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Real Time, An IoT-Based Affordable Air Pollution Monitoring For smart Home 1st

Real Time, An IoT-Based Affordable Air Pollution Monitoring ...

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1st international conference on Sustainable Technologies for Computational Intelligence (ICTSCI)

作者： Kumar, Prashant Purohit, Gaurav Tanwar, Pramod Gautam, Chitra Raju, Kota Solomon Cent Elect Engn Res Inst CSIR Pilani Rajasthan India Acad Sci & Innovat Res AcSIR Ghaziabad India

ISBN: (纸本)9789811500299;9789811500282

The emergence of the Internet of things (IoT) facilitates us to connect all the devices available at home. Also, we can control and access devices and appliances remotely in real time. This article, by taking the advantages of IoT, proposes an IoT-based affordable air quality monitoring with smart home system that brings controlling and monitoring of devices and appliances, and monitoring of home environment together in real time. The system uses the combination of wireless communication and cloud networking, to remote control various appliances (like fans, TV), also getting a glimpse of air quality. In order to monitor the home environment, we develop a practical information acquisition model, a network for indoor and outdoor air quality monitoring which can be carried anywhere as a portable device. The portable nodes can sense pollutants like CO, CO2, PM 2.5, PM 10 with temperature and humidity and monitor the air quality. The collected data are sent to a cloud service platform like things speak and Firebase for real-time visualization and appropriate forecasting and prediction analytics. Also, a user-friendly Android app and web portal increase the usability of this system. Moreover, when critical conditions occur, an alert is reflected on the android app and on web portal in real time. Also, the system starts sending a warning ring on registered mobile.

关键词： Air quality monitoring (AQM) smart home IoT Raspberry Pi

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Automated Task Scheduling for Automotive Industry 24

Automated Task Scheduling for Automotive Industry

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24th IEEE international conference on intelligent Engineering Systems (INES)

作者： Lewandowski, R. Olszewska, J. I. Univ West Scotland Paisley Renfrew Scotland

ISBN: (纸本)9781728110592

Nowadays, the automotive industry requires an increased use of intelligent systems to endure. In this paper, we present a new solution for automated task scheduling to help automotive industry in efficiently managing garage employees' time and improving the effectiveness of the servicing and maintenance tasks of vehicles. The developed approach consists in a set of interconnected web applications with a model-view-controller based-on architecture and expert-knowledge temporal logic rules to automate the assignment of the daily workload for engineers working on multiple workstations within an automotive company. The proposed intelligent system prioritises and selects tasks for these engineers;the scheduled tasks being automatically ordered and displayed accordingly on screens visible in the related garage workstations. This automated task scheduling system has been successfully deployed within Arnold Clark Automobiles Ltd Company, and the performance of this new application used by a group of engineers under real-world operational conditions have been assessed and analysed.

关键词： intelligent Systems Industry 4.0 and smart factory Manufacturing-oriented data analytics intelligent Human-Machine Interaction Expert Systems

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Construction of evaluation-index system of college teachers’ intelligent teaching ability under the background of educational informationization 2.0

Construction of evaluation-index system of college teachers...

引用

international conference on Application of intelligent Systems in Multi-modal Information analytics, MMIA 2020

作者： Sun, Dongyan School of Information Engineering Jilin Engineering Normal University 3050 Kaixuan Road Kuancheng District Changchun China

ISBN: (纸本)9783030515553

With the new round of technologies represented by artificial intelligence, big data, and blockchain, and the innovative integration of various industries, information technology has become the basic driving force for social development. The embedding and application of various new technologies has become a development trend of the times, that is, the informatization of education, which has become an important part of the sustainable development of an information society. The purpose of this article is to build an evaluation-index system for the wisdom teaching ability of college teachers in the context of educational informatization (EI) 2.0. This paper evaluates the teaching ability of teachers in colleges and universities, finds out where teachers have defects, and puts forward corresponding suggestions based on the research results. The sample selects a college teacher as the research object, and uses questionnaires as an experimental method to investigate the college teachers’ ability. This article tests the sample through five dimensions of basic technical literacy, planning and preparation ability, organization and management ability, evaluation and diagnosis ability, learning and development ability, and gets average scores of 2.6881, 3.0086, 2.3986, 2.1572, and 2.6525 respectively. It can be seen that the wisdom teaching ability of college teachers needs to be improved. © Springer Nature Switzerland AG 2021.

关键词： Informatization

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AI Integrated Pedagogical Techniques for Higher Education 1st

AI Integrated Pedagogical Techniques for Higher Education

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1st international conference on intelligent Technology for Educational Applications, ITEA 2024

作者： Dhasarathan, Jayalakshmi Thangasamy, Suresh Balakrishnan Uthirapathy, Samson Ebenezar Rattinamangalam Sakthivel, Arunkumar Devaraj, Santhakumar Fernandez, Mary Herin Fernandez Subbiah, Priyanga Narayanan, Lakshmi Kanthan R.M.D Engineering College KavaraipettaiGummidipoondi Thiruvallur India Saveetha University Tamilnadu Chennai602105 India Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 India

ISBN: (纸本)9789819640898

In recent times, the incorporation of Artificial Intelligence (AI) into higher education has surged, marking a significant shift in teaching and learning methodologies. These include customized tutoring platforms, adaptive and smart tutoring systems, data analytics tools, virtual assistants tailored for academic use, curriculum design and development software, collaborative learning platforms, assessment, and feedback tools, as well as discussions on the ethical implications of AI adoption in higher education. The discussion emphasizes that the thoughtful inclusion of AI not only refines pedagogical methodologies but also facilitates the extensive coverage of contemporary educational strategies. The chapter strategically examines current applications of AI tools, presents successful case studies illustrating their impact, and envisions potential future developments. Educators also prioritize fostering critical thinking and digital literacy skills in students to enable them to engage with AI responsibly. Additionally, collaborative projects, hands-on learning opportunities, and interdisciplinary approaches are encouraged to prompt students to explore AI’s societal impacts and applications across various fields. Through these strategies, higher education institutions seek to equip students for the AI-driven workforce while fostering innovation and creativity in educational practices. This paper aims to throw the limelight on the possibility of integrating advanced computational technologies such as AI, ML, and IoT towards empowering the modern education system to attain par excellence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Application of the big data technology in the enterprise management

Application of the big data technology in the enterprise man...

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1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Guan, Yumei Sanya University SanyaHainan572000 China

ISBN: (纸本)9789811525674

With the rapid development of the information technology, especially in the new situation of the deep implementation of the "Internet +" strategy, the big data technology has been applied to all aspects and all walks of life. This will have a certain impact on the management mode of enterprises. Enterprises should have a clear understanding of this, and in a specific process of its implementation, we should combine the big data technology with the enterprise management closely. We should not only give full play to the positive role of the big data technology, but also make great efforts to innovate the enterprise management mode, so that the big data can play a greater role in promoting the enterprise management reform and development. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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Heart Disease Prediction using Exploratory data Analysis 1

Heart Disease Prediction using Exploratory Data Analysis

引用

1st international conference on smart Sustainable intelligent computing and Applications, ICITETM 2020

作者： Indrakumari, R. Poongodi, T. Jena, Soumya Ranjan School of Computing Science and Engineering Galgotias University Greater Noida U.P. India

Healthcare industries generate enormous amount of data, so called big data that accommodates hidden knowledge or pattern for decision making. The huge volume of data is used to make decision which is more accurate than intuition. Exploratory data Analysis (EDA) detects mistakes, finds appropriate data, checks assumptions and determines the correlation among the explanatory variables. In the context, EDA is considered as analysing data that excludes inferences and statistical modelling. analytics is an essential technique for any profession as it forecast the future and hidden pattern. data analytics is considered as a cost effective technology in the recent past and it plays an essential role in healthcare which includes new research findings, emergency situations and outbreaks of disease. The use of analytics in healthcare improves care by facilitating preventive care and EDA is a vital step while analysing data. In this paper, the risk factors that causes heart disease is considered and predicted using K-means algorithm and the analysis is carried out using a publicly available data for heart disease. The dataset holds 209 records with 8 attributes such as age, chest pain type, blood pressure, blood glucose level, ECG in rest, heart rate and four types of chest pain. To predict the heart disease, K-means clustering algorithm is used along with data analytics and visualization tool. The paper discusses the pre-processing methods, classifier performances and evaluation metrics. In the result section, the visualized data shows that the prediction is accurate. © 2020 The Authors. Published by Elsevier B.V.

关键词： Forecasting

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2nd international conference on Machine Learning and Information Processing, ICMLIP 2020

2nd International Conference on Machine Learning and Informa...

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2nd international conference on Machine Learning and Information Processing, ICMLIP 2020

ISBN: (纸本)9789813348585

The proceedings contain 55 papers. The special focus in this conference is on Machine Learning and Information Processing. The topics include: Performance Analysis of Different Models for Twitter Sentiment;electricity Forecasting Using Machine Learning: A Review;end to End Learning Human Pose Detection Using Convolutional Neural Networks;conference Paper Acceptance Prediction: Using Machine Learning;Object Identification and Tracking Using YOLO Model: A CNN-Based Approach;real-Time Hands-Free Mouse Control for Disabled;accounting Fraud Detection Using K-Means Clustering Technique;transforming the Lives of Socially Dependent to Self-dependent Using IoT;enforcement an Evidence and Quality of Query Services in the Cost-Effective Cloud;Prediction and Classification of Biased and Fake News Using NLP and Machine Learning Models;a Machine Learning Approach Towards Increased Crop Yield in Agriculture;resume Screening Using Natural Language Processing and Machine Learning: A Systematic Review;assessment of Osteogenic Sarcoma with Histology Images Using Deep Learning;SMDSB: Efficient Off-Chain Storage Model for data Sharing in Blockchain Environment;ray Tracing Algorithm for Scene Generation in Simulation of Photonic Mixer Device Sensors;a Study of Quality Metrics in Agile Software Development;Optimization of Ray-Tracing Algorithm for Simulation of PMD Sensors;real-Time Object Detection for Visually Challenged;reinforcement Learning: A Survey;smart Leaf Disease Detection Using Image Processing;data Encryption on Cloud database Using Quantum computing for Key Distribution;prediction and Prevention of Addiction to Social Media Using Machine Learning;analysis of Block Matching Algorithms for Motion Estimation in Video data;information Retrieval Based on Telugu Cross-Language Transliteration;Success of H1-B VISA Using ANN.

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Quantifying Load Uncertainty Using Real smart Meter data

Quantifying Load Uncertainty Using Real Smart Meter Data

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2020 international conference on Communications Control and computing Technologies for smart Grids

作者： Bu, Fankun Dehghanpour, Kaveh Yuan, Yuxuan Wang, Zhaoyu Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

ISBN: (纸本)9781728161273

As we get closer to customers in distribution systems, load stochasticity increases. In the past, due to lack of real-time data, the comprehensive knowledge of load behavior was limited, and simplistic assumptions had to be made for distribution system modeling and analysis, especially in the processes of network design and expansion. With the deployment of Advanced Metering Infrastructure (AMI), ample real-time smart meter data has become available to utilities. In this paper, using real hourly smart meter data, we have quantified load uncertainty in terms of average, maximum and maximum noncoincident demands on a daily basis, as well as load factor and diversity factor. These uncertainty metrics are examined for individual residential, commercial and industrial customers, as well as distribution transformers serving residential customers. This paper provides a benchmark on load uncertainty quantification for practicing engineers and researchers.

关键词： Distribution system modeling load uncertainty smart meters data analytics

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Comprehensive Analysis of Students' Performance by Applying Machine Learning Techniques 3rd

Comprehensive Analysis of Students' Performance by Applying ...

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3rd international conference on smart computing and Informatics (SCI)

作者： HemaMalini, B. H. Suresh, L. Kushal, Mayank BMS Inst Technol & Management VTU Bengaluru India Cambridge Inst Technol Bengaluru India BMS Inst Technol & Management Bengaluru India

ISBN: (纸本)9789813296909;9789813296893

Predicting the performance of students becomes more interesting due to the increase in the number of students opting for engineering courses and parents' inclination to see their wards becoming engineers and getting placed in outstanding companies with good packages. This paper aims at performance analysis of students by applying different machine learning techniques. The collected data contains the students' personal information, family background, friends, study time and other information which contains 45 questions. Various classifiers like Random Forest, SVM and Logical Regression were applied on the collected data by hard-coding in Python using Jupyter Notebook. Based on the classification algorithms, statistics are generated and comparison of all three classifiers is made so as to envisage the truthfulness and to discover the finest acting algorithm. Random Forest classifier gives 96% accuracy, Logistic Regression gives 86% accuracy and SVM classifier gives 84% accuracy. So, the best result is shown by Random Forest classifier.

关键词： Educational data mining EDM Learning analytics Artificial neural network KNN Naive Bayes Random Forest Logical Regression SVM Ministry of education Students' performance Machine learning Academic performance

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Cyber-Physical Systems in smart City: Challenges and Future Trends for Strategic Research 5th

Cyber-Physical Systems in Smart City: Challenges and Future ...

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5th international conference on Advanced intelligent Systems and Informatics (AISI)

作者： Juma, Mazen Shaalan, Khaled British Univ Dubai Fac Engn & Informat Technol Block 111st & 2nd Floor Dubai U Arab Emirates

ISBN: (纸本)9783030311292;9783030311285

Modern cities today compete with each other to be smarter, maintain a more sustainable with high-quality living, acquire talents, and provide jobs. This digital transformation through agile drivers will help address the increasing challenges of urbanization in a couple of decades. The Cyber-Physical System (CPS) is becoming pervasive in every aspect of smart city daily life and considered as one of the four fundamental conceptual approaches of the fourth generation industrial revolution (Industry 4.0). CPS used to describe the next generation of a diverse spectrum of complicated, multidisciplinary, physically comprehending engineered systems that integrates embedded cyber aspects into the physical world. It implants computation technologies, communication control, the convergence of information, and physical processes together with strategic importance internationally. CPS is still a vast research area. As a result, it opens venues for applications across multiple scales. This paper presents an in-depth survey of the related works, focusing on the design and how it relates to different research fields, current concepts, and real-life applications to understand CPS more precisely. Further, it enumerates an extensive set of CPS challenges and opportunities, introducing visionary ideas, research strategies, and future trends expected for future-oriented technological solutions, like cloud computing, Internet of Things, and Big data. These technological solutions are to play a critical role in CPS research and have significant impacts on the smart city.

关键词： Cyber-physical systems smart city Industry 4.0 Big data Cloud computing Internet of Things Research strategies Future trends

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