Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow.

The Triadic Relationship of Sense-Making, Analytics, and Institutional Influences

The current business environment demands the enablement of organization-wide use of analytics to support a fact-based decision making. Such movement within the organization require employees to take advantage of the self-service business analytics tools to independently fulfil their needs. However, assuming independence in data analytics requires employees to make sense of several elements which collectively contribute to the generation of required insights. Building on sense-making, self-service business analytics, and institutions literature, this paper explores the relationship between sense-making and self-service business analytics and how institutions influence and shape such relationship. By adopting a qualitative perspective and using 22 interviews, we have empirically investigated a model developed through our literature review and provided more understanding of the sense-making concept in a self-service business analytics context.

A Smart Integrated Vest for the Canine Companion of the K9 Units

Search and rescue operations can range from small, confined spaces, such as collapsed buildings, to large area searches during missing person operations. K9 units are tasked with intervening in such emergencies and assist in the optimal way to ensure a successful outcome for the mission. They are required to operate in unknown situations were the lives of the K9 handler and the canine companion are threatened as they operate with limited situational awareness. Within the context of the INGENIOUS project, we developed a K9 vest for the canine companion of the unit, aiming to increase the unit’s safety while operating in the field, assist the K9 handler in better monitoring the location and the environment of the K9 and increase the information provided to the Command and Control Center during the operation.

Design Thinking: Methodological Strategy for the Creation of a Playful Application for Children with Dyslexia

The use of a methodology to address a problem facilitates work in an efficient, effective, and highly productive way. The design thinking methodology (also known as design thinking) is user-centric and oriented towards offering solutions by breaking down a problem into small parts to analyze it, to explore it, to test the results, and to create solutions that benefit the end-user. Many children have problems related to learning disorders, such as dyslexia, which occur due to the way that their brain incorporates and processes information. This can lead to them showing difficulty in some learning areas, even when their intelligence or motivation does not appear to be affected. In this research, through a mixed approach, a playful application is developed using new information and communication technologies (ICT), following a design thinking methodology, with the aim of supporting the learning of children with dyslexia through content designed with respect to their needs in order to help improve their academic performance. Data collection was carried out through observation, an interview, and record reviews. Analysis of the didactic materials allowed for the observation that content designed for the specific needs of children can work as a reinforcement for incorporating the information in an entertaining, dynamic, and friendly way, ultimately contributing to improved academic performance.

How 2.5D Maps Design Improve the Wayfinding Performance and Spatial Ability of Map Users

Useful information can be provided by 2.5D maps that can take advantage of the additional dimension. However, aside from stereoscopic landmarks, optimal methods for presenting other essential information is unclear. Two experiments were conducted to explore how the presentation of 2.5D maps can effectively increase wayfinding performance. First, analysis was performed to understand the effects of 2.5D maps on wayfinding behavior and map reading. Then, a 2.5D map design was proposed and verified to optimize the 2.5D map presentation of urban environments. The results showed that compared with users of low view angle maps, those using high view angle maps orientated more easily with elements of the map during wayfinding tasks. High view angle maps allowed superior performance, and including transparency and lines improved wayfinding performance. The participants using maps that were opaque and with lines exhibited the most confusion and hesitation. The participants who used maps that were transparent and had lines exhibited the least confusion and hesitation. Highlighting buildings at intersections can help map users use the intersections as references and increase their intuitive spatial ability.

Proposal for a Standard Architecture for the Integration of Clinical Information Systems in a Complex Hospital Environment

The evolution of technology in clinical environments increases the level of precision in patient care, as well as optimizes the management of healthcare centers. However, the need to have information systems that are more sophisticated and require interoperability between them means that a great deal of effort has to be made to assume the maintenance and scalability of the systems. Therefore, a proposal for a standard information model for the integration of clinical systems in a healthcare environment is presented. In order to elaborate the model, an analysis of the functional needs of the different clinical areas of a clinical environment is made based on the information systems that make up the system and application map. An evaluation of the technical requirements and the technological solutions that can satisfy these requirements is also carried out, delving into the different technical alternatives that allow the exchange of information. From the analysis carried out, an integration model capable of covering the needs that arise in clinical environments with a high level of complexity is obtained, also allowing the continuous evolution of the systems that make up the model, along with the incorporation of new systems. Although the model presented may fully cover the expectations raised, the rapid evolution in terms of both functional needs and technical aspects makes it necessary to continuously monitor and evaluate the model, in order to adapt it to the needs that arise.

Searching Deterministic Chaotic Properties in System-Wide Vulnerability Datasets

Cybersecurity is a never-ending battle against attackers, who try to identify and exploit misconfigurations and software vulnerabilities before being patched. In this ongoing conflict, it is important to analyse the properties of the vulnerability time series to understand when information systems are more vulnerable. We study computer systems’ software vulnerabilities and probe the relevant National Vulnerability Database (NVD) time-series properties. More specifically, we show through an extensive experimental study based on the National Institute of Standards and Technology (NIST) database that the relevant systems software time series present significant chaotic properties. Moreover, by defining some systems based on open and closed source software, we compare their chaotic properties resulting in statistical conclusions. The contribution of this novel study is focused on the prepossessing stage of vulnerabilities time series forecasting. The strong evidence of their chaotic properties as derived by this research effort could lead to a deeper analysis to provide additional tools to their forecasting process.

Analysis and Assessment of Controllability of an Expressive Deep Learning-Based TTS System

In this paper, we study the controllability of an Expressive TTS system trained on a dataset for a continuous control. The dataset is the Blizzard 2013 dataset based on audiobooks read by a female speaker containing a great variability in styles and expressiveness. Controllability is evaluated with both an objective and a subjective experiment. The objective assessment is based on a measure of correlation between acoustic features and the dimensions of the latent space representing expressiveness. The subjective assessment is based on a perceptual experiment in which users are shown an interface for Controllable Expressive TTS and asked to retrieve a synthetic utterance whose expressiveness subjectively corresponds to that a reference utterance.

Fault Detection of Bearing: An Unsupervised Machine Learning Approach Exploiting Feature Extraction and Dimensionality Reduction

The monitoring of rotating machinery is an essential activity for asset management today. Due to the large amount of monitored equipment, analyzing all the collected signals/features becomes an arduous task, leading the specialist to rely often on general alarms, which in turn can compromise the accuracy of the diagnosis. In order to make monitoring more intelligent, several machine learning techniques have been proposed to reduce the dimension of the input data and also to analyze it. This paper, therefore, aims to compare the use of vibration features extracted based on machine learning models, expert domain, and other signal processing approaches for identifying bearing faults (anomalies) using machine learning (ML)—in addition to verifying the possibility of reducing the number of monitored features, and consequently the behavior of the model when working with reduced dimensionality of the input data. As vibration analysis is one of the predictive techniques that present better results in the monitoring of rotating machinery, vibration signals from an experimental bearing dataset were used. The proposed features were used as input to an unsupervised anomaly detection model (Isolation Forest) to identify bearing fault. Through the study, it is possible to verify how the ML model behaves in view of the different possibilities of input features used, and their influences on the final result in addition to the possibility of reducing the number of features that are usually monitored by reducing the dimension. In addition to increasing the accuracy of the model when extracting correct features for the application under study, the reduction in dimensionality allows the specialist to monitor in a compact way the various features collected on the equipment.

The Effectiveness of Online Platforms after the Pandemic: Will Face-to-Face Classes Affect Students’ Perception of Their Behavioural Intention (BIU) to Use Online Platforms?

The purpose of this study is to investigate students’ intention to continue using online learning platforms during face-to-face traditional classes in a way that is parallel to their usage during online virtual classes (during the pandemic). This investigation of students’ intention is based on a conceptual model that uses newly used external factors in addition to the technology acceptance model (TAM) contrasts; hence, it takes into consideration users’ satisfaction, the external factor of information richness (IR) and the quality of the educational system and information disseminated. The participants were 768 university students who have experienced the teaching environments of both traditional face-to-face classes and online classes during the pandemic. A structural equation modelling (SEM) test was conducted to analyse the independent variables, including the users’ situation awareness (SA), perceived ease of use, perceived usefulness, satisfaction, IR, education system quality and information quality. An online questionnaire was used to explore students’ perceptions of their intention to use online platforms accessibly in a face-to-face learning environment. The results showed that (a) students prefer online platforms that have a higher level of content richness, to be able to implement the three dimensions of users’ situation awareness (perception, comprehension and projection); (b) there were significant effects of TAM constructs on students’ satisfaction and acceptance; (c) students are in favour of using a learning platform that is characterised by a high level of educational system quality and information quality and (d) students with a higher level of satisfaction have a more positive attitude in their willingness to use the online learning system.