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： Felix, A. Yovan Sharmila, V. Devi, S. Nandhini Deena, S. Yadav, Ajay Singh Jeyalakshmi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering R.M.D. Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering VSB Engineering College Tamil Nadu Karur India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Physics PSNA College of Engineering and Technology Tamil Nadu Dindigul India

ISBN: (纸本)9781032669663

The Internet of Things (IoT) connects systems, software, cloud computing, activities and could provide new access points for cyber-attacks. Virus attacks and unauthorized downloading are the two main risks to IoT security at the moment. These dangers run the risk of obtaining private information, damaging their reputation and their finances. A frame is employed in this paper’s deep learning to identify threat avoidance in IoT. The cyber security detection systems following database has to be built, after which the benchmark variables are chosen and assessed in order to establish a cyber-security alert system in a large data set. The deep learning strategy to identifying threat avoidance was described for the simulation of this scenario. From the Mailing database, malware samples have been gathered for analysis. The research results demonstrate that the proposed method for assessing risks to cyber security in IoT provides highest accuracy to current approaches. Because of this, Deep Convolution Neural Network (DCNN) is utilized in IoT to detect attack avoidance. © 2024 The Author(s).

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Performance Analytics in Collaborative Learning Environments 2

Performance Analytics in Collaborative Learning Environments

引用

2nd IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security, iSSSC 2022

作者： Kavitha, R.K. Jaisingh, W. Harishma, S. Kumaraguru College of Technology Department of Computer Applications Coimbatore India Vit Bhopal University School of Computing Science and Engineering India

ISBN: (纸本)9781665490566

Collaborative learning has recently become more common in educational settings and has the potential to revolutionise the way students are taught today. Pair programming is a tool for group learning in which two programmers work on the same projects simultaneously on a single computer. The pairs will work together on every aspect of programming, including algorithm development, coding, and testing. The environment that facilitates sharing of understanding and perception is provided by the continual communication that prevails among pair programmers. The goal of this study is to use pair programming as a teaching method in computer labs. To produce insightful findings, predictive analytics was applied to the data set. The study was conducted in appropriate lab settings where students are expected to learn independently and write code. During the regular lab sessions, research participants were required to conduct the lab experiments in pairs. The experiences of the students working in such surroundings were recorded using the necessary tools in order to study the effectiveness of this strategy. The students' learning was evaluated based on a number of factors, including student preferences, increased productivity, enhanced learning, and information sharing. Additionally, projections about the students' success in the final exams were made based on the analysis of the result. © 2022 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Exploring the Impact of Image Filters in Facial Detection through Different Classification Algorithms 4

Exploring the Impact of Image Filters in Facial Detection th...

引用

4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Bisht, Raj Kishor Mohan, Daivik Mohan, Aadershi Joshi, Akshay Kumar Lohumi, Yogesh Graphic Era Hill University School of Computing Dehradun India Graphic Era Hill University Dept. of Mathematics Dehradun India Graphic Era Deemed to be Univeristy Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350354218

In the present work, we have analyzed the effect of applying different filters in face detection through different classification methods. For the purpose, we have created different datasets: first dataset consists of 1600 different unfiltered images of eight students with 200 images of each student and second dataset consists of 8000 filtered images using a mix of filters with 1000 images of each student. Then we created five additional datasets, each dataset created by applying a single filter. Five filters namely blur, cut-out, mosaic, noise and shear are used for the present study. Six different algorithms CNN, Random Forest, SVM, Gaussian Naïve Bayes, KNN, and Logistic Regression were applied and results of unfiltered image dataset and filtered image dataset using a mix of filters have been compared. We found a little improvement in the accuracy in the case of KNN and CNN and a little decline in accuracy of other algorithms applied to filtered dataset created using a mix of filters. The significance of difference between the two results is checked through paired t-test. Based on paired t-test, we conclude that filters in images, in general, do not have much effect in face detection through different classification algorithms. The results of unfiltered dataset and individual filtered datasets show that there is a significant difference in results between the unfiltered dataset and blur filtered dataset, others being identical. This indicates that the filter 'blur' has some effect in face detection and other has no significant effect. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Information Technology (IT) Governance Framework with Artificial Neural Network and Balance Scorecard to Improve the Success Rate of Software Projects 6

Information Technology (IT) Governance Framework with Artifi...

引用

6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Ranesh, M. Maria Antony Samuel, S. Justin Natchadalingam, R. Jeyanthi, P. School of Computing Sathyabama Institute of Science and Technology Dept of Information Technology India Psn Engineering College Dept of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9781665482714

IT governance enables companies to plan and manage their IT investments in order to achieve their strategic objectives. Overall goal of this paper is to discuss about an approach to increase the success of the software projects through Artificial Intelligence. Standard governance standards are analyzed to understand the impact on software project success rates. Enterprise governance is a subset of IT governance that aids in the management of information technology resources. COBIT is a widely used methodology for developing effective enterprise IT governance. ISACA (Information Systems Audit and Control Association) stated implementation solution currently uses a sequentially ordered approach, which has raised issues. Several challenges have arisen as a result of top management's lack of commitment and misplaced solutions. Limitation on this investigation is the customized and confidential governance pattern used across various organization, hence there are very limited real time data for end-to-end analysis. Nonetheless, in combination with the agile project management paradigm, new project life-cycle solutions have evolved. Artificial Intelligence (AI) is an important element to address the increasing demand for machine learning systems. Artificial neural network (ANN) method is highly useful to arrive at best possible solution to improve the project success. This study found that ANN with Balance Score Card was exceptionally effective in providing the optimal solution to increase the project success rates. © 2022 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Demand Response in Smart Grids using SDN 14

Demand Response in Smart Grids using SDN

引用

14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Gautham Krishna, K. Patnaik, Manav Gunananthaa, K.B. Radhika, N. Paul, Sujni Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Higher Colleges of Technology Dubai Men's Campus Dubai United Arab Emirates

ISBN: (纸本)9798350335095

Digitization of analogue meters has enabled the travel of information into duplex in electric power grids. This has fetched the ability of data collection and analysis from the consumer sinks. Transfer of such data requires extensive digital network transmission infrastructure as a parallel space. Being incorporated with a fresh network environment since proposals, harnessing and applying the modern network technologies(Software Defined Networks, Network Virtualization) in smart grids from elemental level provide opportunities for building applications which are dynamic and generic. This study explores the Demand response event, where the various problems of supply constraints (considerably in peak hours) are dealt with possible load smooth and reducing inefficient power generation. This follows the exploration of Variable Pricing possibilities based on factors like seasons, power generation modes, demand times. The whole network infrastructure is built upon Software Defined Networks' (SDN) Virtual Network Functions (VNF) which gives possibilities for customization, easy adaptation to custom behaviours and much. © 2023 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

CVNET - Cardiovascular Disease Detection using Deep Convolutional Neural Network

CVNET - Cardiovascular Disease Detection using Deep Convolut...

引用

Innovation in Technology (APCIT), Asia Pacific Conference on

作者： R Bhuvaneswari Karthigeyan S Sabhanayagam T Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering Galgotias University Greater Noida UP India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Cardiovascular diseases (CVDs) pose a serious threat to world health. This work uses a large dataset with clinical and demographic information to predict CVD risk using deep learning techniques. In order to enhance generalization and reduce overfitting, a Convolutional Neural Network (CNN) architecture was created, combining Batch Normalization and Dropout layers. A thorough assessment of the model’s performance was conducted using metrics including F1-score, accuracy, precision, and recall. With an astounding accuracy rate of 98.54%, the CVNET model outperformed more conventional deep learning techniques in terms of predictive power. These results highlight how deep learning models can improve early cardiovascular disease prediction, which permits prompt diagnosis and tailored treatment strategies. The commercialization of cutting-edge deep learning methods offers a viable strategy for reducing the prevalence of cardiovascular illnesses worldwide.

关键词： Productivity Technological innovation Accuracy Prevention and mitigation Neural networks Predictive models Cardiovascular diseases

来源：评论

学校读者我要写书评

暂无评论

Optimization of Traffic and Time Control with Sensor-Driven Transmission Control System using MANET and Machine Learning 7

Optimization of Traffic and Time Control with Sensor-Driven ...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kathirvel, N. Vidyalakshmi, R. Raihana, A. Mohanraj, A. Uma, S. Saranya, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology School of Computing Department of Information Technology India Karpagam College of Engineerng Department of Eee Coimbatore India Sri Krishna College of Engineering And Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science And Engineering India Hindustan College of Engineering And Technology Department of Computer Science And Engineering India

ISBN: (纸本)9798350359299

In the dynamic landscape of modern urban environments, the optimization of transportation and traffic management systems is paramount for ensuring efficiency, sustainability, and the overall well-being of inhabitants. This study introduces an innovative solution through the deployment of a sophisticated network of strategically positioned sensors at crucial locations, encompassing roadway sections, entry and exit points, and parking spaces within a smart city framework. These sensors continuously capture real-time data on traffic flow, vehicle counts, and parking spot occupancy, forming the foundational elements of an intelligent system. Robust machine learning models, notably Convolutional Neural Networks (CNN), VGG19, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM), are employed to process and analyze the acquired sensor data. CNN, renowned for its prowess in image recognition, emerges as the leading performer, achieving an impressive accuracy of 97.6%. This model excels in deciphering complex spatial patterns, significantly enhancing the precision of traffic forecasts. Additionally, performance metrics, encompassing precision, recall, F1 score, and accuracy, underscore the efficacy of each machine learning model. The exceptional precision and recall values across all models showcase their robustness in identifying and predicting traffic situations, while accuracy scores affirm their overall efficiency in optimizing transportation and traffic management within smart city environments. © 2024 IEEE.

关键词： Sensor networks

来源：评论

学校读者我要写书评

暂无评论

Characterization, synthesis, and optimization of quantum circuits over multiple-control Z-rotation gates: A systematic study

引用

Physical Review A 2023年第2期108卷 022603-022603页

作者： Shihao Zhang Junda Wu Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China

We conduct a systematic study of quantum circuits composed of multiple-control Z-rotation (mczr) gates as primitives, since they are widely used components in quantum algorithms and also have attracted much experimental interest in recent years. We establish a circuit-polynomial correspondence to characterize the functionality of quantum circuits over the mczr gate set with continuous parameters. An analytic method for exactly synthesizing such quantum circuit to implement any given diagonal unitary matrix with an optimal gate count is proposed, which also enables the circuit depth optimal for specific cases with pairs of complementary gates. Furthermore, we present a gate-exchange strategy together with a flexible iterative algorithm for effectively optimizing the depth of any mczr circuit, which can also be applied to quantum circuits over any other commuting gate set. Besides the theoretical investigation, the practical performance of our circuit synthesis and optimization techniques is further evaluated numerically on two typical examples in quantum computing, including diagonal Hermitian operators and quantum approximate optimization algorithm circuits with tens of qubits, which can demonstrate a reduction in circuit depth by 33.40% and 15.55% on average over relevant prior works, respectively. Therefore, our methods and results provide a pathway for implementing quantum circuits and algorithms on recently developed devices.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

PEPC: Parallel and Extensible PC Implementation for Causal Structure Learning 23

PEPC: Parallel and Extensible PC Implementation for Causal S...

引用

7th International Conference on High Performance Compilation, computing and Communications, HP3C 2023

作者： Feng, Yongquan Xu, Liyang Huang, Wanrong Yang, Wenjing Lu, Yutong Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781450399883

Learning the causal relationships among variables from observational data has been a significant problem in statistics and data mining, and one such algorithm is the PC algorithm. However, the existing CPU-based implementations are difficult to support Causal Structure Learning(CSL) on complex observational data due to inefficiency. In this paper, we propose an efficient CPU-based parallel implementation of the PC algorithm, called PEPC. It has an extensible framework, and some optimizations are adopted to reduce the CSL computation of the PC algorithm. Moreover, our PEPC supports efficient multi-thread parallelism. The experimental results demonstrate that PEPC outperforms existing methods in serial efficiency and multi-thread parallelism. In serial tests, our method achieves the maximum speedup ratios of 577.26, 116.74, and 134.68 over three existing approaches, Stable, ***, and Tetrad. In parallel experiments, it achieves 10.66X speedup when running the PC algorithm with 12 threads. © 2023 ACM.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

MetaGraph: Plasmid/Chromosome Classification Enhancement Using Graph Neural Networks 8

MetaGraph: Plasmid/Chromosome Classification Enhancement Usi...

引用

8th International Multidisciplinary Moratuwa engineering Research Conference, MERCon 2022

作者： Alahakoon, Sasindu Dassanayake, Gayal Nandasiri, Chamika Wickramarachchi, Anuradha Perera, Indika University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka Australian National University School of Computing Canberra Australia

ISBN: (数字)9781665487863

ISBN: (纸本)9781665487863

Chromosomes and plasmids are the main sites of genetic information in microorganisms. Separately identifying plasmids and chromosomes is essential for further metagenomic analysis. Computational tools have achieved significant results in classifying DNA into plasmids and chromosomes. However, there is often a trade-off between recall and precision in the currently available tools. Several graph-based tools have been proposed to improve the prediction precision and recall simultaneously by improving upon the results produced by existing tools. We propose MetaGraph, a Graph Neural Network (GNN) based tool for plasmid/chromosome classification enhancement. It uses the high confidence predictions of existing plasmid/chromosome prediction tools and improves the prediction accuracy of low confidence predictions using plasmid probabilities as features for the GNN. We evaluated MetaGraph for a set of simulated DNA sequences. The results significantly improved over stateof-the-art tools like PlasFlow and PlasClass. The results were increased up to 20% from the initial PlasClass predictions. The source code for MetaGraph is freely available at: https://***/MetaGSC/MetaGraph © 2022 IEEE.

关键词： Graph neural networks

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：