Early Consequences Regarding the Impact of Artificial Intelligence on International Trade

2020

Asian bus. rev.

In the last decade, cloud computing has changed dramatically. More providers and administration contributions have entered the market, and cloud infrastructure, once limited to single-provider data centers, is expanding. This article discusses the shifting cloud foundation and the benefits of decentralizing computing from data centers. These patterns necessitate novel cloud computing architectures. These models may affect linking people and devices, data-intensive computing, the service space, and self-learning frameworks. Finally, we compiled a list of issues to consider while assessing modern cloud frameworks. Architectural and urban design projects breach scale and predictability constraints and seek enhanced competency, maintainability, energy performance, and cost-efficiency. Simulation and large-scale information processing drive this cycle. Advances in calculations and computer power help address the complex elements of a coordinated whole-structure framework. Adaptability is a barrier to the configuration, control, and development of whole-system frameworks. This position paper proposes several solutions for semi-or fully automated projects, such as short-plan boundary space exploration, large-scope high-accuracy simulation, and integrated multidisciplinary development. These computer-intensive operations were previously only accessible to the exam network. Once empowered by cloud computing and high-performance computing, these methods can stimulate intelligent plan measures, leading to enhanced results and shorter development times.

Section: Multi-cloudmentioning

confidence: 99%

Cloud and HPC Headway for Next-Generation Management of Projects and Technologies

2020

Asian bus. rev.

“…This will allow for greater levels of productivity. For example, IoT Analytics researched more than 640 IoT deployments and found that 84% of these deployments satisfy the Sustainable Development Goals established by the United Nations (Achar, 2019b).…”

Section: Sustainable Iot Around the Worldmentioning

confidence: 99%

Influence of IoT Technology on Environmental Monitoring

Asia Pac. j. energy environ.

2020

Self Cite

The newly connected globe has the potential to become a more environmentally friendly and sustainable place if more efficiencies can be brought to various industries. We will look at examples of how the Internet of Things technology is helping to improve the world. Relatively recently, the technology of the fourth industrial revolution has given rise to the characteristics of things constantly expanding, and everything, including people, things, people, and the environment, is connected based on the Internet. In particular, the network structure is connected to various Internet of Things devices and transitioning from wired to wireless. Gateways both within and outside the smart home can now be used to control other devices, in contrast to users who previously owned each device individually. However, due to these changes, the environment is now susceptible to attacks from the outside. For example, when an attacker gains access to a gateway, he can attempt various attacks, such as port scans, OS and service detection, and denial of service assaults on Internet of Things devices. As a result, the findings of the study reveal the influence of IoT technology, which has the potential to improve environmental monitoring to enhance environmental safety.

“…Even though big data and artificial intelligence are closely related, the two concepts are not the same. Artificial intelligence is a machine learning strategy that uses data to actualize the algorithms' learning approach, which is also known as artificial intelligence (Achar, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Maximizing the Potential of Artificial Intelligence to Perform Evaluations in Ungauged Washbowls

2020

Eng. int. (Dhaka)

Self Cite

Long short-term memory networks (LSTM) offer precision in the prediction that has never been seen before in ungauged basins. Using k-fold validation, we trained and evaluated several LSTMs in this study on 531 basins from the CAMELS data set. This allowed us to make predictions in basins for which we did not have any training data. The implication is that there is usually sufficient information in available catchment attributes data about similarities and differences between catchment-level rainfall-runoff behaviors to generate out-of-sample simulations that are generally more accurate than current models when operating under ideal (i.e., calibrated) conditions, i.e., when using under idealized conditions. In other words, existing models are generally less accurate when working under idealized conditions than out-of-sample simulations. We found evidence that including physical limits in LSTM models improves simulations, which we believe should be the primary focus of future research on physics-guided artificial intelligence. Putting in place additional physical constraints on the LSTM models.