5 research outputs found
Deep Sequence Models for Text Classification Tasks
The exponential growth of data generated on the Internet in the current
information age is a driving force for the digital economy. Extraction of
information is the major value in an accumulated big data. Big data dependency
on statistical analysis and hand-engineered rules machine learning algorithms
are overwhelmed with vast complexities inherent in human languages. Natural
Language Processing (NLP) is equipping machines to understand these human
diverse and complicated languages. Text Classification is an NLP task which
automatically identifies patterns based on predefined or undefined labeled
sets. Common text classification application includes information retrieval,
modeling news topic, theme extraction, sentiment analysis, and spam detection.
In texts, some sequences of words depend on the previous or next word sequences
to make full meaning; this is a challenging dependency task that requires the
machine to be able to store some previous important information to impact
future meaning. Sequence models such as RNN, GRU, and LSTM is a breakthrough
for tasks with long-range dependencies. As such, we applied these models to
Binary and Multi-class classification. Results generated were excellent with
most of the models performing within the range of 80% and 94%. However, this
result is not exhaustive as we believe there is room for improvement if
machines are to compete with humans
Machine learning based augmented reality for improved learning application through object detection algorithms
Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-of-the-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience
The Emerging Trends of Multi-Label Learning
Exabytes of data are generated daily by humans, leading to the growing need
for new efforts in dealing with the grand challenges for multi-label learning
brought by big data. For example, extreme multi-label classification is an
active and rapidly growing research area that deals with classification tasks
with an extremely large number of classes or labels; utilizing massive data
with limited supervision to build a multi-label classification model becomes
valuable for practical applications, etc. Besides these, there are tremendous
efforts on how to harvest the strong learning capability of deep learning to
better capture the label dependencies in multi-label learning, which is the key
for deep learning to address real-world classification tasks. However, it is
noted that there has been a lack of systemic studies that focus explicitly on
analyzing the emerging trends and new challenges of multi-label learning in the
era of big data. It is imperative to call for a comprehensive survey to fulfill
this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202