Public sentiment can impact the implementation of public policies and even cause policy failure if public support is not received. Therefore, knowledge of public sentiment concerning new and emerging policies is critical for policymakers. During the coronavirus disease 2019 (COVID-19) pandemic, several precautionary measures have been suggested in an attempt to delay or mitigate the spread of the virus. This study presents a framework that applies natural language processing (NLP) techniques, such as sentiment and bigram analyses, to characterize the public sentiment on three prominent mitigation measures (mask wearing, social distancing, and quarantine) as shared by Twitter users in the United States. As part of the framework, we apply a bigram graph-based approach to visualize the most frequent topics in Twitter discussions during the COVID-19 pandemic. The objective is to provide insights into the most commonly discussed topics among Twitter users with similar demographic characteristics (e.g., age and gender). The sentiment and bigram analyses identified the most frequently discussed topics expressing both positive and negative sentiments among different age and gender groups. Discussions containing positive sentiment prevailed and revolved around the benefits of the measures and trust in the government, while the topics of negative sentiment involved conspiracy theories, skepticism, and distrust of government mandates. It is also notable that the discussions among people 19–29 and over 40 years old focus on government officials and political parties, benefits or inefficiency of mitigation measures, and conspiracy theories more often than other demographic groups. Our proposed approaches and results offer a novel and potentially valuable contribution to public policymakers.

Abstract Breast carcinoma is a sort of malignancy that begins in the breast. Breast malignancy cells generally structure a tumour that can routinely be seen on an x‐ray or felt like a lump. Despite advances in screening, treatment, and observation that have improved patient endurance rates, breast carcinoma is the most regularly analyzed malignant growth and the subsequent driving reason for malignancy mortality among ladies. Invasive ductal carcinoma is the most boundless breast malignant growth with about 80% of all analyzed cases. It has been found from numerous types of research that artificial intelligence has tremendous capabilities, which is why it is used in various sectors, especially in the healthcare domain. In the initial phase of the medical field, mammography is used for diagnosis, and finding cancer in the case of a dense breast is challenging. The evolution of deep learning and applying the same in the findings are helpful for earlier tracking and medication. The authors have tried to utilize the deep learning concepts for grading breast invasive ductal carcinoma using Transfer Learning in the present work. The authors have used five transfer learning approaches here, namely VGG16, VGG19, InceptionReNetV2, DenseNet121, and DenseNet201 with 50 epochs in the Google Colab platform which has a single 12GB NVIDIA Tesla K80 graphical processing unit (GPU) support that can be used up to 12 h continuously. The dataset used for this work can be openly accessed from http://databiox.com . The experimental results that the authors have received regarding the algorithm's accuracy are as follows: VGG16 with 92.5%, VGG19 with 89.77%, InceptionReNetV2 with 84.46%, DenseNet121 with 92.64%, DenseNet201 with 85.22%. From the experimental results, it is clear that the DenseNet121 gives the maximum accuracy in terms of cancer grading, whereas the InceptionReNetV2 has minimal accuracy.

Physical traits such as the shape of the hand and face can be used for human recognition and identification in video surveillance systems and in biometric authentication smart card systems, as well as in personal health care. However, the accuracy of such systems suffers from illumination changes, unpredictability, and variability in appearance (e.g. occluded faces or hands, cluttered backgrounds, etc.). This work evaluates different statistical and chrominance models in different environments with increasingly cluttered backgrounds where changes in lighting are common and with no occlusions applied, in order to get a reliable neural network reconstruction of faces and hands, without taking into account the structural and temporal kinematics of the hands. First a statistical model is used for skin colour segmentation to roughly locate hands and faces. Then a neural network is used to reconstruct in 3D the hands and faces. For the filtering and the reconstruction we have used the growing neural gas algorithm which can preserve the topology of an object without restarting the learning process. Experiments conducted on our own database but also on four benchmark databases (Stirling's, Alicante, Essex, and Stegmann's) and on deaf individuals from normal 2D videos are freely available on the BSL signbank dataset. Results demonstrate the validity of our system to solve problems of face and hand segmentation and reconstruction under different environmental conditions.

Irrespective of the educational model, the major challenge is how to achieve maximum efficiency of the education process and keep learners engaged during learning. This paper investigates the relationship between emotions and engagement in the E-learning environment, and how recognizing the learners emotions and changing the content delivery accordingly can affect the efficiency of the E-learning process. The proposed experiment aims to identify ways to increase the engagement of the learners, hence, enhance the efficiency of the learning process and the quality of learning. A controlled experiment was conducted to investigate participants emotions using bio sensors such as eye tracker, EEG, and camera to capture facial images in different emotional states. One-way analysis of variance (ANOVA) test and t-Test was carried out to compare the performance of the three groups and show if there was an effect of using the Affective E-learning system to improve the learners performance. Our findings support the conclusion that using bio sensors as a quantitative research tool to investigate human behaviours and measure emotions in real time can significantly enhance the efficiency of E-learning.

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. This model is used to the representation of motion in image sequences by initialising a suitable segmentation. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.

Automatic sports video segmentation is a fast growth area of research in the visual information retrieval field. This paper presents a semi-parametric algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus performs a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental results shows that the proposed football video segmentation algorithm performs with high accuracy.

We propose the design of a real-time system to recognize and interpret hand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose segmentation, characterization and tracking will be implemented using the growing neural gas (GNG) structure. The capacity of the system to obtain information with a high degree of freedom allows the encoding of many gestures and a very accurate motion capture. The use of hand pose models combined with motion information provided with GNG permits to deal with the problem of the hand motion representation. A natural interface applied to a virtual mirror writing system and a module to estimate hand pose have been designed to demonstrate the validity of the system.

In our work we aim to explore a general framework that addresses the fundamental problem of universal unsupervised extraction of semantically meaningful visual regions. To this end this paper describes a novel region analysis technique using a self-organising map, the growing neural gas, which is adapted so as to improve modelling speed as well as to ensure a double-linkage chain around all region contours to simplify shape analysis. While the growing neural gas has been extensively applied to shape modelling, it has never explicitly been used for curvature analysis, contour description and region similarity. Once a contour network has been obtained, a transformation is applied that converts the closed contour to an open one, facilitating the use of certain angular descriptors. Discriminative descriptors derived from the properties of regions, their contours and their transformed contours are established and define a feature vector used for the representation of regions based on the appearance and contour information.

This paper aims to address the ability of self-organizing neural network models to manage real-time applications. Specifically, we introduce fAGNG (fast Autonomous Growing Neural Gas), a modified learning algorithm for the incremental model Growing Neural Gas (GNG) network. The Growing Neural Gas network with its attributes of growth, flexibility, rapid adaptation, and excellent quality of representation of the input space makes it a suitable model for real time applications. However, under time constraints GNG fails to produce the optimal topological map for any input data set. In contrast to existing algorithms the proposed fAGNG algorithm introduces multiple neurons per iteration. The number of neurons inserted and input data generated is controlled autonomous and dynamically based on a priory learnt model. Comparative experiments using topological preservation measures are carried out to demonstrate the effectiveness of the new algorithm to represent linear and non-linear input spaces under time restrictions.

The Self-Organising Artificial Neural Network Models, of which we have used the Growing Neural Gas (GNG) can be applied to preserve the topology of an input distribution. Traditionally these models neither do include local adaptation of the nodes nor colour information. In this paper, we present an extension to the original growing neural gas network that has probabilistic features and can be applied to preserve the topology of a non-stationary distribution. The network consists of the geometrical position of the nodes, the underline local feature structure of the image, and the distance vector between the modal image and any successive images. Accurate correspondence of the nodes between successive images, is measured through the calculation of the topographic product. The method performs continuously mapping over a distribution that changes over time and works with both smooth and abrupt changes. The method is successfully applied to object modelling and tracking.

The Self-Organising Artificial Neural Network Models, of which we have used the Growing Neural Gas (GNG) can be applied to preserve the topology of an input distribution. Traditionally these models neither do include local adaptation of the nodes nor colour information. In this paper, we extend GNG by presenting an improvement to the network that has both global and local properties and can track in cluttered backgrounds. The method performs continuously mapping over a distribution that changes over time and works with both smooth and abrupt changes. The central mechanism relies on the addition of global and local attributes, and skin colour information to the network which allow us to automatically model and track 2D gestures. Application to hand gesture video tracking is presented.

Self-organising neural networks have shown promise in a variety of applications areas. Their massive and intrinsic parallelism makes those networks suitable to solve hard problems in image-analysis and computer vision applications, especially when non-stationary environments occur. Moreover, this kind of neural networks preserves the topology of an input space by using their inherited competitive learning property. In this work we use a kind of self-organising network, the Growing Neural Gas, to solve some computer vision tasks applied to visual surveillance systems. It has been used their capacity to represent non rigid objects as a result of an adaptive process by a topology-preserving graph that constitutes an induced Delaunay triangulation of their shapes. The neural network is also modified to accelerate the learning algorithm in order to support applications with temporal constraints. This feature has been used to build a system able to track image features in video sequences. The system automatically keeps the correspondence of features among frames in the sequence using its own structure. Information obtained during the tracking process and allocated in the neural network can also be used to analyse the objects motion.

This paper presents a robust approach to nonrigid modelling and tracking. The contour of the object is described by an active growing neural gas (A-GNG) network which allows the model to re-deform locally. The approach is novel in that the nodes of the network are described by their geometrical position, the underlying local feature structure of the image, and the distance vector between the modal image and any successive images. A second contribution is the correspondence of the nodes which is measured through the calculation of the topographic product, a topology preserving objective function which quantifies the neighbourhood preservation before and after the mapping. As a result, we can achieve the automatic modelling and tracking of objects without using any annotated training sets. Experimental results have shown the superiority of our proposed method over the original growing neural gas (GNG) network.