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Fog computing in 5G Mobile Networks: A Review

Fog Computing in 5G Mobile Networks: A Review

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international conference on Advanced science and Engineering (ICOASE)

作者： Fady E. F. Samann Siddeeq Yousif Ameen Shavan Askar Technical College of Administration Duhok Polytechnic University Duhok Iraq Research Center Director Duhok Polytechnic University Duhok Iraq Technical College of Engineering Erbil Polytechnic University Erbil Iraq

The existing Internet infrastructure cannot meet the demands of the exponential growth in data users need to access. Therefore, Fog computing (FC), Internet of Things (IoT), and 5G are upgrading conventional data transfer with innovative solutions and intelligently processing data to improve performance. Fog computing is considered a central component in the growth of the new 5G networks and the Internet of Things. These advanced technologies allow the Internet to provide enhanced services through sensors, continually monitoring a wide range of information. The paper reviews the most recent studies that implemented fog computing in a 5G environment by defining the essential services and network-oriented functionality. Moreover, the surveyed study is also discussed and assessed through sum-up tables with general remarks about the followed trends. The mentioned studies presented legitimate solutions for issues in the Vehicular Network and improved the current network architecture.

关键词： 5G mobile communication Vehicular ad hoc networks Quality of service Network architecture Market research Solids Software

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Salient Region Detection in Images Based on U-Net and Deep Learning

Salient Region Detection in Images Based on U-Net and Deep L...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Krishna Kumar M. Marimuthu Ayan Das Gupta Bhasker Pant Surendra Kumar Shukla Dhiraj Kapila Department of Computer Science and Engineering Bansal Institute of Engineering and Technology Lucknow India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Geography Chandernagore Government College Hugli India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9781665493970

Image analysis tasks use salient object detection because it not only identifies important elements of a visual scene but also lessens computational complexity by removing unimportant elements. In this research, we propose a novel salient object recognition method based on a deep learning network that maintains picture information in the mid and low regions. Using a deep learning model, our technique generates a coarse saliency map for the entire target image. The map is then fine-tuned utilising low-to-mid level information particular to the image. For detection of salient objects, we use a U-Net as our architecture. The saliency map can be predicted pixel by pixel, reducing low-level visual information loss. Our results show that our system regularly outperforms other approaches for detecting salient objects, resulting in superior precision and recall rates.

关键词： Deep learning Visualization Computational modeling Object detection Computer architecture Object recognition Task analysis

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Analysis and Comparison of Routing Protocols in Wireless Sensor Networks

Analysis and Comparison of Routing Protocols in Wireless Sen...

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international conference on Application of Intelligent Systems in Multi-modal Information Analytics, MMIA 2020

作者： Yu, Binbin College of Computer Science and Technology Jilin University Changchun130012 China College of Computer Science and Technology Beihua University Jilin132013 China Jilin Provincial Department of Education Key Laboratory of Intelligent Data Analysis and Processing Jilin132013 China

ISBN: (纸本)9783030514303

With the continuous development of wireless sensor technology, wireless network, big data, cloud computing and other new generation of information technology, wireless sensor networks has become a research hotspot in the field of information security and an indispensable part of people’s lives. Wireless sensor networks are extensive applied into agriculture, forestry, military, medical health, urban rail transit and other fields. Energy saving and power consumption reduction are the key technologies to be solved in wireless sensor networks. Wireless Sensor Networks take common perception and acquisition of the information of the objects to be perceived as the main purpose. In the article, firstly, the classical routing protocols for wireless sensor networks are introduced. Secondly, the relevant effective energy-saving routing protocols are analyzed and compared. Finally, the future development trend of this research is prospected. © 2021, Springer Nature Switzerland AG.

关键词： Routing protocols

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Machine Transliteration for Indian languages: Survey

Machine Transliteration for Indian languages: Survey

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international conference on Parallel Distributed and Grid computing (PDGC)

作者： Palakpreet Kaur Kamal Deep Garg Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665454025

Transliteration is an important aspect when conversing from one language to another. This paper addresses several models designed for the Transliteration of Indian languages. It deals with the methods and approaches used for the languages like Marathi, Hindi, and Punjabi. A Transliteration model is designed on the basis of grapheme, phoneme, correspondence and hybrid methods. Various models have been designed for top-level languages but for Indian languages, the researchers are yet in an infant state as fewer resources are available for them as compared to other languages. Initial research work was rule-based which means rules had to be created for every language taking into consideration of rules and grammar of that language. Later, technology transfer occurred, and alignment models and hybrid models gained popularity. Technological progress in the last few years have improved Transliteration results manifold. advances in this area contributed to the advancement of communication technologies immensely. To improve data retrieval processing, research developments and Transliteration results, the future scope and requirements of several models are discussed briefly in this paper. The analysis of various models in this paper can be further used to improve research in this field.

关键词： Manifolds Analytical models Computational modeling Technology transfer Phonetics Grid computing data models

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Research on Key technologies of Multi-modal Heterogeneous Medical Big data Based on Deep Learning

Research on Key Technologies of Multi-modal Heterogeneous Me...

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ISCTT 2022;7th international conference on Information science, Computer Technology and Transportation

作者： Yan Zeng Wei Liang

With the continuous deepening of social and economic development and medical reform, as well as the rapid development of some new technologies such as artificial intelligence, big data, and cloud computing, intelligent information technology combining medical and industrial has become a necessary trend. Among them, multi-source heterogeneous data fusion based on machine learning theory and supported by perceptual data has become a current hot research field, which is widely used in urban systems such as medical care, homes, and transportation. Massive perception data are generated in different fields or industries, and each type of data contains a large amount of unlabeled data, data sparse areas and domain knowledge, and there are big differences in these data types, data relationships and data quality. How to fully mine The information of single source data, and the fusion of these single sources of information, the establishment of a big data model based on these fusion information, and finally the establishment of an intelligent auxiliary diagnosis system, has become one of the important research contents. This article observes the patient's diagnosis and treatment process in the hospital, combines deep learning network and multi-modal data fusion technology, and uses deep semantic matching network to study the establishment of a hospital intelligent diagnosis and treatment auxiliary system, which provides a reference for medical big data detection and diagnosis and treatment.

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Understanding DNS Query Composition at B-Root

Understanding DNS Query Composition at B-Root

引用

IEEE/ACM international Symposium on Big data computing (BDC)

作者： Jacob Ginesin Jelena Mirkovic Northeastern University USC/ISI

The Domain Name System (DNS) is part of critical internet infrastructure, as DNS is invoked whenever a remote server is accessed (an URL is visited, an API request is made, etc.) by any application. DNS queries are served in hierarchical manner, with most queries served locally from cached data, and a small fraction propagating to the top of the hierarchy – DNS root name servers. Our research aims to provide a comprehensive, longitudinal characterization of DNS queries received at B-Root over ten years. We sampled and analyzed a 28-billion-query large dataset from the ten annual "Day in the Life of the Internet (DITL)" experiments, from 2013 through 2022. We sought to identify and quantify unexpected DNS queries, establish longitudinal trends, and compare our findings with published results of others. We found that unexpected query traffic increased from 39.57% in 2013 to 67.91% in 2022, with 36.55% of queries being priming queries. We also observed growth and decline of Chromium-initiated, random DNS queries. Finally, we analyzed the largest DNS query senders and established that most of their traffic consists of unexpected queries.

关键词： Uniform resource locators Big data Market research Internet Servers Domain Name System

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An Efficient Object Detection and Classification from Restored Thermal Images based on Mask RCNN 6

An Efficient Object Detection and Classification from Restor...

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6th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Thenmozhi, E. Karunakaran, A. Arunkumar, J.R. Chinnammal, V. Kalaivanan, C. Anitha, G. Panimalar Engineering College Department of Information Technology Chennai India S.A Engineering College Department of Electronics and Communication Engineering Chennai India Modern Institute of Technology and Research Centre Department of Computer Science and Engineering Rajasthan Alwar India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

In recent years, thermal cameras are extensively employed in several industries, including biometrics, intelligent surveillance, and health monitoring. The thermal cameras' exorbitant price, meanwhile, makes them difficult to obtain. Furthermore, blurring brought on by camera movement, object movement, and focus settings is a problem with thermal photos. There haven't been many research on thermal image-centered picture restoration that focus on such issues. Additionally, it is critical to accelerate the processing capability of image treatment technologies in order to work in tandem with techniques like object tracking and activity detection that make use of temporal data from thermal recordings. Furthermore, no research has been done on the use of thermal pictures for super-resolution rebuilding and deblurring. Due to the inability to discern reflected on the soil surface or wall caused by the heat emitted by the item, previous research on object recognition using thermal imaging include inaccuracies. This paper suggests a deep learning-based technique for thermal image reconstruction that combines deblurring and super-resolution reconstruction in one step. Recent advances in deep learning have shown that approaches based on generative adversarial networks (GANs) perform well in image-to-image translation challenges because they can maintain texture features in pictures and produce finer, more convincing textures than traditional feed forward encoders. This research study suggests a deblur-SRRGAN for thermal image restoration in light of the benefits of GAN. Additionally, a MR-CNN is also recommended to perform object recognition in the thermal image reconstruction. © 2022 IEEE.

关键词： Image reconstruction

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Research on the Application of Computer Digital Retrieval Technology in the Construction of Library and Information database

Research on the Application of Computer Digital Retrieval Te...

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Applied Physics and computing (ICAPC), international conference on

作者： Chen Wendi Department of Management Henan University of Science and Technology Luoyang China

The key technologies of digital library mainly include the digitization technology of multimedia information; the retrieval technology of multimedia media such as images, sounds and texts; the storage, management and retrieval technology of hypermedia information; the copyright protection technology of digital documents and Web server and multimedia database interface technology. Traditional platforms have always had the problems of poor processing effect on library information data mining and long running time. This paper innovatively proposes and designs a library and information data mining processing platform based on machine learning. At the same time, this paper designs a library information database system based on digital retrieval. The platform designs the data mining module, data storage module and data analysis module of the hardware. The simulation results show that this method has higher precision and better reliability for intelligent library and information identification. The system effectively improves the intelligence authenticity identification and intelligence data retrieval and collection capabilities.

关键词： Simulation Storage management Multimedia databases Libraries Hardware Web servers Software

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Traffic improvisation by identifying the accident locations using ML/data mining approaches

Traffic improvisation by identifying the accident locations ...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Nitish Giri Goswami Pranav Sharma Aditi Arora Nidhi Singh Department of Computer Science Abes Engineering College Ghaziabad U.P. India

Road Traffic Accident Prediction is really widely necessary element that affects the premature death of lots of people and also have the losses of property which is either public or private. Traffic road safety basically is a word which is related to the proper planning and the executing the various approaches to reduce the cause of road traffic accidents. Road traffic accident survey is really most essential to pick out diverse elements connected with road accidents that can surely help in decreasing the accident rate. The homogeneity of road traffic accident data is major problem in traffic road safety survey. Primarily in this paper we are using decision tree, random forest classifier on a current traffic road accident data. Although, in these defined procedures they are efficiently fitted to pull out homogeneity of road traffic accident data. Several studies center on procedure of road accidents. Other elements put in weather and light conditions of the road. Road accident harshness keeps on changing over a period of time and also expand infinitely. Hence in this study we basically examined latest works, data mining methodologies and tools that were demonstrated better in accident factual data analysis and prediction. We also made a web app using flask to deploy our machine learning model and use this app to predict and send a message to alert the user.

关键词： Road accidents Urban areas Government Predictive models Road safety Planning Decision trees

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Literature Survey on Face Recognition of Occluded Faces

Literature Survey on Face Recognition of Occluded Faces

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international conference on Circuit, Power and computing technologies (ICCPCT)

作者： Anil J L. Padma Suresh P. Muthukumar S.H. Krishna Veni P. Asha Rajesh Prasad Department of Electrical and Electronics Engineering Nooral Islam Centre for Higher Education Thuckalay Tamil Nadu India Department of Electrical and Electronics Engineering Baselios Mathews II College of Engineering Sasthamcotta Kollam Kerala India Department of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tiruvallur Chennai Tamilnadu India Department of Computer Science and Engineering Baselios Mathews II College of Engineering Sasthamcotta Kollam Kerala India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Stella Mary’s College of Engineering Tamil Nadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

In this paper, a study of the recent advancements in occluded face recognition is made. Recently, this has become very relevant due to the outburst of corona virus. Because of the seriousness of the spread of the disease, every person wore a mask, and it became challenging for automated systems to identify the person. So, it became the need of the hour to find new techniques for occluded face detection. In the years 2021 to 2022, many papers have been published in this regard. Researchers all over the world have contributed significantly towards occluded face recognition. Also, the advancement of deep learning has helped researchers a lot in implementing innovative techniques to deal with the challenges in face recognition, especially occluded faces, which is considered one of the most difficult challenges to overcome. This paper intends to push the limits further and encourage researchers to find more innovative techniques for occlusion-independent face recognition. In this paper, the recent publications in this field are studied. This paper gives an insight into different algorithms which use Convolutional Neural Networks, Deep Learning, Attention mechanism, Dictionary representation, Simultaneous segmentation, Joint segmentation and identification, etc., for occluded face detection and recognition. Incorporating these strategies in face recognition has substantially improved the recognition rate, specifically for occluded face recognition. Additionally, the databases for training occluded face recognition algorithms lack adequate data. This paper also gives an insight into the recent databases available for training occluded face recognition algorithms. Also, different methods for obtaining synthesised occluded faces are discussed.

关键词： Training Deep learning Surveys Representation learning Attention mechanisms databases Face recognition

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