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How To Choose The Best Machine Learning Algorithm For A Particular Problem? – Analytics India Magazine

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How do you know what machine learning algorithm to choose for your problem? Why dont we try all the machine learning algorithms or some of the algorithms which we consider will give good accuracy. If we apply each and every algorithm it will take a lot of time. So, it is better to apply a technique to identify the algorithm that can be used.

Choosing the right algorithm is linked up with the problem statement. It can save both money and time. So, it is important to know what type of problem we are dealing with.

In this article, we will be discussing the key techniques that can be used to choose the right machine algorithm in a particular work. Through this article, we will discuss how we can decide to use which machine learning model using the plotting of dataset properties. We will also discuss how the size of the dataset can be a considerable measure in choosing a machine learning algorithm.

The dataset is taken from Kaggle, you can find it here. It has information about the diabetic patient and whether or not each patient will have an onset of diabetes. It has 9 columns and 767 rows. Rows and columns represent patient numbers and details.

Practical Implication:

First of all, we will import the required libraries.

After it we will proceed by reading the csv file.

By applying the pair plot we will be able to understand which algorithm to choose.

From the plot, we can see that there is a lot of overlap between the data points.KNN should be preferred as it works on the principle of Euclidean distance. In case KNN is not performing as per the expectation then we can use the Decision Tree or Random Forest algorithm.

A decision tree or Random Forest works on the principle of non-linear classification. We can use it if some of the data points are overlapping with each other.

Many algorithms work on the assumption that classes can be separated by a straight line. In such cases, Logistic regression or Support Vector Machine should be preferred. It easily separates the data points by drawing a line that divides the target class. Linear regression algorithms assume that data trends follow a straight line. These algorithms perform well for the present case.

Import the various algorithm classifiers to check the training time of small and large dataset.

Split the data into train and test. Now we can proceed by applying Decision Tree, Logistic Regression, Random Forest and Support Vector Machine algorithms to check the training time for a classification problem.

Now, we will fit several machine learning models on this dataset and check the training time taken by these models.

From the above results, we can conclude that Decision Trees will take much less time than all algorithms for small dataset. Hence, it is recommended to use a low bias/high variance classifier like a decision tree.

The dataset is taken from Kaggle, you can find it here. It has information about credit card fraud that occurred in two days. Feature Class is a target variable and it takes 1 in case of fraud and 0 otherwise. It has 284807 rows and 31columns.

#Train-Test Split

Now again, on this second dataset, we will fit the above machine learning models on this dataset and check the training time taken by these models.

With the huge dataset size depth of Decision Tree grows, it implements multiple if-else statements which increase complexity and time. Both Random Forest and Xgboost use the Decision Tree algorithm which takes more time. The result shows Logistic regression outperforms others.

I have concluded my analysis in selecting the correct machine learning algorithm. Furthermore, it is always advisable to use two algorithms for addressing the problem statement. This could provide a good reference point for the audience.

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How To Choose The Best Machine Learning Algorithm For A Particular Problem? - Analytics India Magazine

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October 19th, 2020 at 3:56 am

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Lantronix Brings Advanced AI and Machine Learning to Smart Cameras With New Open-Q 610 SOM Based on the Powerful Qualcomm QCS610 System on Chip (SOC)…

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October 15, 2020 07:00 ET | Source: Lantronix, Inc.

IRVINE, Calif., Oct. 15, 2020 (GLOBE NEWSWIRE) -- Lantronix Inc. (NASDAQ: LTRX), a global provider of Software as a Service (SaaS), engineering services and hardware for Edge Computing, the Internet of Things (IoT) and Remote Environment Management (REM), today announced the availability of its new Lantronix Open-Q 610 SOM based on the powerful Qualcomm QCS610System on Chip (SOC). This micro System on Module (SOM) is designed for connected visual intelligence applications with high-resolution camera capabilities, on-device artificial intelligence (AI) processing and native Ethernet interface.

Our long and successful relationship with Qualcomm Technologies enables us to deliver powerful micro SOM solutions that can accelerate IoT design and implementation, empowering innovators to create IoT applications that go beyond hardware and enabletheir wildest dreams, said Paul Pickle, CEO of Lantronix.

The new Lantronix ultra-compact (50mm x 25mm), production-ready Open-Q 610 SOM is based on the powerful Qualcomm QCS610SOC, the latest in the Qualcomm Vision Intelligence Platform lineup targeting smart cameras with edge computing. Delivering up to 50 percent improved AI performance than the previous generation as well as image signal processing and sensor processing capabilities, it is designed to bring smart camera technology, including powerful artificial intelligence and machine learning features formerly only available to high-end devices, into mid-tier camera segments, including smart cities, commercial and enterprise, homes and vehicles.

Bringing Advanced AI and Machine Learning to Smart Camera Application

Created to bring advanced artificial intelligence and machine learning capabilities to smart cameras in multiple vertical markets, the Open-Q 610 SOM is designed for developers seeking to innovate new products utilizing the latest vision and AI edge capabilities, such as smart connected cameras, video conference systems, machine vision and robotics. With the Open-Q 610 SOM, developers gain a pre-tested, pre-certified, production-ready computing module that reduces risk and expedites innovative product development.

The Open-Q 610 SOM provides the core computing capabilities for:

Connectivity solutions include Wi-Fi/BT, Gigabit Ethernet, multiple USB ports and three-camera interfaces.

The Lantronix Open-Q 610 SOM provides advanced artificial intelligence and machine learning capabilities that enable developers to innovate new product designs, including smart connected cameras, video conference systems, machine vision and robotics, said Jonathan Shipman, VP of Strategy at Lantronix Inc. Lantronix micro SOMs and solutions enable IoT device makers to jumpstart new product development and accelerate time-to-market by shortening the design cycle, reducing development risk and simplifying the manufacturing process.

Open-Q 610 Development Kit

The companion Open-Q 610 Development Kit is a full-featured platform with available software tools, documentation and optional accessories. It delivers everything required to immediately begin evaluation and initial product development.

The development kit integrates the production-ready OpenQ 610 SOM with a carrier board, providing numerous expansion and connectivity options to support development and testing of peripherals and applications. The development kit, along with the available documentation, also provides a proven reference design for custom carrier boards, providing a low-risk fast track to market for new products.

In addition to production-ready SOMs, development platforms and tools, Lantronix offers turnkey product development services, driver and application software development and technical support.

For more information, visit Open-Q 610 SOM and Open Q 610 SOM Development kit.

About Lantronix

Lantronix Inc. is a global provider of software as a service (SaaS), engineering services and hardware for Edge Computing, the Internet of Things (IoT) and Remote Environment Management (REM). Lantronix enables its customers to provide reliable and secure solutions while accelerating their time to market. Lantronixs products and services dramatically simplify operations through the creation, development, deployment and management of customer projects at scale while providing quality, reliability and security.

Lantronixs portfolio of services and products address each layer of the IoT Stack, including Collect, Connect, Compute, Control and Comprehend, enabling its customers to deploy successful IoT and REM solutions. Lantronixs services and products deliver a holistic approach, addressing its customers needs by integrating a SaaS management platform with custom application development layered on top of external and embedded hardware, enabling intelligent edge computing, secure communications (wired, Wi-Fi and cellular), location and positional tracking and environmental sensing and reporting.

With three decades of proven experience in creating robust industry and customer-specific solutions, Lantronix is an innovator in enabling its customers to build new business models, leverage greater efficiencies and realize the possibilities of IoT and REM.Lantronixs solutions are deployed inside millions of machines at data centers, offices and remote sites serving a wide range of industries, including energy, agriculture, medical, security, manufacturing, distribution, transportation, retail, financial, environmental, infrastructure and government.

For more information, visit Learn more at the Lantronix blog,, featuring industry discussion and updates. To follow Lantronix on Twitter, please visit View our video library on YouTube at or connect with us on LinkedIn at

Safe Harbor Statement under the Private Securities Litigation Reform Act of 1995: Any statements set forth in this news release that are not entirely historical and factual in nature, including without limitation statements related to our solutions, technologies and products as well as the advanced Lantronix Open-Q 610 SOM, are forward-looking statements. These forward-looking statements are based on our current expectations and are subject to substantial risks and uncertainties that could cause our actual results, future business, financial condition, or performance to differ materially from our historical results or those expressed or implied in any forward-looking statement contained in this news release. The potential risks and uncertainties include, but are not limited to, such factors as the effects of negative or worsening regional and worldwide economic conditions or market instability on our business, including effects on purchasing decisions by our customers; the impact of the COVID-19 outbreak on our employees, supply and distribution chains, and the global economy; cybersecurity risks; changes in applicable U.S. and foreign government laws, regulations, and tariffs; our ability to successfully implement our acquisitions strategy or integrate acquired companies; difficulties and costs of protecting patents and other proprietary rights; the level of our indebtedness, our ability to service our indebtedness and the restrictions in our debt agreements; and any additional factors included in our Annual Report on Form 10-K for the fiscal year ended June 30, 2019, filed with the Securities and Exchange Commission (the SEC) on September 11, 2019, including in the section entitled Risk Factors in Item 1A of Part I of such report, as well as in our other public filings with the SEC. Additional risk factors may be identified from time to time in our future filings. The forward-looking statements included in this release speak only as of the date hereof, and we do not undertake any obligation to update these forward-looking statements to reflect subsequent events or circumstances.

Lantronix Media Contact: Gail Kathryn Miller Corporate Marketing & Communications Manager 949-453-7158

Lantronix Analyst and Investor Contact: Jeremy Whitaker Chief Financial Officer 949-450-7241

Lantronix Sales: Americas +1 (800) 422-7055 (US and Canada) or +1 949-453-3990 Europe, Middle East and Africa +31 (0)76 52 36 744 Asia Pacific + 852 3428-2338 China + 86 21-6237-8868 Japan +81 (0) 50-1354-6201 India +91 994-551-2488

2020 Lantronix, Inc. All rights reserved. Lantronix is a registered trademark, and EMG, and SLC are trademarks of Lantronix Inc. Other trademarks and trade names are those of their respective owners.

Qualcomm is a trademark or registered trademark of Qualcomm Incorporated.

Qualcomm Vision Intelligence Platform and Qualcomm QCS610 are products of Qualcomm Technologies, Inc. and/or its subsidiaries.

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Lantronix Brings Advanced AI and Machine Learning to Smart Cameras With New Open-Q 610 SOM Based on the Powerful Qualcomm QCS610 System on Chip (SOC)...

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October 19th, 2020 at 3:56 am

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AI and Machine Learning Technologies Expected to Play a Key Role in Expanding Multi Billion Dollar Digital Banking Sector: Report – Crowdfund Insider

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The global digital banking platform market size is expected to reach $10.87 billion by 2027, which means that its expanding at about a 13.6% CAGR (compounded annual growth rate), according to estimates from Allied Market Research (AMR).

A release that summarizes that findings of the report notes:

Growing adoption of online banking over traditional banking drives the growth of the global digital banking platforms market. North America contributed the highest share in 2019, and will maintain its dominance throughout the forecast period. During the Covid-19 pandemic, users are preferring digital banking platforms such as internet banking to avoid physical contact with individuals and prevent transmission of coronavirus.

But the report also mentioned that compliance and online data security issues may begin to limit the growth of the virtual banking market. Despite cybersecurity issues, the digital banking sector is on track to grow steadily in the coming years due to advancements in related technologies such as artificial intelligence (AI) and machine learning (ML).

AI and ML are used to make intelligent decisions about key business and banking processes. They may also be used to analyze large amounts of data in order to determine the creditworthiness of an application. Additionally, AI can help detect suspicious or potentially fraudulent transactions by using context clues or by looking for certain patterns in the way payments or transactions are made.

The report further noted that the Reserve Bank of India (RBI) has confirmed that around twice as many transactions were made via digital banking platforms in April 2020 (when compared to March 2020 which was the time the Coronavirus pandemic began).

Virtual banks across the globe appear to be doing quite well and continue to launch new products and promotional offers. Digital banking group Varo Bank has launched Varo Advice, which is a new product that instantly advances up to $100 to qualifying customers. As noted in a release, the new offer is designed to help customers proactively manage their finances, Varo Advance offers instant access to up to $100 cash right in the Varo Bank app.

US digital banking platform Greenwood recently revealed that there have been over 100,000 sign-ups just days after its debut.

As covered, emerging digital technology breakthroughs in AI and IoT are fundamentally changing consumers banking experience, according to a new report. Meanwhile, another report found that consumers in European countries like Germany are not downloading new digital banking apps as much as expected (in a post COVID world). However, theyre still using the virtual banking apps theyve already installed a lot more than before, the report revealed.

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AI and Machine Learning Technologies Expected to Play a Key Role in Expanding Multi Billion Dollar Digital Banking Sector: Report - Crowdfund Insider

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AutoML Alleviates the Process of Machine Learning Analysis – Analytics Insight

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Machine Learning (ML)is constantly being adopted by diverse organizations in an enthusiasm to acquire answers and analysis. As the embracing highly increases, it is often forgotten that machine learning has its flaws that need to be addressed for acquiring a perfect solution.

Applications of artificial intelligence andmachine learning are using new toolsto find practical answers to difficult problems. Companies move forward with the emerging technologies to get a competitive edge on their working style and system. Through the process, organizations are learning a very important lesson that one strategy doesnt fit for all.Business organizations want machine learningto do analysis on large data, which is complex and difficult. They neglect the fact that machine learning cant perform on diverse data storage and even if it does, it will conclude with a wrong prediction.

Analysing unstructured and overwhelming large datasets on machine learning is dangerous. Machine learning might conclude with a wrong solution while performing predictive analysis on such data. The implementation of the misconception in a companys working system might drag down its improvement. Many products that incorporatemachine learning capabilitiesuse predetermined algorithms and many diverse ways to handle data. However, each organizations data has different technical characteristics that might not go well with the existing machine learning configuration.

To address the problems where machine learning falls short, AutoML takes head-on in the companys data analysis perspective. AutoML takes over labour intensive job of choosing and tuning machine learning models. The new technology takes on many repetitive tasks where skilful problem definition and data preparation are needed. It reduces the need to understand algorithm parameters and shortening the compute time needed to produce better models.

Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The technology focuses on the development of computer programs that can access data and use it for themselves. It is a model created and trained on a set of previously gathered data, often known as outcomes. The model can be used tomake predictions using that data.

However, machine learning cant get accurate results all the time. It depends on the data scientist handling the machine learning configurations and data inputs. A data scientist studies the input data and understands the desired output to solve business problems. They choose the apt mathematical algorithm from a dozen and tune those parameters called hyperparameters and evaluate the resulting models. The data scientist has the responsibility to adjust the algorithms tuning parameters again and again until the machine learning model produces the desired result. If the results are not tactic, then the data scientist might even start from the very beginning.

Machine learning system struggles to function when the data is too large or unorganised. Some of the other machine learning issues are,

Classification- The process of labeling data can be thought to as a discrimination problem, modeling the similarities between groups.

Regression- Machine learning staggers to predict the value of a new unpredicted data.

Clustering- Data can be divided into groups based on similarity and other measures of natural structure in data. But, human hands are needed to assign names to the groups.

As mentioned earlier, machine learning alone cant address the datasets of an organisation to find predictions. Here are some reasons why tuning a machine learning algorithm is challenging to choose and how AutoML can prove to be useful at such instances.

Choosing the right algorithm: It is not always obvious to choose a perfect algorithm that might work well for building real-value predictions, anomaly detection and classification models for a particular data set. Data scientists have to go through many well-known algorithms of machine learning that could suit the real-world situation. It could take weeks or even months to come up with the right algorithm.

Selecting relevant information: Data storage has diverse data variables or predictors. Henceforth, it is hard to tell which of those data points are significant for making a decision. This process of selecting relevant information to include in data models is called feature selection.

Training machine learning models: The most difficult process in machine learning is to choose a subset of data that can be used for training a machine learning model. In some cases, training against some data variables or predictors can increase training time while actually reducing the accuracy of the ML model.

Automated machine learning (AutoML)basically involves automating the end-to-end process of applying machine learning to real-world problems that are actually relevant in the industry.AutoML makes well-educated guessesto select a suitable ML algorithm and effective initial hyperparameters. The technology tests the accuracy of training the chosen algorithms with those parameters and makes tiny adjustments, and tests the results again. AutoML also automates the creation of small, accurate subsets of data to use for those iterative refinements, yielding excellent results in a fraction of the time.

In a nutshell, AutoML acts as a right tool that quickly chooses, builds and deploys machine learning models that deliver accurate results.

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AutoML Alleviates the Process of Machine Learning Analysis - Analytics Insight

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October 19th, 2020 at 3:56 am

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Futurism Reinforces Its Next-Gen Business Commerce Platform With Advanced Machine Learning and Artificial Intelligence Capabilities – Yahoo Finance

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New AI capabilities pave way for an ultra-personalized customer experience

PISCATAWAY, N.J., Oct. 14, 2020 /PRNewswire/ --Futurism Technologies, a leading provider of digital transformation solutions, is bringing to life its Futurism Dimensions business commerce suite with additional artificial intelligence and machine learning capabilities. New AI capabilities will help online companies provide an exceptional personalized online customer experience and user journeys. Futurism Dimensions will not only help companies put their businesses online, but would also help to completely digitize their commerce lifecycle. The commerce life cycle includes digital product catalog creation and placement, AI-driven digital marketing, order generation to fulfillment, tracking, shipments, taxes and financial reporting, all from a unified platform.

With the "new norm," companies are racing to provide a better online experience for their customers. It's not just about putting up a website today, it's about creating personalized and smarter customer experiences. Using customer behavioral analysis, AI, machine learning and bots, Futurism's Dimensions creates that personalized experience. In addition, with Futurism Dimensions, companies become more efficient by transforming the entire commerce value chain and back office to digital.

"Companies such as Amazon have redefined online customer experience and set the bar very high. Every company will be expected to offer personalized, easy-to-use, online experience available from anywhere at any time and on any device," said Sheetal Pansare, CEO of Futurism Technologies. "We've armed Dimensions with advanced AI and ML to help companies provide exceptional personalized experiences to their customers. At the same time, with Dimensions, they can digitize their entire commerce value chain and become more efficient with business automation. Our ecommerce platform is affordable and suited for companies of all sizes," added Mr. Pansare.

Story continues

Futurism Dimensions highlights:

Secure and stable platform with 24/7 support and migration

As cybercrimes continue to evolve, e-commerce companies ought to keep up with advanced cybersecurity developments. Futurism Dimensions prides itself on its security for customers allowing them to receive the latest in technological advancements in cybersecurity. Dimensions leverages highly secure two-factor authentication and encryption to safeguard your customers' data and business from potential hackers.

To ensure seamless migration from existing implementations, Dimensions integrates with most legacy systems.

Dimensions offers 24/7 customer support, something you won't find with some of the dead-end platforms of the past. Others will simply have a help page or community forum, but that doesn't necessarily solve the problem. It can also be costly if you need to reach someone for support on other platforms, whereas Dimensions support is included in your plan.

Migrating to Dimensions is a seamless transition with little to no downtime. Protecting online businesses from cyber threats is a top priority while transitioning their websites from another platform or service. You get a dedicated team at your disposal throughout the transition to ensure timely completion and implementation.

Heat Map, Customer Session Playback, Live Chat and Analytics

Dimensions offers intelligent customer insights with Heat Map tracking, Full customer session playback, and live chat allowing you to understand customers' needs. Heat Map will help you identify the most used areas of your website and what your customers are clicking on. Further, customer session playback will help you identify how customers arrived at certain products or pages. Dimensions also has a live customer session that helps you provide prompt support.

Customer insights and analytics are lifeblood for any e-business in today's digital era. Dimensions offers intelligent insights into demographics to help you market to your target audiences.

Highly personalized user experience using Artificial Intelligence

Dimensions lets you deploy smart AI-powered bots that use machine learning algorithms to come up with smarter replies to customer questions thus, reducing response time significantly. Chatbots can help address customer queries that usually drop in after business hours with automated and pre-defined responses. Eureka! Never lose a sale.

Business Efficiency and Automation using AI and Machine Learning

AI and machine learning can help predict inventory and automate processes such as support, payments, and procurement. It can also expand business intelligence to help create targeted marketing plans. Lastly, it can give you live GPS logistics tracking.

Mobile Application

Dimensions team will design your mobile site application to look and function as if a consumer were viewing it on their computer. Fully optimized and designed for ease of use while not limiting anything from your main site.

About Futurism Technologies

Advancements in digital information technology continue to offer companies with the opportunities to drive efficiency, revenue, better understand and engage customers, and redefine their business models. At Futurism, we partner with our clients to leverage the power of digital technology. Digital evolution or a digital revolution, Futurism helps to guide companies on their DX journey.

Whether it is taking a business to the cloud to improve efficiency and business continuity, building a next-generation ecommerce marketplace and mobile app for a retailer, helping to define and implement a new business model for a smart factory, or providing end-to-end cybersecurity services, Futurism brings in the global consulting and implementation expertise it takes to monetize the digital journey.

Futurism provides DX services across the entire value chain including e-commerce, digital infrastructure, business processes, digital customer engagement, and cybersecurity.

Learn more about Futurism Technologies, Inc. at


Leo J Cole Chief Marketing Officer Mobile: +1-512-300-9744 Email:


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Purebase Enhances Its Board of Advisors with An Expert on Machine Learning and Cheminformatics – GlobeNewswire

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October 13, 2020 12:30 ET | Source: Purebase Corporation

IONE, CA, Oct. 13, 2020 (GLOBE NEWSWIRE) -- Purebase Corporation (OTCQB: PUBC), a diversified resource company, headquartered in Ione, California, today announces that Dr. Newell Washburn, PhD, whom is an expert on machine learning and cheminformatics applied to complex materials applications has agreed to join the Purebase Advisory Board.

Dr. Washburn joins Dr. Karen Scrivener, PhD, Dr. Kimberly Kurtis, PhD, and Mr. Joe Thomas as part of the Purebase Advisory Board team that will provide expert guidance in the development and execution of Purebases rollout of next-generation, carbon emission reducing, supplementary cementitious materials (SCMs).

Purebases Chairman and CEO, Scott Dockter stated, We look forward to Dr. Washburn joining our team. He will be an asset and great resource as his primary focus is the use of data-driven approaches to formulate cementitious binders with high SCM content and to design chemical admixture systems for the broad deployment. In addition, his partnering with a broad range of chemical admixture and cement companies and the ARPA-E program in the Department of Energy. We are looking forward to working with him.

Newell R. Washburn, PhD is Associate Professor of Chemistry and Engineering at Carnegie Mellon University and CEO of Ansatz AI. Professor Washburn co-founded Ansatz AI to commercialize the hierarchical machine learning algorithm he and his collaborators developed at CMU for modeling and optimizing complex material systems based on sparse datasets. The company is currently working with clients in the US, Europe, and Japan on using chemical and materials informatics in product development and manufacturing. Professor Washburn received a BS in Chemistry from the University of Illinois at Urbana-Champaign, performed doctoral research at the University of California (Berkeley) on the solid state chemistry of magnetic metal oxides, and then did post-doctoral research in chemical engineering at the University of Minnesota (Twin Cities).

About Purebase Corporation

Purebase Corporation (OTCQB: PUBC) is a diversified resource company that acquires, develops, and markets minerals for use in the agriculture, construction, and other specialty industries.


Emily Tirapelle | Purebase Corporation,and please visit our corporate website

Safe Harbor

This press release contains statements, which may constitute forward-looking statements within the meaning of the Securities Act of 1933 and the Securities Exchange Act of 1934, as amended by the Private Securities Litigation Reform Act of 1995. Those statements include statements regarding the intent, belief, or current expectations of Purebase Corporation and members of its management team as well as the assumptions on which such statements are based. Such forward-looking statements are not guarantees of future performance and involve risks and uncertainties, and that actual results may differ materially from those contemplated by such forward-looking statements. Important factors currently known to management that may cause actual results to differ from those anticipated are discussed throughout the Companys reports filed with Securities and Exchange Commission which are available at as well as the Companys web site at The Company undertakes no obligation to update or revise forward-looking statements to reflect changed assumptions, the occurrence of unanticipated events or changes to future operating results.

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Purebase Enhances Its Board of Advisors with An Expert on Machine Learning and Cheminformatics - GlobeNewswire

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COVID-19 And The Role Of AI, Machine Learning In Logistics: A Conversation With Delhivery CTO Kapil Bharati – Mashable India

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The worldwide spread of the COVID-19 pandemic has disrupted how people buy products and services and how they perceive e-commerce. The standardized lockdown rules across India and the growing hesitation among consumers to go outside and shop for essential goods have tilted the nation towards e-commerce.

Consumers have switched from shops, supermarkets, and shopping malls to online portals for the purchase of products, ranging from basic commodities to branded goods. Since the norm of social distancing has been initiated for almost the entirety of 2020, the scope of online purchases and online businesses is expected to surge. Many people are embracing the concept of online retail and there's been a visible surge in first time users on e-commerce portals. But this boom for e-commerce portals have only meant an increase in pressure on the logistics industry - an industry that employs over 8 million people in the country.

Well, to understand the bearing the pandemic's had on the logistics industry and how's technology played a key role in tackling some of the problems at hand, we spoke to Kapil Bharati, who's the Chief Technology Officer and Co-founder at Delhivery - one of India's biggest B2B & C2C logistic courier service providers.

An IIT Delhi alumnus and a mechanical engineer by qualification, Bharati has been working on the design, development of complex large-scale applications at Delhivery and specifically leads the Technology and Data Science divisions, providing overall technical direction to the organisation.

Here are some of the insights that he had to share with us.

Question: When you think of supply chain services and logistics from a lay-mans point-of-view, the use of technology definitely isn't the first thing that comes to mind. Being one of its co-founders, could you tell us a bit about Delhivery's journey in India over the past 9 years, while helping us understand how key a role technology plays in it?

Kapil Bharati: Since its inception in 2011, Delhivery has become India's largest supply chain services company. Today, with our nationwide network extending beyond 17,500 pin codes and 2,300 cities, we provide a full suite of logistics services such as express parcel transportation, LTL and FTL freight, reverse logistics, cross-border, B2B & B2C warehousing, and technology services.

We aim to build the operating system for commerce and utilise our scale and learning from the Indian market globally.

Our strategy is inherently composed of two interlocking flywheels - logistics and technology. We continue to aggressively invest in building world-class logistics infrastructure and couple it with cutting edge technology, design, and engineering capabilities.

The flywheels represent a fundamental pillar of our philosophy - deliver at scale to reduce costs; generate and process data at scale to bring further cost optimisation and service level improvements in our operations. These have allowed us to deliver speed, reliability, and cost-efficiency to over 10000 customers across our network that reaches over a billion consumers. At the heart of this transformation is the ability to capture, store, and process huge amounts of data.

SEE ALSO: These Tech Trends Will Impact Our Lives In A Post COVID-19 World

As we deliver over 1 million shipments every day, we undergo 30 million changes in the state of shipments in our network and generate over 100 million events from over 40k devices in the network. Leveraging machine learning and artificial intelligence techniques, each event makes our system more efficient - optimising facility locations, transportation routes, and movement of goods, predicting future events, creating dynamic last-mile delivery routes, and ensuring consistently high service levels. This ability acts as a significant differentiator and has powered our growth over the last 9 years.

Our vision is to create a global technology and data platform to provide real-time insights to businesses and create optimisation opportunities and real-time decision support for logistics and supply chain players worldwide.

Question: A large chunk of the country's services has been almost forced to adopt a tech-first approach ever since the COVID-19 pandemic became a part of our lives. How has Delhivery embraced this change and do you think the pandemic has thus served as a blessing in disguise?

Kapil Bharati: The fast spread of COVID-19 and multiple nationwide lockdowns posed serious challenges and uncertainties in our supply chain network.

Our technology stack acted as a significant differentiator during this time, enabling us to answer the holy trinity of what (essential/non-essential goods) could move where (containment zones; red/ orange/ green zone) and how (active lanes/ facilities).

Within the first 48 hours of the lockdown, we repurposed our address disambiguation and product categorization systems to identify customers living in the containment zones and essential shipments. Control systems were put in place to provide visibility and direction to our ground operators. These were crucial ingredients for businesses to reboot, as we advised our clients on the products, they could ship without the fear of getting stuck. We were operational in 4500 pin codes within 48 hours of the lockdown and over 15500 pin codes within a week.

Safety of our customers and employees is a top priority for us. Our teams are following stringent traceability protocols, undergoing daily health checks, and are stocked adequately with face masks, hand gloves and sanitizers. We are doing our best to deliver your packages safely.

The closure of physical retail forced brands, distributors, and retailers to relook at their business models and directly connect with customers. We launched new services to quickly onboard such businesses in the healthcare, pharma, and food domains and set them up to adopt digital technologies for hyperlocal and contactless deliveries.

Internally, one of the major changes has been the organisation's split into an arm that works relentlessly on the ground and another that works from home to power technology, products, BD, and client experience. In the crucible of the pandemic lockdown, our teams showed remarkable resilience in adapting to both environments, expanding for us the possibility of attracting talent irrespective of where they are based.

Question: How big a role do you think machine learning and artificial intelligence play in being components of change in the landscape of the logistics industry as a whole?

Kapil Bharati: AI/ML starts to play an increasingly important role as we scale our operations to deliver millions of shipments a day. It becomes very hard at this scale, if not impossible, for ground operators to make optimal decisions on how shipments must be routed through the network.

AI/ML models allow us to automate this decision-making and push the boundaries of speed and efficiency as systems become capable of simultaneously evaluating thousands of variables that affect a shipments life cycle. As we ingest the huge amounts of data generated by our operations, we are constantly building the intelligence that powers these decision-making abilities.

Meet Mushtaque, our Last-Mile Agent, who is a resident of Mahim, Mumbai and has been associated with us for the last three years. If you wish to partner with us, please login at for more details. #PartnerWithDelhivery #PartnerTestimonials #PartnerProgram

Lets consider a simple example - the resolution of user addresses. In a country like India, one of the fastest-growing consumer markets, the address system is not highly structured. Most companies will lose visibility of an address beyond the pin code (median area of 80 sq km). Our proprietary address resolution engine, Addfix, has been trained over last-mile GPS traces across 750 million successful deliveries to help us accurately predict address locations to within 200m.

Similarly, our other AI/ML systems can predict preferred timeslots for attempting a shipment, predict whether a shipment can be flown or not based on its description, learn on-ground movement constraints based on recent location data, amongst others. These insights feed into our optimisation models, enabling us to ensure our shipments move at the fastest speed at minimum cost while respecting operational constraints and customer preferences.

Altogether, these systems work in tandem to minimise dependency on human decision making to give us a competitive advantage when it comes to both speed and efficiency. Every player in the logistics industry will need to adopt these tools across the board to remain competitive in the days to come.

Question: The global supply chain is already undergoing a major transformation enabled by Big Data and powered by data science teams using advanced technologies like artificial intelligence, blockchain, and robotics. We obviously hear businesses summarize this into fancy terms like 'Industry 4.0' and 'Supply Chain 4.0' but do you see supply chain digitization being a big differentiator in ensuring better efficiency?

Kapil Bharati: The fundamental change over the last few years has been the omnipresence of devices that generate rich data, the rise of the gig/ sharing economy, and the ability to process vast quantities of data in the background to create systemic intelligence or near real-time in the foreground to provide optimal direction.

This paves the way for a transformation from a highly controlled environment with rigid operating procedures, intensive training requirements, and a lack of adaptability to on-ground situations into an environment that continuously builds intelligence and can flexibly and dynamically direct variable supply through mobile/ internet devices to achieve objectives optimally. This kind of environment will not only be more flexible and adaptive but also capable of unlocking never seen before efficiencies and levels of service.

A key feature of this digital transformation will be the ease of collaboration and the participation of multiple types of actors - whether it is a large FMCG enterprise or a trucker with two trucks, or a college student with 4 hours free to earn some money, or someone with spare space in their warehouse.

The future competition will be between the quality of ecosystems that a logistics player operates in and not just other logistics players. These ecosystems will grow stronger, smarter, and larger with the participation of more players and the addition of extensive and diverse data sets. The Delhivery platform will be one of the first such ecosystems that will spur the real-world transition of domain actors to Supply Chain 4.0.

SEE ALSO: Drones Have Proved Their Utility During COVID-19 in India; Laxed Regulations Might Help Kickstart Industry

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COVID-19 And The Role Of AI, Machine Learning In Logistics: A Conversation With Delhivery CTO Kapil Bharati - Mashable India

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October 19th, 2020 at 3:55 am

Posted in Machine Learning

How to Beat Analysts and the Stock Market with Machine Learning – Knowledge@Wharton

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Analyst expectations of firms earnings are on average biased upwards, and that bias varies over time and stocks, according to new research by experts at Wharton and elsewhere. They have developed a machine-learning model to generate a statistically optimal and unbiased benchmark for earnings expectations, which is detailed in a new paper titled, Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases. According to the paper, the model has the potential to deliver profitable trading strategies: to buy low and sell high. When analyst expectations are too pessimistic, investors should buy the stock. When analyst expectations are excessively optimistic, investors can sell their holdings or short stocks as price declines are forecasted.

[With the machine-learning model], we can predict how the prices of the stocks will behave based on whether or not the analyst forecast is too optimistic or too pessimistic, said Wharton finance professor Jules H. van Binsbergen, who is one of the papers authors. His co-authors are Xiao Han, a doctoral student at the University of Edinburgh Business School; and Alejandro Lopez-Lira, a finance professor at the BI Norwegian Business School.

The researchers found that the biases of analysts increase in the forecast horizon, or in the period when the earnings announcement date is not anytime soon. However, on average, analysts revise their expectations downwards as the date of the earnings announcement approaches. These revisions induce negative cross-sectional stock predictability, the researchers write, explaining that stocks with more optimistic expectations earn lower subsequent returns. At the same time, corporate managers have more information about their own firms than investors have, and can use that informational advantage by issuing fresh stock, Binsbergen and his co-authors note.

The Opportunity to Profit

Comparing analysts earnings expectations with the benchmarks provided by the machine-learning algorithm reveals the degree of analysts biases, and the window of opportunity it opens. Binsbergen explained how investors could profit from their machine-learning model. With our machine-learning model, we can measure the mistakes that the analysts are making by taking the difference between what theyre forecasting and what our machine-learning forecast estimates, he said.

We can measure the mistakes that the analysts are making by taking the difference between what theyre forecasting and what our machine-learning forecast estimates. Jules H. van Binsbergen

Using that arbitrage opportunity, investors could short-sell stocks for which analysts are overly optimistic, and book their profits when the prices come down to realistic levels as the earnings announcement date approaches, said Binsbergen. Similarly, they could buy stocks for which analysts are overly pessimistic, and sell them for a profit when their prices rise to levels that correspond with earnings that turn out to be higher than forecasted, he added.

Binsbergen identified two main findings of the latest research. One is how optimistic analysts are substantially over time. Sometimes the bias is higher, and sometimes it is lower. That holds for the aggregate, but also for individual stocks, he said. With our method, you can track over time the stocks for which analysts are too optimistic or too pessimistic. That said, there are more stocks for which analysts are optimistic than theyre pessimistic, he added.

The second finding of the study is that there is quite a lot of difference between stocks in how biased the analysts are, said Binsbergen. So, its not that were just making one aggregate statement, that on average for all stocks the analysts are too optimistic.

Capital-raising Window for Corporations

Corporations, too, could use the machine-learning algorithms measure for analysts biases. If you are a manager of a firm who is aware of those biases, then in fact you can benefit from that, said Binsbergen. If the price is high, you can issue stocks and raise money. Conversely, if analysts negative biases push down the price of a stock, they serve as a signal for the firm to avoid issuing fresh stock at that time.

When analysts biases lift or depress a stocks price, it implies that the markets seem to be buying the analysts forecasts and were not correcting them for over-optimism or over-pessimism yet, Binsbergen said. With the machine-learning model that he and his researchers have developed, you can have a profitable investment strategy, he added. That also means that the managers of the firms whose stock prices are overpriced can issue stocks. When the stock is underpriced they can either buy back stocks, or at least refrain from issuing stocks.

For their study, the researchers used information from firms balance sheets, macroeconomic variables, and analysts predictions. They constructed forecasts for annual earnings that are a year and two years ahead for annual earnings; similarly, they used forecasts that were one, two and three quarters ahead for quarterly earnings. With the benchmark expectation provided by their machine-learning algorithm, they then calculated the bias in expectations as the difference between the analysts forecasts and the machine-learning forecasts.

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How to Beat Analysts and the Stock Market with Machine Learning - Knowledge@Wharton

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October 19th, 2020 at 3:55 am

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AI and Machine Learning Can Help Fintechs if We Focus on Practical Implementation and Move Away from Overhyped Narratives, Researcher Says – Crowdfund…

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Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used by Fintech platform developers to make more intelligent or informed decisions regarding key processes. This may include using AI to identify potentially fraudulent transactions, determining the creditworthiness of a borrower applying for a loan, and many other use cases.

Research conducted by Accenture found that 87% of business owners in the United Kingdom claim that theyre struggling with finding the best ways to adopt AI or ML technologies. Three out of four or 75% of C-Suite executives responding to Accentures survey said they really need to effectively adopt AI solutions within 5 years, so that they dont lose business to competitors.

As reported by IT Pro Portal, theres currently a gap between what may be considered just hype and actual or practical implementation of AI technologies and platforms.

Less than 5% of firms have actually managed to effectively apply Ai, meanwhile, more than 80% are currently just exploring basic proof of concepts for applying AL or ML algorithms. Many firms are also not familiar or dont have the expertise to figure out how to best apply these technologies to specific business use cases.

Yann Stadnicki, an experienced technologist and research engineer, argues that these technologies can play a key role in streamlining business operations. For example, they can help Fintech firms with lowering their operational costs while boosting their overall efficiency. They can also make it easier for a companys CFO to do their job and become a key player when it comes to supporting the growth of their firm.

Stadnicki points out that a research study suggests that company executives werent struggling to adopt AI solutions due to budgetary constraints or limitations. He adds that the study shows there may be certain operational challenges when it comes to effectively integrating AI and ML technologies.

He also mentions:

The inability to set up a supportive organizational structure, the absence of foundational data capabilities, and the lack of employee adoption are barriers to harnessing AI and machine learning within an organization.

He adds:

For businesses to harness the benefits of AI and machine learning, there needs to be a move away from an overhyped theoretical narrative towards practical implementation.It is important to formulate a plan and integration strategy of how your business will use AI and ML, to both mitigate the risks of cybercrime and fraud, while embracing the opportunity of tangible business impact.

Fintech firms and organizations across the globe are now leveraging AI and ML technologies to improve their products and services. In a recent interview with Crowdfund Insider, Michael Rennie, a U.K.-based product manager for Mendix, a Siemens business and the global leader in enterprise low-code, explained how emerging tech can be used to enhance business processes.

He noted:

Prior to low-code, the application and use of cutting-edge technologies within the banking sector have been more academic than actual. But low-code now enables you to apply emerging technologies like AI in a practical way so that they actually make an impact. For example, you could pair a customer-focused banking application built with low-code with a machine learning (ML) engine to identify user behaviors. Then you could make more informed decisions about where to invest in customer experience and most benefit your business.

He added:

Its easy to see the value in this. The problem is that without the correct technology, its too difficult to integrate traditional customer-facing applications with new technology systems. Such integrations typically require millions of dollars in investment and years of work. By the time an organization finishes that intensive work, the market may have moved on. Low-code eliminates that problem, makes integration easy and your business more agile.

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AI and Machine Learning Can Help Fintechs if We Focus on Practical Implementation and Move Away from Overhyped Narratives, Researcher Says - Crowdfund...

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October 19th, 2020 at 3:55 am

Posted in Machine Learning

Proximity matters: Using machine learning and geospatial analytics to reduce COVID-19 exposure risk – Healthcare IT News

Posted: September 20, 2020 at 10:56 pm

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Since the earliest days of the COVID-19 pandemic, one of the biggest challenges for health systems has been to gain an understanding of the community spread of this virus and to determine how likely is it that a person walking through the doors of a facility is at a higher risk of being COVID-19 positive.

Without adequate access to testing data, health systems early-on were often forced to rely on individuals to answer questions such as whether they had traveled to certain high-risk regions. Even that unreliable method of assessing risk started becoming meaningless as local community spread took hold.

Parkland Health & Hospital System, the safety net health system for Dallas County, Texas, and PCCI, a Dallas-based non-profit with expertise in the practical applications of advanced data science and social determinants of health, had a better idea.

Community spread of an infectious disease is made possible through physical proximity and density of active carriers and non-infected individuals. Thus, to understand the risk of an individual contracting the disease (exposure risk), it was necessary to assess their proximity to confirmed COVID-19 cases based on their address and population density of those locations.

If an "exposure risk" index could be created, then Parkland could use it to minimize exposure for their patients and health workers and provide targeted educational outreach in highly vulnerable zip codes.

PCCIs data science and clinical team worked diligently in collaboration with the Parkland Informatics team to develop an innovative machine learning driven predictive model called Proximity Index. Proximity Index predicts for an individuals COVID-19 exposure risk, based on their proximity to test positive cases and the population density.

This model was put into action at Parkland through PCCIs cloud-based advanced analytics and machine learning platform called Isthmus. PCCIs machine learning engineering team generated geospatial analysis for the model and, with support from the Parkland IT team, integrated it with their electronic health record system.

Since April 22, Parklands population health team has utilized the Proximity Index for four key system-wide initiatives to triage more than 100,000 patient encounters and to assess needs, proactively:

In the future, PCCI is planning on offering Proximity Index to other organizations in the community schools, employers, etc., as well as to individuals to provide them with a data driven tool to help in decision making around reopening the economy and society in a safe, thoughtful manner.

Many teams across the Parkland family collaborated on this project, including the IT team led by Brett Moran, MD, Senior Vice President, Associate Chief Medical Officer and Chief Medical Information Officer at Parkland Health and Hospital System.

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Proximity matters: Using machine learning and geospatial analytics to reduce COVID-19 exposure risk - Healthcare IT News

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September 20th, 2020 at 10:56 pm

Posted in Machine Learning

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