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MTeacher: A Gamified and Physiological-Based Web Application Designed for Machine Learning Education 16th

MTeacher: A Gamified and Physiological-Based Web Application...

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16th international conference on UniversalAccess inHuman-Computer Interaction (UAHCI) Held as Part of the 24th international conference on Human-Computer Interaction (HCII)

作者： Hernandez-Cuevas, Bryan Y. Crawford, Chris S. Univ Alabama Dept Comp Sci Tuscaloosa AL 35401 USA

ISBN: (纸本)9783031050282;9783031050275

Everyday devices are becoming smarter. Machine Learning (ML) is increasingly more utilized in technology to augment the user experience. Furthermore, it is applied to improve the overall performance of systems. People constantly use intelligent agents in different forms through online shopping, social media, and entertainment. However, they do not understand the intrinsic functionality of the technology. BrainComputer Interfaces (BCI) are more prominent in non-medical fields. advances in hardware enable better ways to implement and use BCIs in more ubiquitous ways. There are also software advances that allow devices to connect and achieve signal processing in lower-end environments like the web. There is a need for the public to understand the social and ethical implications of Machine Learning. This research project aims to provide novices and non-CS majors with an explainable educational experience. Machine Teacher (MTeacher) is a web-based Machine Learning educational system that aims to implement less typical types of data (e.g. EEG) and gamified elements. Players can explore the concepts ofMachine Learning through interactive activities, and our objective is to help increase their engagement, general ML knowledge, and self-efficacy. Initial results of a study reveal that physiological signals are more appealing to the users than traditional types of data, though they would prefer collecting the data themselves. Our future work aims to explore the addition of real-time physiological data acquisition and improve the application of gamification elements to study their effects on the learner's experience.

关键词： Machine Learning education Explainable machine learning Physiological computing

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data-driven Disaster Management in a Smart City 5th

Data-Driven Disaster Management in a Smart City

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5th EAI international conference on intelligent Transport systems (INTSYS)

作者： Goncalves, Sandra P. Ferreira, Joao C. Madureira, Ana Inst Univ Lisboa ISCTE IUL ISTAR Lisbon Portugal Politecn Porto 2ISRC Inst Super Engn Porto Porto Portugal

ISBN: (纸本)9783030976033;9783030976026

Disasters, both natural and man-made, are extreme and complex events with consequences that translate into a loss of life and/or destruction of properties. The advances in IT and Big data analysis represent an opportunity for the development of resilient environments once the application of analytical methods allows extracting information from a significant amount of data, optimizing the decision-making processes. This research aims to apply the CRISP-DM methodology to extract information about incidents that occurred in the city of Lisbon with emphasis on occurrences that affected buildings, constituting a tool to assist in the management of the city. Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the area most affected by occurrences. Finally, machine learning models were applied to the data and the predictive model Random Forest obtained the best result with an accuracy of 58%.

关键词： Disaster management data mining Machine learning Smart city

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Wireless Communication Design Using Neural Networks and Deep Learning

Wireless Communication Design Using Neural Networks and Deep...

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2022 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2022

作者： Singh, Rajesh Khurana, Virender Sambasiva Reddy, M. Yadav, Ranjeet Jangir, Rachit Kapila, Dhiraj Uttaranchal University Uttaranchal Institute of Technology India Rohtak Department of Computer Science India Department of Electronics and Communication Engineering Andhra Pradesh Mylavaram521230 India Government Polytechnic Daman Varkund Nani Daman UT Administration of Dadra and Nagar Haveli & Daman and Diu Nagar Haveli India Government Polytechnic College Department of Computer Science & Engineering Rajasthan Sikar India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

According to this report, the most innovative and active area of the communications business is wireless communications. data may be sent wirelessly between two places without the need of physical media like wires or cables. Business network providers have had to adjust and adapt as a result of the big data age. Computer science, which is used in a variety of industrial and scholarly scientific fields, is one of the effective AI technologies for analysing this flood of data. This article goes in-depth on big dataset processing, technological breakthroughs, and potential 5G network service usage. Identify the needs of phone users using sophisticated analytics, and then apply that knowledge to increase the effectiveness of "shared wireless communications."Particularly striking is the extensive, comprehensive, data-driven computer vision framework it provides, which covers extraction of features, simulation models, and forecast enhancements. The proposed framework's main advantages include its reliance on a sizable data set that faithfully captures the spectrum and other demanding user requirements, the logic of robust computational models, the formulation of issues in the context of wireless networks, and the capacity to design a method. Through a communication system, data is often sent from the transmitter to the receiver across very long distances. Through wireless communication, the transmitter may be positioned anywhere within a few metres. We'll examine existing strategies, their advantages and disadvantages, as well as upcoming developments in underwater sensor networks. We have discovered a fresh approach to doing research. © 2022 IEEE.

关键词： data acquisition

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Personalized intelligent Recommendation System for Electronic Commerce Based on Multi-dimensional Commodity Attributes 4th

Personalized Intelligent Recommendation System for Electroni...

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4th international conference on Innovative computing, IC 2021

作者： Fan, Ganglong Shen, Jian Henan Key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang Normal University Luoyang China Higher Education Admissions Office of HENAN Province Zhengzhou China

ISBN: (纸本)9789811642579

E-commerce recommendation department is faced with some common problems, such as user cold start problem and data sparsity problem. Solving user cold start problem and data sparsity problem is to improve the accuracy of recommendation results. An effective way is to improve the user experience. driven by big data collected by e-commerce system and supported by natural language processing technology, a new model of e-commerce recommendation system is designed. This paper puts forward the personalized intelligent recommendation system for electronic commerce, which can effectively alleviate the problems of cold start and sparsity, and provide users with accurate and personalized recommendation results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Cri-Astrologer: Predicting Demography of Involved Criminals based on Historical data 22

Cri-Astrologer: Predicting Demography of Involved Criminals ...

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9th international conference on Networking, systems and Security, NSysS 2022

作者： Rahman, Md Atiqur Alim Al Islam, A.B.M. Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9781450399036

Because of the rapid advancement in computer technology, police enforcement agencies are now able to keep enormous databases that contain specific information about crimes. These databases can be utilized to analyze crime patterns, criminal characteristics, and the demographics of both criminals and victims. Through the application of various machine learning algorithms to these datasets, it is possible to generate decision-aid systems that can assist in the conduct of police investigations. When there is a large amount of data accessible, several data-driven deep learning approaches can also be utilized. Within the scope of this investigation, our primary objective is to create a tool that may be utilized during the standard investigative process. To forecast criminal demographic profiles using crime evidence data and victim demographics, we present a deep factorization machine-based DNN architecture. We evaluate the performance of our architecture in comparison to that of traditional machine learning algorithms and deep learning algorithms, and we provide our findings in a comparative study. © 2022 Association for computing Machinery.

关键词： Crime

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STRUCTURED, SYSTEMATIC THREAT BASED APPROACH TO EVALUATE AND IMPROVE data QUALITY TO FACILITATE DIGITAL TRANSFORMATION 14

STRUCTURED, SYSTEMATIC THREAT BASED APPROACH TO EVALUATE AND...

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14th international Pipeline conference (IPC)

作者： Tomar, Pushpendra Kruse, Betsy Hasan, Samah Kondratyuk, Sergiy Dynam Risk The Woodlands TX 77380 USA Enbridge Houston TX USA Dynam Risk Calgary AB Canada Dynam Risk Toronto ON Canada

ISBN: (纸本)9780791886571

Pipeline operators are rapidly and increasingly moving towards digital transformation in order to harvest efficiencies and achieve higher levels of reliability and safety. Fueled by advances in technology such as cloud computing and machine learning, data is considered a key asset, and pipeline operations are increasingly driven by information and analytics. However, successfully achieving a digital transformation toward reliable and high-quality data requires mature processes for obtaining, managing, evaluating, and continuously improving data quality. During a review of pipeline risk assessment results, a pipeline operator (Operator) found that risk results for a particular pipeline were driven by the mainline coating type being listed as "un-coated." However, further review of the records showed that the pipeline, in fact, was coated. One of the Operator's foundational principles is 'data as an asset'. Thus, the Operator understands the critical impact of such data inconsistencies across many potential receptors, from financial impacts to public safety. Additionally, mature processes enhance confidence in prioritizing the "right work." data quality is essential for the use of historical data, interoperability across various data systems, and generation of useful analytics. The data quality process maturity (Process maturity) evaluation aims to assess all processes, capabilities, and governance required for ensuring high data quality. As a result, the Operator decided to rigorously evaluate their data quality and the maturity of data quality processes. The data quality assessment involved creating a comprehensive list of data elements required to assess a particular threat, prioritizing data elements, and documenting data storage by the source system. The data quality was then evaluated using Key Performance Indicators (KPIs), establishing a baseline. An organization's Process maturity varies from level one (Initial) to level five (Optimized). The Process maturity

关键词： data quality evaluation data maturity Digital transformation Bayesian network KPIs Process maturity a step-by-step approach

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Research on the Influence of Aging Information System Optimization Based on Big data on China’s Rural Revitalization 3rd

Research on the Influence of Aging Information System Optimi...

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3rd international conference on Advanced intelligent Technologies, ICAIT 2022

作者： Wang, Lina Zhang, Qiuyue Cui, Jian Wang, Yitong Pan, Yongqiang School of Economy and Management Hainan Normal University Hainan Haikou571158 China

ISBN: (纸本)9789819952021

The strategy of rural revitalization is an important part of resolving the main contradictions of China’s current development, and it is of great significance to China's rural development. However, the long-term urban–rural dual structure has led to serious loss of rural population, and the gradual transformation of rural areas to hollow and aging is an important factor in the steady implementation of the rural revitalization strategy. Rural revitalization has five connotations, namely, industrial prosperity, ecological livability, rural civilization, effective governance, and affluent life. This paper applies advanced intelligent technologies such as cloud computing and big data and green computing, selects 20 indicators to evaluate the five dimensions of rural revitalization, and carries out data analysis. At the same time, a system dynamics model is constructed to simulate rural revitalization and the interactive relationship between the level of information system on aging and predict the five dimensions of rural revitalization from 2020 to 2030. The results found that the deepening of the aging level has a positive effect on rural revitalization;it has different degrees of promotion effect on the five dimensions, and the basic sequence from fast to slow is: living affluent > rural culture > industrial prosperity > ecological livability > governance effective;the overall rating of rural revitalization has also shown rapid growth. In the context of big data, discuss the impact of aging information system on rural revitalization from a quantitative perspective, hoping to provide a certain reference for the implementation of rural revitalization. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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intelligent Tutoring for Large-Scale Personalized Programming Learning Based on Knowledge Graph 53

Intelligent Tutoring for Large-Scale Personalized Programmin...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Li, Ying Qiu, Jincheng Yang, Runze Zhu, Tongyu Sheng, Hao Gui, ShiJie Liang, Yu School of Computer Science and Engineering Beihang University Beijing China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

ISBN: (纸本)9798350336429

The development of learning analytics technology has created the necessary conditions for the implementation of large-scale personalized education. By taking advantage of the current advancements available, we can adopt a deep integration of 'artificial intelligence + education', take educational big data as the research object, aim at achieving precise teaching, and use cognitive diagnosis as a means to construct an intelligent guidance model. The model will conform to a learner's cognitive patterns and curriculum characteristics to meet his personalized learning needs under different knowledge states. Firstly, use knowledge map and abstract syntax trees to extract the characteristics of teaching objectives from questions and standard programing codes. Secondly, Use 'target characteristics - learning characteristics - learning sequence - learning effect' cognitive diagnosis model to assess student achievement in learning. Finally, according to the correlation between knowledge points, the initial learning path consisting of all relevant knowledge points is identified from a knowledge map. And the most optimal learning path is selected based on the importance of knowledge points and student's learning situation. There are three innovation points in this paper. First, the traditional method which only focuses on discovering the one-dimensional knowledge features implicated in the questions, is extended to construct a knowledge-ability multidimensional feature matrix. Secondly, it optimized static cognitive diagnosis into dynamic cognitive arbitrariness, which has a modeling capability of dynamic temporal sequence. Thirdly, it regarded the correlation between knowledge points, and the importance of knowledge points as the heuristic conditions for constructing a learning path. © 2023 IEEE.

关键词： Cognitive Diagnosis intelligent Guidance Model Knowledge Graph Personalized Learning Path

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IoT-Enabled Black Box for Driver Behavior Analysis Using Cloud computing

IoT-Enabled Black Box for Driver Behavior Analysis Using Clo...

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advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： J. Jegan M. Raja Suguna M. Shobana H. Azath S. Murugan M. Rajmohan Department of Computer Science and Engineering School of Technology The Apollo University Chittoor Andhra Pradesh India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Networking and Communication SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Bhopal India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The Internet of Things (IoT)-powered black box for advanced driver behavior analysis and vehicle safety is a revolutionary strategy for improving driver behavior and road safety via the use of black boxes powered by the Internet of Things. This project captures realtime data on driving behavior, encompassing characteristics such as speed, acceleration, braking, and adherence to lanes via the seamless integration of IoT technologies into the vehicle's black box. It does this by providing in-depth insights into drivers' behaviors, which encourages adopting safe driving practices thanks to the advanced data analytics it employs. In addition to its behavior monitoring capabilities, the IoT-driven black box enhances the vehicle's safety by including collision detection technologies. If an accident occurs, the system immediately sends an alert, communicating the severity of the collision and the exact GPS locations to the emergency services, which speeds up the process of responding to the incident. This attempt combines cutting-edge Internet of Things capabilities with vehicle safety to develop responsible driving behaviors and, eventually, a safer highway environment for all parties involved. This invention can modify driving norms thanks to its comprehensive approach, highlighting the potential of technology-driven solutions in promoting safer roadways.

关键词： Technological innovation data analysis Vehicle safety Emergency services Road safety Internet of Things Collision avoidance

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A Topic-Based Publish/Subscribe System in a Fog computing Model for the IoT 14th

A Topic-Based Publish/Subscribe System in a Fog Computing Mo...

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14th international conference on Complex, intelligent and Software Intensive systems (CISIS)

作者： Saito, Takumi Nakamura, Shigenari Enokido, Tomoya Takizawa, Makoto Hosei Univ Tokyo Japan Rissho Univ Tokyo Japan

ISBN: (纸本)9783030504540;9783030504533

In order to reduce the traffic of networks and servers in the IoT, types of the fog computing (FC) models are proposed, which are composed of fog nodes. A fog node supports application processes to calculate output data on sensor data and forward the output data to servers. A topic-based PS (publish/subscribe) model is a new contents-aware, event-driven model of a distributed system. Here, a process publishes a message whose contents are denoted by publication topics. A process specifies subscription topics and only is delivered messages whose publication topics include some of the subscription topics. In our previous studies, the MPSFC (Mobile PS (publish/subscribe) Fog computing) model is proposed where mobile fog nodes like vehicles are interconnected and fog nodes communicate with one another in wireless networks by the PS model. In this paper, we propose a TBDT (Topic-based data Transmission) protocol for mobile fog nodes to deliver messages to target nodes. In the evaluation, we show the number of messages in the TBDT protocol is fewer than the epidemic routing protocol while the delivery ratio is smaller.

关键词： IoT Fog computing (FC) model Mobile Fog computing (MFC) model Topic-based publish/subscribe system Mobile Publish/Subscribe Fog computing (MPSFC) model

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