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检索条件"任意字段=4th International Conference on Computing and Data Science, CONF-CDS 2022"

共 1120 条记录，以下是191-200 订阅

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A Decision Support System using Machine Learning -IoT to tackle the Covid-19 pandemic in Uttarakhand 4

A Decision Support System using Machine Learning -IoT to tac...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Negi, Harendra Singh Dimri, Sushil Chandra Kumar, Bhawnesh Kumar, Naveen Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Dit University School of Computing Dehradun India

ISBN: (纸本)9781665474368

Covid-19 spread is worldwide;India is now at the second place where this epidemic is spreading with high rate. the state of Uttarakhand, a hilly state of India also has a significant impact of Covid-19. this paper suggests that machine learning techniques with IOT can equipped the doctors, and lab technicians to deal with this pandemic. Here, we also design a prediction system to help the doctors so that they can keep the records of infected patients. We used IoT, machine learning and ensemble methods for healthcare to store infected patient's data in the cloud database, and enable doctors/others to screen patient's data about their disease. We developed a decision support system to detect the diseases quickly and the treatment can be initiated immediately. © 2022 IEEE.

关键词： Decision support systems

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Quantitative Analysis of the Impact of Online + Offline Intelligent Evaluation of SQL database Algorithm on Courses 4

Quantitative Analysis of the Impact of Online + Offline Inte...

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4th international conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Tian, Hu The School of Education Science Xian Yang Normal University Xian Yang Shaanxi712000 China

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

As a highly specialized subject, the large-scale database application course plays a very important role in the social and economic development of our country. From the current point of view, the traditional teaching system of large-scale database application courses can no longer meet the development needs of the era of big data. Analyze the video attraction of online courses. through statistical analysis, establish a single video attraction factor R, and study the attraction of a single video based on multiple regression algorithms Mathematical mechanism that force is affected by video attributes. It is found that the attractiveness of a single video is negatively linearly related to both video duration and video release time. Judge the relative importance of the curriculum, and then explore the establishment of a quantitative evaluation model for the curriculum system. © 2022 IEEE.

关键词： Curricula

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Hybrid Classification Model for Network Traffic Classification using SMOTE and PCA 4

Hybrid Classification Model for Network Traffic Classificati...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Rana, Rohit Bharti, Vishal Chandigarh University Department of Computer Science Mohali India

ISBN: (纸本)9781665474368

Network Traffic classification (NTC) involves identifying diverse kinds of applications or traffic data for which the received data packets are analyzed. It is an important technique in communication networks, in recent times. the process to classify the network traffic classification is executed such as to take the data for input, pre-process the data, extract the attributes, classification, and performance analysis. Classifying the network traffic is a crucial process to manage the network and rapid advances in machine learning have driven the utility of learning methods to categorize traffic across a network. this work introduces a new framework to classify the traffic in network so that the efficacy is enhanced. the proposed model is the combination of SMOTE which solve problem of class unbalancing, PCA for the feature reduction and for the classification three classifiers are merged together which are SVM, KNN and random forest. the performance of proposed model is tested in Python is executed using Anaconda to test the efficacy of the introduced framework. © 2022 IEEE.

关键词： Learning systems

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A Novel Approach to Panel data Clustering: Integrating Feature Extraction and Weighted Distance Functions 4

A Novel Approach to Panel Data Clustering: Integrating Featu...

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4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

作者： Ao, Xiqin Abisado, Mideth College of Computing and Information Technologies National University Manila Philippines Institute of Big Data and Artificial Intelligence Anhui Xinhua University Anhui China

ISBN: (纸本)9798400708831

the traditional clustering method for two-dimensional information is unsuitable for processing panel data. A weighted panel data clustering method based on feature extraction is proposed based on the investigation of multiple information features of panel data. the technique first defines a distance function that considers the absolute value of the indicator, the growth rate of adjacent time points, and the degree of fluctuation variation. then, the indicator weights and time weights are aggregated into a comprehensive weighted distance through a linear model, and the clustering process of panel data is implemented based on the Ward clustering method. Finally, empirical analysis using consumption expenditure data of various regions in China shows that this clustering analysis method has better discrimination ability and can improve the accuracy of sample clustering. © 2023 ACM.

关键词： Feature extraction

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Making AI Explainable in the Global South: A Systematic Review 22

Making AI Explainable in the Global South: A Systematic Revi...

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4th ACM SIGCAS/SIGCHI conference on computing and Sustainable Societies (COMPASS)

作者： Okolo, Chinasa T. Dell, Nicola Vashistha, Aditya Cornell Univ Comp Sci Ithaca NY 14853 USA Cornell Tech Informat Sci New York NY USA

ISBN: (纸本)9781450393478

Artificial intelligence (AI) and machine learning (ML) are quickly becoming pervasive in ways that impact the lives of all humans across the globe. In an effort to make otherwise "black box" AI/ML systems more understandable, the field of Explainable AI (XAI) has arisen with the goal of developing algorithms, toolkits, frameworks, and other techniques that enable people to comprehend, trust, and manage AI systems. However, although XAI is a rapidly growing area of research, most of the work has focused on contexts in the Global North, and little is known about if or how XAI techniques have been designed, deployed, or tested with communities in the Global South. this gap is concerning, especially in light of rapidly growing enthusiasm from governments, companies, and academics to use AI/ML to "solve" problems in the Global South. Our paper contributes the first systematic review of XAI research in the Global South, providing an early look at emerging work in the space. We identified 16 papers from 15 different venues that targeted a wide range of application domains. All of the papers were published in the last three years. Of the 16 papers, 13 focused on applying a technical XAI method, all of which involved the use of (at least some) data that was local to the context. However, only three papers engaged with or involved humans in the work, and only one attempted to deploy their XAI system with target users. We close by reflecting on the current state of XAI research in the Global South, discussing data and model considerations for building and deploying XAI systems in these regions, and highlighting the need for human-centered approaches to XAI in the Global South.

关键词： Artificial Intelligence Machine Learning Algorithmic Fairness Explainability HCI4D XAI4D ICTD Global South

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Machine Learning based Sentiment Analysis of Hindi data with TF-IDF and Count Vectorization 7

Machine Learning based Sentiment Analysis of Hindi Data with...

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7th international conference on computing, Communication and Security, ICCCS 2022 and 2022 4th international conference on Big data and Computational Intelligence, ICBDCI 2022

作者： Gupta, Ashwani Sharma, Utpal Tezpur University Department of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350309904

Sentiment refers to emotions. Sentiment analysis, often known as opinion mining, is the technique of identifying and extracting subjective data from pre-web and post-web reviews using text analytics, computational linguistics, and natural language processing. Hindi is an Indian language which is used by many of Indians. Due to phenomenal growth of online product reviews in Hindi post-web Hindi reviews are also increasing rapidly. A machine learning based method in this paper to analysis postweb text data. the present method is divided into four steps. First of all, an annotated Hindi review data set is developed from post-web sources. In second step, feature extraction is performed on annotated Hindi review dataset using the Term-Frequency/ Inverse-Document Frequency (TF-IDF) and count vectorization techniques. In the third step, the retrieved features are given to the classifier so it can make predictions. Moreover, annotated dataset translated into English. Second step and third step are performed on annotated English dataset in last step. A range of evaluation criteria, including precision, recall, and F1- score, are presented in the results. In both instances, the support vector machine produced the most pertinent results. © 2022 IEEE.

关键词： Sentiment analysis

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Privacy Preserving Task Allocation with Multi-objectives in Edge computing Enhanced Mobile Crowdsensing 4

Privacy Preserving Task Allocation with Multi-objectives in ...

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4th international conference on Industrial Artificial Intelligence, IAI 2022

作者： Yu, Longxin Meng, Haofei Yu, Wenwu Southeast University School of Mathematics School of Cyber Science and Engineering Nanjing210096 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Mobile crowdsensing (MCS) uses participants' computing resources to collect and analyze data and it has been applied in several areas to bring the convenience to people's lives. In MCS, the minimization of travel distance with location privacy is a common objective but should not be the only one practically. Different from the single objective of travel distance minimization, in this paper we formulate a multi-objective optimization model based on bit flipping mechanism, i.e., travel distance minimization and sensing quality score maximization, which is more suitable for a practical scenario. In order to solve the large-scale optimization problem, a Multi-Objective Simulated Annealing approach (MOSA) is utilized to derive a Pareto solution for decision makers. Extensive simulation results illustrate the feasibility and effectiveness of the proposed scheme. © 2022 IEEE.

关键词： Simulated annealing

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Secure and Efficient V2G Scheme through Edge computing and Federated Learning 4

Secure and Efficient V2G Scheme through Edge Computing and F...

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4th international conference on Smart Power and Internet Energy Systems (SPIES)

作者： Shang, Yitong Li, Zekai Shao, Ziyun Jian, Linni Southern Univ Sci & Tech Jiaxing Res Inst Jiaxing Peoples R China Guangzhou Univ Sch Elect & Commun Engn Guangzhou Peoples R China Southern Univ Sci & Tech Dept Elect & Elect Engn Shenzhen Peoples R China

ISBN: (纸本)9781665489577

Since the road transport accounts for 15% of total carbon emissions, electric vehicles (EVs) have made great strides and large-scale uncoordinated EV charging will greatly increase the load pressure of power system. the vehicle-to-grid (V2G) technology can optimize the idle EVs to charge during the grid load valley period and feeding the grid as a power source during the load peak period. this bidirectional energy flow technology builds a bridge between the power grid and EVs. However, the user privacy-preserving and data asset protection have always been ignored in previous works. In this paper, the secure and efficient V2G scheme through edge computing and federated learning from the double layer has been proposed. Firstly, the edge computing unit is set in the data source, viz., the charging point, to avoid sensitive data leakage. And then, the desensitized charging data will be stored in charging station. Next, the federated learning is utilized among charging stations to jointly train a global model without breaching data asset. Finally, the real dataset is applied to the experiment, and the effectiveness of the proposed architecture is verified.

关键词： vehicle-to-grid user privacy-preserving data asset protection edge computing federated learning

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Research on privacy fraud detection of Logistic regression based on homomorphic encryption 4

Research on privacy fraud detection of Logistic regression b...

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4th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2022

作者： Chen, Zhuang Cai, Mingdian Wang, Zhikun School of Computer Science and Engineering Chongqing University of Technology Chongqing400065 China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

Fraud detection is an integral part of financial security monitoring tool;however, the traditional fraud detection method cannot detect the existing malicious fraud, and the clouds will produce data revealing that the risk of fraud detection system can not protect the privacy of detected object, so the fraud detection data privacy security becomes a significant problem,Homomorphic encryption as a demonstrable cryptography cloud privacy computing outsourcing scheme can ensure that cloud computing can perform ciphertext polynomial calculation under the dense state data without direct contact with the accurate data of users, so as to ensure data privacy security. Aiming at the data privacy security problems in the process of fraud detection, this paper combined homomorphic encryption and Logistic regression fraud detection technology to study the Logistic regression fraud detection algorithm under homomorphic ciphertext and constructed a cloud privacy fraud detection method based on customer service and cloud computing services. CKKS encryption scheme is used to encrypt the fraud data set and realize the Logistic regression fraud detection algorithm under ciphertext. the experiment proves that the difference between the fraud detection accuracy on ciphertext and plaintext is less than 3%. Under the condition of ensuring the privacy of sensitive data to be detected, the effect of the fraud detection model is not affected. © 2022 IEEE.

关键词： Object detection

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Using Dynamic data Driven Cyberinfrastructure for Next Generation Disaster Intelligence 4th

Using Dynamic Data Driven Cyberinfrastructure for Next Gener...

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4th international conference on Dynamic data Driven Applications Systems, DDDAS 2022

作者： Altintas, Ilkay University of California San Diego La JollaCA92093 United States

ISBN: (纸本)9783031526695

Wildfires and related disasters are increasing globally, making highly destructive megafires a part of our lives more frequently. A common observation across these large events is that fire behavior is changing, making applied datadriven fire research more important and time critical. Significant improvements towards modeling wildland fires and the dynamics of fire related environmental hazards and socio-economic impacts can be made through intelligent integration of modern data and computing technologies with techniques for data management, machine learning and artificial intelligence. However, there are many challenges and opportunities in integration of the scientific discoveries and datadriven methods for hazards with the advances in technology and computing in a way that provides and enables different modalities of sensing and computing. the WIFIRE cyberinfrastructure took the first steps to tackle this problem with a goal to create an integrated infrastructure, data and visualization services, and workflows for wildfire mitigation, monitoring, simulation, and response. Today, WIFIRE provides an end-to-end management infrastructure from the data sensing and collection to artificial intelligence and dynamic data-driven modeling efforts using a continuum of computing methods that integrate edge, cloud, and high-performance computing. through this cyberinfrastructure, the WIFIRE project provides data driven knowledge for a wide range of public and private sector users, enabling scientific, municipal, and educational use. this paper summarizes the talk reviewing our recent work on building this dynamic data driven cyberinfrastructure and impactful application solution architectures that showcase integration of a variety of existing technologies and collaborative expertise. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Disasters

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