Relating Machine Learning to the Real-World: Analogies to Enhance Learning Comprehension

Machine learning is an exciting field for many, but the rigor, math, and its rapid evolution are often found to be formidable, keeping them away from studying and pursuing a career in this area. Similarity has been substantially explored in machine learning algorithms such as in the K-nearest neighbors, Kernel methods, Support Vector Machines, but not so much in human learning, particularly when it comes to teaching machine learning. In the course of teaching the subject to undergraduate, graduate, and general pool of students, the author found that relating the concepts to real-world examples greatly enhances student comprehension and makes the topics much more approachable despite the math and the methods involved. This paper relates some of the concepts, artifacts, and algorithms in machine learning such as overfitting, regularization, and Generative Adversarial Networks to the real world using illustrative examples. Most of the analogies included in the paper were well appreciated by the students in the course of the author’s teaching and acknowledged as enhancing comprehension. It is hoped that the material presented in this paper will benefit larger audiences, drawing more learners to the field, resulting in enhanced contributions to the area. The paper concludes by suggesting deep learning for automatically generating similarities and analogies as a future direction

Misinformation Containment Using NLP and Machine Learning: Why the Problem Is Still Unsolved

Despite the increased attention and substantial research into it claiming outstanding successes, the problem of misinformation containment has only been growing in the recent years with not many signs of respite. Misinformation is rapidly changing its latent characteristics and spreading vigorously in a multi-modal fashion, sometimes in a more damaging manner than viruses and other malicious programs on the internet. This chapter examines the existing research in natural language processing and machine learning to stop the spread of misinformation, analyzes why the research has not been practical enough to be incorporated into social media platforms, and provides future research directions. The state-of-the-art feature engineering, approaches, and algorithms used for the problem are expounded in the process

Analyzing and Addressing Data-driven Fairness Issues in Machine Learning Models used for Societal Problems

This work aims to systematically analyze and address fairness issues arising in machine learning models because of class imbalances present in data, specifically used for addressing societal problems and providing unique insights. Using a specific data set, spectral analysis is first performed to present evidence and characterize the fairness issues. Subsequently, a series of class imbalance correction techniques are applied before the data is used to generate various machine learning models. The models so generated are then evaluated using multiple metrics. The results are then analyzed to compare the various approaches to determine the relative merits of each. As the experiments described in this paper confirm, not all oversampling techniques help in correcting data-induced model biases. Based on the Kappa statistic, F-1 score, and accuracy measured by the area under the Receiver Operating Characteristic curve, among the approaches evaluated, the Majority Weighted Minority Oversampling Technique, MWMOTE oversampling technique addresses the fairness issues the best and also improves the performance of the models at least for the dataset in consideration. The experiments also demonstrate that some of the oversampling techniques can degrade the models both in terms of performance and fairness. The results are interpreted using the evaluation metrics

Reconnoitering Generative Deep Learning Through Image Generation From Text

A picture is worth a thousand words goes the well-known adage. Generating images from text understandably has many uses. In this chapter, the authors explore a state-of-the-art generative deep learning method to produce synthetic images and a new better way for evaluating the same. The approach focuses on synthesizing high-resolution images with multiple objects present in an image, given the textual description of the images. The existing literature uses object pathway GAN (OP-GAN) to automatically generate images from text. The work described in this chapter attempts to improvise the discriminator network from the original implementation using OP-GAN. This eventually helps the generator network\u27s learning rate adjustment based on the discriminator output. Finally, the trained model is evaluated using semantic object accuracy (SOA), the same metric that is used to evaluate the baseline implementation, which is better than the metrics used previously in the literature

An Overview of Carbon Footprint Mitigation Strategies. Machine Learning for Societal Improvement, Modernization, and Progress

Among the most pressing issues in the world today is the impact of globalization and energy consumption on the environment. Despite the growing regulatory framework to prevent ecological degradation, sustainability continues to be a problem. Machine learning can help with the transition toward a net-zero carbon society. Substantial work has been done in this direction. Changing electrical systems, transportation, buildings, industry, and land use are all necessary to reduce greenhouse gas emissions. Considering the carbon footprint aspect of sustainability, this chapter provides a detailed overview of how machine learning can be applied to forge a path to ecological sustainability in each of these areas. The chapter highlights how various machine learning algorithms are used to increase the use of renewable energy, efficient transportation, and waste management systems to reduce the carbon footprint. The authors summarize the findings from the current research literature and conclude by providing a few future directions

Spectral analysis perspective of why misinformation containment is still an unsolved problem

Misinformation is still a major societal problem. The arrival of ChatGPT only added to the problem. This paper analyzes misinformation in the form of text from a spectral analysis perspective to find the answer to why the problem is still unsolved despite multiple years of research and a plethora of solutions in the literature. A variety of embedding techniques are used to represent information for the purpose. The diverse spectral methods used on these embeddings include t-distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA). The analysis shows that misinformation is quite closely intertwined with genuine information and the machine learning algorithms are not as effective in separating the two despite the claims in the literature

Deep Learning for Conversions Between Melodic Frameworks of Indian Classical Music

The CycleGAN deep learning framework has been successfully used for image style transfer in important domains such as medical diagnosis. This paper describes attempts, first of their kind, at using the framework for converting Indian Classical music from one melodic framework, called raga or raag, to another. From the audio samples generated and their visualizations, it is evident that the experiments were reasonably successful in converting music in Hindustani Classical raga to music in Indian Carnatic raga and vice versa. The insights presented in the paper are hoped to inspire further work to revolutionize the use of technology to improvise Indian Classical music

Prediction of Formation Conditions of Gas Hydrates Using Machine Learning and Genetic Programming

The formation of gas hydrates in the pipelines of oil, gas, chemical, and other industries has been a significant problem for many years because the formation of gas hydrates may block the pipelines. Hence, the knowledge of the phase equilibrium conditions of gas hydrate became necessary for the economic and safe working of oil, gas, chemical industries. Various thermodynamic approaches with various mathematical techniques are available for the prediction of formation conditions of gas hydrates. In this chapter, the authors have discussed the least square support vector machine and artificial neural network models for the prediction of stability conditions of gas hydrates and the use of genetic programming (GP) and genetic algorithm (GA) to develop a generalized correlation for predicting equilibrium conditions of gas hydrates

A Framework for Detecting Injected Influence Attacks on Microblog Websites Using Change Detection Techniques

Presidential elections can impact world peace, global economics, and overall well-being. Recent news indicates that fraud on the Web has played a substantial role in elections, particularly in developing countries in South America and the public discourse, in general. To protect the trustworthiness of the Web, in this paper, we present a novel framework using statistical techniques to help detect veiled Web fraud attacks in Online Social Networks (OSN). Specific examples are used to demonstrate how some statistical techniques, such as the Kalman Filter and the modified CUSUM, can be applied to detect various attack scenarios. A hybrid data set, consisting of both real user tweets collected from Twitter and simulated fake tweets is constructed for testing purposes. The efficacy of the proposed framework has been verified by computing metrics, such as Precision, Recall, and Area Under the ROC curve. The algorithms achieved up to 99.9% accuracy in some scenarios and are over 80% accurate for most of the other scenarios

A framework for detecting injected influence attacks on microblog websites using change detection techniques

Presidential elections can impact world peace, global economics, and overall well-being. Recent news indicates that fraud on the Web has played a substantial role in elections, particularly in developing countries in South America and the public discourse, in general. To protect the trustworthiness of the Web, in this paper, we present a novel framework using statistical techniques to help detect veiled Web fraud attacks in Online Social Networks (OSN). Specific examples are used to demonstrate how some statistical techniques, such as the Kalman Filter and the modified CUSUM, can be applied to detect various attack scenarios. A hybrid data set, consisting of both real user tweets collected from Twitter and simulated fake tweets is constructed for testing purposes. The efficacy of the proposed framework has been verified by computing metrics, such as Precision, Recall, and Area Under the ROC curve. The algorithms achieved up to 99.9% accuracy in some scenarios and are over 80% accurate for most of the other scenarios