Top 10 Research and Thesis Topics for ML Projects in 2022
This article features the top 10 research and thesis topics for ML projects for students to try in 2022
Text mining and text classification, image-based applications, machine vision, optimization, voice classification, sentiment analysis, recommendation framework project, mall customers’ project, object detection with deep learning.
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M.Tech/Ph.D Thesis Help in Chandigarh | Thesis Guidance in Chandigarh
Latest thesis topics in Machine Learning for research scholars:
Choosing a research and thesis topics in Machine Learning is the first choice of masters and Doctorate scholars now a days. Though, choosing and working on a thesis topic in machine learning is not an easy task as Machine learning uses certain statistical algorithms to make computers work in a certain way without being explicitly programmed. The algorithms receive an input value and predict an output for this by the use of certain statistical methods. The main aim of machine learning is to create intelligent machines which can think and work like human beings. Achieving the above mentioned goals is surely not very easy because of which students who choose research topic in machine learning face difficult challenges and require professional thesis help in their thesis work.
Below is the list of the latest thesis topics in Machine learning for research scholars:
- The classification technique for the face spoof detection in artificial neural networks using concepts of machine learning .
- The iris detection and reorganization system using classification and glcm algorithm in machine learning.
- Using machine learning algorithms in the detection of pattern system using algorithm of textual feature analysis and classification
- The plant disease detection using glcm and KNN classification in neural networks merged with the concepts of machine learning
- Using the algorithms of machine learning to propose technique for the prediction analysis in data mining
- The sentiment analysis technique using SVM classifier in data mining using machine learning approach
- The heart disease prediction using technique of classification in machine learning using the concepts of data mining.
So let’s start with machine learning.
First of all…
What exactly is machine learning?
Find the link at the end to download the latest topics for thesis and research in Machine Learning
What is Machine Learning?
Machine Learning is a branch of artificial intelligence that gives systems the ability to learn automatically and improve themselves from the experience without being explicitly programmed or without the intervention of human. Its main aim is to make computers learn automatically from the experience.
Requirements of creating good machine learning systems
So what is required for creating such machine learning systems? Following are the things required in creating such machine learning systems:
Data – Input data is required for predicting the output.
Algorithms – Machine Learning is dependent on certain statistical algorithms to determine data patterns.
Automation – It is the ability to make systems operate automatically.
Iteration – The complete process is iterative i.e. repetition of process.
Scalability – The capacity of the machine can be increased or decreased in size and scale.
Modeling – The models are created according to the demand by the process of modeling.
Methods of Machine Learning
Machine Learning methods are classified into certain categories These are:
- Supervised Learning
- Unsupervised Learning
Supervised Learning – In this method, input and output is provided to the computer along with feedback during the training. The accuracy of predictions by the computer during training is also analyzed. The main goal of this training is to make computers learn how to map input to the output.
Unsupervised Learning – In this case, no such training is provided leaving computers to find the output on its own. Unsupervised learning is mostly applied on transactional data. It is used in more complex tasks. It uses another approach of iteration known as deep learning to arrive at some conclusions.
Reinforcement Learning – This type of learning uses three components namely – agent, environment, action. An agent is the one that perceives its surroundings, an environment is the one with which an agent interacts and acts in that environment. The main goal in reinforcement learning is to find the best possible policy.
How does machine learning work?
Machine learning makes use of processes similar to that of data mining. Machine learning algorithms are described in terms of target function(f) that maps input variable (x) to an output variable (y). This can be represented as:
There is also an error e which is the independent of the input variable x. Thus the more generalized form of the equation is:
In machine the mapping from x to y is done for predictions. This method is known as predictive modeling to make most accurate predictions. There are various assumptions for this function.
Benefits of Machine Learning
Everything is dependent on machine learning. Find out what are the benefits of machine learning.
Decision making is faster – Machine learning provides the best possible outcomes by prioritizing the routine decision-making processes.
Adaptability – Machine Learning provides the ability to adapt to new changing environment rapidly. The environment changes rapidly due to the fact that data is being constantly updated.
Innovation – Machine learning uses advanced algorithms that improve the overall decision-making capacity. This helps in developing innovative business services and models.
Insight – Machine learning helps in understanding unique data patterns and based on which specific actions can be taken.
Business growth – With machine learning overall business process and workflow will be faster and hence this would contribute to the overall business growth and acceleration.
Outcome will be good – With machine learning the quality of the outcome will be improved with lesser chances of error.
Branches of Machine Learning
- Computational Learning Theory
- Adversarial Machine Learning
- Quantum Machine Learning
- Robot Learning
Computational Learning Theory – Computational learning theory is a subfield of machine learning for studying and analyzing the algorithms of machine learning. It is more or less similar to supervised learning.
Adversarial Machine Learning – Adversarial machine learning deals with the interaction of machine learning and computer security. The main aim of this technique is to look for safer methods in machine learning to prevent any form of spam and malware. It works on the following three principles:
Finding vulnerabilities in machine learning algorithms.
Devising strategies to check these potential vulnerabilities.
Implementing these preventive measures to improve the security of the algorithms.
Quantum Machine Learning – This area of machine learning deals with quantum physics. In this algorithm, the classical data set is translated into quantum computer for quantum information processing. It uses Grover’s search algorithm to solve unstructured search problems.
Predictive Analysis – Predictive Analysis uses statistical techniques from data modeling, machine learning and data mining to analyze current and historical data to predict the future. It extracts information from the given data. Customer relationship management(CRM) is the common application of predictive analysis.
Robot Learning – This area deals with the interaction of machine learning and robotics. It employs certain techniques to make robots to adapt to the surrounding environment through learning algorithms.
Grammar Induction – It is a process in machine learning to learn formal grammar from a given set of observations to identify characteristics of the observed model. Grammar induction can be done through genetic algorithms and greedy algorithms.
Meta-Learning – In this process learning algorithms are applied on meta-data and mainly deals with automatic learning algorithms.
Best Machine Learning Tools
Here is a list of artificial intelligence and machine learning tools for developers:
ai-one – It is a very good tool that provides software development kit for developers to implement artificial intelligence in an application.
Protege – It is a free and open-source framework and editor to build intelligent systems with the concept of ontology. It enables developers to create, upload and share applications.
IBM Watson – It is an open-API question answering system that answers questions asked in natural language. It has a collection of tools which can be used by developers and in business.
DiffBlue – It is another tool in artificial intelligence whose main objective is to locate bugs, errors and fix weaknesses in the code. All such things are done through automation.
TensorFlow – It is an open-source software library for machine learning. TensorFlow provides a library of numerical computations along with documentation, tutorials and other resources for support.
Amazon Web Services – Amazon has launched toolkits for developers along with applications which range from image interpretation to facial recognition.
OpenNN – It is an open-source, high-performance library for advanced analytics and is written in C++ programming language. It implements neural networks. It has a lot of tutorials and documentation along with an advanced tool known as Neural Designer.
Apache Spark – It is a framework for large-scale processing of data. It also provides a programming tool for deep learning on various machines.
Caffe – It is a framework for deep learning and is used in various industrial applications in the area of speech, vision and expression.
Veles – It is another deep learning platform written in C++ language and make use of python language for interaction between the nodes.
Machine Learning Applications
Following are some of the applications of machine learning:
Machine Learning in Bioinformatics
Bioinformatics term is a combination of two terms bio, informatics. Bio means related to biology and informatics means information. Thus bioinformatics is a field that deals with processing and understanding of biological data using computational and statistical approach. Machine Learning has a number of applications in the area of bioinformatics. Machine Learning find its application in the following subfields of bioinformatics:
Genomics – Genomics is the study of DNA of organisms. Machine Learning systems can help in finding the location of protein-encoding genes in a DNA structure. Gene prediction is performed by using two types of searches named as extrinsic and intrinsic. Machine Learning is used in problems related to DNA alignment.
Proteomics – Proteomics is the study of proteins and amino acids. Proteomics is applied to problems related to proteins like protein side-chain prediction, protein modeling, and protein map prediction.
Microarrays – Microarrays are used to collect data about large biological materials. Machine learning can help in the data analysis, pattern prediction and genetic induction. It can also help in finding different types of cancer in genes.
System Biology – It deals with the interaction of biological components in the system. These components can be DNA, RNA, proteins and metabolites. Machine Learning help in modeling these interactions.
Text mining – Machine learning help in extraction of knowledge through natural language processing techniques.
Deep Learning is a part of the broader field machine learning and is based on data representation learning. It is based on the interpretation of artificial neural network. Deep Learning algorithm uses many layers of processing. Each layer uses the output of previous layer as an input to itself. The algorithm used can be supervised algorithm or unsupervised algorithm. Deep Learning is mainly developed to handle complex mappings of input and output. It is another hot topic for M.Tech thesis and project along with machine learning.
Deep Neural Network
Deep Neural Network is a type of Artificial Neural Network with multiple layers which are hidden between the input layer and the output layer. This concept is known as feature hierarchy and it tends to increase the complexity and abstraction of data. This gives network the ability to handle very large, high-dimensional data sets having millions of parameters. The procedure of deep neural networks is as follows:
Consider some examples from a sample dataset.
Calculate error for this network.
Improve weight of the network to reduce the error.
Repeat the procedure.
Applications of Deep Learning
Here are some of the applications of Deep Learning:
Automatic Speech Recognition
Natural Language Processing
Customer Relationship Management
Advantages of Deep Learning
Deep Learning helps in solving certain complex problems with high speed which were earlier left unsolved. Deep Learning is very useful in real world applications. Following are some of the main advantages of deep learning:
Eliminates unnecessary costs – Deep Learning helps to eliminate unnecessary costs by detecting defects and errors in the system.
Identifies defects which otherwise are difficult to detect – Deep Learning helps in identifying defects which left untraceable in the system.
Can inspect irregular shapes and patterns – Deep Learning can inspect irregular shapes and patterns which is difficult for machine learning to detect.
From this introduction, you must have known that why this topic is called as hot for your M.Tech thesis and projects. This was just the basic introduction to machine learning and deep learning. There is more to explore in these fields. You will get to know more once you start doing research on this topic for your M.Tech thesis. You can get thesis assistance and guidance on this topic from experts specialized in this field.
Research and Thesis Topics in Machine Learning
Here is the list of current research and thesis topics in Machine Learning :
Machine Learning Algorithms
Supervised Machine Learning
Unsupervised Machine Learning
For starting with Machine Learning, you need to know some algorithms. Machine Learning algorithms are classified into three categories which provide the base for machine learning. These categories of algorithms are supervised learning, unsupervised learning, and reinforcement learning. The choice of algorithms depends upon the type of tasks you want to be done along with the type, quality, and nature of data present. The role of input data is crucial in machine learning algorithms.
Computer Vision is a field that deals with making systems that can read and interpret images. In simple terms, computer vision is a method of transmitting human intelligence and vision in machines. In computer vision, data is collected from images which are imparted to systems. The system will take action according to the information it interprets from what it sees.
It is a good topic for machine learning masters thesis. It is a type of machine learning algorithm in which makes predictions based on known data-sets. Input and output is provided to the system along with feedback. Supervised Learning is further classified into classification and regression problems. In the classification problem, the output is a category while in regression problem the output is a real value.
It is another category of machine learning algorithm in which input is known but the output is not known. Prior training is not provided to the system as in case of supervised learning. The main purpose of unsupervised learning is to model the underlying structure of data. Clustering and Association are the two types of unsupervised learning problems. k-means and Apriori algorithm are the examples of unsupervised learning algorithms.
Deep Learning is a hot topic in Machine Learning. It is already explained above. It is a part of the family of machine learning and deals with the functioning of the artificial neural network. Neural Networks are used to study the functioning of the human brain. It is one of the growing and exciting field. Deep learning has made it possible for the practical implementation of various machine learning applications.
Neural Networks are the systems to study the biological neural networks. It is an important application of machine learning and a good topic for masters thesis and research. The main purpose of Artificial Neural Network is to study how the human brain works. It finds its application in computer vision, speech recognition, machine translation etc. Artificial Neural Network is a collection of nodes which represent neurons.
Reinforcement Learning is a category of machine learning algorithms. Reinforcement Learning deals with software agents to study how these agents take actions in an environment in order to maximize their performance. Reinforcement Learning is different from supervised learning in the sense that correct input and output parameters are not provided.
Predictive Learning is another good topic for thesis in machine learning. In this technique, a model is built by an agent of its environment in which it performs actions. There is another field known as predictive analytics which is used to make predictions about future events which are unknown. For this, techniques like data mining, statistics, modeling, machine learning, and artificial intelligence are used.
It is a network that represents probabilistic relationships via Directed Acyclic Graph(DAG). There are algorithms in Bayesian Network for inference and learning. In the network, a probability function is there for each node which takes an input to give probability to the value associated with the node. Bayesian Network finds its application in bioinformatics, image processing, and computational biology.
Data Mining is the process of finding patterns from large data-sets to extract valuable information to make better decisions. It is a hot area of research. This technology use method from machine learning, statistics, and database systems for processing. There exist data mining techniques like clustering, association, decision trees, classification for the data mining process.
Click on the following link to download the latest thesis and research topics in Machine Learning
Latest Thesis and Research Topics on Machine Learning(pdf)
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8 Best Topics for Research and Thesis in Artificial Intelligence
Imagine a future in which intelligence is not restricted to humans!!! A future where machines can think as well as humans and work with them to create an even more exciting universe. While this future is still far away, Artificial Intelligence has still made a lot of advancement in these times. There is a lot of research being conducted in almost all fields of AI like Quantum Computing, Healthcare, Autonomous Vehicles, Internet of Things , Robotics , etc. So much so that there is an increase of 90% in the number of annually published research papers on Artificial Intelligence since 1996. Keeping this in mind, if you want to research and write a thesis based on Artificial Intelligence, there are many sub-topics that you can focus on. Some of these topics along with a brief introduction are provided in this article. We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.
So without further ado, let’s see the different Topics for Research and Thesis in Artificial Intelligence!
1. Machine Learning
Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate. However, generally speaking, Machine Learning Algorithms are divided into 3 types i.e. Supervised Machine Learning Algorithms, Unsupervised Machine Learning Algorithms , and Reinforcement Machine Learning Algorithms.
2. Deep Learning
Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data. Basically, Deep Learning uses artificial neural networks to implement machine learning. These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!). This web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach. An example of a deep neural network is RankBrain which is one of the factors in the Google Search algorithm.
3. Reinforcement Learning
Reinforcement Learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hypothetical student learns from its own mistakes over time (like we had to!!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error. This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. And like humans, this works for machines as well! For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.
Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence comes in! AI allows robots to act intelligently in certain situations. These robots may be able to solve problems in a limited sphere or even learn in controlled environments. An example of this is Kismet , which is a social interaction robot developed at M.I.T’s Artificial Intelligence Lab. It recognizes the human body language and also our voice and interacts with humans accordingly. Another example is Robonaut , which was developed by NASA to work alongside the astronauts in space.
5. Natural Language Processing
It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation , etc. NLP is currently extremely popular for customer support applications, particularly the chatbot . These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.
Some Research Papers published in the field of Natural Language Processing are provided here. You can study them to get more ideas about research and thesis on this topic.
6. Computer Vision
The internet is full of images! This is the selfie age, where taking an image and sharing it has never been easier. In fact, millions of images are uploaded and viewed every day on the internet. To make the most use of this huge amount of images online, it’s important that computers can see and understand images. And while humans can do this easily without a thought, it’s not so easy for computers! This is where Computer Vision comes in. Computer Vision uses Artificial Intelligence to extract information from images. This information can be object detection in the image, identification of image content to group various images together, etc. An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.
7. Recommender Systems
When you are using Netflix, do you get a recommendation of movies and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online. A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering. Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on Natural Language Processing done on the books. On the other hand, Collaborative Filtering is done by analyzing your past reading behavior and then recommending books based on that.
8. Internet of Things
Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention. Internet of Things , on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other. Now, all these IoT devices generate a lot of data that needs to be collected and mined for actionable results. This is where Artificial Intelligence comes into the picture. Internet of Things is used to collect and handle the huge amount of data that is required by the Artificial Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the IoT devices.
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Trending research topics in machine learning.
Trending Masters and Phd Research Topics in Machine Learning
Machine learning constitutes a vital role in Artificial intelligence and deals with the ability of machines to learn from the massive amount of data using knowledge representation, processing, and storing. Recently, machine learning has received great unprecedented popularity with the development of several new areas. The previously established research areas have also gained new momentum in big data analysis. The tremendous growth in the quantity of digital data, affordable computing resources, and optimization algorithms has enabled machine learning techniques for the breakthrough of artificial intelligence. For instance, large quantities of medical data are analyzed for diagnosis and treatment. The machine learning techniques analyze the medical data and determine the patterns in the bio-signals. They drive advances in healthcare and medical research. The recent research on machine learning algorithms attempts to solve the following challenges, 1) Developing the machine learning algorithms that can computationally scale to Big data, 2) Designing algorithms that do not require large amounts of labeled data, 3) Designing a resource-efficient machine learning methods, and 4) developing a privacy preservation techniques for various applications.
Machine Learning -Topics Coverage
Machine Learning Models: Supervised - Unsupervised - Semi-Supervised - Regression - Ensemble - Reinforcement Deep Learning Models: Deep Neural Networks - Deep Recurrent Neural Networks - Deep Belief Networks - Deep Boltzmann Machine - Deep Autoencoder -Generative Neural Networks - Deep Ensemble Learning - Deep Reinforcement Learning - Convolutional Neural Networks- Transfer Learning - Extreme Learning Machines - Deep Generative Models - Dynamic Neural Networks - Radial Basis Function Networks - Long Short-Term Memory Networks - Restricted Boltzmann Machines - Self Organizing Maps - Transfer Reinforcement Learning - Multi-Goal Reinforcement Learning - Unsupervised Representation Learning - Distributional Reinforcement Learning -Extreme Multi-Label Classification - Generalized Few-Shot Classification - Multimodal Deep Learning - Quantum Machine Learning - One-Shot Learning - Hierarchical Reinforcement Learning - Multiple Instance Learning - Interpretable Machine Learning - Imitation Learning - Federated Learning - Active Learning - Few-Shot Learning - Meta-Learning - Representation Learning - Deep Cascade Learning- Explainable Deep Neural Networks - Evidential Deep Learning -Graph Representation Learning - Meta Reinforcement Learning - Graph Convolutional Networks - Hopfield Neural Networks - Quaternion Factorization Machines - Adversarial Machine Learning - Hyperbolic Deep Neural Networks - Few-Shot Class-Incremental Learning - Non-Local Graph Neural Networks -Distributed Active Learning - Triple Generative Adversarial Network - Shallow Broad Neural Network - Spiking Neural Networks - Bayesian Neural Networks - Word Embedding Models-Neural Machine Translation - Attention Mechanisms - Domain Adaptation - Data Augmentation - Image Augmentation - Text Augmentation -Neural Architecture Search - Hyperparameter Optimization - Neural Architecture Search - Feature Engineering Applications: Natural Language Processing - Stream Processing - Recommendation Systems - Sentiment Analysis - Opinion Mining - Time Series Data Analysis - Medical Machine Learning - Disease Prediction - Multimedia - Stock Market Prediction - Cyber security - Pattern Recognition - Medical Imaging - Healthcare - Speech Recognition - Computer Vision - Malware Detection System - Intrusion Detection System - Intelligent Wireless Networks - Big Data Analytics - Intelligent Vehicular Networks -Autonomous Vehicles - Time Series Forecasting - Edge Intelligence - Cloud Computing - Internet of Vehicles - Semantic Similarity
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Machine Learning Research Topics 2023
By : Aksaht Gaurav , Ronin Institute, U.S
Machine learning (ML) is a rapidly evolving field, with new technologies and approaches being developed at a breakneck pace [1-6]. As we approach the year 2023, the field is poised to make significant advancements in a number of areas. This blog post will explore some of the top machine-learning research topics for 2023.
Explainable Artificial Intelligence (XAI)
XAI is an area of research that focuses on developing machine learning algorithms that can provide clear explanations for their decisions [7-11]. As machine learning becomes more widespread, there is a growing need for algorithms that can be easily understood and interpreted by humans. XAI research is expected to make significant strides in 2023 and beyond, with new models and algorithms that offer more transparency and accountability.
Federated learning is a technique that allows multiple devices to contribute to a shared machine learning model without sending their data to a centralized server [12-16]. This technique has many potential applications, from improving personalized recommendations to developing better predictive models for medical research. In 2023, we can expect to see significant advancements in federated learning, with new algorithms and techniques that improve its efficiency and accuracy.
Continual learning is an area of research that focuses on developing machine learning algorithms that can learn new tasks without forgetting their previous knowledge . This is an important area of research, as current machine learning models are often unable to learn new tasks without significant retraining. In 2023, we can expect to see significant advancements in continual learning, with new models and algorithms that can learn new tasks more efficiently.
Reinforcement learning is a type of machine learning that focuses on training agents to make decisions in complex environments [16-20]. In 2023, we can expect to see significant advancements in reinforcement learning, with new techniques that improve the stability and efficiency of training algorithms.
Machine Learning for Healthcare
Machine learning has the potential to revolutionize the healthcare industry, from improving diagnosis and treatment to developing better predictive models for disease outbreaks . In 2023, we can expect to see significant advancements in machine learning for healthcare, with new models and algorithms that offer better accuracy and efficiency.
Machine Learning for Metaverse
The metaverse is an emerging concept that refers to a virtual world that combines elements of gaming, social media, and other online experiences . As the metaverse becomes more widespread, there will be a growing need for machine learning algorithms that can analyze and interpret the vast amounts of data generated by these virtual worlds. In 2023, we can expect to see significant advancements in machine learning for the metaverse, with new models and algorithms that offer better insights and predictions.
Natural Language Processing
Natural language processing (NLP) is an area of machine learning that focuses on developing algorithms that can analyze and understand human language . In 2023, we can expect significant NLP advancements, with new models and algorithms that offer better accuracy and efficiency. This could have many potential applications, from improving voice assistants and chatbots to developing better text-to-speech and speech-to-text systems.
Autonomous systems, such as self-driving cars and drones, rely on machine learning algorithms to make decisions and navigate complex environments . In 2023, we can expect significant advancements in autonomous systems, with new models and algorithms offering better accuracy and safety. This could have many potential applications, from improving transportation and logistics to developing better surveillance and security systems.
Quantum Machine Learning
Quantum computing is an emerging technology that has the potential to revolutionize machine learning. In 2023, we can expect to see significant advancements in quantum machine learning, with new algorithms and techniques that offer better efficiency and scalability. This could have many potential applications, from improving drug discovery and materials science to developing better machine learning models for financial and insurance industries.
Overall, the field of machine learning is rapidly evolving, with many exciting research topics on the horizon. From machine learning for metaverse and autonomous systems to natural language processing and quantum machine learning, there is a lot to look forward to in 2023 and beyond. By staying up to date with the latest research, we can help shape the future of machine learning and its impact on our world.
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- Pai, M. L., et al., (2020). Application of Artificial Neural Networks and Genetic Algorithm for the Prediction of Forest Fire Danger in Kerala . In Intelligent Systems Design and Applications: 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018) held in Vellore, India, December 6-8, 2018, Volume 2 (pp. 935-942). Springer International Publishing.
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Top 10 Machine Learning Research Ideas 2022
- Machine Learning Research Ideas
Table of contents
Image processing, data visualization, predictive maintenance, social media analysis, natural language processing, sentiment analysis, voice interfaces, virtual assistants.
Machine learning is a branch of artificial intelligence in which machines can learn and make predictions without being programmed. Machine learning enables computers to detect patterns that humans might not see. It’s also great for automating tasks that are too complex or time-consuming for an individual. This article will provide 10 machine learning project ideas to get you started with machine learning.
Machine learning can make processes of recognizing and classifying images faster and more accurate. Even if you’re not interested in your photographs, the time and effort that can be saved with an application like Photoshop is considerable.
Start by using a Google tool for machine learning, Vision, to take a quick image reading test and build the image recognition algorithm you’re most interested in.
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Projects based on Image processing are gettingting huge attention.
Data visualization is the process of presenting information in a visually appealing way. This can be done by using graphics, charts, and maps, among other visualization tools. Challenge: Data visualization is a valuable tool for raising awareness of the data that’s necessary for machine learning .
If you have a data visualization website, create a gallery of useful data visualizations to present the topic. Instead of creating a cartographic map of your location to represent your body size, develop a globe so that it can represent how your body would be distributed across different areas of the world. The best way to make this easier is to visualize your body by drawing a human skeleton.
Predictive maintenance is the process of predicting when components or systems will fail by using machine learning to capture historical data and apply statistical analysis.
Predictive maintenance can improve reliability and decrease repair costs for applications that are sensitive to changing conditions, such as data centers and power grids.
Predictive maintenance could be used for airplane components, mining equipment, or even car models.
Predictive maintenance can also be used to predict possible delays or delays in purchasing decisions by predicting the price of similar products.
Considerations and Benefits of Machine Learning Machine learning enable computers to do tasks without being explicitly programmed.
Facebook’s “trending topics” let users see what topics are trending. If you’re really into knowing what’s happening in the news, you can simply start analyzing the trending topics on Facebook.
Do you know how many countries are talking about each topic, and how many users are talking about each topic? What’s the sentiment behind each conversation?
You can generate similar metrics on Twitter. Make sure to use a powerful machine-learning algorithm to interpret this data.
Polling For example, if you’re a local food or hotel chain, you can send SMS polling to every member in the country to know what their experience with your brand is. You can also apply machine learning to analyze the responses.
This is a popular and well-known machine learning technique used to understand a person’s voice. Artificial intelligence uses natural language processing to help make the computer understand the tone of voice and speech patterns, and infer a person’s attitude.
This type of analysis is useful for detecting situations such as what a person is saying, whether it’s positive or negative, and to judge the content of the conversation.
This type of analysis is usually done by a computer in a microphone to capture speech patterns and understand whether the communication is friendly or aggressive.
Some machine learning companies are working with chatbots and instant messenger bots to make it easy for customers to contact them with messaging software such as Facebook Messenger, Skype, and WhatsApp.
The sentiment is the emotional response to an article, image, or text. At the end of every article, the Internet Archive has a collection of .docx files that contain comments, tags, and user ratings. These are often used for sentiment analysis.
For instance, sentiment analysis might conclude that the tweet above was negative or positive. This can help marketers decide how to react to each piece of content in their email campaigns.
Reaction Detection If you have a website, you may not even know if users are interacting with your site. You can use sentiment analysis to determine if people are happy, disappointed, or confused.
The voice user interface (VUI) is a type of conversational interface that responds to human verbal requests and commands.
Voice interfaces also help automate different applications like shopping, controlling home appliances, and getting directions.
VUI systems are becoming more popular in modern applications such as the Amazon Echo and the Apple Siri. Most of the solutions that follow will use an Amazon Echo or the Apple Siri.
Wearables The wearable device market is growing and becoming more important as people seek solutions to various problems. Wearables can provide additional capabilities to existing hardware devices.
Computer interfaces can be intimidating to use for people new to computers. Fortunately, virtual assistants like Siri or Alexa are simplifying the process.
Virtual assistants act as intermediaries between users and the computer. The assistant takes over for users in the case of difficulty with the user interface or text input.
To use virtual assistants, it’s important to train the assistant with data. For example, if you wanted your virtual assistant to automatically return a list of nearby restaurants after saying “where is the closest restaurant”, you’d have to first train the assistant to recognize nearby restaurants.
If you have access to an audio dataset, you can record yourself and record your conversations with an assistant.
These are all the major machine learning research ideas to kick start your next project.
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147 Top Machine Learning Topics To Get Your Paper Easy
First of all, let’s determine – what is machine learning ? Generally, it refers to the study of computer algorithms that improve automatically through the use of data and experience. Machine learning is seen as part of AI that makes decisions or predictions without being entirely programmed.
The complexity of developing conventional algorithms for performing the much-needed tasks makes this field a choice for the chosen few. Statistics show that the number of college students pursuing this course is few. Are you among the chosen few who would like to improve and excel in your computer science course? Well, our expert help will help springboard you to the next level.
How To Find Topics in Machine Learning
The task of sourcing for impeccable machine learning topics is the least trodden path. The few resources available in the field and the course’s technicality make it all the more difficult. However, there are places you can find top-tier ideas in machine learning:
Reputable online sources Your well-stocked college library Available computer science papers (articles, journals, theses, etc.) Tech programs such as TED-X
With these readily available sources, you can be confident of a long list of machine learning topics that can impress your professor. That said, our professionals have compiled a handpicked list of writing ideas for your inspiration. Have a look at them:
The Best Machine Learning Topics
- Discuss supervised learning algorithms approach in machine learning
- Evaluate iterative optimization of an object function
- How can a function be used to determine the output for inputs correctly?
- How to improve the accuracy of predictions or outputs with an algorithm
- A case study of the classification, active learning, and regression in college
- Analyze the effectiveness of learning from examples using a similarity function
- The application of unsupervised learning in density estimation in statistics
- Manifold learning algorithms for solving machine learning problems
- Discuss the application of machine learning in data mining
- How to detect anomalies and deviations in machine learning
- The role of developmental robotics in machine learning
- Determine the relationship between variables in large databases
- How to develop artificial immune systems
- Discuss the concept of strict rules in machine learning
- Learning classifier systems in machine learning algorithms
Easy Machine Learning Research Topics
- Challenges involved in creating intelligent machines that mimic human behavior
- Discuss the process of data observations in machine learning
- How to enable computers to learn automatically without human intervention
- How to analyze training data and produce an inferred function
- Drawing inferences from datasets comprising of input data without labeled responses
- The role of Artificial Neural Network in learning from observational data
- Evaluate the input and output layers of artificial neural networks
- How to stack multiple layers of neural networks to create a huge network
- Discuss the dependence of machine learning on linear regression
- Dealing with the classification problem using logistic regression
- The random forest machine learning technique in college
Hot Topics in Machine Learning
- New computing technologies that have contributed to machine learning
- The essence of machine learning in developing the self-driving Google car
- An analysis of online recommendation offers: A case of Netflix
- How to know what customers are saying about a product using machine learning
- The crucial role of fraud detection in machine learning
- Discuss the crucial relationship between AI and machine learning
- What has contributed to the resurging interest in machine learning?
- The role of machine learning in computational processing
- The impact of machine learning in developing faster and more accurate results
- The role of data preparation capabilities in machine learning
- Discuss the place of machine learning in today’s world
Interesting Machine Learning Thesis Topics
- How to apply machine learning to the progressive Internet of Things
- Why industries using large amounts of data need machine learning knowledge
- The role of machine learning in banks and other financial institutions
- How government agencies use machine learning in ensuring public safety
- Analyze how sensor data is used in identifying ways to increase efficiency
- The role of wearable devices to the healthcare industry
- How website recommending systems are transforming the retail sector
- The process of finding new energy sources using machine learning
- How to identify patterns and trends in transportation using machine learning
- Discuss prediction and gradient boosting as machine learning methods
- Compare and contrast between machine learning, deep learning, and data mining
Top Machine Learning Project Topics
- How to pair the best algorithms with the right tools in machine learning
- The important role of the rich, sophisticated heritage of statistics in machine learning
- The role of machine learning in huge enterprise environments
- Discuss some of the local search optimization techniques: A case of genetic algorithms
- How to handle multivariate adaptive regression splines
- Discuss the effectiveness of the singular value decomposition
- Tools and processes involved in machine learning: A case of algorithms
- Evaluate the process of comprehensive data quality and management
- The interactive data exploration and visualization model
- Compare and contrast the different machine learning models today
- How the automated sensor ensemble model is used in identifying flaws
College Research Topics in Machine Learning
- How to determine the best machine-learning algorithm to use
- The role of curiosity in meeting the challenges that lie ahead of machine learning
- How scientists have incorporated machine learning in combating the pandemic
- The place of innovation, agility, and customer-centricity in machine learning
- The underpinnings of resilience in the machine learning process
- The role of machine learning in the face of unpredictability
- Top-rated analytical skills gained through machine learning
- Getting repeatable data using the easy model deployment
- Discuss the Graphical User Interfaces for building models and process flaws
- Evaluate the sequential covering rule building
- Principal component analysis in the machine learning process
Machine Learning Hot Topics
- Developing a stock price detector using machine learning
- Discuss how to predict wine quality using a wine quality dataset
- The process of developing human activity recognition using a smartphone dataset
- Evaluate object detection with deep learning
- Why do we need to develop machine learning projects?
- Why there are a lot of unearthed projects in software development
- Machine learning: The efficiency of using textbooks and study materials
- Getting hands-on experience through machine learning
- Effective software for developing projects in machine learning
- Why data scientists are going to be the future of the world
- How to leverage various Artificial Intelligence technologies
Current Research Topics in Machine Learning
- How to cartoony an image with machine learning
- The role of machine learning in aiding coronavirus patients
- How easy is it to classify human facial expressions and map them to emojis?
- The role of machine learning in the increased cyberbullying claims
- Why most developing countries are slow to incorporating machine learning
- The effectiveness of the machine learning curriculum in colleges and universities
- Are internet sources watering down the essence of machine learning
- Discuss the role of machine learning in developing bioweapons
- Using machine learning to solve daily problems in life
- How effectively can machines recognize handwritten digits?
- The role of convolutional neural networks in machine learning
Advanced Topics in AI & Machine Learning
- Discuss the latest generative models in machine learning
- The role of the Bayesian inference in the mathematics of machine learning
- How probabilistic programming is transforming machine learning
- Model selection and learning: The challenges herein
- Discuss the application of machine learning in natural language processing
- The development of neural Turing machines
- Evaluate syntactic and semantic parsing in the process of machine learning
- Discuss GPU optimization for neural networks
- Back-propagation of time through machine learning processes
- The role of MIT in advancing research in machine learning
- Long-short term memory: A case study of the applications of machine learning
Best Machine Learning Project Topics
- Advances made in machine learning in the recent years
- A simple way of preventing neural networks from overfitting
- How to use deep residual learning for image recognition
- The process of accelerating deep network training through batch normalization
- Discuss large-scale video classification with convolutional neural networks
- Evaluate some of the common objects in Microsoft COCO
- Describe how to learn deep machine features for scene recognition
- Developing a new framework for generative adversarial nets
- The impact of high-speed tracking with kernelled correlation features
- A review of the multi-label learning algorithms
- Describe how to transfer features in deep neural networks
Top-Rated Machine Learning Research Project Topics
- Why we do not have hundreds of classifiers to solve real-world problems of classification
- A web-scale approach to dealing with probabilistic knowledge
- Supervised machine learning methods for fusing distinct information sources
- Suggest new algorithms for evaluating and comparing algorithms
- A review of the existing trends in extreme learning machines
- A survey of the concept drift adaptation in machine learning
- Describe the simultaneous segmentation and detection process
- Discuss the most used feature selection methods today
- The problem of Face Alignment for a single image
- Evaluate the various multiple classifier systems in the world
- How to achieve a super-real-time performance with high-quality predictions
Credible Machine Learning Dissertation Topics
- Describe a semi-supervised setting in machine learning
- Concepts of hypothesis sets in machine learning
- Preprocessing of data: A case study of data normalization
- Some of the most common problems in machine learning
- Terminology and basic concepts: A case study of convex optimization
- Discuss batch gradient descent and stochastic gradient descent
- Assess the notion of support vectors in support machines
- Online tools used for getting some intuition of an algorithm
- Describe the generative model and basic ideas of parameter estimation
- Discuss the memory-based neural networks
- What is the Markov decision process
Research Topics in Human Visual System and Machine Learning
- The role of video processing experts
- Understanding the psychology of vision
- Using the HVS model
- Discuss the process of Chroma subsampling
- Image compression techniques
- The low-pass filter characteristic of the HVS model
- Describe the human eye
- The impact of 3D resolution
- How does a depth-inverted face look like?
- Brightness resolution
- Complex visual systems
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Machine learning research paper topics, how do choose machine learning research paper topics.
Choosing a machine learning research paper topic is the first decision a student has to choose in their masters or doctorate degree. But getting there, choosing and working on a thesis topic is not that simple but need a statistical algorithm to make computers work without being explicitly programmed. The algorithms receive an input value and choose an output using statistical methods. The main goal of machine learning is to create intelligent machines that work like humans.
In order to reach those goals and identified as research topics in machine learning, we require professional thesis help and support.
What are Some Best Machine Learning Research Topics?
Here are some lists and ideas on machine learning research topics :
- The classification technique for the face spoof detection in artificial neural networks using concepts of machine learning.
- The plant disease detection using glam and KNN classification in neural networks merged with the concepts of machine learning
- Using the algorithms of machine learning to propose techniques for the prediction analysis in data mining
- The heart disease prediction uses the technique of classification in machine learning using the concepts of data mining.
- The sentiment analysis technique using SVM classifier in data mining using a machine learning approach
- The iris detection and reorganization system using classification and glam algorithm in machine learning.
- Using machine learning algorithms in the detection of pattern systems using the algorithm of textual feature analysis and classification
How to Prepare a Research Paper Example in Machine Learning?
Writing a research paper example in machine learning is an expert job. If you are planning to write a research paper , then do thorough research before deciding the topic.
Start with stating the goals of the research and the criteria that readers will evaluate the approach. Then, categorize the paper into varied familiar cases such as formal analysis, description of the new algorithm, and evaluate the approach.
Collect all your findings related to your approach, methods, discuss similarities, differences, and other previous research. Further, you can state all the limitations of the approach and suggest what could be the directions for prospective research.
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Available Master's thesis topics in machine learning
Here we list topics that are available. You may also be interested in our list of completed Master's theses .
Learning and inference with large Bayesian networks
Most learning and inference tasks with Bayesian networks are NP-hard. Therefore, one often resorts to using different heuristics that do not give any quality guarantees.
Task: Evaluate quality of large-scale learning or inference algorithms empirically.
Advisor: Pekka Parviainen
Traditionally, probabilistic graphical models use a graph structure to represent dependencies and independencies between random variables. Sum-product networks are a relatively new type of a graphical model where the graphical structure models computations and not the relationships between variables. The benefit of this representation is that inference (computing conditional probabilities) can be done in linear time with respect to the size of the network.
Potential thesis topics in this area: a) Compare inference speed with sum-product networks and Bayesian networks. Characterize situations when one model is better than the other. b) Learning the sum-product networks is done using heuristic algorithms. What is the effect of approximation in practice?
Bayesian Bayesian networks
The naming of Bayesian networks is somewhat misleading because there is nothing Bayesian in them per se; A Bayesian network is just a representation of a joint probability distribution. One can, of course, use a Bayesian network while doing Bayesian inference. One can also learn Bayesian networks in a Bayesian way. That is, instead of finding an optimal network one computes the posterior distribution over networks.
Task: Develop algorithms for Bayesian learning of Bayesian networks (e.g., MCMC, variational inference, EM)
Large-scale (probabilistic) matrix factorization
The idea behind matrix factorization is to represent a large data matrix as a product of two or more smaller matrices.They are often used in, for example, dimensionality reduction and recommendation systems. Probabilistic matrix factorization methods can be used to quantify uncertainty in recommendations. However, large-scale (probabilistic) matrix factorization is computationally challenging.
Potential thesis topics in this area: a) Develop scalable methods for large-scale matrix factorization (non-probabilistic or probabilistic), b) Develop probabilistic methods for implicit feedback (e.g., recommmendation engine when there are no rankings but only knowledge whether a customer has bought an item)
Bayesian deep learning
Standard deep neural networks do not quantify uncertainty in predictions. On the other hand, Bayesian methods provide a principled way to handle uncertainty. Combining these approaches leads to Bayesian neural networks. The challenge is that Bayesian neural networks can be cumbersome to use and difficult to learn.
The task is to analyze Bayesian neural networks and different inference algorithms in some simple setting.
Deep learning for combinatorial problems
Deep learning is usually applied in regression or classification problems. However, there has been some recent work on using deep learning to develop heuristics for combinatorial optimization problems; see, e.g.,  and .
Task: Choose a combinatorial problem (or several related problems) and develop deep learning methods to solve them.
References:  Vinyals, Fortunato and Jaitly: Pointer networks. NIPS 2015.  Dai, Khalil, Zhang, Dilkina and Song: Learning Combinatorial Optimization Algorithms over Graphs. NIPS 2017.
Advisors: Pekka Parviainen, Ahmad Hemmati
Estimating the number of modes of an unknown function
Mode seeking considers estimating the number of local maxima of a function f. Sometimes one can find modes by, e.g., looking for points where the derivative of the function is zero. However, often the function is unknown and we have only access to some (possibly noisy) values of the function.
In topological data analysis, we can analyze topological structures using persistent homologies. For 1-dimensional signals, this can translate into looking at the birth/death persistence diagram, i.e. the birth and death of connected topological components as we expand the space around each point where we have observed our function. These observations turn out to be closely related to the modes (local maxima) of the function. A recent paper  proposed an efficient method for mode seeking.
In this project, the task is to extend the ideas from  to get a probabilistic estimate on the number of modes. To this end, one has to use probabilistic methods such as Gaussian processes.
 U. Bauer, A. Munk, H. Sieling, and M. Wardetzky. Persistence barcodes versus Kolmogorov signatures: Detecting modes of one-dimensional signals. Foundations of computational mathematics17:1 - 33, 2017.
Advisors: Pekka Parviainen , Nello Blaser
Causal Abstraction Learning
We naturally make sense of the world around us by working out causal relationships between objects and by representing in our minds these objects with different degrees of approximation and detail. Both processes are essential to our understanding of reality, and likely to be fundamental for developing artificial intelligence. The first process may be expressed using the formalism of structural causal models, while the second can be grounded in the theory of causal abstraction.
This project will consider the problem of learning an abstraction between two given structural causal models. The primary goal will be the development of efficient algorithms able to learn a meaningful abstraction between the given causal models.
Advisor: Fabio Massimo Zennaro
"Multi-armed bandit" is an informal name for slot machines, and the formal name of a large class of problems where an agent has to choose an action among a range of possibilities without knowing the ensuing rewards. Multi-armed bandit problems are one of the most essential reinforcement learning problems where an agent is directly faced with an exploitation-exploration trade-off.
This project will consider a class of multi-armed bandits where an agent, upon taking an action, interacts with a causal system. The primary goal will be the development of learning strategies that takes advantage of the underlying causal system in order to learn optimal policies in a shortest amount of time.
Causal Modelling for Battery Manufacturing
Lithium-ion batteries are poised to be one of the most important sources of energy in the near future. Yet, the process of manufacturing these batteries is very hard to model and control. Optimizing the different phases of production to maximize the lifetime of the batteries is a non-trivial challenge since physical models are limited in scope and collecting experimental data is extremely expensive and time-consuming.
This project will consider the problem of aggregating and analyzing data regarding a few stages in the process of battery manufacturing. The primary goal will be the development of algorithms for transporting and integrating data collected in different contexts, as well as the use of explainable algorithms to interpret them.
Reinforcement Learning for Computer Security
The field of computer security presents a wide variety of challenging problems for artificial intelligence and autonomous agents. Guaranteeing the security of a system against attacks and penetrations by malicious hackers has always been a central concern of this field, and machine learning could now offer a substantial contribution. Security capture-the-flag simulations are particularly well-suited as a testbed for the application and development of reinforcement learning algorithms.
This project will consider the use of reinforcement learning for the preventive purpose of testing systems and discovering vulnerabilities before they can be exploited. The primary goal will be the modelling of capture-the-flag challenges of interest and the development of reinforcement learning algorithms that can solve them.
Approaches to AI Safety
The world and the Internet are more and more populated by artificial autonomous agents carrying out tasks on our behalf. Many of these agents are provided with an objective and they learn their behaviour trying to achieve their objective as best as they can. However, this approach can not guarantee that an agent, while learning its behaviour, will not undertake actions that may have unforeseen and undesirable effects. Research in AI safety tries to design autonomous agent that will behave in a predictable and safe way.
This project will consider specific problems and novel solution in the domain of AI safety and reinforcement learning. The primary goal will be the development of innovative algorithms and their implementation withing established frameworks.
Automatic hyperparameter selection for isomap
Isomap is a non-linear dimensionality reduction method with two free hyperparameters (number of nearest neighbors and neighborhood radius). Different hyperparameters result in dramatically different embeddings. Previous methods for selecting hyperparameters focused on choosing one optimal hyperparameter. In this project, you will explore the use of persistent homology to find parameter ranges that result in stable embeddings. The project has theoretic and computational aspects.
Advisor: Nello Blaser
Validate persistent homology
Persistent homology is a generalization of hierarchical clustering to find more structure than just the clusters. Traditionally, hierarchical clustering has been evaluated using resampling methods and assessing stability properties. In this project you will generalize these resampling methods to develop novel stability properties that can be used to assess persistent homology. This project has theoretic and computational aspects.
Topological Ancombs quartet
This topic is based on the classical Ancombs quartet and families of point sets with identical 1D persistence ( https://arxiv.org/abs/2202.00577 ). The goal is to generate more interesting datasets using the simulated annealing methods presented in ( http://library.usc.edu.ph/ACM/CHI%202017/1proc/p1290.pdf ). This project is mostly computational.
Persistent homology vectorization with cycle location
There are many methods of vectorizing persistence diagrams, such as persistence landscapes, persistence images, PersLay and statistical summaries. Recently we have designed algorithms to in some cases efficiently detect the location of persistence cycles. In this project, you will vectorize not just the persistence diagram, but additional information such as the location of these cycles. This project is mostly computational with some theoretic aspects.
Divisive covers are a divisive technique for generating filtered simplicial complexes. They original used a naive way of dividing data into a cover. In this project, you will explore different methods of dividing space, based on principle component analysis, support vector machines and k-means clustering. In addition, you will explore methods of using divisive covers for classification. This project will be mostly computational.
Multimodality in Bayesian neural network ensembles
One method to assess uncertainty in neural network predictions is to use dropout or noise generators at prediction time and run every prediction many times. This leads to a distribution of predictions. Informatively summarizing such probability distributions is a non-trivial task and the commonly used means and standard deviations result in the loss of crucial information, especially in the case of multimodal distributions with distinct likely outcomes. In this project, you will analyze such multimodal distributions with mixture models and develop ways to exploit such multimodality to improve training. This project can have theoretical, computational and applied aspects.
Detecting small clusters
Standard clustering methods are good at detecting clusters of a certain size and density. Detecting small clusters is difficult, because they lie in low density regions. In this project, you will use methods from anomaly detection coupled with clustering techniques to overcome this challenges. In addition, you will test the new techniques on real-world mass cytometry data. This project will be computational and applied.
We are looking for 2-3 students to join an interdisciplinary project where you will work together with medical doctors to analyse mass cytometry data. This is data on single cells and we are considering both suspension and image data. Potential projects range from applied data analysis to the development of new specialized methods to solve problems that arise in mass cytometry.
Advisors: Nello Blaser , Sonia Gavasso
Simulated underwater environment and deep learning
Using data from the Mareano surveys or the LoVe underwater observatory, create a simulator for underwater benthic (i.e. sea bed) scenes by placing objects randomly (but credibly) on a background. Using the simulated data, train deep learning neural networks to:
a) recognize presence of specific objects b) locate specific objects c) segment specific objects
Test the systems on real data and evaluate the results.
Advisor: Ketil Malde
Evaluating the effects and interaction of hyperparameters in convolutional neural networks
Neural networks have many hyperparameters, including choice of activation functions, regularization and normalization, gradient descent method, early stopping, cost function, and so on. While best practices exist, the interactions between the different choices can be hard to predict. To study this, train networks on suitable benchmark data, using randomized choices for hyperparameters, and observe parameters like rate of convergence, over- and underfitting, magnitude of gradient, and final accuracy.
Online learning in real-time systems
Build a model for the drilling process by using the Virtual simulator OpenLab ( https://openlab.app/ ) for real-time data generation and online learning techniques. The student will also do a short survey of existing online learning techniques and learn how to cope with errors and delays in the data.
Advisor: Rodica Mihai
Building a finite state automaton for the drilling process by using queries and counterexamples
Datasets will be generated by using the Virtual simulator OpenLab ( https://openlab.app/ ). The student will study the datasets and decide upon a good setting to extract a finite state automaton for the drilling process. The student will also do a short survey of existing techniques for extracting finite state automata from process data. We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN. We do this using Angluin's L*algorithm as a learner and the trained RNN as an oracle. Our technique efficiently extracts accurate automata from trained RNNs, even when the state vectors are large and require fine differentiation.arxiv.org
Applications of causal inference methods to omics data
Many hard problems in machine learning are directly linked to causality . The graphical causal inference framework developed by Judea Pearl can be traced back to pioneering work by Sewall Wright on path analysis in genetics and has inspired research in artificial intelligence (AI) .
The Michoel group has developed the open-source tool Findr  which provides efficient implementations of mediation and instrumental variable methods for applications to large sets of omics data (genomics, transcriptomics, etc.). Findr works well on a recent data set for yeast .
We encourage students to explore promising connections between the fiels of causal inference and machine learning. Feel free to contact us to discuss projects related to causal inference. Possible topics include: a) improving methods based on structural causal models, b) evaluating causal inference methods on data for model organisms, c) comparing methods based on causal models and neural network approaches.
1. Schölkopf B, Causality for Machine Learning, arXiv (2019): https://arxiv.org/abs/1911.10500
2. Wang L and Michoel T. Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data. PLoS Computational Biology 13:e1005703 (2017). https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005703
3. Ludl A and and Michoel T. Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast. arXiv:2010.07417 https://arxiv.org/abs/2010.07417
Advisors: Adriaan Ludl , Tom Michoel
Space-Time Linkage of Fish Distribution to Environmental Conditions
Conditions in the marine environment, such as, temperature and currents, influence the spatial distribution and migration patterns of marine species. Hence, understanding the link between environmental factors and fish behavior is crucial in predicting, e.g., how fish populations may respond to climate change. Deriving this link is challenging because it requires analysis of two types of datasets (i) large environmental (currents, temperature) datasets that vary in space and time, and (ii) sparse and sporadic spatial observations of fish populations.
The primary goal of the project is to develop a methodology that helps predict how spatial distribution of two fish stocks (capelin and mackerel) change in response to variability in the physical marine environment (ocean currents and temperature). The information can also be used to optimize data collection by minimizing time spent in spatial sampling of the populations.
The project will focus on the use of machine learning and/or causal inference algorithms. As a first step, we use synthetic (fish and environmental) data from analytic models that couple the two data sources. Because the ‘truth’ is known, we can judge the efficiency and error margins of the methodologies. We then apply the methodologies to real world (empirical) observations.
Advisors: Tom Michoel , Sam Subbey .
Towards precision medicine for cancer patient stratification
On average, a drug or a treatment is effective in only about half of patients who take it. This means patients need to try several until they find one that is effective at the cost of side effects associated with every treatment. The ultimate goal of precision medicine is to provide a treatment best suited for every individual. Sequencing technologies have now made genomics data available in abundance to be used towards this goal.
In this project we will specifically focus on cancer. Most cancer patients get a particular treatment based on the cancer type and the stage, though different individuals will react differently to a treatment. It is now well established that genetic mutations cause cancer growth and spreading and importantly, these mutations are different in individual patients. The aim of this project is use genomic data allow to better stratification of cancer patients, to predict the treatment most likely to work. Specifically, the project will use machine learning approach to integrate genomic data and build a classifier for stratification of cancer patients.
Advisor: Anagha Joshi
Unraveling gene regulation from single cell data
Multi-cellularity is achieved by precise control of gene expression during development and differentiation and aberrations of this process leads to disease. A key regulatory process in gene regulation is at the transcriptional level where epigenetic and transcriptional regulators control the spatial and temporal expression of the target genes in response to environmental, developmental, and physiological cues obtained from a signalling cascade. The rapid advances in sequencing technology has now made it feasible to study this process by understanding the genomewide patterns of diverse epigenetic and transcription factors as well as at a single cell level.
Single cell RNA sequencing is highly important, particularly in cancer as it allows exploration of heterogenous tumor sample, obstructing therapeutic targeting which leads to poor survival. Despite huge clinical relevance and potential, analysis of single cell RNA-seq data is challenging. In this project, we will develop strategies to infer gene regulatory networks using network inference approaches (both supervised and un-supervised). It will be primarily tested on the single cell datasets in the context of cancer.
Developing a Stress Granule Classifier
To carry out the multitude of functions 'expected' from a human cell, the cell employs a strategy of division of labour, whereby sub-cellular organelles carry out distinct functions. Thus we traditionally understand organelles as distinct units defined both functionally and physically with a distinct shape and size range. More recently a new class of organelles have been discovered that are assembled and dissolved on demand and are composed of liquid droplets or 'granules'. Granules show many properties characteristic of liquids, such as flow and wetting, but they can also assume many shapes and indeed also fluctuate in shape. One such liquid organelle is a stress granule (SG).
Stress granules are pro-survival organelles that assemble in response to cellular stress and important in cancer and neurodegenerative diseases like Alzheimer's. They are liquid or gel-like and can assume varying sizes and shapes depending on their cellular composition.
In a given experiment we are able to image the entire cell over a time series of 1000 frames; from which we extract a rough estimation of the size and shape of each granule. Our current method is susceptible to noise and a granule may be falsely rejected if the boundary is drawn poorly in a small majority of frames. Ideally, we would also like to identify potentially interesting features, such as voids, in the accepted granules.
We are interested in applying a machine learning approach to develop a descriptor for a 'classic' granule and furthermore classify them into different functional groups based on disease status of the cell. This method would be applied across thousands of granules imaged from control and disease cells. We are a multi-disciplinary group consisting of biologists, computational scientists and physicists.
Advisors: Sushma Grellscheid , Carl Jones
Machine Learning based Hyperheuristic algorithm
Develop a Machine Learning based Hyper-heuristic algorithm to solve a pickup and delivery problem. A hyper-heuristic is a heuristics that choose heuristics automatically. Hyper-heuristic seeks to automate the process of selecting, combining, generating or adapting several simpler heuristics to efficiently solve computational search problems [Handbook of Metaheuristics]. There might be multiple heuristics for solving a problem. Heuristics have their own strength and weakness. In this project, we want to use machine-learning techniques to learn the strength and weakness of each heuristic while we are using them in an iterative search for finding high quality solutions and then use them intelligently for the rest of the search. Once a new information is gathered during the search the hyper-heuristic algorithm automatically adjusts the heuristics.
Advisor: Ahmad Hemmati
Machine learning for solving satisfiability problems and applications in cryptanalysis
Advisor: Igor Semaev
Hybrid modeling approaches for well drilling with Sintef
Several topics are available.
"Flow models" are first-principles models simulating the flow, temperature and pressure in a well being drilled. Our project is exploring "hybrid approaches" where these models are combined with machine learning models that either learn from time series data from flow model runs or from real-world measurements during drilling. The goal is to better detect drilling problems such as hole cleaning, make more accurate predictions and correctly learn from and interpret real-word data.
The "surrogate model" refers to a ML model which learns to mimic the flow model by learning from the model inputs and outputs. Use cases for surrogate models include model predictions where speed is favoured over accuracy and exploration of parameter space.
Surrogate models with active Learning
While it is possible to produce a nearly unlimited amount of training data by running the flow model, the surrogate model may still perform poorly if it lacks training data in the part of the parameter space it operates in or if it "forgets" areas of the parameter space by being fed too much data from a narrow range of parameters.
The goal of this thesis is to build a surrogate model (with any architecture) for some restricted parameter range and implement an active learning approach where the ML requests more model runs from the flow model in the parts of the parameter space where it is needed the most. The end result should be a surrogate model that is quick and performs acceptably well over the whole defined parameter range.
Surrogate models trained via adversarial learning
How best to train surrogate models from runs of the flow model is an open question. This master thesis would use the adversarial learning approach to build a surrogate model which to its "adversary" becomes indistinguishable from the output of an actual flow model run.
GPU-based Surrogate models for parameter search
While CPU speed largely stalled 20 years ago in terms of working frequency on single cores, multi-core CPUs and especially GPUs took off and delivered increases in computational power by parallelizing computations.
Modern machine learning such as deep learning takes advantage this boom in computing power by running on GPUs.
The SINTEF flow models in contrast, are software programs that runs on a CPU and does not happen to utilize multi-core CPU functionality. The model runs advance time-step by time-step and each time step relies on the results from the previous time step. The flow models are therefore fundamentally sequential and not well suited to massive parallelization.
It is however of interest to run different model runs in parallel, to explore parameter spaces. The use cases for this includes model calibration, problem detection and hypothesis generation and testing.
The task of this thesis is to implement an ML-based surrogate model in such a way that many surrogate model outputs can be produced at the same time using a single GPU. This will likely entail some trade off with model size and maybe some coding tricks.
Uncertainty estimates of hybrid predictions (Lots of room for creativity, might need to steer it more, needs good background literature)
When using predictions from a ML model trained on time series data, it is useful to know if it's accurate or should be trusted. The student is challenged to develop hybrid approaches that incorporates estimates of uncertainty. Components could include reporting variance from ML ensembles trained on a diversity of time series data, implementation of conformal predictions, analysis of training data parameter ranges vs current input, etc. The output should be a "traffic light signal" roughly indicating the accuracy of the predictions.
Transfer learning approaches
We're assuming an ML model is to be used for time series prediction
It is possible to train an ML on a wide range of scenarios in the flow models, but we expect that to perform well, the model also needs to see model runs representative of the type of well and drilling operation it will be used in. In this thesis the student implements a transfer learning approach, where the model is trained on general model runs and fine-tuned on a most representative data set.
(Bonus1: implementing one-shot learning, Bonus2: Using real-world data in the fine-tuning stage)
ML capable of reframing situations
When a human oversees an operation like well drilling, she has a mental model of the situation and new data such as pressure readings from the well is interpreted in light of this model. This is referred to as "framing" and is the normal mode of work. However, when a problem occurs, it becomes harder to reconcile the data with the mental model. The human then goes into "reframing", building a new mental model that includes the ongoing problem. This can be seen as a process of hypothesis generation and testing.
A computer model however, lacks re-framing. A flow model will keep making predictions under the assumption of no problems and a separate alarm system will use the deviation between the model predictions and reality to raise an alarm. This is in a sense how all alarm systems work, but it means that the human must discard the computer model as a tool at the same time as she's handling a crisis.
The student is given access to a flow model and a surrogate model which can learn from model runs both with and without hole cleaning and is challenged to develop a hybrid approach where the ML+flow model continuously performs hypothesis generation and testing and is able to "switch" into predictions of a hole cleaning problem and different remediations of this.
Advisor: Philippe Nivlet at Sintef together with advisor from UiB
Explainable AI at Equinor
In the project Machine Teaching for XAI (see https://xai.w.uib.no ) a master thesis in collaboration between UiB and Equinor.
Advisor: One of Pekka Parviainen/Jan Arne Telle/Emmanuel Arrighi + Bjarte Johansen from Equinor.
Explainable AI at Eviny
In the project Machine Teaching for XAI (see https://xai.w.uib.no ) a master thesis in collaboration between UiB and Eviny.
Advisor: One of Pekka Parviainen/Jan Arne Telle/Emmanuel Arrighi + Kristian Flikka from Eviny.
If you want to suggest your own topic, please contact Pekka Parviainen or Nello Blaser .
200 Latest Artificial Intelligence Topics to Consider for Research
Table of Contents
Are you searching for the best artificial intelligence topics to prepare your research paper? If yes, then explore this blog post. Basically, artificial intelligence is a wide and complex subject that focuses on problem-solving by combining computer science and robust datasets. Since, the subject also encompasses sub-fields of machine learning and deep learning, for writing your artificial intelligence research paper, you can also choose any topic related to the sub-fields. In case, you are confused about what topic to choose or if you are unaware of how to prepare your academic paper on artificial intelligence topics, then get help from this blog. Here, we have presented a list of the best 200 artificial intelligence research paper topics along with some important topic selection and writing tips.
Artificial Intelligence Topic Selection Tips
If you are reading this blog, perhaps you have been stuck somewhere, while choosing your artificial intelligence research topics.
Follow the pointers below so that you will choose your artificial intelligence topics easily, without any doubts in your mind.
- Make use of exclusive ideas in your information and technology topics because your professor will love original ideas.
- Also, if you discuss something new, then of course your professor will grant you bonus points. Yes, you heard it right, our artificial intelligence topics will therefore fetch you good marks.
- Moreover, good educational research topics will develop easy ways of writing an essay. Consequently, there is sufficient information on the internet on the majority of our topics.
Therefore, feel free to choose any artificial intelligence topics, but just remember to reframe them, which avoids plagiarism.
Tips for Writing an Academic Paper on Artificial Intelligence Topic
Though artificial intelligence has become a scientific field, efficient communication is still a matter of art. Nonetheless, thumb rules also exist for art practices, and in this blog, we present amazing ideas that will improve your research paper.
- Mention the objectives as well as criteria for your research, accordingly, the professor will evaluate your paper. Also divide your paper into some appropriate classes, for example, a formal analysis or a computational framework of human learning.
- Moreover, mention the learning tasks as well as the performance, making it the goal of your research. Accordingly, differentiate between the two dimensions that will make your goals clear.
- Furthermore, elucidates the representation and organization of the system’s mastery as well as the depiction of training information.
- Elucidate the system’s learning components and performance insufficient details. Preferably make use of an algorithm to explain your learning algorithm.
Additionally, mention the drawbacks of the perspective and recommend directions for future research. Simultaneously, research beyond a list of issues to suggest possible solutions.
List of Artificial Intelligence Topics and Ideas
For your convenience, below we have added a list of the latest artificial intelligence topics on various themes related to the subject. Go through them all and pick the one that matches your academic paper writing guidelines.
Outstanding Artificial Intelligence Topics
- General artificial intelligence versus narrow artificial intelligence.
- Explain Deep Learning.
- The threats of narrow AI executions.
- Machine learning complexities in 2021.
- AI in the Mars Rovers.
- Problem resolution using AI.
- Explain and elucidate NLP.
- Define social intelligence.
- The cybernetics perspective to AI.
- Brain duplication is a component of AI projects.
- AI approaches and reasoning art.
- Elaborate on inductive logic programming.
- Big database management using AI.
- Knowledge depiction in AI.
- The future of programming- Prolog.
Also read: Outstanding Big Data Research Topics for Every Student to Explore
Simple Artificial Intelligence Topics
- Vision system application of AI.
- The eligibility issues in the AI system.
- Planning from an AI approach.
- Discuss the future of AI (Artificial Intelligence) and Machine Learning in business
- Application of Reinforcement learning
- Discuss Banking bot and Transaction bot
- What are the major challenges in AI?
- Recommender Systems: Definition and application
- Artificial intelligence and deep learning: SPECT and PET imaging
- Deep learning-enabled medical computer vision
- What are deep learning, reinforcement learning, and natural language processing?
- Artificial Intelligence as well as huge data influence the citizens
- Artificial Intelligence and ethical implications
- Artificial Intelligence Pros and Cons: Essay Sample
- Artificial Intelligence in Healthcare
Difficult Artificial Intelligence Topics
- Popular AI experiments that failed.
- Training a computer on how to paint.
- The recent machine learning algorithm is defined.
- AI robots are going to dominate the world- Explain.
- Predictive sales and how AI intelligence support.
- Lead production applying artificial intelligence.
- Artificial intelligence and data science.
- Movement planning in artificial intelligence.
- Will robots replace our jobs?
- Tesla is a robot car- Explain the concept.
- Automatic weapon system.
- How does an artificial intelligence algorithm breaches privacy?
- Wrong AI theories and the risk they pose.
- AI communication problems with the man.
- Recognizing online spam making use of AI.
Amazing Research Topics on Artificial Intelligence
- Brain duplicate techniques of AI.
- Image identification algorithm of AI.
- Computational intelligence and soft computing.
- Social intelligence in artificial intelligence.
- AI in customer service operations.
- Limitations of AI machines.
- The automated machine learns technology.
- AI functions in robotics.
- HiDeNN (Hierarchical Deep Learning Neural Network): A computational science and engineering in AI architecture
- Experimental quantum speed-up in reinforcement learning agents
- Potential-based multi-objective reinforcement learning approaches to low-impact agents for AI safety
- Regional anesthesia: Use of AI and robotics
- Third-millennium life-saving smart cyberspace driven by AI and robotics
- Artificial intelligence and Machine Learning in hematology
- Advantages and disadvantages of deep learning-enabled medical computer vision
Miscellaneous Artificial Intelligence Topics
- AI for Human Learning and Behavior Change.
- AI in Business.
- AI in Food, Agriculture, and Water.
- Artificial Intelligence in Finance.
- Big Data and AI in High Energy Physics.
- Establishing an easy Chabot.
- AI functions in medicines.
- Fraud detection through AI.
- Deep learning.
- Industrial robots.
- Explain generative theories.
- The Monte Carlo techniques.
- Discuss amortized inference.
- Explore probabilistic programming.
Essay Topics on Artificial Intelligence
- Define Bayesian inference.
- Searching for a livable planet through AI.
- How AI helps in space exploration?
- Application of AI in Mars Rover Perseverance.
- Training AI robots to walk on Mars.
- How AI is used to discover craters on the Moon?
- AI in the International Space Station.
- Applying AI in cybersecurity
- Relate the present AI to that in the movies.
- What are AI engineering’s principles?
- Machine learning, AI, and the Internet of Things.
- Role of machine learning and hyper-automation.
- AI perspective and business learning.
- Machine learning apps of 2022.
- Use of AI and Machine Learning in healthcare systems to fight against the COVID-19 pandemic
Also read: Business Research Topics- Easy and Scoring for the Students
Excellent Artificial Intelligence Ideas for Research
- Discuss the risks of stranded electricity assets with artificial intelligence and machine learning
- Use of Big Data analytics in marketing
- Discuss the qualification problem in artificial intelligence
- Misuse of AI and its Consequences
- Privacy and human rights violations by using automated weapon systems
- Recent Deep Learning algorithm.
- Explain Large Scale Machine Learning.
- Embracing AI systems in companies.
- Define reinforcement learning.
- State 3 important types of artificial intelligence.
- Discussing a simple AI issue.
- How does AI technology forecast housing pricing?
- AI learning for high school students.
- High school and AI education.
- List the AI technologies that transformed the world.
Artificial Intelligence Research Topics for College Students
- Will AI technology distinguish right from wrong?
- Does the AI technology suit the weapon system?
- Discrimination problems of the AI.
- Applying artificial intelligence to terminate high-value targets.
- Importance of human judgment in AI technology.
- Monitoring the AI industry.
- 3 ethical issues of AI.
- Present trends in AI.
- Discuss the industries benefitting from AI technology.
- Unassisted surgery performance- AI
- Analyze the connections between AI, digital media, and democracy
- How can AI technology be positively deployed to underpin political institutions?
- How should eGovernment systems be designed and built to be compliant with democratic principles?
- Describe the process of audio event detection, recognition, and monitoring with AI
- Discuss some rising stars in AI for Medicine and Public Health 2022
Top-Notch Artificial Intelligence Topics
- Responsible for the engineering of AI.
- Obtaining 100% safety against virtual attacks.
- Complex system and its dynamics.
- Intelligent foraging.
- Machine learning and de-noising.
- Data analysis and machine learning.
- Human-like open domain chatbot.
- Neuromorphic computing.
- Internet of Things.
- Computational mechanism design and algorithmic game theory.
- Recommender system.
- Natural language processing.
- Computer vision
- Broad-scale machine learning.
Latest Artificial Intelligence Topics
- Deep learning
- Evolutionary computer system.
- Monitoring AI development.
- Automated financial investment.
- Application AI in hospitals.
- How does AI view the world?
- Explain the recent machine learning algorithm.
- The key principles of AI risk management.
- Forecasting locations using AI in the future.
- Applying water quality using AI.
- Security applications.
- Machine learning.
- Anticipating election results using AI.
- Recognizing online spam using AI.
- Discuss the limitation of machine technology.
Top Artificial Intelligence Research Paper Topics
- In what ways has deep learning changed the course of AI research?
- What impact has natural language processing had on global interaction?
- How might artificial intelligence change the way we interact with computers?
- What are the limitations of artificial learning in the field of medicine?
- How has AI influenced future robotics engineering research?
- What impact does artificial intelligence have on computer vision technologies?
- How does artificial intelligence forecast financial trends?
- Is artificial intelligence influencing how people trade stocks?
- Do streaming device film recommendations learn accurately?
- Will computer technology be able to keep up with AI advancements?
- What role do parallel processing systems play in AI?
- Why are supercomputers so costly?
- What impact does cloud computing have on parallel processing?
- How will artificial intelligence interact with medical devices in the future?
- In what ways does AI influence the creation of media advertisements?
Also read: Is Computer Software Prepackaged Software A Good Career Path?
Trending Artificial Intelligence Topics
- Is it possible for artificial intelligence to incorporate true reinforcement learning?
- How far has robotic AI come in the last decade?
- Will artificial intelligence help us in our daily lives?
- How does algorithmic game theory fit into the context of artificial intelligence?
- How can artificial intelligence mimic human tasks?
- 3 issues with artificial intelligence from an ethical standpoint
- Governing the realm of artificial intelligence
- Is a suitable for use in weaponry?
- Utilizing artificial intelligence to kill important targets
- Can Ai distinguish between right and wrong?
- Human judgment’s place in artificial intelligence
- Ai discrimination issues
- International Space Station artificial intelligence
- Using artificial intelligence to find new moon craters
- Using AI to find habitable planets
Artificial Intelligence Topics for University Assignments:
- Educating a robot to walk on Mars using AI
- Machine learning’s function in hype automation.
- Uses of artificial intelligence in cybersecurity.
- Internet of Things, AI, and machine learning.
- Use artificial intelligence to kill important targets.
- Contrasting current AI with fictional AI.
- Talk about the foundations of AI engineering.
- The three main categories of artificial intelligence
- Uses of AI that revolutionized society.
- Creating an AI challenge (a simple one).
- Machine learning’s function in hyper-automation.
- Big data and AI for genomics and therapeutics – Proceedings of the 19th Annual Meeting of the MCBIOS (MidSouth Computational Biology and Bioinformatics Society)
- Compare and contrast the limitations and benefits of using Human-Robot and Human-Machine Interaction (e.g. exoskeletons, cobots)
- Describe Human-Computer Interaction, Worker well-being, Intelligible user interfaces, XR for industry, and Explainable AI
- Critical analysis of Industry 5.0 including the research and innovation related to it
- Future of Industry 5.0 in society: human-centric solutions, challenges, and prospective research areas
- Critical analysis of the emerging methods and algorithms in Pathology Computer Vision
- Compare and contrast the use of AI in Digital Pathology, Computational Pathology, and Histopathology
- Analyze the pros and cons of the use of Fuzzy Univariate and Multivariate Time Series along with its implementations with examples
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