Drone strikes in Ethiopia's Tigray kill one amid fears of renewed conflict One person has been killed and another injured in drone strikes in Ethiopia's northern Tigray region, a senior Tigrayan official and a humanitarian worker said, in another sign of renewed conflict between regional and federal forces. The Tigrayan official on Saturday said the drone strikes hit two Isuzu trucks near Enticho and Gendebta, two places in Tigray about 20km (12 miles) apart. A local humanitarian worker confirmed the strikes had happened. Both asked not to be named, the Reuters news agency reported. It was not immediately clear what the trucks were carrying.

Karoline Leavitt's family member'abruptly arrested' by ICE after living in US for decades Sir Richard Branson reveals his wife Joan died'quickly and painlessly' while in hospital for a back injury - as he says'life will never be the same' without his'shining star' Residents in liberal Western US city feel'isolated' as state turns extremely red What HAS happened to Beyoncé? Suddenly desperate, I know what's really going on... and it's ugly: CAROLINE BULLOCK LIZ JONES: Sorry, but it's now time for Kate to stop making excuses Teenager dragged from car'by migrant gang' and raped in front of her fiancé describes her night of hell and reveals they warned her'if you scream we'll kill you' Virginia Giuffre's family is at war over who gets Andrew's multi-million payout after she died without leaving a will Prince Philip nicknamed Meghan Markle'DOW' and warned Royal Family about her'eerie similarities' with Wallis Simpson, royal author reveals Sports broadcaster's wife suffers unimaginable tragedy just before he goes on air New'Hollywood of the South' emerges as booming industry generates $1bn... but long-time residents are furious University of Minnesota program offers guidelines to'reverse the whiteness pandemic' Putin'sends top general to Venezuela along with troops tasked with training up President Maduro's forces' as US considers attacking South American country Forget Yellowstone or Etna! 'Hidden' volcanoes pose the greatest risk to the world, scientists warn - after little-known mount erupts in Ethiopia READ MORE: Scientists discover a new hole in one of Yellowstone's basins A little-known Ethiopian volcano has erupted for the first time in at least 12,000 years - sparking fears that'hidden' volcanoes are being missed. Professor Mike Cassidy, a volcanologist at the University of Birmingham, says the world's overlooked volcanoes'pose the greatest threat'. Known as'hidden' volcanoes, they're less famous than Yellowstone or Etna even among scientists - which means they're not being monitored as much. Examples include El Chichón in Mexico, Mount Pinatubo in the Philippines, Mount Merapi in Indonesia and La Soufrière on the Caribbean island of Saint Vincent.

Malaria remains a major public health concern in Ethiopia, particularly in the Amhara Region, where seasonal and unpredictable transmission patterns make prevention and control challenging. Accurately forecasting malaria outbreaks is essential for effective resource allocation and timely interventions. This study proposes a hybrid predictive modeling framework that combines time-series forecasting, multi-output regression, and conventional regression-based prediction to forecast the incidence of malaria. Environmental variables, past malaria case data, and demographic information from Amhara Region health centers were used to train and validate the models. The multi-output regression approach enables the simultaneous prediction of multiple outcomes, including Plasmodium species-specific cases, temporal trends, and spatial variations, whereas the hybrid framework captures both seasonal patterns and correlations among predictors. The proposed model exhibits higher prediction accuracy than single-method approaches, exposing hidden patterns and providing valuable information to public health authorities. This study provides a valid and repeatable malaria incidence prediction framework that can support evidence-based decision-making, targeted interventions, and resource optimization in endemic areas.

Malnutrition among pregnant women is a major public health challenge in Ethiopia, increasing the risk of adverse maternal and neonatal outcomes. Traditional statistical approaches often fail to capture the complex and multidimensional determinants of nutritional status. This study develops a predictive model using ensemble machine learning techniques, leveraging data from the Ethiopian Demographic and Health Survey (2005-2020), comprising 18,108 records with 30 socio-demographic and health attributes. Data preprocessing included handling missing values, normalization, and balancing with SMOTE, followed by feature selection to identify key predictors. Several supervised ensemble algorithms including XGBoost, Random Forest, CatBoost, and AdaBoost were applied to classify nutritional status. Among them, the Random Forest model achieved the best performance, classifying women into four categories (normal, moderate malnutrition, severe malnutrition, and overnutrition) with 97.87% accuracy, 97.88% precision, 97.87% recall, 97.87% F1-score, and 99.86% ROC AUC. These findings demonstrate the effectiveness of ensemble learning in capturing hidden patterns from complex datasets and provide timely insights for early detection of nutritional risks. The results offer practical implications for healthcare providers, policymakers, and researchers, supporting data-driven strategies to improve maternal nutrition and health outcomes in Ethiopia.

Language identification is the task of determining the languages for a given text. In many real-world scenarios, text may contain more than one language, particularly in multilingual communities. Bilingual Language Identification (BLID) is the task of identifying and distinguishing between two languages in a given text. This paper presents BLID for languages spoken in the southern part of Ethiopia, namely Wolaita and Gofa. The presence of words' similarities and differences between the two languages makes the language identification task challenging. To overcome this challenge, we employed various experiments on various approaches. Then, the combination of the Bert-based pre-trained language model and LSTM approach performed better, with an F1-score of 0.72 on the test set. As a result, the work will be effective in tackling unwanted social media issues and providing a foundation for further research in this area.

As part of nationwide efforts aligned with the United Nations' Sustainable Development Goal 3 on Universal Health Coverage, Ethiopia's Ministry of Health is strengthening health posts to expand access to essential healthcare services. However, only a fraction of this health system strengthening effort can be implemented each year due to limited budgets and other competing priorities, thus the need for an optimization framework to guide prioritization across the regions of Ethiopia. In this paper, we develop a tool, Health Access Resource Planner (HARP), based on a principled decision-support optimization framework for sequential facility planning that aims to maximize population coverage under budget uncertainty while satisfying region-specific proportionality targets at every time step. We then propose two algorithms: (i) a learning-augmented approach that improves upon expert recommendations at any single-step; and (ii) a greedy algorithm for multi-step planning, both with strong worst-case approximation estimation. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we demonstrated the empirical efficacy of our method on three regions across various planning scenarios.

Vision-Language Models (VLMs) are increasingly deployed in diverse cultural contexts, yet their internal biases remain poorly understood. In this work, we propose a novel framework to systematically evaluate how VLMs encode cultural differences and biases related to race, gender, and physical traits across countries. We introduce three retrieval-based tasks: (1) Race to Country retrieval, which examines the association between individuals from specific racial groups (East Asian, White, Middle Eastern, Latino, South Asian, and Black) and different countries; (2) Personal Traits to Country retrieval, where images are paired with trait-based prompts (e.g., Smart, Honest, Criminal, Violent) to investigate potential stereotypical associations; and (3) Physical Characteristics to Country retrieval, focusing on visual attributes like skinny, young, obese, and old to explore how physical appearances are culturally linked to nations. Our findings reveal persistent biases in VLMs, highlighting how visual representations may inadvertently reinforce societal stereotypes.

Surprisingly a number of Earth's waterways remain unmapped, with a significant number in low and middle income countries. Here we build a computer vision model (WaterNet) to learn the location of waterways in the United States, based on high resolution satellite imagery and digital elevation models, and then deploy this in novel environments in the African continent. Our outputs provide detail of waterways structures hereto unmapped. When assessed against community needs requests for rural bridge building related to access to schools, health care facilities and agricultural markets, we find these newly generated waterways capture on average 93% (country range: 88-96%) of these requests whereas Open Street Map, and the state of the art data from TDX-Hydro, capture only 36% (5-72%) and 62% (37% - 85%), respectively. Because these new machine learning enabled maps are built on public and operational data acquisition this approach offers promise for capturing humanitarian needs and planning for social development in places where cartographic efforts have so far failed to deliver. The improved performance in identifying community needs missed by existing data suggests significant value for rural infrastructure development and better targeting of development interventions.

Hate speech is a growing problem on social media. It can seriously impact society, especially in countries like Ethiopia, where it can trigger conflicts among diverse ethnic and religious groups. While hate speech detection in resource rich languages are progressing, for low resource languages such as Amharic are lacking. To address this gap, we develop Amharic hate speech data and SBi-LSTM deep learning model that can detect and classify text into four categories of hate speech: racial, religious, gender, and non-hate speech. We have annotated 5k Amharic social media post and comment data into four categories. The data is annotated using a custom annotation tool by a total of 100 native Amharic speakers. The model achieves a 94.8 F1-score performance. Future improvements will include expanding the dataset and develop state-of-the art models. Keywords: Amharic hate speech detection, classification, Amharic dataset, Deep Learning, SBi-LSTM

The prevalence of digital media and evolving sociopolitical dynamics have significantly amplified the dissemination of hateful content. Existing studies mainly focus on classifying texts into binary categories, often overlooking the continuous spectrum of offensiveness and hatefulness inherent in the text. In this research, we present an extensive benchmark dataset for Amharic, comprising 8,258 tweets annotated for three distinct tasks: category classification, identification of hate targets, and rating offensiveness and hatefulness intensities. Our study highlights that a considerable majority of tweets belong to the less offensive and less hate intensity levels, underscoring the need for early interventions by stakeholders. The prevalence of ethnic and political hatred targets, with significant overlaps in our dataset, emphasizes the complex relationships within Ethiopia's sociopolitical landscape. We build classification and regression models and investigate the efficacy of models in handling these tasks. Our results reveal that hate and offensive speech can not be addressed by a simplistic binary classification, instead manifesting as variables across a continuous range of values. The Afro-XLMR-large model exhibits the best performances achieving F1-scores of 75.30%, 70.59%, and 29.42% for the category, target, and regression tasks, respectively. The 80.22% correlation coefficient of the Afro-XLMR-large model indicates strong alignments.