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A method that can accurately use machine learning algorithms for Bitcoin price prediction

Bitcoin is probably the most talked about cryptocurrency that is somehow on top of everyones lists when it comes to thinking about investing. However, when it comes to actual investing, most people would like to have some magic ball to see the future instead of exposing themselves to the enormous risk that naturally comes with it. Have you wondered what machine learning could do in this regard? Lets find out. Bitcoin price prediction with machine learning can be made by leveraging the Google Trends search volume index and Baidu media search volume, essential measures of investor attention and media hype that reflect the sentiment in the highly speculative cryptocurrency market.

Also, by integrating gold spot price with regular features such as property, network, trading, and market in the machine learning algorithm, it is possible to develop higher-dimensional features and avoid the problem of simplifying Bitcoin price prediction.

As Bitcoin price fluctuates significantly, machine learning models are applicable and valuable. Various popular machine learning algorithms, including recurrent neural networks, long short-term memory, support vector machines, and random forest models, have therefore been implemented in previous studies.

After the global financial meltdown in 2008, the BTC blockchain was conceived as a new type of currency with a mechanism that can sidestep existing banking systems. Since then, it and other cryptocurrencies have become a prevalent means of exchanging value. Although the platform initially mainly attracted traders who preferred to wager on volatile assets, it has become a new type of investment serving as a keeper of value and protecting against inflation.

Several years ago, retail investors and traders gambled on an increasing price without basing them on reason or facts, which caused previous price oscillations. However, that has changed. As the crypto markets mature, regulatory authorities develop rules specifically for investors. That being said, even though Bitcoin price still fluctuates, many are now considering it the future of the mainstream economy.

The cryptocurrency market is highly volatile, and your investments are at risk. BTC first became available in 2009 and was worthless at the start. Its price increased to US$0.09 in 2010 and US$1.00 by February 2011, after which it surged to US$29.60. The crypto market took a nosedive after that, with BTCs price falling to just US$2.05 by mid-November 2011.

2016 saw a gradual increase, with prices reaching over US$900 before the years end. They skyrocketed by the end of December 2017, reaching US$19,345.49. The coin once again gained friction during the start of the COVID-19 pandemic, reaching US$29,000 by the end of 2020. At present, Bitcoin is hovering around US$26k.

As the digital asset market reels from the SEC crackdown on Binance and Coinbase, Bitcoin and other cryptocurrencies are hovering at crucial levels. Bitcoin has lost 1.8% of its value in the last week, and experts forecast that this trend will continue.

Bitcoin has a value of US$26,568.11 with a market cap of US$515B, down by 0.05% overnight. The Bitcoin trading volume has also taken a hit, falling by 20.22% in that same time and now sitting at US$11,783,962,824. With Bitcoin slowly losing its value, analysts foresee a drop below US$26,000.

In conclusion, as you can tell, the future of the cryptocurrency king is still uncertain, and several possible scenarios could play out. Different approaches are available for Bitcoin price prediction but can never be 100% correct. Whatever happens, it will be interesting to see how the crypto market evolves in the next few years.

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Bitcoin Price Prediction: A Machine Learning Approach - Analytics Insight

By Hub staff report

Johns Hopkins scientists have developed a method involving artificial intelligence to visualize and track changes in the strength of synapsesthe connection points through which nerve cells in the brain communicatein live animals. The technique, described in Nature Methods, should lead to a better understanding of how such connections in human brains change with learning, aging, injury, and disease, the scientists say.

"If you want to learn more about how an orchestra plays, you have to watch individual players over time, and this new method does that for synapses in the brains of living animals," says Dwight Bergles, professor in the Department of Neuroscience at the Johns Hopkins University School of Medicine.

Image caption: Thousands of SEP-GluA2 tagged synapses (shown in green) surround a sparsely labeled dendrite (show in magenta) before and after XTC image resolution enhancement. Scale bar is 5 microns.

Image credit: Xu, Y.K.T., Graves, A.R., Coste, G.I. et al. Nat Methods

Bergles co-authored the study with colleagues Adam Charles and Jeremias Sulam, both assistant professors in the Department of Biomedical Engineering, and Richard Huganir, Bloomberg Distinguished Professor at JHU and director of the neuroscience department. All four researchers are members of Johns Hopkins' Kavli Neuroscience Discovery Institute.

Nerve cells transfer information from one cell to another by exchanging chemical messages at synapses, or junctions. In the brain, the authors explain, different life experiences, such as exposure to new environments and learning skills, are thought to induce changes at synapses, strengthening or weakening these connections to allow learning and memory. Understanding how these minute changes occur across the trillions of synapses in our brains is a daunting challenge, but it is central to uncovering how the brain works when healthy and how it is altered by disease.

To determine which synapses change during a particular life event, scientists have long sought better ways to visualize the shifting chemistry of synaptic messaging, necessitated by the high density of synapses in the brain and their small sizetraits that make them extremely hard to visualize even with new state-of-the-art microscopes.

"We needed to go from challenging, blurry, noisy imaging data to extract the signal portions we need to see," Charles says.

To do so, Bergles, Sulam, Charles, Huganir, and their colleagues turned to machine learning, a computational framework that allows flexible development of automatic data processing tools. Machine learning has been successfully applied to many domains across biomedical imaging, and in this case, the scientists leveraged the approach to enhance the quality of images composed of thousands of synapses. Although it can be a powerful tool for automated detection, greatly surpassing human speeds, the system must first be "trained," teaching the algorithm what high quality images of synapses should look like.

In these experiments, the researchers worked with genetically altered mice in which glutamate receptorsthe chemical sensors at synapsesglowed green, or fluoresced, when exposed to light. Because each receptor emits the same amount of light, the amount of fluorescence generated by a synapse in these mice is an indication of the number of synapses, and therefore its strength.

As expected, imaging in the intact brain produced low quality pictures in which individual clusters of glutamate receptors at synapses were difficult to see clearly, let alone to be individually detected and tracked over time. To convert these into higher quality images, the scientists trained a machine learning algorithm with images taken of brain slices (ex vivo) derived from the same type of genetically altered mice. Because these images weren't from living animals, it was possible to produce much higher quality images using a different microscopy technique, as well as low quality imagessimilar to those taken in live animalsof the same views.

This cross-modality data collection framework enabled the team to develop an enhancement algorithm that can produce higher resolution images from low quality ones, similar to the images collected from living mice. In this way, data collected from the intact brain can be significantly enhanced and able to detect and track individual synapses (in the thousands) during multiday experiments.

To follow changes in receptors over time in living mice, the researchers then used microscopy to take repeated images of the same synapses in mice over several weeks. After capturing baseline images, the team placed the animals in a chamber with new sights, smells, and tactile stimulation for a single five-minute period. They then imaged the same area of the brain every other day to see if and how the new stimuli had affected the number of glutamate receptors at synapses.

Although the focus of the work was on developing a set of methods to analyze synapse level changes in many different contexts, the researchers found that this simple change in environment caused a spectrum of alterations in fluorescence across synapses in the cerebral cortex, indicating connections where the strength increased and others where it decreased, with a bias toward strengthening in animals exposed to the novel environment.

The studies were enabled through close collaboration among scientists with distinct expertise, ranging from molecular biology to artificial intelligence, who don't normally work closely together. The researchers are now using this machine learning approach to study synaptic changes in animal models of Alzheimer's disease, and they believe the method could shed new light on synaptic changes that occur in other disease and injury contexts.

"We are really excited to see how and where the rest of the scientific community will take this," Sulam says.

The experiments in this study were conducted by Yu Kang Xu, a PhD student and Kavli Neuroscience Discovery Institute fellow at JHU; Austin Graves, assistant research professor in biomedical engineering at JHU; and Gabrielle Coste, a neuroscience PhD student at JHU. This research was funded by the National Institutes of Health (RO1 RF1MH121539).

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Machine learning helps scientists see how the brain adapts to ... - The Hub at Johns Hopkins

A chilled CBD-infused Labatt Breweries beverage is coming to a market near you this December.

Fluent Beverage Company, the joint-partnership between the massive brewer Anheuser-Busch Inbev NV (EBR:ABI) and global cannabis pioneer Tilray Inc. (NASDAQ:TLRY), announced this week it will commercialize a non-alcoholic, CBD-infused beverage for Canadians likely hitting markets in December 2019.

Beer drinkers will know Anheuser-Busch by its Canadian subsidiary Labatt Breweries, which employs over 3,400 canucks and brews Budweiser, Kokanee, Stella Artois, Corona, Palm Bay and Mikes Hard Lemonade, to name a few.

The joint venture was announced in December 2018 when High Park, a wholly-owned subsidiary of Tilray, and Labatt partnered to research a non-alcoholic drink containing weed cannabinoids tetrahydrocannabinol (THC) and cannabidiol (CBD).

Each company is investing up to $50 million in the partnership, according to Benzinga.

The companies need more time to research beverages containing THC and will only be providing CBD-drinks in December, Fluents chief executive Jorn Socquet told the Canadian Press.

THC, the intoxicating compound in cannabis, is unstable and degrades too quickly for a reasonable shelf life whereas CBD, the non-intoxicating compound, remains potent and stable for longer, said Socquet.

What the drink will actually look like, taste like, or smell like isnt being revealed, but Socquet told the Canadian Press the non-alcoholic CBD-infused drink will likely be sparkling, slightly sweet and tea-like.

The partnership between Labatt and Tilray comes after two similar beer and weed partnership announcements from August 2019.

Molson Coors Brewing Co. (TSX:TPX.B) and Quebec-based HEXO Corp. (NYSE:HEXO) are partnering to get cannabis-infused non-acloholic drinks to Canadians, and Constellation Brands Inc.(NYSE:STZ)(NYSE:STZ.B) bought a 38 per cent majority share of Canopy Growth Corp. (NYSE:CGC)(TSE:WEED) in August to invest in a similar venture.

Canadians wont be able to crack a cold CBD one till the government passes the second wave of cannabis legalization, set for October 17 which will legalize beverages, edibles, vapes and topicals. Even then consumers will have to wait 60 days while companies give a mandatory notice to Health Canada before drinks sales kick off.

If everything goes according to plan, expect the tsunami of CBD-drinks to hit one week before Christmas.

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Finlay Minerals to use machine-learning to increase exploration success in British Columbia project - Mugglehead

HOLMDEL, N.J., June 13, 2023 /PRNewswire/ -- Over 67 percent of CISOs plan to embrace new technology including machine learning tools to detect ransomware activity over the next year, research conducted by Evaluator Group determined, with earlier detection of ransomware corruption and support for faster discovery of the last clean backup the top analytics requested.

"Machine learning and analytics are critical in the race against cyber criminals"

Evaluator Group conducted a survey of 163 CISOs to define the top data management challenges, at the behest of Index Engines, whose CyberSense software detects signs of data corruption due to ransomware and facilitates an intelligent and rapid restoration.

"Machine learning and analytics are critical in the race against cyber criminals and CISOs have realized this," said Jim McGann, VP of Business Development and Marketing at Index Engines. "Ransomware attacks are getting more sophisticated, evading thresholds and metadata-level security tools. Machine learning and analytics can observe data, look deep into files and make deterministic decisions on whether it's been corrupted by ransomware or give you confidence that it's clean for recovery."

CISOs struggle to detect attacks and find the last known good copy of data for recovery, the study found, along with bare minimum recovery expected to take hours with full recovery expected to take weeks or months often resulting in data that is forever lost due to malicious corruption.

Currently, security professionals lack in-house ability to use deep forensic analysis to determine what happened and how to recover intelligently, the report stated. Only 11% of respondents indicated they have all the capabilities they need from their current vendors.

Two-thirds of the respondents said they plan to add data analytics and/or machine learning tools to detect suspicious activity over the next year, the report showed. More than half said they planned to add data loss prevention software and tools to continuously monitor for malicious software. Rounding out the top five choices were audit data for sensitive content (48%) and data forensics analysis for post-ransomware attack (47%).

Budgets are increasing to support the increasing sophistication of ransomware attacks, the report showed, with 84% reporting their cyber security budget is increasing this year, with 49% of budgets increasing up to 10%. Only 12% said it would increase more than 25%, the same number who said there would be no change. Only 4% said their cybersecurity budget is decreasing.

When asked what they wanted most for cyber resiliency analytics, 71% of respondents said "earlier detection of a cyberattack," with 43% listing "faster identification of last known good recovery point" and 41% selecting "increased confidence that malware was eradicated from the environment."

"Organizations need features such as anomaly detection and the ability to find the last known good copy of data following an attack to fully recover," Evaluator Group senior analyst Dave Raffo said. "Data forensics tools and processes that focus on analyzing, identifying, monitoring and reporting on digitally stored data can help facilitate successful data recovery."

To read the full report, go to: https://go.indexengines.com/eg_data_management_challenges_CISO

SOURCE Index Engines

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Two-Thirds of CISOs Plan to Ramp Up the Battle Against ... - PR Newswire

The AI and machine learning (ML) craze taking tech by storm is a gold rush. And as in a traditional gold rush, there are plenty of picks and shovels to be sold.

For large language models and other cutting-edge AI models, the tools come in the form of specialized chips for more efficient computing. Chipmaker Nvidia was propelled briefly to a trillion-dollar market cap due to interest in its AI-focused graphics cards. Startups around the world are designing their own hardware that is optimized for AI and ML applications.

The market map below outlines the global AI and ML VC ecosystem and where the capital is going. Explore the AI and ML semiconductors segment by clicking on the blue tile below.

Notable deals include Moore Threads, an AI chip startup that raised $213.2 million in venture funding, and Bolttech, an insurtech startup using AI to automate processes, which raised a $300 million Series B.

Almotive, a startup creating automated driving systems, was acquired by the auto conglomerate behind Fiat and Chrysler Stellantis for an undisclosed amount in December. ECARX, a startup developer of AI-centric chips acquired COVA Acquisition for $300 million and went public in December.

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Market map: Investors bet on the chips powering AI and machine ... - PitchBook News & Analysis