检索结果-内蒙古大学图书馆

9th International Conference on System Modeling and Advancement in Research Trends, SMART 2020

作者： Srivastava, Rohit Mishra, R.P. Kumar, Vivek Shukla, Himanshu Kumar Goyal, Neha Singh, Chandrabhan Computer Science and Engineering F.O.E.T. University Lucknow India Electronics and Communication Dr R.M.L. Awadh University Ayodhya India Computer Science and Engineering Teerthanker Mahaveer University Moradabad India Computer Science and Engineering K.M. C. Language University Lucknow India Computer Science and Engineering B.N. College of Engg. and Tech. Lucknow India

ISBN: (纸本)9781728189086

Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights about malware attacks in COVID-19 on mobile devices. To meet the objectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as benign or malicious. © 2020 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Channel allocation in cognitive radio networks using energy detection technique

引用

Materials Today: Proceedings 2020年 33卷 934-938页

作者： Deepak Kumar Nayak Arjuna Muduli Mohammad Tahir Hussain Asgar Ali Mirza Jagadeesh Reddy Gummadipudi Nalluri Sravan Kumar Department of ECE Koneru Lakshmaiah Education Foundation K L University Vaddeswaram Guntur District India Final Year B.Tech in Electronics & Communication Engineering in Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District India

Cognitive radio (CR) is a modern and innovative technology that seeks to solve this issue of bandwidth depletion by increasing the use of the mobile cellular connectivity bandwidth. The distribution and re-allocation of channels is a crucial aspect of cellular mobile networks (CMNs) to maximize the utilization of the CMS. This work reviews the transmitter based cognitive radio (CR) techniques and analysis them based on signal to noise ratio . This paper also proposes a complete model for energy detection. The analysis and simulation are carried out in Matlab by taking bPSK and QPSK modulation. This energy detection has the advantages as no prior knowledge of the primary consumer is needed and this system's computational cost is very small.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Ambient Parametric Monitoring of Farms Using Embedded IoT & LoRa

Ambient Parametric Monitoring of Farms Using Embedded IoT & ...

引用

IEEE bombay Section Signature Conference (IbSSC)

作者： Tejaswi Raj Thoms A Johny Suyash Khetawat Rajeshwari b Sathya Prasad B.Tech Department of Electronics & Communication Engineering PES University Bengaluru

ISBN: (纸本)9781538674765

This paper presents the development of a cost effective, self-sustainable and solar rechargeable smart farming product based on embedded IoT and providing end to end solution using the cutting edge LoRa technology. It portrays the innovative methods developed to monitor ambient parameters using the LoRa LPWAN. The product leverages the capabilities of the Arduino Mini MCU capitalizing the low power and ultra-low power optimization techniques to enhance self-sustainability of the device. It also provides an insight of the alternative, creative approaches developed for some soil sensors (NPK and pH) to make the product cost-effective. These real time ambient data & parametric trends can be monitored over the cloud at user's convenience on an android application.

关键词： IoT LoRa Arduino power optimization soil sensors real time ambient data farm houses Farms Parametric embedding Clouds Methyltestosterone Androids micro-controller unit MCU gene Leverage

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

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

A study on hybrid recommender system with deep learning and deployment in big data

引用

Test engineering and Management 2019年第11-12期81卷 1869-1875页

作者： Ramanujam, Srivats S. Sivaneshwar, P. Naren, J. Madhumitha, S. Vithya, G. B. Tech Computer Science and Engineering School of Computing SASTRA Deemed University Thanjavur India B. Tech Information and Communication Technology School of Computing SASTRA Deemed University Thanjavur India School of Computing SASTRA Deemed University Tirumalaisamudram ThanjavurTamil Nadu India B. Tech Electronics and Communication Engineering School of Computing SASTRA Deemed University Thanjavur India School of Computing KL University Vijayawada India

A recommender system is a filtering tool that provides customized suggestions of products to users by using various techniques. A hybrid recommender system combines the approaches of two or more techniques, primarily focusing on Content-based recommender systems and Collaborative recommender systems, thus proving better suggestions by exploiting the advantages of both the strategies. This paper provides a detailed review of the various ways to implement a Hybrid Recommender System, enabling the reader to get a bird’s eye view on the concept. The study allows the user to understand the relationship between data characteristics and the relative performance of various approaches to a Hybrid Recommender System. The flow of the research starts with a description of the initial models of Recommender Systems, followed by a study on the application of Machine Learning and Deep Learning algorithms to the Hybrid Recommenders. The contributions to the study are dichotomous: 1) to identify and discuss the various techniques for Hybrid Recommendation Systems via a Systematic literature review and 2) to compare the various techniques to get a clear view of the efficiency and the accuracy of each technique. At the end of the study, the reader understands the development flow of Hybrid Recommender Systems and gets a clear idea of how various Recommender Systems work. It is observed that the technique which incorporates Deep Learning provides better results. © 2019 JPR Solutions. All rights reserved.

关键词： Recommender systems

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

Extracting Facial Features in Filter Modified Images using Neural Networks

引用

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

作者： M Sumithra M Shyamala Devi A Poornima Shatadru Sarkar Harish Patel Preethi Mahankali boggula Navya Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai 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

Facial identification plays a vital role in this digital era. Recent advancements in technology provide various filtering effects while capturing the images. Extracting the facial features from the filter modified image becomes challenging. To identify the important facial attribute values from those images, special features extraction techniques needs to be applied. Facial points are the key factors to recognize the face. When these factors are altered, facial detection becomes crucial. Initially apply the image pre-processing techniques to reduce the noise level in the images then perform feature selection. To overcome the curse of dimensionality, Occam's razor approach is followed to limit the selection of attributes. In different stages the features are going to be filtered by replacing the weaker attributes. Later Pearson Correlation, a filter based correlation technique is applied to evaluate the deviated numerical value from the target. The deviated features can be treated with Lasso effect to suppress the poor weighted features. Finally through recursive feature elimination, highest influencing attribute factor can be identified. In this approach some of the facial feature which is used in training the neural network can be discarded based on the deviation rates. In terms of accuracy the proposed system can recognize the dataset with 70% of accuracy with filter effected images.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Android Malware Detection Amid COVID-19

Android Malware Detection Amid COVID-19

引用

International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Rohit Srivastava R.P. Mishra Vivek Kumar Himanshu Kumar Shukla Neha Goyal Chandrabhan Singh Computer Science and Engineering F.O.E.T. University Lucknow India Electronics and Communication Dr R.M.L. Awadh University Ayodhya India Computer Science and Engineering Teerthanker Mahaveer University Moradabad India Computer Science and Engineering K.M. C. Language University Lucknow India Computer Science and Engineering B.N. College of Engg. and Tech. Lucknow India

ISBN: (数字)9781728189086

ISBN: (纸本)9781728189093

关键词： COVID-19 XML Static analysis Machine learning Feature extraction Malware Smart phones

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：