Publications

Abstract: Healthcare management plays a crucial role in ensuring the efficient delivery of healthcare services. This responsibility encompasses strategic planning, resource management, and regulatory compliance to enhance patient care outcomes. In this paper, we delve into the multifaceted nature of healthcare management, highlighting the expertise required to optimize processes within dynamic healthcare environments. Furthermore, we explore the potential of machine learning in addressing operational challenges within healthcare. By examining various machine learning algorithms, we identify their advantages and limitations, proposing a structured method of application. Through this analysis, we aim to illuminate how machine learning can minimize patient waiting times and optimize overall healthcare operations. This inspection aims to elucidate how machine learning methodologies can mitigate patient wait times and refine overall healthcare logistics. By amalgamating cutting-edge technologies with strategic methodologies, healthcare entities can leverage the transformative capabilities of machine learning to enhance operational efficiency and elevate the delivery of patient care.

Abstract: Text simplification is crucial in bridging the comprehension gap in today’s information-rich environment. Despite advancements in English text simplification, languages with intricate grammatical structures, such as Greek, often remain under-explored. The complexity of Greek grammar, characterized by its flexible syntactic ordering, presents unique challenges that hinder comprehension for native speakers, learners, tourists, and international students. This paper introduces a comprehensive dataset for Greek text simplification, containing over 7500 sentences across diverse topics such as history, science, and culture, tailored to address these challenges. We outline the methodology for compiling this dataset, including a collection of texts from Greek Wikipedia, their annotation with simplified versions, and the establishment of robust evaluation metrics. Additionally, the paper details the implementation of quality control measures and the application of machine learning techniques to analyze text complexity. Our experimental results demonstrate the dataset’s initial effectiveness and potential in reducing linguistic barriers and enhancing communication, with initial machine learning models showing promising directions for future improvements in classifying text complexity. The development of this dataset marks a significant step toward improving accessibility and comprehension for a broad audience of Greek speakers and learners, fostering a more inclusive society.

Abstract: The importance of monitoring earthquakes for disaster management, public safety, and scientific research can hardly be overstated. The emergence of low-cost seismic sensors offers potential for widespread deployment due to their affordability. Nevertheless, vehicular noise in low-cost seismic sensors presents as a significant challenge in urban environments where such sensors are often deployed. In order to address these challenges, this work proposes the use of an amalgamated deep neural network constituent of a DNN trained on earthquake signals from professional sensory equipment as well as a DNN trained on vehicular signals from low-cost sensors for the purpose of earthquake identification in signals from low-cost sensors contaminated with vehicular noise. To this end, we present low-cost seismic sensory equipment and three discrete datasets that—when the proposed methodology is applied—are shown to significantly outperform a generic stochastic differential model in terms of effectiveness and efficiency.

Abstract: Melanoma is a serious form of skin cancer that begins in cells known as melanocytes. While it is less common than basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), melanoma is more dangerous because of its ability to spread to other organs more rapidly if it is not treated at an early stage. The basic examination for melanoma is dermoscopy, an image modality of the skin part that is affected. In this work, we propose a new deep learning approach, based on convolutional neural networks, to classify dermoscopic images in one out of 32 categories. An existing dataset, containing 2013 images from different categories of melanoma has been used for the training and validation of our approach.