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Predictive Modeling for Student Grade Prediction Using Machine Learning and Visual analytics 19

Predictive Modeling for Student Grade Prediction Using Machi...

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19th international conference on New Trends in intelligent Software Methodologies, Tools and Techniques (SoMeT)

作者： Bujang, Siti Dianah Abdul Selamat, Ali Krejcar, Ondrej Univ Teknol Malaysia Malaysia Japan Int Inst Technol MJIIT Jalan Sultan Yahya Petra Kuala Lumpur Malaysia UTM Sch Comp Fac Engn Johor Baharu Malaysia Univ Teknol Malaysia Media & Games Ctr Excellence MagicX Johor Baharu Malaysia Univ Hradec Kralove Fac Informat & Management Rokitanskeho 62 Hradec Kralove 50003 Czech Republic

ISBN: (纸本)9781643681153;9781643681146

data-driven plays an important role in determining the quality of services in institutions of higher learning (HEIs). Increasingly data in education is encouraging institutions to find ways to improve student academic performance. By using machine learning with visual analytics, data can be predicted based on valuable information and presented with interactive visualizations for institutions to improve decision making. Therefore, predicting students' academic performance is critical to identifying students at risk of failing a course. In this paper, we propose two approaches, such as (i) a prediction model for predicting students' final grade based on machine learning that interacts with computational models;(ii) visual analytics to visualize predictive models and insightful data for educators. The data were tested using student achievement records collected from one of the Malaysian Polytechnic databases. The data set used in this study involved 489 first semester students in Computer System Architecture (CSA) course from 2016 to 2019. The decision tree algorithms (J48), Random Tree (RT), Random Forest (RF), and REPTree) was used on the student data set to produce the best predictions of the model. Experimental results show that J48 returns the highest accuracy with 99.8 %, among other algorithms. The findings of this study can help educators predict student success or failure for a particular course at the end of the semester and help educators make informed decisions to improve student academic performance at Polytechnic Malaysia.

关键词： Predictive Model Predictive analytics Machine Learning Visual analytics data Science Grade Prediction Student Academic Performance

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Application of the data Mining Technology in the Economic Management in the Age of Big data

Application of the Data Mining Technology in the Economic Ma...

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international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Luo, Wenbiao Guangzhou Vocat Coll Sci & Technol Guangzhou 510550 Guangdong Peoples R China

ISBN: (纸本)9783030152352;9783030152345

Under the background of the continuous acceleration of the reform process of the domestic market economy, relying on the continuous improvement of the information technology level, the Internet era has come in an allround way. Under this background, the traditional economic management model obviously cannot meet the needs of the current economic market. In view of the problems existing in the traditional economic management, this paper discusses the informatization of the economic management under the background of the big data era.

关键词： Big data era data mining technology Economic management Application mechanism

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A Novel Secure Big data Cyber Incident analytics Framework for Cloud-Based Cybersecurity Insurance 2

A Novel Secure Big Data Cyber Incident Analytics Framework f...

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2nd IEEE international conference on Big data Security on Cloud (BigdataSecurity)

作者： Gai, Keke Qiu, Meikang Elnagdy, Sam Adam Pace Univ Dept Comp Sci New York NY USA

ISBN: (纸本)9781509024032

The remarkable increasing demands of mitigating losses from cyber incidents for financial firms has been driving the rapid development of the Cybersecurity Insurance (CI). The implementations of CI have covered a variety of aspects in cyber incidents, from hacking to frauds. However, CI is still at its exploring stage so that there are a number of dimensions that are uncovered by the current applications. The cyber attack on critical infrastructure is one of the serious issues that prevents the expansions of CI. This paper addresses CI implementations focusing on cloud-based service offerings and proposes a secure cyber incident analytics framework using big data. Our approach is designed for matching different cyber risk scenarios, which uses repository data. Our simulation has provided the theoretical proof of the adoptability and feasibility.

关键词： Cybersecurity insurance incident analytics framework cloud computing big data

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Proceedings - 2020 IEEE 6th international conference on Big data computing Service and Applications, BigdataService 2020

Proceedings - 2020 IEEE 6th International Conference on Big ...

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6th IEEE international conference on Big data computing Service and Applications, BigdataService 2020

ISBN: (纸本)9781728170220

The proceedings contain 38 papers. The topics discussed include: crack detection with multi-task enhanced faster R-CNN model;data structure for packet de-duplication in distributed environments;ScholarFinder: knowledge embedding based recommendations using a deep generative model;intracranial hemorrhage detection in CT scans using deep learning;classifying cognitive load for a proactive in-car voice assistant;distributed fog computing architecture for real-time anomaly detection in smart meter data;classifying cognitive load for a proactive in-car voice assistant;an ensemble of multiple boosting methods based on classifier-specific soft voting for intelligent vehicle crash injury severity prediction;and the delta big data architecture for mobility analytics.

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Big data analytics and machine learning techniques to manage the smart grid

Big data analytics and machine learning techniques to manage...

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2023 international conference on Computer Communication and Informatics, ICCCI 2023

作者： Elavarasi, M. Kolikipogu, Ramakrishna Kotha, Mahesh Santhi, M.V.B.T. Vels University Pallavaram School of Computing Sciences Chennai India Information Technology Hyderabad India Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

ISBN: (纸本)9798350348217

Big data is a crucial part of the Energy Internet. Integrating renewables and smart grids is vital and exciting. Any power plant must collect and evaluate data to make educated choices. This study examines big data analytics in renewable energy based power plants. This paradigm implements big data analytics for renewable energy utilities and smart grids. The authors describe a five-step machine-learning approach to predict smart grid dependability. Using 64,000 occurrences and 12 attributes from an intelligent grid data system, we expected the system's stability using three machine-learning algorithms. The penalized linear regression model achieves 95% accuracy using 70% of the training data. The random forest model is 84% accurate vs 77% for the decision tree. CNN and gradient-boosted decision tree models obtained 86% classification accuracy. This study's small dataset prevents extensive data analyses. Cloud computing and real-time event analysis are well-suited for a data analytics infrastructure. Future research should include data from additional countries and renewable energy sources. © 2023 IEEE.

关键词： smart power grids

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Dependability of V2I Services in the Communication Network of the intelligent Transport Systems 6

Dependability of V2I Services in the Communication Network o...

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6th international conference on Models and Technologies for intelligent Transportation Systems (MT-ITS)

作者： Kabashkin, Igor Transport & Telecommun Inst Riga Latvia

ISBN: (纸本)9781538694848

intelligent Transport System (ITS) applications are focused on improving road safety and creating new transport services. To realize the potential of ITS applications, data exchange should be efficiently carried out through direct communication over short distances both between vehicles (V2V) and between vehicles and infrastructure (V2I). High demands are placed on ITS performance indicators. These requirements are increasing especially in the context of the new mobility patterns using autonomous vehicles. The ITS big data processing can be centralized or distributed. The centralized approach utilizes the power of cloud computing. In contrast to cloud computing, intelligent data processing with faster responses and higher quality can be provided by fog computing. Fog computing is a horizontal, system-level architecture that distributes computing, storage, control and networking functions closer to the users along a cloud-to-thing continuum. Implementing fog computing in the dynamic environment of ITS to provide real-time big data analytics is facing many challenges that are not covered by previous research studies. The article analyzes the availability of V2I services in distributed communication network of ITS with fog-based architecture from reliability point of view. The dependability of V2I services for end users of smart vehicle in fog operating ITS is defined. The mathematical model of the reliability of a separate dedicated service in fog-based ITS for end user applications in the real conditions of operation is developed.

关键词： reliability availability intelligent transport systems fog architecture V2I communications

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Multi-Objective Value-Driven smart Building Management Solutions

Multi-Objective Value-Driven Smart Building Management Solut...

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international conference on Cloud computing and Big data

作者： Lin, Chien Ye, Jiun-Hau Yu, Cheng-Juei Weng, Ming-Lung Chang, Juiwen Wu, Tsung-Lin Hsiao, Morris H. Lin, Grace Inst Informat Ind Data Analyt Technol & Applicat Res Inst Taipei Taiwan

ISBN: (纸本)9781467383509

The intelligent Building Management System (iBMS) is a building management system which was developed from the needs of building owners for centralized energy management and accomplished by the multi-objective value-driven approach. Firstly building monitoring and service/maintenance optimization were conducted by establishing objectives, collecting data, and defining the mapping functions between values of interests and sensor/actuator factors in building operations. Then the operational goal of the smart green buildings to balance efficiency, health, safety, energy saving, and comfort was achieved by performing data analytics, prediction, and operations optimization. This research proposed a multi-objective value-driven management system and methodology from the viewpoint of the building owners to optimize the operational efficiency of smart buildings, seeking for the benefits that the building owners believe is the biggest in a multiple objective situation. The results of this research had been introduced into three commercial buildings in cooperation with enterprises in the green industry to demonstrate and verify the applicability of the research, showing that 18.9% energy saving was accomplished while the balance between comfort and energy-saving objectives was retained.

关键词： smart building energy analysis and management value-driven

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IoT-Enabled smart Agriculture System Using Cognitive computing 3

IoT-Enabled Smart Agriculture System Using Cognitive Computi...

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international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Rajababu, Durgam Boddepalli, Eswararao Survase, Rajesh Bhaskar Pal, Arunabha Dandagala, Sreenivasulu Chakravarthi, M. Kalyan SR Univ Dept EEE Sch Engn Warangal Telangana India Virginia Tech Bradley Dept ECE Blacksburg VA USA Savitribai Phule Pune Univ ES Divekar Coll Varvand Dept Geog Pune Maharashtra India Centurion Univ Technol & Management MS Swaminathan Sch Agr Dept Soil Sci Parlakhemundi 761211 Odisha India Annamacharya Univ Annamacharya Inst Technol & Sci Dept Civil Engn Rajampet 516126 Andhra Pradesh India VIT AP Univ Sch Elect Engn Amaravathi 522237 Andhra Pradesh India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Together, the Internet of Things (IoT) and cognitive computing have revolutionized the agricultural industry by enabling the implementation of intelligent husbandry systems. Investigating the creation and use of an Internet of Things-enabled smart husbandry system that leverages cognitive computing techniques is the aim of this study. The system includes detectors to gather data on environmental factors like crop health, soil temperature, and humidity in real-time. After that, a centralized platform receives these data. This data is processed by algorithms used in cognitive computing to give growers useful insights like optimal irrigation timing and early detection of crop issues. The application of this method raises agricultural operations' productivity and enhances decision-making processes' effectiveness. This study covers the following topics: the system's armature;the choice and incorporation of Internet of Things bias;the use of cognitive computing models;and the assessment of system performance in actual agricultural scripts. The results show how cognitive computing and the Internet of Things (IoT) may revolutionize traditional husbandry methods into data-driven, intelligent husbandry systems.

关键词： IoT smart agriculture cognitive computing sensor networks data analytics precision farming

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DEVELOPING USER- AND TECHNOLOGY-DRIVEN USE CASES FOR smart CITY APPLICATIONS 16

DEVELOPING USER- AND TECHNOLOGY-DRIVEN USE CASES FOR SMART C...

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16th international conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th international conference on Connected smart Cities, CSC 2022, 7th international conference on Big data analytics, data Mining and Computational Intelligence, BigDaCI 2022, and 11th international conference on Theory and Practice in Modern computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information Systems, MCCSIS 2022

作者： Otto, Robert Guedey, Myriam Uckelmann, Dieter University of Applied Sciences Stuttgart Schellingstraße 24 Stuttgart70174 Germany

ISBN: (纸本)9789898704429

Within the transformation and digitalization in the area of smart public buildings and infrastructures, finding, selecting and implementing the "right" use cases is an actual challenge for cities, public utilities, and other stakeholders. The question is where to start, what knowledge and capacities are available and what should be prioritized. This paper introduces a first concept for a methodology that supports the co-development of use cases with respect to current activities, existing infrastructures, and know-how on behalf of the public partners. As a preliminary result, three use cases were identified that represent the current priorities of the involved partner smart cities. © MCCSIS *** rights reserved.

关键词： Internet of things

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Innovating smart Buildings and Cities Through Agent-Based Modeling, Discrete Event Simulation and Sensor-Based data analytics 12

Innovating Smart Buildings and Cities Through Agent-Based Mo...

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12th IEEE international conference on intelligent data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Lopinski, Juliane Sachweh, Sabine Muller, Claudia IDiAL Institute University of Applied Sciences and Arts Dortmund Dortmund Germany University Siegen Siegen Germany

ISBN: (纸本)9798350358056

The paper emphasizes the significance of sensor-based data analytics (SBDA) for modeling and simulation in a smart city, using the methods discrete event simulation (DES) and agent-based modeling (ABM). Moreover, the project reviewing paper presents several selected project examples, such as CitySim, AIRE, smart Grid, and the Gotland project that demonstrate the potential of SBDA in smart buildings and urban systems and analyse possible advantages of the application of the SBDA method. Furthermore, the result table demonstrates the possibilities of using SBDA in ABM and DES in other sectors, thus obtaining accurate results in a modeling system in the future. These findings have significant implications for policymakers, urban planners, and stakeholders in smart city development who seek to enhance the quality of life for urban citizens. The paper contributes to a better understanding of advantages of using sensor-based data analysis in ABM and DES and the method's potential benefit for other sectors. It provides a basis for analysis on some methodologies in the field of smart cities and buildings. It is intended to serve as an overview for future researchers and to contribute to the understanding of general concepts of research institutions. © 2023 IEEE.

关键词： efficiency modelling simulation smart city systems

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