Fixed two tactics not displaying the image properly

S2-group · Dec 14, 2023 · ab93a0f · ab93a0f
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diff --git a/docs/_posts/green-ml-enabled-systems/2023-12-13-reduce-number-of-data-features.md b/docs/_posts/green-ml-enabled-systems/2023-12-13-reduce-number-of-data-features.md
@@ -21,5 +21,5 @@ t-relatedQA: "Accuracy, Data Representativeness"
 t-measuredimpact: "Reducing number of input features can result in a reduction of energy consumption while still maintaining accuracy."
 t-source: "Roberto Verdecchia, Luis Cruz, June Sallou, Michelle Lin, James Wickenden, and Estelle Hotellier. 2022. Data-Centric Green AI: An Exploratory Empirical Study. (2022). In 2022 International Conference on ICT for Sustainability (ICT4S). IEEE, 35–45"
 t-source-doi: "https://doi.org/10.1002/widm.1507"
-T-diagram: "reduce-number-of-data-features.png"
+t-diagram: "reduce-number-of-data-features.png"
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diff --git a/docs/_posts/green-ml-enabled-systems/2023-12-13-remove-redundant-data.md b/docs/_posts/green-ml-enabled-systems/2023-12-13-remove-redundant-data.md
@@ -1,7 +1,7 @@
 ---
 layout: tactic
 
-title: "Remove redundant data"
+title: "Remove Redundant Data"
 tags: machine-learning data-centric measured
 t-sort: "Awesome Tactic"
 t-type: "Architectural Tactic"
@@ -16,6 +16,6 @@ t-targetQA: "Energy Efficiency"
 t-relatedQA: "Accuracy, Data Representativeness"
 t-measuredimpact: "Removing redundant data from the dataset leads to a smaller input data that further decreases computation, computational time, energy consumption, and memory space"
 t-source: "Priyadarshan Dhabe, Param Mirani, Rahul Chugwani, and Sadanand Gandewar. 2021. Data Set Reduction to Improve Computing Efficiency and Energy Consumption in Healthcare Domain. In Digital Literacy and Socio-Cultural Acceptance of ICT in Developing Countries. Springer, 53–64. [DOI](https://doi.org/10.1007/978-3-030-61089-0_4); Phyllis Ang, Bhuwan Dhingra, and Lisa Wu Wills. 2022. Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models. In Proceedings of NLP Power! The First Workshop on Efficient Benchmarking in NLP. Association for Computational Linguistics, Dublin, Ireland, 113–121. [DOI](https://aclanthology.org/2022.nlppower-1.12)"
-T-diagram: "remove-redundant-data.png"
+t-diagram: "remove-redundant-data.png"
 t-source-doi: 
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