A Survey of Dense Object Detection Methods Based on Deep Learning by Yang Zhou, Hui Li

“…In recent years, thanks to the development and advancement of deep learning methods, object detection algorithms and frameworks have made significant breakthroughs, allowing many detection methods to be applied to real-world scenarios. …”
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Graph-Based Extractive Text Summarization Models: A Systematic Review by Abdulkadir Bichi, Pantea Keikhosrokiani, Rohayanti Hassan, Khalil Almekhlafi

“…This necessitates the use of technique such as automatic text summarization. Many approaches and algorithms have been proposed for automatic text summarization including; supervised machine learning, clustering, graph-based and lexical chain, among others. …”
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CLINICAL SIGNIFICANCE OF CYTOMEGALOVIRUS INFECTION IN PEDIATRIC LIVER TRANSPLANT RECIPIENTS by O. M. Tsiroulnikova, I. V. Zhilkin, D. G. Akhaladze

“…Liver transplantation is a life-saving procedure for many forms of end-stage liver disease in pediatrics. …”
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Modern Approaches to Detect and Classify Comment Toxicity Using Neural Networks by Sergey V. Morzhov

“…It is a non-trivial task even if the word “understand” here means only “to classify”. the rapid evolution of machine learning technologies has led to ubiquitous implementation of new algorithms. A lot of tasks, which for many years were considered almost impossible to solve, are now quite successfully solved using deep learning technologies. this article considers algorithms built using deep learning technologies and neural networks which can successfully solve the problem of detection and classification of toxic comments. …”
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Tradeoffs Associated With the Cloud Masking for SGLI Sea Surface Temperature by Yukio Kurihara, Rigen Shimada, Eri Yoshizawa, Fumiaki Kobashi

“…Cloud masking is an essential process for retrieving the sea surface temperature (SST) from infrared data observed by satellites. Many cloud masking algorithms have been proposed, but cloud contamination still remains as a difficult issue. …”
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Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism by Hong Yin, Shuqiang Yang, Xiaoqian Zhu, Songchang Jin, Xiang Wang

“…To most of the classification algorithms, it will degrade the performance of model. …”
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An FPGA-Based Quantum Computing Emulation Framework Based on Serial-Parallel Architecture by Y. H. Lee, M. Khalil-Hani, M. N. Marsono

“…Quantum Fourier transform and Grover’s search are chosen as case studies in this work since they are the core of many useful quantum algorithms. Experimental work shows that, with the proposed emulation architecture, a linear reduction in resource utilization is attained against the pipeline implementations proposed in prior works. …”
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Improving the Efficiency and Quality of Education as the Basis for Successful Digitalization of the Economy by I. I. Shpak, S. N. Kasanin, A. S. Stepanets

“…Examples of results obtained during many years of professional activity are given.…”
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Hybrid Viscosity Approaches to General Systems of Variational Inequalities with Hierarchical Fixed Point Problem Constraints in Banach Spaces by Lu-Chuan Ceng, Saleh A. Al-Mezel, Abdul Latif

“…Our results extend, improve, supplement, and develop the recent results announced by many authors.…”
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Objective method of comparing DNA microarray image analysis systems by Edward L. Korn, Jens K. Habermann, Madhvi B. Upender, Thomas Ried, Lisa M. McShane

“…Many image analysis systems are available for processing the images produced by laser scanning of DNA microarrays. …”
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From nature to computation: Bio-inspired optimization techniques and applications by Goswami Chandrashekhar, Shieh Chin-Shiuh, Chakrabarti Prasun

“…This paper delves into the idea of self-organization in bio-inspired algorithms and how they have been used in many studies. …”
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Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network by Muhammad Sohaib, Jong-Myon Kim

“…Due to enhanced safety, cost-effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. …”
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Applications of machine learning in acute care research by Ikechukwu Ohu, Paul Kummannoor Benny, Steven Rodrigues, Jestin N. Carlson

“…Artificial intelligence algorithms are currently being applied to enable faster prognosis and diagnosis of diseases and to improve patient outcomes.1,2 Despite the successful application of artificial intelligence, it is still fraught with limitations and “unknowns” pertaining to the fact that a model's accuracy is dependent on the amount of information available for training the model, and the understanding of the complexity presented by current artificial intelligence and machine learning algorithms is often limited in many individuals outside of those involved in the field. …”
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Assessing Unconfined Vapor Cloud Explosions (Uvce) Physical Effects: A Software Built For Modelling With Bst Methodology by Tahsin Aykan Kepekli

“…Usually, a large portion of risk assessment studies neglect overpressure damage assessment as these algorithms cannot be understood or implemented easily due to difficulties in usage. …”
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Conformal prediction for uncertainty quantification in dynamic biological systems. by Alberto Portela, Julio R Banga, Marcos Matabuena

“…As an alternative, we propose using conformal predictions methods and introduce two novel algorithms designed for dynamic biological systems. …”
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Efficient Prefetch and Issue Scheduling Approaches for Simultaneous Multithreading Applied to Superscalar RISC-V Processor by Hananya Ribo, Shlomo Greenberg

“…This work also proposes efficient prefetch-scheduling and Issue-scheduling algorithms for Simultaneous Multithreading (SMT) processors. …”
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Generative Adversarial Network for Damage Identification in Civil Structures by Zahra Rastin, Gholamreza Ghodrati Amiri, Ehsan Darvishan

“…In recent years, many efforts have been made to develop efficient deep-learning-based structural health monitoring (SHM) methods. …”
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Machine Learning to Assess Relatedness: The Advantage of Using Firm-Level Data by Giambattista Albora, Andrea Zaccaria

“…Traditionally, relatedness is measured using networks derived by country-level co-occurrences of product pairs, that is counting how many countries export both. In this work, we compare networks and machine learning algorithms trained not only on country-level data, but also on firms, which is something not much studied due to the low availability of firm-level data. …”
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Significance of individual management plan for the patients with hereditary lymphangioedema during pregnancy and lactation, and in newborns by E. N. Bobrikova, A. R. Denisova, T. S. Kotomina, S. A. Serdotetskova, D. S. Fomina

“…A limited number of observations determine the value of the analysis for each clinical case. Many facets of clinical manifestations, a list of predisposing and triggering factors, as well as limitations of some diagnostic and therapeutic algorithms, require the development of individual management schemes under distinct clinical situations. …”
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Geochemical Pathways Defined by Predictive Regolith-Landform Models Using TanDEM-X Data in the Tanami Region, Australia by Richard Llewellyn Langford

“…This paper will demonstrate that neither the classical approach of mapping boundaries based on visual estimates of breaks in slope, nor the alternative approach of producing maps and models derived from algorithms, can be used in isolation. Visual estimates of many boundaries are complex, unreliable and time consuming, and algorithmic mapping is driven by parameters than can produce an infinite set of models. …”
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