检索结果-内蒙古大学图书馆

2019 IEEE International Conference on System, Computation, Automation and Networking, ICSCAN 2019

作者： Shah, Chinmay Shaikh, Maruf Shah, Dhruvesh Samdani, Krishna B-Tech Computer Engineering MPSTME NMIMS University Mumbai India Computer Engineering Department MPSTME NMIMS University Mumbai India

ISBN: (纸本)9781728115252

In recent years, there has been a rapid advancement in the healthcare sector. A major part of this development is possible due to the availability of electronic health records for the patients. Traditionally, the healthcare system used to consist of health records which were handwritten by doctors and other specialists and the medical history of the patients were filed up and they were kept either with the patients or at the hospital. This method not only required a huge amount of manual work and time but also resulted in extensive usage of papers. Thus, an enormous volume of data was generated that was physically present on paper. Simultaneously, with the advancements in the field of computer science, we are able to store the medical record as electrical health records. Also, with the help of clinical analytics, we now have various models and algorithms for storing and computing a huge amount of data and gain certain insights from them. This is known as big data. And healthcare, being an information-rich enterprise, a lot of big data applications can be used here. In this paper, we discuss the different frameworks that are used to store such data and, various methods used to predict certain diseases at an early stage. Such predictive diagnosis will help in cost reduction and can be lifesaving in certain cases. © 2019 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Review of Convolutional Neural Network based Image Enhancement techniques

A Comprehensive Review of Convolutional Neural Network based...

引用

IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Katyani Singh Avi Seth Harshit Singh Sandhu Krishna Samdani B.Tech Computer Engineering MPSTME NMIMS University Computer Engineering Department MPSTME NMIMS University

Image enhancement is the most important field in image research and processing. The main aim here is to artificially enhance the photo quality and visual appearance. Many photos end up with unusually high amount of noise and grain due to poor sensor quality and control, or with low sharpness. It is expensive and sometimes impossible to increase the resolution of the sensors (even counter intuitive at times) inside the cameras. Convolutional Neural Networks (CNN) rely on image structure and the texture to serve as differentiating features. In this paper, we do a literature review of CNN based techniques for Image denoising, Image super resolution, contrast enhancement, improving images in low light conditions, and then propose a method utilizing the above mechanisms, and our own devised system which uses nearest neighbor classification to rebuild images, along with an HDR photography approach similar to Google RAISR's.

关键词： Cameras Image resolution Convolutional neural networks Image enhancement Image color analysis Google Adaptation models

来源：评论

学校读者我要写书评

暂无评论

VLSI implementation using fully connected neural networks for energy consumption over neurons

引用

SUSTAINAbLE ENERGY techNOLOGIES AND ASSESSMENTS 2022年第PartA期52卷

作者： Mehbodniya, Abolfazl Kumar, Ravi Mohanty, Sachi Nandan Geetha, A. bedi, Pradeep Tripathi, Rohit [a]Department of Electronics and Communications Engineering Kuwait College of Science and Technology (KCST) Doha Area 7th Ring Road Kuwait [b]Department of Electronics and Communication Engineering Jaypee University of Engineering and Technology Guna Madhya Pradesh India [c]Department of ECE Saveetha School of Engineering SIMATS Thadalam Chennai - 602105 Tamil Nadu India [d]Department of Computer Science & Engineering Vardhaman College of Engineering (Autonomous) Hyderabad India [e]B.Tech Department of ECE Indian Institute of Space Science and Technology Thiruvananthapuram 695547 Kerala India [f]Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603203 India

Increasingly, the capacity of artificial neural networks to identify and express top-level concepts in sets of data has become quite significant. Deep learning models like fully connected layer machine learning have demonstrated outstanding results in range of identification and verification applications. It's technology deployments suffered as from complexity and huge silicone areas with energy consumption. Memory accesses, the bulk from which happens in fully connected systems, comprise the energy usage of machine learning. It has many of the depths of neural network. Throughout this work, researchers suggest densely connected networks by demonstrating that there is indeed a drop in the number of links inside fully connected systems of up to 90% enhancing the quality of three popular sets of data. To decrease the memory needs have suggested low-connected grid networks, researchers present perhaps an effective hardware implementation based on the linear response registers. In comparison to the traditional design of fully connected layer neural network models, the suggested design could save up to 90 percent of storage. Findings of execution further reveal that artificial neurons of suggested weak network connections have a power consumption decrease up to 84 percent compared to one single atom of fully connected layer machine learning.

关键词： Neural Network Deep learning Energy consumption VLSI

来源：评论

学校读者我要写书评

暂无评论

SOUL: Simulation of Objects in Unity for Learning

SOUL: Simulation of Objects in Unity for Learning

引用

International Conference on Intelligent Communication and Computational techniques (ICCT)

作者： Lavina Nagpal Meghna Jaglan Anuraj Kathait Aakil Mathur Abhishek Vichare B.Tech Computer Engineering MPSTME NMIMS University Mumbai India

This paper explores the realms of creating a user-friendly and highly interactive environment for e-learning. It offers an in depth explanation about how a web based application along with Unity can be built to allow the user to learn myriad of subjects with ease. The end user can access a farrago of e-learning modes such as basic mode, game mode, fast track mode, and certification mode for a wide range of concepts and their topics. The paper gives detailed information about the requirement specifications for building such an e-learning portal and its proposed architecture and flow diagrams. It then further explains how to build these modes in Unity using C# to write the scripts and then embed them into a web application subsequently. The paper concludes by offering critical analysis on developing such an e-learning application and the benefits of each mode.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Face Spoof Detection Using Gray Level Co-Occurrence Matrix and Discrete Wavelet Transform Feature Extractor

Face Spoof Detection Using Gray Level Co-Occurrence Matrix a...

引用

International Conference on Computing Sciences (ICCS)

作者： Amal Mathew Kaushik Daiv Polkumpally Rohan Goud Piyush Talreja Sai Sanjana Reddy Vatte B.tech Computer Science Ms Ramaiah University of Applied Sciences Bangalore Karnataka India B.E. Computer Science MIT College of Engineering Pune Maharashtra India B.E Computer Science Chaitanya Bharathi Institute of Technology Hyderabad India B.E Computer Science Maharashtra Institute of Technology Pune Maharashtra India

ISBN: (纸本)9781665494465

Face identification using ML (machine learning) is well-known. Attendance structures may benefit from this method. Using this method, you may achieve the desired area, as well as beneficial attributes and a dataset, by preparing two sets of data again for test and training phases. To distinguish between a testing set and a test sets, a photograph is used as a testing set. An ensemble classification method is used to sort the test images into categories like “identified” and “unidentified.” This model can't provide reliable findings since it simply divides data into two categories. The development of GLCM was motivated by the need to use texture properties to identify faces. The existence of the query picture is noted once face detection has taken place. In simulation findings, the new model outperforms the baseline models in terms of accuracy. Keywords—Ensemble classifier, GLCM, Face Spoof, SVM, DWT

关键词： Training Image segmentation Face recognition Training data Machine learning Transforms Feature extraction

来源：评论

学校读者我要写书评

暂无评论

SD-measure: a social distancing detector

arXiv

引用

arXiv 2020年

作者： Gupta, Savyasachi Kapil, Rudraksh Kanahasabai, Goutham Joshi, Shreyas Srinivas Joshi, Aniruddha Srinivas B.Tech Department of Computer Science and Engineering National Institute of Technology Warangal Telangana506004 India

The practice of social distancing is imperative to curbing the spread of contagious diseases and has been globally adopted as a non-pharmaceutical prevention measure during the COVID-19 pandemic. This work proposes a novel framework named SD-Measure for detecting social distancing from video footages. The proposed framework leverages the Mask R-CNN deep neural network to detect people in a video frame. To consistently identify whether social distancing is practiced during the interaction between people, a centroid tracking algorithm is utilised to track the subjects over the course of the footage. With the aid of authentic algorithms for approximating the distance of people from the camera and between themselves, we determine whether the social distancing guidelines are being adhered to. The framework attained a high accuracy value in conjunction with a low false alarm rate when tested on Custom Video Footage Dataset (CVFD) and Custom Personal Images Dataset (CPID), where it manifested its effectiveness in determining whether social distancing guidelines were practiced. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Twitter Sentiments Analysis Using Machine Learninig Methods

Twitter Sentiments Analysis Using Machine Learninig Methods

引用

Emerging technology (INCET), International Conference for

作者： Lokesh Mandloi Ruchi Patel B.Tech Computer Science and Engineering Meidcaps University Indore India Dept. Computer Science and Engineering Medicaps University Indore India

ISBN: (数字)9781728162218

ISBN: (纸本)9781728162225

Analysis of sentiments is the method of deciding whether the sentiments in the text is positive, negative or neutral. It is also known as material polarity or mining of opinions. The growth and advancement in social media platforms engaged a huge number of users. Social media platform like twitter where users can post their tweets in 280 characters. because of the limited number of characters in tweets, it becomes easy for the sentiment analysis. On Twitter 550 millions of tweets are posted daily. Twitter also represents all age group people and also a fair representation of gender. Therefore, the sentiment analysis of twitter data becomes somewhat general sentiments of society. In this paper, we will compare various Machine Learning methods like the Naïve bayes Classification method, Support Vector Machine Classification Method and Maximum Entropy Classification method. We will see how sentiments analysis is done by this classification algorithm and what is the accuracy and precision in these cases.

关键词： Sentiment analysis Twitter Machine learning Feature extraction Support vector machines Entropy

来源：评论

学校读者我要写书评

暂无评论

Mental Health Monitoring System using Artificial Intelligence: A Review

Mental Health Monitoring System using Artificial Intelligenc...

引用

International Conference for Convergence of technology (I2CT)

作者： Vidhi Mody Vrushti Mody B. Tech Department of Computer Engineering NMIMS-MPSTME

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

Mental health is an important public health concern worldwide and should be a prominent part of the healthcare industry. However, the pace of development in this field appears to be quite slow. Recently AI techniques have attracted a lot of attention in different industries, including mental health. With the help of advanced AI techniques and machine learning algorithms, a personalized care that focuses on providing emotional support catered to a specific individual has been facilitated. In this paper, we analyse various systems for mental health monitoring namely virtual counselling, precision therapy, and diagnostic systems by reviewing the algorithms and parameters used in each system. We conclude by proposing a system which combines the above-mentioned systems and is planned to provide personalized mental care.

关键词： Medical treatment Feature extraction Support vector machines Monitoring Artificial intelligence Decision trees Employee welfare

来源：评论

学校读者我要写书评

暂无评论

Variant component principal linear reduction for prediction of hypothyroid disease using machine learning

引用

Journal of Physics: Conference Series 2021年第1期1767卷

作者： M. Shyamala Devi P. Swathi Ayesha Jahangir A. Dhanunjai Sai Kumar A. Naga bhushan Naidu M. Raghuram Manas Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Third Year B.Tech Students Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

With the tremendous technological growth, the world is shifted to adapt the different food and life style by the people that results in the improper working of the body organs. The change in the food habits leads to a major problems that we face in the current scenario is the presence of hypothyroid in the body. The likelihood of hypothyroid still ruins as a challenging issue due to the uncertainty of proper symptoms. With this background, the machine learning can be used towards health care scenarios for the prediction of disease based on the patients past history. This paper focus on predicting the existence of hypothyroid with respect to the patients' medical parameters. The hypothyroid patient dataset is taken from the UCI Metadata repository with 24 columns and 3163 unique patient's records is used for the experimentation of hypothyroid with the following contributions. Firstly, the hypothyroid dataset from UCI machine repository is subjected with the data processing and exploratory analysis of the dataset. Secondly, the unrefined data set is fixed with different classifier algorithm to find the presence of hypothyroid and to examine the efficiency metrics before and after feature scaling. Thirdly, the data is processed to PCA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Fourth, the data is processed to LDA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Experimental results show that the Kernel Support Vector Machine classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced PCA dataset. Similarly, the Logistic Regression, Kernel Support Vector Machine and Gaussian Naïve bayes classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced LDA dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Activation Layers Implication of CNN Sequential Models for Facial Expression Recognition

引用

IOP Conference Series: Materials Science and engineering 2021年第1期1074卷

作者： M. Shyamala Devi Ankita Sagar Karan Thapa Mreenmoy Hazarika P. Swathi Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Facial Expression Recognition is the critical part in the human emotional detection in the field of image processing. The application tends to soft or hard real time application based on the power of expression detection of idle image or videos. The Social communication with any object can be done by verbal or non-verbal format. Expression and Emotion detection completely rely on the non-verbal communication and facial expression. Machine Learning play an important role in the recognition of facial expression. An attempt is made in this paper to analyse the performance of Convolutional neural network models with diverse activation layer for the performance ***, the facial expression dataset is extracted from the website http://***/ckagree/, http://***/database/faces/ is subjected with the data processing. Secondly, the data analysis is done for the distribution of expression image in the training and testing dataset. Thirdly, the facial expression images are detected with HAAR cascade and then the images are cropped with (350, 350). Fourth, the facial expression images is applied with normalized and the bottleneck features are created for training and testing data. Fifth, the training dataset is fitted with convolutional sequential neural network models with various activation layers like Sigmoid, Elu, Relu, Selu, Tanh, Softsign and Softplus. Sixth, the performance analysis is done with loss and accuracy for all the epoch of all CNN models for all the activation layers. Experimental results show that CNN sequential model with Relu activation layer is found to have the accuracy of 100%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：