Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective.

Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments.

Deep Neural Networks Applied to Stock Market Sentiment Analysis.

The volume of data is growing exponentially and becoming more valuable to organizations that collect it, from e-commerce data, shipping, audio and video logs, text messages, internet search queries, stock market activity, financial transactions, the Internet of Things, and various other sources. The major challenges are related with the way to extract insights from such a rich data environment and whether Deep Learning can be successful with Big Data. To get some insight on these topics, social network data are employed as a case study on how sentiments can affect decisions in stock market environments. In this paper, we propose a generalized Deep Learning-based classification framework for Stock Market Sentiment Analysis. This work comprises the study, the development, and implementation of an automatic classification system based on Deep Learning and the validation of its adequacy and efficiency in any scenario, particularly Stock Market Sentiment Analysis. Distinct datasets and several Deep Learning approaches with different layers and embedded techniques are used, and their performances are evaluated. These developments show how Deep Learning reacts to distinct contexts. The results also give context on how different techniques with different parameter combinations react to certain types of data. Convolution obtained the best results when dealing with complex data inputs, and long short-term layers kept a memory of data, allowing inputs which are not as common to still be considered for decisions. The models that resulted from Stock Market Sentiment Analysis datasets were applied with some success to real-life problems. The best models reached accuracies of 73% in training and 69% in certain test datasets. In a simulation, a model was able to provide a Return on Investment of 4.4%. The results contribute to understanding how to process Big Data efficiently using Deep Learning and specialized hardware techniques.

Using Data Mining to Assist in Predicting Reproductive Outcomes Following Varicocele Embolization.

We carried out a retrospective analysis of infertile couple data using several methodologies and data analysis techniques, including the application of a novel data mining approach for analyzing varicocele treatment outcomes. The aim of this work was to characterize embolized varicocele patients by ascertaining the improvement of some of their clinical features, predicting the success of treatment via pregnancy outcomes, and identifying data patterns that can contribute to both ongoing varicocele research and the more effective management of patients treated for varicocele. We retrospectively surveyed the data of 293 consenting couples undergoing infertility treatment with male varicocele embolization over a 10-year period, and sperm samples were collected before and at 3, 6, and 12 months after varicocele embolization treatment and analyzed with World Health Organization parameters-varicocele severity grades were assessed with medical assessment and scrotal ultrasound, patient personal information (e.g., age, lifestyle, and embolization complications) was collected with clinical inquiries, and varicocele embolization success was measured through pregnancy outcomes. Varicocele embolization significantly improved sperm concentration, motility, and morphology mean values, as well as sperm chromatin integrity. Following this study, we can predict that a male patient without a high varicocele severity grade (with grade I or II) has a 70.83% chance of conceiving after embolization treatment if his partners' age is between 24 and 33 with an accuracy of 70.59%. Furthermore, male patients successful in achieving pregnancy following embolization are mostly characterized by having a normal sperm progressive motility before treatment, a normal sperm concentration after treatment, a moderate to low varicocele severity grade, and not working in a putatively hazardous environment.

Applying Text Analytics for Studying Research Trends in Dependability.

The dependability of systems and networks has been the target of research for many years now. In the 1970s, what is now known as the top conference on dependability-The IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)-emerged gathering international researchers and sparking the interest of the scientific community. Although it started in niche systems, nowadays dependability is viewed as highly important in most computer systems. The goal of this work is to analyze the research published in the proceedings of well-established dependability conferences (i.e., DSN, International Symposium on Software Reliability Engineering (ISSRE), International Symposium on Reliable Distributed Systems (SRDS), European Dependable Computing Conference (EDCC), Latin-American Symposium on Dependable Computing (LADC), Pacific Rim International Symposium on Dependable Computing (PRDC)), while using Natural Language Processing (NLP) and namely the Latent Dirichlet Allocation (LDA) algorithm to identify active, collapsing, ephemeral, and new lines of research in the dependability field. Results show a strong emphasis on terms, like 'security', despite the general focus of the conferences in dependability and new trends that are related with 'machine learning' and 'blockchain'. We used the PRDC conference as a use case, which showed similarity with the overall set of conferences, although we also found specific terms, like 'cyber-physical', being popular at PRDC and not in the overall dataset.