Rescue Drone: Increasing Autonomy and Implementing Computer Vision
Focuses on developing a rescue drone with increased autonomy and implementing computer vision for advanced object detection. The team, consisting of Cody Campbell (Hardware Engineer), Alexandra Borgesen (Computer Engineer), Halil Yonter (Team Leader), Shawn Cho (Software Engineer), Peter Burchell (M
78 views • 44 slides
Sports Volunteers Movement in Botswana: Challenges and Strategies
The Sports Volunteers Movement (SVM) in Botswana, under the Botswana National Sport Commission, aims to promote volunteerism in sports for community development. Despite successful events, there is a decline in volunteer participation. Reasons include lack of incentives, burnout, and feeling unappre
0 views • 11 slides
Counterfeit Detection Techniques in Currency to Combat Financial Fraud
Currency counterfeiting poses a significant challenge to the financial systems of countries worldwide, impacting economic growth. This study explores various counterfeit detection techniques, emphasizing machine learning and image processing, to enhance accuracy rates in identifying counterfeit curr
0 views • 15 slides
Machine Learning Algorithms and Models Overview
This class summary covers topics such as supervised learning, unsupervised learning, classification, clustering, regression, k-NN models, linear regression, Naive Bayes, logistic regression, and SVM formulations. The content provides insights into key concepts, algorithms, cost functions, learning a
0 views • 39 slides
Understanding Kernel Tricks in Machine Learning
Kernel tricks in machine learning involve transforming inputs into higher-dimensional spaces to make linear models work for nonlinear data. Kernels can be applied to various algorithms like SVM, ridge regression, and more, allowing for better model performance with complex datasets.
0 views • 15 slides
Efficient Anomaly Detection for Batch Systems Using Machine Learning
Explore a lightning talk session focusing on using Collectd metrics and job data in HTCondor batch systems for anomaly detection. Challenges with raw historical data are addressed through data collection, manipulation, and application of anomaly detection techniques using ML. Various algorithms such
0 views • 14 slides
NLDB 2020 Pattern Learning for Detecting Defect Reports and Improvement Requests
This research paper focuses on automatically learning patterns to detect actionable feedback in mobile app reviews, specifically identifying defect reports and improvement requests. The main goal is to develop a mechanism that can effectively classify feedback types using both manual and learned pat
0 views • 17 slides
Functional Approximation Using Gaussian Basis Functions for Dimensionality Reduction
This paper proposes a method for dimensionality reduction based on functional approximation using Gaussian basis functions. Nonlinear Gauss weights are utilized to train a least squares support vector machine (LS-SVM) model, with further variable selection using forward-backward methodology. The met
0 views • 23 slides
Evolution of Sentiment Analysis in Tweets and Aspect-Based Sentiment Analysis
The evolution of sentiment analysis on tweets from SemEval competitions in 2013 to 2017 is discussed, showcasing advancements in technology and the shift from SVM and sentiment lexicons to CNN with word embeddings. Aspect-Based Sentiment Analysis, as explored in SemEval2014, involves determining asp
0 views • 23 slides
Gender Identification in SMS Texts: An Exploration of Authorship Characteristics
Cyber forensics methods play a crucial role in detecting SMS authors for potential use in criminal persecution cases as visual anonymity in text messages can be exploited by criminals. This study delves into the authorship characterization of SMS texts by categorizing authors based on sociolinguisti
0 views • 17 slides
Stream Management and Online Learning in Data Mining
Stream management is crucial in scenarios where data is infinite and non-stationary, requiring algorithms like Stochastic Gradient Descent for online learning. Techniques like Locality Sensitive Hashing, PageRank, and SVM are used for critical calculations on streaming data in fields such as machine
0 views • 46 slides
Implicit Citations for Sentiment Detection: Methods and Results
This study focuses on detecting implicit citations for sentiment detection through various tasks such as finding zones of influence, citation classification, and corpus construction. The research delves into features for classification, highlighting the use of n-grams, dependency triplets, and other
0 views • 14 slides
Overview of Linear Classifiers and Perceptron in Classification Models
Explore various linear classification models such as linear regression, logistic regression, and SVM loss. Understand the concept of multi-class classification, including multi-class perceptron and multi-class SVM. Delve into the specifics of the perceptron algorithm and its hinge loss, along with d
0 views • 51 slides
Object Detection Techniques Overview
Object detection techniques employ cascades, Haar-like features, integral images, feature selection with Adaboost, and statistical modeling for efficient and accurate detection. The Viola-Jones algorithm, Dalal-Triggs method, deformable models, and deep learning approaches are prominent in this fiel
0 views • 21 slides