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Review of Recent Advancement on Nature/Bio-inspired Antenna Designs
PublicationThis article presents an extensive examination of antennas rooted in nature and biology, showcasing their remarkable performance across a wide spectrum of frequencies—from microwave to terahertz. The limitations of traditional antenna design have become increasingly evident in the face of burgeoning demands for novel communication technologies. Conventional analytical-equation-based approaches struggle to deliver the combined performance...
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Intelligent Decision Forest Models for Customer Churn Prediction
PublicationCustomer churn is a critical issue impacting enterprises and organizations, particularly in the emerging and highly competitive telecommunications industry. It is important to researchers and industry analysts interested in projecting customer behavior to separate churn from non‐churn consumers. The fundamental incentive is a firm’s intent desire to keep current consumers, along with the exorbitant expense of gaining new ones....
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The effect of road restraint systems on the level of road safety - Polish experience
PublicationRoadside accidents happen when a vehicle runs off the road. The majority of these accidents are very severe because leaving the road is usually followed by hitting a solid obstacle (tree, pole, support, culvert front wall, barrier). Roadsides are some of the most important issues of road safety. They have been studied for years to identify roadside hazards and the effectiveness of road safety measures such as restraint systems....
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Screening of predicted synergistic multi-target therapies in glioblastoma identifies new treatment strategies
PublicationAbstract Background IDH-wildtype glioblastoma (GBM) is a highly malignant primary brain tumor with a median survival of 15 months after standard of care, which highlights the need for improved therapy. Personalized combination therapy has shown to be successful in many other tumor types and could be beneficial for GBM patients. Methods We performed the largest drug combination screen to date in GBM, using a high-throughput effort...
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Iterative Global Sensitivity Analysis Algorithm with Neural Network Surrogate Modeling
PublicationGlobal sensitivity analysis (GSA) is a method to quantify the effect of the input parameters on outputs of physics-based systems. Performing GSA can be challenging due to the combined effect of the high computational cost of each individual physics-based model, a large number of input parameters, and the need to perform repetitive model evaluations. To reduce this cost, neural networks (NNs) are used to replace the expensive physics-based...
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Study of various machine learning approaches for Sentinel-2 derived bathymetry
PublicationIn recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it...
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Ultrasound and Clinical Preoperative Characteristics for Discrimination Between Ovarian Metastatic Colorectal Cancer and Primary Ovarian Cancer: A Case-Control Study
PublicationThe aim of this study was to describe the clinical and sonographic features of ovarian metastases originating from colorectal cancer (mCRC), and to discriminate mCRC from primary ovarian cancer (OC). We conducted a multi-institutional, retrospective study of consecutive patients with ovarian mCRC who had undergone ultrasound examination using the International Ovarian Tumor Analysis (IOTA) terminology, with the addition of evaluating...
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Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier
PublicationIn recent years, a lot of attention has been paid to using radiology imaging to automatically find COVID-19. (1) Background: There are now a number of computer-aided diagnostic schemes that help radiologists and doctors perform diagnostic COVID-19 tests quickly, accurately, and consistently. (2) Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia....
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Knowledge-based performance-driven modeling of antenna structures
PublicationThe importance of surrogate modeling techniques in the design of modern antenna systems has been continuously growing over the recent years. This phenomenon is a matter of practical necessity rather than simply a fashion. On the one hand, antenna design procedures rely on full-wave electromagnetic (EM) simulation tools. On the other hand, the computational costs incurred by repetitive EM analyses involved in solving common tasks...
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Designing acoustic scattering elements using machine learning methods
PublicationIn the process of the design and correction of room acoustic properties, it is often necessary to select the appropriate type of acoustic treatment devices and make decisions regarding their size, geometry, and location of the devices inside the room under the treatment process. The goal of this doctoral dissertation is to develop and validate a mathematical model that allows predicting the effects of the application of the scattering...
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Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework
PublicationThe rapid development of shipping trade pushes automated container terminals toward the direction of intelligence, safety and efficiency. In particular, the formulation of AGV scheduling tasks and the safety and stability of transportation path is an important part of port operation and management, and it is one of the basic tasks to build an intelligent port. Existing research mainly focuses on collaborative operation between...
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A perfect hashing incremental scheme for unranked trees using pseudo-minimal automata
PublicationWe describe a technique that maps unranked trees to arbitrary hash codes using a bottom-up deterministic tree automaton (DTA). In contrast to other hashing techniques based on automata, our procedure builds a pseudo-minimal DTA for this purpose. A pseudo-minimal automaton may be larger than the minimal one accepting the same language but, in turn, it contains proper elements (states or transitions that are unique) for every input...
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Study of Multi-Class Classification Algorithms’ Performance on Highly Imbalanced Network Intrusion Datasets
PublicationThis paper is devoted to the problem of class imbalance in machine learning, focusing on the intrusion detection of rare classes in computer networks. The problem of class imbalance occurs when one class heavily outnumbers examples from the other classes. In this paper, we are particularly interested in classifiers, as pattern recognition and anomaly detection could be solved as a classification problem. As still a major part of...
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Use of LIDAR Data in the 3D/4D Analyses of the Krakow Fortress Objects
PublicationThe article presents partial results of studies within the framework of the international project "Cultural Heritage Through Time" (CHT2). The subject of the study were forts of the Krakow Fortress, which had been built by the Austrians between 1849-1914 in order to provide defence against the Russians. Research works were aimed at identifying architectural changes occurring in different time periods in relation to selected...
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Elective surgery system strengthening: development, measurement, and validation of the surgical preparedness index across 1632 hospitals in 119 countries
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