检索结果-内蒙古大学图书馆

international conference on Contemporary computing and Informatics (IC3I)

作者： N. Deeban P.Shyamala Bharathi Department of VLSI Microelectronics Institute of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

The fifth-generation (5G) network provides support for a wide variety of systems, including applications that demand the highest level of security as well as dependable communication. Because of recent advancements in smart devices, we are currently experiencing an explosion in the generation of data as well as a heterogeneity that necessitates the development of new network solutions for improved traffic analysis and comprehension. In order to automatically manage the massive amounts of data, these solutions need to be both intelligent and scalable. It is now feasible and easy to deploy machine learning (ML) to solve complex problems, and its effectiveness has been validated in a number of different domains. This is due to the progress that has been made in high-performance computing. It is anticipated that the deployment of 5G wireless communication systems will start in the year 2020. The traffic management for 5G networks will present significant technical challenges due to the introduction of new use cases, new technologies, and new network architectures. In this paper, we have concentrated on analyzing network data traffic for 5G Network using an ensemble method known as Bagging ensemble with Random Forest. Bootstrap sampling is utilized in the standard ensemble method known as “bagging.” The random forest technique is an improvement on the bagging method that can result in better variable selection. First, we will go over the concept of bagging, and then we will go over the enhancement that brought about random forest. The random forest algorithm is objective because it consists of multiple trees, each of which is trained on a different subset of the data. When you have both categorical and numerical features to work with, the random forest algorithm performs particularly well. On the basis of the test data, the classification accuracy is an average of 96 percent, significantly outperforming other methods.

关键词： 5G mobile communication Telecommunication traffic Network architecture Classification algorithms Ensemble learning Security smart devices

来源：评论

学校读者我要写书评

暂无评论

A Review and development of research framework on Technological Adoption of Blockchain and IoT in Supply Chain Network Optimization

A Review and development of research framework on Technologi...

引用

2020 international conference on Mainstreaming Block Chain Implementation (ICOMBI)

作者： Rajeshwar S Kadadevaramth D Sharath B Ravishankar P. Mohan Kumar Siddaganga Institute of Technology Tumakuru India Industrial Engineering & Management Audencia Business school Nantes France BMS College of Engineering Bangalore India

ISBN: (数字)9789354069017

ISBN: (纸本)9781728172880

Industry 4.0 started to revolutionize the industrial processes, forcing industries of all the sector to digitize the current operation in the organization. Fast growing economy and increased customer demands also place important role in digitizing process. According to Infield Solutions research in 2017, currently 70% of the Fortune companies are using IoT in their business model, IoT is used for varies purposes for predicting maintenance, intelligent sensors on the jet engines, smart grid are used for utility services and energy control. Current IoT networks are connected to centralized system which is facing issues related to security and data reliability. Blockchain (chronological chain of block) is a peer-to-peer distributed ledger forged by agreement, shared with a system for “smart contracts”. The decentralized feature of blockchain overcomes the security and data reliability issues in IoT. However, on the other hand supply chain disruptions are causing huge impact on the logistics network. For creating resilient logistics network, all the parties across the supply chain (beyond tier 1) must be collaborated in order to trust each stakeholder. Based on these issues I devised two research questions: Research methods include preliminary analysis of the literature review for theoretical knowledge. Conduct interview or send research questions to the managers. Finally, concluding on how Blockchain and IoT can create resilient logistics network by Risk Management. Based on the survey results will be drawn.

关键词： Supply chains Cloud computing Edge computing Contracts

来源：评论

学校读者我要写书评

暂无评论

A Deep learning Technique for smart Gender Classification System

A Deep learning Technique for Smart Gender Classification Sy...

引用

Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Vijaya Krishna Sonthi Rishi Sikka Anusha R P. Bala Shanmuga Devi Sachin K Kamble Bhasker Pant Department Of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department Of Electronics Sanskriti University Mathura Uttar Pradesh India English A. P. C. Mahalaxmi College for Women Thoothukudi India Design Terna Engineering College Navi Mumbai CSE Graphic Era Deemed To Be University Dehradun Uttarakhand India

The examination of the possibility of employing deep learning techniques for transportation planning and modelling is the basis of this essay. The writers look at the transportation issues that have developed for urbanized populations. intelligent transportation systems will become increasingly crucial to satisfy people’s transportation demands as the urban population grows. Automated data gathering devices demand the usage of analytics tools in order to give insights for actual decision-making and policy planning. This research has two objectives. This research begins with a review of academic literature on gender and age identification using Convolutional Neural Network-based approaches estimate, followed by a thorough examination of the present methodology. Then, using the classification techniques, we conduct a review of roughly 30 publications to draw valid conclusions and forecasts about how the various CNN approaches and transportation systems will develop in the *** to the existence of a complicated background, object occlusion, and varying lighting conditions, gender determination from facial photos is a difficult process. Face photos may be used for a variety of purposes, including tracking, identification, and expression analysis. Two deep learning-based approaches are examined in this research for facial image-based gender classification. Convolutional neural networks (CNN) and Alex Net are two examples of these techniques. The effectiveness of both models for identifying the male and female classes from face photos was tested through experiments. Results indicate that both approaches were successful in classifying people by gender. Additionally, a comparison between these two models and a few of the common techniques for gender categorization was done.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Experimental Evaluation of Deep Learning Based Liver Cancer Identification Scheme Using Hybrid Classification Logic

Experimental Evaluation of Deep Learning Based Liver Cancer ...

引用

Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Mouleeswaran SK J. Bhuvaneswari Sakthi Saravanan. N S. Hemajothi Sivabalan A K. Vijayakumar Department of CSE Dayananda Sagar University Bangalore Department of Artificial Intelligence and Data Science RMK college of Engineering and Technology Chennai Department of EEE S. A. Engineering College Chennai Department of ECE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of ECE Easwari Engineering College Chennai Department of Information Technology St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Companies in the healthcare industry would be foolish to disregard medical analytics and decision-making tools that employ multi-modal intelligence. Liver cancer, among the most prevalent malignancies, can only be effectively treated if caught early. The contrast and brightness of liver tumours are identical to those of the surrounding tissues. Also, depending on the stage of cancer and the kind of tumour, abnormal tumour forms might be a major cause for concern. Segmenting a liver tumour entails first locating the cancer, and then cutting it into smaller pieces. However, while traditional methods of interactive segmentation require a large number of intensity levels, newer methods based on convolutional neural networks (CNN) and the Learning Scheme for Liver Cancer Identification (LSLCI) have limited presentation on images of liver tumours. This study showcases a new method for segmentation utilizing Coot Extreme Learning Model, which is based on deep learning, and it shows great efficiency in identifying tumors using accessible information of liver scans. The research process the first segmentation with minimal further user clicks in order to optimize the segmentation process. Doing so allows the suggested geodesic distance encoding method to incorporate inner boundary points. Liver cancer survival rates can be improved with early detection and treatment. The gold standard for liver tumour differential diagnosis is dynamic contrast-enhanced magnetic resonance imaging (MRI). But since MRI diagnoses are susceptible to human error, deep learning may provide a fresh approach to the diagnostic process. We trained a deep learning system using LSLCI and CNN to identify liver tumours from a variety of sources, including enhanced and unenhanced magnetic resonance images, as well as textual and numerical clinical data, as well as the results of laboratory tests.

关键词： Deep learning Liver cancer Image segmentation Magnetic resonance imaging Liver Magnetic resonance Medical services Convolutional neural networks Medical diagnostic imaging Tumors

来源：评论

学校读者我要写书评

暂无评论

Vulnerability Analysis of the Urban Transport System in the Context of smart Cities

Vulnerability Analysis of the Urban Transport System in the ...

引用

IEEE international smart Cities conference (ISC2)

作者： Antônio A. F. G. M. Pertence Raquel A. F. Mini Humberto Torres Marques-Neto Graduate Program in Informatics Pontifical Catholic University of Minas Gerais Belo Horizonte Brazil

ISBN: (数字)9781728182940

ISBN: (纸本)9781728182957

Large cities seek to incorporate intelligent systems into their infrastructure, industrial, educational, and social activities to improve the quality of service provided to citizens and make all processes more efficient. To this end, these cities use technologies that involve the areas of the Internet of Things, Big data, and Governance to make decisions by governments and authorities. In the field of Urban computing, it is possible to use open data in the GTFS format to solve the problems of the urban transport system using complex network metrics that make it possible to model complex interactions between objects in high dimensionality. Stops and routes are modeled as a graph, characterized by complex network metrics. According to the literature, if there is an interruption of the urban transport system, we make an evaluation of the degree of vulnerability of the system in the search for solutions. The main goal of this paper is to propose an approach to analyze the vulnerability of the urban transport system using complex network metrics along with GTFS data in scenarios targeted failures. As a contribution, the proposed approach uses the concept of skewness in the methodology established in the literature. The skewness enables a better understanding and differential the failure conditions from the evaluation of the vulnerability the urban transport system, through a quantitative indication of which local metrics most influence the decay of network metrics. The analysis of the results obtained in the proposed approach serves as a subsidy for studies in the search for solutions to the problems of the urban transport system, providing a tool to the set of technological options for the planning of urban transport systems of smart cities available to governments and authorities. The proposed approach can be applied in different cities, regardless of their size and location, as long as you have access to open geographic data in the GTFS format.

关键词： Measurement smart cities Government Complex networks Tools Search problems Planning

来源：评论

学校读者我要写书评

暂无评论

Fog Node Optimum Placement and Configuration Technique for VANETs

Fog Node Optimum Placement and Configuration Technique for V...

引用

international conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Rehab Shahin Ali A. El-Moursy Sherif M. Saif Hazem M. Abbas Salwa M. Nassar Faculty of Engineering Ain Shams University Cairo Egypt University of Sharjah Sharjah UAE Electronics Research Institute Cairo Egypt

ISBN: (数字)9781728165356

ISBN: (纸本)9781728165363

intelligent Transportation Systems (ITS) are nowadays considered very important applications of smart cities. One of the most important technologies that are utilized to support ITS is Vehicular Ad-hoc Networks (VANETs). In VANETs, vehicles communicate with each other (V2V) or with the infrastructure (Roadside Units) (V2I). Roadside Units (RSUs) collect data from vehicles in the coverage area and send it to cloud servers through the Internet. Cloud servers have high performance computational and storage capabilities that ITS applications require for data processing. However, due to the real-time requirements of the ITS applications, cloud approach alone cannot be guaranteed to satisfy the strict time constraints due to long latency access of the centralized cloud server. Fog computing is an emerging approach that extends the services of cloud computing to the edge of the network. Fog computing can be utilized in VANETs through deployment of fog nodes into RSUs. One of the major challenges is identifying the optimum number, locations and computational capabilities of the RSUs particularly in urban regions where obstacles exist heavily inside the coverage area of the RSUs. In this paper, we consider the optimization problem of fog-based RSU placement where the objective is to maximize the achieved level of service quality in a cost-effective way. The problem is formulated as a Satisfiability Modulo Theories (SMT) problem and solved using Microsoft Z3. The proposed approach is able to generate a set of solutions as Pareto front. We obtained data from OpenStreetMap for Cairo city. Our approach outperforms other solutions in the literature in terms of cost.

关键词： Cloud computing smart cities Signal processing Servers Time factors Optimization Edge computing

来源：评论

学校读者我要写书评

暂无评论

Architecture of an intelligent Intrusion Detection System for smart Home

Architecture of an intelligent Intrusion Detection System fo...

引用

IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Julian Graf Katrin Neubauer Sebastian Fischer Rudolf Hackenberg Ostbayerische Technische Hochschule Regensburg Germany Secure Systems Engineering Fraunhofer AISEC Berlin Germany

ISBN: (数字)9781728147161

ISBN: (纸本)9781728147178

Increasing cyber-attacks on Internet of Things (IoT) environments are a growing problem of digitized households worldwide. The purpose of this study is to investigate how an intelligent Intrusion Detection System (iIDS) can provide more security in IoT networks with a novel architecture, combining multiple classical and machine learning approaches. By combining classical security analysis methods and modern concepts of artificial intelligence, we increase the quality of attack detection and can therefore conduct dedicated attack suppression. The architectural image of the iIDS consists of different layers, which in parts achieve self-sufficient results. The results of the different modules are calculated by means of statement variables and evaluation techniques adapted for the individual module elements and subsequently combined by limit value considerations. The architecture image combines approaches for the analysis and processing of IoT network traffic and evaluates it to an aggregated score. From this result it can be determined whether the analyzed data indicates device misuse or attempted break-ins into the network. This study answers the questions whether a connection between classical and modern concepts for monitoring and analyzing IoT network traffic can be implemented meaningfully within a reliable architecture of an iIDS.

关键词： Artificial intelligence Intrusion detection Protocols Sensors Wireless fidelity Computer architecture

来源：评论

学校读者我要写书评

暂无评论

intelligent Navigation System for the Visually Impaired - A Deep Learning Approach

Intelligent Navigation System for the Visually Impaired - A ...

引用

international conference on computing Methodologies and Communication (ICCMC)

作者： Deepak Kumar Yadav Somsankar Mookherji Joanne Gomes Siddhant Patil Department of Information Technology St. Francis Institute of Technology Mumbai University

ISBN: (数字)9781728148892

ISBN: (纸本)9781728148908

Visually impaired individuals have been gradually claiming a significant stake in the population demographics. The proposed autonomous device aims to provide a holistic solution by engineering a smart navigation system that relentlessly scans the environment, detects and classifies neighboring objects using a 4 layered Convolutional Neural Network (CNN) that has been trained on a data set containing 2513 permutations of various images of household objects that an individual may encounter in daily life. The CNN follows the 80-20 rule for testing and training the self-learning model enabling it to learn recursively from the error rate. The proposed system then calculates distances of neighboring objects from the user and provides adaptive solutions in real time to manoeuvre the user to safety by providing auditory input in a simplistic manner which considers 10-24 frames per second while drafting the kinematic response for the user. The device has achieved an unprecedented success rate of serving within a response time of less than 50 ms. The accuracy of the CNN algorithm being at 94.6%, also sets a distinguished benchmark as an object detection algorithm thereby contributing to the success in simulations of the proposed device in a constrained environment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Distributed Machine Learning for Resilient Operation of Electric Systems

Distributed Machine Learning for Resilient Operation of Elec...

引用

international conference on smart Energy Systems and Technologies (SEST)

作者： M. Hadi Amini Ahmed Imteaj Javad Mohammadi School of Computing and Information Sciences Florida International University Miami FL USA Sustainability Optimization and Learning for InterDependent networks laboratory (solid lab) FIU Miami FL USA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9781728147017

ISBN: (纸本)9781728147024

Power system resilience is crucial to ensure secure energy delivery to electricity consumers. Power system outages lead to economical and societal burdens for the society and industries. To mitigate the socio-economical impacts of a power outage, we need to develop efficient algorithms to ensure resilient operation of the power system. In this paper, we first explain the notion of data-driven resilience. Then, we present a pathway of leveraging edge intelligence to improve resilience. To this end, we propose a novel distributed machine learning paradigm. Our proposed structure relies on local Resilience Management Systems (RMS) that serve as intelligent decision-making entities in each area, e.g. an autonomous micro-grid or a smart home can act as RMS. The RMS agents, which are available in different areas, can share their local data (i.e., a microgrid's operational data) with their neighboring RMS to coordinate their decisions in a distributed fashion. This will provide two major advantages: 1) distributed intelligence replaces centralized decision-making leading to robust decision-making and enhanced resilience; 2) since local data are locally shared among all entities within an RMS, if one of the RMS agents fails to communicate with the rest of network, we still can maintain a feasible solution (which is not necessarily optimal). Finally, we presents different scenarios in the simulation results section that showcases the system performance for two buildings under various outage scenarios.

关键词： Resilience Power system reliability Decision making Optimization Load management Collaborative work

来源：评论

学校读者我要写书评

暂无评论

IEEE ACCESS SPECIAL SECTION EDITORIAL: 5G AND BEYOND MOBILE WIRELESS COMMUNICATIONS ENABLING intelligent MOBILITY

引用

IEEE ACCESS 2020年 8卷 208892-208897页

作者： Guan, Ke Rupp, Markus Kuerner, Thomas Briso, Cesar Matolak, David W. Takada, Jun-Ichi Wang, Wei Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Vienna Univ Technol Inst Telecommun A-1040 Vienna Austria Tech Univ Carolo Wilhelmina Braunschweig Inst Commun Technol D-38106 Braunschweig Germany Univ Politecn Madrid Ingn Audiovisual & Comunicac Madrid 28040 Spain Univ South Carolina Coll Engn & Comp Columbia SC 29208 USA Tokyo Inst Technol Dept Int Dev Engn Tokyo 1528550 Japan Changan Univ Dept Informat Engn Xian 710054 Peoples R China

Increasing urbanization is one major trend that shapes tomorrow's society;by 2050, more than 85% of the developed world's population will live in a comparatively small number of ever-growing cities. Within such cities and their commuter belts, reliable high-rate wireless communication will not only be required for (quasi-) static users but also for hosts of people moving in public and private transportation networks. Yet, wireless connectivity is not restricted to people;frictionless functioning of such a society in motion is supported by intelligent Mobility, where each connected transportation vehicle (car, train, bus, ship, aircraft, motorcycle, and bicycle) is expected to be a smart object equipped with a powerful multi-sensor platform, communication capability, computing units, and Internet protocol (IP)-based connectivity, such as to be highly efficient in various vehicular and transportation applications. This vision requires a more pervasive and ubiquitous communications and networking core, which will not be only driven by the existing research on 5G but also enabled by future mobile wireless communications that employ new concepts, such as data analytics, artificial intelligence, machine learning, and cloud computing. Therefore, this IEEE Access Special Section focuses on various theoretical and experimental views on researching and developing the required technological enhancements of 5G and beyond mobile wireless communications, to efficiently support the vision of intelligent mobility, providing mobility as a service, and enabling dependable Internet services. Twenty-three high-quality contributions to this Special Section have been selected in a strict peer-review process supported by reputed international experts.

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：