Machine Learning Algorithms For Ra...

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international conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Sapna Kumari Muhammad Owais Raza Arsha Kumari Dept. of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Dept. of Information and Computing University of Sufism and Modern Sciences Bhitshah Pakistan Dept. of Electronics Engineering Mehran University of Engineering & Technology Jamshoro Pakistan

ISBN: (数字)9798350335316

ISBN: (纸本)9798350335323

In the last few decades, tremendous change is observed in rainfall patterns which are majorly influenced by two major factors 1) climate change and 2) CO2 emission. Erratic rainfall patterns caused catastrophic effects on agriculture and human life in developing countries like Pakistan, where major economic growth is largely dependent on agriculture. the main objective of this study is to evaluate a performance different machine learning algorithms for forecasting rainfall patterns using dimensionality reduction techniques on climate change indicators. For this purpose rainfall data was collected for Pakistan. Principle component analysis (PCA), Pearson correlation, and Greedy search algorithms were used for feature selection and the evolution of models was performed using Root Mean Square error (RMSE), Root Absolute Error (RAE), and Coefficient of determination metrics. Results show that features obtained using the Pearson correlation produced the least error and Bayesian linear regression performed with the highest accuracy followed by Neural Network regression.

关键词： Climate change Rain pattern recognition Predictive models machine learning Neural networks

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Identification Of Medicinal Plants from Leaf Images

Identification Of Medicinal Plants from Leaf Images

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Global conference for Advancement in Technology (GCAT)

作者： Parth Jhunjhunwala Riya Sanjay Dhawas Shristi Tiwari Abhishek Sharma Electronics and Communication International Institute of Information Technology Naya Raipur India Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India International Institute of Information Technology Naya Raipur India

recognition quest for medicinal herbs is a continuous field of exploitation and is crucial in Ayurveda’s industries. Incorrect identification leads to adverse effects. One of the most entrusted classification tools is Support Vector machine (SVM), which deals with high dimensional feature space and is generally a crucial aspect of image data. this paper proposes a comparison of a novel multi-class prediction to segregate plant leaves based on an SVM classifier and deep learning based on Resnet50 and InceptionV3 for leaf classification implemented, each model was individually trained before comparison. the findings from the trained model can be integrated into a web application. the SVM model was built in four stages. Stage 1 involves data preprocessing using techniques like grayscaling, smoothing, thresholding, morphological transformation, and boundary extraction to make the data suitable for SVM-based classification. In Stage 2, shape-based, colour-based, and texture-based features are extracted, and a comma-separated delimiter file is generated that would be fitted in an SVM classification model. Stage 3 and 4 is the model-building and testing stage wherein classification, feature scaling, and parameter tuning is done to obtain optimal hyperparameters for the SVM classifier. Using the transfer learning approach parameters were initialized on the pre-trained Neural networks namely InceptionV3, and ResNet50. When training the models on the relevant dataset, the softmax layer coupled to the dense layer served as the classifier and was used to extract features from the input photos. the last stage involves integrating the solution into a web application. the real-time leaf images can be captured by the user utilizing a camera to obtain plant names from images and upload them to a web interface. the whole framework is compared with the existing frameworks for plant leaf classification.

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New Graph Embedding Approach for 3D Protein Shape Classification 9

New Graph Embedding Approach for 3D Protein Shape Classifica...

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Joint 9th international conference on Informatics, Electronics and Vision (ICIEV) / 4th international conference on Imaging, Vision and pattern recognition (ICIVPR)

作者： Madi, Kamel Paquet, Eric Umanis Res & Innovat F-92300 Levallois Perret France Natl Res Council Canada Ottawa ON Canada

ISBN: (纸本)9781728193311

We address the problem of 3D protein deformable shape classification. Proteins are macromolecules characterized by deformable and complex shapes which are related to their function making their classification an important task. their molecular surface is represented by graphs such as triangular tessellations or meshes. In this paper, we propose a new graph embedding based approach for the classification of these 3D deformable objects. Our technique is based on graphs decomposition into a set of substructures, using triangle-stars, which are subsequently matched with the Hungarian algorithm. the proposed approach is based on an approximation of the Graph Edit Distance which is characterized by its robustness against both noise and distortion. Our algorithm defines a metric space using graph embedding techniques, where each object is represented by a set of selected 3D prototypes. We propose new approaches for prototypes selection and features reduction. the classification is performed with supervised machine learning techniques. the proposed method is evaluated against 3D protein benchmark repositories and state-of-the-art algorithms. Our experimental results consistently demonstrate the effectiveness of our approach. Contributions-We propose a new graph embedding approach to classify 3D deformable protein shapes and new techniques for prototypes selection and dimensionality reduction then we performed the classification using a Naive Bayes (NB) classifier and we achieve better results than the state-of-the-art.

关键词： 3D Protein Shape Classification 3D object recognition 3D Deformable object recognition pattern recognition Graph Embedding Graph matching Graph edit distance Graph decomposition Graph metric Metric learning

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ICICoS 2020 - Proceeding: 4th international conference on Informatics and Computational Sciences

ICICoS 2020 - Proceeding: 4th International Conference on In...

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4th international conference on Informatics and Computational Sciences, ICICoS 2020

ISBN: (纸本)9781728195261

the proceedings contain 40 papers. the topics discussed include: uncertainty avoidance and individualism collectivism on acceptance of smart city mobile applications;waste classification using support vector machine with SIFT-PCA feature extraction;improving the quality of learning management system (LMS) based on student perspectives using UTAUT2 and trust model;analysis of deep feature extraction for colorectal cancer detection;simulation study using average difference algorithm to analyze the outlierness degree of spatial observations;development of the smart chicken eggs incubator based on internet of things using the object oriented analysis and design method;code-mixed sentiment analysis using machine learning approach – a systematic literature review;implementation of textrank algorithm in product review summarization;and Indonesian ethnicity recognition based on face image using uniform local binary pattern (ULBP) and color histogram.

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Utilizing Smartphone Usage pattern for Gender recognition applying machine learning Algorithms 4

Utilizing Smartphone Usage Pattern for Gender Recognition ap...

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4th international conference on Electronics, Communication and Aerospace Technology, ICECA 2020

作者： Polin, Johora Akter Khan, Omayer Marouf, Ahmed Al Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728163871

Smartphones can connect people over long distance within few seconds and perform critical tasks easily. the concept of global village has turned into reality through the optimum usage of smartphones. Smartphone is not just being used as a helping hand, it also contains the user's identity and behavior through its usage pattern. this paper has utilized the smartphone usage pattern to identify the gender of its user. In this process, the proposed research work has analyzed 1284 instances of male and female of a specific age group (18-30 years) are collected with a survey that incorporates relevant features from the subjects and their gender to create a labelled dataset. For better approximation and experimentation, 10 different machine learning algorithms such as Naïve Bayes, decision tree, random forest etc. are compared. the compared algorithms are taken from five different categories of classification algorithms such as tree-based classifiers, Bayes, function-based etc. To evaluate performance of different algorithms, accuracy, precision, recall and f-score measurements are utilized. Among these algorithms, random forest has performed better by showing accuracy of about 84.11%. From this research, the factors involved in smartphone are known and its usage is impactful for predicting users' gender. With the understanding of this traits, security, biometric, and privacy concerns can be developed to a higher extent. © 2020 ieee.

关键词： Smartphones

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A Proficient Model to Classify Bangladeshi Bank Notes for Automatic Vending machine Using a Tiny Dataset with One-Shot learning Siamese Networks 11

A Proficient Model to Classify Bangladeshi Bank Notes for Au...

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11th international conference on Computing, Communication and Networking Technologies, ICCCNT 2020

作者： Hossain, Md. Ekram Islam, Arni Islam, Md. Sanzidul Daffodil International University Department of Software Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728168517

Automatic vending machine is a necessity at this technological era. It's a step to go toward the vendor-less shop management, which supports the commandment of the 4th Industrial Revolution. But image recognition system needs a lot of data to get the pattern. Deep learning applications is very computationally extravagant for getting good features and find in many cases that little data cannot give good learning features. We have used and reworked the architecture of Siamese Neural Network for One-shot learning to recognize the Bangladeshi bank notes with a tiny dataset. In this article we analyzed 20 images only for 5 different Bangladeshi bank notes what people used regularly. this research will help general people to get better experience with vending machines which can recognize notes with one data example only. We used 5 notes (5,10,20,50,100 TAKA) and get excellent result with 97.38% accuracy using help of convolutional architecture. © 2020 ieee.

关键词： Deep learning

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Data Augmentation Based on 3D Model Data for machine learning 4

Data Augmentation Based on 3D Model Data for Machine Learnin...

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4th ieee international conference on Computer and Communication Systems (ICCCS)

作者： Iwasaki, Masumi Yoshioka, Rentaro Univ Aizu Grad Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan

ISBN: (纸本)9781728113227

A new method of data augmentation for machine learning using 3D model data is proposed. the method involves the use of STL data of an object to automatically generate a set of training data covering a continuous range of view angles and various backgrounds. It also involves the use of two CNN's, one corresponding to the 'object (parent class)' and another to the 'view angle (child class)', that provides a two-stage classification to improve tolerance against over-classification. the performance of the method is demonstrated by comparing categorization results with conventional approach based on real-world photographs. the method shows satisfactory improvements over conventional method using photographed images.

关键词： component machine-learning pattern recognition image-processing

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Rumor Detection on Twitter using Extracted patterns from Conversational Tree 4

Rumor Detection on Twitter using Extracted Patterns from Con...

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4th international conference on Web Research (ICWR)

作者： Yavary, Arefeh Sajedi, Hedieh Univ Tehran Coll Sci Sch Math Stat & Comp Sci Dept Comp Sci Tehran Iran

ISBN: (纸本)9781538653647

these days, Twitter social network is one of the main platforms for getting news, among people over the world. this is because of the high volume of data generated by this social media, which makes Twitter up to date with news and information. Nevertheless, the existence of invalid information over the social network makes the users unhappy and also arises some problems in the real world, particularly in the crisis. To overcome these problems and other possible issues, automatic detection of rumor on Twitter must be taken into account. Despite such issues, in this paper, rumor detection in Twitter is studied. In this paper, the rumor is validated by considering the user's feedback, as the source data for rumor study. In our proposed method, pattern recognition and its analysis of the user conversational tree in Twitter is studied. these recognized patterns feed into as features for training a classifier for rumor detection. the model for training a classifier is an Extreme learning machine and its extension. the dataset for experiments of our method is the standard dataset of SemEval-2017 Task 8. Experiments of our proposed method with respect to competitor methods in rumor detection show that our method outperforms the state of the art methods.

关键词： Rumor Detection Twitter Social Network Conversational Tree pattern recognition Extreme learning machine

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Face recognition using interest points and ensemble of classifiers 4

Face recognition using interest points and ensemble of class...

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4th ieee international conference on Recent Advances in Information Technology, RAIT 2018

作者： Vinay, A. Sampat, Pratik Rajesh Belavadi, Sagar V. Pratik, R. Rao, B. S. Nikitha Ragesh, Rahul Murthy, K. N. Balasubramanya Natarajan, S. Centre for Pattern Recognition and Machine Intelligence PES University Bangalore Karnataka India

ISBN: (纸本)9781538630396

In human beings, it is the responsibility of the temporal lobe of the brain for recognition of faces. Certain features of the face trigger the neurons of the temporal lobe which are then stored. these eventually lead to the identification of the face. In machine learning system, a huge dataset of images is used as a reference by the face recognition algorithms. the most prominent features are extracted from what is referred to as landmarks on the face and are analyzed based on their relative position amongst other features. the main challenge in computer vision lies in the identification and extraction of these features which in turn assist in face recognition owing to the exponentially large degree of variability that can be seen in appearances. Speed and accuracy are some of the most prominent challenges for the same. We explore ELM critically and study its performance. Owing to its extremely fast learning, it finds potential in real-time applications. the goal of this paper is to evaluate the performance of the extreme learning machine algorithm for facial recognition when used as a standalone model as well as when it is used with an ensemble learner. We then conclude with a voting classifier which is responsible for the final predictions made. Various combinations of ensembles are explored to come up with the best performing model. © 2018 ieee.

关键词： Face recognition

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A Privacy-Preserving Classifier in Statistic pattern recognition 4th

A Privacy-Preserving Classifier in Statistic Pattern Recogni...

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4th international conference on Cloud Computing and Security (ICCCS)

作者： Wang, Qi Zhou, Dehua Guan, Quanlong Li, Yanling Yang, Jimian Jinan Univ Dept Comp Sci Guangzhou Guangdong Peoples R China Jinan Univ Network & Educ Technol Ctr Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783030000097;9783030000080

machine learning classification and pattern recognition are widely used in various scenarios nowadays, such as medical diagnosis and face recognition, both need to estimate the similarity measure between different samples. In such applications, it is critical to protect the privacy of both the private input data and the machine learning model. In this paper, we propose a privacy-preserving Mahalanobis distance scheme for the statistic pattern recognition, and construct a protocol for the privacy-preserving prediction phase by using the labeled-homomorphic encryption scheme, which combines linearly homomorphic encryption and pseudo-random function. And we consider an outsouring scenario, which most work to be outsourced to the cloud server. Our design goal is to ensure that the client's private data is permanently confidential and protect the secret model in cloud server. Most of the previous work proposed complex schemes with too many interactions between the clients and cloud server, and we propose an efficient scheme to minimal the complexity of the client side.

关键词： Classification Homomorphic encryption Cloud computing

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