Yet the healthcare industry has been slow to commit to its potential. As recently as 2016, a Forrester Consulting survey found that just 34% of healthcare organizations had adopted predictive analytics, compared with 51% in all other industries. Healthcare was similarly behind in cognitive computing, at 23% versus 40% elsewhere. Among the pioneers, some organizations are already using AI to reduce physical trips to the doctor's office, improve patient care and rethink care delivery models that date to the 19th century. "I believe we can get to a world where we aren't just identifying your likelihood of utilizing the emergency room or being hospitalized, but getting in front of those situations and delivering proactive care," says Dr. Arta Bakshandeh, senior medical officer at Alignment Healthcare.

Summary: Artificial Intelligence in Healthcare Market Overview: Artificial intelligence (AI) can be defined as the science and engineering adopted to design intelligent machines, especially intelligent computer programs. AI is an intelligent system that applies various human intelligence based functions such as reasoning, learning, and problem-solving skills on different disciplines such as biology, computer science, mathematics, linguistics, psychology, and engineering. AI is widely applicable in medication management, treatment plans, and drug discovery. The global AI in healthcare market was valued at $1,441 million in 2016, and is estimated to reach at $22,790 million by 2023, registering a CAGR of 48.7% from 2017 to 2023. The growth of the global AI in healthcare market is driven by the ability of AI to improve patient outcomes, need to increase coordination between healthcare workforce & patients, increase in adoption of precision medicine, and a notable rise in venture capital investments.

UAE-based automotive company W Motors' has announced the unveiling of MUSE at Auto Shanghai 2019 on April 16. The fully-electric MUSE features a Level 4 / Level 5 autonomous driving system, innovative user interfaces and cloud-computed connectivity, as well as several interior configurations catering to different business needs and consumer requirements. It will be fully produced in Dubai, UAE by W Motors at the all-new production facility of which the first phase is set to be completed in the last quarter of 2019. Pioneers of the future of driving, W Motors is the first and only automotive developer in the Middle East - in partnership with sister company ICONIQ Motors - to release a self-driving vehicle, designed to be on the road for EXPO 2020 in Dubai. MUSE was developed by W Motors and ICONIQ Motors in collaboration with international partners AKKA Technologies, Magna Steyr and Microsoft USA, each offering highly specialized and cutting-edge technologies in the realm of advanced autonomous driving solutions.

The world is inundated with data. Take just the global financial markets. They generate vast amounts of data – share prices, commodity prices, indices, option and futures prices, to name just a few. But data is of no use if there aren't people able to collect, collate, analyse and apply it to the benefit of society. All that data generated by global financial markets gets used for asset and wealth management – and it must be properly analysed and understood to inform good decision making.

Nowadays the increasing and rapid progress of technology and the availability of electronic documents from a variety of sources have made a huge amount of textual data available. Hence, one of the prominent research topics of statistical andmachine learning communities is to provide suitable and feasible methods to extract high-quality information from unstructured textual data (Lata and Loar, 2018) for the different purposes of clustering, classification and document retrieval (Khan et al., 2010). This work originates from an empirical problem of classification of the content ofcalls made to the customer service of an important mobile phone company inItaly. The received calls are written down by an operator and classified into relevant classes (e.g.

The objective of this research is to enhance performance of Stochastic Gradient Descent (SGD) algorithm in text classification. In our research, we proposed using SGD learning with Grid-Search approach to fine-tuning hyper-parameters in order to enhance the performance of SGD classification. We explored different settings for representation, transformation and weighting features from the summary description of terrorist attacks incidents obtained from the Global Terrorism Database as a pre-classification step, and validated SGD learning on Support Vector Machine (SVM), Logistic Regression and Perceptron classifiers by stratified 10-K-fold cross-validation to compare the performance of different classifiers embedded in SGD algorithm. The research concludes that using a grid-search to find the hyper-parameters optimize SGD classification, not in the pre-classification settings only, but also in the performance of the classifiers in terms of accuracy and execution time.

Abstract--Accurate taxi demand-supply forecasting is a challenging applicationof ITS (Intelligent Transportation Systems), due to the complex spatial and temporal patterns. We investigate the impact of different spatial partitioning techniques on the prediction performance of an LSTM (Long Short-Term Memory) network, in the context of taxi demand-supply forecasting. We consider two tessellation schemes: (i) the variable-sized Voronoi tessellation, and (ii) the fixed-sized Geohash tessellation. While the widely employed ConvLSTM (Convolutional LSTM) can model fixed-sized Geohash partitions, the standard convolutional filters cannot be applied on the variable-sized Voronoi partitions. To explore the Voronoi tessellation scheme, we propose the use of GraphLSTM (Graph-based LSTM), by representing the Voronoi spatial partitions as nodes on an arbitrarily structured graph. The GraphLSTM offers competitive performance against ConvLSTM, atlower computational complexity, across three realworld large-scale taxi demand-supply data sets, with different performance metrics. To ensure superior performance across diverse settings, a HEDGE based ensemble learning algorithm is applied over the ConvLSTM and the GraphLSTM networks. I. INTRODUCTION Spatiotemporal forecasting has a wide range of applications, rangingfrom epidemic detection [1], energy management [2], to cellular traffic [3], among others. Location-based taxi demand and supply forecasting, one of the key components ofITS (Intelligent Transportation Systems), also relies heavily on accurate spatiotemporal forecasting. Mobility-on- Demand services such as e-hailing taxis, which have gained tremendous popularity in the recent years, often face taxi demand-supply imbalances. During peak and off-peak hours, mismatches occur between the spatial distributions of the taxi demand and the available drivers, resulting in either scarcity or abundance of vacant taxis. For example, Figure 1 presents a case of demand-supply mismatch averaged over all Mondays near the city center in Bengaluru, India.

The Stellar Daisy, a massive South Korean tanker that sank in March 2017, was spotted on the floor of the South Atlantic Ocean nearly two years later, the CEO of an ocean exploration company revealed Sunday. This discovery could shed new light on exactly what caused the vessel to tilt and sink and provide some closure to the families of the 22 crew members who died. "We are pleased to report that we have located Stellar Daisy, in particular for our client, the South Korean Government, but also for the families of those who lost loved ones in this tragedy," Ocean Infinity CEO Oliver Plunkett said. "Through the deployment of multiple state of the art (autonomous underwater vehicles), we are covering the seabed with unprecedented speed and accuracy." The Stellar Daisy sank on March 31, 2017, nearly 2,500 miles east of Uruguay, while transporting iron ore from Brazil to China.

In 2015, all 193 member countries of the United Nations ratified the 2030 "Sustainable Development Goals" (SDG): a call to action to "end poverty, protect the planet and ensure that all people enjoy peace and prosperity." The 17 goals – shown in the chart below – are measured against 169 targets, set on a purposefully aggressive timeline. The first of these targets, for example, is: "by 2030, [to] eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day". The UN emphasizes that Science, Technology and Innovation (STI) will be critical in the pursuit of these ambitious targets. Rapid advances in technologies which have only really emerged in the past decade – such as the internet of things (IoT), blockchain, and advanced network connectivity – have exciting SDG applications.

The human rights job landscape is changing rapidly. Current and future challenges in combating human rights violations require new skills and tactics. We have compiled a list of 7 free online courses and specializations that will equip you with the knowledge and skills for the human rights jobs of the future. Machine learning and artificial intelligence create new opportunities and challenges for the protection of human rights. Artificial intelligence can help make education, health and economic systems more efficient but also bears the risk to amplify polarization, bias and discrimination against certain groups.