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In the 1950s, the concept of machine learning was discovered and developed as a subfield of artificial intelligence. However, there were no significant developments or research on it until this decade. Typically, this field of study has developed and expanded since the 1990s. It is a field that will continue to develop in the future due to the difficulty of analysing and processing data as the number of records and documents increases. Due to the increasing data, machine learning focuses on finding the best model for the new data that takes into account all the previous data. Therefore, machine learning research will continue in correlation with this increasing data. This research focuses on the history of machine learning, the methods of machine learning, its applications, and the research that has been conducted on this topic. Our study aims to give researchers a deeper understanding of machine learning, an area of research that is becoming much more popular today, and its applications.
Manisha More
Machine learning is the fastest growing areas of computer science. It has the ability to lets the computer to create the program. It is a subset of Artificial Intelligence (AI), and consists of the more advanced techniques and models that enable computers to figure things out from the data and deliver. It is a field of learning and broadly divided into supervised learning, unsupervised learning, and reinforcement learning. There are many fields where the Machine learning algorithms are used. The objective of the paper is to represent the ML objectives, explore the various ML techniques and algorithms with its applications in the various fields from published papers, workshop materials & material collected from books and material available online on the World Wide Web.
Pattern Recognition Letters
Ramon Lopez De Mantaras
pankaj verma
The field of machine learning is introduced at a conceptual level. The main goal of machine learning is how computers automatically learn without any human invention or assistance so that they can adjust their action accordingly. We are discussing mainly three types of algorithms in machine learning and also discussed ML's features and applications in detail. Supervised ML, In this typeof algorithm, the machine applies what it has learned in its past to new data, in which they use labeled examples, so that they predict future events. Unsupervised ML studies how systems can infer a function, so that they can describe a hidden structure from unlabeled data. Reinforcement ML, is a type of learning method, which interacts with its environment, produces action, as well as discovers errors and rewards.
Journal of Advances in Mathematical & Computational Science. Vol 10, No.3. Pp 1 – 14.
Jerry Sarumi
Machine learning and associated algorithms occupies a pride of place in the execution of automation in the field of computing and its application to addressing contemporary and human-centred problems such as predictions, evaluations, deductions, analytics and analysis. This paper presents types of data and machine learning algorithms in a broader sense. We briefly discuss and explain different machine learning algorithms and real-world application areas based on machine learning. We highlight several research issues and potential future directions
IJESRT Journal
Machine learning [1], a branch of artificial intelligence, that gives computers the ability to learn without being explicitly programmed, means it gives system the ability to learn from data. There are two types of learning techniques: supervised learning and unsupervised learning [2]. This paper summarizes the recent trends of machine learning research.
International Journal for Research in Applied Science & Engineering Technology (IJRASET)
Dr. Manish Kumar Singh
Machine learning has become one of the most envisaged areas of research and development field in modern times. But the area of research related to machine learning is not new. The term machine learning was coined by Arthur Samuel in 1952 and since then lots of developments have been made in this field. The data scientists and the machine learning enthusiasts have developed myriad algorithms from time to time to let the benefit of machine learning reach to each and every field of human endeavors. This paper is an effort to put light on some of the most prominent algorithms that have been used in machine learning field on frequent basis since the time of its inception. Further, we will analyze their area of applications.
International Journal of Advanced Technology and Engineering Exploration
Akash Badone
International Journal of Engineering Applied Sciences and Technology
vishal bari
Today, huge amounts of data are available everywhere. Therefore, analyzing this data is very important to derive useful information from it and develop an algorithm based on this analysis. This can be achieved through data mining and machine learning. Machine learning is an essential part of artificial intelligence used to design algorithms based on data trends and past relationships between data. Machine learning is used in a variety of areas such as bioinformatics, intrusion detection, information retrieval, games, marketing, malware detection, and image decoding. This paper shows the work of various authors in the field of machine learning in various application areas.
Ioannis Vlahavas
IJRASET Publication
This paper describes essential points of machine learning and its application. It seamlessly turns around and teach about the pros and cons of the ML. As well as it covers the real-life application where the machine learning is being used. Different types of machine learning and its algorithms. This paper is giving the detail knowledge about the different algorithms used in machine learning with their applications. There is brief explanation about the Weather Prediction application using the machine learning and also the comparison between various machine learning algorithms used by various researchers for weather prediction.
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Sumeet Agarwal
JMSS, A2Z Journals
Journal of Management and Service Science (JMSS), A 2 Z Journals
Applied Sciences
Grzegorz Dudek
Pooja Ambatkar
Journal of Physics: Conference Series
Jafar Alzubi
IRJET Journal
Kostantinos Demertzis
International Journal of Computer Applications
IJERA Journal
International journal of engineering research and technology
Dr Nitin Rajvanshi
International Journal of Engineering Research and Advanced Technology
rama prasad
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT
Zachary Barillaro
International Journal of Innovative Technology and Exploring Engineering
atul kathole
Iqbal Muhammad
Artificial Intelligence
mplab.ucsd.edu
Paul Ruvolo
Foundation of Computer Applications
Editor IJATCA , nikhil katoch
International Journal of Scientific Research in Science, Engineering and Technology
International Journal of Scientific Research in Science, Engineering and Technology IJSRSET
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Since its inception, the field of heterogeneous catalysis has evolved to address the needs of the ever-growing human population. Necessity, after all, fosters innovation. Today, the world faces numerous challenges related to anthropogenic climate change, and that has necessitated, among other things, a search for new catalysts that can enable renewable energy conversion and storage, sustainable food and chemicals production, and a reduction in carbon emissions. This search has led to the emergence of many promising classes of materials, each having a unique set of catalytic properties. Among such candidate materials, high-entropy alloys (HEAs) have very recently shown the potential to be a new catalyst design paradigm. HEAs are multimetallic, disordered alloys containing more than four elements and, as a result, possess a higher configurational entropy, which gives them considerable stability. They have many conceivable benefits over conventional bimetallic alloy catalysts—greater site heterogeneity, larger design space, and higher stability, among others. Consequently, there is a need to explore their application in a wide range of thermal and electrocatalytic reaction systems so that their potential can be realized.
In the past few decades, first principles-based approaches involving Density Functional Theory (DFT) calculations have proven to be effective in probing catalytic mechanisms at the atomic scale. Fundamental insights from first principles studies have also led to a detailed understanding of reactivity and stability trends for bimetallic alloy catalysts. However, the express application of first principles approaches to study HEA catalysts remains a challenge, due to the large computational cost incurred in performing DFT calculations for disordered alloys, which can be represented by millions of different configurations. A combination of first principles approaches and computationally efficient machine learning (ML) approaches can, however, potentially overcome this limitation.
In this thesis, combined workflows involving first principles and machine learning-based approaches are developed. To map catalyst structure to properties graph convolutional network (GCN) models are developed and trained on DFT-predicted target properties such as formation energies, surface energies, and adsorption energies. Further, the Monte Carlo dropout method is integrated into GCN models to provide uncertainty quantification, and these models are in turn used in active learning workflows that involve iterative model retraining to both improve model predictions and optimize the target property value. Dimensionality reduction methods, such as principal components analysis (PCA) and Diffusion Maps (DMaps), are used to glean physicochemical insights from the parameterization of the GCN.
These workflows are applied to the analysis of binary, ternary, and quaternary alloy catalysts, and a series of fundamental insights regarding their stability are elucidated. In particular, the origin and stability of “Pt skins” that form on Pt-based bimetallic alloys such as Pt 3 Ni in the context of the oxygen reduction reaction (ORR) are investigated using a rigorous surface thermodynamic framework. The active learning workflow enables the study of Pt skin formation on stepped facets of Pt 3 Ni (with a complex, low-symmetry geometry), and this analysis reveals a hitherto undiscovered relationship between surface coordination and surface segregation. In another study, an active learning workflow is used to identify the most stable bulk composition in the Pd-Pt-Sn ternary alloy system using a combination of exhaustively sampled binary alloy data and prudently sampled ternary alloy data. Lastly, a new GCN model architecture, called SlabGCN, is introduced to predict the sulfur poisoning characteristics of quaternary alloy catalysts, and to find an optimal sulfur tolerant composition.
On another front, the electrocatalytic activity of quinary HEAs towards the ORR is investigated by performing DFT calculations on HEA structures generated using the High-Entropy Alloy Toolbox (HEAT), an in-house code developed for the high-throughput generation and analysis of disordered alloy structures with stability constraints (such as Pt skin formation). DFT-predicted adsorption energies of key ORR intermediates are further deconvoluted into ligand, strain, and surface relaxation effects, and the influence of the number of Pt skins on these effects is expounded. A Sabatier volcano analysis is performed to calculate the ORR activities of selected HEA compositions, and correspondence between theoretical predictions and experimental results is established, to pave the way for rational design of HEA catalysts for oxygen reduction.
In summary, this thesis examines stability and reactivity trends of a multitude of alloy catalysts, from conventional bimetallic alloys to high-entropy alloys, using a combination of first principles approaches (involving Density Functional Theory calculations) and machine learning approaches comprising graph convolutional network models.
Office of Basic Energy Sciences
Additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.
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Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023. Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023. Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023.
This dissertation explores three topics related to random forests: tree aggregation, variable importance, and robustness. 10. Climate Data Computing: Optimal Interpolation, Averaging, Visualization and Delivery. This dissertation solves two important problems in the modern analysis of big climate data.
This dissertation revisits and makes progress on some old but challenging problems concerning least squares estimation, the work-horse of supervised machine learning. Two major problems are addressed: (i) least squares estimation with heavy-tailed errors, and (ii) least squares estimation in non-Donsker classes.
Based on this background, the aim of this thesis is to select and implement a machine learning process that produces an algorithm, which is able to detect whether documents have been translated by humans or computerized systems. This algorithm builds the basic structure for an approach to evaluate these documents. 1.2 Related Work
that a machine can be made to simulate it." [3] In the AI field, there are several terms. Artificial intelligence is the largest collection, machine learning is a subset of artificial intelligence, and deep learning is a subset of machine learning, as shown in Exhibit 2.3 [4]. This thesis mainly
Machine Learning, a natural outgrowth at the intersection of Computer Science and Statistics, has evolved into a broad, highly successful, and extremely dynamic discipline. ... In this thesis, we develop theoretical foundations and new algorithms for several important emerging learning paradigms of significant practical importance, including ...
Machine learning (ML) systems are remarkably successful on a variety of benchmarks across sev-eral domains. In these benchmarks, the test data points, though not identical, are very similar to ... This thesis focuses on an extreme version of this brittleness, adversarial examples, where even imperceptible (but carefully constructed) changes ...
Ph.D. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. In this article, I present 10 compelling machine learning dissertations that I found interesting in terms of my own areas of pursuit. I hope you'll find several of them that match your own interests.
Machine learning applications to both decision-making and decision-support are growing. Further,witheachsuccessfulapplication,learningalgorithmsaregain- ing increased autonomy and control over decision-making. As a result, research into intelligent decision-making algorithms continues to improve. For example, theStanfordResearchInstitute ...
For dissertations published prior to 2008, please consult the following Dissertation LibGuide. Active filters Keyword Machine learning Cancel selected facet. ... Advancements in machine learning techniques have encouraged scholars to focus on convolutional neural network (CNN) based solutions for object detection and pose estimation tasks. ...
The main three chapters of the thesis explore three recursive deep learning modeling choices. The rst modeling choice I investigate is the overall objective function that ... feature engineering in machine learning, but after my rst deep learning project there was no going back. I also want to thank you for your perspective and helping me
However, traditional analysis techniques and human intuition are of limited use on so-called "big-data" environments, and one of the most promising areas to prepare for this influx of complex training data is the field of machine learning. Thus, the objective of this thesis was to lay the foundations for the use of machine learning algorithms ...
This article provides a list of 20 potential thesis ideas for an undergraduate program in machine learning and deep learning in 2023. Each thesis idea includes an introduction, which presents a brief overview of the topic and the research objectives. The ideas provided are related to different areas of machine learning and deep learning, such ...
Building a finite state automaton for the drilling process by using queries and counterexamples. Machine Learning for Drug Repositioning in Parkinson's Disease. Exploring Graph Neural Networks for Analyzing Prescription Data to Predict Parkinson's Disease Risk. Scaling Laws for Language Models in Generative AI.
This thesis proposes a mode of inquiry that considers the inter- active qualities of what machine learning does, as opposed the tech- nical specifications of what machine learning is. A shift in focus from the technicality of ML to the artifacts it creates allows the interaction designer to situate its existing skill set, affording it to engage ...
Quantum computing is widely thought to provide exponential speedups over classical algorithms for a variety of computational tasks. In classical computing, methods in artificial intelligence such as neural networks and adversarial learning have enabled drastic improvements in state-of-the-art performance for a variety of tasks. We consider the intersection of quantum computing with machine ...
Student PerformanceM. Sc. Thesis, 35 pages June 2017This thesis examines the application of machine learning algorithms t. predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in term. of how much they improve the prediction ...
attacks. It is obvious by multiple reasons that machine learning in Cybersecurity is far more than merely applying well-established methods to datasets of cyber entities. Cybersecurity domain involves machine learning challenges that require efficient methodological and theoretical handling.
Video (online) Consult the top 50 dissertations / theses for your research on the topic 'AI, Machine Learning.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA ...
Machine learning is an essential part of artificial intelligence used to design algorithms based on data trends and past relationships between data. Machine learning is used in a variety of areas such as bioinformatics, intrusion detection, information retrieval, games, marketing, malware detection, and image decoding.
Some of the learning algorithm which is used to make machine smart is studied in the types of AI. 2.1 Introduction to artificial intellligence AI is a wide term that relates to advancements that make machines "intelligent." John McCarthy coined the term AI in 1956. There are numerous different terms related to AI, for example, deep learning, ML,
In this thesis, combined workflows involving first principles and machine learning-based approaches are developed. To map catalyst structure to properties graph convolutional network (GCN) models are developed and trained on DFT-predicted target properties such as formation energies, surface energies, and adsorption energies.
3) Machine Learning algorithms allowed us to analyze clinical data, draw. relationships between diagnostic variables, design the predictive model, and. tests it against the new case. The predictive model achieved an accuracy of 89.4. percent using RandomForest Classifier's default setting to predict heart diseases.
iques based on machine learning, deep learning, and blockchain technology from 2009 to 2018. The survey identifies applications, drawbacks, and challenges of these th. ee intrusion detection methodologies that identify threats in computer network environments.The second half of this thesis proposes a new machine learning model f.