For island nations and countries without the infrastructure for reliable transportation, drones can do more than take photos or collect data: they can transport supplies to save lives. The Pacific island country of Vanuatu, for instance, has teamed up with UNICEF and two drone companies to deliver vaccines to rural areas. Vanuatu is composed of 83 islands spread over an area that covers 1,600 kilometers ( 1,000 miles). To deliver vaccines to its more rural communities, health workers often have to walk for hours -- sometimes, it can even take them days by cars and/or boats. Drones could ensure that local health facilities have quick access to lifesaving supplies when needed.

Why data could be the deciding factor in Africa's agricultural transformation. The world has a palm oil problem. It's a global, billion-dollar industry and its end result is irreversible environmental damage, ranging from deforestation and fires, to the loss of species such as tigers, pygmy elephants and orangutans. Palm oil is used in 50% of the products we buy (think bread, shampoo, soaps and even chocolate) due to the fact that it is the highest-yielding vegetable oil crop. Yet, in a country like Uganda, where 80% of the population is involved in agriculture as a way of life, many Ugandans farm oil palm on small plots, barely making a living. "The use of data for purposes of precision agricultural systems is being used around the world to optimize farms, from anticipating natural disasters such as droughts and flooding, to predicting the best time to harvest crops, to anticipating outbreaks of pests and disease before they impact the produce," says AgriSA's Janse Rabie.

OVER THE past five years researchers in artificial intelligence have become the rock stars of the technology world. A branch of AI known as deep learning, which uses neural networks to churn through large volumes of data looking for patterns, has proven so useful that skilled practitioners can command high six-figure salaries to build software for Amazon, Apple, Facebook and Google. The top names can earn over $1m a year. The standard route into these jobs has been a PhD in computer science from one of America's elite universities. Earning one takes years and requires a disposition suited to academia, which is rare among more normal folk.

To improve customer experiences, brands are increasingly turning to data to give them insights and direction. Some of the benefits include helping brands personalize the customer experience. This creates a more enjoyable and memorable moment for customers. In return, they can become repeat customers. Plus, they may tell others about their (hopefully) great experience.

In 2017, United Nations Secretary-General António Guterres noted the difficult challenge that policymakers, particularly those in the Global South face with respect to AI. He said that "The implications for development are enormous. Developing countries can gain from the benefits of AI, but they also face the highest risk of being left behind." For example, in Nigeria doctors are using AI to help reduce the incidence of birth asphyxia, a leading cause of under-five death in Africa, and yet at the same time there are real concerns about AI's impact on rising unemployment and the influence that Google, China, and others are exerting across the Global South. AI technologies are raising complex social, political, technological, economic, and ethical questions.

Text classification is a challenging problem which aims to identify the category of texts. Recently, Capsule Networks (CapsNets) are proposed for image classification. It has been shown that CapsNets have several advantages over Convolutional Neural Networks (CNNs), while, their validity in the domain of text has less been explored. An effective method named deep compositional code learning has been proposed lately. This method can save many parameters about word embeddings without any significant sacrifices in performance. In this paper, we introduce the Compositional Coding (CC) mechanism between capsules, and we propose a new routing algorithm, which is based on k-means clustering theory. Experiments conducted on eight challenging text classification datasets show the proposed method achieves competitive accuracy compared to the state-of-the-art approach with significantly fewer parameters.

Most new technologies tend to see people react with scepticism, suspicion and fear, with some even becoming alarmist and broadcasting extreme negative scenarios up to and including some pending cataclysm. And this has certainly been the case for Artificial Intelligence (AI)! But the reality is all our technologies are benign and any risk is entirely down to people and what they might do. In the case of AI, the advantages afforded to medical science are the most visible, well-known and radical with dramatic improvements in diagnosis, treatment and outcomes. Lives are being saved, survival rates improved, and the quality of life for millions of people improved.

Abstract-- Prognostics and Health Management (PHM) offers several benefits for predictive maintenance. It predicts the future behavior of a system as well as its Remaining Useful Life (RUL). This RUL is used to planned the maintenance operation to avoid the failure, the stop time and optimize the cost of the maintenance and failure. However, with the development of the industry the assets are nowadays distributed this is why the PHM needs to be developed using the new IT. In our work we propose a PHM solution based on Cyber physical system where the physical side is connected to the analyze process of the PHM which are developed in the cloud to be shared and to benefit of the cloud characteristics Keywords-- Cyber physical systems CPS, Prognostics Health Management PHM, Decision post-prognostics, cloud computing, Internet of Things.

Since the arrival of the fall armyworm (Spodoptera frugiperda) caterpillar in West Africa in early 2016, true to its name, it has been marching quickly and mercilessly through the continent, eating maize (corn) along with sorghum, millet, and rice and causing billions of dollars in crop losses. It has now been confirmed or reported in every sub-Saharan African country and was recently found in southern India, beginning its likely spread into much of the Asian continent. The time for eradication has long passed, and scientists, NGOs, and governments are now focused on control. For some, this means chemical pesticides, but these are expensive and many smallholders do not know how to safely apply the chemicals, making them a threat to human and environmental health, including the survival of other insects and their predators. Additionally, many farmers have said that even when they spray the pesticides, they are ineffective.

This study seeks to improve the running time of the Local Directional Pattern (LDP) during feature extraction using a newly proposed acceleration scheme to LDP. LDP is considered to be computationally expensive. To confirm this, Shabat and Tapamo compared the running time of the LDP to gray level co-occurrence matrix (GLCM) were it was established that the running time for LDP was two orders of magnitude higher than that of the GLCM. In this study, the performance of the newly proposed acceleration scheme was evaluated against LDP and LBP using images from the publicly available extended Cohn-Kanade (CK) dataset. Based on our findings, the proposed acceleration scheme significantly improves the running time of the LDP by almost 3 times during feature extraction.