Recently, machine learning has gained a lot of popularity since it can be applied to a wide range of industries to quickly and effectively solve complex problems. Unlike a common assumption, finding use cases for Machine Learning(ML) is not difficult. Facebook’s picture tagging and email providers’ spam detection are the two applications of machine learning that are most frequently used to solve problems.
1. What is Machine Learning?
Software systems can predict outcomes more correctly with the use of machine learning (ML), a type of artificial intelligence (AI), without needing to be explicitly told to do so. ML algorithms use historical data as input to forecast new output values.
Machine learning is frequently used in recommendation engines. Business process automation, spam detection, maintenance schedules, fraud cases, and maintenance schedules were very few of the important functions.
Top 5 business cases that are better solved by Machine Learning.
2.1. Disease prediction using machine learning
The disease prediction using machine learning system detects diseases in reaction to user or healthcare doctor reports. The program calculates the likelihood that the user will experience the illness by examining their symptoms. The architecture for sickness detection permits the use of the supervised ML method with a Naive Bayes classifier.
The likelihood of the disease is calculated using the Nave Bayes method. Accurate analysis of medical data helps in the early detection of diseases and the treatment of patients as the number of biological and healthcare data sources grows. With tree structure and regression analysis, we can predict the development of illnesses like diabetes, malaria, jaundice, dengue fever, and cancer.
In the medical sciences, there is considerable data growth each year. The increase in technology in the healthcare and hospitality industries has improved the effectiveness of collecting patient records, offering faster patient care. With the use of disease data, data mining reveals hidden patterns in the massive amount of medical data.
People today are affected by a variety of illnesses because of their surroundings and lifestyle choices. Therefore, it is crucial to spot problems at an early stage. While it is also very difficult for a doctor to make accurate predictions based only on indications. The most challenging task is to accurately diagnose a problem. Data mining is vital for preventing disease and solving this problem.
2.2 Fake currency detection using machine learning
This post looks into the issue of identifying if the given sample of currency is actual or fake. It is possible to fake currency detection using machine learning using a variety of common methods and techniques based on the color, lengths, and serial numbers shown. Various ML techniques are proposed by image processing in the advanced era of computer science and high computing approaches, which provide 99.9% accuracy for the fake identity of the cash.
Due to their distinctive qualities, Indian currency is challenging to distinguish from counterfeit. There aren’t many technologies available to address these issues. Numerous research has been conducted on this issue. Even so, these-related technologies are being upgraded. Each Indian banknote has some traits in common and many others that set it apart. The system has to be modified as currencies’ characteristics change. It uses a variety of machine-learning approaches to identify notes. The procedures for spotting real banknotes are as follows:
- Using ORB, identify the key points and descriptors
- Machine learning training dataset: Predictive models utilizing machine learning algorithms are taught how to extract pertinent features using training data.
- Comparing each of the training photos to the query image: The idea of image matching is crucial to computer vision and object recognition.
Finding a machine-learning model to recognize the Indian rupee note is the aim of this paper. The right note should be able to be matched by the model. To identify a banknote, the following actions must be taken:
- Data gathering
- Data Preparation
- Model Construction
2.3. Fake news detection using machine learning
Because content on social media platforms is so detailed to obtain and develops so quickly, it can be challenging to distinguish between fake and authentic content. Information sharing has made it simple to disseminate information, which has dramatically increased information fraud. The credibility of social media networks is at risk in situations where the distribution of false information is widespread.
Similar to this, it has become challenging to determine a piece of information’s accuracy right away by comparing its sources, quality, and delivery. Despite these drawbacks, ML has been crucial in the classification of information. The major focus of this research is the classification of authentic and “fake news detecting using machine learning techniques. The drawbacks of such strategies and improvisational deep learning implementation are also examined.
One of the most difficult things for a human to complete is spotting fake news. Machine learning may be used to quickly identify fake news. Different machine learning classifiers can assist in determining whether a piece of news is accurate or false. These days, it is simple to gather the dataset needed to train these classifiers. Machine learning classifiers were employed by many academics to verify the veracity of the news.
False information spread through social media can influence people’s opinions. People alter their opinions in response to false information without checking it first. A method that can identify such news is required. The researchers have utilized machine learning classifiers for this objective. The K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Linear Support Vector Machine, Decision Tree, and Stochastic Gradient Descent classifiers are among those employed by various academics. The results show that the linear support vector machine had good accuracy in identifying fake news.
2.4. Loan prediction using machine learning
Typically, loan prediction entails the lender reviewing the borrower’s history to determine whether the bank should approve the loan.. Things like credit score, loan amount, lifestyle, career, and assets are among the factors that determine whether the loan is approved. If past borrowers with requirements comparable to yours have made on-time payments, your loan’s approval is more likely.
This is dependent on prior experience and connections with other applicants and can be used by ML algorithms to develop a data science problem to determine the loan status of an application using a collection of similar parameters.
A variety of data sets with various details on prior loan applications may be used to assess the loan condition. A machine-learning model may look at this data, which may be static or time-series, and predict the likelihood that the loan will be approved. Let’s examine a few databases.
2.4.1 Top 5 Loan Prediction Datasets to Practice Loan Prediction Projects
126.96.36.199. Univ.AI Loan Prediction Dataset Based on Customer Behavior
11 parameters are used in this Univ. AI loan prediction dataset to map their relationships to the applicant’s loan default. This aids in identifying actions that might make lending to that consumer riskier. If the risk forecast is high, the bank will deny the applicant’s loan status. The factors include age, career, home ownership, car ownership, and income, and there are 252,000 samples.
188.8.131.52. Future Loan Status Dataset on Kaggle
Using 17 features and over 80,000 samples, the Future Loan Status Prediction Dataset trains a machine learning model to predict whether this loan will be paid off based on the past behavior of other customers.
184.108.40.206. Home Loan Prediction Dataset Kaggle
To use a variety of factors, like gender, marital status, education, the number of dependents, income, loan size, and credit history, the data attempts to estimate the probability each application will be approved. There are 614 values in this dataset. Due to this dataset’s ease of use and simplicity, we will use it to demonstrate how ML may be used to forecast loan status and status changes.
220.127.116.11. UCI Credit Risk Dataset
This collection looks at the credit histories of overseas clients who have fallen behind on credit payments and divides them into dangerous and trustworthy clients. This adds another variable that can be used to predict loans. The collection contains 23 properties, the majority of which track prior transactions and invoices. This is a rather thorough dataset with over 30,000 cases. You may find it in the UCI Credit Risk Dataset for Predicting Loan Eligibility.
18.104.22.168. UCI German Credit Risk: Kaggle
With 1000 samples and 20 categorical characteristics, this loan prediction dataset from actual German financial institutions represents each consumer who has obtained a loan from the bank. German Credit Risk Dataset from UCI.
2.5 Security Improvement
The spread of web-based technology has led to an increase in the world’s reliance on web services. A more convenient and connected lifestyle has resulted as a result. But there are also certain dangers attached to it:
- Attacks through phishing
- Identity fraud
- A violation of data
- Privacy issues
To protect the general security of their users and employees, businesses employ a variety of safeguarding and management practices. Security systems, security devices, threat management software, and strict data storage policies are a few of them. Successful corporations use mostly expert security teams to track out, patch, and resolve web application problems.
By giving part of the monitoring and security risk activities to an algorithmic system, machine learning might be used to support the work of the current security personnel.
3. Scope and limitations of machine learning
3.1. Scope of Machine Learning
The scope of ML is not limited to the investment sector. It is spreading into all industries, including those in banking and finance, information technology, media and entertainment, gaming, and the auto industry.
There are many scopes in the future of machine learning
- Computer Vision
- Quantum Computing
- Automotive Industry
Both academia and the general public are continually drawn to the topic of robotics. The first programmable robot, Unimate, was created by George Devol in 1954. The first artificial intelligence (AI) robot was made by Hanson Robotics in the twenty-first century after then. These technological advancements were made feasible by AI and ML. The goal of scientists worldwide is to build robots that closely resemble the human brain. In this research, a wide range of technologies—including neural networks, AI, machine learning, computer vision, and many more—are being used. Robots that can do a variety of duties just like humans could one day be created through technology.
3.1.2. Computer Vision
Computer vision and machine learning have grown closer together. Computer vision is much better at tracking and identifying things thanks to machine learning. It provides practical approaches for using computer vision to gather, analyze, and concentrate images on objects. In turn, machine learning has expanded in breadth thanks to computer vision. Digital photos or films, sensing equipment, interpreting equipment, and the interpretation stage are all used in this process. ML is utilized in computer vision at every stage, from analysis to interpreting tools.
The techniques that can be used in other domains show that machine learning is, relative to other subjects, a larger field. The analysis of a digital recording is another use of ML ideas. Contrarily, computer vision focuses exclusively on digital photos and movies. It also has connections with signal processing, physics, neuroscience, and information engineering.
3.1.3. Quantum Computing
The field of ML is still in its infancy. There are many improvements that may be made in this area. Quantum computing is one of many that will advance ML. It is a sort of computing that makes use of the entanglement and superposition mechanical properties of quantum mechanics. We can create quantum systems that exhibit several states simultaneously by leveraging the quantum phenomenon of superposition. Entanglement, on the other hand, is a situation in which two dissimilar states can be related to one another. It aids in expressing the relationship between a quantum system’s attributes.
Advanced quantum algorithms that process data quickly are used to build these quantum devices. The processing power of machine learning models is increased via quick processing. When ML is applied in more future applications, the processing speed of an automation system, that is used in several various technologies, will improve.
3.1.4. Automotive Industry
Driving safety standards are being developed and altered by ML in the automotive industry. Some big companies, like Google, Tesla, Mercedes Benz, Nissan, etc., have invested a lot of money in ML to create ground-breaking technologies. Tesla’s self-driving vehicle is the best in the business, though. These self-driving cars are built using ML, IoT sensors, high-definition cameras, voice recognition systems, etc.
All you have to do is get in your car and drive to the destination. It will determine the most efficient path there and make sure the driver gets there safely. What a joy it would be to see such a magnificent creation of mankind. ML has made all of this feasible.
3.2 Limitations of Machine Learning
ML offers a novel method for developing projects that need to process a lot of data. But what crucial factors should you take into account before using ML as a tool to create for your startup or company? You should be aware of any potential drawbacks and risks before using this technology. Four broad categories can be used to categorize potential ML problems, which we mention below.
3.2.1. Ethical concerns
Of course, trusting algorithms has numerous benefits. The use of computer algorithms to automate procedures, analyze vast volumes of data, and make difficult judgments has helped the world. Trusting algorithms does have certain disadvantages, though. Bias can exist in algorithms at any stage of development. Additionally, because algorithms are developed and trained by people, bias cannot be completely eliminated.
Many ethical issues are still unresolved. Who is responsible, for instance, if something goes wrong? Take the most straightforward illustration—self-driving automobiles. In the event of a road collision, who should be held responsible? Who is more responsible—the driver, the automobile company, or the software creator?
3.2.2. Deterministic problems
The powerful technology of ML has a wide range of uses, including predicting the weather and studying the environment and climate. By utilizing ML models to calibrate and correct the sensors, you can change the behavior of sensors that measure environmental indicators like temperature, pressure, and humidity.
For instance, models can be created to forecast pollution by modeling weather and air pollutants. Depending on the amount of data and the accuracy of the model, this might be extremely costly and take up to a month.
Can ML be used to predict the weather by humans? Maybe. A simple forecasting algorithm with data from weather stations and satellites can be used by experts. They can give the information required to train a neural network to predict tomorrow’s weather, such as air pressure in a particular area, air humidity, wind speed, etc.
3.2.3. Lack of Data
Given their intricate designs, neural networks need a ton of training data in order to function well. The amount of data needed by a neural network increases with its size. In these cases, some might decide to reuse the data, but this will never result in good results.
The scarcity of good data is a further issue. This is distinct from merely lacking data. Imagine that your neural network requires more information and you give it enough low-quality input to do so. The accuracy of the model may be severely hampered as a result.
3.2.4. Lack of interpretability
Interpretability is a significant issue with deep learning algorithms. Imagine you are developing a model for a financial company to spot fraud cases. Your model should be able to defend how it categorizes transactions in this situation. For this task, a deep learning system might do well in terms of accuracy and responsiveness, but it might not validate its conclusions.
Perhaps you are employed by an AI consulting company. You want to offer your services to a client who only uses traditional statistical methods. If AI models cannot be understood, they can become useless, because human interpretation entails subtleties that go well beyond technical skill. How likely is it that your client will believe you and your experience if you can’t persuade them that you know how an algorithm works?
4. Perspectives and issues in machine learning
Ontologies are important from the Semantic Web perspective. The inductive method can be used to make knowledge that is implicit in web resources visible by using them as selection processes for super. Deductive reasoning is no longer as easily applicable in the shared and distributed Web setting due to the possibility of noisy or opposing fundamental knowledge bases. Inductive learning approaches, in particular, could be successfully applied in this situation.
Additionally, ML techniques could be used to uncover new knowledge from an ontological knowledge base that cannot be rationally deduced when combined with conventional reasoning techniques. The session’s main topic will be various knowledge extraction problems and potential ML fixes.
The automatic growth and improvement of current models, the identification of idea drift and innovations inside concepts, and the uncovering of hidden knowledge patterns by utilizing the wealth of data present inside a domain are all areas where ML techniques could be extremely beneficial.
On the one hand, this might mean forgoing reliable and thorough deductive procedures in favor of speculative conclusions, but on the other, it might make it possible to use deductive reasoning on a large scale and deal with the inherent uncertainty that exists on the Web because it may, by its very nature, contain clashing or incomplete information.
In this article, we discuss: What is machine learning? and in which business cases ML is used to better solve the scope and limitations of machine learning, as well as the prospects and issues of machine learning. We hope you understand the details of ML.
Making predictions from data is a strong use of machine learning. But it’s crucial to keep in mind that machine learning is only as effective as the data used to train the algorithms. It is crucial to use high-quality data that is indicative of the real-world data that the algorithm will be employed on in order to create precise predictions.