Dynamic pricing is a promising strategy to address the challenges of smart charging, as traditional time-of-use (ToU) rates and stationary pricing (SP) do not dynamically react to changes in operating conditions, reducing revenue for charging station (CS) vendors and affecting grid stability. Previous studies evaluated single objectives or linear combinations of objectives for EV CS pricing solutions, simplifying trade-offs and preferences among objectives. We develop a novel formulation for the dynamic pricing problem by addressing multiple conflicting objectives efficiently instead of solely focusing on one objective or metric, as in earlier works. We find optimal trade-offs or Pareto solutions efficiently using Non-dominated Sorting Genetic Algorithms (NSGA) II and NSGA III. A dynamic pricing model quantifies the relationship between demand and price while simultaneously solving multiple conflicting objectives, such as revenue, quality of service (QoS), and peak-to-average ratios (PAR). A single method can only address some of the above aspects of dynamic pricing comprehensively. We present a three-part dynamic pricing approach using a Bayesian model, multi-objective optimization, and multi-criteria decision-making (MCDM) using pseudo-weight vectors. To address the research gap in CS pricing, our method selects solutions using revenue, QoS, and PAR metrics simultaneously. Two California charging sites' real-world data validates our approach.

In recent years, high-frequency trading has emerged as a crucial strategy in stock trading. This study aims to develop an advanced high-frequency trading algorithm and compare the performance of three different mathematical models: the combination of the cross-entropy loss function and the quasi-Newton algorithm, the FCNN model, and the vector machine. The proposed algorithm employs neural network predictions to generate trading signals and execute buy and sell operations based on specific conditions. By harnessing the power of neural networks, the algorithm enhances the accuracy and reliability of the trading strategy. To assess the effectiveness of the algorithm, the study evaluates the performance of the three mathematical models. The combination of the cross-entropy loss function and the quasi-Newton algorithm is a widely utilized logistic regression approach. The FCNN model, on the other hand, is a deep learning algorithm that can extract and classify features from stock data. Meanwhile, the vector machine is a supervised learning algorithm recognized for achieving improved classification results by mapping data into high-dimensional spaces. By comparing the performance of these three models, the study aims to determine the most effective approach for high-frequency trading. This research makes a valuable contribution by introducing a novel methodology for high-frequency trading, thereby providing investors with a more accurate and reliable stock trading strategy.

Using AI, Microsoft might finally mount a challenge to Google's dominance in search. Earlier this month, Microsoft unveiled the new Bing search engine, which uses artificial intelligence to not just give users links, but also to provide answers to questions. Bing can respond, for example, to a query to create a spreadsheet of a company's revenue for the past five years or to check if a certain-size futon will fit in a specified vehicle. Since Bing's update, Google has come out with its own competitor, Bard. "We've been steadily focused on Bing and we've been working. We've been at it for the last two decades or so," said Yusuf Mehdi, corporate vice president at Microsoft, over a Microsoft Teams video chat last week.

In Meta Platform's (FB -3.82%) most recent earnings report, there were some signs of a pullback with net income down year over year and more competition has played a part. In this video clip from "The Virtual Opportunities Show" on Motley Fool Live, recorded on May 24, Fool.com contributor Jose Najarro discusses how the company's investment in artificial intelligence is encouraging for the business going forward. Jose Najarro: First if we take a quick look, Meta Platforms for their financial results total revenue was $27.9 billion dollars. That was up 7% year over year. Again, this is a company that was probably growing at strong double digits and now we're seeing a bit of a pullback.

Thinking artificial intelligence could boost your portfolio right about now? But which AI stocks beyond the usual suspects: Alphabet Inc. (NASDAQ: GOOG), Apple Inc. (NASDAQ: AAPL), Twilio Inc. (NYSE: TWLO), ServiceNow, Inc. (NYSE: NOW), NVIDIA Corporation (NASDAQ: NVDA) and QUALCOMM Incorporated (NASDAQ: QCOM). Whether you believe some of the famous companies above are bearish and/or overvalued (another argument for another day) or are just looking to inject some new blood into your portfolio, let's go through some options you might consider. But first, in the interest of education, what are AI stocks and which ones should you consider right now? Let's find out. At its most basic level, artificial intelligence (AI) refers to an algorithm or dynamic machine that learns and interprets the data given to it.

Artificial intelligence (AI) promises to be one of the most transformative modern technologies we've ever seen, capable of quickly completing complex tasks that once required thousands of hours of human input. Machine learning is a subset of AI that uses data to improve processes, and also make predictions. It's already being applied to dozens of industries globally, streamlining everything from racing to internet search results. According to one industry report, the machine learning market was worth $1.6 billion in 2017 and is set to explode to $20.8 billion by 2023. That's a compound annual growth rate of 53%, and the following two companies are leading the charge.

Tuesday's top trending stocks come to us from every sector, from 3D manufacturing to semiconductor manufacturers to, surprisingly, a global gambling guru. Q.ai runs factor models daily to get the most up-to-date reading on stocks and ETFs. Our deep-learning algorithms use Artificial Intelligence (AI) technology to provide an in-depth, intelligence-based look at a company – so you don't have to do the digging yourself. Sign up for the free Forbes AI Investor newsletter here to join an exclusive AI investing community and get premium investing ideas before markets open. IBM IBM closed up almost 0.5% on Monday to $146.17 per share, starting off the week with nearly 7 million trades on the docket and ticking up over 16% YTD.

Airlines have been making use of highly complex Revenue Management Systems to maximize revenue for decades. Estimating the impact of changing one component of those systems on an important outcome such as revenue is crucial, yet very challenging. It is indeed the difference between the generated value and the value that would have been generated keeping business as usual, which is not observable. We provide a comprehensive overview of counterfactual prediction models and use them in an extensive computational study based on data from Air Canada to estimate such impact. We focus on predicting the counterfactual revenue and compare it to the observed revenue subject to the impact. Our microeconomic application and small expected treatment impact stand out from the usual synthetic control applications. We present accurate linear and deep-learning counterfactual prediction models which achieve respectively 1.1% and 1% of error and allow to estimate a simulated effect quite accurately.

If you have a job or a steady income from other sources, it can be easy to sometimes forget the United States is in a recession. After all, you have money coming in and the S&P 500 index has largely, though not entirely, recovered from its steep drop that began in mid-February triggered by the worldwide spread of COVID-19. It's not wise, however, to bury your head in the sand to some hard facts. In the last couple of months, the U.S. unemployment rate has been higher than at any time since the Great Depression and the COVID-19 pandemic is worsening in this country. This is extremely troubling from several aspects, including an economic one.

In late 2016, Adobe announced that Sensei, the company's AI technology, would begin to power and assist in some of its Digital Media applications, such as Photoshop and Illustrator. While that was only 3 short years ago, in the dawning of the AI era, Sensei's role makes Adobe one of the pioneers of machine learning- and deep learning-powered AI. What started in 2016 as narrow AI technology aimed at narrow use cases has become an AI engine that, according to Tatiana Mejia, group Product Marketing manager for Sensei, now powers dozens of different features across Adobe. "We don't tag any of the features with Sensei, it's just the engine behind Adobe products," said Mejia. Whether the technology is effective or not, the concept is advanced AI thinking.