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Introduction to IoT Technologies

Hadoop, Data Science, Statistics & others

Top new IoT Technologies

Given below are some new IoT Technologies:

1. Driverless Cars

IoT is the future of autonomous vehicles. Driverless autonomous vehicles seamlessly drive traffic across highways. The passengers can input their destination and the software will seamlessly coordinate with the traffic grid to drive passengers to the destination at very high speeds.

These cars will be fitted with sensors, cloud architecture, gyroscopes, proximity sensors, and other technologies which co-ordinate with the IoT software to get real-time data on pedestrian movement, traffic conditions, and road condition such as speed breakers, signals, stop signs. These cars will be fitted with AI and machine learning software which will adapt to road conditions instantaneously (Huh, Cho, & Kim, 2023, February).

2. Smart Wearable’s

Smart wearable technologies can be integrated into the IoT ecosystem to provide real-time data to users. The Google Glass project has possibilities that enable the user to get information and entertainment on the go. Another smart wearable device is the Fit bit which enables activity tracking, heart rate monitoring, several sleep steps climbed, and other personal metrics to its users (Chen et al., 2014). These devices use the IoT ecosystem to connect to AI-based monitoring systems to provide real-time data analysis to smartphones through mobile applications like IOS and Android-based apps.

3. IoT in Healthcare

Some hospitals have implemented smart beds that can provide real-time data for patient occupancy and provide information related to patient health to nurses. IoT enabled sensors can also be used to monitor pacemakers. IoT enabled platforms have also use IoMT sensors along with RFID electronics which can be fabricated on e-textiles to be wirelessly connected to patient monitoring and diagnostic systems (Jia, Feng, Fan, & Lei, 2012, April).

4. Smart Homes and Building Automation

IoT enabled technologies such as electronic sensors, smart home appliances, and electrical and mechanical systems, can be used to automate homes and connect them in a single network. Different systems such as smart lighting can be adjusted by the user’s mobile phones or home climate control systems that can adjust according to the outside climate through metrological data and the users’ requirements.

Smart homes also include home appliances such as smart refrigerators microwaves etc., which can be connected to the user’s mobile phones and provide alerts to the refrigerator’s contents and microwave settings. IoT also seamlessly connects home entertainment systems such as smart televisions, music, and entertainment systems and provides a single user platform, where the user can effectively control all of these devices through a single platform (Kelly, Suryadevara, & Mukhopadhyay, 2013).

All of the IoT enabled home automation systems can be connected to an AI platform that can control all of these devices and provide a seamless user experience. Cloud Computing based systems can store user data seamlessly in a virtual platform and use it anywhere through IoT-enabled devices. Smart energy grids connect IoT-based technology to improve energy efficiency for homes (Dorri, Kanhere, Jurdak, & Gauravaram, 2023, March).

5. IoT in Security and Defence

IoT provides real-time tracking systems such as restraint bands which provide real-time data on criminals such as their geographical locations. The internet of military things is IoT systems usage for combat operations and warfare (Lee & Lee, 2024). Applications of these systems are limitless with the potential for cyber warfare and battles dominated by machine intelligence. Real-time data and coordination can be made with soldiers on the field and they can interact with their Central command irrespective of geographical location.

IoT also enables autonomous drones and smart combat platforms which can be used to put humans out of harm’s way. Internet of Battlefield things has enabled the use of technology for reconnaissance, unmanned warfare, and battlefield surveillance to be entirely handed over to AI-based systems which can be monitored remotely. The ocean of things is a DARPA Enabled program designed for collecting and analyzing environmental and marine data that contract military and Commercial Ocean going vessels.

Military-based applications for IoT include ambient intelligence and autonomous devices such as robots and drones which can take over from human control. Deep reinforcement learning uses IoT systems where IoT devices adapt to an environment where conventional machine learning algorithms cannot be used. Cyberwarfare and espionage also find applications in IoT.

Conclusion

IoT has come a long way since its inception, IoT will become ingrained into all aspects of life from smart home automation and entertainment, personal devices, revolutionize modern warfare and improve healthcare. Overall, IoT has the potential to simplify every aspect of human life and make the world a more connected place.

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Ai In Marketing: Comprehensive Guide

Thanks to the increasing amount of data gathered from customers, businesses’ marketing strategies become more data-driven. Considering AI is better than humans when it comes to processing and analyzing large datasets, it is not a surprise that AI is becoming widespread among marketers. According to a study, 87% of companies that have adopted AI were using it to improve email marketing. 61% of marketers were also planning to use artificial intelligence in sales forecasting.

Why is AI in marketing important?

Source: Google Trends

Level of interest in using marketing in AI is increasing. Marketers are always chasing ways to increase their impact without increasing marketing budgets. AI technologies provide them a way to achieve these. Marketers can combine customer data and AI-powered tools to anticipate their customers’ next move and improve their journey. To do that, they can use AI to

understand the market better,

create unique content,

and perform personalized marketing campaigns.

provide accurate insights and suggest smart marketing solutions that would directly reflect on profits.

Below is our initial framework explaining the impact of AI in marketing. We have developed that over time, and you can see our latest view below the framework. To get more information, please visit the relevant page to see references, videos, and detailed explanations:

1-Optimize Pricing & Placement

Dynamic pricing: Businesses can optimize their pricing strategy by using AI to scrape competitors’ pricing data automatically and to maximize revenue by setting competitive prices for products.

Physical placement

Merchandising optimization: You can leverage machine learning and big data to optimize your online or offline merchandising. 

Shelf audit/analytics: Your business can use videos, images, or robots in the retail area to audit and analyze your use of shelf space. You can identify and manage stock-outs or sub-optimal use of shelf space. One of the most leading-edge examples in this area is Lowe’s autonomous retail service robot. It will explore how robots can meet the needs of both customers and employees. Meet the LoweBot

More mainstream solutions in this space include leveraging images taken by employees to manage and analyze shelf space. Trax Image Recognition explains their solution in this space in detail.

Digital Placement

Product Information Management: Businesses manage and improve all product information centrally to improve product discoverability and appeal. As a result, they automatically modify and update the product description, box description, and all other related information.

Visual Search Capability: Businesses can leverage machine vision to enable their customers to search for products by image or video to immediately reach their desired products. In the today’s world too easy to ensure customer’s desires with the AI algorithms thanks to the lots of snapping and sharing images. For example, Flipkart, one of India’s largest online retailers, uses visual search.

Image tagging to improve product discovery: You can leverage machine vision to tag your images, taking into account your users’ preferences and relevant context for your products.

2-Optimize marketing communication

An optimized marketing communication reaches customers at the right time at the right channel with the right message. There are numerous emerging AI companies specializing in these areas. For example, companies like Appzen track customers’ cross-device behavior to ensure that your messages target customers at the device they are using at the time of your marketing communication.

Just a few years ago, most messages to customers were handcrafted for specific macro-segments. Today companies like Phrasee suggest personalized messages to ensure that customers receive messages they prefer to read. For example, Dell has increased its page visits by 22% by introducing Persado’s AI-powered marketing tools.

Enablers

Neuromarketing: Your business can leverage neuroscience and biometric sensors to understand how your content impacts your audience’s emotions and memory. You can test your content in private until it achieves the desired effect.

Omnichannel: Businesses can personalize marketing communication across different paid marketing platforms. They can create a coherent strategy for the overall marketing strategy and analyze comparatively the impact on different platforms.

Retargeting: You can retarget customers who have already expressed interest in your products or services. As a result, sales numbers increase by engaging the right customer.

Channel specific optimizations

Content generation: For content creators, it is always a challenge to come up with a matchless marketing strategy. In this field, AI can provide creative solutions. From the topics chosen for content marketing, Natural Language Generation models can create unique content and deliver creative suggestions. You can read more about content generation by reading our article.

Mobile Marketing: With mobile marketing, businesses can create personalized, individual messaging based on each customer’s real-time and historical behaviors. Most of the users are active in mobile platforms, develop your strategy to gain a greater share in mobile traffic.

Email Marketing: Email marketing includes emails that are tailored to individual behavior. Detect which email type performs better with your product and customer. Customize by including the necessary email structure and images.

Video Commerce: You can make videos with embedded products shoppable by inserting relevant links and auto-identify products in videos.

3- Personalized Marketing Strategies

Businesses can leverage customer data to reach customers with personalized recommendations via email, site search, or other channels. These systems ensure your company to make the right offer taking into account all

digital and analog interactions (page views, purchases, email opens…) of that customer with your brand

that customer’s interactions on different web properties

interactions of similar customers

These are all designed to lead the consumer more reliably towards a sale and support companies to increase their revenues. 

If you are looking for vendors in the space, feel free to check our transparent, data driven vendors lists on the topic:

4- Connect & Leverage Customer Feedback

Emotion Recognition: You can capture the emotional state of your customers by analyzing micro gestures and mimics. Computer vision will help you to capture the details and provide you with the real emotions of your customer.

5- Analytics

Analytics: You connect all your marketing data and KPIs automatically. AI tools enable you to manage campaigns, trigger alerts, and improve your marketing efficiency. AI-powered marketing analytics can lead companies to identify their customer groups more accurately. By discovering their loyal customers, companies can develop accurate marketing strategies and also retarget customers who have expressed interest in products or services before.  

Channel specific analytics

Social analytics & automation: Businesses can leverage Natural Language Processing and machine vision to analyze and act upon all content generated by your actual or potential customers on social media, surveys, and reviews.

PR analytics: You can learn from, analyze, and measure your PR efforts by using AI tools for marketing. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue. With the increasing demand in digital media to specify the targets will more easy for the brands in competitive marketing.

If you believe you can benefit from AI in your business, you can view our data-driven lists of AI Consultants, and AI/ML Development Services

Now that you have checked out AI applications in marketing, feel free to look at our AI in the marketing section. You can check out AI applications in sales, customer service, IT, data, or analytics. And if you have a business problem, we can help you find the right solution to resolve it:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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A Comprehensive Guide On Markov Chain

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

Overview

· Markovian Assumption states that the past doesn’t give a piece of valuable information. Given the present, history is irrelevant to know what will happen in the future.

· Markov Chain is a stochastic process that follows the Markovian Assumption.

· Markov chain had given a new dimension to probability theory. The applicability can be seen almost in every field. Also, several value-able ideas have been developed with the basis of The Markov Chain; their importance is paramount in the field of data analytics.

Introduction

Being a student of Engineering, I often wondered how I used to pass in a few subjects after spending one week with them just before the final exam. Even after spending 3-4 hours per day, I could not unearth anything during the entire semester. My road to enlightenment for those subjects was a topsy-turvy journey until one week before the semester exam. I thought I’d fail. But from the final result, I used to think about how I have turned the tide within one week just before the exam?

A few years before, when I had started my journey in data analytics, I came across a theory, The Markov Chain. It gave the answer and the explanation that I was looking for in the prementioned scenario. One of the critical properties of the Markov Chain that caught my attention during that time was.

‘Only the most recent point in the trajectory affects what happens next.’

After this, I started to dig deep into the spiral of the Markov Chain. I’ll try to simplify the concept as much as I can with the help of real-life examples. I know the content itself is enormous and time-consuming to read. But you know what I have found while going through various materials. It’s full of some high-level mathematics. I feel from my learning experience that first, you need to have an intuition of the subject which comes from understanding which examples can give, followed by mathematics and then implementation via coding. This content is a mixture of more examples and a bit of Mathematics. I hope you will enjoy it.

Table Contents 

· A Brief History

· Difference between Stochastic and Random Processes

· Importance of ‘Chain’ in Markov Chain

· Basics of Markov Chain

· Markovian and Non-Markovian Process

· Types of States in Markov Chain and concept of Random Walk

· Basics of Transition Matrix

· Hidden Markov Model

· Conclusion

· Coding Reference

· YouTube Links

· Used References

· Reference for Exercise and Solutions

· Reference for some Thought-Provoking Research Paper and Patents using Markov Chain

A Brief History on Markov Chain

More than a century ago, the Russian mathematician Andrei Andreevich Markov had discovered a complete novel branch of probability theory. Well, it has an exciting story. While going through Alexander Pushkin’s novel ‘Eugene Onegin,’ Markov spent hours sorting through patterns of vowels and consonants. On January 23, 1913, he addressed the Imperial Academy of Sciences in St. Petersburg with a synopsis of his finding. His findings did not alter the understanding or appreciation of Pushkin’s poem. Still, the technique he developed—now known as a Markov chain—extended probability theory in a new direction. Markov’s methodology went beyond coin-flipping and dice-rolling situations (where each event is independent of all others) to chains of linked events (where what happens next depends on the system’s current state).

Difference between Stochastic and Random Processes

So in a Markov chain, the future depends only upon the present, NOT upon the past. Let’s dig deep into it. As per Wikipedia, ‘A Markov chain or Markov process is a stochastic model which describes a sequence of possible events where the probability of each event depends only on the state attained in the previous event.’

For me, most of the time, we are confused with a word like Stochastic and Random. We often say ‘Stochastic means Random.’ But understanding the difference will help us comprehend the essence of the Markov Chain from a new perspective. Let me give you an example to explain it better. Let’s say I am at a pub and having a beer-drinking fun game with my friend. We have set out a rule. I’ll drink the number of bottles within a certain amount of time; my friend will drink exactly twice more than what I am drinking within that particular time. Like if I drink one bottle within one minute. He will drink two bottles within one minute. Or if I drink two bottles within one minute, he will drink four bottles within one minute and so on. I can define it like below :

1st event

For me, bottle consumption :

x

Following Event

For my friend, bottle consumption :

2x

So Stochastic is not random. It is a combination of processes that must have at least a random process. And the deterministic process may or may not depend upon a random method. I hope I have cleared it. If you are confused about what a deterministic approach is, let me give you an example. The sun always rises in the east. It’s a deterministic statement, a universal truth. The sun will never rise in the west. Hey, but please don’t try the competition that I have mentioned before. Drinking is injurious to health. It’s a fictitious condition that I have mentioned.

Importance of ‘Chain’ in Markov Chain

One of the intrinsic philosophies of the Markov Chain is the realization of interconnectivity, which is quite an amusing factor for solving real-life problems. Folks who are familiar with the ‘Domino Effect ‘are aware that when you make a change of one behavioural attribute will activate a chain reaction and cause a shift in related behavioural attributes as well. Our Real-world is full of that examples. Please check the below examples.

Basics of Markov Chain

with any graph.

Now let me give you an intuition about the recent past that governs the present outcome with my competitive pub gaming example. I am not going to explain it; observing the picture will be self-explanatory.

I hope the above depiction gives you some idea about the process. One thing you may be thinking Markov chain does not speak about any ‘Past.’ But why have I mentioned terms like ‘Near Past’ and ‘Far Past’? But I feel you can build a solid conceptual intuition by considering this. Just think ‘Near Past’ is ‘Yesterday’ and ‘Far Past’ starts from the day before ‘Yesterday’ believe me it will help you more. It is like Yesterday you have studied hard that is why Today you have a good result or like ‘Today’ you are motivated because ‘Yesterday’ you have met someone. I’ll give you another example from a real-life problem. Before that, let me define Markov Chain from a probabilistic point of view. Three elements determine a Markov chain.

· A state-space(S): If we define the seasonal states throughout a country, we can say, Summer, Monsoon, Autumn, Winter, Spring. So on Season State-space, we have prementioned five seasonal states.

If the transition operator doesn’t change during the transition. The Markov chain is called ‘Time Homogenous.’ So if t1à∞ the chain will reach an equilibrium known as Stationary Distribution. For this the equation will look like below,

Now come to another example; you have seen that Gmail editor, while typing a mail, always suggests the next word. Most of the time, I have seen at least my case it’s right. Its an application of the Markov chain in the field of Natural Language Processing(NLP). More precisely, it uses the concept of’ n-gram.’ Our example is based on a bit of that. Consider the below three sentences.

Sentence 1: I am a Boy.

Sentence 2: I am a Girl.

Sentence 3: I am a Star.

Here we have considered the concept of uni-gram. I hope the below picture will give you the intuition of the n-gram concept.

From the below transition diagram, we can see that our previous example has six states.

So one question may arise in your mind that how many states are possible. A theoretically infinite number of the states are possible. This type of Markov chain is known as the Continuous Markov Chain. But when we have a finite number of states, we call it Discrete Markov Chain.

Markovian and Non-Markovian Process 

Then you may ask, what is the use of it? Well, every coin has two sides. Let me bring you another example. Consider you have a dice with six sides, and you roll it. X. denotes the outcome. You roll the dice, and the first outcome is 3. Hence X1=3. Now you move the dice one again; this time, you got 5. Now we can say X2=3+5=8. Now let’s say we are interested in the probability where X5=15, Imagin X4=12; well, if we know the value of X4 does X3, X2, X1 matter to us? The example just I have given is an example Markovian process. And the previous example was not Markovian.

Types of States in Markov Chain and concept of Random Walk

Before looking at some more cases of transition diagrams, get familiar with some more terms. One of the two most important terms is Recurrent and Transient state. I had found a good definition of these two during my research work. Let me share with you.

· Recurrent states :

♪ You can check out any time you like, but you can never leave. ♪

If you start at a Recurrent State, you will for sure return to that state at some point in the future.

· Transient states :

♪ You don’t have to go home, but you can’t stay here. ♪

Otherwise, you are in a transient state. In case some positive probability that once you leave, you will never return.

Markov chain is a directed graph. A random walk on a directed graph always has a sequence of vertices generated from a start vertex by selecting an edge, traversing the edge to a new vertex, and repeating the process. Suppose the graph by nature is firmly connected. In that case, the fraction of time the walk spends at the various vertices of the graph will converge into a stationary probability distribution, as discussed earlier via a mathematical equation. Since the graph is directed, there is a possibility of vertices with no outer edges and nowhere for the walk to go. Vertices in a strongly connected component with no edges from the graph’s remainder can never be reached unless the component must contain the start vertex. When a walk leaves a strongly connected component, it can never return. Exciting, right? Form this discussion, we have another vital conception of An absorbing Markov chain. It is a Markov chain where it is impossible to leave some states, and any state with positive probability (after some number of steps) can reach such a state. The perfect example of an absorbing Markov chain is drunkard’s walk. Exciting right? Now let’s see. I will give an overview of it. But you can also do a little bit of google search and find more details. Now let’s see the transition diagram first, then I will explain.

The drunkard will stagger from one location(In this case, let’s consider those locations as states) to the next while he is in-between the pub and his home, but once he reaches one of the two locations(home or pub), he will stay there permanently (or at least for the night). The drunkard’s walk usually occurs where the probability of moving to the left or right is equal.

Now let’s have some more state transition diagrams

Sentence 1: I am a star.

Sentence 2: am I a star?

The above picture is an example of a transition graph where we have a closed-loop. Also, it bears a critical property of a Markov Chain: the probability of all edges leaving out of a specific node must be the sum of 1. See the S1 and S2 nodes. Also, observe that a Transient state is any state where the return probability is less than 1. See S1.

Let’s look at one more example.

Sentence 1: I had had too many chocolates.

Sentence 2: I had chocolates.

You can see a recurrence state over here with S2. There is some mathematical notation I want to explain here.

I. Every state communicates with itself, i↔i;(Reflexive)

II. Also, if i↔j, then j↔i;(Symmetric)

III. Also, if i↔j and j↔k, then i↔k. (Transitive)

Hence, the states of a Markov chain can be classified into communicating classes so that only members of the same class can communicate with each other. Two states i and j belong to the same class if and only if i↔j.

A Markov chain is Irreducible if all states communicate, meaning it belongs to one communication class. Also, in the case of two or more Communication classes, it’s known as the Reducible Markov chain.

There is also a conception of Periodic and Aperiodic Markov state. I will give an example of both first. Let’s say we are going to measure the maximum temperature of consecutive days at a particular location. And below is the transition diagram.

I’ll give some intuition about Ergodic property as I feel in the present word being a data scientist you must have some notion of it. So, What is Ergodic property? The ergodic hypothesis says that the time spent by a system in some region of the phase space of microstates with the same energy is proportional to the volume of this region, i.e., that all accessible microstates are equally probable over a long period. The ergodic hypothesis says that the time spent by a system in some region of the phase space of microstates with the same energy is proportional to the volume of this region, i.e., that all accessible microstates are equally probable over a long period. Is it confusing, right? Let me simplify it a bit more. Ergodicity is a statement about how averages in one domain relate to another domain. It is about how we draw some conclusion about something while having information about something else. I hope the last statement can give you some correlation between the Markovian process and Ergodicity. Let me give you some examples now. You have been to a local football game. You are supporting your team. And there is a new player who is playing for the first time. During the match, he had scored a world-class solo goal while dribbling past five players. Now during the next game, you are confident he will score an important goal.

Another excellent example of this is In an election; each party gets some percentage of votes, party A receives x%, party B receives y%, and so on. However, it does not mean that throughout their lifetime, each person who has voted votes with party A in x% of elections, with B in y% of elections, and so on. I won’t give the mathematical explanation over here. You can do a little bit of research on it.

Basics of Transition Matrix

Now, let’s give some more intuitiveness about the Transition matrix. You have already become aware of the state transition diagram. Now it’s time for the Transition matrix. The basics of the Transition matrix are given below.

I will try to give some more examples, hope they will be sufficient to understand the concept. Let me share a very small sample. I hope the following diagram will be very much self-explanatory.

Now, let’s find out some properties of the transition matrix. Please try to correlate these properties with the above example.

Property 1: The value in the ith row and jth column marks the transition probabilities from state i to state j.

Property 2: Rows of this matrix must sum to 1, until and unless you do not have to go anywhere from the last state. In that case, the sum of that row will be 0.

Property 3: The transition matrix is square.

Property 4: The outcome of each state must be discrete.

Property 5: No state must remain constant with each generation.

Hidden Markov Model(HMM)

Before stating Hidden Markov Model, I’d like to clear intuition about posterior and prior probabilities. I know this is content-based on Bassian Statistics, but intuition will be helpful to understand the topic very well. Let’s begin. When do you see a word like ‘World,’ we consider it as a Noun. It is coming from our prior knowledge in the absence of data, or rather I’ll term it as context. So we see a word like ‘World,’ we put a higher probability with Noun than any other parts of speech. If we write ‘ World History,’ then ‘World’ becomes an Adjective in the context, right? Now you have data (context), and the probability of ‘World’ (Adjective) is higher than that of ‘World (Noun), given the data. This probability, wherein your condition on the data, is your posterior. Now the prior knowledge that I was talking about is hidden. We don’t know what factors motivated us to gain that kind of knowledge are. Now you may argue that you have studied English grammar well or read lots of English content for the preceding example. It is a specific case, but our base of prior ideas is generally always hidden while making any decision.

Let me give you another example. Suppose you are a fan of an English football club. You regularly follow their games in EPL. Even though they have a glorious history of performing at the top level in recent years, they are performance not up to the mark. On a Sunday, while watching a game of your favorite team with a bottom-listed club, your club is three goals down within 20 minutes of it. You are very upset. And suddenly, your doorbell rings. A neighbor walks in. He is looking for some old newspaper, which you need to search from a pile of a newspaper. As you were upset with your favorite team’s performance, you got cranky. You behaved very rudely and said you would do it later. Now your neighbor has seen you and talked with you some time. He used to think you were a very decent, respectful guy( prior belief). But the way you acted remains unclear to him. Maybe if he had been aware of the situation, he could have opted for a different time to visit. So here, what has driven your action to your neighbor, is hidden to him.

You can read the above example once again, and then please read what I am writing here. HMM requires an observable process Y(Our case, rude behavior of that person to his neighbor) whose outcomes are “influenced” by the consequences of X ( The lasting result of the football match) in a known way. Since X cannot be observed directly, the goal is to learn about X by watching chúng tôi has an additional requirement that the outcome of Y at time t=t0( particular moment of a time when the incident happened) may be “influenced” exclusively by the effect of X at t=t0 (At that time the result was against his favorite team )and that the outcomes of X and Y at t<t0 ( different time like maybe next morning) must not affect the outcome of Y at t=t0.

· Xn is a Markov process whose behavior is not visible(hidden).

For a continuous system, we also can define HMM. For more understanding, I am adding the common graphical interpretation that I have found in Wikipedia and trying to fit it into my example.

The usability of HMM in Speech processing is huge. Speech signals have different phonemes. Phonemes are segments of the spoken speech signal.A phoneme based HMM for say the word ‘cat’ would have /k/ /a/ and /t/ as states. In this approach, we will need to create an HMM for every word in the corpus and strengthen the model’s utterances. In the modern world, it’s more relevant as different people have different accent and phonetical property so using this model always helps to approximate the outcome statistically. Below is one example of the same.

Conclusion

The main weakness of the Markov chain is its incapability to represent non-transitive dependencies; As a result, many valuable independencies go unrepresented in the network. Bayesian networks use the richer language of directed graphs To overcome this deficiency, where the directions of the arrows permit us to distinguish actual dependencies from spurious dependencies induced by hypothetical observations. But still, the Markov chain has contributed hugely in the field of statistical analysis. In the current day, we say our world is a VUCA world. VUCA means Volatile, Uncertain, Complex, and Ambiguous. I feel the use of this kind of model can somewhat control the unpredictability of the VUCA world.

Head on to our blog and read about the overview of the Markov Chain.

Markov Random Field: Markov random fields (ermongroup.github.io)

Coding Reference: 

Note: To implement Markov Model, you must know the NUMPY library in Python very well.

YouTube Links:

I found the below links are very helpful while studying Markov Chain. You can look for it.

Used References:

[7] “Probabilistic reasoning in intelligent systems: Networks of Plausible Inference” written by Judea Pearl, chapter 3: Markov and Bayesian Networks: Two Graphical Representations of Probabilistic Knowledge, p.116:

Reference for some Thought-Provoking Research Paper and Patents using Markov Chain

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

Related

Linux Date Format: A Comprehensive Guide

As a Linux user, you may come across situations where you need to display or manipulate date and time information. Linux provides a powerful built-in utility known as the date command that allows users to get, set, and format the system date and time.

In this article, we will discuss the Linux date format and how to use it effectively to display and manipulate date and time information.

What is the Linux Date Format?

The Linux date format is a string of characters used to represent date and time information in a specific format. The date format is used to specify how the date and time information should be displayed or parsed. The date format string consists of a combination of format specifiers and literal characters.

How to Display the Current Date and Time in Linux

To display the current date and time in Linux, you can use the date command followed by the desired format. By default, the date command displays the system date and time in the YYYY-MM-DD HH:MM:SS format.

$ date 2024-01-01 10:00:00

To display the date and time in a custom format, you can use the format specifiers. For example, to display the date and time in the DD-MM-YYYY HH:MM:SS format, you can use the following command:

$ date '+%d-%m-%Y %H:%M:%S' 01-01-2024 10:00:00

In the above command, the + sign indicates that we are specifying a custom format. The %d, %m, %Y, %H, %M, and %S are format specifiers that represent the day, month, year, hour, minute, and second respectively.

Common Format Specifiers

Here are some of the common format specifiers used in the Linux date format:

%d: The day of the month (01 to 31)

%m: The month (01 to 12)

%Y: The year (e.g., 2023)

%H: The hour in 24-hour format (00 to 23)

%M: The minute (00 to 59)

%S: The second (00 to 59)

Formatting Date and Time Strings

In addition to displaying the date and time, you can also format date and time strings using the date command. To format a date and time string, you can use the -d option followed by the date and time string in quotes.

$ date -d "2024-01-01 10:00:00" '+%d-%m-%Y %H:%M:%S' 01-01-2024 10:00:00

In the above command, we are formatting the date and time string 2024-01-01 10:00:00 in the DD-MM-YYYY HH:MM:SS format.

Converting Date and Time Formats

You can also convert date and time formats using the date command. To convert a date and time from one format to another, you can use the -d option followed by the input date and time string in quotes, and then specify the output format using the + sign and the desired format.

$ date -d "01/01/2024 10:00:00 AM" '+%Y-%m-%d %H:%M:%S' 2024-01-01 10:00:00

In the above command, we are converting the input date and time string 01/01/2024 10:00:00 AM from the MM/DD/YYYY HH:MM:SS AM/PM format to the YYYY-MM-DD HH:MM:SS format.

Conclusion

In conclusion, the Linux date format is a powerful tool that allows users to display, manipulate, and convert date and time information in a specific format. By using the format specifiers and literal characters, you can customize the date and time output to meet your specific requirements. We hope this guide has been helpful in understanding the Linux date format and how to use it effectively in your Linux system.

How To Use Adobe Podcast Ai: A Comprehensive Guide

In the world of podcasting, creating high-quality content is essential for engaging your audience. However, the production process can be time-consuming and require technical expertise. That’s where Adobe Podcast AI comes in. Adobe Podcast AI is a cloud-based service that leverages artificial intelligence to simplify and optimize the podcast creation workflow. In this guide, we will walk you through the steps How to Use Adobe Podcast AI and take your podcasting game to the next level.

See More : What is Adobe Podcast AI?

After signing up, you may need to request access to Adobe Podcast AI. Depending on the availability of the service, you might get instant access or be put on a waiting list. Don’t worry, though; Adobe aims to make the service accessible to as many podcast creators as possible. While waiting for access, you can familiarize yourself with the platform and prepare your podcast files for upload.

Adobe Podcast AI allows you to upload your audio files directly to the platform. You can choose to record your audio directly on the website or upload existing files from your computer or cloud storage. This flexibility ensures that you can seamlessly integrate Adobe Podcast AI into your existing podcast production workflow. Whether you prefer recording on professional equipment or using a mobile device, Adobe Podcast AI has you covered.

One of the standout features of Adobe Podcast AI is its ability to automatically transcribe your audio and generate a script. Once you’ve uploaded your podcast files, Adobe Podcast AI’s powerful AI algorithms will analyze the audio and convert it into text. This transcription can save you countless hours of manual transcribing, allowing you to focus on refining your content and storytelling.

With Adobe Podcast AI, editing your podcast audio is a breeze. The platform allows you to edit your podcast by making changes directly to the generated transcript. You can cut, copy, paste, delete, or rearrange words in the transcript, and the audio will automatically sync with your changes. This intuitive editing process streamlines your workflow, making it easy to fine-tune your podcast and create a polished final product.

Also Read : How to Use AI Image Upscaler Free: Boost Clarity and Detail

In addition to editing, Adobe Podcast AI offers a range of tools to enhance your podcast audio. You can apply filters, noise reduction, and other audio effects to optimize the sound quality. These features are especially valuable for podcast creators who may not have access to professional audio editing software or expertise. With Adobe Podcast AI, you can achieve professional-sounding audio without the need for extensive technical knowledge.

Q. What is Adobe Podcast AI?

Adobe Podcast AI is a cloud-based service that utilizes artificial intelligence to simplify and optimize the podcast creation workflow.

Q. How do I sign up for Adobe Podcast AI?

Q. Is there a waiting list for Adobe Podcast AI?

Depending on the availability of the service, you may need to request access and be put on a waiting list. However, Adobe aims to make the service accessible to as many podcast creators as possible.

Q. How can I upload audio files to Adobe Podcast AI?

You can upload your audio files directly to the platform by recording on the website or uploading existing files from your computer or cloud storage. Adobe Podcast AI supports various recording methods to fit your preference.

Q. Can Adobe Podcast AI transcribe my podcast automatically?

Yes, one of the standout features of Adobe Podcast AI is its automatic transcription capability. It can analyze your audio files and convert them into text, saving you time and effort in manual transcribing.

Q. Can I edit my podcast audio through the transcript in Adobe Podcast AI?

Absolutely. Adobe Podcast AI allows you to edit your podcast audio by making changes directly to the generated transcript. You can cut, copy, paste, delete, or rearrange words, and the audio will sync accordingly.

Q. What audio enhancements can I apply with Adobe Podcast AI?

Adobe Podcast AI provides a range of tools to enhance your podcast audio. You can apply filters, noise reduction, and other audio effects to optimize the sound quality, even if you don’t have professional audio editing knowledge.

Q. Does Adobe Podcast AI integrate with other Adobe Creative Cloud apps?

Yes, Adobe Podcast AI seamlessly integrates with other Adobe Creative Cloud applications such as Adobe Audition, Adobe Premiere Pro, and Adobe Spark. This integration allows you to leverage multiple tools to enhance your podcasting experience.

Q. Can I access Adobe Podcast AI from any device?

Yes, Adobe Podcast AI is entirely cloud-based, making it accessible from any device with an internet connection and a web browser. You can log in and work on your podcasts from your desktop computer, laptop, or even a mobile device.

Adobe Podcast AI is a powerful tool that simplifies and optimizes the podcast creation process through the use of artificial intelligence. By automatically transcribing your audio files, generating scripts, and providing intuitive editing features, it saves podcast creators valuable time and effort. The platform also offers various audio enhancement tools, allowing users to achieve professional-quality sound without extensive technical knowledge. Furthermore, seamless integration with other Adobe Creative Cloud apps expands creative possibilities. With its cloud-based accessibility, Adobe Podcast AI ensures that podcasters can work on their projects from any device with an internet connection. By utilizing Adobe Podcast AI, podcast creators can take their content to the next level and engage their audience with high-quality productions.

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Related

5 Major Technologies Ruling The Physician’s World

Physicians can gather new insights from data to provide customized medical services.

The plenitude of innovative choices can be overwhelming from the outset, so putting resources into an organization to assist with practice management solutions might be the initial move toward going digital. Advisors like these can spread out all the choices and costs required for your office, as well as implement staff training and smooth the transition to using new technology.  

Telehealth

The direction is clear regarding repayment, guidelines, and market influences: Telehealth is well en route to getting typical. Advancements are getting more refined to help telehealth, says Todd Evenson, chief operating officer at the Medical Group Management Association (MGMA). Indeed, even five years back, the probability that patients had a camera on their home PC that would allow them to collaborate with providers was a lot smaller, while today it is automatic. Evenson adds. “I can see physicians utilizing that to engage the patient, particularly those in distant areas or who can’t go to the workplace because of physical issues. It offers an opportunity to be less problematic to their day. You could be in your office and connect with your opportunity as opposed to taking off part of the day to go to the physician’s office.”  

Electronic Health Records (EHR)

In spite of your opinion, EHR at last permits office and expert staff to invest more one-on-one time with patients, reinforcing relationships and conceivably getting more business. Taking out the basic front desk clutter, loose papers and tons of documents make for a more organized office, and one that is probably going to be alluring to patients. When somebody’s record is electronic, it is simpler to track and make changes to their data. Follow-up appointments, medication reminders and test results would all be able to be emailed to the patient conveniently.  

Augmented Reality

Augmented reality is unique in relation to virtual reality and keeps you from putting some distance between reality by placing the information into eyesight as fast as possible. It assists clinical students with getting ready for real-life operations and empowers doctors to improve their capacities. Patients will be equipped for portraying their symptoms with more precision. Drug organizations can likewise offer more innovative drug information to patients.  

Artificial Intelligence and Machine Learning

In the future, nascent machine learning technologies may discover their way into clinical decision support tools for physicians. Cognitive computing technologies, for example, IBM’s Watson, ceaselessly gain from past interactions, picking up value and insight over the long run. Watson Health is devoted to improving the capability of researchers and physicians to gather new insights from data to provide customized medical services. Organizations, for example, IBM and Epic Systems are working together with Mayo Clinic to bring the cognitive computing capabilities of Watson to EHRs. Epic is extricating patient information from health records, providing it to Watson to be immediately compared and monstrous volumes of relevant clinical data, and afterward sending results once again into the Epic EHR. This could prompt more quick and intensive analysis of the multitude of factors affecting patient care.  

Wearable Technology

Wearable technology like wellness trackers and pulse screens are now famous among some patient groups. However, this innovation is progressively picking up clinical importance. For instance, patients with type 2 diabetes have increasing access to affordable continuous glucose

Personal Health Data

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