Master of Science in Artificial Intelligence
http://dl.lib.uom.lk/handle/123/15823
2024-03-29T04:31:20ZSummarization of large-scale videos to text format using supervised based simple rule - based machine learning models
http://dl.lib.uom.lk/handle/123/21478
Summarization of large-scale videos to text format using supervised based simple rule - based machine learning models
Sugathadasa UKHA
Video Summarization has been one of the most interested research and development
field since the late 2000s, thanks to the evolution of social media and the internet, due
to the influence to provide a concise and meaningful summary of large-scale video.
Even though the video summarization has been elongated through several non-ML and
traditional based techniques and ML-based techniques, generation of correct and
required summaries from the video is yet a limitation. To overcome this concern,
different techniques have been attempted including vision-based approaches and NLP
related approaches. With the inspiration of NLP related Transformer networks,
researchers are looking to integrate such sequence-based learning algorithm into the
video dimension as to apply spatiotemporal extractions. Despite the VS
implementations, another extension of VS has been exponentially emphasized, namely
TVS which generates the summaries of the video via a text format.
Simply the evolution of VS towards TVS is not a straightforward journey since a lot
of blockers have been eliminated using UL, RL, and SL based frameworks. When it
comes to the STOA methods in TVS, Transformer based methods are eventually
highlighted along the T5 based NLP frameworks. Since this area is still at the ground
level, a lot of unknow facts and issues can be explored. Especially the attention-based
sequence modelling of the learning algorithm should be carefully imitated to achieve
the best accuracy improvements. All the improvements are subjected to apply into a
real-time application ulteriorly. To tackle such improvements, a novel standalone
method should be introduced with the simplest network layout which can be applicable
to the embedded devices. This is where the Simple Rule-based Machine Learning
Network to Text-based Video Summarization (SiRuML-TVS) has been unveiled.
Though the network contains a single input of large-scale video and a single output of
meaningful description for the given video, the high-level network layout
compromises three ML modules for Video Recognition, Object Detection, and finally
Text Generation. Each module is subjected to different evaluation criterions however,
the end-to-end full network is evaluated on a single metric. Different combination of
each module can be affected to the performance of the entire pipeline however, the
combination of Transformers and CNNs provide the better tradeoff between accuracy
and the computational inferencing. This makes a hope to deploy the proposed method
in an edged device thus, the gap between theoretical explanation to practical
implementation will be filled.
2022-01-01T00:00:00ZAutomated tourism knowledge graph and intent generation from audio content extracted from videos, by utilizing NLP
http://dl.lib.uom.lk/handle/123/21477
Automated tourism knowledge graph and intent generation from audio content extracted from videos, by utilizing NLP
Seneviratne SS
Generating a knowledge graph for a chatbot is a time-consuming exercise which needs the
help of an expert relevant to the field. This thesis presents our approach to synthesizes the
creation of a knowledge graph and intents for a chatbot. Currently, the creation of a knowledge
graph and intents for a chatbot is a tedious process and this process does not extract data from
videos. Developing a chatbot also requires the support of experienced software engineers.
This platform allows a user to build a customized chatbot according to a specific requirement
in any field, without the intervention of experts. It also allows for the seamless development
of a comprehensive knowledge graph from the video content through a simple and less tedious
approach. The platform uses Natural Language Processing (NLP) machine learning models
such as Naive Bayes and Logistic Regression and grammar correction techniques to
supplement the experience of the users.
The working process of this proposed system is Knowledge Extraction and generating the
Knowledge Base. The user inserts keywords related to the chatbot’s domain as the first step
of the process. The system retrieves the search results from YouTube. Finally, NLP will be
used to retrieve data contained in videos to create a preliminary knowledge graph and intents
for a chatbot. A scheduler is then activated automatically from time to time to update the
knowledge graph and intents. The knowledge graph and intents generated have been tested on
a chatbot created using the Rasa framework, with the chatbot giving the correct answers when
questioned by a user.
2022-01-01T00:00:00ZTopological pruner a neural network pruner using topological data analysis
http://dl.lib.uom.lk/handle/123/21476
Topological pruner a neural network pruner using topological data analysis
Perera WMMJU
Architectural damage due to neural network pruning has been a research problem. To recover the
accuracy loss, after pruning, pruned neural network needed to be trained further for a certain time
period to gain the accuracy back. If the damage done by the pruning process is severe, some layers
can collapse and at worse, the entire model may become untrainable. Therefore, pruning process
needs to be done carefully to prevent any significant damage to the neural network. Although some
existing approaches have been used to overcome this issue by identifying the salience of a neuron
with respect to the overall architecture, it is not computationally efficient. Further, the exiting
solutions do not count the topological meaning of the neural network architecture during the
pruning process. We believe that identifying the salience of neuron with respect to the layer is
sufficient to avoid severe damages to the overall architecture.
Topology, the champion of mathematical shapes, has been introduced to solve the aforesaid
problem. We introduce ‘Topological Pruner’, a novel pruner that uses a genetic algorithm powered
by a topological fitness function to identify removable neurons of each layer of a pre trained neural
network. After pruning is done, the model is retrained so that the parameters of the remaining
neuron can be readjusted to recover the model. As per to our knowledge this is the first ever attempt
to use persistence homology, a topological tool for pruning.
Number of parameters, FLOPs and recovery time of the new pruner is evaluated on CIFAR10
dataset on VGG-16 architecture against L1Filter Pruner, L2Filter Pruner and FPGM Pruner.
Evaluation results show that the new pruner competes well with the existing pruners. We conclude
that, topological data analysis can be used to explain the recoverability and mitigate damage cause
by neural network pruning.
2022-01-01T00:00:00ZUsing web scraping in social media to determine market trends with product feature - based sentiment analysis
http://dl.lib.uom.lk/handle/123/21475
Using web scraping in social media to determine market trends with product feature - based sentiment analysis
Nanayakkara T
Customer product reviews are openly available online and they are now widely used for deciding quality
of product or service and to determine market trends and influence decision making of users. Due to the
availability of a massive number of customer reviews on the web, summarizing them requires a fast
classification system. Compared to supervised and unsupervised machine learning techniques for binary
classification of reviews, fuzzy logic can provide a simple and comparatively faster way to model the
fuzziness existing between the sentiment polarities classes due to the uncertainty present in most of the
natural languages. But the fuzzy logic techniques are not much considered in this domain. This thesis
proposes a model which measures product market value by using sentiment analysis conducted on the
reviews of online products which are collected from a well known ecommerce website “Amazon”. Fuzzy
logic approach is used in calculating the final product market demand.
Hence, in this paper we propose a fine grained classification of customer reviews into weak positive,
average positive, strong positive, weak negative, average negative and strong negative classes using a
fuzzy logic model based on the most popularly known sentiment based lexicon SentiWordNet. By
creating rules and relationships between fuzzy membership functions and linguistic variables, we can
analyze the customer opinions towards online products. This proposed model provides the most
reasonable sentiment analysis because we try to reduce all the problems from the related past researches.
The outcomes can allow the business organization to understand their customer‟s sentiments and improve
customer loyalty and customer retention techniques in order to increase customer values and profits
result. Fine grained classification accuracy approximately in the range of 74% to 77% has been obtained
by experiments conducted on datasets of electronic products containing reviews of smart phones, TV and
laptops.
2022-01-01T00:00:00Z