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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.

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2. Radiology and images

This is a health care field whose practitioners spend substantial time and experience taking a look at pictures in addition to patients. This makes it a fantastic match for premature AI adoption. With computer vision technologies, systems may be trained to examine x-rays or other scans and employ deep learning to comprehend what pictures reveal.

Since the outcomes of this AI discovery can then be delivered to a physician for to double-check the exact outcomes, AI for radiology is currently being used in hospitals. In November, as an instance, the University of Rochester Medical Center announced it had been utilizing technology from Aidoc, an AI radiology firm, to help identify and prioritize critical instances to ensure urgent-care patients may be found by a radiologist first, providing those patients that the very best of both worlds: AI and a physician together.

Obviously, as machine learning and artificial intelligence services technologies develop, it will not be long until AI radiology alternatives are always faster and more precise than human physicians could be. Personalized medicine: Quicker, more precise diagnosis

3. Personalized medicine: Faster, more accurate diagnosis

This starts at the identification phase. Home-based AI-driven analysis is at its infancy, but a few fascinating programs are being analyzed. Remidio, by way of instance, creates a mobile-phone based identification for diabetes by analyzing photos of a consumer’s eye; this technique has been used efficiently.

4. Robot surgeons

In the opposite end of this scale, AI can also help in one of the very”hands on” regions of medicine: operation.

However, AI is arriving to robots, also. The wise Tissue Autonomous Robot (STAR) can suture stitches that are far cleaner and more precise than that which a human surgeon may perform; and early evaluations reveal the technology may also correctly eliminate a tumor without damage to the surrounding tissues. And with no necessity for eyes, a number of these robotic processes can be run laparoscopically (a.k.a.”keyhole surgery”), making recovery much quicker and lowering the probability of disease.

The acceptance procedure for AI robot surgeons is very likely to be more compared to those technologies that assist physicians do what they are already doing. However, the benefits can be huge.

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5. Cybersecurity

Hospitals have been hacked at a alarming rate, forcing all stakeholders to adopt stricter cybersecurity policies. Earlier this season, healthcare cybersecurity seller CyberMDX found a vulnerability in a popular syringe pump which could permit an attacker to take over the apparatus and administer deadly doses of medication.

That is where AI can help. Advanced cybersecurity solutions may use machine learning how to comprehend normal network behavior and identify and prevent any anomalous actions that may indicate vulnerabilities or attacks. Luckily, it is coming to healthcare, and it is coming soon. If you are an entrepreneur at the medical industry, you have to take note.

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How Ai Robotics Are Transforming The Health Care Industry

Two of the most futuristic technologies that the world is leveraging today are AI and Robotics. Implementing these two technologies can lead to innovations in several industry verticals, including the healthcare industry.

AI Robotics are Transforming the Health Care Industry

AI and Robotics are already working in several healthcare establishments. They’re carrying out tasks such as genetic testing, robotic surgery, cancer research, data collection, and more.

Additionally, in the dermatology sector, AI is detecting skin cancer. The process of detecting skin cancer involves a technology, “MelaFind,” that uses infrared light to evaluate the skin condition. Afterward, with its sophisticated algorithms, AI evaluates the scanned data to determine skin cancer’s seriousness.

When it comes to the healthcare industry, there are several disciplines that AI and Robotics need to cover

AI and Robotics require more unveiling and continued experimentation to become an integral part of the industry and bring innovations through these emerging technologies.

AI and Robotics can fill in the medical and healthcare industry gaps

On the other hand, the most crucial question remains unanswered; “are we prepared to deliver the entire life and death decisions to the machines” ,”can machines indicate if the care has been provided to a patient’s sufficient or not?”

Assessing the questions mentioned previously might not be simple since there are various challenges and problems the program of AI and Robotics can contribute to. But, 1 thing is for certain: AI and Robotics are poised to develop into an essential component of the medical market.

The implementations of AI and Robotics in the healthcare industry.

We are going to be covering many areas of the medical sector where this technology can facilitate the overstraining procedures in health care delivery.

Below are some of the Present examples of execution of AI and Robotics at the healthcare domainname:

Determining the patient’s priority assessments in emergency service.

Automated health tracking of patients.

Quick and continuous supply of medicine and equipment throughout the hospital floors via intelligent robots.

Interacting with patients via vocals or facial recognition.

Programming personalized health programs in robots enable users to leverage them for multifunctional purposes.

Intro to AI & Robotics in Healthcare Sector

AI and Robotics have proved to be widespread in the medical market. The ubiquitous development of both of these technology has the capacity to transform a lot of facets of healthcare.

From providing personal services to patients to expedite the medication manufacturing process, AI and Robotics can make sure a quicker roll-out date and a efficient and precise performance.

In addition, there are numerous major tech companies out there which are capitalizing on AI and Robotics to enhance the medical infrastructure. By way of instance, Google is currently cooperating with the health care delivery system to construct prediction models.

These forecast models by Google derive from large information and machine learning technologies. They could warn the clinicians of high risk states of the individual, like heart failure or sepsis.

Additional Microsoft is focusing on altering the healthcare sector by fostering and building a culture of smart health through AI and Cloud in health care organizations.

1. Supplementary Robots

These bots are usually aimed towards distributing shares all over the hospital or where they are needed. We have discussed these kinds of robots above too nevertheless, their significance and viability have to be clarified also.

In hospitals, there are instances when multiple patients need immediate drugs or help. In instances like this, the team is generally in a rush to aid the individual rather than carrying out other jobs. Therefore, supplementary robots these days are quickly focusing on tasks like restocking, carrying out the garbage, and cleaning while the people are spending additional time together with the individual.

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2. Exceptional Precision

Multiple autonomous systems employed from the planet’s top hospitals now supply immense feasibility in executing more complicated jobs at a quick and precise rate.

Robot’s real concentrate and robots that are attentive further fortify their core performance and permit them to perform jobs with intense precision. These bots are supported by AI which enables them to understand while doing jobs. Because of these characteristics of bots, their significance in a healthcare organization can’t be refused.

It is a simple fact that robots require continual checkup and upkeep to work correctly; therefore, human intervention is essential for the time being.

What’s more, some robots will also be tasked with reallocate supplies during the hospital using especially designed paths and lifts within a hospital.

Another nice example of outstanding precision would function as micro-robots utilized to do micro-surgeries like unclogging blood vessels. Nevertheless, human intervention or oversight may be asked to overlook the whole procedure or always keep the autonomous systems.

3. Remote Treatment

The concept of distant therapy was around for at least a decade today. The technology was originally halted as a result of the inadequate network connectivity in the time of its implementation. However, further improvements and experiments have been conducted following the creation of 4G and 5G networks.

Nowadays, even though human intervention is necessary in the distant therapy industry, machines can execute many complex tasks individually.

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4. Accurate Diagnostics

The accurate and precise diagnostics of individual health requirements is where AI actually shines. The AI finds patterns which are directing the individual towards different health conditions. It decides the patient’s present condition by analyzing and analyzing the health records and information.

Tests so far have concluded that AI is capable of correctly diagnosing disorders in 87 percent of those instances. By comparison, health state detection by people had an 86% accuracy rate.

Additionally, IBM Watson, health care technology, has struck the 99 percent markers in diagnosing cancer. Therefore, considering the proportions mentioned previously, I assume that AI and Robotics could rival even the best physicians of earth when it comes to diagnostics.

5. Performing Daily Tasks

Robots can do daily tasks and execute many functions that were being done by people. By way of instance, a robot may make a sick or older person/patient feel attended all of the time, reducing the need for human existence. These bots are programmed to behave as private aid.

Additionally, robots may also participate patients in discussions, help them take their medication in time by alerting them, and also execute a simple checkup on them to examine their health state from time to time.

6. Assisting Patients & Medical Practitioners

Some particular robots are made to help the health personnel concentrate on other essential facets of the hospital. These robots are assisting patients by helping them in monitoring or walking their health condition.

Prior to the dawn of AI and Robots, tasks like helping a patient walk, helping the patient to get his/her checkup, carrying the background of the patient’s disease were guide and time-consuming. But these jobs are readily automated and quickly completed by machines.

Additionally, today, robots are assisting people with all from minimally invasive procedures to complicated open-heart surgeries.

Another superb illustration of those robots may function as prosthetic legs made by Hugh Herr. The biomechatronic feet enable the consumer to walk separately without human intervention.

The detectors in those bionic feet link with the individual’s muscles and also allow more freedom and stability. These detectors perfectly replicate human toes’ functioning and provide the consumer more edge over the first feet.

The Promising Future of AI & Robotics in Healthcare

But, AI or robots won’t be taking complete control within the medical sector anytime soon. Human intervention and oversight would nevertheless be an important element in ensuring 100% accuracy in the general procedure.

Additionally, the patients have been known to create a closer relationship with their physicians, nurses, or other health staff. This particular relationship provides patients a sense of never being alone. This atmosphere can never be replicated by robots or machines. Hence, people will always be there using AI and robotics to treat patients and provide a soothing and calming experience collectively.

In addition, the viability of AI and robotics has been destined to flourish in forthcoming years.

By enhancing the clinical workflow to determining the specific cause of an individual’s specific health condition, AI and robotics benefit the health care domain. Consequently, it’s fairly evident that AI will finally master the health care domain later on.

The Mental Health Business: Money And Managed Care

Even though the therapeutic relationship is more akin to a friendship than a more professional relationship, it is, in the end, the latter. It is mediated by monetary payment as the therapist or psychologist is a working professional. Systems like Managed Care have emerged to moderate this transaction, and there are several guidelines that a professional has to abide by when handling money matters.

What is Managed Care?

Managed care in psychology refers to the system of delivering mental health care services coordinated, monitored, and controlled by a third-party payer, such as an insurance company. This system aims to control costs and improve the quality of care by managing access to services and determining the most appropriate treatment options.

Advantages of Managed Care

One of the main benefits of managed care in psychology is that it can help to improve the accessibility of mental health services. Insurance companies negotiate rates with providers, making it more affordable for individuals to access mental health care. Additionally, managed care organizations often have networks of providers that individuals can choose from, increasing the likelihood that individuals will find a provider that is a good fit for them.

Managed care in psychology also aims to improve the quality of care by ensuring that individuals receive appropriate and evidence-based treatments. Insurance companies and managed care organizations often have utilization management programs that review treatment plans and ensure that they align with established guidelines and standards. This can ensure that individuals receive treatments that are likely to be effective rather than those that may be unnecessary or ineffective.

Drawbacks of Managed Care

However, there are also some potential drawbacks to managed care in psychology. One concern is that managed care organizations may limit the number of sessions or treatments that an individual can receive, potentially limiting the effectiveness of treatment. Additionally, managed care organizations may prioritize cost-cutting measures over the needs of the individual, which could lead to suboptimal care. Another concern is that managed care in psychology may lead to a reduction in the autonomy of mental health providers. Managed care organizations often have strict guidelines and protocols that providers must follow, which can limit their ability to tailor treatment to the individual’s unique needs.

Handling Money during Therapy Session

Handling money and payments in therapy is an important aspect of providing mental health services. A therapist must have clear and transparent policies and procedures for billing and collecting payment.

Establishing a fee schedule is one of the first steps in handling money and payments in therapy. This should include the cost of each session and any additional fees for materials or assessments. It is important to be transparent about these fees and to provide clients with a detailed breakdown of the costs associated with therapy. Another important aspect of handling money and payments in therapy is having clear policies and procedures for billing and collecting payments. This may include accepting different forms of payment, such as cash, check, or credit card. It is also important to have a system for tracking payments and following up with clients who may have outstanding balances.

It is also important to consider clients’ needs when handling money and payments in therapy. Many individuals seeking mental health services may be dealing with financial challenges, and it is important to be sensitive to these challenges and to offer flexible payment options when necessary. For example, some therapists offer a sliding scale fee based on the client’s income or accept insurance as payment. In addition, therapists need to be aware of the legal and ethical implications of handling money and payments in therapy. This may include understanding and complying with billing and collections laws and adhering to professional, ethical guidelines regarding accepting gifts or other forms of payment.

Managing Money in Psychological Research

Payments during psychological studies refer to the compensation or reimbursement provided to participants for their time and involvement in research. This compensation can take various forms, such as monetary payments, vouchers, or other incentives. Using monetary payments or incentives as compensation for participants in psychological studies is a common practice. Monetary payments can take the form of cash, checks, or gift cards and are typically given at the end of the study or on a per-session basis.

The payment amount can vary depending on the study but is usually based on the time and effort required of the participant. Vouchers are another form of compensation that is often used in psychological studies. Vouchers can be used for various purposes, such as purchasing goods or services or redeeming cash. Vouchers are often provided as an alternative to cash payments and can be used to incentivize participation in research studies.

Incentives are another form of compensation used in psychological studies, and these can include prizes, raffles, or other rewards for participating in the study. Incentives can be used to increase participant engagement and motivation and can be tailored to the specific needs and preferences of the participants. It is important to note that payments or incentives should be provided ethically and responsibly and not be used to coerce or unduly influence participants to participate in a study. Additionally, the use of payments or incentives should be clearly explained to participants. They should be informed of the amount and form of compensation they will receive before they agree to participate in the study.


Managed care organizations oversee mental health services and ensure they meet certain standards of quality and cost-effectiveness. They use financial incentives, utilization reviews, and various other mechanisms to control costs and the use of mental health services. While managed care can benefit some patients, it can also negatively affect others.

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;







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.


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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Role Of Artificial Intelligence In Smart Meters


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 2024 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 2024 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.


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.


from statsmodels.tsa.seasonal import seasonal_decompose dec = seasonal_decompose(meter['Large SE meter in SM'],model='additive', period=5) dec.plot() meter['Year'] = meter.indexdf = pd.DataFrame() df['ds'] = meter['Year']df['y'] = meter['Large SG meters in SM'] df.head()split_date = 2024df_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() csv from pmdarima.arima import auto_arima df['Year'] = df.index split_date = 2024 df_train = df.loc[df.ds <= split_date].copy() model = auto_arima(df_train['y'], trace=True, error_action='ignore', suppress_warnings=True)['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.


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 = 2024 m = Prophet() = 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 = 2024 df_train = df.loc[df.ds <= split_date].copy() m = Prophet() 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 = 2024 m = Prophet() = 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 = 2024 m = Prophet() = 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.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 


Examples Of Artificial Intelligence (Ai) In 7 Industries

blog / Artificial Intelligence and Machine Learning Examples of Artificial Intelligence (AI) in 7 Industries

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Artificial intelligence (AI) is a hot topic. But for many, the application of AI beyond Apple’s Siri and Amazon’s Alexa remains murky. 

New and emerging use cases for AI are expected to transform nearly every industry in the coming years and decades. And savvy companies and business leaders stand to benefit.

What Is Artificial Intelligence


Broadly speaking, AI refers to computer systems that can perform problem-solving and decision-making tasks normally associated with human intelligence. These can include:

Recognizing images and speech

Making decisions

Translating languages

Providing recommendations

And more

Examples of Artificial Intelligence Across Industries

So, how is artificial intelligence used? 

AI applications range from consumer-oriented solutions (such as chatbots) to highly complex industrial use cases, like predicting the need for manufacturing equipment maintenance. Examples from seven industries provide a glimpse into the breadth and depth of the possibilities for AI. 

1. Financial Services

AI has numerous applications in both consumer finance and global banking operations. Examples of artificial intelligence in this industry include the following.

Fraud Detection

Financial fraud attempts, whether on a massive scale or through day-to-day crimes (like credit card skimming), continue to rise rapidly in frequency and cause major disruptions to both organizations and individuals. According to Business Insider, banks like J.P. Morgan Chase use proprietary artificial intelligence algorithms to flag transactions that don’t fit normal patterns for further inspection.

Algorithmic Trading

Gone are the days of traders shouting on the stock market floor. Today, most major trading transactions are handled by algorithms that react and make decisions far faster than humans ever could. In fact, by 2024, the algorithmic trading industry is expected to reach $19 billion annually. 

2. Insurance

Within the broader landscape of financial services, insurance stands out for its unique applications of artificial intelligence. These include:

AI-Powered Underwriting

For decades, underwriting decisions have relied on highly manual processes and data inputs, as well as invasive processes like medical exams. Today, insurance companies use AI to assess risks using massive data sets that draw on factors ranging from prescription drug history to pet ownership. 

Claims Processing

Today, artificial intelligence can handle much of the claims process for simple claims. Examples of artificial intelligence range from handling client interactions through chatbots (like Progressive’s Flo) to using machine vision to assess auto damage. As machine vision and AI capabilities increase, human involvement will likely play less of a role in claims decisions. 

3. Healthcare

While healthcare has traditionally relied heavily on human labor and care, an increasing number of tasks can now be outsourced to AI. Below are two examples of AI in healthcare.

Precision Medicine and Algorithms

An individual’s health outcomes, or even their response to a certain treatment, can vary significantly based on numerous factors, from lifestyle to genetics. These factors are difficult for human doctors to parse. AI can take in huge amounts of data to identify optimal treatments for patients, or even to identify emerging health concerns before they rise to a level that a human might notice.

Computer Vision for Diagnosis and Surgery

Increasingly, computer vision and machine learning are showing promise for diagnosing conditions such as skin cancers, and even for assisting during complex surgeries. For instance, AI can ensure physicians perform all necessary steps correctly during an operation. 

4. Life Sciences

Since the life sciences by nature involve large data sets generated by experiments, it’s not surprising that artificial intelligence has numerous potential applications in the field. Artificial intelligence examples in life sciences include:

Drug Discovery

The search for novel treatments still requires large-scale experiments and confirmation of hypotheses. However, machine learning has been used since the 1990s to speed the process considerably. It can predict how certain compounds will interact with each other and even how a drug will work against its target.

Predicting Disease Spread

Throughout the COVID-19 pandemic, experts have used AI and machine learning extensively to predict the spread and impacts of the virus, particularly as it has mutated. Data from these models has allowed public health and healthcare leaders to adopt policies and prepare resources to minimize spikes and reduce stress on the broader healthcare system.

5. Telecommunications

While many of us often take internet and communications availability for granted, the telecommunications industry depends on a series of highly complex processes and constant adjustments. AI can address these needs in several ways.

Network Optimization

To maintain flawless operations, networks need to adapt to changing traffic and quickly address anomalies. Currently, 63.5% of telecommunications providers are using AI to monitor and improve their networks and deliver the best possible performance for their end customers.

Predictive Maintenance

Telecommunications networks rely on broadly distributed sets of hardware. And problems within this infrastructure can ripple throughout the network. Artificial intelligence offers telecommunications companies the opportunity to use predictive algorithms to identify when problems are most likely to arise, allowing 

6. Oil, Gas, Energy

Oil, gas, and energy is an increasingly complex field, and one with little room for error given safety and environmental considerations. Artificial intelligence is allowing energy companies to increase their efficiency without increasing costs. Applications include:

Image-Processing to Identify Maintenance Needs

AI’s increasing ability to process images and recognize patterns is making it possible to use drones and other image sources to check power infrastructures for equipment breakdowns or even downed wires. This is a tactic that has already been implemented across the United Kingdom’s power grid. 

Anticipating Energy Demand

As the transition to renewable energy continues, predictive data on energy demand and availability will be essential for energy providers as they make decisions about storage and utilization.  This can include identifying how much solar power needs to be stored for nighttime or rainy days. AI will help companies parse the factors that impact demand and make informed decisions for the future.

7. Aviation

Safe, efficient aviation, especially in the context of rising fuel prices, depends on the careful use of data to optimize both individual flights and the broader aviation infrastructure. AI applications in this sector include:

Predicting Route Demand

To maximize profits while retaining customer loyalty, airlines must strike a careful balance between providing enough flights between specific destinations without flying more routes than is economical. AI models can take factors like internet traffic, macroeconomic trends, and seasonal tourism data into account to help airlines make informed decisions about their route offerings. 

Providing Customer Service

During major disruptions, such as those caused by massive weather events, few airlines have the staffing capacity to handle individual customer queries and needs one-on-one. In addition to automated messaging, airlines increasingly rely on AI to extract key pieces of information from customer messages and provide an appropriate response. For example, this may involve directing a customer inquiring about their luggage to information about reporting lost baggage. 

The Future of AI Across Industries

As the breadth and depth of AI applications demonstrate, applications of big data, machine learning, and more have major implications across industries. While some of these applications are nascent, they are likely only the tip of the iceberg as this technology matures. Organizations, therefore, should consider acting quickly to scale up their internal capacity to investigate and apply AI. 

By Rachel Hastings

Ready to build artificial intelligence capabilities within your organization? View our selection of online artificial intelligence courses. You can also learn more about a new Emeritus Enterprise course on Designing and Building AI Products and Services for Regulated Industries, available for individual and group enrollment.

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