Deception in the eyes of deceiver: A computer vision and machine learning based automated deception detection

Khan, Wasiq; Crockett, Keeley; O’Shea, James; Hussain, Abir Jaafar; Khan, Bilal

doi:10.1016/j.eswa.2020.114341

Cited by 57 publications

(34 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…to draw conclusions from multiple combinations of different attributes. One effective means of dealing with multi-dimensional data visualization is SOM, an unsupervised form of artificial neural networks, performing a nonlinear projection of a high-dimensional space onto a lower-dimensional (typically, two-dimensional) map [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching

et al. 2021

Self Cite

View full text Add to dashboard Cite

Since the coronavirus disease (COVID-19) outbreak in December 2019, studies have been addressing diverse aspects in relation to COVID-19 and Variant of Concern 202012/01 (VOC 202012/01) such as potential symptoms and predictive tools. However, limited work has been performed towards the modelling of complex associations between the combined demographic attributes and varying nature of the COVID-19 infections across the globe. This study presents an intelligent approach to investigate the multi-dimensional associations between demographic attributes and COVID-19 global variations. We gather multiple demographic attributes and COVID-19 infection data (by 8 January 2021) from reliable sources, which are then processed by intelligent algorithms to identify the significant associations and patterns within the data. Statistical results and experts' reports indicate strong associations between COVID-19 severity levels across the globe and certain demographic attributes, e.g. female smokers, when combined together with other attributes. The outcomes will aid the understanding of the dynamics of disease spread and its progression, which in turn may support policy makers, medical specialists and society, in better understanding and effective management of the disease.

show abstract

Section: Methodsmentioning

confidence: 99%

Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Analyzing the associations and patterns in high-dimensional datasets using conventional statistical approaches is impractical. The employment of machine intelligence for the analysis of complex dataset and identification of patterns, have been significantly increased in various domains such as medical imaging [30,31], hand and facial gesture classification [32][33][34], eHealth systems [35], unstructured medical data analysis [36,37] and many more. The pMan-HD in this study, contains 11 non-motor factors (see Table 1) each comprising multiple categories making it more complicated to be analysed by the human experts or conventional PLOS ONE statistical approaches.…”

Section: Pattern Analysis and Rule Miningmentioning

confidence: 99%

The relationship between non-motor features and weight-loss in the premanifest stage of Huntington’s disease

et al. 2021

Self Cite

View full text Add to dashboard Cite

Weight-loss is an integral part of Huntington’s disease (HD) that can start before the onset of motor symptoms. Investigating the underlying pathological processes may help in the understanding of this devastating disease as well as contribute to its management. However, the complex behavior and associations of multiple biological factors is impractical to be interpreted by the conventional statistics or human experts. For the first time, we combine a clinical dataset, expert knowledge and machine intelligence to model the multi-dimensional associations between the potentially relevant factors and weight-loss activity in HD, specifically at the premanifest stage. The HD dataset is standardized and transformed into required knowledge base with the help of clinical HD experts, which is then processed by the class rule mining and self-organising maps to identify the significant associations. Statistical results and experts’ report indicate a strong association between severe weight-loss in HD at the premanifest stage and measures of certain cognitive, psychiatric functional ability factors. These results suggest that the mechanism underlying weight-loss in HD is, at least partly related to dysfunction of certain areas of the brain, a finding that may have not been apparent otherwise. These associations will aid the understanding of the pathophysiology of the disease and its progression and may in turn help in HD treatment trials.

show abstract

“…In recent years, Computer Vision has been involved in several, heterogeneous tasks which include rehabilitation [1][2][3][4][5], virtual/augmented reality [6][7][8][9][10], deception detection [11][12][13][14], robotics [15][16][17][18][19], and much more. Focusing on the latter, one of the most prominent applications involves the usage of drones (hereinafter, UAVs).…”

Section: Introductionmentioning

confidence: 99%

MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images

et al. 2021

View full text Add to dashboard Cite

Tracking objects across multiple video frames is a challenging task due to several difficult issues such as occlusions, background clutter, lighting as well as object and camera view-point variations, which directly affect the object detection. These aspects are even more emphasized when analyzing unmanned aerial vehicles (UAV) based images, where the vehicle movement can also impact the image quality. A common strategy employed to address these issues is to analyze the input images at different scales to obtain as much information as possible to correctly detect and track the objects across video sequences. Following this rationale, in this paper, we introduce a simple yet effective novel multi-stream (MS) architecture, where different kernel sizes are applied to each stream to simulate a multi-scale image analysis. The proposed architecture is then used as backbone for the well-known Faster-R-CNN pipeline, defining a MS-Faster R-CNN object detector that consistently detects objects in video sequences. Subsequently, this detector is jointly used with the Simple Online and Real-time Tracking with a Deep Association Metric (Deep SORT) algorithm to achieve real-time tracking capabilities on UAV images. To assess the presented architecture, extensive experiments were performed on the UMCD, UAVDT, UAV20L, and UAV123 datasets. The presented pipeline achieved state-of-the-art performance, confirming that the proposed multi-stream method can correctly emulate the robust multi-scale image analysis paradigm.

show abstract

Deception in the eyes of deceiver: A computer vision and machine learning based automated deception detection

Cited by 57 publications

References 53 publications

Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching

Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching

The relationship between non-motor features and weight-loss in the premanifest stage of Huntington’s disease

MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images

Contact Info

Product

Resources

About