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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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Towards a Recommended Documentation System Using Data Traceability and machine learning in a Big Data Environment 2nd

Towards a Recommended Documentation System Using Data Tracea...

引用

2nd international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019

作者： Rahmaoui, Othmane Souali, Kamal Ouzzif, Mohammed ESTC RITM Lab ENSEM CED Hassan II University Casablanca Morocco

ISBN: (纸本)9783030366735

In this article we describe our recommended documentation system, which allows organizations to take the information adapted to their objectives with a search engine set, in order to make results and prepare reports according to the organization approaches documentation. We use for the recommendation, data traceability based on the information retrieval techniques to load data (traces) with processing jobs;to give the useful information by analyzing and filtering the documentation most adapted to the rating of the search engine objectives. We propose four levels of the recommended documentation system;the first one is to collect data by using a multimedia management system with an automatic classification of files dragged and dropped on the first interface (API) after authentication, the second is the search engine and the information needed that let system to clean and to format the mass collection of data saved and stocked on Hadoop Distributed File System (HDFS);that keeps growing exponentially, with a machine learning (MLlib) based on apache Spark like a third level, to adapt and analyze traces by making some processing jobs with respecting the search objectives, and the last component consist of making results and more measures on a dashboard (Mobile App and Desktop App). Our work therefore is to propose a conceptual study of the recommended documentation system using data traceability and machine learning in a big data environment in order to implement and evaluate the proposed method in our next work. © 2020, Springer Nature Switzerland AG.

关键词： Search engines

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Analyzing Social Media Opinions Using Hybrid machine learning Model Based on Artificial Neural Network Optimized by Particle Swarm Optimization 1

引用

2nd international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019

作者： Khourdifi, Youness Bahaj, Mohamed Faculty of Sciences and Techniques Hassan 1st University Settat Morocco

ISBN: (数字)9783030366742

ISBN: (纸本)9783030366735

Sentiment Analysis (SA) is one of the concepts of Natural Language processing, also called Opinion Mining. This area of computer science is used to extract the feeling of a text to give useful information about the author’s tendency towards a specific subject. This paper presents an implementation of a sentiment analyzer for Twitter. We analyze tweets to extract feelings on a specific subject. We used a data set of 100000 tweets with 98469 unique values, collected on Twitter. The collected tweets were analyzed using a hybrid machine learning model based on Artificial Neural Network optimized by Particle Swarm Optimization (PS-ANN). The results are compared with three algorithms, Artificial Neural Network (ANN), Support Vector machine (SVM) and Naïve Bayes. The results obtained by cross-validation on the same data set confirm the effectiveness of the proposed approach (PS-ANN) compared to existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Neural networks

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Model of Sentiment Analysis with Deep learning in Social Network Environment 2

Model of Sentiment Analysis with Deep Learning in Social Net...

引用

IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： Wanda, Putra Huang JinJie Univ Respati Yogyakarta Fac Sci & Technol Yogyakarta 55281 Indonesia Harbin Univ Sci & Technol Sch Automat Engn Harbin Peoples R China

ISBN: (纸本)9781538692981

Currently, the digital environment such as social network needs real-time and adaptive security model. Deep learning is becoming increasingly popular for various applications. In this research, we proposed a Dynamic Deep learning algorithm, dubbed Dynamic Convolutional Neural Networks (CNN). Different from common CNN, it assigns similar signal parts to the same CNN channel and solves signal alignment. Therefore, it can better deal with the problem of data noise, alignment, and other data variations. We achieve an increase in CNN graph's performance with dynamic k-max pooling model with a benchmark dataset for sentiment analysis.

关键词： OSN Sentiment Dynamic CNN Social Messenger

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Validation Feedback based Image Transfer Network for Data Augmentation 2

Validation Feedback based Image Transfer Network for Data Au...

引用

2nd international conference on Video, signal and Image processing, VSIP 2020

作者： Chen, Weili Kamata, Seiichiro Sun, Zitang Waseda University Japan

ISBN: (纸本)9781450388931

Modern image classifiers are often suffering over-fitting problems because of the insufficient number of images in the dataset. Data augmentation is a strategy to increase the number of training samples. However, recent data augmentation methods are designed manually and cannot generate real-like images. Some neural network-based image generation methods such as GAN and VAE can also be used for data augmentation, but they are usually applied to unbalanced datasets. Since the generated images cannot be guaranteed to be from the same label, using them to extend a balanced dataset may lead to decreasing the accuracy of the classifier. In this paper, we propose an image transfer network to produce images that automatically adapt to a specific dataset and classifier. The image transfer network will search for the output images which can maximize the validation accuracy and help the classifier to overcome the over-fitting problems. Through the experiments, our method achieves high accuracy on CIFAR-10 and CIFAR-100 datasets. Moreover, since it could combine with other data augmentation methods, we show that using our method can push the state-of-the-art results furthermore. © 2020 ACM.

关键词： machine learning

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Human Pose Estimation and Activity Classification Using machine learning Approach 1

引用

2nd international conference on Soft Computing and signal processing, ICSCSP 2019

作者： Arunnehru, J. Nandhana Davi, A.K. Sharan, R. Raghul Nambiar, Poornima G. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus ChennaiTamil Nadu India

ISBN: (数字)9789811524752

ISBN: (纸本)9789811524745

Human pose estimation is mainly used for the purpose of training the robots to incorporate in a way which the actions are performed in reality. The human pose estimation is the highly exploring topic in the field of computer vision research. The objective of the dynamic pose estimation is to estimate the human pose in all the available datasets. It begins with mapping the skeletal coordinates and also visualizing the coordinates. The features within the coordinates are being obtained with the help of proportionate distance features and the divergence between the coordinates. The feature extraction has taken place separately for RGB and depth dataset. The computed data is being present in the same format as that of the vector space that is being allocated for the feature extractor. The classifiers such as support vector machines (SVMs), k-nearest neighbor (KNN), and the decision tree are being used. The major use of this learning process is to help the robot to train it similar to that of the human functionalities. This functionality can be used in any learning and training procedures to build the human-robotic system. The proper understanding of how every action takes place is learned in this process and thereby training any system with similar measures is simplified with this procedure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Age Group Estimation from Human Iris 2nd

Age Group Estimation from Human Iris

引用

2nd international conference on Soft Computing and signal processing, ICSCSP 2019

作者： Rajput, Minakshi R. Sable, Ganesh S. NIELIT Dr. B.A.M. University AurangabadMH India Maharashtra Institute of Technology Dr. B.A.M. University AurangabadMH India

ISBN: (纸本)9789811524745

The paper presents the approach to determine the age group of a person from an iris structure using less number of features. The performance of a proposed method is evaluated based on five different classifiers. Our methodology improves on earlier methods in terms of classification accuracy and F1 score. The study also proved that human iris structure has age-related information and therefore can be used to predict age. The existing iris biometric systems can be advanced with this method if they show an age in spite of just accepting or rejecting a person. © 2020, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Vehicle Type Classification Using Deep learning 2nd

Vehicle Type Classification Using Deep Learning

引用

2nd international conference on Soft Computing and signal processing, ICSCSP 2019

作者： Bhujbal, Avinash Mane, D.T. Department of Computer Engineering PICT Pune India

ISBN: (纸本)9789811524745

As the rate of data generation is growing rapidly which can be from a number of sources. Information collected can be used for and processed for its commercial or business value. Here, one of the characteristics is the significance of data in terms of time. In time-dependent applications, the need for analysis and quick processing is a necessity. Using YOLO (you look only once) method, we have performed recognition of vehicle type in which for each of the objects the model is trained. Here, the dataset used is from cityscapes where 2659 input set images are taken for training purpose and the performance is calculated in terms of accuracy which is 87%. Using the approach detection for different vehicle categories, i.e., car, bus, truck, motorbike is performed. YOLO model works well and does not require any intrusive approach for detection also due to less to no dependency on any other system optimization and reliability is attained. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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machine learning on Biomedical Images: Interactive learning, Transfer learning, Class Imbalance, and Beyond 2

Machine Learning on Biomedical Images: Interactive Learning,...

引用

2nd IEEE international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Khan, Naimul Mefraz Abraham, Nabila Hon, Marcia Guan, Ling Ryerson Univ Ryerson Multimedia Res Lab Toronto ON Canada

ISBN: (纸本)9781728111988

In this paper, we highlight three issues that limit performance of machine learning on biomedical images, and tackle them through 3 case studies: 1) Interactive machine learning (IML): we show how IML can drastically improve exploration time and quality of direct volume rendering. 2) transfer learning: we show how transfer learning along with intelligent pre-processing can result in better Alzheimer's diagnosis using a much smaller training set 3) data imbalance: we show how our novel focal Tversky loss function can provide better segmentation results taking into account the imbalanced nature of segmentation datasets. The case studies are accompanied by in-depth analytical discussion of results with possible future directions.

关键词： Training Image color analysis Biomedical imaging Computer architecture Feature extraction Rendering (computer graphics) machine learning

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Performance Comparison of machine learning Algorithms for Classification of Chronic Kidney Disease (CKD) 2

Performance Comparison of Machine Learning Algorithms for Cl...

引用

2nd Joint international conference on Emerging Computing Technology and Sports, JICETS 2019

作者： Abdullah, Azian Azamimi Hafidz, Syazwani Adli Khairunizam, Wan School of Mechatronic Engineering Universiti Malaysia Perlis Pauh Putra Campus Arau-Perlis02600 Malaysia

Kidney is one of the vital organs in a human body while ironically, chronic kidney disease (CKD) is one of the main causes of death in the world. Due to the low rate of loss of kidney function, the disease is often overlooked until it is in a really bad condition. Dysfunctional kidney may lead to accumulation of wastes in blood which would affect several other systems and functions of the body such as blood pressure, red blood cell production, vitamin D and bone health. machine learning algorithms can help in classifying the patients who have CKD or not. Even though several studies have been made to classify CKD on patients using machine-learning tool, not many researchers perform pre-processing and feature selection technique to obtain quality and dependable result. machine learning used with feature selection techniques are shown to have better and more dependable result. In this study, feature selection methods such as Random Forest feature selection, forward selection, forward exhaustive selection, backward selection and backward exhaustive selection were identified and evaluated. Then, machine learning classifiers such as Random Forest, Linear and Radial SVM, Naïve Bayes and Logistic Regression were implemented. Lastly, the performance of each machine-learning model was evaluated in terms of accuracy, sensitivity, specificity and AUC score. The results showed that Random Forest classifier with Random Forest feature selection is the most suitable machine learning model for classification of CKD as it has the highest accuracy, sensitivity, specificity and AUC with 98.825%, 98.04%, 100% and 98.9% respectively which outperformed other classifiers. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Feature Selection

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Multi-static Active Sonar Target Recognition Method Based on Bionic signal 2

Multi-static Active Sonar Target Recognition Method Based on...

引用

2nd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Liu, Jiheng Zhou, Zemin Zeng, Xinwu Natl Univ Def Technol Coll Meteorol & Oceanog Changsha Peoples R China

ISBN: (纸本)9781728151021

In modern sonar systems, automatic recognition of underwater targets has always been one of the key technologies in research. In recent years, classification and recognition methods based on machine learning have been widely used in underwater acoustic field where good results have been achieved. Compared with monostatic active sonar, multi-static active sonar can simultaneously acquire the forward, lateral, and backscattering information of the target, and can obtain more accurate and stable target recognition result. Furthermore, the transmit waveform of active sonar effort the performance in complex ocean environment. As all known, the signals transmitted by cetaceans have the characteristics of strong anti-jamming ability, high positioning accuracy, et al. Accordingly, the performance of multi-static active sonar target recognition based on bionic signal is investigated in this paper. Besides, the machine learning methods are applied to the recognition of echo signals, so that further good results and conclusions are obtained.

关键词： Underwater target recognition Active sonar machine learning Bionic signal

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