检索结果-内蒙古大学图书馆

Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Aniket Kulkarni P. Anitha John Yesudas Valluri M V Sunena Rose U Hemavathi Omar M. Hussein Software QA Manager Drata Inc San Diego CA USA Department of Computer Applications Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu India Faculty of Management Studies CMS Business School Jain (Deemed-to-be University) Bengaluru India Department of Computer Science and Engineering Jain (Deemed-to-be University) Bengaluru India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Medical Technical College Medical Instrumentation Engineering Al-Farahidi University Baghdad Iraq

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic stream prediction; this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML and DL computations suggest that their application to intelligent traffic light management is feasible.

关键词： Measurement Power engineering Machine learning algorithms Statistical analysis Smart cities Transportation Machine learning Prediction algorithms Timing Junctions

来源：评论

学校读者我要写书评

暂无评论

What if?.. Fabulating African HCI Futures within the Veil of HCI 23

What if?.. Fabulating African HCI Futures within the Veil of...

引用

Proceedings of the 4th African Human computer Interaction Conference

作者： Shaimaa Lazem Muhammad Sadi Adamu Yasmeen Abdrabou Chris Muashekele Sheena Erete Jakita Thomas Hafeni Mthoko Heike Winschiers-Teophilus Nicola Bidwell Aderonke Sakpere Anicia Peters City of Scientific Research and Technology Applications (SRTA-City) Egypt Imagination Lancaster Lancaster University United Kingdom School of Computing and Communications Lancaster University United Kingdom Aalborg University Denmark College of Information Studeies University of Maryland USA Department of Computer science and software Engineering (CSSE) Auburn University USA NurtureID8 Namibia Department of Software Engineering Namibia University of Science and Technology Namibia Intenational Uiversity of Management Namibia and University of Melbourne Australia University of Ibadan Nigeria National Commission of Research Sicence and Technology (NCRST) Namibia

ISBN: (纸本)9798400708879

This workshop aims to bring together researchers and practitioners (African and otherwise) engaging with diverse communities and industries to discuss the geopolitics of knowledge production in computing research and design. Our proposal builds on existing works that have explored the modalities of the knowledge economy - particularly as knowledge is created, legitimized, and circulated as objective truth via the exercise of power. For example, engaging indigenous communities in conferences and knowledge fairs have sought to challenge disciplinary silos and boundaries imposed on non-Western academics and practitioners. This workshop, as a platform for interaction and discussion with no “sage on the stage”, is the first of its kind within AfriCHI and it seeks to explore and promote more subtle discussions about HCI knowledge production and dissemination practices.

关键词： African HCI Critical fabulation Decolonizing HCI Indigenous knowledge alt.HCI

来源：评论

学校读者我要写书评

暂无评论

A Maple Exploration of Problem 6 of the IMO 88 4th

A Maple Exploration of Problem 6 of the IMO 88

引用

4th Maple Conference, MC 2020

作者： Zeng, Zhenbing Sun, Xiang Huang, Yong Xu, Yaochen Chen, Xiaoru Yang, Lu Department of Mathematics Shanghai University Shanghai200444 China South China Institute of Software Engineering Guangzhou University Guangzhou510990 China Institute of Computing Sciences and Technology Guangzhou University Guangzhou510006 China Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610041 China

ISBN: (纸本)9783030816971

In this paper, we show that by using Maple software, some direct searching computation could derive a solution to Problem 6 of the 1988 International Mathematics Olympiad, which asks to prove that if a and b are integers such that ab+ 1 divides a2+ b2, then (a2+ b2) / (ab+ 1 ) is the square of an integer. © 2021, Springer Nature Switzerland AG.

关键词： Integer International Mathematics Olympiad Maple software Square

来源：评论

学校读者我要写书评

暂无评论

Context-aware and class imbalance invariant threat severity assessment for heterogeneous IoT

TechRxiv

引用

TechRxiv 2021年

作者： Nitish, A. Hanumanthappa, J. Prakash, S.P. Shiva Krinkin, Kirill The Department of Studies in Computer Science University of Mysore India The Department of Information Science & Engineering JSS Science and Technology University Mysuru India The Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University "LETI" Russia

Due to demand for information ubiquity and large-scale automation, proliferating Internet-connected heterogeneous devices exhibit significant variations in data processing capacities, purposes, operating principles, underlying protocols, and dynamic contexts. As a result, adversarial entities exploit the increasing heterogeneous network (HetIoT) vulnerabilities, leading to frequent high-impact attacks due to anomalous device interactions and scarce knowledgebase. This paper presents a two-fold solution to the problem through a network intrusion detection and prevention framework for HetIoT, called HetIoT-NIDPS. Firstly, we assign fault scores to the Expert-curated Knowledgebase (EK) framework, correlating with low-level alerts to assess threat severity and achieve context-awareness. Secondly, the proposed Beta distribution-based HetIoT traffic behavior approximation facilitates class imbalance invariance and improves classifier performance. Additionally, the HetIoT-NIDPS can detect zero-day attacks by identifying known attack variations upon encountering unseen traffic instances. Furthermore, the dynamic HetIoT contexts necessitate real-time threat assessment through online training—performed by analyzing small batches of network traffic samples. We propound the CorrELM classifier based on the extreme learning machine algorithm and test the hypotheses on the Bot-IoT dataset. Finally, we prioritize the correlated alerts based on their severity, determined from root cause analysis and threat severity assessment tables. The results obtained prove that the proposed HetIoT-NIDPS framework is context-aware—producing reduced false alerts, class imbalance invariant—facilitating near real-time threat assessment with unbiased classifier performance, and generalizable—applicable to many NID datasets, which the existing techniques lack when combined. © 2021, CC BY-NC-SA.

关键词： Heterogeneous networks

来源：评论

学校读者我要写书评

暂无评论

WSN-IoT Clustering for Secure Data Transmission in E-Health Sector using Green Computing Strategy 9

WSN-IoT Clustering for Secure Data Transmission in E-Health ...

引用

9th International Conference on Cyber and IT Service Management, CITSM 2021

作者： Srivastava, Anurag Singh, Abhishek Joseph, Susheel George Rajkumar, M. Borole, Yogini Dilip Singh, Hari Kumar Lakshmi Narain College of Technology and Science Indore India Software Developer IT Researcher NorthboroughMA United States Kristu Jyoti College of Management and Technology Department of Computer Applications Kerala India Department of Information Technology Tamilnadu India G.H. Raisoni Institute of Engineering and Technology Department of Electronics and Telecommunications Engineering Pune India M.J.P Rohilkhand University Department of Electronics and Communication Uttar Pradesh Bareilly India

ISBN: (纸本)9781665435482

Energy was one of the 21st century's most critical components. Electronics and computer technologies have been widely used nowadays to simplify work every day. The crisis and the constant lack of power have popularized and integrated Green Computing algorithms for multidimensional applications on a broad scale. The author attempts to defuse times of crisis related to medical crises using the Green Computing method. Wireless Sensor Networks (WSN) are made up of numerous sensor nodes (SN) that are linked to and play a key part in many applications and the Internet of Things (IoT). To establish a dynamic information exchange network, IoT links physical items like sensors. In a number of areas, the Internet of Things was used. Wearable medical sensors are used to monitor health indicators in patients in this crisis situation. This battery-powered medical sensor has a limited quantity of energy. This is an important hurdle for increased network lifetime. In order to address the current issue effectively, a new technique dubbed Jarvis Patrick regressive resource-efficient gaussian process clustering was suggested to prolong the lifetime of the network. This method is based on the green technology concept that requires the hour. IoT devices are first used in SN to detect and collect patient information. SN is categorized in several clusters using the Jarvis Patrick clustering method after the data collection process. Clustering by Jarvis Patrick is a graphical clustering technique used for dividing SN using the Gaussian process regression function. The regression function reviews the SN and performs the process of grouping based on anticipated bandwidth and energy. The Cluster Head (CH) is selected to enhance data transmission and decrease latency after the clustering process. The information is transmitted to the CH from the source node. Then CH locates the nearest CH using the flight-time method. After then, data are sent through a cluster head from the source node to th

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules

Cybersecurity Threats in the Era of AI: Detection of Phishin...

引用

Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Mohammad Aljaidi Ayoub Alsarhan Dimah Al-Fraihat Ahmed Al-Arjan Bashar Igried Subhieh M. El-Salhi Muhammad Khalid Qais Al-Na'amneh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince AL-Hussein bin Abdullah II of Information Technology The Hashemite University Jordan Department of Computer Science and Applications The Hashemite University Zarqa Jordan The Hashemite University Zarqa Jordan Department of Computer Sciences and Technology University of Hull Kingston upon Hull UK Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing.

关键词： Adaptation models Accuracy Phishing Identity theft Refining Machine learning Passwords

来源：评论

学校读者我要写书评

暂无评论

Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

arXiv

引用

arXiv 2024年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J.P.C. School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Electrical and Information Engineering Zhengzhou University the Henan International Joint Laboratory of Intelligent Health Information System The National Telemedicine Center The National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom The Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of School of Software Nanjing University of Information Science and Technology Nanjing210044 China Amazonas State University AM Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11%, increasing the final user utility function value by 0.24%, and decreasing the variance of results by 95.45%. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

InBiodiv-O: An ontology for indian biodiversity knowledge management

arXiv

引用

arXiv 2021年

作者： Patel, Archana Jain, Sarika Debnath, Narayan C. Lama, Vishal Department of Software Engineering School of Computing and Information Technology Eastern International University Viet Nam Department of Computer Applications National Institute of Technology Kurukshetra Haryana India Amdocs Development Centre LLP India

To present the biodiversity information, a semantic model is required that connects all kinds of data about living creatures and their habitats. The model must be able to encode human knowledge for machines to be understood. Ontology offers the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in the biodiversity domain. Various ontologies are developed for the biodiversity domain however a review of the current landscape shows that these ontologies are not capable to define the Indian biodiversity information though India is one of the megadiverse countries. To semantically analyze the Indian biodiversity information, it is crucial to build an ontology that describes all the essential terms of this domain from the unstructured format of the data available on the web. Since, the curation of the ontologies heavily depends on the domain where these are implemented hence there is no ideal methodology is defined yet to be ready for universal use. The aim of this article is to develop an ontology that semantically encodes all the terms of Indian biodiversity information in all its dimensions based on the proposed methodology. The comprehensive evaluation of the proposed ontology depicts that ontology is well built in the specified domain. © 2021, CC BY.

关键词： Biodiversity

来源：评论

学校读者我要写书评

暂无评论

SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

SmartEdge: Smart Healthcare End-to-End Integrated Edge and C...

引用

IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Alain Hennebelle Qifan Dieng Leila Ismail Rajkumar Buyya Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Department of Computer Science and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of Information Technology Emirates Center for Mobility Research National Water and Energy Center UAE University Al-Ain Abu Dhabi United Arab Emirates

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction.

关键词： Cloud computing Accuracy Smart cities Computational modeling Transportation industry Surveillance Machine learning Predictive models Diabetes Time factors

来源：评论

学校读者我要写书评

暂无评论

Research of Dimension Reduction Algorithms of Feature Space in Data Analysis Task

Research of Dimension Reduction Algorithms of Feature Space ...

引用

IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Nikita V. Popov Dmitry M. Klionskiy Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University “LETI” St. Petersburg Russia

ISBN: (数字)9781728157610

ISBN: (纸本)9781728157627

Today data describing a particular subject area, often contain a large number of different features that determine the properties of any processes or objects. At the same time, these data sets can reach enormous sizes, so working with data turns into very resource-intensive and long processes. Reducing the dimension of the feature space entails a reduction in the used memory and getting rid of noisy and duplicate information. To reduce the space, various machine-learning algorithms are used, each of which has its own individual degree of efficiency and validity for its area of application. Client databases are no exception, they often contain large amounts of information, some of which are redundant. Therefore, reducing the space of signs in client data is a topic whose relevance is only increasing every day. This paper presents a study of such methods as reduction of features with low variability, Univariate feature selection, recursive feature elimination, selection based on a decision tree, principal component analysis. Based on the results of the study, requirements are formulated for an algorithm for reducing the dimension of the feature space specifically for the task of working with client data.

关键词： Feature extraction Classification algorithms Principal component analysis Decision trees Machine learning algorithms Libraries Machine learning

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：