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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是1651-1660 订阅

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Shared Word Embedding Space Modeling Method Based on Orthogonal Projection 3

Shared Word Embedding Space Modeling Method Based on Orthogo...

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3rd international conference on Vision, Image and Signal Processing (ICVISP)

作者： Liu, Gang Cao, Yang Wang, Kai Liu, Wangyang Li, Guang Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China CETC Big Data Res Inst Co Ltd Guiyang Peoples R China

ISBN: (纸本)9781450376259

With the continuous development of computer technology, machine learning has been applied in more and more fields. However, the application of word embedding technology in bilingual Chinese and English still needs to be developed. In this paper, we propose a model construction process based on orthogonal projection, and analyze the validity of the model from multiple perspectives. We carry out word sense similarity experiments and word analogy experiments for the quality of single language in the model, and cross-language text similarity experiments for different linguistic quality in the model. Through the analysis of the experimental results, it can be proved that the proposed shared word embedding space model achieves good results compared with the traditional word embedding model, and the effect of the model achieves the desired purpose.

关键词： machine learning Orthogonal Projection Shared Word Embedding Technology Cross Language Document Similarity

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Feature Selection Using Improved Teaching learning Based Algorithm on Chronic Kidney Disease Dataset 3

Feature Selection Using Improved Teaching Learning Based Alg...

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3rd international conference on Computing and Network Communications, CoCoNet 2019

作者： Manonmani, M. Balakrishnan, Sarojini Department of Computer Science Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India

Feature selection plays an important role in almost any data mining application especially in medical data mining to solve the problem of 'curse of dimensionality' and provide early diagnosis with relevant features and high accuracy. Innumerable feature selection methods have been presented in state-of-arts literature to tackle the problems of high dimensional data. Many evolutionary and swarm intelligence algorithms find solutions based on algorithm-specific control parameters. However, it is a challenging task to identify the optimal feature subset using a feature selection algorithm that is not dependent on the controlling parameters of an algorithm that is specific to a particular problem in hand. Hence, the present research work is based on the working principle of the original TLBO algorithm which does not require any algorithm-specific parameters. The proposed research work is known as Improved Teacher Learner Based Optimization (ITLBO) algorithm which aims to select the best feature subset based on Chebyshev distance formula in the evaluation of the fitness function and common control parameters viz., population size and number of generations to find the optimal feature subset for early diagnosis of chronic diseases. The proposed feature selection technique was applied to Chronic Kidney Disease (CKD) dataset and has achieved a significant feature reduction of 36% compared to the feature reduction of 25 % obtained by applying the original TLBO algorithm. The derived optimal feature subset obtained from TLBO algorithm and feature subset obtained from ITLBO algorithm is validated by evaluating the accuracy of Support Vector machine (SVM), Convolution Neural Networks (CNN) and Gradient Boosting classification algorithms. Experimental results reveal that there is an overall improvement of classification accuracy for the three algorithms for the derived feature subset from the proposed feature selection algorithm compared to the original TLBO algorithm. © 2020 The

关键词： Support vector machines

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BioSMART 2019 - Proceedings: 3rd international conference on Bio-Engineering for Smart Technologies

BioSMART 2019 - Proceedings: 3rd International Conference on...

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3rd international conference on Bio-Engineering for Smart Technologies, BioSMART 2019

ISBN: (纸本)9781728135786

The proceedings contain 34 papers. Topics discussed include: a cooperating metaheuristic approach for MR image segmentation;multibiometric score-level fusion through optimization and training;doppler radar system for an automatic transfer phase detection using wavelet transform analysis;an IoT-cloud based solution for real-time and batch processing of big data: application in healthcare;content-based image retrieval for breast ultrasound images using convolutional autoencoders: a feasibility study;a computer-aided diagnosis system for glioma grading using three dimensional texture analysis and machine learning in MRI brain tumor;a sparse variable step-size LMS algorithm for impulsive noise;haptic feedback in surgical robotic interface controller;and a fuzzy adaptive binary global learning colonization-MLP model for body fat prediction.

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Research on Artificial Intelligence machine learning Character Recognition Based on Online machine learning Method

Research on Artificial Intelligence Machine Learning Charact...

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IEEE international conference of Safety Produce Informatization (IICSPI)

作者： Yuan Zhang School of Computer Science and Technology Shandong University of Finance and Economics Jinan Shandong China

ISBN: (数字)9781728177380

ISBN: (纸本)9781728177397

The development of artificial intelligence technology has completely transformed human daily life and industrial production mode. Due to the improvement of scientific level and the progress of Internet technology, people have gradually entered the information society, and information technology plays an indispensable role in many application scenarios of daily life and production. With the progress of social science, artificial intelligence technology has also developed rapidly, and people have made breakthrough progress in the research of machine learning. In the past, character recognition methods were based on the image representation of characters, and then the differences between characters were analyzed and processed. With the development of artificial intelligence technology, researchers have studied it more widely and deeply. As a new technical field in artificial intelligence technology, machine learning generally plays an important role in network search, language recognition and other scenarios. This paper will discuss and study the research of character recognition method based on machine learning, and make corresponding arrangement and summary.

关键词： machine vision Social sciences machine learning Production learning (artificial intelligence) Character recognition Artificial intelligence

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Pretrained Convolutional Neural Networks for Cancer Genome Classification

Pretrained Convolutional Neural Networks for Cancer Genome C...

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international conference on computer applications & Information Security (ICCAIS)

作者： Aisha Abdullahi Khlood Bawazeer Salwa Alotaibai Elmaha Almoaither Mashael Al-Otaibi Haya Alaskar Thavavel Vaiyapuri Computer Science Department Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia

ISBN: (数字)9781728142135

ISBN: (纸本)9781728142142

Deep learning techniques, particularly convolutional neural networks (CNNs), have proved their success and popularity recently in many fields, especially distinguishing and analyzing medical diseases. Motivated by this direction, our work attempts for the first time to investigate the application of a state-of-the-art deep learning technique on genomic sequences to classify tumours of different classes. The novelty of our approach lies in the application of the popular pre-trained AlexNet on an image version of the RNA-Sequence data. Our methodology demonstrated an outstanding performance with good sensitivity results of 98.3%, 94.1%, 96.6%, 100%, and 100% for selected types of breast, colon, kidney, lung and prostate cancers respectively. The outcome of this work is expected to provide a new direction for genomics data classification and designing accurate automated diagnosis tools.

关键词： machine learning Cancer Genomics Bioinformatics Training Tumors

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Deep learning in Image Processing: Revolutionizing Diagnosis in the Field of Dermatology 3rd

Deep Learning in Image Processing: Revolutionizing Diagnosis...

引用

3rd international conference on Advanced Informatics for Computing Research (ICAICR)

作者： Nayak, Richa Hasija, Yasha Delhi Technol Univ Bawana Rd Delhi India

ISBN: (纸本)9789811501081;9789811501074

The modern-day society is increasingly dependent on computer-aided tools and techniques. Digital imaging techniques have a tremendous impact on our day-to-day lives. Image processing is a vital component in the field of biological sciences and has the potential to drastically change the computer-human interface. Image processing refers to the conversion of an image into a digital form followed by enhancement of the image in order to extract useful information from it that are indiscernible by human ocular perceivers. Rapid advances in image processing, computerized reconstruction of an image and allied advancements in image analysis algorithms and the application of artificial intelligence has spurred a revolution in the field of medical and diagnostic imaging. Deep learning, a type of Artificial Neural Network (machine learning), is resurfacing as a powerful tool for its utilization in big healthcare data. The integration of deep learning techniques to image processing has the potential to add momentum to the dermatological imaging and promote early and accurate diagnosis of skin lesions. This review attempts to discuss the fundamentals of image processing, its importance, various clinical imaging modalities in use in the field of dermatology and application of deep learning algorithms in dermatological imaging, accentuating the inadequacies and future research prospects.

关键词： Image processing Image segmentation Dermatological imaging Deep learning

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Diabetes prediction by using Big Data Tool and machine learning Approaches

Diabetes prediction by using Big Data Tool and Machine Learn...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： Srinivasa Rao Swarna Sumati Boyapati Pooja Dixit Rashmi Agrawal Tata Consultancy Services Edison NJ IRIS Software INC Edision NJ Bundelkhand University Jhansi UP Faculty of Computer Application Manav Rachna International Institute of Research and Studies

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

The use of big data in daily life is increasing from health care, social networks, banking systems, entry into the banking system, use of sensors and smart devices, leading to large amounts of data. That's why, it is necessary to develop a model and device that handles data in optimized form. In this paper, diabetes predicated from a data set with the help of various machine-learning algorithms such as Naive bayes, KNN algorithm, Random forest, logistic regression. The main objective of the paper is to observe diabetes disease with the help of big data tools and machine learning model. For doing this, the authors can select more accurate model with the help of some matrices. This paper predict diabetes disease using four machine learning models and then compare their performance among themselves. machine learning provides more flexible and scalability than the older bio statistical method that helps it perform a variety of tasks such as risk detection, diagnosis, classification, and prediction.

关键词： Diabetes Medical services Big Data Diseases Classification algorithms machine learning algorithms machine learning

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Ford vehicle classification based on extreme learning machine optimized by bat algorithm 3rd

Ford vehicle classification based on extreme learning machin...

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3rd international conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the international conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Zhao, Yile Lu, Zhihai School of Education Science Nanjing Normal University NanjingJiangsu210023 China

ISBN: (纸本)9789811519246

The application of automobile identification in life is more and more extensive, so research on related technologies is receiving widespread attention. This article focuses on research on Ford vehicle identification, the theoretical method of identification is proposed and its effectiveness is verified in experiments. We first obtain the side-view image of the Ford car. Secondly, we use gray level co-occurrence to extract the feature of Ford car. Third, we use extreme learning machine as the classifier. Finally, we use bat algorithm to optimize the algorithm, and employ 10-fold cross-validation to ensure the validity of the data. The results of the research indicate that in the same kind of research, the method we employ has the highest accuracy (84.92 ± 0.64%). © Springer Nature Singapore Pte Ltd. 2019.

关键词： machine learning

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Convolutional Neural Network for Control Chart Patterns Recognition 19

Convolutional Neural Network for Control Chart Patterns Reco...

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3rd international conference on computer Science and application Engineering (CSAE)

作者： Hong, Zhiyong Li, Yuqiang Zeng, Zhiqiang Wuyi Univ Fac Intelligent Mfg Jiangmen Guangdong Peoples R China

ISBN: (纸本)9781450362948

Control chart, as a basic tool of statistical process control (SPC), has been widely used in monitoring the fluctuation of variables in manufacturing processes. Unnatural control chart patterns (CCPs) are associated with specific causes which can help practitioners control the manufacturing processes. Over the last several decades, many machine learning methods have been applied to the control chart pattern recognition, and have obtained outstanding results. Convolutional neural networks (CNNs), as a state-of-the-art deep machine learning approach, have been demonstrated remarkable performance in many machine learning fields. In this paper, a novel convolutional neural network was proposed to recognize CCPs. Different from traditional classification methods need hand-crafted features which will need a great engineering effort to improve the classifier accuracy, the proposed method extracts multilevel features from input data automatically. Besides, different kind of basic CPPs and concurrent CCPs (two or more basic CCPs occurring simultaneously) recognition model have been investigated by many researchers, but most models would start from scratch and the previous ones would be abandoned when a new CCPs recognition model was built in the research. To solve this problem, the strategy of transfer learning was used in a finely-tuned previous model to accomplish a new CPPs recognition task. The experiment showed that the proposed CNN model outperformed the support vector machine (SVM) model, and that building a CCPs classifier based on transfer learning has a higher performance than building a classifier from scratch.

关键词： Control chart patterns Concurrent patterns CNN Transfer learning

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Galvanic Skin Response signal based Cognitive Load classification using machine learning classifier 3

Galvanic Skin Response signal based Cognitive Load classific...

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3rd international conference on Electrical, computer and Telecommunication Engineering, ICECTE 2019

作者： Elahi, Md. Toufick E Binta Islam, Iffath Khulna University of Engineering Technology Department of Electrical and Electronic Engineering Khulna9203 Bangladesh

ISBN: (纸本)9781728164083

Cognitive load (CL) classification is an important research issue in the human-computer interaction paradigm. It is evident from recent research that Galvanic Skin Response (GSR) can be used to sense cognitive load. CL analysis is important for understanding the mental growth of the child and the psychology of patients going through the different traumatic situation. Inspired by such a novel application, this model is designed. In this work, a technique has been demonstrated to measure and evaluate the level of human cognitive load for different tasks by collecting GSR from 40 student participants. The students are asked to sit for a test to solve three different tasks-reading comprehension, solving mathematics, and cracking Sudoku. Time-domain features have been extracted from participant's GSR signals while these tasks are being analyzed. Some parameters such as Correlation Dimension (CD), Lempel-Ziv Complexity (LZC), Hurst Exponent (HE) and Shannon Entropy (SE) are used to analyze CL and these are used as features while accomplishing classification. The Level of stress or cognitive load is strongly observed with the one-way ANOVA test and box-whisker plots. Next, several machine learning algorithms are used to classify the various level of cognitive load. Using the 10 fold cross-validation and Naïve Bayes algorithm 91.5% accuracy is obtained. © 2019 IEEE.

关键词： Classification (of information)

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