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Artificial intelligence is creating a negative impact on online casinos and sports bookies

Online resources for US online sports gamblers

Nowadays, things move fast in the online gambling industry and, for some people, it can be hard to keep up with new legislation in each state. Fortunately, nowadays there are many online resources for people who have doubts about online sports betting. the experts at chúng tôi have created an exhaustive guide about where are online sports betting legal in the USA. Just by reading the guide, we can fully access accurate and up-to-date information about betting laws and regulations in each state. Just by selecting a state on the map, we can access information about the legal situation of online sports gambling in each territory and keep up with the latest updates on the issue.

The influence of Artificial Intelligence on sports gambling

Artificial Intelligence (AI) is making so many changes in the casino industry. Over the latest ten years, we have witnessed how online casinos evolved to become a massive industry with a broad catalog of games to gamble and entertain users of all ages above 18. In the past decade, sports gambling went through a significant digital transformation through which a completely new way of betting was created. Currently, the Artificial Intelligence (AI) revolution is affecting the online sports gambling business, bringing new ideas on how to create a better experience for gamblers. One of the key factors of Artificial Intelligence is data and how it can provide accurate information to transform a specific business through statistical analysis. Thanks to the insight provided by data, gambling companies can make decisions based on statistics and make a difference in risk management, rewards programs, fraud detection, or customer profiling, among others. Also, Artificial Intelligence is also making a difference in sports betting through real-time information which is crucial for betting. AI is able to provide minute information on each gameplay, facilitating the betting process. With the help of Artificial Intelligence, better predictions can be done, providing the best experience for bettors, and acting as a protection against the undesirable effects of the business. Sports bookies may also be affected by the influence of Artificial Intelligence since we will probably be able to place bets automatically. Anyway, we can’t fully predict what will happen with bookies or have a final answer to the title question.

Technologies evolve and impact our way of living, transforming how we carry out activities in our daily life. Regarding gambling and bet placing, online casinos are changing the industry, bringing new experiences on how we spend our free time, share a game with others or entertain ourselves. Online casinos have rapidly evolved from a marginal business that was not fully regulated to well-stablished enterprises that are completely legal and have a solid controlled frame, providing sports gamblers with a fun and safe environment to place bets. Online casinos are now a reliable business that is growing rapidly after showing how much fun we can have just by sitting in our living room with our mobile phones or tablet. Also, online gambling is much more attractive for those under-40 who are particularly used to computers and video games. Anyway, there are still some people that feel hesitant about online gambling for many reasons. On the one hand, they love the ambiance of land-based casinos and won’t accept losing it by going online for sports gambling. On the other hand, they have doubts about legal and safe issues.Nowadays, things move fast in the online gambling industry and, for some people, it can be hard to keep up with new legislation in each state. Fortunately, nowadays there are many online resources for people who have doubts about online sports betting. The experts at chúng tôi has created an exhaustive guide through which we can learn everything about which state has authorized online sports betting and what are the conditions to bet legally and safely within the United States. Recently, sports betting was authorized by the US Supreme Court, which has lifted the ban on the activity, leading every state to regulate its own sports gambling market. Thanks to the fact thatabout where are online sports betting legal in the USA. Just by reading the guide, we can fully access accurate and up-to-date information about betting laws and regulations in each state. Just by selecting a state on the map, we can access information about the legal situation of online sports gambling in each territory and keep up with the latest updates on the issue.Artificial Intelligence (AI) is making so many changes in the casino industry. Over the latest ten years, we have witnessed how online casinos evolved to become a massive industry with a broad catalog of games to gamble and entertain users of all ages above 18. In the past decade, sports gambling went through a significant digital transformation through which a completely new way of betting was created. Currently, the Artificial Intelligence (AI) revolution is affecting the online sports gambling business, bringing new ideas on how to create a better experience for gamblers. One of the key factors of Artificial Intelligence is data and how it can provide accurate information to transform a specific business through statistical analysis. Thanks to the insight provided by data, gambling companies can make decisions based on statistics and make a difference in risk management, rewards programs, fraud detection, or customer profiling, among others. Also, Artificial Intelligence is also making a difference in sports betting through real-time information which is crucial for betting. AI is able to provide minute information on each gameplay, facilitating the betting process. With the help of Artificial Intelligence, better predictions can be done, providing the best experience for bettors, and acting as a protection against the undesirable effects of the business. Sports bookies may also be affected by the influence of Artificial Intelligence since we will probably be able to place bets automatically. Anyway, we can’t fully predict what will happen with bookies or have a final answer to the title question. In some cases, new technologies fully transform the way in which we perform a specific action, but there are also cases in which the new ways coexist with the old ways for some time. So, there is no straight answer to what will exactly happen with our current way of placing sports bets after the Artificial Intelligence revolution changes it. But it will probably provide a better experience for betters because that is the way the industry can actually grow.

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Artificial Intelligence In Higher Education: Transforming The College Experience

Technology has changed how people learn and has made access to knowledge faster and easier. In particular, artificial intelligence (AI) is changing the educational landscape forever. Now, students can access learning in a more engaging, efficient, and personalized manner. As technology improves, it will become more useful to university and college students.

To understand how AI transforms the learning experience, this article presents how it enhances learning outcomes. It also highlights how these tools can streamline administrative processes and offer personalized guidance to learners.

How AI Improves Learning Outcomes

There are several ways AI improves students’ learning outcomes, as highlighted below.

Customized Learning Paths

Artificial intelligence gives educational institutions access to great analytic abilities. As such, educators may quickly analyze students’ data to determine their:

Assimilation rate;

Strengths;

Weaknesses;

Potential.

Armed with these details, educators can develop customized learning paths specific to an individual. In the course of their learning, students may require the services of Writing Universe for their academic writing. This platform guarantees free essay samples that will help boost students’ imagination.

Intelligent Tutoring

Intelligent tutoring is the learning tool of the future. It is fast, effective, and available 24/7. Also, it can easily guide students through challenging concepts. In addition, it may answer questions and deliver feedback instantaneously. As such, learners that access learning via AI-powered platforms should receive personalized support that simplifies complex concepts.

Adaptive Assessments

Knowledge is always dynamic. Likewise, students’ capacities are not equal. Therefore, a traditional assessment may be inaccurate and unsuitable for gauging understanding. With AI-based adaptive assessments, learners can access dynamic tests and exams that adjust according to a student’s responses. If you are looking for digital aids and learning tools to enhance your academic experience, see here to find apps you should try out while in college. These applications should help you organize your education better.

Streamline Administrative Processes

For administrators and faculty members, artificial intelligence can make administrative processes easier. Below are some AI-based tools that can simplify mundane tasks.

Automated Grading

Typically, it takes a few weeks for learners to receive their grades. It may take longer for a larger class. However, using an AI-powered automated grading system, feedback on assignments, tests, exams, and projects becomes faster. In some cases, students may receive instantaneous feedback for some assessments.

Intelligent Chatbots

Intelligent chatbots can give the public access to information without wasting any time. Also, people from different places can access information in various languages through real-time translation. Speaking of translation, the TheWordPoint platform offers access to professional Spanish Translators. Furthermore, clients are sure of fast and affordable services.

Smart Scheduling

A professor may find balancing between classes, seminars, and other commitments challenging. With smart scheduling applications, an educator can optimize their schedule using AI-powered tools, which reduces conflicts and save time.

Enhanced Student Services

Student services are crucial for college students. With the help of AI, student support can become more efficient in the following ways.

Personalized Guidance

For students seeking guidance, optimized AI support can provide recommendations for career and academic paths based on skills, goals, and interests. Armed with this information, students can make the best decisions regarding their future. For learners looking to graduate from college, the write my thesis platform is an ideal service for seeking project help. Plus, students can get access to talented writers and editors.

Mental Health Assistance

With the help of intelligent virtual assistants and chatbots, learners can access vital resources and learn coping strategies for mental health issues. In addition, this support can help students identify warning signs before they become big problems.

Conclusion

Artificial intelligence is actively transforming students’ college experiences. It achieves this by customizing learning paths and providing intelligent tutoring to adaptive assessments. On the other hand, it helps educators and administrators streamline grading, access to information, and scheduling through automated grading, intelligent chatbots, and smart scheduling. For students seeking information, AI-powered tools offer personalized guidance on a wide range of subjects. In addition, it gives students access to critical mental health resources through AI-based virtual assistance and chatbots.

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How Artificial Intelligence Is Helping To Prevent Blindness

AI For Eye Care Monitoring – Controlling Eye Strain

Eyecare monitors can also be used to avoid eye strain due to prolonged computer usage. The entire system is controlled by a user’s pattern of eye movement, facial expressions, and eye blinking as well as background lighting and ambient noise levels in the surrounding environment. These factors are monitored by the AI monitors, which automatically adjust the screen brightness, visual contrast level, and contrast ratio to prevent prolonged or inefficient use of computers for long periods of time.

This is considered to be a huge help for users who wish to reduce eye strain and improve their eyesight. However, people with macular diseases, such as age-related macular degeneration (AMD), may need to use glasses to avoid the buildup of permanent damage.

AI For Eye Care App

Artificial intelligence (AI) is even being used by doctors to detect and diagnose eye conditions. The AI Eye Test is a tool that can use an image of the eye to detect conditions such as glaucoma, macular degeneration, diabetic retinopathy, and cataracts. Furthermore, AI is being applied in the healthcare industry to help medical professionals make diagnoses of patients faster. AI monitors are able to analyze and process information in a faster manner.

In fact, GlaucomaCalc, which is a web-based tool and AI application, has been created by a group of scientists to help provide more accurate and timely results. This is achieved by using an AI algorithm to process medical imaging data from retinal scans, presenting the information to the user in a simple and intuitive manner.

Diabetic Retinopathy Detection

Diabetic retinopathy (DR) is a disease that affects the blood vessels in the eyes and can lead to further complications such as eye damage and even blindness. In fact, it is one of the leading causes of blindness in working-age Americans. Thus, the ability to detect signs of DR as early as possible and in an efficient way could have a huge impact on preventing blindness.

One study has shown that AI algorithms can be used to automatically detect DR in retinal images with 83-96% accuracy rate. In fact, 71 vision centers are now using these AI algorithms to help provide faster and more accurate diagnoses for patients with DR.However, the technology is still far from perfect, as it does not yet have the ability to automatically identify all possible DR conditions.

Eye Care Screening In Children

In fact, the ability to use artificial intelligence to screen and diagnose eye diseases at a very early stage is now being applied to children as well. AI monitors can now analyze images to detect signs of various eye diseases in children like amblyopia, strabismus, and glaucoma. Furthermore, AI is being used to detect cataracts or whether surgery is going to be successful for patients or not by analyzing images from MRIs and CT scans. Additionally, this is especially important when operating in a developing country where data could be inaccurate and cannot be used for screening.

AI To Study The Human Eye

Artificial intelligence is also being used to study human eye tissue. This is all in the hope of finding better ways to diagnose or treat eye conditions. AI monitors are able to process data in milliseconds, while human experts may take hours or even days per diagnosis. Thus, artificial intelligence is being used to analyze images and determine whether degeneration or damage to the retina has occurred.

Furthermore, a technology known as optical coherence tomography (OCT) uses light waves with different wavelengths to scan through the tissue of the human eye and gives high-definition images of it. These images can then be used for various purposes such as seeing if ADME (absorption, distribution, metabolism, excretion) occurs when a patient administers certain drugs during treatment.

AI To Determine Better Glasses Or Contact Lenses Are Needed For A Patient

The technology of artificial intelligence is now being developed to calculate if a certain contact lens or eyeglass prescription is suitable for an individual. In fact, AI monitors can gather and process data from thousands of patients and even analyze their medical records and use 3D models to determine the best prescription that they should use to prevent eye diseases and maximize their visual acuity.

This can lead to faster and more accurate diagnoses and better treatment outcomes. For example, doctors can now gather data from exams, MRIs, CAT scans, and x-rays and use it to decide on the best prescription for a patient, whether it’s for glasses or contacts.

Artificial intelligence is a very useful tool that can be used to provide high-quality and efficient treatments for patients. It can also be used to prevent problems and improve the overall quality of life. Artificial intelligence is often used in the healthcare industry, but it will soon be used in many other sectors.

Knowing John Mccarthy: The Father Of Artificial Intelligence

AI has become so ingrained in our everyday lives that it’s difficult to comprehend life without it

It is undeniable that the technology industry has seen a wide variety of innovations over the years. The use of

What is the root of the word “Artificial Intelligence”?

After playing a significant role in defining the area devoted to the creation of intelligent machines, John McCarthy, an American computer scientist pioneer and inventor, was called the “Father of Artificial Intelligence.” In his 1955 proposal for the 1956 Dartmouth Conference, the first artificial intelligence conference, the cognitive scientist coined the term. The intention was to see if there was a way to create a machine that could think abstractly, solve problems, and develop itself like a human. “Every aspect of learning or any other feature of intelligence can, in principle, be described so precisely that a machine can be made to simulate it,” he claimed.

Major AI Accomplishments by John McCarthy

It is undeniable that the technology industry has seen a wide variety of innovations over the years. The use of artificial intelligence at any level has proved to be fantastic. It automated a significant number of workers, reducing human effort and has led everyone to believe that there is even more to come. As per report of Artificial Solutions , “Recent results from a large survey of machine learning researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024) all the way to working as a surgeon (by 2053). Researchers also believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years.” Nearly every aspect of our lives is being affected by artificial intelligence machines in order to boost profitability and enhance our human capabilities. AI has become so ingrained in our everyday lives that it’s difficult to comprehend life without it. As a result, we will be eternally grateful to those who were the driving force behind this incredible technology and who have contributed to making computer science even more human-like and efficient.After playing a significant role in defining the area devoted to the creation of intelligent machines, John McCarthy, an American computer scientist pioneer and inventor, was called the “Father of Artificial Intelligence.” In his 1955 proposal for the 1956 Dartmouth Conference, the first artificial intelligence conference, the cognitive scientist coined the term. The intention was to see if there was a way to create a machine that could think abstractly, solve problems, and develop itself like a human. “Every aspect of learning or any other feature of intelligence can, in principle, be described so precisely that a machine can be made to simulate it,” he claimed.Programming languages, the Internet, the web, and robots are just a few of the world’s technological innovations that John paved the way for. He coined the term “Artificial Intelligence,” invented the first programming language for symbolic computation, LISP (which is still used as a preferred language in the field of AI), and invented and established time-sharing. Human-level AI and commonsense reasoning were two of his major contributions. According to Britannica , “McCarthy received (1951) a doctorate in mathematics from Princeton University, where he briefly taught. He also held professorships at Dartmouth College (1955–58), the Massachusetts Institute of Technology (1958–62), and Stanford University (1953–55 and 1962–2000).” His efforts in the field of artificial intelligence have been immaculate throughout his career. McCarthy’s contributions were widely recognized and he received numerous awards. He has won a number of prestigious awards, including: In 1971, he received the Turing Award from the Association for Computing Machinery. In 1988, the Kyoto Prize was awarded. In 1990, he was awarded the National Medal of Science in Statistical, Computational Sciences, and Mathematics by the United States of America. In 2003, the Franklin Institute awarded him the Benjamin Franklin Medal in Cognitive Science and Computers.

Role Of Artificial Intelligence In Smart Meters

Introduction

Global transformations are taking place to get the most out of the data because of the widespread deployment of smart meters, which present more than 16 million in the United Kingdom.

Aim of researchers and utilities are Timely and accurate billing, a better understanding of home energy use, easing the transition to renewable energy and electric vehicles, and improved management of electricity generation and distribution. By reducing unnecessary energy use, households and utilities can cut costs and achieve goals related to energy efficiency and climate change. But how ? Artificial intelligence is the solution.

Emerging technologies like Artificial Intelligence have a role in industries. Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, and other related fields have received attention from businesses recently.

Today, we can use the power of Artificial Intelligence to analyze data from smart meters with the help of machine learning to reduce our energy consumption. Time-series machine learning algorithms make it simple for an algorithm to “learn” how much energy is consumed from historical data and predict the future. That can help the Artificial Intelligence engine decide demand-response and ensures security.

Learning Objectives

1. We will discuss the need for Smart meters and why it is necessary.

2. We will take the Smart energy csv file and do the time series analysis of the number of Smart meters applied by Large and small suppliers domestically. We will take the period analysis by fbprophet and ARIMA.

In India, work is in progress. In England, all houses and others working places will be available by the end of 2025.

This article was published as a part of the Data Science Blogathon.

Table of Contents Why do we Need Smart Meters?

Often they have questions in mind.

1. How to reduce electricity bills?

2. How to stop the coming of inaccurate bills?

Smart meters are the answers to all the questions.

Electronic or Smart meters are self-automatic reading machines that read the meter reading and show us the data stored in rupees, pounds, dollars, or pence.

Smart meters cover gas, water, and electricity. It helps to take readings of gas and electricity, send them to the suppliers, and show off the amount of gas and electricity used by the device installed in your home or office and how you can reduce your energy bill.

The use of Smart meters helps reduce your bills. Reduction in CO2 emissions and accurate readings.

Now no Complaints about inaccurate or missing bills.

Why are Smart Meters Helpful to Customers and Suppliers?

1. No more inaccurate bills: A smart meter design helps to know how much you have used electricity and gas and how much is remaining. It will display the days left based on the daily use of electricity and gas. In other words, if you have a non-smart meter, then based on the use of electricity and gas in the past, Suppliers issue bills that can be inaccurate. With the help of a Smart meter, false billing gets stopped, and you know how to limit your use of electricity and gas.

2. By Smart meters, more data will come out that can help the suppliers to know about the supplies of energy customers build and make a profit. For example, if the energy bill exceeds suddenly at night will mean some fault or applications were running at night.

3. Benchmarking analysis is another way to know your competitors in the same field.

Altogether Smart meters are necessary options to be taken to reduce CO2 emissions into the atmosphere.

With the help of the SMILE project in the United Kingdom, the energy usage by a Smart meter in care-taking homes or patients will let us know when the person uses which day electricity more, leading to the observation of health in the disabled and elderly.

Smart meters will let us know when the person uses which day electricity more, which will detect health issues in the disabled and elderly.

How can a Smart Meter be Acquired?

Advanced metering infrastructure is a system with controller, Suppliers that controls your energy usage by communicating in two ways. It is not the like an Automated meter system. It conveys two ways by sending home the energy used at a particular time. It is a host to collect the reading by broadband over a power line or landline and then sends it to the meter data management system.

A Distribution Company or Discom is a business that delivers electricity to customers. These companies don’t make electricity but buy electricity from the people who make it and sell it to people. Discoms are the owners of the grids that you see all over your city. These are part of the electricity chain with two companies named GENCOs that generate and TRANSCOs that transmit the electricity.

Discoms are two types owned by the state and private. By the state, like Kerala Electricity Board and Karnataka Power Corporation Limited, and private discoms, like Tata Power, BSES Rajdhani, and Reliance.

About 1.7 million Smart meters got installed by Energy Efficiency Service Limited, IntelliSmart, and other agencies.

facilitate the implementation of smart meters through a BOOT (Build, Own, Operate, Transfer) model. Energy Efficiency Services Ltd (EESL) is an Indian government energy service company.

The government has mandated that energy providers provide their customers with smart meters.

Contact your energy provider for smart meter installation at a time and date that work best for you. It will not cost you anything.

According to our most recent analysis, the country is gaining more benefits from the rollout than it implemented.

What are Solar Panels?

Solar panels comprise solar cells of silicon, phosphorous (negative charge), and boron (positive) in layers.

The Photovoltaic effect is where the photons from solar panels start an electric current that hits the solar panel’s surface and releases electrons from orbits into the solar cells’ electric field, which pulls these free electrons into a directional Current.

A home roof space often has enough room for the number of solar panels needed to generate enough electricity to meet all of its needs. Any unwanted goes to the power grid, which saves money on electricity bills at night.

Solar Panels and Smart Meters

Energy Suppliers

There are two meters, one for electricity and one for gas; Smart meters will take their places. An In-Home Display (IHD) is a handheld device attached to the home and is simple to use.

We took the data of England’s domestic and non-domestic gas and electricity meters by small and all suppliers for September 2023.

Engineers will have better information about what caused power outages and will be able to detect them much more quickly. They will be able to complete repairs more quickly and affordably.

Additionally, smart meters are contributing to our decrease in reliance on imported fossil fuels. We can save money using energy during off-peak hours or when there is more clean electricity. Some customers have even got an award for using electricity on windy days.

The Sum of Small suppliers for gas meters was highest for 2023 at 137450, followed by 2023 and 2023. The maximum amount of applying of gas meters is in 2023 by the large suppliers.

The suppliers of large gas meters were more in 2023, 2023, and 2023. The suppliers for small electricity meters are more in 2023 and 2023.

Time Series data are the data that make changes or move a period, and to know the future data values, we need Time Series forecasting.

Time Series Analysis

A procedure of analyzing a sequence of data values collected over a specific time is called time series analysis.

Time Series data analysis puts insight into seasonal patterns, trends, and the future that can help Electricity and Gas Suppliers to make profits.

Models and Techniques for Time Series Analysis

ARIMA (Autoregressive, Moving Average model): It takes past values to predict the future.

Autoregressive – An autoregression model assumes that previous time step observations can predict the value at the subsequent time step.

Integrated – The difference between the new data values and the previous values takes their place to make the data stationary.

Moving Average – A moving average takes the arithmetic mean of a particular set of values over a specific period.

Univariate ARIMA- Jenkins model: Ony Single dependent variable like temperature.

The single variable data in the univariate ARIMA model is forecast. For example

Temp Date 2023-02-01 60 2023-02-02 70 2023-02-03 55

Multivariate ARIMA – Jenkins models: Multiple dependents like temperature and humidity.

The two or more variable data in the multivariate ARIMA model are forecast. For example

Temp Humidity Date 2023-02-01 60 75.2 2023-02-02 70 60.1 2023-02-03 55 52.3

Time series data analysis involves the following steps.

1. Stationary – To check for seasonal patterns of the data. A series whose properties do not change over time is called a stationary time series. Variance, mean, and covariance are these characteristics. Trends and seasonality are absent from stationary time series.

2. Autocorrelation – Future values are correlated to past values or not.

The relationship between two variables is termed correlation means variables are related to each other.

Positive correlation occurs when both variables change in the same direction (e.g., simultaneously go up or down). A negative correlation occurs when two variables change values in opposite directions (e.g., one goes up and one goes down).

Autocorrelation is the term used to describe the correlation between the variable and itself at earlier time steps.

Interestingly, the time series problem may not be predictable if all lag variables have low or no correlation with the output variable.

For Stationary data, we will use Dickey-Fuller Test.

It will give p-values. If we accept the null theory, the data is stationary, and if we reject it, not Stationary data.

We calculate the rolling mean and the amount of variance (STD) for seven months.

rolling_mean = meter['Large SE meter in SM'].rolling(7).mean() rolling_std = meter['Large SE meter in SM'].rolling(7).std()#import csv

We imported the adfuller from the stats model and passed out the data meter and parameter AIC.

from statsmodels.tsa.stattools import adfuller af = adfuller(meter['Large SE meter in SM'],autolag="AIC") data_out = pd.DataFrame({"Values":[af[0],af[1],af[2],af[3], af[4]['1%'], af[4]['5%'], af[4]['10%']] , "Metric":["Test Statistics","p-value","No. of lags used","Number of observations used","cvalue(1%)", "cvalue (5%)", "cvalue (10%)"]}) print(data_out)

The p-value is more than 0.05, and the critical value is less than the test statistics results. The data is not stationary and has increasing trends.

Autocorrelation

autoc_lag1 = meter['Large SE meter in SM'].autocorr(lag=1) print("One Month Lag: ", autoc_lag1) autoc_lag3 = meter['Large SE meter in SM'].autocorr(lag=3) print("Three Months Lag: ", autoc_lag3) autoc_lag6 = meter['Large SE meter in SM'].autocorr(lag=6) print("Six Months Lag: ", autoc_lag6) autoc_lag9 = meter['Large SE meter in SM'].autocorr(lag=9) print("Nine Months Lag: ", autoc_lag9) One Month Lag: 0.9916188803959796Three Months Lag: 0.9549079337204182 Six Months Lag: 0.9261679644654887 Nine Months Lag: 1.0

The results show data is highly correlated.

Decomposition

from statsmodels.tsa.seasonal import seasonal_decompose dec = seasonal_decompose(meter['Large SE meter in SM'],model='additive', period=5) dec.plot()plt.show() meter['Year'] = meter.indexdf = pd.DataFrame() df['ds'] = meter['Year']df['y'] = meter['Large SG meters in SM'] df.head()split_date = 2023df_train = df.loc[df.ds <= split_date].copy() plt.plot(df_test, color = "red")plt.title("Train/Test split for Suppliers") plt.ylabel("Suppliers") plt.xlabel('Year-Month')sns.set()plt.show()#import csv from pmdarima.arima import auto_arima df['Year'] = df.index split_date = 2023 df_train = df.loc[df.ds <= split_date].copy() model = auto_arima(df_train['y'], trace=True, error_action='ignore', suppress_warnings=True) model.fit(df_train['y']) forecast = model.predict(n_periods=len(df_test['y'])) forecast = pd.DataFrame(forecast,index = df_test['y'].index,columns=['Prediction']) ARIMA(1,0,1)(0,0,0)[0] intercept : AIC=inf, Time=0.22 sec ARIMA(0,0,0)(0,0,0)[0] intercept : AIC=153.606, Time=0.26 sec ARIMA(1,0,0)(0,0,0)[0] intercept : AIC=155.409, Time=0.09 sec ARIMA(0,0,1)(0,0,0)[0] intercept : AIC=inf, Time=0.14 sec ARIMA(0,0,0)(0,0,0)[0] : AIC=154.292, Time=0.00 sec Best model: ARIMA(0,0,0)(0,0,0)[0] intercept Total fit time: 0.700 seconds plt.plot(forecast) from math import sqrtfrom sklearn.metrics import mean_squared_error rms = sqrt(mean_squared_error(df_test['y'],forecast)) print("RMSE: ", rms)RMSE: 6731165.9583494775 def mape(ac, pre): ac, pre = np.array(actual), np.array(pre) return np.mean(np.abs((ac - pre) / actual)) * 100 mape(df['y'], forecast)4098.346771463499 Time Series Analysis of Large supplier’s Gas Meters by Fbprophet

Fbprophet, an open-source library developed or built by Facebook, is used for time series analysis. It requires two columns where ds refers to the year and the y column to the data variable.

Why Fbprophet?

It hands out several outliers and null values and shows results in seconds.

The user can manually add seasonality and holiday values.

Code

from sklearn.metrics import mean_squared_error, mean_absolute_errorfrom chúng tôi import add_changepoints_to_plot meter=pd.read_csv("Meter_energy_domestics.csv") meter=meter.fillna(0)meter.head() sns.boxplot(x =meter['Large SG meters in SM']) meter=meter.set_index("Year") meter.plot()#import csv

We then converted the Year column into the ds column and took the y column.

meter=meter.reset_index()df = pd.DataFrame()df['ds'] = meter['Year'] df['y'] = meter['Large SG meters in SM']df.head()split_date = 2023 m = Prophet()m.fit(df_train)future = m.make_future_dataframe(periods=365) forecast = m.predict(df_test) forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper', 'trend', 'trend_lower', 'trend_upper']].tail() fig1 = m.plot(forecast)#import csv m.plot_components(forecast) print("MSE:", mean_squared_error(y_true = df_test["y"], y_pred = forecast['yhat'])) print("MAE:", mean_absolute_error(y_true = df_test["y"], y_pred = forecast['yhat'])) MSE: 788768128821.7513MAE: 790450.4288312361 print("MAPE: ", mean_abs_perc_err(y_true = (df_test["y"]), y_pred = (forecast['yhat']))) MAPE: 57.08464486655384#import csv

Time series analysis of Large supplier’s gas meter in Traditional mode by fbprophet.

df = pd.DataFrame() df['ds'] = meter['Year'] df['y'] = meter['Large SG meters in TM'] df.head() split_date = 2023 df_train = df.loc[df.ds <= split_date].copy() m = Prophet()m.fit(df_train) future = m.make_future_dataframe(periods=365) forecast = m.predict(df_test) forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper', 'trend', 'trend_lower', 'trend_upper']].tail() fig1 = m.plot(forecast)#import csv

Large Suppliers’ gas meter non-smart

df = pd.DataFrame()df['ds'] = meter['Year'] df['y'] = meter['Large SG meters non-smart'] df.head()split_date = 2023 m = Prophet()m.fit(df_train)future = m.make_future_dataframe(periods=365) forecast = m.predict(df_test) forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper', 'trend', 'trend_lower', 'trend_upper']].tail() fig1 = m.plot(forecast)#import csv

Large Suppliers’ Electricity meters in Smart mode

df = pd.DataFrame()df['ds'] = meter['Year']df['y'] = meter['Large SE meter in SM']df.head()split_date = 2023 m = Prophet()m.fit(df_train)future = m.make_future_dataframe(periods=365) forecast = m.predict(df_test) forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper', 'trend', 'trend_lower', 'trend_upper']].tail() fig1 = m.plot(forecast)#import csv Conclusion

We discuss the few benefits of Smart meters, their uses, and how they can be the foundation of the future in health-related issues. We further discussed Solar Panel and went for an analysis of the data and its future predictions.

Key Points

Before Smart meters, there was often a complaint about inaccurate or missing bills.

1. The smart meter helps reduction of electricity bills and misinformation about electricity bills.

2. Facebook created Prophet, an open-source library for automatically forecasting univariate time series data.

3. An analysis of the data and its future predictions by fbprophet and ARIMA.

4. fbprophet results show a lower MAPE value.

5. The analysis results show the future of Large suppliers’ gas meters in Smart mode rises. The large suppliers’ gas meters non-smart will decrease, traditionally, with similar values.

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5 Ways Artificial Intelligence May Affect Health Care Industry In The Upcoming Years

Technology is changing quickly, and the entire world is shifting it. Concepts which were only science fiction just a few decades ago — such as artificial intelligence development (AI) — are rapidly becoming commonplace. Computers have become strong enough to manage complicated AI computations; machine learning algorithms are more precise and quicker than everand the cloud and the internet of things have made it possible for small devices to get artificial intellgence’s tremendous capabilities.

1. Digital consultations

Digital consultations are not brand new. For several decades, there are medical diagnostic methods on the internet or on the telephone, for example WebMD or the United Kingdom’s NHS 111 system. All these”dumb” systems have considerable limitations.

Rather than blindly following a record, AI digital appointment programs have heard out of countless genuine instance records to ask questions which are related to the specific patient.

Secondly, progress natural language processing can comprehend complicated paragraphs instead of force individuals to pick predefined choices. Collectively, these two AI technologies will help answer individual inquiries and recommend courses of actions like creating a GP appointment or visiting the ER.

Many companies are currently offering AI-driven digital consultation solutions. Finally, digital consultations must help cut back on unnecessary physicians’ visits and enhance healthcare efficiency.