From Thursday, African leaders will gather in the Ethiopian capital, Addis Ababa, home of the African Union (AU), for the continental body's annual summit. According to AU Commission Chairperson Moussa Faki Mahamat, regional integration and "maintaining momentum in addressing issues of peace and security" is high on the agenda. But in an ironic twist, the host of the summit has either initiated or been involved in multiple conflicts in the last three years. Ethiopia's two-year civil war with the state of Tigray may have ended in November 2022 after a Pretoria pact, but federal troops are currently upping drone strikes against rebels known as Fano militia in the state of Amhara, next door to Tigray. This week, the Ethiopian Human Rights Council said "at least 45 civilians" had been killed by federal troops in Amhara.

Predicting the probability of non-performing loans for individuals has a vital and beneficial role for banks to decrease credit risk and make the right decisions before giving the loan. The trend to make these decisions are based on credit study and in accordance with generally accepted standards, loan payment history, and demographic data of the clients. In this work, we evaluate how different Machine learning models such as Random Forest, Decision tree, KNN, SVM, and XGBoost perform on the dataset provided by a private bank in Ethiopia. Further, motivated by this evaluation we explore different feature selection methods to state the important features for the bank. Our findings show that XGBoost achieves the highest F1 score on the KMeans SMOTE over-sampled data. We also found that the most important features are the age of the applicant, years of employment, and total income of the applicant rather than collateral-related features in evaluating credit risk. Work done when the authors were a research intern at Chapa. Equally contributed to this work.

COVID-19 officially became a global pandemic on Wednesday. As public health officials and governments respond; businesses brace for losses; and events like trade shows, SXSW, and Google's I/O shutter around the world, the disease is also impacting scientific conferences. Ironically, a coronavirus conference got canceled this week, and on Tuesday the International Conference on Learning Representations (ICLR), one of the fastest-growing machine learning conferences in the world, shared that it will now be a virtual event held entirely online. Papers will be presented in prerecorded five-minute videos with a slide deck, while researchers invited to make longer presentations can submit 15-minute videos. In a post about the change to an all-digital conference, organizers called the cancellation of an in-person event an "… opportunity to innovate on how to host an effective remote conference."

WASHINGTON - Embattled U.S. aviation giant Boeing on Thursday insisted on the "fundamental safety" of its 737 Max aircraft but pledged to take all necessary steps to ensure the jets' airworthiness. The statements came hours after Ethiopian officials said pilots of a doomed plane that crashed last month, leaving 157 people dead, had followed the company's recommendations. The preliminary findings released Thursday by transportation authorities in Addis Ababa put the American aircraft giant under even greater pressure to restore public trust amid mounting signs the company's onboard anti-stall systems were at fault in crashes involving its formerly top-selling 737 Max aircraft -- incidents that left nearly 350 people dead in less than five months. "We remain confident in the fundamental safety of the 737 Max," CEO Dennis Muilenburg said in a statement, adding that impending software fixes would make the aircraft "among the safest airplanes ever to fly." Muilenburg also acknowledged, however, that an "erroneous activation" of Boeing's Maneuvering Characteristics Augmentation System had occurred. The system is designed to prevent stalls but may have forced the Ethiopian and Indonesian jets into the ground.

Road traffic accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury; Ethiopia in particular experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this work, we applied data mining technologies to link recorded road characteristics to accident severity in Ethiopia, and developed a set of rules that could be used by the Ethiopian Traffic Agency to improve safety.