Imagine a world where online transactions are safer and more secure, thanks to cutting-edge technology. Alibaba has developed a powerful fraud risk management system that uses big data to detect and prevent fraudulent activities in real-time. By analyzing vast amounts of user behavior data, this system can accurately identify suspicious transactions and protect both buyers and sellers. Alibaba’s innovative approach extends beyond its own platform with AntBuckler, a product designed to help merchants and banks combat cybercrime. AntBuckler uses advanced risk models to assess the threat level of transactions, providing a clear, visual representation of potential risks. This groundbreaking work not only enhances security but also builds trust in digital commerce, paving the way for a safer online environment for everyone.

Chen, J. et al. (2015). Big data based fraud risk management at Alibaba. The Journal of Finance and Data Science, Vol. 1, No. 1, pp. 1-10. https://doi.org/10.1016/j.jfds.2015.03.001

Discover how the powerful combination of High Performance Computing (HPC) and Artificial Intelligence (AI) is reshaping the financial industry. From lightning-fast data processing to enhanced fraud detection and personalized customer insights, this technological duo is driving efficiency and innovation in finance. This article spotlights the world where milliseconds can mean millions and highlights how financial firms are leveraging these cutting-edge tools to stay ahead in a rapidly evolving landscape.

Whitefield-Madrano, A. (2024), “How Does High Performance Computing and AI Help Financial Firms?”, BizTech Magazine,  https://biztechmagazine.com/article/2024/02/how-does-high-performance-computing-and-ai-help-financial-firms-perfcon

Imagine a city equipped with advanced surveillance cameras that can automatically detect violent behavior in real-time, alerting authorities without needing someone to constantly monitor the footage. This study from Peru provides the foundation for such technology, making public spaces safer by quickly identifying and responding to dangerous situations. By improving the efficiency and accuracy of violence detection, this research supports efforts to enhance community safety and can guide future developments in technology aimed at preventing violence.

Díaz, J.E.G., Rodríguez, C. (2024). Classification of physical violence actions using Convolutional Neural Networks with transfer learning. International Journal of Safety and Security Engineering, Vol. 14, No. 5, pp. 1347-1355. https://doi.org/10.18280/ijsse.140501

Imagine a tool that helps city planners and law enforcement anticipate crime patterns more accurately, allowing them to allocate resources effectively and improve public safety. This study introduces a groundbreaking model called Multi-type Relations Aware Graph Neural Networks (MRAGNN), which analyzes the relationships between different types of crimes to predict future occurrences. By understanding these correlations, the model provides more precise predictions, addressing challenges like data imbalance and prediction bias. Tested on crime data from Los Angeles and Chicago, MRAGNN outperforms existing methods, offering valuable insights for urban management and helping create safer communities.

Wang, S. et al. (2025). MRAGNN: Refining urban spatio-temporal prediction of crime occurrence with multi-type crime correlation learning, International Journal of Expert Systems with Applications, Vol. 265. https://doi.org/10.1016/j.eswa.2024.125940

Imagine living in a bustling city where traffic congestion is a daily struggle, causing delays and frustration. Researchers have developed a cutting-edge solution using artificial intelligence to optimize traffic lights, making your commute smoother and faster. By analyzing real-world traffic patterns, this system intelligently adjusts signal timings at intersections, reducing congestion and improving traffic flow. Tested in Chattanooga, Tennessee, this approach has shown significant improvements, helping drivers spend less time in traffic and more time enjoying their day. In fact, this innovative system has been shown to reduce the average number of vehicles in congested areas by nearly 20% compared to traditional methods, significantly easing traffic woes and enhancing urban mobility. This innovation promises to transform city life, making it more efficient and less stressful for everyone.

Z. Yin, T. Liu, C. Wang, H. Wang and Z. -P. Jiang, “Reducing Urban Traffic Congestion Using Deep Learning and Model Predictive Control,” in _IEEE Transactions on Neural Networks and Learning Systems_, vol. 35, no. 9, pp. 12760-12771, Sept. 2024, doi: 10.1109/TNNLS.2023.3264709

This work systematically examines green-aware management techniques for sustainable data centers, emphasizing the integration of renewable energy, optimization of resource use, and waste heat recovery. By adopting strategies such as workload management, virtual resource consolidation, and advanced cooling techniques, data centers can significantly reduce energy consumption and carbon emissions. For instance, implementing liquid cooling systems can lower Power Usage Effectiveness (PUE) to below 1.09, while integrating renewable energy sources can increase solar utilization by up to 39.6% and save energy costs by 11.1%. These advancements are pivotal in driving the transition towards greener  computing infrastructures, supporting the digital economy, and contributing to global carbon neutrality goals.

Lin, W. et al. (2024). A systematic review of green-aware management techniques for sustainable data center, Sustainable Computing: Informatics and Systems, Vol. 42, https://doi.org/10.1016/j.suscom.2024.100989

Imagine a world where farming is smarter and more efficient, helping to feed a growing global population while protecting the environment. This vision is becoming a reality with the use of edge artificial intelligence (AI) in agriculture. Edge AI involves using smart devices on farms to collect and analyze data in real-time, allowing farmers to make informed decisions about water use, pest control, and crop health. This technology not only boosts productivity but also reduces resource consumption, making farming more sustainable. By addressing challenges like climate change and resource scarcity, edge AI is paving the way for a future where agriculture can thrive without compromising the planet’s health. This innovative approach promises to enhance food security and sustainability, benefiting farmers, consumers, and the environment alike.

El Jarroudi et al. (2024). Leveraging edge artificial intelligence for sustainable agriculture. Nature Sustainability, Vol. 7, pp. 846–854, https://doi.org/10.1038/s41893-024-01352-4

Imagine a tool that helps farmers predict how much fruit their trees will produce, allowing them to plan better and manage resources more efficiently. This study introduces a groundbreaking approach using artificial intelligence to estimate crop yields and monitor plant growth stages, specifically tailored for fruit farms in Chile. By analyzing satellite images, climate data, and detailed pictures of fruit trees, the tool provides farmers with accurate predictions, helping them decide when to harvest and how to allocate resources effectively. This innovative method not only boosts productivity but also supports farmers in making informed decisions, ultimately enhancing their ability to meet market demands and improve their livelihoods.

Altimiras, F. et al. (2025). A Computational Framework for Crop Yield Estimation and Phenological Monitoring. In: Guerrero, G., San Martín, J., Meneses, E., Barrios Hernández, C.J., Osthoff, C., Monsalve Diaz, J.M. (eds) High Performance Computing. CARLA 2024. Communications in Computer and Information Science, vol 2270. Springer, Cham. https://doi.org/10.1007/978-3-031-80084-9_14