Archive for the ‘Machine Learning’ Category

Enhancing airport operations through cloud-native technology, AI … – Airport Technology

Through its portfolio, Airport management software company AeroCloud strives to provide customers with a crystal ball, managing airports in real-time as well as predicting passenger flow and gate optimisation.

The company is developing a reputation for introducing new technology and being customer centric when offering solutions.

George Richardson, CEO of AeroCloud, has spoken with Airport Technology about the companys strategy, challenges and opportunities in the sector, and the importance of business models.

Jasleen Mann: How and when was AeroCloud founded?

George Richardson: AeroCloud was founded in Macclesfield in the North West of England in 2019 by myself and my co-founder and our CTO, Ian Forde-Smith. He has worked in the airport sector his entire career, and I was a retired racing driver, having driven competitively for 10 years.

I was excited at the prospect of bringing together Ians extensive sector and technical knowledge and my commercial mindset to create solutions that enable the airport sector to seize on the benefits of the cloud.

We built a matchbox business plan designed to compete with and displace one of five legacy operators in the airport space. Were a typical David and Goliath story and its been a tremendous journey since we first launched.

Were forever grateful that our first customers at Northwest Florida Beaches International Airport and Tampa International Airport trusted in us. And to our investors for taking the time to understand the airport technology sector and subsequently getting as excited as we are about the unforeseen potential we can bring to airports as a SaaS company.

JM: What are AeroClouds key areas of focus?

GR: AeroCloud is an intelligent management platform designed for the airport sector and the only cloud-native player in this space. The platform enables everything from faster passenger processing times to improved self-service check-in and bag drop and facilitates, increased communication between stakeholders to deal with real-time fluctuations in processes to ensure that airports work better and communicate with their customers.

We recently also launched an industry-first computer vision solution for airports that offers kerb to gate insights for the first time. AeroCloud Optic uses computer vision to intelligently, anonymously and accurately track passengers as they move through an airport. The real-time monitoring of passengers triggers alerts in response to operational bottlenecks such as extended wait times at security, which can then be immediately addressed.

The AI and machine learning algorithms also allow airport staff to identify trends and predict future scenarios to inform more accurate decision-making and long-term planning. This enables better resource management and enhanced retail opportunities for concession partners, which in turn improves the airports passenger experience.

JM: What are the challenges in this area?

GR: Airports are fast-moving complex domains, requiring smooth coordination of multiple factors in a high-pressure environment from security to passengers and airlines. A lack of synergy between these different factors can affect an airports performance in some instances, operates at only around 66% capacity.

We want to solve problems the aviation industry has struggled with for decades due to the reliance on on-site legacy technology, which isnt fit for purpose because its clunky, needs on-site maintenance, and doesnt take advantage of the latest technological innovations enabled through the cloud.

JM: What opportunities have you embraced?

GR: We have put in so much hard work to ensure that what we bring to the market is revolutionary for our sector, leveraging new technologies and practices to solve issues that have existed for decades.

We have also created an innovative ecosystem of evangelical customers, and meaning we invite them to tell our product team their problems. We then collaborate with them to find solutions and that informs the features that we create and deliver. This means that we remain customer centric across every business area, from product design and development through to customer support.

Our mission is to be the largest provider of airport operation automation software for the small to medium-sized airport market globally. In February 2023, we successfully raised $12.6million in Series A funding after we were able to demonstrate our commitment to and progress against this goal.

Indeed, when commenting on the motivation behind investing in AeroCloud, one of our new investors, Liz Christo at Stage 2 Capital, said In only a small time, AeroCloud has become the definitive operating software for small to medium-sized airports. With this new funding, we plan to deliver on our bold ambitions to expand our business, employ local people in the North West where we were founded, and continue to displace our competitors.

JM: What is the importance of democratisation of data across airports?

GR: Coordination and communication are key to operational management. Yet in many airports, most stakeholders are in the dark about the current state of play. Data is not readily accessible and many third parties might never see it beyond periodical reports.

Putting data in the hands of all relevant parties helps them understand how their services are performing and how that impacts the airport operations as a whole. That is why we offer unlimited licenses to our cloud-based platform. We dont want airports to have to choose who has access to data nor reduce its potential in supporting better operational decision-making.

JM: How does the companys strategy differ to competitors?

GR: The opportunity, we think, is a $20 billion market in which legacy players dominate; AeroCloud is the only 100% cloud-native supplier, and we are shaking up the status quo.

We can centralise all an airports operational data and flight data in about 48 hours. Our data also operates in real-time and is up to 30% more accurate than our competitors. When airports require updates or issues fixed, we deliver these via the cloud, saving resourcing and money unlike our legacy competitors who have to send a technician on site.

We also enable unlimited licenses per customer so an airports entire stakeholder base can access to the platform at no extra cost. This means AeroCloud can be used on any device wherever an airports team is based, whether thats onsite or remote that ensures the platform is more secure than legacy systems which are often run on centrally located stack servers.

JM: How do airport business models now compare to pre-pandemic models?

GR: Even before the 2020 Covid-19 pandemic, which caused escalating passenger processing times and labour shortages, these issues were difficult to manage, particularly for small and medium-sized airports that dont have the budgets and capacities that their larger peers do. As travel returns to pre-pandemic levels, airports have struggled with adjusting to heightened demand, affecting operations worldwide.

And while it is a difficult time still for many of their airports as they whole industry faces due to the debts that arose during the pandemic, it is the time when they need to invest in improving the operational efficiencies of their airports to help them boost passenger experience and revenue in the long-term.

JM: What are the implications of the 80:20 rule?

GR: The 80:20 rule requires airlines to use 80% of their take-off and landing slots or risk losing them to a competitor the following year. The rule was relaxed during the pandemic after IATA highlighted the changing schedules that many airlines were facing.

From an airport perspective, the reintroduction of this rule will necessitate a seamless journey of passengers through the terminal so that they arrive to their gate on time and support airlines to leave within their allotted timing slot.

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Enhancing airport operations through cloud-native technology, AI ... - Airport Technology

AI and Machine Learning in Wealth Management: Customized Portfolios, Predictive Analytics – Finance Magnates

Wealthmanagement is a complex and constantly evolving field, with a vast amount ofdata to analyze and complex decisions to make. With the rise of artificialintelligence (AI) and machine learning (ML), the field of wealth management hasexperienced a significant transformation in recent years.

In thisarticle, we will explore the benefits of AI and ML in wealth management,including customized portfolios and predictive analytics.

One of the mostsignificant benefits of AI and ML in wealth management is the ability to createcustomized portfolios for clients. Traditionally, wealth managers relied onmanual analysis and intuition to create investment portfolios for theirclients.

This processwas time-consuming, costly, and often resulted in portfolios that were notfully optimized for the client's unique financial situation and goals.

Keep Reading

AI and MLtechnologies can analyze vast amounts of data quickly and accurately, providingwealth managers with the insights needed to create customized investmentportfolios that meet the unique needs of each client.

Thesetechnologies can analyze factors such as risk tolerance, investment goals, andfinancial situation to create a portfolio that is tailored to the client'sspecific needs.

In addition, AIand ML can continually monitor the portfolio and adjust it as needed to ensurethat it remains aligned with the client's goals and objectives. This can helpto optimize portfolio performance and reduce the risk of losses due to marketfluctuations or other factors.

Anothersignificant benefit of AI and ML in wealth management is the ability to usepredictive analytics to make more informed investment decisions.

Predictiveanalytics involves using historical data and machine learning algorithms tomake predictions about future market trends and asset performance.

By analyzingvast amounts of data, including economic indicators, market trends, and assetperformance, AI and ML technologies can provide wealth managers with insightsand predictions that would be impossible to obtain through manual analysisalone.

Thesetechnologies can identify patterns and trends in the data that humans maynot be able to detect, providing wealth managers with a more comprehensive andaccurate view of the market.

Thisinformation can be used to make more informed investment decisions, such aswhich assets to invest in and when to buy or sell them. Predictive analyticscan also help wealth managers to identify potential risks and opportunities,allowing them to make proactive decisions to mitigate risk and capitalize onmarket opportunities.

While AI and MLtechnologies offer significant benefits for wealth management, there are alsochallenges and considerations to keep in mind. One of the primary challenges isensuring the accuracy and reliability of the data used to train the machinelearning algorithms.

If the data isbiased or incomplete, the algorithms may produce inaccurate or unreliablepredictions, leading to poor investment decisions and potential losses.

Anotherconsideration is the ethical and regulatory implications of using AI and ML inwealth management. As these technologies become increasingly sophisticated, itis essential to ensure that they are used ethically and in compliance withregulatory requirements.

This includesconsiderations such as data privacy, transparency, and accountability.

AI has theability to analyze large sets of data and provide insights that humans may notbe able to uncover. However, as with any technology, there are risks involved,and AI can backfire on wealth management in several ways.

One of the mostsignificant risks of using AI in wealth management is the potential for biasedalgorithms. AI is only as good as the data it is trained on, and if the data isbiased, the algorithms will also be biased. This can lead to unequal treatment ofclients and inaccurate investment decisions. For example, if the AI algorithmis trained on data that disproportionately represents wealthy individuals, itmay not be able to accurately predict the needs and goals of lower-incomeclients.

Moreover, AIrelies on historical data to make predictions about the future, and if thatdata is biased, the algorithm will also be biased. Biased algorithms can leadto inaccurate predictions and investment decisions, which can result infinancial losses for clients. For example, an algorithm trained on historicaldata that disproportionately represents a certain industry or demographic maynot be able to accurately predict the performance of other industries ordemographics.

While AI cananalyze vast amounts of data quickly, it cannot replace human expertise andjudgment entirely. Overreliance on technology can lead to missed opportunitiesor suboptimal investment decisions. A combination of human expertise andAI-powered analytics can lead to better investment decisions, but it isimportant to strike a balance between the two.

There is a riskthat AI can reinforce existing inequalities in wealth management. Wealthmanagement firms that use AI may be more likely to cater to wealthy clients whocan afford their services while ignoring lower-income clients. This can createa vicious cycle where wealthy clients continue to benefit from AI-poweredwealth management services, while those with less wealth are left behind.

AI and MLtechnologies are transforming the field of wealth management, providing wealthmanagers with new insights and capabilities to create customized portfolios andmake more informed investment decisions.

By analyzingvast amounts of data and using predictive analytics, these technologies canhelp wealth managers to optimize portfolio performance, reduce risk, andcapitalize on market opportunities.

However, it isessential to keep in mind the challenges and considerations associated withusing AI and ML in wealth management.

Wealth managersmust ensure the accuracy and reliability of the data used to train the machinelearning algorithms and consider the ethical and regulatory implications ofusing these technologies.

Overall, AI andML have the potential to revolutionize the field of wealth management andprovide significant benefits for both wealth managers and their clients. Asthese technologies continue to evolve, it is essential for wealth managers tostay informed and embrace them to remain competitive in a rapidly evolvingindustry.

Wealthmanagement is a complex and constantly evolving field, with a vast amount ofdata to analyze and complex decisions to make. With the rise of artificialintelligence (AI) and machine learning (ML), the field of wealth management hasexperienced a significant transformation in recent years.

In thisarticle, we will explore the benefits of AI and ML in wealth management,including customized portfolios and predictive analytics.

One of the mostsignificant benefits of AI and ML in wealth management is the ability to createcustomized portfolios for clients. Traditionally, wealth managers relied onmanual analysis and intuition to create investment portfolios for theirclients.

This processwas time-consuming, costly, and often resulted in portfolios that were notfully optimized for the client's unique financial situation and goals.

Keep Reading

AI and MLtechnologies can analyze vast amounts of data quickly and accurately, providingwealth managers with the insights needed to create customized investmentportfolios that meet the unique needs of each client.

Thesetechnologies can analyze factors such as risk tolerance, investment goals, andfinancial situation to create a portfolio that is tailored to the client'sspecific needs.

In addition, AIand ML can continually monitor the portfolio and adjust it as needed to ensurethat it remains aligned with the client's goals and objectives. This can helpto optimize portfolio performance and reduce the risk of losses due to marketfluctuations or other factors.

Anothersignificant benefit of AI and ML in wealth management is the ability to usepredictive analytics to make more informed investment decisions.

Predictiveanalytics involves using historical data and machine learning algorithms tomake predictions about future market trends and asset performance.

By analyzingvast amounts of data, including economic indicators, market trends, and assetperformance, AI and ML technologies can provide wealth managers with insightsand predictions that would be impossible to obtain through manual analysisalone.

Thesetechnologies can identify patterns and trends in the data that humans maynot be able to detect, providing wealth managers with a more comprehensive andaccurate view of the market.

Thisinformation can be used to make more informed investment decisions, such aswhich assets to invest in and when to buy or sell them. Predictive analyticscan also help wealth managers to identify potential risks and opportunities,allowing them to make proactive decisions to mitigate risk and capitalize onmarket opportunities.

While AI and MLtechnologies offer significant benefits for wealth management, there are alsochallenges and considerations to keep in mind. One of the primary challenges isensuring the accuracy and reliability of the data used to train the machinelearning algorithms.

If the data isbiased or incomplete, the algorithms may produce inaccurate or unreliablepredictions, leading to poor investment decisions and potential losses.

Anotherconsideration is the ethical and regulatory implications of using AI and ML inwealth management. As these technologies become increasingly sophisticated, itis essential to ensure that they are used ethically and in compliance withregulatory requirements.

This includesconsiderations such as data privacy, transparency, and accountability.

AI has theability to analyze large sets of data and provide insights that humans may notbe able to uncover. However, as with any technology, there are risks involved,and AI can backfire on wealth management in several ways.

One of the mostsignificant risks of using AI in wealth management is the potential for biasedalgorithms. AI is only as good as the data it is trained on, and if the data isbiased, the algorithms will also be biased. This can lead to unequal treatment ofclients and inaccurate investment decisions. For example, if the AI algorithmis trained on data that disproportionately represents wealthy individuals, itmay not be able to accurately predict the needs and goals of lower-incomeclients.

Moreover, AIrelies on historical data to make predictions about the future, and if thatdata is biased, the algorithm will also be biased. Biased algorithms can leadto inaccurate predictions and investment decisions, which can result infinancial losses for clients. For example, an algorithm trained on historicaldata that disproportionately represents a certain industry or demographic maynot be able to accurately predict the performance of other industries ordemographics.

While AI cananalyze vast amounts of data quickly, it cannot replace human expertise andjudgment entirely. Overreliance on technology can lead to missed opportunitiesor suboptimal investment decisions. A combination of human expertise andAI-powered analytics can lead to better investment decisions, but it isimportant to strike a balance between the two.

There is a riskthat AI can reinforce existing inequalities in wealth management. Wealthmanagement firms that use AI may be more likely to cater to wealthy clients whocan afford their services while ignoring lower-income clients. This can createa vicious cycle where wealthy clients continue to benefit from AI-poweredwealth management services, while those with less wealth are left behind.

AI and MLtechnologies are transforming the field of wealth management, providing wealthmanagers with new insights and capabilities to create customized portfolios andmake more informed investment decisions.

By analyzingvast amounts of data and using predictive analytics, these technologies canhelp wealth managers to optimize portfolio performance, reduce risk, andcapitalize on market opportunities.

However, it isessential to keep in mind the challenges and considerations associated withusing AI and ML in wealth management.

Wealth managersmust ensure the accuracy and reliability of the data used to train the machinelearning algorithms and consider the ethical and regulatory implications ofusing these technologies.

Overall, AI andML have the potential to revolutionize the field of wealth management andprovide significant benefits for both wealth managers and their clients. Asthese technologies continue to evolve, it is essential for wealth managers tostay informed and embrace them to remain competitive in a rapidly evolvingindustry.

Original post:
AI and Machine Learning in Wealth Management: Customized Portfolios, Predictive Analytics - Finance Magnates

Application of machine learning in predicting non-alcoholic fatty liver … – Nature.com

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Originally posted here:
Application of machine learning in predicting non-alcoholic fatty liver ... - Nature.com

Machine Learning in Finance Market 2023 Analytical Assessment, Key Drivers, Growth and Opportunities to 2032 – EIN News

Machine Learning in Finance Market 2023 Objectives of the Study, Research Methodology and Assumptions, Value Chain Analysis and Forecast by 2032

The industry's behavior is discussed in detail. It also outlines the future direction that will ensure strong profits over the coming years. This report will provide a practical overview of the global market and its changing environment to help readers make informed decisions about market projects. This report will focus on that will allow the market to expand its operations in existing markets.

: https://market.us/report/machine-learning-in-finance-market/request-sample

(Use Company eMail ID to Get Higher Priority)

This report helps both to analyze the market in-depth. This will help the leading players decide on their business strategy and set goals. This report provides critical market information, including Machine Learning in Finance market size, growth rates and forecasts in key regions and countries, as well as growth opportunities in niche markets.

The Machine Learning in Finance report contains data based on - using proven research methods. This report provides all-around information that aids in the estimation of every part of the Machine Learning in Finance market. This report was created by considering several aspects of market research and analysis. These include market size estimates, market dynamics, company and market best practices. Entry-level marketing strategies, positioning, segmentation, competitive landscaping and economic forecasting. Industry-specific technology solutions, roadmap analysis, targeting key buying criteria and in-depth benchmarking of vendor offerings.

Ignite Ltd Yodlee Trill A.I. MindTitan Accenture (NYS:ACN) ZestFinance

Machine Learning in Finance Based on Type:

Supervised Learning Unsupervised Learning Semi Supervised Learning Reinforced Leaning

Machine Learning in Finance By Application

Banks Securities Company Others

:

- North America (the U.S and Canada and the rest of North America)

- Europe (Germany, France, Italy and Rest of Europe)

- Asia-Pacific (China, Japan, India, South Korea and Rest of Asia-Pacific)

- LAMEA (Machine Learning in Financezil, Turkey, Saudi Arabia, South Africa and Rest of LAMEA)

Interested in Procure The Data? Inquire here at: https://market.us/report/machine-learning-in-finance-market/#inquiry

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1. Industry trends (2015-2020 historic and future 2022-2031)

2. Key regulations

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6. Porters Five Forces Model, PESTLE and SWOT Analysis

:

How is the Machine Learning in Finance market along with regions like , , -, are growing?

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What are the challenges that the Global (North America and Europe and Asia-Pacific and South America) must overcome to be commercially viable? Are their growth and commercialization dependent on cost declines or technological/application breakthroughs?

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Philogen Announces Publication of a New Study in Collaboration with Google focused on Machine Learning models applied to DNA-Encoded Chemical Library…

Philogen

Philogen Announces Publication of a New Study in Collaboration with Google focused on Machine Learning models applied to DNA-Encoded Chemical Library Technology

The collaboration has focused on the use of Googles Machine Learning models combined with Philochems DNA-Encoded Chemical Libraries

The research activity promises to have a direct impact on the discovery of novel tumor-targeting small organic ligands with broad applicability in a number of different indications

Siena, Italy, March 28, 2023 - Philogen S.p.A., a clinical-stage biotechnology company focused on the development of innovative antibody and small molecule ligands, announces the publication of a new study conducted in collaboration with Google focused on the use of Machine Learning models applied to the screening of DNA-Encoded Chemical Libraries (DELs). Philogen practices DEL technology within its fully-owned Swiss-based Philochem AG subsidiary.

DELs emerged as an efficient and cost-effective ligand discovery tool. The technology allows the rapid selection of specific binders (Phenotype), physically connected to unique DNA tags (Genotype) that work as amplifiable identification barcodes. Philochem has synthesized several DNA-Encoded Chemical Libraries, featuring different designs, that have yielded high affinity and selective binders to a variety of target proteins of pharmaceutical interest.

Results of DEL selection are extremely data-rich, as they may contain enrichment information for billions of compounds on a variety of different targets. In principle, this information can be exploited using computational methods both for the affinity maturation of DEL-derived HIT compounds and for the characterization of binding specificities.

In this collaborative project, Google and Philochem, a fully-owned subsidiary of Philogen, have applied DEL Technology and Instance-Level Deep Learning Modelling to identify tumor-targeting ligands against Carbonic Anhydrase IX (CAIX), a clinically validated marker of hypoxia and of clear cell Renal Cell Carcinoma. The approach yielded binders that showed accumulation on the surface of CAIX-expressing tumor cells in cellular binding assays. The best compound displayed a binding affinity of 5.7 nM and showed preferential tumor accumulation in in vivo pre-clinical models of Renal Cell Carcinoma.

Story continues

The successful translation of LEAD candidates for in vivo tumor-targeting applications demonstrates the potential of using machine learning with DEL Technology to advance real world drug discovery.

The results of the study are available as preprint on the BioRXiv website at http://www.biorxiv.org/content/10.1101/2023.01.25.525453v1.

Dario Neri, Chief Executive Officer of Philogen commented: We are excited by the potential of the synergy between DNA-Encoded Chemical Libraries and Artificial Intelligence. The powerful discovery approach that we have developed together with Google should be broadly applicable to additional targets of pharmaceutical interest for the discovery of novel drug prototypes.

Philogen Group Description

Philogen is an Italian-Swiss company active in the biotechnology sector, specialized in the research and development of pharmaceutical products for the treatment of highly lethal diseases. The Group focuses on the discovery and development of targeted anticancer drugs, exploiting high-affinity ligands for tumor markers (also called tumor antigens). These ligands - human monoclonal antibodies or small organic molecules - are identified using Antibody Phage Display Libraries and DNA-Encoded Chemical Library technologies.

The Group's main therapeutic strategy for the treatment of these diseases is represented by the concept of tumor targeting. This approach is based on the use of ligands capable of selectively delivering very potent therapeutic active ingredients (such as pro-inflammatory cytokines) to the tumor mass, sparing healthy tissues. Over the years, Philogen has mainly developed monoclonal antibody-based ligands that are specific for antigens expressed in tumor-associated blood vessels, but not expressed in blood vessels associated with healthy tissues. These antigens are usually more abundant, more stable and better accessible than those expressed directly on the surface of tumor cells. The elaborate expertise in the field of vascular targeting enabled the generation of a strong portfolio with many ongoing projects which are currently pursued by the Group.

The Group's objective is to generate, develop and market innovative products for the treatment of diseases for which medical science has not yet identified satisfactory therapies. This is achieved by exploiting (i) proprietary technologies for the isolation of ligands that react with antigens present in certain diseases, (ii) experience in the development of products which selectively accumulate at the disease sites, (iii) experience in drug manufacturing and development, and (iv) an extensive portfolio of patents and intellectual property rights.

Although the Group's drugs are primarily oncology applications, the targeting approach is also potentially applicable to other diseases, such as certain chronic inflammatory diseases.

FOR MORE INFORMATION:

Philogen - Investor Relations

IR@philogen.com - Emanuele Puca | Investor Relations

Consilium Strategic Communications contacts

Mary-Jane Elliott, Davide Salvi

Philogen@consilium-comms.com

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Philogen Announces Publication of a New Study in Collaboration with Google focused on Machine Learning models applied to DNA-Encoded Chemical Library...