Visual scene recognition with biologically relevant generative models

This research focuses on developing visual object categorization methodologies that are based on machine learning techniques and biologically inspired generative models of visual scene recognition. Modelling the statistical variability in visual patterns, in the space of features extracted from them by an appropriate low level signal processing technique, is an important matter of investigation for both humans and machines. To study this problem, we have examined in detail two recent probabilistic models of vision: a simple multivariate Gaussian model as suggested by (Karklin & Lewicki, 2009) and a restricted Boltzmann machine (RBM) proposed by (Hinton, 2002). Both the models have been widely used for visual object classification and scene analysis tasks before. This research highlights that these models on their own are not plausible enough to perform the classification task, and suggests Fisher kernel as a means of inducing discrimination into these models for classification power. Our empirical results on standard benchmark data sets reveal that the classification performance of these generative models could be significantly boosted near to the state of the art performance, by drawing a Fisher kernel from compact generative models that computes the data labels in a fraction of total computation time. We compare the proposed technique with other distance based and kernel based classifiers to show how computationally efficient the Fisher kernels are. To the best of our knowledge, Fisher kernel has not been drawn from the RBM before, so the work presented in the thesis is novel in terms of its idea and application to vision problem

Risk Factors Leading to Meconium Aspiration Syndrome in Meconium-Stained Amniotic Fluid

OBJECTIVES This study aimed to find out the risk factors leading to meconium aspiration syndrome in patients having meconium-stained amniotic fluid. METHODOLOGY This comparative study was conducted in the department of Obstetrics and Gynaecology at Hayatabad Medical Complex from January 2022- June 2022. All patients admitted to the labour ward with the diagnosis of meconium-stained liquor (MSL) were included in the study through a convenient sampling technique. Patients were divided into two groups, group 1 having only meconium-stained amniotic fluid (MSAF) without meconium aspiration syndrome while group 2 having babies with the diagnosis of meconium aspiration syndrome (MAS). Both groups were compared for different risk factors for the development of MAS. Differences in the risk factors between the two groups were analyzed using Pearson’s correlation with a p-value of <0.05 considered significant. SPSS vs 20 was used for statistical analysis. RESULTS 84 patients were included in the study, i.e., 61 in group 1 and 23 in group 2. The mean age of the patients was 25± 3.45. The frequency of meconium-stained amniotic fluid was 3.83%. Meconium aspiration syndrome developed in 23 babies out of 84 MSAF deliveries (27.38%). Low APGAR score (< 0.00), patients handled outside the hospital (<0.001) and prolonged second stage (0.003) were significant risk factors for the development of MAS. CONCLUSION In the prolonged second stage, patients handled outside the hospital by unauthorized personnel and low APGAR score at birth were statistically significant risk factors for developing meconium aspiration syndrome

Diversified Fisher kernel: encoding discrimination in Fisher features to compete deep neural models for visual classification task

Fisher kernels derived from stochastic probabilistic models such as restricted and deep Boltzmann machines have shown competitive visual classification results in comparison to widely popular deep discriminative models. This genre of Fisher kernels bridges the gap between shallow and deep learning paradigm by inducing the characteristics of deep architecture into Fisher kernel, further deployed for classification in discriminative classifiers. Despite their success, the memory and computational costs of Fisher vectors do not make them amenable for large‐scale visual retrieval and classification tasks. This study introduces a novel feature selection technique inspired from the functional characteristics of neural architectures for learning discriminative feature representations to boost the performance of Fisher kernels against deep discriminative models. The proposed technique condenses the large dimensional Fisher features for kernel learning and shows improvement in its classification performance and storage cost on leading benchmark data sets. A comparison of the proposed method with other state‐of‐the‐art feature selection techniques is made to demonstrate its performance supremacy as well as time complexity required to learn in reduced Fisher space

Detecting moments of change and suicidal risks in longitudinal user texts using multi-task learning

This work describes the classification system proposed for the Computational Linguistics and Clinical Psychology (CLPsych) Shared Task 2022. We propose the use of multitask learning approach with a bidirectional long-short term memory (Bi-LSTM) model for predicting changes in user’s mood (Task A) and their suicidal risk level (Task B). The two classification tasks have been solved independently or in an augmented way previously, where the outputof one task is leveraged for learning another task, however this work proposes an ‘all-in-one’ framework that jointly learns the related mental health tasks. Our experimental results (ranked top for task A) suggest that the proposed multi-task framework outperforms the alternative single-task frameworks submitted to the challenge and evaluated via the timeline based and coverage based performance metrics shared by the organisers. We also assess the potential of using various types of feature embedding schemes that could prove useful in initialising the Bi-LSTM model for better multitask learning in the mental health domain

Revisiting deep fisher vectors: using fisher information to improve object classification

Although deep learning models have become the gold standard in achieving outstanding results on a large variety of computer vision and machine learning tasks, the use of kernel methods has still not gone out of trend because of its potential to beat deep learning performances at a number of occasions. Given the potential of kernel techniques, prior works have also proposed the use of hybrid approaches combining deep learning with kernel learning to complement their respective strengths and weaknesses. This work develops this idea further by introducing an improved version of Fisher kernels derived from the deep Boltzmann machines (DBM). Our improved deep Fisher kernel (IDFK) utilises an approximation of the Fisher information matrix to derive improved Fisher vectors. We show IDFK can be utilised to retain a high degree of class separability, making it appropriate for classification and retrieval tasks. The efficacy of the proposed approach is evaluated on three benchmark data sets: MNIST, USPS and Alphanumeric, showing an improvement in classification performance over existing kernel approaches, and comparable performance to deep learning methods, but with much reduced computational costs. Using explainable AI methods, we also demonstrate why our IDFK leads to better classification performance

Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics

Background In general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype–phenotype interrelation is possible. However, determining correct genotype–phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available. Methods The proposed methodology, visual diagnostic decision support system (visual diagnostic DSS), employs machine learning (ML) algorithms and digital image processing techniques in a hybrid approach for automated diagnosis in medical genetics. This approach uses facial features in reference images of disorders to identify visual genotype–phenotype interrelationships. Our statistical method describes facial image data as principal component features and diagnoses syndromes using these features. Results The proposed system was trained using a real dataset of previously published face images of subjects with syndromes, which provided accurate diagnostic information. The method was tested using a leave-one-out cross-validation scheme with 15 different syndromes, each of comprised 5–9 cases, i.e., 92 cases in total. An accuracy rate of 83% was achieved using this automated diagnosis technique, which was statistically significant (p < 0.01). Furthermore, the sensitivity and specificity values were 0.857 and 0.870, respectively. Conclusion Our results show that the accurate classification of syndromes is feasible using ML techniques. Thus, a large number of syndromes with characteristic facial anomaly patterns could be diagnosed with similar diagnostic DSSs to that described in the present study, i.e., visual diagnostic DSS, thereby demonstrating the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes

Socio-technical trust For multi-modal hearing assistive technology

The landscape of opportunity is rapidly changing for audio-visual (AV) hearing assistive technology. While hearing assistive devices, such as hearing aids, have traditionally been developed for populations of deaf and hard of hearing (DHH) communities, the ubiquitous use of in-ear technology and recent advances in edge computing are reformulating what drives research and development in this domain. With that comes new challenges to consider from the perspective of multiple different stakeholders. In this position paper, we elaborate on seven key socio-technical challenges that may impede the adoption of trustworthy multi-modal hearing assistive technologies. We also draw upon a recent survey being piloted in the UK to examine perceptions of trust for audio systems in the context of human rights. We strongly encourage the research community to consider trust as a factor in developing new AV assistive hearing technologies, as trust may ultimately drive adoption of this technology within broader society

ConversationMoC: encoding conversational dynamics using multiplex network for identifying moment of change in mood and mental health classification

Understanding mental health conversation dynamics is crucial,yet prior studies often overlooked the intricate interplay of social interactions. This paper introduces a unique conversationlevel dataset and investigates the impact of conversational context in detecting Moments of Change (MoC) in individual emotions and classifying Mental Health (MH) topics in discourse. In this study, we differentiate between analyzing individual posts and studying entire conversations, using sequential and graph-based models to encode the complex conversation dynamics. Further, we incorporate emotion and sentiment dynamics with social interactions using a graph multiplex model driven by Graph Convolution Networks (GCN). Comparative evaluations consistently highlight the enhanced performanceof the multiplex network, especially when combining reply, emotion, and sentiment network layers. This underscores the importance of understanding the intricate interplay between social interactions, emotional expressions, and sentiment patterns in conversations, especially within online mental health discussions. We are sharing our new dataset (ConversationMoC) and models with the broader research community to facilitate further research

On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks

This paper presents cooperative routing scheme to improve data reliability. The proposed protocol achieves its objective, however, at the cost of surplus energy consumption. Thus sink mobility is introduced to minimize the energy consumption cost of nodes as it directly collects data from the network nodes at minimized communication distance. We also present delay and energy optimized versions of our proposed RE-AEDG to further enhance its performance. Simulation results prove the effectiveness of our proposed RE-AEDG in terms of the selected performance matrics