PG Theses – Page 3

Unveiling the Dynamics of Crowdfunding Success: A Network Analysis and Machine Learning Approach to Kickstarter Technology Projects

This thesis investigates the dynamics of crowdfunding success in Kickstarter’s technology category using a blend of network analysis and machine learning. By sourcing a comprehensive dataset from Webrobots.io, which includes detailed monthly data dumps of Kickstarter projects, this study focuses on United States (U.S.)-based technology projects to uncover patterns and factors contributing to successful crowdfunding […]

Socio-economic and Geographical factors for Crime Incidents

Spatio-temporal data mining techniques are used for crime analysis for their knowledge oriented and meaningful visual representation of crime incidents. Visual representation of crime patterns assist analysts with in-depth understanding of crime behavior with time and location. The representation can be made more knowledgeable and perceptible by incorporating details of socio-economic factor and areafis geographical […]

A framework at DAAS layer for handling interoperabilty and security in data integration

Data integration is a known technique to provide uniform view on sets of heterogeneous data sources. It not only provides better analysis of data but also facilitates users to query without any knowledge on the heterogeneous data sources. With the advent of Service Oriented Architecture and Cloud Computing it is possible for users to access […]

A Framework For Multi Class Prediction Of Dengue Using Early Clinical And Laboratory Indicators

This research proposes the use of machine learning methods to classify dengue severity level for better management of patients and at the same time emergency healthcare services are not disturbed.

Low Resource Summarization Using Pre-Trained Language Models

Our approach involves a fusion of deformable style transfer (DST), an optimization-centric technique that harmonizes the texture and geometry of a given content image to closely align with a chosen style image. This methodology is coupled with diffusion model inpainting, which is applied to content images originating from Calliar: an online dataset featuring handwritten Arabic calligraphy. Diverging from previous generative art methodologies, our approach presents aesthetically pleasing results, all the while preserving the integrity and structure of the Arabic script.

Textual Art Generator Using Hybrid Learning Techniques

Detection and Analysis of Mental Health Illness using social media

Recently social media has been a widely used network that connects people around the world. Not only this but people sharing their life events, thoughts through posts, status updates all gather up as a big data resource. This resource is helpful in conducting various researches, analyses including big data and machine learning. In this study, we analyzed six mental health issues using Reddit’s data. The data obtained summarizes; Depression, Anxiety, Bipolar, Bipolar Disorder, Schizophrenia, Autism and Mental Health which is a general class which discusses mental health. Experimentation is done using various deep learning and NLP techniques applied for classification such as Convolutional Neural Network, Long-short term memory network, Gated Recurrent Unit, Bi- Long-short term memory network and Bi-Gated Recurrent Unit. In addition to these traditional techniques, pre-trained BERT model and …

A Deep Learning based Framework for low-light image enhancement

We develop a CNN-based exposure fusion framework that can detect and eliminate hidden degradations in the darkness, as well as adjust different lightning conditions. The framework helps in extracting optimized feature representations using denoising, enhancement, and fusion module. Moreover, we perform a variety of ablation studies of low-light enhancement methods as well as comparative analysis of our proposed method with existing method is performed both qualitatively and quantitatively.

Personality Detection Using Deep Learning Techniques

This research proposes a machine learning model and a deep learning model to predict the personality of an individual based on Myers–Briggs Type Indicator (MBTI) personality model. The proposed machine learning models (SVM, LR, MLP and XGBoost) were trained on MBTI and MBTI500 datasets with imbalanced and balanced instances (using SMOTE). The proposed deep learning model was trained using CNN with GloVe word embeddings. SVM model achieved the highest accuracy of 96.81% for machine learning model on MBTI500 dataset with SMOTE. However, CNN exhibited the highest accuracy of 99.54% on MBTI dataset which supersedes the existing models

An Automated Teeth Lesion Diagnosis based on Deep Learning Techniques

A pipeline based on context-aware light-weight transformers with the goal of improving image quality without sacrificing the naturalness of the image, as well as reducing the inference time and size of the model. In this study, we trained a deep network-based transformer model on two standard datasets, i.e., Large-Scale Underwater Image (LSUI) and Underwater Image Enhancement Benchmark Dataset (UIEB), so that the network becomes more generalized, which subsequently improved the performance. Our real-time underwater image enhancement system shows superior results on edge devices. Also, we provide a comparison with other transformer-based methods.