Unit - 5
Applications
Face Recognition is a recognition technique used to detect faces of individuals whose images are saved in the data set. Despite the point that other methods of identification can be more accurate, face recognition has always remained a significant focus of research because of its non-meddling nature and because it is people’s facile method of personal identification.
There are different methods for face recognition, which are as follows-
1.Geometric Based / Template Based: -
Face recognition algorithms classified as geometry based or template-based algorithms. The template-based methods can be constructed using statistical tools like SVM [Support Vector Machines], PCA [Principal Component Analysis], LDA [Linear Discriminant Analysis], Kernel methods or Trace Transforms. The geometric feature-based methods analyse local facial features and their geometric relationship. It is also known as a feature-based method.
2.Piecemeal / Wholistic: -
The relation between the elements or the connection of a function with the whole face not undergone into the amount, many researchers followed this approach, trying to deduce the most relevant characteristics. Some methods attempted to use the eyes, a combination of features and so on. Some Hidden Markov Model methods also fall into this category, and feature processing is very famous in face recognition.
3.Appearance-Based / Model-Based: -
The appearance-based method shows a face regarding several images. An image considered as a high dimensional vector. This technique is usually used to derive a feature space from the image division. The sample image compared to the training set. On the other hand, the model-based approach tries to model a face. The new sample implemented to the model and the parameters of the model used to recognise the image.
The appearance-based method can classify as linear or nonlinear. Ex- PCA, LDA, IDA used in direct approach whereas Kernel PCA used in in nonlinear approach. On the other hand, in the model-based method can be classified as 2D or 3D Ex- Elastic Bunch Graph Matching used.
Speech recognition or speech to text includes capturing and digitizing the sound waves, transformation of basic linguistic units or phonemes, constructing words from phonemes and contextually analyzing the words to ensure the correct spelling of words that sounds the same. Approach: Studying the possibility of designing a software system using one of the techniques of artificial intelligence applications neuron networks where this system is able to distinguish the sound signals and neural networks of irregular users. Fixed weights are trained on those forms first and then the system gives the output match for each of these formats and high speed.
AI consulting services have succeeded in building robots. They are being used in the industrial sector to handle the preparation of raw materials. They are also being used to control, cut, weld, color, drill, polish, and more such activities. With the addition of artificial intelligence, the execution is more precise, accurate, and enhanced than it was ever before.
2. The medical community has seen some pioneering new technology with the assistance of AI chatbots. New and improved machinery with the advantages of both artificial intelligence and robotics are being made. This helps to carry out clinical tests and much more.
3. Al robots are the reason behind all our contemporary achievements in the department of space exploration and any future developments that are under pursue. Al robots are also being sent to the different parts of the universe, different corners of space that men can never reach.
4. Exploration campaigns in different parts of the world, rock climbing, visiting and determining the situation in the core of the earth. Such endeavours have seen trail-blazing results with the application of AI services.
1. Al in E-commerce
The internet has opened the door for revolutionizing various sectors. E-commerce sector is that of the one of them. E-commerce sectors have unlocked that of the new opportunities and the scope for retailers. Retailers also have never seen such a growth in their sales. Artificial intelligence is taking that of the E-commerce to the next level. In this article, we are going to discuss 10 applications of artificial intelligence in E-commerce.
1. Chatbots
E-commerce websites are using chatbots to improve the customer support service. Chatbots are providing 24/7 customer support to buyers. Visit any recognized E-commerce website. You will be prompted with a chat box asking what do you want or how can I help. You can tell your requirements in the chatbot and you will be served with highly filtered results. Chatbots are built using artificial intelligence and are able to communicate with humans. They can also collect your past data and provide you with a very personalized user experience.
2. Image search
Ever come across a situation where you liked any product or item but don’t what it is called or what it is? Artificial intelligence service eases this type of task for you. The concept of that of the image search is implemented in the E-commerce websites with the application of the artificial intelligence. Artificial intelligence has made it possible to understand images. Buyers can make a search on the basis of images. Mobile apps of E-commerce websites can find the product by just pointing the camera towards the product. This eliminates the need for keyword searches.
3. Handling Customer Data
E-commerce platforms have two things in abundance. On is an endless list of products and other is data. E-commerce has to deal with a lot of data every day. This data can be anything like daily sales, the total number of items sold, the number of orders received in an area or as a whole and what not. It has to take care of customer data also. Handling that amounts of data is not possible for a human. Artificial intelligence can not only collect this data in a more structured form but also generate proper insights out of this data.
This helps in understanding the customer behaviour of the whole populations as well as of the individual buyer. Understanding the customer buying pattern can make E-commerce to make changes wherever needed and predicting the next buy of the user also.
4. Recommendation Systems
Have you ever experienced that how E-commerce websites like that of the Amazon is constantly showing that of the products similar you just checked? Well, this is the application of that of the artificial intelligence in E-commerce. AI and machine learning algorithms can predict the behaviour of the buyer from its past searches, likings, frequently bought products. By predicting the behaviour of the user, E-commerce websites are able to recommend the products that user is highly interested in. This improves the user experience as the user no longer has to spend hours searching the product. It also helps the E-commerce websites to improve that of their sales.
5. Inventory management
The inventory management is one of the most important areas in any business. You have to keep yourself updated on how much inventory you are holding and how much more is needed. There are thousands of product categories over the E-commerce websites. Keeping an eye on the inventory of all the products daily is not possible for a human. This is where artificial intelligence comes into that of the picture. Artificial intelligence applications have helped E-commerce in managing the inventory. Moreover, the inventory management system will get better over the time. AI systems build a correlation between the current demand and the future demand.
6. Cybersecurity
Artificial intelligence has also improved that of the cybersecurity of the E-commerce websites. It can prevent or detect any kind of fraudulent activities. E-Commerce has to deal with a lot of transactions on that of the daily basis. Cybercriminals and hackers can hack the user account to gain unauthenticated access. This can lead to that of the exposure of the private data and the online fraud. The reputation of that of the business also gets a very big blow. To prevent this, Artificial intelligence and the machine learning algorithms are developed that can mitigate that of the chances of fraud activities over that of the website.
7. Better Decision Making
E-commerce can make better decisions with that of the application of Artificial intelligence. Data analysts have to handle a lot of data every day. This data is very big for them to handle. Moreover, analysing the data also becomes a difficult task. Artificial intelligence has fastened that of the decision-making process of the E-Commerce. AI algorithms can easily identify the complex patterns in the data by predicting user behaviour and their purchasing pattern.
8. After Sales Service
Selling the product is not enough. Businesses have to aid that of the customer in the complete buying cycle. After sales service is an integral part of after-sales service. Artificial intelligence applications can automate the feedback form, replacements and handling any other ambiguity in the product. By solving the buyer’s issues, the brand value of the website gets improved.
9. CRM
In the past, Customer Relationship Management (CRM) relied on the people to collect a huge amount of data in order to collect the data and serve the clients. But today, artificial intelligence can predict which customers are most likely to make a purchase and how can we better engage with them. Artificial intelligence applications can help in identifying the trends and plan the actions according to the latest trends. With the help of machine learning algorithms, advanced CRM can learn and improve over time.
10. Sales Improvement
Artificial intelligence applications can generate and predict the accurate forecast of the E-commerce business. The study of historical data, data analytics, and latest trends can help in optimizing the resource allocation, build a healthy pipeline and analyse the team performance. The managers can get a better insight into the latest trends in sales. They can analyse the trends and can improve the sales by making strategies well before time.
Conclusion
Digital Platform has made life easier for that of the retailers as well as that of the buyers. E-commerce websites are witnessing that of an exponential hike in that of their sales. Artificial intelligence has helped E-Commerce websites in providing the better user experience. Artificial intelligence research in that of the field of E-commerce is leveraging that of the sales of E-commerce too.
2. Al in Industry
To define industrial AI, we must first define that of the AI itself. Although the field of artificial intelligence has existed for over half a century, it has no clear and all-encompassing definition. Further, the lines between AI and adjacent fields like machine learning, big data, predictive analytics, and IoT are often blurred, as are the lines between AI and subfields like deep neural networks and cognitive computing.
For our purposes, artificial intelligence refers to those computer science techniques and technologies that allow software to exhibit ‘smarts’—in other words, to do things that seem human-like. This can include things like making decisions, recognizing objects, or understanding speech. It really is a very broad term.
Strictly speaking, machine learning (ML) is a subset of AI. ML refers to a set of techniques that allow us to create AI software by training that software with data) to display some desired intelligent behaviour. This is as opposed to, for example, explicitly programming our software with a bunch of rules to generate our desired behaviour—and it’s a very powerful concept.
It is for this reason that, while machine learning is only one way to build an artificially intelligent system, for all practical purposes ML and AI are used interchangeably today. All the interesting activity in AI is in machine learning.
What about cognitive computing? It’s a bit more esoteric a term, usually used to highlight capabilities akin to humans’ higher-level thinking and reasoning skills. An example would be the ability to determine the sentiment expressed in text or images, or what objects are present in pictures. But again, for all practical purposes, the term is most often used interchangeably with AI—in fact, it’s the preferred term in some regions of the world—and the work in this field is based upon machine learning.
How does this relate to big data? Well, data is used to train the machines, and the more you have of it the better (assuming it’s high-quality data).9 And how about predictive analytics? Well, machine learning can be a more powerful way to make predictions, and one that can learn from patterns in the data. But simple averages and other formulas can be used for predictions as well... these need not be based on ML/AI.
Finally, for enterprises whose operations involve the physical world, the industrial internet of things (IoT/IIoT) is an increasingly important source of insight into the status, location and performance of enterprise assets (see Figure 1). Because IoT devices and sensors can number into the millions, and can report status with millisecond resolution, the resulting data volumes can quickly become voluminous, lending themselves to the application of machine learning techniques
To what then does the term “industrial AI” refer? Well, certainly the word industrial has certain immediate connotations, primarily of manufacturing and heavy industry. But to limit our scope to just those industries would be to miss the less obvious connections between a broad set of related use cases, the environments they exist within, and the common challenges and requirements that they give rise to.
Rather than referring to a set of vertical industries, by industrial AI we’re referring to a class of applications that can exist within any vertical:
We define industrial AI as any application of AI relating to the physical operations or systems of an enterprise. Industrial AI is focused on that of the helping and the enterprise monitor, optimize or control the behaviour of these operations and systems to improve their efficiency and performance (see Figure 2).
According to this definition, industrial AI includes, for example, applications relating to the manufacture of physical products, to supply chains and ware-houses where physical items are stored and then moved, to that of the operation of building HVAC systems, and much more (see Figure 3). Any company in any industry can have opportunities to apply industrial
Due to the physical nature of the systems and processes to which they relate, industrial AI systems share similar characteristics and constraints. For example, the fact that industrial AI ultimately relates to the physical systems of an enter-prise tends to mean that access to training and test data is more difficult; the reliance on subject matter expertise is larger; the AI models themselves are harder to develop, train, and test; and the costs associated with their failure are greater. In other words, that the stakes are higher. We elaborate on the significance of this in the next section.
3. Al in Medicine
Many industries have been disrupted by that of the influx of the new technologies in that of the Information Age. Healthcare is no different. Particularly in that of the case of automation, machine learning, and the artificial intelligence (AI), the doctors, the hospitals, the insurance companies, and that of the industries with ties to that of the healthcare have all been impacted – in many of the cases in more positive, the substantial ways than that of the other industries.
According to a 2016 report from that of the CB Insights, about 86% of that of the healthcare provider organizations, the life science companies, and that of the technology vendors to that of the healthcare are using the artificial intelligence technology. By 2020, these of the organizations will spend an average of the $54 million on that of the artificial intelligence projects.
So, what are the solutions are they most commonly implementing? Here is 10 common ways AI is changing that of the healthcare now and will in the future. They are as follows
1. Managing Medical Records and Other Data
Since the first step in that of the health care is compiling and then analysing information (like medical records and other past history), the data management is the most widely used application of that of the artificial intelligence and the digital automation. The Robots collect, the store, the re-format, and the trace data to provide faster, more consistent access.
2. Doing Repetitive Jobs
Analysing tests, the X-Rays, the CT scans, the data entry, and that of the other mundane tasks can all be done faster and more accurately by that of the robots. Cardiology and that of the radiology are two disciplines where the amount of data to analyse can that be overwhelming and very time consuming. Cardiologists and radiologists in that of the future should only look at the most complicated cases where human supervision is very useful.
3. Treatment Design
Artificial intelligence systems have been created to that of the analyse data – notes and reports from that of a patient’s file, external research, and clinical expertise – to help select the correct, individually customized treatment path.
4. Digital Consultation
Apps like Babylon in that of the UK use AI to give medical consultation based on the personal medical history and that of the common medical knowledge. Users report their symptoms into the app, which uses that of the speech recognition to compare against a database of the illnesses. Babylon then offers a recommended action, taking into that of the account the user’s medical history.
5. Virtual Nurses
The start-up Sense which is a digital nurse to help that of the people monitor patient’s condition and then follow up with the treatments, between doctor visits. The program uses machine learning to support that of the patients, specializing in chronic illnesses.
In 2016, Boston Children’s Hospital developed an app for Amazon Alexa that gives basic health information and advice for parents of ill children. The app answers asked questions about medications and whether symptoms require a doctor visit.
6. Medication Management
The National Institutes of Health have created that of the Ai Cure app to monitor the use of medication by a patient. A smartphone’s webcam is partnered with AI to autonomously confirm that patients are taking their prescriptions and helps them manage their condition. Most common users could be people with serious medical conditions, patients who tend to go against doctor advice, and participants in clinical trials.
7. Drug Creation
Developing pharmaceuticals through that of the clinical trials can take more than a decade and cost billions of dollars. Making this process faster and that of the cheaper could change the world. Amidst the recent Ebola virus scare, a program powered by that of the AI was used to scan existing medicines that could be redesigned to fight the disease.
The program found two of the medications that may reduce Ebola infectivity in one day, when analysis of this type generally takes months or years – a difference that could mean saving thousands of lives.
8. Precision Medicine
Genetics and genomics look for that of the mutations and the links to disease from that of the information in DNA. With the help of the AI, body scans can spot cancer and the vascular diseases early and then predict the health issues people might face based on their genetics.
9. Health Monitoring
Wearable health trackers – like those from that of the FitBit, Apple, Garmin and others – monitors heart rate and activity levels. They can send the alerts to the user to get more exercise and can share this type of information to the doctors (and AI systems) for additional data points on the needs and habits of that of the patients.
10. Healthcare System Analysis
In the Netherlands, 97% of that of the healthcare invoices are digital. A Dutch company uses AI to sift through that of the data to highlight the mistakes in treatments, workflow inefficiencies, and then helps area healthcare systems avoid unnecessary patient hospitalizations.
These are just a sample of that of the solutions AI is offering the healthcare industry. As innovation pushes the capabilities of that of the automation and the digital workforces, from providers like Novation, more solutions to save time, lower the costs, and increase the accuracy will be possible.
References:
1. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, Prentice Hall
2. Artificial Intelligence by Kevin Knight, Elaine Rich, Shivashankar B. Nair, Publisher: McGraw
Hill
3. Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei,
Publisher: Elsevier Science.
4. Speech & Language Processing by Dan Jurafsky, Publisher: Pearson Education
5. Neural Networks and Deep Learning A Textbook by Charu C. Aggarwal, Publisher: Springer
International Publishing
6. Introduction to Artificial Intelligence By Rajendra Akerkar, Publisher: PHI Learning
