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.

At least nine people, including three journalists, have been killed and several others wounded in an Israeli drone attack on Beit Lahiya in northern Gaza, according to Palestinian media. The attack on Saturday reportedly targeted a relief team that was accompanied by journalists and photographers. At least three local journalists are among the dead. The Palestinian Journalists' Protection Center said in a statement that "the journalists were documenting humanitarian relief efforts for those affected by Israel's genocidal war" and called on Gaza ceasefire mediators to pressure Israeli Prime Minister Benjamin Netanyahu to move forward with implementing the agreed truce and prisoner exchange. Israel has rejected opening talks on the second phase of the ceasefire between it and Hamas, which would require it to negotiate over a permanent end to the war, a key Hamas demand.

This study aims to understand the factors that resulted in under-five children's malnutrition from the Multiple Indicator Cluster (MICS-2019) nationwide surveys and classify different malnutrition stages based on the four well-established machine learning algorithms, namely - Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP) neural network. Accuracy, precision, recall, and F1 scores are obtained to evaluate the performance of each model. The statistical Pearson correlation coefficient analysis is also done to understand the significant factors related to a child's malnutrition. The eligible data sample for analysis was 21,858 among 24,686 samples from the dataset. Satisfactory and insightful results were obtained in each case and, the RF and MLP performed extraordinarily well. For RF, the accuracy was 98.55%, average precision 98.3%, recall value 95.68%, and F1 score 97.13%. For MLP, the accuracy was 98.69%, average precision 97.62%, recall 90.96%, and F1 score of 97.39%. From the Pearson co-efficient, all negative correlation results are enlisted, and the most significant impacts are found for the WAZ2 (Weight for age Z score WHO) (-0.828"), WHZ2 (Weight for height Z score WHO) (-0.706"), ZBMI (BMI Z score WHO) (-0.656"), BD3 (whether child is still being breastfed) (-0.59"), HAZ2 (Height for age Z score WHO) (-0.452"), CA1 (whether child had diarrhea in last 2 weeks) (-0.34"), Windex5 (Wealth index quantile) (-0.161"), melevel (Mother's education) (-0.132"), and CA14/CA16/CA17 (whether child had illness with fever, cough, and breathing) (-0.04) in successive order.

In the face of climate change-induced droughts, vulnerable regions encounter severe threats to food security, demanding urgent humanitarian assistance. This paper introduces a causal inference framework for the Horn of Africa, aiming to assess the impact of cash-based interventions on food crises. Our contributions encompass identifying causal relationships within the food security system, harmonizing a comprehensive database, and estimating the causal effect of humanitarian interventions on malnutrition. Our results revealed no significant effects, likely due to limited sample size, suboptimal data quality, and an imperfect causal graph resulting from our limited understanding of multidisciplinary systems like food security. This underscores the need to enhance data collection and refine causal models with domain experts for more effective future interventions and policies, improving transparency and accountability in humanitarian aid.

Malnutrition is a serious and prevalent health problem in the older population, and especially in hospitalised or institutionalised subjects. Accurate and early risk detection is essential for malnutrition management and prevention. M-health services empowered with Artificial Intelligence (AI) may lead to important improvements in terms of a more automatic, objective, and continuous monitoring and assessment. Moreover, the latest Explainable AI (XAI) methodologies may make AI decisions interpretable and trustworthy for end users. This paper presents a novel AI framework for early and explainable malnutrition risk detection based on heterogeneous m-health data. We performed an extensive model evaluation including both subject-independent and personalised predictions, and the obtained results indicate Random Forest (RF) and Gradient Boosting as the best performing classifiers, especially when incorporating body composition assessment data. We also investigated several benchmark XAI methods to extract global model explanations. Model-specific explanation consistency assessment indicates that each selected model privileges similar subsets of the most relevant predictors, with the highest agreement shown between SHapley Additive ExPlanations (SHAP) and feature permutation method. Furthermore, we performed a preliminary clinical validation to verify that the learned feature-output trends are compliant with the current evidence-based assessment.

Ending global hunger has long been a critical goal for the global community. When the United Nations' Sustainable Development Goals were released in 2014, ending hunger, food insecurity and all forms of malnutrition formed SDG2. Though there has been some progress in the fight against hunger – ongoing conflicts, climate change, economic downturns and the COVID-19 pandemic have been major barriers to achieving SDG2. As of 2020, according to the UN, 720 and 811 million people globally faced hunger, and current estimates suggest that 660 million people may still face hunger in 2030. Professor Salah Sukkarieh, a robotics engineer at the University of Sydney's Australian Centre for Field Robotics, will this week speak at the United Nations Food and Agriculture Organization's (FAO) Global Conference on Sustainable Plant Production in Rome (2-4 November).

Malnutrition is a global health crisis and is the leading cause of death among children under five. Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. However, measuring them accurately is a challenge, especially in the global south, due to limited resources. In this work, we propose a CNN-based approach to estimate the height of standing children under five years from depth images collected using a smart-phone. According to the SMART Methodology Manual [5], the acceptable accuracy for height is less than 1.4 cm. On training our deep learning model on 87131 depth images, our model achieved an average mean absolute error of 1.64% on 57064 test images. For 70.3% test images, we estimated height accurately within the acceptable 1.4 cm range. Thus, our proposed solution can accurately detect stunting (low height-for-age) in standing children below five years of age.

Welthungerhilfe, one of Germany's largest private aid organisations, in collaboration with the India chapter of Action Against Hunger, has launched an app called Child Growth Monitor. The app uses Microsoft's Artificial Intelligence technology to monitor children's growth and levels of nutrition, and helps in fighting malnutrition. Child Growth Monitor has been launched as a pilot project in India. The app scans nearly 10,000 children under the age of five for signs of malnutrition. It is powered by Microsoft Azure and AI services.

With one or other battle, war or conflict raging in many parts of world, what this world needs is peace. And as Nobel prize winner and father of green revolution, Dr.Norman Borlaug famously said, "There cannot be any peace on hungry stomach". If people are well fed and hence happy, they are less likely to engage in conflicts. A group of researchers from Cornell University would use ML techniques to analyse food and market conditions, to predict poverty and malnutrition in poorest region of the planet. The method would use available satellite data to measure solar induced chlorophyll fluorescence (SIF).

A look at how Yemen's brutal civil war is creating a market for prosthetic limbs. Each is missing a vital part of their body – a hand, a leg, an arm. Inside that building is new hope for each: Prosthetic limbs are being cut, carved, melted and molded. Young patient recently outfitted with a new leg waits for his training session outside the Ma'rib prosthetics center in Yemen (Fox News/Hollie McKay) "Sometimes I go to my office to cry for each of these miserable stories," Dr. Haitham Ahmed Ali Ahmed, a Sudanese volunteer with Physicians Across Continents, told Fox News. "It isn't fair, but we do whatever we can to give them another chance."