检索结果-内蒙古大学图书馆

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Neelakandan, Viduran Voleti, Sai Suraj Srinivasan, Dhanush Gouthaman, P. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331530013

This research discusses how data analytics techniques can be applied in the enhancement of building energy management. It is therefore with the purpose of stimulating the right utilization of the practically inexhaustible quantity of data obtained through smart grids that this research seeks to design original approaches to energy management, building performance, and counteracting of negative impacts on the environment. Since Apache spark process is very efficient we do an excellent job in pre-processing and analyzing the data collected. Combining high-level artificial intelligence decision making tools like linear regression as well as the random forest, the relationship was established between energy utilization and determinants, forecast patterns and create accurate prognosis models. In order to present the results in a format that can be easily understood with the help of Tableau to build a data visualization where depending on the values chosen by the stakeholders further steps can be taken in achieving a more efficient use of energy and less expenditure. In our work, we strive to generate knowledge that will support improved energy efficiency of the built environment, and therefore a more sustainable future. The implications of our work have the ability to affect the processes within and energy usage of buildings, saving energies and making cities greener. Furthermore, our work can help with the creation of further reference points for future research in the field of building energy optimization. © 2024 IEEE.

关键词： Big Data Building Efficiency Energy Optimization Machine Learning Predictive Analytics Smart Grids Sustainability

来源：评论

学校读者我要写书评

暂无评论

Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

引用

Distributed Generation and Alternative Energy Journal 2024年第1期39卷 111-136页

作者： SwornaKokila, M.L. Venkatarathinam, R. Rose Bindu, Joseph P. Manivasagam, M.A. Kishore, Kakarla Hari Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology Sri BalajiChockalingam Engineering College A.C.S. Nagar Irumbedu Thiruvannamalai District Tamil Nadu Arni India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Computer Science and Engineering Siddartha Institute of Science and Technology Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time ( © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Symptom Checker: Leveraging Big Data Technologies for Real-Time Healthcare Triage and Diagnostics 5

AI-Driven Symptom Checker: Leveraging Big Data Technologies ...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Banerjee, Augnik Baibhav, Piyush Kumar, Nitin Singh, Aniket Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331530013

The healthcare industry continues to face significant challenges in delivering timely and accurate diagnoses, particularly in remote and underserved areas where access to medical professionals is limited. The work aims to develop an AI-driven symptom checker that leverages big data technologies to offer preliminary diagnoses and support patient triage, ensuring quicker medical response times. Utilizing Hadoop for scalable data storage, Hive for advanced data analysis, and Spark for real-time distributed data processing, the system is designed to efficiently handle large volumes of patient data. Tableau is integrated for intuitive data visualization, allowing healthcare professionals to gain actionable insights and make informed decisions promptly. The proposed architecture enables the swift analysis of patient symptoms in real-time, significantly reducing diagnostic delays and enhancing the quality of care. By focusing on scalable and efficient data processing, the work addresses the need for a robust healthcare solution capable of operating in environments with limited resources. This system has the potential to transform patient care in remote areas by providing real-time analysis, identifying critical cases, and aiding in effective resource allocation. The solution's emphasis on rapid diagnosis and visualization offers a comprehensive approach to improving healthcare outcomes in underserved regions. © 2024 IEEE.

关键词： AI-Driven Symptom Checker Apache Spark Hadoop Healthcare Triage Hive Tableau

来源：评论

学校读者我要写书评

暂无评论

MUTS-Based Cooperative Target Stalking for A Multi-USV System

引用

IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1582-1592页

作者： Chengcheng Wang Yulong Wang Qing-Long Han Yunkai Wu Shanghai Key Laboratory of Power Station Automation Technology and the School of Mechatronic Engineering and AutomationShanghai UniversityShanghai 200444China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia College of Automation Jiangsu University of Science and TechnologyZhenjiang 212100China

This paper is concerned with the cooperative target stalking for a multi-unmanned surface vehicle(multi-USV)*** on the multi-agent deep deterministic policy gradient(MADDPG)algorithm,a multi-USV target stalking(MUTS)algorithm is ***,a V-type probabilistic data extraction method is proposed for the first time to overcome shortcomings of the MADDPG *** advantages of the proposed method are twofold:1)it can reduce the amount of data and shorten training time;2)it can filter out more important data in the experience buffer for ***,in order to avoid the collisions of USVs during the stalking process,an action constraint method called Safe DDPG is ***,the MUTS algorithm and some existing algorithms are compared in cooperative target stalking *** order to demonstrate the effectiveness of the proposed MUTS algorithm in stalking tasks,mission operating scenarios and reward functions are well designed in this *** proposed MUTS algorithm can help the multi-USV system avoid internal collisions during the mission ***,compared with some existing algorithms,the newly proposed one can provide a higher convergence speed and a narrower convergence domain.

关键词： Cooperative target stalking improved deep reinforcement learning multi-unmanned surface vehicle(multi-USV)systems V-type probabilistic data extraction

来源：评论

学校读者我要写书评

暂无评论

Predictive Analytics in Hotel Recommendations Using Machine Learning 5

Predictive Analytics in Hotel Recommendations Using Machine ...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Singh, Siddharth Dey, Shayan Sharma, Shreyanshi Prathaban, Banu Priya School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331518097

In a world where travellers want the perfect stay, a new system emerges as the one that goes beyond simple hotel recommendations. By analysing details like interaction words and prices, it predicts hotel ratings using a powerful Random Forest Regressor which performs better than methods like Linear Regressor. With careful data processing and encoding, it achieves high accuracy, measured through precise metrics like RMSE, MSE and Max Error. It achieves a great RMSE score of 0.424. This system offers a scalable solution, turning every hotel suggestion into a personalized experience, redefining the way people find their ideal stay. © 2024 IEEE.

关键词： Hotels

来源：评论

学校读者我要写书评

暂无评论

Analysis of Encryption Techniques for Secure Data in Cloud Environment 1

Analysis of Encryption Techniques for Secure Data in Cloud E...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, M. Ravi Jeya, I. Jasmine Selvakumari Athira, S. Hindusthan College of Engineering and Technology Department of Computer Science And Engineering Coimbatore India Vit Bhopal University School of Computing Science and Engineering Sehore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331529246

Over the years, Cloud computing is becoming increasingly popular due to the continually changing technology. The primary goal of the cloud computing network is to offer consumers pay-per-use usage of on-demand processing and storage resources, enabling low-cost, efficient infrastructure utilization for smaller businesses. However, because of the feature of cloud resource sharing, data security and privacy create issues, and prevent smart systems from continuously transferring produced data to the cloud computing resources. Many encryption methods were presented to address data security concerns. This article presents the processes of several data protection and encryption systems currently in use and critically analyzes them based on common factors. The purpose of this survey is to compile data encryption methods now in use, often discussed in the survey of smart systems for data security when applied to the cloud platforms. © 2025 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

Adaptive Web Application Firewall for Multi-Threat Detection 5

Adaptive Web Application Firewall for Multi-Threat Detection

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Maheshwari, Muskan Nayak, Aniket Sethy, Asmit Sujatha, G. College of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Department of Networking and Communications Chennai India

ISBN: (纸本)9798331518097

Cyber threats targeting web applications have increased in frequency and sophistication, thus the need of such applications to be protected with advanced mechanisms. We introduce in this paper an Adaptive Web Application Firewall WAF that is capable of protecting applications from a wide range of threats that includes but are not limited to SQL injection, DDoS attacks, directory traversal, and CSRF. Using machine learning and traffic analysis, the proposed ABS adapts in real-time to the changing attack vectors and axes of engagement against web applications that are already compromised. With the ever-growing amount of incoming traffic, it is the Adaptive WAF that will enable web applications to maintain various forms of attacks and in the process develop varied defensive approaches. This paper explores how the Adaptive WAF can be used to enhance AWS security, integrity, and availability through the reduction of available cyber risk. The KNN model, with its introduction, strengthens the system further by highly increasing the precision and accuracy of the classification of web attacks to a level of 80.3% in terms of accuracy and 74.8% in terms of precision. This model enables the reliable detection of threats through distinguishing between malicious patterns with fewer false positives, thus further enhancing the ability of the system to detect and respond accordingly to the various web-based threats. © 2024 IEEE.

关键词： Adaptive Web Application Firewall Internet machine learning security protocols web application

来源：评论

学校读者我要写书评

暂无评论

Utilizing Big Data Analytics for Social Media Insights 5

Utilizing Big Data Analytics for Social Media Insights

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Vedhika Srinidhi, V.S. Sanjana, S. Rozario, Neo Joseph Dewan, Sia Gouthaman, P. School of Computing College of Engineering & Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331518097

Big Data analytics is a rapidly evolving field that offers profound insights into various sectors by enabling the analysis of massive and diverse datasets. This research paper focuses on the application of Big Data techniques to social media and airline networks, leveraging tools such as sentiment analysis, clustering, and data visualization to extract actionable information. The study analyses Twitter data to uncover public sentiment trends and classifies topics using advanced machine learning techniques. Furthermore, the research explores the use of clustering algorithms within the Hadoop ecosystem to process large-scale datasets efficiently. Insights from airline customer feedback and network planning data demonstrate how Big Data analytics can optimize operational efficiency and enhance customer satisfaction. Key findings include the identification of distinct sentiment patterns in social media data, the efficiency of novel clustering techniques in handling high-volume datasets, and the utility of advanced visualization methods to convey complex data. These results have significant implications for industries ranging from social media platforms to the airline sector, providing valuable tools for enhancing decision-making processes and improving user experiences across diverse domains. The study highlights the potential of Big Data analytics to drive innovation and operational excellence in dynamic environments. © 2024 IEEE.

关键词： Airline networks Big Data analytics Clustering algorithms Data visualization Sentiment analysis Social media

来源：评论

学校读者我要写书评

暂无评论

Real-Time Weapon Detection System for Enhanced Security Monitoring 4

Real-Time Weapon Detection System for Enhanced Security Moni...

引用

4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Kuppili, Rohan Agarwal, Khusboo Bhosale, Tanmay Gouthaman, P. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331529635

Innovative technology for weapon detection during security monitoring is necessary today due to the rising threats of violence and terror. The paper discusses a detailed design of a weapon detection system intended for application in enhancing security surveillance specifically using a deep learning approach. The system employs YOLOv3 (You Only Look Once, Version 3) mood and adaption aimed at live footage weapon detection. To do this on broader scale, this system incorporates big data tools Hadoop, Docker, Spark and Kafka for reasons of real time processing and effectiveness in high volume data situations. This model is applied on the live footage surveillance of public and private places to determine if the model can detect weapons, to warn relevant authorities in the aim of decreasing response time and averting security breach occurrences. The experiments conducted confirm that the model is capable of performing weapon detection tasks in an indoor environment with changes in illumination conditions with few false alarms. © 2024 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Weather Prediction from Historical Data using Big Data Technologies 5

Weather Prediction from Historical Data using Big Data Techn...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Choudha, Shaurya Singh, Aniruddh Kumar, Aman Gouthaman, P. School of Computing College of Engineering and Technology Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur603203 India

ISBN: (纸本)9798331518097

Weather forecasting is an important function in a variety of activities such as agriculture, environment monitoring, and disaster management among others. Today with the development of technology, vast amounts of meteorological data are able to be collected and analyzed with the intention of improving prediction accuracy. The purpose of this work is to analyze the contribution of Big Data frameworks such as, Hadoop, MapReduce and Apache Spark in effective weather prediction processes. In a distributed system, historical weather data, satellite images and sensor readings are kept and manipulated through the HDFS storage. For effective data manipulation, MapReduce and Spark are used along with Hive and Pig for searching large datasets. Predictive analytics are performed using machine learning algorithms included in the MLlib package of Spark. These results are presented using the Tableau application, which shows the speed and accuracy of this Big Data approach in comparison to classical ones. The proposed work ends with a look into the prospects of Big Data in the field of weather forecasting and relevant problems in this area. © 2024 IEEE.

关键词： MapReduce

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：