Enhancing Hate Speech Detection in Sinhala Language on Social Media using Machine Learning

To counter the harmful dissemination of hate speech on social media, especially abusive outbursts of racism and sexism, automatic and accurate detection is crucial. However, a significant challenge lies in the vast sparsity of available data, hindering accurate classification. This study presents a novel approach to Sinhala hate speech detection on social platforms by coupling a global feature selection process with traditional machine learning, the research scrutinizes hate speech intricacies. A class-based variable feature selection process evaluates significance via global and local scores, identifying optimal values for prevalent classifiers. Utilizing class-based and corpus-based evaluations, we pinpoint optimal feature values for classifiers like SVM, MNB, and RF. Our results reveal notable enhancements in performance, specifically the F1-Score, underscoring how feature selection and parameter tuning work in tandem to boost model efficacy. Furthermore, the study explores nuanced variations in classifier performance across training and testing datasets, emphasizing the importance of model generalization

Helmet use detection of tracked motorcycles using CNN-based multi-task learning.

Automated detection of motorcycle helmet use through video surveillance can facilitate efficient education and enforcement campaigns that increase road safety. However, existing detection approaches have a number of shortcomings, such as the inabilities to track individual motorcycles through multiple frames, or to distinguish drivers from passengers in helmet use. Furthermore, datasets used to develop approaches are limited in terms of traffic environments and traffic density variations. In this paper, we propose a CNN-based multi-task learning (MTL) method for identifying and tracking individual motorcycles, and register rider specific helmet use. We further release the HELMET dataset, which includes 91,000 annotated frames of 10,006 individual motorcycles from 12 observation sites in Myanmar. Along with the dataset, we introduce an evaluation metric for helmet use and rider detection accuracy, which can be used as a benchmark for evaluating future detection approaches. We show that the use of MTL for concurrent visual similarity learning and helmet use classification improves the efficiency of our approach compared to earlier studies, allowing a processing speed of more than 8 FPS on consumer hardware, and a weighted average F-measure of 67.3% for detecting the number of riders and helmet use of tracked motorcycles. Our work demonstrates the capability of deep learning as a highly accurate and resource efficient approach to collect critical road safety related data

Finding neural signatures for obesity through feature selection on source-localized EEG

Obesity is a serious issue in the modern society since it associates to a significantly reduced quality of life. Current research conducted to explore the obesity-related neurological evidences using electroencephalography (EEG) data are limited to traditional approaches. In this study, we developed a novel machine learning model to identify brain networks of obese females using alpha band functional connectivity features derived from EEG data. An overall classification accuracy of 0.912 is achieved. Our finding suggests that the obese brain is characterized by a dysfunctional network in which the areas that are responsible for processing self-referential information such as energy requirement are impaired.Comment: 5 pages, 3 figures, conference submissio

Chronic pain detection from resting-state raw EEG signals using improved feature selection

We present an automatic approach that works on resting-state raw EEG data for chronic pain detection. A new feature selection algorithm - modified Sequential Floating Forward Selection (mSFFS) - is proposed. The improved feature selection scheme is rather compact but displays better class separability as indicated by the Bhattacharyya distance measures and better visualization results. It also outperforms selections generated by other benchmark methods, boosting the test accuracy to 97.5% and yielding a test accuracy of 81.4% on an external dataset that contains different types of chronic painComment: 9 pages, 4 figures, journal submissio

Deep Adversarial Transition Learning using Cross-Grafted Generative Stacks

Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains. In this paper, we present a novel "deep adversarial transition learning" (DATL) framework that bridges the domain gap by projecting the source and target domains into intermediate, transitional spaces through the employment of adjustable, cross-grafted generative network stacks and effective adversarial learning between transitions. Specifically, we construct variational auto-encoders (VAE) for the two domains, and form bidirectional transitions by cross-grafting the VAEs' decoder stacks. Furthermore, generative adversarial networks (GAN) are employed for domain adaptation, mapping the target domain data to the known label space of the source domain. The overall adaptation process hence consists of three phases: feature representation learning by VAEs, transitions generation, and transitions alignment by GANs. Experimental results demonstrate that our method outperforms the state-of-the art on a number of unsupervised domain adaptation benchmarks.Comment: 12 pages, 8 figure

Segment-based predominant learning swarm optimizer for large-scale optimization

Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified