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class='section' id='blog'><div class='widget FeaturedPost' data-version='1' id='FeaturedPost1'> </div><div class='widget Blog' data-version='1' id='Blog1'> <div class='tensorsite-container--narrow'> <div class='tensorsite-detail'> <a aria-hidden='true' class='tensorsite-detail__next-url' hidden='true' href='https://blog.tensorflow.org/2019/02/my-notes-on-tensorflow-2-0.html'></a> <div aria-hidden='true' class='tensorsite-detail__current-url' hidden='true'>https://blog.tensorflow.org/2019/02/air-cognizer-predicting-air-quality.html</div> <div aria-hidden='true' class='tensorsite-detail__tags' hidden='true'> <span>Community</span> <b class='label-divider-dot'>·</b> <span>TensorFlow Lite</span> </div> <div aria-hidden='true' class='tensorsite-detail__main-image' hidden='true'> https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBUi-G7WDQP5RK20G9PioqDgmKs-OGdrZUVv-OCsNwdHEo7SS2YmnV1P3egXrHU3J26l3pFNiQYZvxuav_HE7FhgVs62LJClZm08j9ySLuq_kHuCjdSImB936c9mqr_vxJIvU0uB4AVKw/s1600/0_AZaB7fy1qAAVgN_i.png </div> <p aria-hidden='true' class='tensorsite-detail__description' hidden='true'> <span class='tensorsite-content__info'> February 08, 2019 — </span> <em>A guest article by <a href="https://www.linkedin.com/in/prernakhanna97/">Prerna Khanna</a>, <a href="http://bit.ly/tanmayLinked">Tanmay Srivastava </a>and Kanishk Jeet</em><br><br> Large cities like Delhi can suffer from air pollution, especially in winter, and we’ve seen headlines like “<a href="https://www.ndtv.com/delhi-news/cold-morning-in-delhi-air-quality-continues-to-be-severe-1971865">Cold Morning In Delhi, Air Quality Continues To Be Severe</a>” appear on the front page of newspapers. Poor air quality in the winter months can lead to smog, which can restrict outdoor activities and cause health concerns.<br>As … </p> <div class='tensorsite-content__subtitle'> <a href='https://blog.tensorflow.org/search?label=Community&max-results=20'> <span>Community</span> </a> <b class='label-divider-dot'>·</b> <a href='https://blog.tensorflow.org/search?label=TensorFlow+Lite&max-results=20'> <span>TensorFlow Lite</span> </a> <b class='label-divider-dot'>·</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-detail__title'> Air Cognizer: Predicting Air Quality with TensorFlow Lite </div> <div class='tensorsite-detail__contact'> <div class='tensorsite-detail__info'> <span class='tensorsite-detail__timestamp'>February 08, 2019</span> </div> <a class='icon-link' href='https://twitter.com/intent/tweet?text=%22Air Cognizer: Predicting Air Quality with TensorFlow Lite%22 from the TensorFlow Blog%0A%0Ahttps://blog.tensorflow.org/2019/02/air-cognizer-predicting-air-quality.html' rel='noopener noreferrer' target='_blank' title='Share this post on Twitter'> <svg alt='Twitter Social Icon' class='twitter-icon social-icon' height='19' viewBox='0 0 23 19' width='23' xmlns='http://www.w3.org/2000/svg'> <g fill='none' fill-rule='evenodd' transform='translate(-7 -9)'> <rect height='36' width='36'></rect> <path d='M14.076,27.2827953 C22.566,27.2827953 27.21,20.2477953 27.21,14.1477953 C27.21,13.9477953 27.21,13.7487953 27.197,13.5507953 C28.1,12.8977953 28.88,12.0887953 29.5,11.1617953 C28.657,11.5347953 27.764,11.7797953 26.848,11.8877953 C27.812,11.3107953 28.533,10.4037953 28.878,9.33479527 C27.972,9.87179527 26.98,10.2507953 25.947,10.4547953 C24.198,8.59579527 21.274,8.50679527 19.415,10.2547953 C18.217,11.3817953 17.708,13.0617953 18.08,14.6647953 C14.368,14.4787953 10.91,12.7257953 8.566,9.84279527 C7.341,11.9507953 7.967,14.6497953 9.995,16.0047953 C9.261,15.9827953 8.542,15.7837953 7.9,15.4267953 L7.9,15.4847953 C7.9,17.6827953 9.449,19.5747953 11.603,20.0107953 C10.924,20.1957953 10.211,20.2227953 9.519,20.0897953 C10.124,21.9707953 11.856,23.2587953 13.832,23.2957953 C12.197,24.5797953 10.178,25.2777953 8.098,25.2747953 C7.731,25.2747953 7.364,25.2527953 7,25.2087953 C9.111,26.5627953 11.567,27.2817953 14.076,27.2787953' fill='#545454'></path> </g> </svg> </a> </div> <div class='divider divider--article-top'></div> <div class='tensorsite-detail__body'> <em>A guest article by <a href="https://www.linkedin.com/in/prernakhanna97/">Prerna Khanna</a>, <a href="http://bit.ly/tanmayLinked">Tanmay Srivastava </a>and Kanishk Jeet</em><br /> <a name='more'></a><br /> Large cities like Delhi can suffer from air pollution, especially in winter, and we’ve seen headlines like “<a href="https://www.ndtv.com/delhi-news/cold-morning-in-delhi-air-quality-continues-to-be-severe-1971865">Cold Morning In Delhi, Air Quality Continues To Be Severe</a>” appear on the front page of newspapers. Poor air quality in the winter months can lead to smog, which can restrict outdoor activities and cause health concerns.<br /> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody> <tr><td style="text-align: center;"><img border="0" data-original-height="900" data-original-width="1600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBUi-G7WDQP5RK20G9PioqDgmKs-OGdrZUVv-OCsNwdHEo7SS2YmnV1P3egXrHU3J26l3pFNiQYZvxuav_HE7FhgVs62LJClZm08j9ySLuq_kHuCjdSImB936c9mqr_vxJIvU0uB4AVKw/s1600/0_AZaB7fy1qAAVgN_i.png" style="margin-left: auto; margin-right: auto;" /></td></tr> <tr><td class="tr-caption" style="text-align: center;">PM 2.5 estimation using a mobile device</td></tr> </tbody></table>As engineering students, we strive to use technology for social good. A crucial first step in solving the air pollution problem is to enable citizens to gauge the quality of air they breathe.<br /> <br /> This could be done with pollution sensors — although they can be expensive to deploy at scale. Our goal was to design a reliable and inexpensive air quality estimation solution, accessible to everyone with a smartphone.<br /> <br /> Research like <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734658/">Particle Pollution Estimation Based on Image Analysis</a> has shown that machine learning can be effectively used to estimate air quality using camera images, although previous work was usually limited to images from a few static cameras.<br /> <br /> Our goal was to develop an Android-based mobile application to provide local, real-time air quality estimation using smartphone camera images. The <a href="http://www.celestiniprojectindia.com/">Celestini Project India</a> from the <a href="http://marconisociety.org/">Marconi Society</a> inspired us, and provided an internship opportunity at IIT Delhi and the resources to develop our project.<br /> <br /> <iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/sBTqLHA95rs" width="560"></iframe><br /> <br /> We decided to focus on predicting air quality in terms of “PM 2.5”, or particles that have a diameter less than 2.5 micrometers. To visualize results, we predict the PM 2.5 values and map them on a color gradient <a href="https://en.wikipedia.org/wiki/Air_quality_index#India">Air Quality Index (AQI) scale</a>. This is a standard scale set by each country’s government. Warnings are then displayed according to the AQI values.<br /> <h3>Using TensorFlow Lite to Predict Air Quality</h3>The application we developed collects images from the camera on the mobile phone, and processes them on-device using <a href="https://www.tensorflow.org/lite/">Tensorflow Lite</a> to provide an AQI estimate. Before developing our app, we trained an AQI estimation model on cloud. The model is automatically downloaded using <a href="https://firebase.google.com/products/ml-kit/">Firebase ML Kit</a> in the Android application.<br /> We describe the system in detail below.<br /> <ol><li>The Mobile Application. This is used to capture images and predict AQI levels. The application processes images on-device.<br /> </li> <li>TensorFlow Lite is used to power on-device inference, in a small binary size (which is important for download speed, when bandwidth is limited) for the trained machine learning model.<br /> </li> <li>Firebase. Parameters extracted from the images (described below) are sent to Firebase. Whenever a new user uses the app, a unique ID is created for them. This can be used later to customize the machine-learning model for different geo-locations.<br /> </li> <li>Amazon EC2<strong>.</strong> We train our models here, using these parameters and the PM values from the geo-location.<br /> </li> <li>ML Kit. Trained models are hosted on <a href="https://firebase.google.com/docs/ml-kit/use-custom-models">ML Kit</a>, and automatically on to the device, then run with TensorFlow Lite.</li> </ol><img border="0" data-original-height="547" data-original-width="1600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgQVNe2QLoX8zrtmQs6wFQ5FkVR_XXzh0G8MbiRtWMGzCZqIMLOE0T8JxyxSKnFCRnGI9n2Pt1n1ngZYpaBgEm9ZGbozmchPeV6QHoYxd-GJEXVIh61CWPOnogyEbF0516zA3xuZVX25uc/s1600/1_Gb1InVl1xxu7JDvktxVhXw.png" /> <br /> <h3>Two Models</h3>Here are more details about how we analyze images to predict AQI. We train two image-based machine learning models to build the application: the first model predicts the AQI using the features of the user-uploaded photo, and the second model filters out images where no skyline is present. <br /> <h4>AQI model</h4>We predict the AQI from user photos using the following features. These features are extracted by traditional image processing techniques, and combined by a linear model. Our second model (discussed later), works with images directly as is common in deep learning. <b>Transmission:</b> This describes scene attenuation and the amount of light entering the phone camera after being reflected by air particles. It can be described from this equation: <br /> <center><img border="0" data-original-height="86" data-original-width="744" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEioz4d8Ex4EO6GxVbYa_P4EtxGEFR-DQ1hZLwnNvudNuUyzwSLMTJgcMVwbc4BlQUVADpJXhb4haFzPwYg6ebpKL4zV745pcTIjeMKdkaB3rh2jKzWetqkOhmHGsZwLWyZCrwqCYhIq3Uk/s1600/eq1.png" style="width: 500px;" /></center>where I is the observed hazy image, t is the Transmission from the scene to the camera, J is the scene radiance, A is the airlight color vector. Transmission for a single hazy image was found by using the concept of dark channel, which assumes the existence of some pixels with zero or very low intensity at least for one color channel in all the outdoor images. For a haze–free image J, the dark channel is: <br /> <center><img border="0" data-original-height="100" data-original-width="560" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjVBE9OXLTyjA-rM7J-h_AIpcoPS-80DdS53zTzvaFY1InMD5H9CCub_qYoCQ2-6-NiOwuo06XMpI-4psJS0x1Fe_m57QhERjVEJauqSmWe6wNnK0WoEzx6XcuYKNWWxR6wdwRe7C1w6s4/s1600/eq2.png" style="width: 500px;" /></center>where Jc is one of the color channels of J, and Ω(x) is a local patch centered at x. The airlight can be estimated from the sky or the brightest region, so the transmission can be obtained by: <br /> <center><img border="0" data-original-height="114" data-original-width="528" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjbYI12XFEIfn9ZpOZyUKiKrZwAf4BUKsJAsjP12Itc_x8sB3ZVd3OvzBPAoRLwmMvKYNgQUpTSpWzK3_qLCzk8Kc5f914u9nBKss3l1uFqf9RF7e5SQRRr1p9wC8xSUcq5dfRS5FTmgK8/s1600/eq3.png" style="width: 500px;" /></center>where Ic(y)/A is the hazy image normalized by air-light A, and the second term on the right is the dark channel of the normalized hazy image. <b>Blue color of the Sky:</b> This feature is somewhat similar to how we perceive a polluted day. If the sky is gray, we perceive it is as a polluted day. Blue color was estimated using RGB splitting. <br /> <b>Gradient of Sky:</b> Now the sky could appear gray due to cloud cover, so to take this possibility into account we incorporated this feature. The gradient was calculated by making a mask of sky region and then calculating the Laplacian of the region. <br /> <b>Entropy, RMS Contrast:</b> These features also tell us about the details contained in an image. If it is a polluted day, an image loses its details. RMS contrast is defined as the standard deviation of the image pixel intensities. Following is the equation for RMS contrast: <br /> <center><img border="0" data-original-height="202" data-original-width="798" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgI0y4JgBpyj88MAfgwx1ylWJZDnvA_zPtxImwZQ6RtrMKMLmn3641KW8TN89KsK4VuXbgwQSpEdU4lH2OGf5JxwqqsWZbVAfGzWyPYrYq0JZsbbpD4u0E4f8Zdlw4qVSsvD_qPQgt26hg/s1600/eq4.png" style="width: 500px;" /></center>where Iij is intensity at (i,j) pixel of the image with size M by N, and avg(I) is the average intensity of all pixels in the image. Thus Contrast has an inverse relation with PM 2.5. For estimating entropy the following equation was used: <br /> <center><img border="0" data-original-height="146" data-original-width="570" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhnRIhYgO8OrABb6ulq1qdJJt_1fDpQrYQH0N70S-x11K1Eii474jsLHrZPbrQgGWMUFNpcrLYBcr0Z46IXHYcmpjO2-mY0FngezX0pRVlTuD9zI_ulKlvEq0w1L-DxAf0aWBV-3Gbk8pg/s1600/eq5.png" style="width: 500px;" /></center>where pi is the probability that the pixel intensity is equal to i, and M is the maximum intensity of the image. As the PM concentration increases, the image increasingly loses its details, and the image entropy decreases. Thus it follows an Inverse relation with PM 2.5 <b>Humidity</b>: Through our research we concluded that on humid days, pollution levels rose, as PM 2.5 absorb moisture and lowers the visibility. <br /> <center><img border="0" data-original-height="185" data-original-width="340" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg6fzWBZLC3eet1SM9n1k-5y8CGn3D3ufEApWQ22IZp6NEdY3l1lGpvt-wS8XIbVBGXyZVwTGYC6W2wVBxaaS8upukOFq6BhI6dEL0Ii1GjENW2qd4Ky7BSPCZ7MtI7ATcakZR_jCh01hg/s1600/gif.gif" style="width: 300px;" /></center><h4>Skyline model</h4>Initially, when the app was released, people were curious if they were able to use it to predict AQI inside their houses as well as outside. Our model is capable of predicting if an image contains at least 50% skyline, and will accept skyline images by making use of a binary classifier. <br /> <br /> We used Transfer Learning<strong> </strong>to create this classifier and re-trained the model on our labeled dataset using <a href="https://www.tensorflow.org/hub/">TensorFlow Hub</a>. The dataset consisted of 2 classes: 500 images with 50% skyline, and 540 images which don’t contain a skyline (or less than 50% skyline). This includes images of rooms, offices, gardens, outdoor scenes etc. We used the<a href="https://ai.googleblog.com/2017/06/mobilenets-open-source-models-for.html"> MobileNet</a> 0.50 architecture and while testing on unseen 100 samples, an accuracy of 95% was achieved. <a href="https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/">TF for Poets</a> is helpful in Image retraining. <br /> <br /> The confusion matrix for the retrained Model is as follows: <br /> <img border="0" data-original-height="252" data-original-width="1600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMaQq45vAHMQWRX8hDhGDtCspPW0bGORB54UKrD6j_MXqZr137oqtkOo988rl7ZiAXOsyHrpi0SPPDip9kjvfny846oHeBwJ1rY0mPpYP8xyUHW7pfEF16esasD3kJAPsYUHtYXvjLpPw/s1600/table.png" /> <br /> <br /> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody> <tr><td style="text-align: center;"><img border="0" data-original-height="572" data-original-width="1470" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVpNfdPUcw6saybp2UuJyzcgK0IDdfa2iJX-MrX6cJDW_LKk5Z-IqB1IetcH6zwW_n6ZGa2OJjmk1ef0-8qUQ9_D1yGvZkyEW_vh2pbrU3Ya0Yv-0gzODU4A1ei3pxwTA3tObKSRdPBcA/s1600/images.png" style="margin-left: auto; margin-right: auto;" /></td></tr> <tr><td class="tr-caption" style="text-align: center;">Left: images with > 50% skyline. Right, images with < 50% skyline</td></tr> </tbody></table><h3>Custom models for each user</h3>We realized that each user needs a custom ML model, because each smartphone has different camera specification. To train such a model, we collect images from each user. <br /> We decided to combine the results from two models, an image-based model and a temporal model using meteorological parameters. The temporal model using meteorological parameters helps achieve higher inference accuracy and give some results to the user while the image-based machine learning model is being trained, while the image-based machine learning model helps us customize the model to the specific user; thereby improving the inference accuracy by reducing the estimation error. <br /> <br /> To create a small training dataset for each user, 7 images are taken from which features are extracted and used for training. The images must be of 7 consecutive days with half of them covering the sky and no direct source of light, for instance the sun. After extracting the features from the image, they are used to train a Regression Model. The model is linear, as all the image features were more or less linearly proportional to the PM 2.5 values.<br /> <br /> After the training dataset and the model were created, a second set of images for testing are created. Once the dataset has image features of 7 different days, testing starts. If the training RMSE is less than 5 for 7 different days, the model is frozen and sent to ML Kit, which is downloaded in the application. In case RMSE is not less than 5, more training data is collected.<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody> <tr><td style="text-align: center;"><img border="0" data-original-height="800" data-original-width="1200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhV79cGmGCNi8_J1c-rfvFCvHLIQNBDG_QNRCS3nA4oUx1gNRGuZ97BaPYRXHEBQ74-qvVt6t4Y2Mz4vm6DqzBb0vWcXNyCJcguGLnj_XON4YrKFKKn4ifEVXJCibpiCpYWbfzACNiaC88/s1600/finalgraph1.png" style="margin-left: auto; margin-right: auto;" /></td></tr> <tr><td class="tr-caption" style="text-align: center;">Relation of Image features with PM 2.5</td></tr> </tbody></table><h4>Meteorological Parameters</h4>We also use a temporal model using meteorological data to predict AQI based on historical AQI available for nearest location and the temporal model supplements the image-based model to improve the inference accuracy. We collected meteorological datasets for Delhi from <a href="https://data.gov.in/catalog/historical-daily-ambient-air-quality-data">Government website</a> for 2015 to 2017 and performed Ridge Regression with LASSO Optimization to select key parameters for affecting the PM 2.5 levels. The key parameters selected were: Previous hour’s PM 2.5 concentration, concentration of various gases like NO2, SO2, O3 and dew point. The data was then split for training and testing. For training we used data from Jan 2015 — Jan 2017. Testing was performed on data from Jan 2017 — June 2017. We achieved an accuracy of 90% on our dataset.<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody> <tr><td style="text-align: center;"><img border="0" data-original-height="560" data-original-width="960" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhyWMegAUVUHwRlNU4vSigv5WR_IDnjWKU5mLYuNBGvN4pr0rAh7FPL_ghArM_lcEuhbBlWWWcckAB6eJF6x4dnyXuQGgp15FnPlXUmCPIORF_xPb2F9qyjvRe72HeBLOzRaF7a1EqLr6o/s1600/finalgraph2.png" style="margin-left: auto; margin-right: auto;" /></td></tr> <tr><td class="tr-caption" style="text-align: center;">The line graph representing the values of RMS error given by the 3 models over a time period of 21 days.</td></tr> </tbody></table>This <a href="https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/#0">codelab</a> helped us with TFLite on Android. The next challenge was to host the adaptive image based model for each user. To solve for this we came across an interesting solution by Firebase ML Kit. It allows custom and adaptive ML models to be hosted in cloud and on-device. We followed this <a href="https://firebase.google.com/docs/ml-kit/android/use-custom-models">documentation</a>.<br /> <h3>The road ahead</h3>We intend to make the following improvements to improve the application in future:<br /> <ul><li>Generate results on photos taken at night.<br /> </li> <li>Extend reachability to other major cities.<br /> </li> <li>Make the model robust in various weather conditions.</li> </ul>We started this project with the aim of spreading awareness about the toxic levels of pollution. We hope overtime we will all be motivated to take steps to reduce activities which reduce air quality. <br /> <br /> Lastly, we would like to thank Dr. Aakanksha Chowdhery (Google) and Prof. Brejesh Lall (IIT Delhi) for mentoring us throughout our journey. We acknowledge the financial and learning support provided by <a href="http://marconisociety.org/">Marconi Society</a> via Celestini Program India, providing us a wonderful platform to prototype this application. We could not have completed without this mentoring and support. <br /> <br /> You can follow this project by visiting our <a href="http://bit.ly/Air_Cognizer">website</a> and demonstration video on <a href="https://www.youtube.com/watch?v=sBTqLHA95rs">YouTube</a>. The application is available on the Play Store under the name<a href="http://bit.ly/AirCognizer"> Air Cognizer.</a> </div> </div> <div class='tensorsite-detail-footer'> <div class='article-divider'> <img alt='Diamond Article Divider' src='https://www.gstatic.com/tf_blog/images/ic_article_end.svg'/> </div> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=Community&max-results=20'> Community </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=TensorFlow+Lite&max-results=20'> TensorFlow Lite </a> </div> </div> <div class='tensorsite-next'> <div class='tensorsite-container--large'> <div class='tensorsite-next__title'>Next post</div> <div class='tensorsite-card'> <a aria-label='Next Card' class='tensorsite-card__href next' href='https://blog.tensorflow.org/2019/02/air-cognizer-predicting-air-quality.html'></a> <div class='tensorsite-content__image-wrapper'> <img alt='Air Cognizer: Predicting Air Quality with TensorFlow Lite' class='tensorsite-content__image' src='https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBUi-G7WDQP5RK20G9PioqDgmKs-OGdrZUVv-OCsNwdHEo7SS2YmnV1P3egXrHU3J26l3pFNiQYZvxuav_HE7FhgVs62LJClZm08j9ySLuq_kHuCjdSImB936c9mqr_vxJIvU0uB4AVKw/s1600/0_AZaB7fy1qAAVgN_i.png'/> </div> <div class='tensorsite-content'> <div class='tensorsite-content__subtitle next'> <span>Community</span> <b class='label-divider-dot'>·</b> <span>TensorFlow Lite</span> <b class='label-divider-dot'>·</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-content__title next'> Air Cognizer: Predicting Air Quality with TensorFlow Lite </div> <p class='tensorsite-content__description next'> <span class='tensorsite-content__info'> February 08, 2019 </span> — <span> <em>A guest article by <a href="https://www.linkedin.com/in/prernakhanna97/">Prerna Khanna</a>, <a href="http://bit.ly/tanmayLinked">Tanmay Srivastava </a>and Kanishk Jeet</em><br><br> Large cities like Delhi can suffer from air pollution, especially in winter, and we’ve seen headlines like “<a href="https://www.ndtv.com/delhi-news/cold-morning-in-delhi-air-quality-continues-to-be-severe-1971865">Cold Morning In Delhi, Air Quality Continues To Be Severe</a>” appear on the front page of newspapers. Poor air quality in the winter months can lead to smog, which can restrict outdoor activities and cause health concerns.<br>As … </span> </p> </div> </div> </div> </div>  </div></div> </div>  <div class='tensorsite-full-footer'> <div class='section' id='footer'><div class='widget HTML' data-version='1' id='HTML2'> <section class='tensorsite-footer'> <div class='tensorsite-container tensorsite-footer__container'> <div class='tensorsite-footer__side tensorsite-footer__side--left'></div> <div class='tensorsite-footer__side tensorsite-footer__side--right'></div> <div class='tensorsite-footer__content'> <div class='tensorsite-content'> <div class='tensorsite-content__title tensorsite-content__title--grow'> Build, deploy, and experiment easily with TensorFlow </div> <div class='tensorsite-content__cta-wrapper'> <a class='tensorsite-content__button tensorsite-button tensorsite-button--white' href='https://www.tensorflow.org/'> Get started </a> </div> </div> </div> </div> <div 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