Artificial intelligence and applications (Commerce, Calif.)最新文献_第7页

Hyper-Parameter Tuning in Deep Neural Network Learning 深度神经网络学习中的超参数整定

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-10-29 DOI: 10.5121/csit.2022.121809

Tiffany Zhan

{"title":"Hyper-Parameter Tuning in Deep Neural Network Learning","authors":"Tiffany Zhan","doi":"10.5121/csit.2022.121809","DOIUrl":"https://doi.org/10.5121/csit.2022.121809","url":null,"abstract":"Deep learning has been increasingly used in various applications such as image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. In deep learning, a convolutional neural network (CNN) is regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The full connectivity of these networks makes them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include penalizing parameters during training or trimming connectivity. CNNs use relatively little pre-processing compared to other image classification algorithms. Given the rise in popularity and use of deep neural network learning, the problem of tuning hyperparameters is increasingly prominent tasks in constructing efficient deep neural networks. In this paper, the tuning of deep neural network learning (DNN) hyper-parameters is explored using an evolutionary based approach popularized for use in estimating solutions to problems where the problem space is too large to get an exact solution.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77009137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Context-Aware and Adaptive System to Automate the Control of the AC Windshield using AI and Internet of Things 使用人工智能和物联网自动控制空调挡风玻璃的环境感知和自适应系统

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-10-29 DOI: 10.5121/csit.2022.121803

Joshua Tian, Y. Sun

引用次数: 0

LabBuddy: A Game-based Interactive and Immersive Educational Platform for Physics Lab Learning using Artificial Intelligence and 3D Game Engine LabBuddy:一个基于游戏的交互式沉浸式物理实验室学习平台，使用人工智能和3D游戏引擎

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-10-29 DOI: 10.5121/csit.2022.121814

Yu-Zi Ji, Mingze Gao, Yu Sun

引用次数: 0

Anomaly Detection based on Alarms Data 基于告警数据的异常检测

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-10-29 DOI: 10.5121/csit.2022.121810

Michel Kamel, Anis Hoayek, M. Batton-Hubert

{"title":"Anomaly Detection based on Alarms Data","authors":"Michel Kamel, Anis Hoayek, M. Batton-Hubert","doi":"10.5121/csit.2022.121810","DOIUrl":"https://doi.org/10.5121/csit.2022.121810","url":null,"abstract":"Alarms data is a very important source of information for network operation center (NOC) teams to aggregate and display alarming events occurring within a network element. However, on a large network, a long list of alarms is generated almost continuously. Intelligent analytical reporting of these alarms is needed to help the NOC team to eliminate noise and focus on primary events. Hence, there is a need for an anomaly detection model to learn from and use historical alarms data to achieve this. It is also important to indicate the root cause of anomalies so that immediate corrective action can be taken. In this paper, we aim to design an anomaly detection model in the context of alarms data (categorical data) in the field of telecommunication and that can be used as a first step for further root cause analysis. To do this, we introduce a new algorithm to derive four features based on historical data and aggregate them to generate a final score that is optimized through supervised labels for greater accuracy. These four features reflect the likelihood of occurrence of events, the sequence of events and the importance of relatively new events not seen in the historical data. Certain assumptions are tested on the data using the relevant statistical tests. After validating these assumptions, we measure the accuracy on labelled data, revealing that the proposed algorithm performs with a high anomaly detection accuracy.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81290622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Approach using Machine Learning Model for Breast Cancer Prediction 基于机器学习模型的乳腺癌预测方法

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-10-29 DOI: 10.5121/csit.2022.121815

Fatema Nafa, Enoc Gonzalez, Gurpreet Kaur

引用次数: 0

Low-Carbon Innovation Decision Considering Quality Differences and Government Subsidies under the Three-Party Trading Platform 三方交易平台下考虑质量差异和政府补贴的低碳创新决策

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-10-29 DOI: 10.5121/csit.2022.121823

Xu Wang, Longzhen Zhou, Zusheng Zhang, Yingbo Wu, Longxi Li

{"title":"Low-Carbon Innovation Decision Considering Quality Differences and Government Subsidies under the Three-Party Trading Platform","authors":"Xu Wang, Longzhen Zhou, Zusheng Zhang, Yingbo Wu, Longxi Li","doi":"10.5121/csit.2022.121823","DOIUrl":"https://doi.org/10.5121/csit.2022.121823","url":null,"abstract":"In the context of low-carbon innovation, reasonable subsidy, innovation, and pricing strategies are important to achieve resource decarbonization and supply-demand matching, while the quality differentiation of resources has a significant impact on the strategy formulation. In this paper, we study low-carbon innovation and government subsidy in different innovation scenarios with two providers offering differentiated manufacturing resources on a resource trading platform, integrating two variables of resource quality difference and demand-side lowcarbon preference. Using utility theory and the Stackelberg game, a decision model of low carbon innovation and government subsidy is constructed, and the equilibrium solution is obtained with inverse induction. Then, the low-carbon innovation and subsidy strategies under different innovation scenarios are compared and the effects of relative coefficients of quality and innovation cost coefficients on the strategies are analyzed. The findings show that when the difference in resource quality is small, the level of green innovation is higher in the low carbon innovation scenario with high-quality resources compared to the low carbon innovation scenario with low-quality resources, and the rate of government subsidy for innovation investment is also higher. In case of the large difference in resource quality, the relative magnitudes of green innovation level and government subsidy rate for innovation inputs in different scenarios are related to innovation cost coefficients.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75821319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The NP-Completeness of Quay Crane Scheduling Problem 码头起重机调度问题的np -完备性

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-10-29 DOI: 10.5121/csit.2022.121802

A. Skaf, Samir Dawaliby, Arezki Aberkane

引用次数: 0

An Intelligent and Social-Oriented Sentiment Analytical Model for Stock Market Prediction using Machine Learning and Big Data Analysis 基于机器学习和大数据分析的股票市场预测智能和面向社会的情绪分析模型

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-10-29 DOI: 10.5121/csit.2022.121819

Muqing Bai, Yu Sun

引用次数: 0

F-low: A Promising Countermeasure Against DDoS Attacks based on Split Sketch and PCA F-low:一种基于分割草图和PCA的DDoS攻击防范方法

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-10-29 DOI: 10.5121/csit.2022.121821

Fei Wang, Zhenxi Li, Xiaofeng Wang

引用次数: 0

Image Segmentation in Shape Synthesis, Shape Optimization, And Reverse Engineering 形状合成、形状优化和逆向工程中的图像分割

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-10-29 DOI: 10.5121/csit.2022.121824

M. Ćurković, A. Curkovic, D. Vucina, D. Samardžić

引用次数: 0