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Expand Up @@ -621,22 +621,16 @@ <h2 data-number="19.2" class="anchored" data-anchor-id="agriculture"><span class
</div><div id="ref-tirtalistyani2022indonesia" class="csl-entry" role="listitem">
Tirtalistyani, Rose, Murtiningrum Murtiningrum, and Rameshwar S. Kanwar. 2022. <span>“<span>Indonesia</span> Rice Irrigation System: <span>Time</span> for Innovation.”</span> <em>Sustainability</em> 14 (19): 12477. <a href="https://doi.org/10.3390/su141912477">https://doi.org/10.3390/su141912477</a>.
</div></div><p>With greater investment and integration into rural advisory services, TinyML could transform small-scale agriculture and improve farmers’ livelihoods worldwide. The technology effectively brings the benefits of precision agriculture to disconnected regions most in need.</p>
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Exercise&nbsp;19.1: Crop Yield Modeling
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<h3 data-number="19.2.1" class="anchored" data-anchor-id="crop-yield-modeling"><span class="header-section-number">19.2.1</span> Crop Yield Modeling</h3>
<p>This exercise teaches you how to predict crop yields in Nepal by combining satellite data (Sentinel-2), climate data (WorldClim), and on-the-ground measurements. You’ll use a machine learning algorithm called XGBoost Regressor to build a model, split the data for training and testing, and fine-tune the model parameters for the best performance. This notebook lays the foundation for implementing TinyML in the agriculture domain. Consider how you could adapt this process for smaller datasets, fewer features, and simplified models to make it compatible with the power and memory constraints of TinyML devices.</p>
<p><a href="https://colab.research.google.com/github/developmentseed/sat-ml-training/blob/main/_notebooks/2020-07-29-Crop_yield_modeling_with_XGBoost.ipynb#scrollTo=GQd7ELsRWkBI"><img src="https://colab.research.google.com/assets/colab-badge.png" class="img-fluid"></a></p>
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Exercise&nbsp;19.1: <a href="https://colab.research.google.com/github/developmentseed/sat-ml-training/blob/main/_notebooks/2020-07-29-Crop_yield_modeling_with_XGBoost.ipynb#scrollTo=GQd7ELsRWkBI"><img src="https://colab.research.google.com/assets/colab-badge.png" class="img-fluid figure-img"></a>
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<section id="healthcare" class="level2 page-columns page-full" data-number="19.3">
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Jia, Zhenge, Dawei Li, Xiaowei Xu, Na Li, Feng Hong, Lichuan Ping, and Yiyu Shi. 2023. <span>“Life-Threatening Ventricular Arrhythmia Detection Challenge in Implantable Cardioverter<span></span>defibrillators.”</span> <em>Nature Machine Intelligence</em> 5 (5): 554–55. <a href="https://doi.org/10.1038/s42256-023-00659-9">https://doi.org/10.1038/s42256-023-00659-9</a>.
</div></div><p>An on-device algorithm for early and timely life-threatening VA detection will increase the chances of survival. The proposed AI/ML algorithm needed to be deployed and executed on an extremely low-power and resource-constrained microcontroller (MCU) (a $10 development board with an ARM Cortex-M4 core at 80 MHz, 256 kB of flash memory and 64 kB of SRAM). The submitted designs were evaluated by metrics measured on the MCU for (1) detection performance, (2) inference latency, and (3) memory occupation by the program of AI/ML algorithms.</p>
<p>The champion, GaTech EIC Lab, obtained 0.972 in <span class="math inline">\(F_\beta\)</span> (F1 score with a higher weight to recall), 1.747 ms in latency, and 26.39 kB in memory footprint with a deep neural network. An ICD with an on-device VA detection algorithm was <a href="https://youtu.be/vx2gWzAr85A?t=2359">implanted in a clinical trial</a>.</p>
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Exercise&nbsp;19.2: Clinical Data: Unlocking Insights with Named Entity Recognition
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<h3 data-number="19.3.5" class="anchored" data-anchor-id="clinical-data-unlocking-insights-with-named-entity-recognition"><span class="header-section-number">19.3.5</span> Clinical Data: Unlocking Insights with Named Entity Recognition</h3>
<p>In this exercise, you’ll learn about Named Entity Recognition (NER), a powerful tool for extracting valuable information from clinical text. Using Spark NLP, a specialized library for healthcare NLP, we’ll explore how NER models like BiLSTM-CNN-Char and BERT can automatically identify important medical entities such as diagnoses, medications, test results, and more. You’ll get hands-on experience applying these techniques with a special focus on oncology-related data extraction, helping you unlock insights about cancer types and treatment details from patient records.</p>
<p><a href="https://colab.research.google.com/github/JohnSnowxColabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.Clinical_Named_Entity_Recognition_Model.ipynb#scrollTo=I08sFJYCxR0Z"><img src="https://colab.research.google.com/assets/colab-badge.png" class="img-fluid"></a></p>
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Exercise&nbsp;19.2: <a href="https://colab.research.google.com/github/JohnSnowxColabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.Clinical_Named_Entity_Recognition_Model.ipynb#scrollTo=I08sFJYCxR0Z"><img src="https://colab.research.google.com/assets/colab-badge.png" class="img-fluid figure-img"></a>
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<h2 data-number="19.12" class="anchored" data-anchor-id="sec-ai-for-good-resource"><span class="header-section-number">19.12</span> Resources</h2>
<p>Here is a curated list of resources to support students and instructors in their learning and teaching journeys. We are continuously working on expanding this collection and will be adding new exercises soon.</p>
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<p>These slides are a valuable tool for instructors to deliver lectures and for students to review the material at their own pace. We encourage students and instructors to leverage these slides to enhance their understanding and facilitate effective knowledge transfer.</p>
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<li><p><a href="#exr-agri" class="quarto-xref">Exercise&nbsp;<span>19.1</span></a></p></li>
<li><p><a href="#exr-hc" class="quarto-xref">Exercise&nbsp;<span>19.2</span></a></p></li>
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<p>In addition to exercises, we offer a series of hands-on labs allowing students to gain practical experience with embedded AI technologies. These labs provide step-by-step guidance, enabling students to develop their skills in a structured and supportive environment. We are excited to announce that new labs will be available soon, further enriching the learning experience.</p>
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