检索结果-内蒙古大学图书馆

International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Fiaz, A.S. Syed Sonai, Veeramani Computer Science and Engineering Amrita Viswa Vidhyapeetham School of Computing Chennai India Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai India

ISBN: (纸本)9781032669663

The vast changes in the computing utilization have created a need with the improvised resource availability and reliability. The virtualization of servers and data centers to increase their efficiency has transformed how enormous data is stored and handled. Cloud computing has created a supreme and impressive approach to virtualize servers and data centers and to make them energy efficient in order to leverage on diverse IT resources. Massive power and energy consumption by IT resources leads to an energy crisis and a change in the planet’s climate. But because these IT resources use so much energy and power, they end up being a significant source of CO2 emissions. Due to this, green cloud computing is now required in order to make IT resources cost-and energy-effective. It is necessary to do a thorough analysis of the cloud’s power efficiency in order to arrive at the best options for green cloud computing. Green Cloud computing is well known to be a broad domain and a popular field for research because of the increasing rise of enormous data storage and computational demand. It can generate solutions that not only make IT resources energy efficient but also reduce operational expenses. This review’s objective is to highlight some of the best practices for achieving green cloud computing, including workload clustering, Auction-Based Resource Optimization techniques, Hardware and Software controlled solutions, Minimize cooling requirements scheduling, clustering, virtualization and other energy-saving techniques. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Carbon footprint

来源：评论

学校读者我要写书评

暂无评论

Cyber Chronicles: Tracking Behavior Patterns for Detecting Threats in Large Networks 3

Cyber Chronicles: Tracking Behavior Patterns for Detecting T...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Ramasubbu, Senthilnathan Thangavel, Senthil Kumar Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372120

One of the primary challenges in cybersecurity is that even one un-detected, appropriately unanalyzed malicious security event can hide the attack vectors of a potential hacker. It is essential to detect the data breach at the earliest stage to reduce the impact on the business. The malicious actor's activities will have higher visibility during the initial attacks to compromises or exploitation at the beginning of the attack cycle. After the initial compromise, the attacker studies the environment and establishes the persistence and covert channel communication. At this successive phase of the attack cycle, the attacker camouflages with the production data flow, access, and noises in the network and application logs. It is essential to detect the attacks during the reconnaissance and initial attacks and should not miss any malicious activities. Most of our security tools focus on reducing false positives;however, reducing false negatives is critical to cyber security. Hence, this paper is focused on suitable deep learning to reduce the False Negatives (FNs) with minimal Type II error to detect the attacks well in advance. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Blockchain-based Decentralized Digital Forensics Case Management System using IPFS 3

Blockchain-based Decentralized Digital Forensics Case Manage...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Viswanathan, Jayaraj Kumar, S. Udhaya Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350354348

Blockchain technology has gained significant attention for its ability to provide a decentralized and immutable platform for various applications. In this paper, we propose a Blockchain based Decentralized Case Management System using the InterPlanetary File System (IPFS) protocol. The decentralized nature of blockchain ensures data integrity and transparency, while IPFS facilitates efficient and decentralized storage of case files. Our system allows for secure and fine-grained access control to case data, ensuring that only authorized users can view and manage cases. We present the architecture of the system, which includes smart contracts deployed on the blockchain to manage case data and access control rules. Additionally, we have discussed the implementation details and use case scenarios to demonstrate the functionality of the proposed system. Performance testing revealed that our system significantly enhances data integrity and transparency. Comparative analysis demonstrated that IPFS, despite having slightly higher latencies than traditional FTP, provides superior security and redundancy. Through our research, we aim to provide a secure, transparent, and efficient solution for managing cases in various domains, including legal, healthcare, and finance. © 2024 IEEE.

关键词： Data integrity

来源：评论

学校读者我要写书评

暂无评论

Empowering data structure visualization through modification in Python 15

Empowering data structure visualization through modification...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Puvvadi, Harsha Vardhan Angala, Hari Prasad Swaroop, Bandi Vishnu Reddy, Busa Thanuj Sathwik Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This article introduces a novel approach to data structure visualization through the development of a new programming language, utilizing Python's Lex-YACC library for lexical analysis and parsing, and the Turtle module for graphics in the backend. The proposed language enables users to visualize various data structures such as arrays, stacks, and queues, along with their operations, facilitating a deeper understanding of these fundamental concepts in computer science. By combining the power of a custom scripting language with Python's versatile libraries, this research aims to empower users to interactively explore and comprehend complex data structures through intuitive visualizations. © 2024 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Hybrid model with improved score level fusion for heart disease classification

引用

Multimedia Tools and Applications 2024年第18期83卷 54951-54987页

作者： Maithani, Ankit Verma, Garima Department of Computer Science and Engineering School of Computing DIT University Makka Wala Uttarakhand Dehradun248009 India School of Computing DIT University Uttarakhand Dehradun248009 India

The heart, an essential organ of the body, pumps blood filled with oxygen to every area of the body through a complex network of veins and arteries. Any condition affecting the heart is referred to as heart disease. It is essential to start the right treatments early on in order to reduce the death toll. Predicting cardiac disease before heart attacks or strokes is therefore a significant challenge for the healthcare industry. This article introduces a methodology for predicting heart disease which consists of the three steps: (1) pre-processing, (2) feature extraction, and (3) disease prediction. Initially, the input data is pre-processed using improved data normalization. Next, the features are extracted from the normalized data;they are higher-order statistical features, information gain-based features, mutual information-based features, and improved entropy-based features. The results are then decided utilising an improved score-level fusion procedure, which is used to the suggested hybrid classification model that incorporates models like Deep Maxout and QNN algorithms. By adjusting the Deep Maxout and QNN classifiers' ideal weights, a self-improved Beluga whale optimisation is developed for optimal training to increase the performance of the hybrid model. When using dataset 1, the Hybrid + SI-BWO produced a precision of 94.25%, while CNN, DBN, Bi-LSTM, SVM, RNN, DL, and RF all produced precisions of 75.04%, 77.38%, 80.17%, and 86.52%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

引用

computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu Pradip Kumar Sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

来源：评论

学校读者我要写书评

暂无评论

Comparison of CNN-based deep learning architectures for rice diseases classification

引用

Artificial Intelligence in Agriculture 2023年第3期9卷 22-35页

作者： Md Taimur Ahad Yan Li Bo Song Touhid Bhuiyan Department of Computer Science and Engineering Daffodil International UniversitySavarBangladesh School of Mathematics Physics and Computing University of Southern QueenslandToowoombaAustralia School of Engineering University of Southern Queensland ToowoombaAustralia Department of Computer Science and Engineering Faculty of Science and Information TechnologyDaffodil International UniversityBangladesh

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice ***,most CNN-based rice disease detection studies only considered a small number of diseases in their *** these shortcomings were addressed in this *** this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in *** addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble *** results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf *** high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural *** research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

关键词： Deep learning Convolutional neural networks(CNNs) Transfer learning Plant leaf disease detection

来源：评论

学校读者我要写书评

暂无评论

Optimizing DDoS Attack Detection Using Machine Learning 2

Optimizing DDoS Attack Detection Using Machine Learning

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

引用

High-Confidence computing 2024年第4期4卷 91-104页

作者： Shabab Intishar Rahman Shadman Ahmed Tasnim Akter Fariha Ammar Mohammad Muhammad Nayeem Mubasshirul Haque Sriram Chellappan Jannatun Noor Department of Computer Science and Engineering BRAC UniversityDhaka 1212Bangladesh Department of Computer Science and Engineering University of South FloridaFL 33620USA Computing for Sustainability and Social Good(C2SG)Research Group Department of Computer Science and EngineeringSchool of Data and SciencesBRAC UniversityDhaka 1212Bangladesh

By analyzing data gathered through Online Learning(OL)systems,data mining can be used to unearth hidden relationships between topics and trends in student ***,in this paper,we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system *** our implementation,we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%.By focusing on marks below this threshold,we aim to identify and establish associations based on the patterns of weakness observed in the past ***,we leverage K-means clustering to provide instructors with visual representations of the generated *** strategy aids instructors in better comprehending the information and associations produced by the *** clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights,enabling them to support the verification of the relationship between *** can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their *** paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

关键词： Recommendation system Clustering F-P growth Apriori Associative pattern E-learning sphere Online learning

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral imagery quality assessment and band reconstruction using the prophet model

引用

CAAI Transactions on Intelligence Technology 2025年第1期10卷 47-61页

作者： Ping Ma Jinchang Ren Zhi Gao Yinhe Li Rongjun Chen National Subsea Centre School of ComputingEngineering and TechnologyRobert Gordon UniversityAberdeenUK School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhouChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina

In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land *** study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet *** introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global *** metric effectively captures the intricate noise and distortions inherent in the HSI ***,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality *** validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are *** a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is *** authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed *** addition,the authors assess the classification accuracy utilising these reconstructed *** various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band ***,the authors’approach obviates the need for manually prefiltering of noisy *** comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.

关键词： band reconstruction band quality hyperspectral image(HSI) prophet model

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：