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The volume of Amharic digital documents has grown rapidly in recent years. As a result, automatic document categorization is highly essential. In this paper, we present a novel dimension reduction approach for improving classification accuracy by combining feature selection and feature extraction. The new dimension reduction method utilizes Information Gain (IG), Chi-square test (CHI), and Document Frequency (DF) to select important features and Principal Component Analysis (PCA) to refine the features that have been selected. We evaluate the proposed dimension reduction method with a dataset containing 9 news categories. Our experimental results verified that the proposed dimension reduction method outperforms other methods. Classification accuracy with the new dimension reduction is 92.60%, which is 13.48%, 16.51% and 10.19% higher than with IG, CHI, and DF respectively. Further work is required since classification accuracy still decreases as we reduce the feature size to save computational time.

Artificial Intelligence (AI) research has always been embedded in complex networks of cultural imagination, corporate business, and sociopolitical power relations. The great majority of AI research around the world, and almost all commentary on that research, assumes that the imagination, business, and political systems of Western culture and the Global North are sufficient to understand how this technology should develop in future. This article investigates the context within which AI research is imagined and conducted in the Amhara region of Ethiopia, with implications for public policy, technology strategy, future research in development contexts, and the principles that might be applied as practical engineering priorities.

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