[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127492-en":3,"doc-seo-127492-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127492,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",6,"Technology","Machine Learning-Based IOT Air Quality and Pollution Detection","This work addresses the need for prior detection of harmful chemical gases and air pollution in factory and disaster-prone areas. The system identifies gas composition and intensity using an IoT terminal equipped with MQ series gas sensors and DHT11, interfaced with an STM32 microcontroller and a WiFi circuit. Sensor streams are transported to a cloud database, then cleaned with preprocessing and feature extraction techniques to support pollution attribute detection and reporting via an Air Pollution Index (API) approach.","Machine Learning-Based IOT Air Quality and  \nPollution Detection  \nK. Siva Krishna1, Dr Thatavarti Satish2, Dr. Jyotirmaya Mishra3  \n1Research Scholor,Department of Computer Science and Engineering,GIET University, AP, India  \n2Department ofCSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.  \n3Department of Computer Science and Engineering,GIET University, AP, India  \nEmail Id: [drsatishthatavarti@kluniversity.in](drsatishthatavarti@kluniversity.in)  \nAbstract  \nIn India, gas leakage from the different factories harmful to human surveying in the last fifty years is very low. However, there is a lack of prior detection of the chemical gases detection system in the situation raised. So, In this regard, there is a gap identification of chemical gases intensity detection needed. In this work, the main objective is to identify chemical gases intensity and maintain the stream data in the database from different locations. To fill this gap, that is identifying the high-intensity chemical gases from the chemical gas disaster areas. The first step needs to identify the different chemical gases and natural gas compositions. In this regard in this work for design internet-based gases in the air system. So, the sensors MQ2(Ethanol i-Butane Methane Alcohol Gas Sensor Sensor), MQ3(Sensitivity Alcohol Detector), MQ4 (Methane and Natural Gas (CNG)), MQ-5 ( LPG GAS SENSOR), MQ-7 (CO Gas Sensor Module Test Carbon Monoxide Detector), MQ-8 (hydrogen Gas Sensor), MQ-9 (carbon monoxide), MQ-135 Sensor(Air Quality Sensor Hazardous Gas Detector) and DHT11 Digital Temperature Humidity Sensors. These sensors are interfacing with the micro-control STM 32 board. It is also called one Pollution identification terminal by using it to pull the sensor stream data from location to centralized data. This stream data transportation is a service to pull the data. For this Data pulling, design an algorithm store into a cloud database. In this research work, design the electronic terminal with a wifi circuit using IoT technologies.Moreover, getting these attributes as data. Data need to apply the preprocessing techniques and extracted feature techniques also. This paper discusses mainly designing the terminal for pollution attributes , cleaning the data, and applying the Machine Learning based Feature extraction techniques.  \nKeywords:-Internet of things, Data as a Request, Cloud, Sensors, Pollution, Micro Controller,feature extraction and data cleaning  \nI. INTRODUCTION  \nThe Internet of Things (IoT) is a vast network of autonomous and heterogeneous devices and sensors that send massive amounts of data to monitoring systems that analyze the data and make decisions. Smart Homes, Smart Grid, Public Safety and Environment Monitoring such as weather monitoring and water quality, Medical and Healthcare (Internet of Medical Things IoMT), Industrial Processing such as California (Cf) CoAP framework, Agriculture and Breeding such as Climate-Smart Agriculture (CSA), and connected vehicles (IoCV) are some of the applications that use IoT technology and devices [1, 2] . Nowadays, the use of these applications and their benefits play an important role in improving one's quality of life. As a result, the future of IoT devices, technology, and applications will shape our future. [3] .  \nOver the past 130 times, the world has warmed by around0 .85 °C. The situation calculated that between 2030 – 2050, climate change will beget over deaths worldwide andwill add between€1.8-3.6 billion per time to health care costs. Air pollution in numerous overpopulated metropolises is far too high for mortal exposure, causing resides to wear  \nface masks or original PPE all time round. In numerous metropolises, people may not indeed be apprehensive of the high situations of dangerous adulterants they expose to at certain times of the day or time. This leads to significant health pitfalls to help—contributors to climate change[4] . In this regard, the Internet of Thing Technol","cbCaijygVbSsN3Ry","https://ap.wps.com/l/cbCaijygVbSsN3Ry","pdf",928971,1,14,"English","en",105,"# Introduction\n## Applications of IoT and environmental monitoring\n## Air pollution background and health impacts\n## Proposed system overview with gas sensors and STM32\n# Related Works\n## Prior pollution detection systems and data pulling techniques","[{\"question\":\"What is the main goal of this air quality and pollution detection work?\",\"answer\":\"It aims to identify chemical gas intensity and transport sensor stream data from different locations to a centralized cloud database for analysis and detection.\"},{\"question\":\"Which sensors and microcontroller are used in the proposed terminal?\",\"answer\":\"The system uses MQ2, MQ3, MQ4, MQ-5, MQ-7, MQ-8, MQ-9, MQ-135 and DHT11 sensors, interfaced with an STM32 microcontroller and connected via a WiFi circuit.\"},{\"question\":\"How does the work handle the sensor data before machine learning analysis?\",\"answer\":\"It applies preprocessing techniques, then performs data cleaning and machine-learning based feature extraction to derive pollution attributes from the collected streams.\"}]","Machine Learning-Based IOT Air Quality and Pollution Detection | 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is the main goal of this air quality and pollution detection work?","Question",{"text":76,"@type":77},"It aims to identify chemical gas intensity and transport sensor stream data from different locations to a centralized cloud database for analysis and detection.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which sensors and microcontroller are used in the proposed terminal?",{"text":81,"@type":77},"The system uses MQ2, MQ3, MQ4, MQ-5, MQ-7, MQ-8, MQ-9, MQ-135 and DHT11 sensors, interfaced with an STM32 microcontroller and connected via a WiFi circuit.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the work handle the sensor data before machine learning analysis?",{"text":85,"@type":77},"It applies preprocessing techniques, then performs data cleaning and machine-learning based feature extraction to derive pollution attributes from the collected 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