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Noise Pollution Detection And Notification Using Flet And Arduino

Noise Pollution Detection And Notification Using Flet And Ar...

Smart Systems for Electrical, Electronics, Communication and computer engineering (ICSSEECC), International Conference on

作者： I. Kala Swathy S Vishnu Priya V Harshavarthan P K Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore Tamil Nadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This project seeks to identify urban, traffic, and industrial noise hazards. This is accomplished by Arduino, which uses sound sensors to recognize and differentiate between light and loud noise. With the help of the ThingSpeak server, the noise level data for different time is also tracked. The proj ect extends its benefits to the users by notifying users via mail if they are in an area with high noise levels. The Flet User Interface framework was used in the development of this web application.

关键词： Pollution Environmentally friendly manufacturing techniques Working environment noise Noise Pollution measurement Frequency measurement Noise measurement

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Usability Evaluation of Kids' Learning Apps 2

Usability Evaluation of Kids' Learning Apps

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ibrahim, Amer Al-Rajab, Murad Hamid, Khalid Aqeel, Muhamamd Muneer, Salman Parveen, Mehvish Saleem, Maryam American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates Abu Dhabi University Computer Scienc and IT College of Engineering United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Superior University Department of Software Engineering Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NCBAE School of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The main objective of learning applications is to use modern electronic technologies to impart knowledge, communicate knowledge, and Direct learning models with their learning activities in a timely and effective manner. The preponderance of the apps were designed to up-skill the kids in the fundamentals of letters and numbers. Generally, they were grounded and practice skills in nature, based on a lack of abilities in critical thinking, favoring parrot-fashion skills, and contributing to a deep conceptual comprehension of more specific concepts are unable. By using appropriate evaluation methodologies, the purposed study observes essential characteristics and aspects that affect the usability of interfaces. An evaluation of the app's features will figure out to ensure user contentment. When a kid interacts with a learning interface, the ease of use provided by the interface is validated. Because a large number of the apps were designed to train kids' fundamentals of alphabetic and numeric. The focus of this research is to see whether self-proclaimed learning apps for kids are built according to developmentally appropriate standards to help kids in formal and informal learning settings develop socially, emotionally, and academically. © 2023 IEEE.

关键词： Learning systems

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A comprehensive review of deep learning power in steady-state visual evoked potentials

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Neural Computing and Applications 2024年第27期36卷 16683-16706页

作者： Al-Qaysi, Z.T. Albahri, A.S. Ahmed, M.A. Hamid, Rula A. Alsalem, M.A. Albahri, O.S. Alamoodi, A.H. Homod, Raad Z. Shayea, Ghadeer Ghazi Duhaim, Ali M. Tikrit Iraq Technical College Imam Ja’afar Al-Sadiq University Baghdad Iraq Baghdad Iraq Department of Management Information Systems College of Administration and Economics University of Mosul Mosul Iraq Australian Technical and Management College Melbourne Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Applied Science Research Center Applied Science Private University Amman Jordan Department of Oil and Gas Engineering Basra University of Oil and Gas Basra1004 Iraq Ministry of Education Thi-Qar Education Directorate Thi Qar Iraq MEU Research Unit Middle East University Amman Jordan Baghdad Iraq

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of Science, PubMed, ScienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, include

关键词： Deep learning

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Cloud-Computing Trend Analysis of Urbanization Impacts on Land Surface Temperature and Electricity Demand in Gaborone, Botswana

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Remote Sensing in Earth Systems Sciences 2024年第1期8卷 283-306页

作者： Moyo, Boineelo Schröder, Dietrich Moreri, Kealeboga Institute for living conditions in rural areas Johann Heinrich von Thuenen-Institut Bundesallee 50 Lower Saxony Braunschweig 38116 Germany Department of Geomatics Computer Science and Mathematics Stuttgart University of Applied Sciences Schellingstr. 24 Baden-Wuerttemberg Stuttgart 70174 Germany Department of Civil Engineering University of Botswana Notwane Rd Gaborone Botswana

Land use land cover (LULC) changes are critical drivers of global environmental changes, profoundly impacting sustainability in rapidly urbanizing regions. In urban areas, where vegetation cover is reduced, LULC changes significantly affect the distribution and intensity of Land Surface Temperature (LST), which in turn influences electricity demand and consumption. This study explores the relationship between urbanization, vegetation loss, surface temperature and electricity demand in Gaborone, Botswana. The study utilized the Landsat data to conduct a 15-year (2005–2020) spatio-temporal analysis of LULC changes and LST dynamics, identified surface urban heat islands (SUHI) using the Getis-Ord Gi* hot-spot analysis and assessed the correlation between LST and key spectral indices adopted in the study. The analysis employed Google Earth Engine (GEE) cloud computing platform and the Random Forest (RF) machine learning algorithm, known for their accuracy and efficiency in classification and change detection studies. Findings reveal substantial LULC transformations in Gaborone with approximately 12 km² of land converted to Built-Up areas, driving a 2.36% annual urban growth rate and shrinking green and bare spaces. Consequently, surface temperatures rose by about 2.1 °C, impacting energy demand, particularly for cooling during hot summers. This demand surge, coupled with urban expansion, has fueled higher electricity prices, posing new challenges in climate adaptation and mitigation. The study provides critical insights into the interplay between urbanization, rising temperatures, and energy consumption in Gaborone. These insights highlight the urgent need for developing sustainable urban planning and energy strategies to address the impacts of climate change, land degradation, and energy poverty. © The Author(s) 2024.

关键词： Climate change Electricity demand Google earth engine Land surface temperature Land use land cover Random forest

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A Fused Machine Learning Approach for Intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins 9

Traffic Signal Optimization by Integrating Reinforcement Lea...

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9th IEEE Smart World Congress, SWC 2023

作者： Kumarasamy, Vijayalakshmi K Saroj, Abhilasha Jairam Liang, Yu Wu, Dalei Hunter, Michael P. Guin, Angshuman Sartipi, Mina University of Tennessee at Chattanooga Department of Computer Science and Engineering United States Oak Ridge National Laboratory Applied Research for Mobility Systems United States Georgia Institute of Technology School of Civil and Environmental Engineering United States

ISBN: (纸本)9798350319804

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario. © 2023 IEEE.

关键词： Digital Twin Fuel Consumption Graph Neural Network Multi-Agent Reinforcement Learning Traffic Signal Optimization

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Stability and Hopf bifurcation of TB-COVID-19 coinfection model with impact of time delay

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SeMA Journal 2024年 1-41页

作者： Verma, V.S. Kaushik, Harshita Singh, Ram Jain, Sonal Akgül, Ali Department of Mathematics and Statistics Deen Dayal Upadhayaya Gorakhpur University Uttar Pradesh Gorakhpur 273009 India Departmental of Mathematical Science Baba Ghulam Shah Badshah University Jammu & Kashmir Rajouri 185234 India School of Technology Woxsen University Telangana Hyderabad 502345 India Art and Science Faculty Department of Mathematics Siirt University Siirt 56100 Turkey Department of Computer Engineering Biruni University Topkapı Istanbul 34010 Turkey Mathematics Research Center Department of Mathematics Near East Boulevard Near East Boulevard Nicosia/Mersin 10 99138 Turkey

In this work, a compartmental approach used for modelling and surveillance of dynamics of Tuberculosis and COVID-19 coinfection by considering the post-infection and molecular variants compartments in addition to existing compartments. We have tried to explain how variants can affect the infection rate and its severity. Also, the post-infection compartment is studied to understand the impact after recovery from the disease and the consequences of the post-infection period. We highlight the conceptual idea and mathematical tools and techniques needed for such co-infection disease modelling. We investigate the basic properties of the co-infectious disease model, including the local and global stability of the equilibria for the delayed and non-delayed systems, and calculate the points of equilibria and the basic reproduction number of the suggested model. It is proved that when the time delay caused by the latent phase of tuberculosis exceeds the threshold value, the constructed model undergoes a Hopf bifurcation around the tuberculosis existence. The center manifold theory is used to study the direction and stability of the Hopf bifurcation which is essential to study the delayed system. Both delayed and non-delayed systems differ significantly and we have conducted numerical analyses to verify the efficacy of the deduced outcomes. © The Author(s), under exclusive licence to Sociedad Española de Matemática Aplicada 2024.

关键词： 34D23 34K18 34K20 Centre manifold theory COVID-19 Hopf bifurcation TB Time delay Vaccination

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Word2Minecraft: Generating 3D Game Levels through Large Language Models

arXiv

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arXiv 2025年

作者： Huang, Shuo Nasir, Muhammad Umair James, Steven Togelius, Julian Department of Computer Science and Engineering Tandon School of Engineering New York University New York10012 United States School of Computer Science and Applied Mathematics University of the Witwatersrand South Africa

We present Word2Minecraft, a system that leverages large language models to generate playable game levels in Minecraft based on structured stories. The system transforms narrative elements—such as protagonist goals, antagonist challenges, and environmental settings—into game levels with both spatial and gameplay constraints. We introduce a flexible framework that allows for the customization of story complexity, enabling dynamic level generation. The system employs a scaling algorithm to maintain spatial consistency while adapting key game elements. We evaluate Word2Minecraft using both metric-based and human-based methods. Our results show that GPT-4Turbo outperforms GPT-4o-Mini in most areas, including story coherence and objective enjoyment, while the latter excels in aesthetic appeal. We also demonstrate the system’s ability to generate levels with high map enjoyment, offering a promising step forward in the intersection of story generation and game design. We open-source the code at https://***/JMZkk/Word2Minecraft/tree/word2mc_v0 Copyright © 2025, The Authors. All rights reserved.

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Intelligent IoT-Based Water Quality Monitoring System Using TD-CNN-LSTM Approach

Intelligent IoT-Based Water Quality Monitoring System Using ...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Velammal, M. Rakesh, Vittam Kartheeban, K. Ali Qidwai, Khalid Shyamala, G. Thulasimani, T. Karpagam College of Engineering Department of Computer Science and Technology Tamil Nadu Coimbatore India Geethanjali College of Engineering and Technology Electrical and Electronics Engineering Telangana Hyderabad India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Tamilnadu Anand Nagar India College of Computer Science and Information Technology Jazan University Computer Science Jazan Saudi Arabia School of Engineering Sr University Civil Engineering Telangana Warangal India Bannari Amman Institute of Technology Department of Mathematics Tamilnadu Sathyamangalam India

ISBN: (纸本)9798350300857

Today, water contamination affects a large portion of the world's population. Manually collecting water samples from various sites and analyzing them in a lab as part of the routine monitoring technique. These approaches used to be effective, but they are now too slow and laborious to be practical. In addition, cutting-edge methods consider a wide range of physical and chemical elements. The standard method of quality monitoring and data sharing is time-consuming, unreliable, and costly. This emphasizes the need for constant, around-the-clock water quality monitoring. Basic Data Preprocessing, feature selection, and model training are the three main pillars of the proposed approach. The proposed method employs the preprocessing tools. The proposed system used the SOFM method (Structure-From-Function-Movement) to identify and eliminate unnecessary features selection. A model's accuracy can be measured with the help of prediction tools like utilizing CNN, LSTM, and TD-CNN-LSTM, models are trained. The proposed model outperforms two current top-tier contenders. The proposed method achieves higher accuracy (about 98.37%) when compared to other approaches like CNN and LSTM. © 2023 IEEE.

关键词： Water pollution

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Lean Model for Improving Educational Institute grade and Student Performances 2

Lean Model for Improving Educational Institute grade and Stu...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Waiker, Vinod Shrinath, Sanket Siddiqui, Asfar H. Wankhede, Dilip Shahu, Vijendra Datta Meghe Institute of Management Studies Maharashtra Nagpur440020 India Yeshwantrao Chavan College of Engineering Department of Applied Mathematics M.S. Nagpur441110 India Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798331528713

Lean thinking already proved to be the most efficient process for improvement in the production and service industry. Educational Institute is also getting benefitted by the lean model and improved their processes for better prospects for the students. This study focus on lean principles for educational institute to transform their process, eliminate waste and improve the productivity. This study also find out the gaps in achieving the target goal and implement lean tools to satisfy the student need to meet the target. © 2024 IEEE.

关键词： Systems thinking

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