Category Archives: Machine Learning
Artificial Intelligence and Machine Learning are the topics of a lecture 10 a.m. to noon Friday, May 21, as part of the new Data Analytics Certificate Program Speaker Series at Youngstown State University.
The lecture features Quentin Fisher, founder and chief technology officer of Health Care Analytics, whose cloud technologies and digital solutions for close to a quarter century across more than 90 clients are now accessible to everyone.
The event is via Zoom. Register online.
Fisher previously held vice president- and partner-level positions with CSC (now DXC) and HCL, where he has led Global Business Analytics Services for manufacturing and public service industries. He has a long history in consulting delivery and operations where hes managed consulting business portfolios of $100 million and 600 consultants across the globe. Originally from Canada, where he earned an Industrial Engineering degree from the University of Manitoba, Fisher currently lives in Northeast Ohio.
The lecture will include examples of how AI and Machine Learning are being used to impact operations, will explore the differences between analytics, Big Data, AI and data visualization, and will examine how these technologies can enable organizations to predict events and increase operational efficiency.
For more information, contact Ou Hu, the Paul J. Thomas Endowed Chair and Professor in Economics at YSU, at firstname.lastname@example.org.
A year ago, YSU introduced new certificate programs in Data Analytics aimed at helping graduates improve and broaden their job prospects. The new certificates on both the undergraduate and graduate levels are composed of three courses - Data Management, Data Visualization and Predictive Modeling.
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First lecture in new speaker series focuses on AI and machine learning - YSU.edu
Machine learning security vulnerabilities are a growing threat to the web, report highlights – The Daily Swig
Security industry needs to tackle nascent AI threats before its too late
As machine learning (ML) systems become a staple of everyday life, the security threats they entail will spill over into all kinds of applications we use, according to a new report.
Unlike traditional software, where flaws in design and source code account for most security issues, in AI systems, vulnerabilities can exist in images, audio files, text, and other data used to train and run machine learning models.
This is according to researchers from Adversa, a Tel Aviv-based start-up that focuses on security for artificial intelligence (AI) systems, who outlined their latest findings in their report, The Road to Secure and Trusted AI, this month.
This makes it more difficult to filter, handle, and detect malicious inputs and interactions, the report warns, adding that threat actors will eventually weaponize AI for malicious purposes.
Unfortunately, the AI industry hasnt even begun to solve these challenges yet, jeopardizing the security of already deployed and future AI systems.
Theres already a body of research that shows many machine learning systems are vulnerable to adversarial attacks, imperceptible manipulations that cause models to behave erratically.
BACKGROUND Adversarial attacks against machine learning systems everything you need to know
According to the researchers at Adversa, machine learning systems that process visual data account for most of the work on adversarial attacks, followed by analytics, language processing, and autonomy.
Machine learning systems have a distinct attack surface
With the growth of AI, cyberattacks will focus on fooling new visual and conversational Interfaces, the researchers write.
Additionally, as AI systems rely on their own learning and decision making, cybercriminals will shift their attention from traditional software workflows to algorithms powering analytical and autonomy capabilities of AI systems.
Web developers who are integrating machine learning models into their applications should take note of these security issues, warned Alex Polyakov, co-founder and CEO of Adversa.
There is definitely a big difference in so-called digital and physical attacks. Now, it is much easier to perform digital attacks against web applications: sometimes changing only one pixel is enough to cause a misclassification, Polyakov told The Daily Swig, adding that attacks against ML systems in the physical world have more stringent demands and require much more time and knowledge.
Read more of the latest infosec research news
Polyakov also warned about vulnerabilities in machine learning models served over the web such as API services provided by large tech companies.
Most of the models we saw online are vulnerable, and it has been proven by several research reports as well as by our internal tests, Polyakov. With some tricks, it is possible to train an attack on one model and then transfer it to another model without knowing any special details of it.
Also, you can perform CopyCat attack to steal a model, apply the attack on it and then use this attack on the API.
Most machine learning algorithms require large sets of labeled data to train models. In many cases, instead of going through the effort of creating their own datasets, machine learning developers search and download datasets published on GitHub, Kaggle, or other web platforms.
Eugene Neelou, co-founder and CTO of Adversa, warned about potential vulnerabilities in these datasets that can lead to data poisoning attacks.
Poisoning data with maliciously crafted data samples may make AI models learn those data entries during training, thus learning malicious triggers, Neelou told The Daily Swig. The model will behave as intended in normal conditions, but malicious actors may call those hidden triggers during attacks.
RELATED TrojanNet a simple yet effective attack on machine learning models
Neelou also warned about trojan attacks, where adversaries distribute contaminated models on web platforms.
Instead of poisoning data, attackers have control over the AI model internal parameters, Neelou said. They could train/customize and distribute their infected models via GitHub or model platforms/marketplaces.
Unfortunately, GitHub and other platforms dont yet have any safeguards in place to detect and defend against data poisoning schemes. This makes it very easy for attackers to spread contaminated datasets and models across the web.
Attacks against machine learning and AI systems are set to increase over the coming years
Neelou warned that while AI is extensively used in myriads of organizations, there are no efficient AI defenses.
He also raised concern that under currently established roles and procedures, no one is responsible for AI/ML security.
AI security is fundamentally different from traditional computer security, so it falls under the radar for cybersecurity teams, he said. Its also often out of scope for practitioners involved in responsible/ethical AI, and regular AI engineering hasn't solved the MLOps and QA testing yet.
Check out more machine learning security news
On the bright side, Polyakov said that adversarial attacks can also be used for good. Adversa recently helped one of its clients use adversarial manipulations to develop web CAPTCHA queries that are resilient against bot attacks.
The technology itself is a double-edged sword and can serve both good and bad, he said.
Adversa is one of several organizations involved in dealing with the emerging threats of machine learning systems.
Last year, in a joint effort, several major tech companies released the Adversarial Threat ML Matrix, a set of practices and procedures meant to secure the machine learning training and delivery pipeline in different settings.
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Can machine learning help save the whales? How PNW researchers use tech tools to monitor orcas – GeekWire
Aerial image of endangered Southern Resident killer whales in K pod. The image was obtained using a remotely piloted octocopter drone that was flown during health research by Dr. John Durban and Dr. Holly Fearnbach. (Vulcan Image)
Being an orca isnt easy. Despite a lack of natural predators, these amazing mammals face many serious threats most of them brought about by their human neighbors. Understanding the pressures we put on killer whale populations is critical to the environmental policy decisions that will hopefully contribute to their ongoing survival.
Fortunately, marine mammal researchers like Holly Fearnbach of Sealife Response + Rehab + Research (SR3) and John Durban of Oregon State University are working hard to regularly monitor the condition of the Salish Seas southern resident killer whale population (SKRW). Identified as J pod, K pod and L pod, these orca communities have migrated through the Salish Sea for millennia. Unfortunately, in recent years their numbers have dwindled to only 75 whales, with one new calf born in 2021. This is the lowest population figure for the SRKW in 30 years.
For more than a decade, Fearnbach and Durban have flown photographic surveys to capture aerial images of the orcas. Starting in 2008, image surveys were performed using manned helicopter flights. Then beginning in 2014, the team transitioned to unmanned drones.
As the remote-controlled drone flies 100 feet or more above the whales, images are captured of each of the pod members, either individually or in groups. Since the drone is also equipped with a laser altimeter, the exact distance is known making calculations of the whales dimensions very accurate. The images are then analyzed in whats called a photogrammetric health assessment. This assessment helps determine each whales physical condition, including any evidence of pregnancy or significant weight loss due to malnourishment.
As a research tool, the drone is very cost effective and it allows us to do our research very noninvasively, Fearnbach said. When we do detect health declines in individuals, were able to provide management agencies with these quantitative health metrics.
But while the image collection stage is relatively inexpensive, processing the data has been costly and time-consuming. Each flight can capture 2,000 images with tens of thousands of images captured for each survey. Following the drone work, it typically takes about six months to manually complete the analysis on each seasons batch of images.
Obviously, half a year is a very long time if youre starving or pregnant, which is one reason why SR3s new partnership with Vulcan is so important. Working together, the organizations developed a new approach to process the data more rapidly. The Aquatic Mammal Photogrammetry Tool (AMPT) uses machine learning and an end-user tool to accelerate the laborious process, dramatically shortening the time needed to analyze, identify and categorize all of the images.
Applying machine learning techniques to the problem has already yielded huge results, reducing a six-month process to just six weeks with room for further improvements. Machine learning is a branch of computing that can improve its performance through experience and use of data. The faster turnaround time will make it possible to more quickly identify whales of concern and provide health metrics to management groups to allow for adaptive decision making, according to Vulcan.
Were trying to make and leave the world a better place, primarily through ocean health and conservation, said Sam McKennoch, machine learning team manager at Vulcan. We got connected with SR3 and realized this was a great use case, where they have a large amount of existing data and needed help automating their workflows.
AMPT is based on four different machine learning models. First, the orca detector identifies those images that have orcas in them and places a box around each whale. The next ML model fully outlines the orcas body, a process known in the machine learning field as semantic segmentation. After that comes the landmark detector which detects the rostrum (or snout) of the whale, the dorsal fins, blowhole, shape of the eye patches, fluke notch and so forth. This allows the software to measure and calculate the shape and proportions of various parts of the body.
Of particular interest is whether the whales facial fat deposits are so low they result in indentations of the head that marine biologists refer to as peanut head. This only appears when the orca has lost a significant amount of body fat and is in danger of starvation.
Finally, the fourth machine learning model is the identifier. The shape of the gray saddle patch behind the whales dorsal fin is as unique as a fingerprint, allowing each of the individuals in the pod to be identified.
There are a lot of different kinds of information needed for this kind of automation. Fortunately, Vulcan has been able to leverage some of SR3s prior manual work to bootstrap their machine learning models.
We really wanted to understand their pain points and how we could provide them the tools they needed, rather than the tools we might want to give them, McKennoch said.
As successful as AMPT has been, theres a lot of knowledge and information that has yet to be incorporated into its machine learning models. As a result, theres still the need to have users in-the-loop in a semi-supervised way for some of the ML processing. The interface speeds up user input and standardizes measurements made by different users.
McKennoch believes there will be gains with each batch they process for several cycles to come. Because of this, they hope to continue to improve performance in terms of accuracy, workflow and compute time to the point that the entire process eventually takes days, instead of weeks or months.
This is very important because AMPT will provide information that guides policy decisions at many levels. Human impact on the orcas environment is not diminishing and if anything, is increasing. Overfishing is reducing food sources, particularly chinook salmon, the orcas preferred meal. Commercial shipping and recreational boats continue to cause injury and their excessive noise interferes with the orcas ability to hunt salmon. Toxic chemicals from stormwater runoff and other pollution damage the marine mammals health. Ongoing monitoring of each individual whale will be critical to maintaining their wellbeing and the health of the local marine ecosystem.
Vulcan plans to open-source AMPT, giving it a life of its own in the marine mammal research community. McKennoch said they hope to extend the tool so it can be used for other killer whale populations, different large whales, and in time, possibly smaller dolphins and harbor seals.
Read more from the original source:
Can machine learning help save the whales? How PNW researchers use tech tools to monitor orcas - GeekWire
Attabotics Partners With AltaML and Amii to Bolster Artificial Intelligence and Machine Learning Cap – DC Velocity
Attabotics, the 3D robotics supply chain company, today announced a partnership with AltaML, a leading Canadian applied artificial intelligence and machine learning company, and the Alberta Machine Intelligence Institute (Amii), one of the worlds preeminent centers of artificial intelligence research and application, to develop capabilities in artificial intelligence (AI) and machine learning (ML) that further optimize efficiency and productivity in Attabotics innovative supply chain infrastructure. Together, the three organizations will begin operationalizing the partnership through projects that combine AI technologies with IoT (Internet of Things) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance Attabotics data management and capabilities.
Requiring 85 percent less space than typical fulfillment warehouses, Attabotics is an entirely new way to store and pick goods in warehouses that is tailor-made to help retailers respond to changing e-commerce demands and empower brands. The company transforms the rows and aisles of a typical warehouse into a single, vertical storage structure thats modular and scalable, and uses 3D robots internally to store and retrieve items for box packers on the outside perimeter. Attabotics offers an ideal applied platform to utilize emerging technologies to optimize the supply chain for modern commerce.
Integrating AI technology into the supply chain for transparency, predictive analytics and network optimization is integral as the pandemic has shown that the traditional supply chain doesnt and wont support modern consumer behavior. Attabotics is building advanced AI/ML capabilities that maximize supply chain system throughput by predictively optimizing fulfillment while minimizing downtime. Attabotics drives these advanced AI models by leveraging IoT data derived from modern cloud based robotic operations. With AltaML and Amii, Attabotics is taking another step toward building out its digitally integrated, distributed network that is optimized for modern commerce.
Were excited to work with two world-renowned organizations to build the future of innovation in Canada, said Scott Gravelle, Attabotics CEO. Creating alliances with industry-leading partners is something weve put an emphasis on, which is why were so grateful to have identified the right partners in AltaML and Amii to help further to optimize our platform as we revolutionize the supply chain.
This collaboration draws on the strengths of three Alberta technology leaders to expand the data analytics capabilities for customers. Combining Attabotics expertise in warehousing and fulfillment with AltaMLs expertise developing applied AI solutions and Amiis world-leading research expertise, the collaboration will enable innovation in areas such as maximizing system automation uptime and throughput. This partnership will also support the growth of Calgary and Alberta as an innovation hub and contributes to an ecosystem where technology and innovation continue to thrive.
AltaML builds and deploys AI-powered software for complex problems, creating new competitive advantage for our partners, said Nicole Janssen, AltaML co-CEO. Attabotics has disrupted traditional warehousing, and we are thrilled to work with them, and Amii, to optimize their processes through applied AI. We are already seeing promising results and look forward to many more to come.
Amii is thrilled to be part of this one-of-a-kind collaboration bringing together three of Albertas leading technology organizations. Together, were demonstrating the provinces reputation as a hub for technology and artificial intelligence through the combination of Attabotics transformational work in advanced robotics for supply chain, Amiis leadership and expertise in artificial intelligence research and development and AltaMLs proven record in applying AI to create business impact. This partnership shows the power of public-private partnerships and is further proof of Albertas leadership in the research and application of AI, said Cam Linke, Amii CEO.
What is artificial intelligence?Artificial intelligence (AI) is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
Simply put, AIs goal is to make computers/computer programs smart enough to imitate the human mind behaviour.Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings. AI must have access to properties, categories, objects and relations between all of them to implement knowledge engineering. AI initiates common sense, problem-solving and analytical reasoning power in machines, which is much difficult and a tedious job.
What is machine learning?Artificial Intelligence and Machine Learning are much trending and also confused terms nowadays. Machine Learning (ML) is a subset of Artificial Intelligence. ML is a science of designing and applying algorithms that are able to learn things from past cases. If some behaviour exists in past, then you may predict if or it can happen again. Means if there are no past cases then there is no prediction.
ML can be applied to solve tough issues like credit card fraud detection, enable self-driving cars and face detection and recognition. ML uses complex algorithms that constantly iterate over large data sets, analysing the patterns in data and facilitating machines to respond different situations for which they have not been explicitly programmed. The machines learn from the history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict rational outputs.
Safety & quality:Artificial intelligence frameworks deliver more secure, more precise production lines results with more prominent speed and more consistency than human workers. Whats more, on the processing plant floor, AI-based detection can be utilised to keep employees and equipment safer, recognising likely dangers, for example, a worker who has forgotten to wear the appropriate safety gear.
Waste reduction and transparency:
Obviously, the beverage industrys waste angle is a profoundly discussed and scrutinised part of the business. All through the worlds supply chains, AI is being utilised to follow each and every stage of the manufacturing and supply chain process, for example, tracking costs, overseeing stock levels, and even nations of origin.
Improving food safety standards:Regardless of where you go on the planet, food safety standards are consistently imperative to follow, and guidelines appear to be turning out to be stricter constantly. Particularly with Covid-19, and nations become more mindful of how contaminated food can be.
Luckily, robots that utilise AI and machine learning can handle and process food, fundamentally disposing of the odds that contamination can happen through touch. Robots and machinery cant communicate infections and such that people can, accordingly limiting the risk of it turning into an issue.
PackagingArtificial intelligence-driven robotics are proving key to meeting the pressing and picking demands quickened by customers expanding utilisation of e-commerce. The complex and work escalated nature of the process offers remarkable potential for intelligent automation.
Supply chain managementAI is essentially programming computers so they can receive data, evaluate it, make a decision based on the evaluation, and then perform a given task based on the decision. This emerging technology helps the beverage industry with Supply Chain Management through logistics, predictive analytics, and transparency. The dairy industry is using AI to improve quality assurance, provide better forecasting models, and keep up with consumer trends.
Rising technologies based on artificial intelligence & machine learning in beverage industry
RoboticsRobots refer to machines that can perform tasks or operations by themselves after being programmed using a computer. Those tasks may be either simple and repetitive, or adaptive and more complex, in which the latter requires the integration of other AI methods, such as CV and ML, to continually retrain and learn to carry out more advanced operations.In tea and coffee, robots have been developed for brewing and dispensing purposes. a tea brewing machine is able to make cups of tea in specific times with adequate water temperature and record the consumption patterns. Another recent development was a robot named Teforia; it consists of an in-home tea maker in which the user is able to add any combination of tea leaves and water, then the robot is controlled using a smartphone application to start the process; it claims to be able to brew the beverage to achieve the optimal flavour profile for each consumer. In the case of coffee, a robotic coffee maker, Mugsy, was developed using Raspberry Pi and it is possible to integrate it with different applications such as text messages, Twitter or an Alexa device, which are used to indicate to the robot to start brewing the coffee.
A commercial robotic pourer has been developed, which consists of two arms and a screen; the authors integrated it with a camera, which is able to detect the level of the liquid to predict when to stop serving. A robotic arm was designed to pick a cup and serve the beverage ordered (orange juice, apple juice or iced tea), a second robot was programmed to pick the bottled soft drinks and take them to the clients table, while a third machine was intended to have a conversation with clients while waiting for the order.
Computer vision techniquesThe CV technique refers to a subdivision of AI, which consists of automatic information extraction from either images or videos by imitating the human eye functions. It can be coupled with robotics, specific equations or algorithms, basic statistics, and ML algorithms, to fully automate the technique as an AI system; this may allow the procedure to be stand-alone and to classify or predict the quality parameters of the product. Some advantages include that it is non-destructive, non-contact, may be replicated, is automatic, and therefore, considered as a rapid method, which is more accurate and reliable than some traditional procedures such as visual inspection and sensory analysis, which include human error as a possible drawback.
Machine learningMachine learning (ML) is a branch of artificial intelligence, which refers to a computer-based system that may be trained to find patterns among a dataset to classify or predict specific parameters, and it is able to improve its performance by feeding new data. Machine learning may be divided into supervised and unsupervised algorithms, which, at the same time, may be classified into different subtypes.The use of ML has been increasing in recent years in the food and beverage industry due to its ability to improve production and assess the quality in a faster, more accurate, objective, and cost-effective way.
1. Machine learning in hot beveragesCompared to other types of beverages, the application of ML has been less explored in hot drinks. By using machine learning, the quality of green or black tea can be predicted with the help of different inputs. Green teas can be classified according to the quality grade using both back propagation neural networks and probabilistic neural networks with the outputs of an e-nose as inputs of the models.
A model has been developed using the flavonoids, catechins, and total methyl-xanthines content to predict the antioxidant activity. CIELab colour parameters of coffee beans are used to classify them according to their colour through ANN with a 100% accuracy; however, that perfect classification is due to the direct relationship of the categories and the inputs, which makes the model senseless and useless. A model is developed to predict the roasting degree of coffee using results from hyperspectral images (8741734 nm) through support vector machine with a 90% accuracy.
2. Machine learning in non-alcoholic beveragesThere are several studies using ML in non-alcoholic beverages; however, it has not been applied extensively for water quality assessment, and nothing has been done in bottled water. An ANN model is used to assess the quality of drinking water when entering the distribution system, using microbiological and chemical data as inputs with 100% accuracy. No recent studies have been published regarding the application of ML in soft drinks; however, there are some papers in fruit juices. The use of e-nose outputs as inputs to model fruit juices quality has been popular. ML has been used to classify strawberry juice samples according to the processing treatment with 100% accuracy.
Conclusion:Despite the increasing trend in the applications of emerging technologies, which involve the use of AL, robotics, ML and CV in the beverage industry, there are still several gaps to be covered. Robotics science needs to be more explored in beverages as a tool to aid other AI components, which would maximise the use of some emerging methods. Regarding CV, most approaches developed mainly for the assessment of hot drinks and non-alcoholic beverages are based on the analysis of colour; however, more research needs to be conducted to apply this technology to measure other parameters related to the quality traits specific to each product.
The main issue with ML is that there is still a lack of knowledge among researchers concerning the proper development techniques, usage, and interpretation of the algorithms and modelling, as well as the way to select the best models to avoid over- or under-fitting, which are common problems within the existing publications. Furthermore, the combination of two or more of the aforementioned methods should be considered to be implemented as an approach to AI in the different beverage categories, especially for hot and other non-alcoholic drinks in which these technologies have not been very popular among the companies.
(Sanjay is head, SafeFoodz Solutions, a food safety-regulatory advisor-trainer. The article is co-authored by Arya and supported by Saisha Raut, They can be reached at email@example.com)
Insights and Prediction of Machine Learning in Medical Imaging Global Market (2020-2027) KSU | The Sentinel Newspaper – KSU | The Sentinel Newspaper
The Machine Learning in Medical Imaging market research in this report provided by Global Market Monitor includes historical and forecast market data, consumer demand, application segmentation details, and price trends. This report also provides a detailed overview and data analysis of major Machine Learning in Medical Imaging companies during the forecast period.
Medical imaging analytics tools become more readily available, providers are likely to find strong incentives to investigate the best way to integrate artificial intelligence/machine learning into their imaging strategies. Machine Learning in Medical Imaging has awfully increasing development trend.
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Leading Company for Driving Market GrowthThe global Machine Learning in Medical Imaging market growth is also reliant on the development of active players in the industry, which are:Alibaba Zebra MaxQ AI Aidoc Google Arterys Tencent
View the Comprehensive Analysis on Various Segment:https://www.globalmarketmonitor.com/reports/655502-machine-learning-in-medical-imaging-market-report.html
Application SynopsisThe Machine Learning in Medical Imaging Market by Application are:Breast Lung Neurology Cardiovascular Liver Others
Global Machine Learning in Medical Imaging market: Type segmentsSupervised Learning Unsupervised Learning Semi Supervised Learning Reinforced Leaning
Table of Content1 Report Overview1.1 Product Definition and Scope1.2 PEST (Political, Economic, Social and Technological) Analysis of Machine Learning in Medical Imaging Market2 Market Trends and Competitive Landscape3 Segmentation of Machine Learning in Medical Imaging Market by Types4 Segmentation of Machine Learning in Medical Imaging Market by End-Users5 Market Analysis by Major Regions6 Product Commodity of Machine Learning in Medical Imaging Market in Major Countries7 North America Machine Learning in Medical Imaging Landscape Analysis8 Europe Machine Learning in Medical Imaging Landscape Analysis9 Asia Pacific Machine Learning in Medical Imaging Landscape Analysis10 Latin America, Middle East & Africa Machine Learning in Medical Imaging Landscape Analysis 11 Major Players Profile
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Regional Segment AnalysisThe report focuses on detailed analysis of major regions like North America (United States, Canada and Mexico), Europe (Germany, France, UK, Russia and Italy), Asia-Pacific (China, Japan, Korea, India and Southeast Asia), South America (Brazil, Argentina, Columbia), and Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa).
?Target Audience:Machine Learning in Medical Imaging manufacturersDistributors and resellers of Machine Learning in Medical ImagingMachine Learning in Medical Imaging industry associationsProduct managers, Machine Learning in Medical Imaging industry administrator, C-level executives of the industriesMarket research and consulting firmsSmall and Medium-sized Enterprises (SMEs) Machine Learning in Medical Imaging potential investorsMachine Learning in Medical Imaging key stakeholders Machine Learning in Medical Imaging end-user sectorsResearch and Development (R&D) companies
Key Questions Answered by Global Market Monitor Research Report:What is the size and CAGR of the global Machine Learning in Medical Imaging Market?Which are the leading segments of the global market?Which region may hit the highest market share in the coming era?What are the main strategies adopted in the global market?What growth impetus or acceleration market carries during the forecast period?What are the key driving factors of the most profitable regional market?What trends, challenges, and barriers will impact the development and sizing of the Global Machine Learning in Medical Imaging Market?
About Global Market MonitorGlobal Market Monitor is a professional modern consulting company, engaged in three major business categories such as market research services, business advisory, technology consulting.We always maintain the win-win spirit, reliable quality and the vision of keeping pace with The Times, to help enterprises achieve revenue growth, cost reduction, and efficiency improvement, and significantly avoid operational risks, to achieve lean growth. Global Market Monitor has provided professional market research, investment consulting, and competitive intelligence services to thousands of organizations, including start-ups, government agencies, banks, research institutes, industry associations, consulting firms, and investment firms.ContactGlobal Market MonitorOne Pierrepont Plaza, 300 Cadman Plaza W, Brooklyn,NY 11201, USAName: Rebecca HallPhone: + 1 (347) 467 7721Email: firstname.lastname@example.orgWeb Site: https://www.globalmarketmonitor.com
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Thanks to advanced technology, consumers can now access, spend, and invest their money in safer ways. Lenders looking to win new business should apply technology to make processes faster and more efficient.
Artificial intelligence has transformed the way we handle money by giving the financial industry a smarter, more convenient way to meet customer demands.
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Machine learning helps financial institutions develop systems that improve user experiences by adjusting parameters automatically. It's become easier to handle the extensive amount of data related to daily financial transactions.
Machine learning and AI are changing how the financial industry does business in these ways:
The need to enhance fraud detection and cybersecurity is no longer an option. People pay bills, transfer money, trade stocks, and deposit checks through smartphone applications or online accounts.
Many businesses store their information online, increasing the risk of security breaches. Fraud is a major concern for companies that offer financial services--including banks--which lose billions of dollars yearly.
Machine learning and artificial intelligence technologies improve online finance security by scanning data and identifying unique activities. They then highlight these activities for further investigation. This technology can also prevent credential stuffing and credit application fraud.
Cognito is a cyber-threat detection and hunting software impacting the financial space positively. Its built by a company called Vectra. Besides detecting threats automatically, it can expose hidden attackers that target financial institutions and also pinpoint compromised information.
Making Credit Decisions
Having good credit can help you rent an apartment of your choice, land a great job, and explore different financing options. Now more than ever, many things depend on your credit history, even taking loans and credit cards.
Lenders and banks now use artificial intelligence to make smarter decisions. They use AI to accurately assess borrowers, simplifying the underwriting process. This helps save time and financial resources that would have been spent on humans.
Data--such as income, age, and credit behavior--can be used to determine if customers qualify for loans or insurance. Machine learning accurately calculates credit scores using several factors, making loan approval quick and easy.
AI software like ZestFinance can help you to easily find online lenders, all you do is type title loans near me. Its automated machine learning platform (ZAML) works with companies to assess borrowers without credit history and little to no credit information. The transparent platform helps lenders to better evaluate borrowers who are considered high risk.
Many businesses depend on accurate forecasts for their continued existence. In the finance industry, time is money. Financial markets are now using machine learning to develop faster, more exact mathematical models. These are better at identifying risks, showing trends, and providing advanced information in real time.
Financial institutions and hedge fund managers are applying artificial intelligence in quantitative or algorithmic trading. This trading captures patterns from large data sets to identify factors that may cause security prices to rise or fall, making trading strategic.
Tools like Kavout combine quantitative analysis with machine learning to simultaneously process large, complex, unstructured data faster and more efficiently. The Kai Score ranks stocks using AI to generate numbers. A higher Kai Score means the stock is likely to outperform the market.
Online lenders and other financial institutions can now streamline processes thanks to faster, more efficient tools. Consumers no longer have to worry about unnecessary delays and the safety of their transactions.
About The Author:
Aqib Ijaz is a content writingguru at Eyes on Solution. He is adept in IT as well. He loves to write on different topics. In his free time, he likes to travel and explore different parts of the world.
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3 Applications of Machine Learning and AI in Finance - TAPinto.net
Latest innovative report on Machine Learning in Medicine Market by 2028| Industry Supply Chain Analysis, Growth Opportunities, and Business…
The Latest Released Global Machine Learning in Medicine Market study by Market Research Inc. offers a critical assessment of key growth dynamics, emerging avenues, investment trends in key regional markets, and the competitive landscape in various regions and strategies of top key players. The study also offers insight into the share and size of various segments in the market. The report presents the market analysis based on several factors. Different exploratory techniques such as qualitative and quantitative analysis have been used to give data accurately. For a better understanding of the customers, it uses effective graphical presentation techniques, such as graphs, charts, tables as well as pictures.
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The Global Machine Learning in Medicine Market Report gives a clear idea about the global competitive landscape; it offers unique insights into the businesses by providing detailed data about some significant strategies to get customers rapidly. To get a clear idea about the ups-downs of the businesses some significant case studies have been included in terms of statistical data. Additionally, it offers informative data on recent trends, tools, methods, and technologies that are driving the growth of the market. Different approaches have been used to analyze the different restraining factors in front of the businesses.
Major Market Players Profiled in the Global Machine Learning in Medicine Market Report includes:
Proceeding further, The Machine Learning in Medicine report of Market incorporates segmentation studies including product and application categories, and Regional-level analysis of the top geographies. Moving to the market competitive scenario, product and service offering of the prominent organizations along with business strategies employed by them to maintain a strong hold in this marketplace are reviewed thoroughly.
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Global Machine Learning in Medicine Market segmentation:
Based on Type:
Based on Application:
Geographically, the market has been fragmented into several regions such as:
This report provides a detailed and analytical look at the various companies that are working to achieve a high market share in the global Machine Learning in Medicine market. Data is provided for the top and fastest growing segments. This report implements a balanced mix of primary and secondary research methodologies for analysis. Markets are categorized according to key criteria. To this end, the report includes a section dedicated to the company profile. This report will help you identify your needs, discover problem areas, discover better opportunities, and help all your organizations primary leadership processes. You can ensure the performance of your public relations efforts and monitor customer objections to stay one step ahead and limit losses.
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The years considered to estimate the market size in this study are as follows:
Key Highlights of the report:
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Artificial Intelligence and other disruptive technology are spreading their wings in the current scenario. Technology has become a mandatory element for all kinds of businesses across all industries around the globe. Let us travel back to 1958 when Frank Rosenblatt created the first artificial neural network that could recognize patterns and shapes. From such a primitive stage we have now reached a place where machine learning is an integral part of almost all softwares and applications.
Machine learning is resonating with everything now, be it automated cars, speech recognition, chatbots, smart cities, and whatnot. The abundance of big data and the significance of data analytics and predictive analytics has made machine learning an imperative technology.
Machine learning, as the name suggests is a process in which machines learn and analyze the data fed to it and predict the outcome. There are different types of machine learning like supervised, unsupervised, semi-supervised, etc. Machine learning is the stairway to reach artificial intelligence and it learns from algorithms based on the database and derives answers and correlations from them.
Machine learning is an integral part of automation and digital transformation. In 2016, Google introduced its graph-based machine learning tool. It used the semi-supervised learning method to connect clusters of data based on their similarities. Machine learning technology helps industries identify market trends, potential risks, customer needs, and business insights. Today, business intelligence and automation are the norms and ML is the foundation to achieve these and enhance the efficiency of your business.
A term identified by Gartner, Hyperautomation is the new tech trend in the world. It enables industries to automate all possible operations and gain intelligent and real-time insights from the data collected. ML, AI, and RPA are some of the important technologies behind the acceleration of hyperautomation. AIs ability to augment human behaviour is aided by machine learning. Machine learning algorithms can automate various tasks once the algorithm is trained. ML models along with AI will enhance the capacity of machines and software to automatically improve and respond to changes according to the business requirements.
According to Industry Research, the Global Machine Learning market is projected to grow by USD11.16 billion between 2020 and 2024, progressing at a CAGR of 39% during the forecast period.
This data is enough to indicate the growth and acceptance of ML across the world. Let us understand how different industries are using ML.
Other industries leveraging ML include banking and finance, cybersecurity, manufacturing, media, automobile, and many more.
Executives and C-Suite professionals should consider it a norm to have a strategy or goal before putting out ML into practice. The true capability of this technology can only be extracted by developing a strategy for its use. Otherwise, the disruptive tech might remain inside closed doors just automating routine and mundane tasks. MLs capability to innovate should not be chained just to automate repetitive tasks.
According to McKinsey, companies should consist two types of people, quants, and translators to unleash the power of ML. Translators should be the ones connecting the vague lines between the complex data analysis by algorithms and convert it into readable and understandable business insights for the executives.
Machine learning is not an unfamiliar technology these days, but it still takes time and patience to leave the legacy systems behind and embrace the power of disruptive technologies. Companies should focus on democratizing ML and data analytics for their employees and create a transparent ecosystem to leverage the capabilities of these techs by demystifying them.
AI and ML tools are gradually entering the area of cybersecurity systems. (Ars Electronica)
Although many years ago we couldnt even think about this, today, Artificial Intelligence and Machine Learning technologies are gradually entering our life. Their presence can be noticed everywhere from complicated quantum computing systems to powerful medical diagnostic systems. To substantiate our statement, lets take a deeper look at the statistical data. In 2020, the revenue generated by AI services and technologies reached $158 billion. Therefore, if you are going to invest in building a mobile app for your business, you need to make sure you are dealing with a reputable software development team, that knows for sure how to work with AI tools, and keeps track of the latest trends in this niche. Following this link, you can find out more about artificial intelligence. In this post, well take a closer look at all emerging AI and ML trends to pay attention to in 2021.
According to Garner, hyper-automation is one of the main trends in the area of IT. The main concept that lies behind this idea is the fact that absolutely everything within one company can be automated. The COVID pandemic promoted the development and adoption of this idea. Sometimes, it is also called intelligent process automation. And ML and AI elements are the main drivers of hyper-automation.
The thing is that to get the best possible results hyper-automation initiatives cant be contingent on static packaged software. To achieve success, automated business processes must bring in sync with modern rapidly changing circumstances and quickly respond to vicissitudes of life. And this is where the use of AI and ML technologies might come in handy! Their powerful learning algorithms allow systems to quickly react to changing processes and can provide users with only up-to-date information.
It goes without saying that 2020 wasnt easy, but this year proved that modern business simply cant do without such experts as data scientists or practitioners. Last year, we noticed a surge in demand for such experts as data practitioners and AI/ML experts that can make your business more efficient and can help speed up decision-making.
Professional data scientists can surely make your business grow. They use their skills to translate business demands into data science, find new channels of operating, etc. Besides, they also use data science to help deal with a myriad of significant and complicated business processes.
In current times, the demand for professional data practitioners that understand statistical data is higher than ever before.
AI and ML tools are gradually entering the area of cybersecurity systems. Programmers of such systems are mostly focused on updating their technology to prevent the software from malware, DDS attacks, and ransomware. AI and ML tools can help find threats faster. On top of that, cybersecurity systems are also used for collecting data from various communication networks, transactional systems, and websites of a company. AI algorithms are also used to find threatening activity (dangerous IP addresses or potential data breaches).
Currently, AI is also widely used in home security systems but its functionality is still limited. Experts say that very soon, the functionality of such systems will be improved and they will be able to learn the habits, ways of behavior, and preferences of users. This information will surely help them recognize intruders faster.
So far, AI and ML technologies are a hot trend in software development. No matter what business strategy you have, these tools can easily be integrated into it. Therefore, we strongly suggest paying attention to such trends if you want to make your business grow.
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Emerging AI and Machine Learning Trends to Watch in 2021 - FlaglerLive.com