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Preprint ARTICLE | doi:10.20944/preprints202305.0789.v1

Unlocking the Power of Music: A Novel Approach to Language Teaching and Learning

Edgar R. Eslit

Subject: Social Sciences, Education Keywords: Language Learning; Novel Approach; Power of Music

Online: 11 May 2023 (04:58:08 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202305.0600.v1

Adaptive Modeling Multi-Agent Learning System for Video Behavior Clustering Recognition

Xingyu Qian, Aximu Yuemaier, Wen-Chi Yang, Xiaogang Chen, Shunfen Li, Weibang Dai, Zhitang Song

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Continuous Learning; Multi-agent System; Prediction; Adaptability

Online: 9 May 2023 (08:23:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0391.v1

Precision and Accuracy Assessment of Cephalometric Analyses Performed by Deep Learning Artificial Intelligence with and without Human Augmentation

Sumer Panesar, Alyssa Zhao, Eric Hollensbe, Ariel Wong, Surya Sruthi Bhamidipalli, George Eckert, Vinicius Dutra, Hakan Turkkahraman

Subject: Medicine And Pharmacology, Dentistry And Oral Surgery Keywords: artificial intelligence; machine learning; orthodontics; radiology; cephalometry

Online: 6 May 2023 (08:33:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0117.v1

Redesign and implementation of the Electromagnetism Course For Engineering Students Under The Backward Design Methodology

Jesús David González, Liliana Selene Martínez, Roberto Aguas, Jhon De La Hoz, Henry Sánchez

Subject: Social Sciences, Education Keywords: Backward Design; Virtual Learning; Ecosystem; Design; Thinking

Online: 3 May 2023 (08:36:19 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0546.v1

Factors Affecting the Adoption of Online Database Systems for Learning among Students at Economics Universities in Vietnam

Thi Minh Phuong Nguyen

Subject: Social Sciences, Education Keywords: Economics universities; Learning; Online database systems, Students

Online: 19 April 2023 (05:01:25 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202303.0345.v2

FTSO: Effective NAS via First Topology Second Operator

Likang Wang, Lei Chen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: neural architecture search; machine learning; computer vision

Online: 29 March 2023 (02:14:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202303.0254.v1

An Experimental Case Study on How AI May Improve Communication and Resource Allocation

Nwosu Obinnaya Chikezie Victor

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial Intelligence; Machine Learning; Natural Language Processing

Online: 14 March 2023 (09:59:51 CET)

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This paper explores how Artificial Intelligence (AI) can help reduce the communication gap and streamline the distribution of resources. It uses an exploratory case study to examine the current state of the communication gap and the potential opportunities AI can bring to close it. The paper will discuss the current state of the communication gap, the current methods of resource distribution, and the potential opportunities AI can bring to reduce the communication gap. It will also explore the potential implications of AI on resource distribution and the communication gap. The paper will review the existing literature on the communication gap, resource distribution, and AI. It will then focus on the opportunities AI can bring to the communication gap. It will analyze the use of machine learning, natural language processing, and other AI-based technologies to reduce the communication gap. It will also examine the potential implications of AI on resource distribution, such as developing more efficient distribution channels, increasing transparency, and reducing costs. Finally, the paper will analyze the impact of AI on the communication gap and resource distribution. It will discuss the potential challenges and benefits of using AI, such as the need for data security, the potential for bias, and the limitations of AI technology. The paper will also explore the potential for AI to improve the distribution of resources and reduce the communication gap. Overall, this paper provides an exploratory case study on the opportunities of how AI can contribute to reducing the communication gap and streamlining the distribution of resources. It discusses the potential implications of AI on the communication gap and resource distribution, and it reviews the potential challenges and benefits of using AI. The paper provides an in-depth analysis of the potential opportunities of using AI to reduce the communication gap and streamline the distribution of resources.

Preprint REVIEW | doi:10.20944/preprints202303.0113.v1

Self-regulation and Peer Collaboration among Students of Higher Education in Digital Learning Space: A Systematic Literature Review of JCR High Impact Factor

Muhammad Uzair Ul-Hassan, Iram Parveen, Sahib Khan, Joiciane de Souza

Subject: Social Sciences, Education Keywords: Self-regulation; Peer collaboration; online learning; communication

Online: 6 March 2023 (16:08:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0557.v1

PDF Malware Detection Using Machine Learning

Awss AlMahadeen, mouhammd alkasassbeh

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: PDF; Malware; Machine Learning; Python; Random Forest

Online: 30 January 2023 (12:55:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0360.v1

Using Game-Based Learning to Improve Learning Outcomes in K-12 Mathematics Education

John Knight

Subject: Social Sciences, Education Keywords: GBL; Games; Education; Learning; Motivation; Mathematics; Gamification

Online: 19 January 2023 (12:17:57 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0222.v1

Unveiling Circumnutation in Pea Plants via Supervised Machine Learning

Qiuran Wang, Tommaso Barbariol, Gian Antonio Susto, Bianca Bonato, Silvia Guerra, Umberto Castiello

Subject: Social Sciences, Behavior Sciences Keywords: plant movement; circumnutation; machine learning; classification; kinematics

Online: 12 January 2023 (09:34:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0252.v1

Predictive Model for LNG Ship Routing applying Machine Learning

Ramón Fernández-Reyes Doallo, Alberto Camarero Orive, Nicoleta González Cancelas

Subject: Engineering, Civil Engineering Keywords: LNG; shipping optimization; machine learning; predictive model.

Online: 14 December 2022 (07:57:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0027.v1

A Novel Data-Driven Fuzzy for Accurate Coagulant Dosage in Drinking Water Treatment

Adriano Bressane, Ana Paula Garcia Goulart, Isadora Gurjon Gomes, Anna Isabel Silva Loureiro, Rogério Galante Negri, Rodrigo Braga Moruzzi, Adriano Gonçalves dos Reis, Jorge K. S. Formiga, Carrie Peres Melo, Gustavo H. R. Silva

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: coagulant dosage; fuzzy; machine-learning; water treatment

Online: 1 December 2022 (14:49:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0014.v1

Efficient Anomaly Heartbeat Detection Approach for Intermediate Nodes of Internet‐of‐Things Platforms

Leonardo Juan Ramirez Lopez

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Machine Learning; Fog layer; Heartrate; Performance; IoT

Online: 1 December 2022 (08:07:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202211.0053.v1

Examining Learner Autonomy in EFL Learning of the Undergraduate Students

Mohammad Arshad Ali, Alaul Alam

Subject: Social Sciences, Education Keywords: EFL learning; learner autonomy; undergraduate education level

Online: 2 November 2022 (10:45:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202209.0176.v1

Differentially Private Block Coordinate Descent for Linear Regression on Vertically Partitioned Data

Jins De Jong, Bart Kamphorst, Shannon Kroes

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Differential privacy; Federated learning; Vertically partitioned data

Online: 13 September 2022 (10:57:53 CEST)

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Preprint REVIEW | doi:10.20944/preprints202209.0006.v1

The Evolution of Well-Being: An Anthropology Based, Multidisciplinary Review

Seth Jace Dornisch

Subject: Social Sciences, Psychology Keywords: well-being; evolution; emotion; learning; language; motivation

Online: 1 September 2022 (04:29:47 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0441.v1

Knowledge Map of Digital Learning and Sustainable Development (2004-2022): a Scientometric Analysis by Using Citespace

Youhua Shen, Lehui Huang, And Xueshi Wu

Subject: Social Sciences, Education Keywords: digital learning; CiteSpace; visualization; Web of Science

Online: 29 July 2022 (02:48:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202206.0412.v1

Dictionary Learning for Personalized Multimodal Recommendation

Bo Liu, Henri Millett, Bruno L. Rebola, Plank Svensen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Dictionary learning, Recommender system, Personalized recommendation, Multimodal

Online: 30 June 2022 (03:43:30 CEST)

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Preprint REVIEW | doi:10.20944/preprints202203.0293.v1

Advances in Non-invasive Biosensing Measures to Monitor Wound Healing Progression

Walker Short, Oluyinka Olutoye, Benjamin Padon, Umang Parikh, Daniel Colchado, Hima Vangapandu, Shayan Shams, Taiyun Chi, Jangwook Jung, Swathi Balaji

Subject: Medicine And Pharmacology, Pulmonary And Respiratory Medicine Keywords: extracellular matrix; biosensing; machine learning; wound healing

Online: 22 March 2022 (03:50:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202111.0034.v2

A Blockchain-Based Intelligent Machine Learning System for Smart Health Care

Mir Hassan

Subject: Engineering, Control And Systems Engineering Keywords: Blockchain technology; security; Machine learning; Smart System

Online: 17 March 2022 (10:53:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202203.0231.v1

Teacher Oriented Factors Affecting Effectiveness of Online Learning in the Covid-19 Pandemic Environment in Kenya

Khushali Kerai, Tilak Shah

Subject: Social Sciences, Education Keywords: Pandemic; Covid-19; Online learning; Teachers; Kenya

Online: 16 March 2022 (10:13:01 CET)

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Preprint REVIEW | doi:10.20944/preprints202203.0057.v1

A Systematic Review on Digitalisation, Parenting, and Children’s Mental Health in China amid the Pandemic

Jason Hung

Subject: Social Sciences, Sociology Keywords: digitalisation; digital learning; mental health; parenting; China

Online: 3 March 2022 (07:50:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202110.0416.v1

Impact of Madden - Julian Oscillation on North Indian Ocean Cyclone Intensity

M M Ali, Uppalapati Naga Tanusha, C. Purna Chand, B Himasri, Mark A. Bourassa, Y Zheng

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Madden Julian Oscillation; depressions; cyclones; machine learning

Online: 27 October 2021 (13:49:00 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202110.0059.v1

Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values

Abdul Karim, Zheng Su, Phillip K.West, Matthew Keon, The NYGC ALS Consortium, Jannah Shamsani, Samuel Brennan, Ted Wong, Oggy Milicevic, Guus Teunisse, Hima Nikafshan Rad, Abdul Sattar

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Machine learning; ALS; Classification; Interpretation; Target Identification

Online: 4 October 2021 (12:50:04 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202109.0077.v1

Relationship between the STEAM Approach and SUMAK KAWSAY in Vulnerable Social Sectors

Galo Hurtado

Subject: Social Sciences, Education Keywords: STEAM; Teaching and learning; Sumak Kawsay; vulnerable

Online: 6 September 2021 (07:17:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202107.0698.v1

Higher Education Instructors' Use, Perceptions, and Practices of Mobile Learning in Morocco

Youness Zidoun, Nabil Zary

Subject: Social Sciences, Education Keywords: Mobile learning; Integration framework; Design-based research

Online: 30 July 2021 (11:43:12 CEST)

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Background: In an age where information is generally accessible, most of the interest these days has focused on how accessible and convenient technology can be. So small and personal, mobile devices can transform our perception of learning by combining both mobility and convenience. Mobile learning is part of the digital learning landscape alongside e-learning and serious games. However, knowledge about effective design of mobile learning experiences remains of interest with a focus on appropriate design models and the embodiments that can be implemented to achieve the intended educational outcomes. Exploring the instructor's perspective on mobile learning is essential. Therefore, the aim of this study was to investigate the Moroccan instructors' perception and practice of mobile learning to inform the development of an ecologically valid mobile learning integration model. Methods: Higher education Instructors (n=41) were recruited to the study. The Moroccan instructors' perception and their experiences regarding their adoption of mobile learning were collected using an online survey. The analysis focused on their mobile use, perceived IT competency, and opinions on mobile learning. Results: We described most of the instructors' considerations regarding integrating mobile technologies into their teaching activities. We found that most of the mobile learning activities defined by the respondents corresponded to relatively advanced use of mobile devices. More promising, instructors have found innovative ways to use the educational potential of mobile devices. However, the prospect of mobile devices was still to challenge. No or poor Wi-Fi connection, number of devices or limited access, sometimes fees or applications incompatibility were identified as reasons and obstacles to mobile learning usage. Conclusion: Mobile learning is mostly perceived positively among Moroccan instructors allowing many applications and usage to enhance teaching and learning. In this study, a better understanding of aspects and factors influencing the integration of mobile learning in the Moroccan educational context is exposed, helping further the development of an ecologically valid mobile learning integration model. Future work on mobile learning should consider the highly paced evolution of mobile technologies, emphasizing the flexibility of integration frameworks to support instructors and learners.

Preprint ARTICLE | doi:10.20944/preprints202106.0669.v1

Computational Generation of Virtual Concrete Mesostructures

Vijaya Holla, Giao Vu, Jithender J. Timothy, Fabian Diewald, Christoph Gehlen, Günther Meschke

Subject: Engineering, Civil Engineering Keywords: Concrete, Mesoscale, Modeling, Virtual Mesostructure, Machine Learning

Online: 28 June 2021 (14:49:38 CEST)

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Preprint HYPOTHESIS | doi:10.20944/preprints202102.0349.v4

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

Pranjal Kumar

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Learning (artificial intelligence); Neural networks; Activity recognition

Online: 7 June 2021 (13:04:11 CEST)

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Working Paper ARTICLE

Estimating Neural Network’s Performance with Bootstrap: a Tutorial

Umberto Michelucci, Francesca Venturini

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Neural Networks; Machine Learning; Bootstrap; Resampling; Algorithms

Online: 22 March 2021 (16:09:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints202103.0301.v1

Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

Alexander Scheinker

Subject: Physical Sciences, Acoustics Keywords: machine learning; adaptive control; time-varying systems

Online: 11 March 2021 (08:40:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202101.0085.v1

Challenges and Experiences of Online Evaluation in Courses of Civil Engineering During the Lockdown Motivated by the COVID-19 Crisis

Marcos García-Alberti, Fernando Suárez, Isabel Chiyón, Juan Carlos Mosquera Feijoo

Subject: Engineering, Automotive Engineering Keywords: COVID-19; online learning; higher education; evaluation

Online: 5 January 2021 (11:39:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints202101.0013.v1

Social Support and Emotional Maturity to Reduce Students’ Online Learning Anxiety During the Covid-19 Pandemic

Andik Matulessy, Sahat Saragih, Nawafil Nawafil, Hengki Perdana, Moses Pandin

Subject: Social Sciences, Education Keywords: Social support; emotional maturity; anxiety; online learning

Online: 4 January 2021 (11:26:27 CET)

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Preprint ARTICLE | doi:10.20944/preprints202011.0010.v1

Distributed Correlation-Based Clustering Mechanism for Large-Scale Datasets

Kuei-Sheng Lee, Meng-Feng Tsai, Chi-Sheng Huang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Big Data; Clustering; Distributed system; Machine learning

Online: 2 November 2020 (10:00:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202009.0636.v1

The Effect of Students’ Experience with the Transition from Primary to Secondary School on Self-Regulated Learning and Motivation

Ana Uka, Arban Uka

Subject: Business, Economics And Management, Accounting And Taxation Keywords: motivation; self-regulated learning; transition; secondary school

Online: 26 September 2020 (13:40:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202009.0521.v1

Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

E. Patricia Becerra-Sánchez, Angélica Reyes, Antonio Guerrero-Ibañez

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: electroencephalographic; feature selection; machine learning; prediction model

Online: 22 September 2020 (11:27:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202009.0444.v1

Adjusting Mineral Nutrition of Lowbush Blueberry to Agroecosystem Conditions

Serge-Étienne Parent, Jean Lafond, Maxime Paré, Léon Etienne Parent, Noura ZIadi

Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: blueberry; crop modeling; plant nutrition; machine learning

Online: 19 September 2020 (03:27:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202009.0333.v1

Ten Tips for Blended Practicals

Andrew Garrard, Stephen Beck, Matteo Di Benedetti, Adam Funnell

Subject: Social Sciences, Education Keywords: practical engineering education; remote practicals; blended learning

Online: 15 September 2020 (06:13:10 CEST)

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Working Paper REVIEW

Computational Biology and Machine Learning Approaches to Study Mechanistic Microbiomehost Interactions

Padhmanand Sudhakar, Kathleen Machiels, Severine Vermeire

Subject: Biology And Life Sciences, Immunology And Microbiology Keywords: Computational methods; machine learning; microbiome-host interactions

Online: 7 September 2020 (14:50:42 CEST)

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The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on inflammation, nutrition, and homeostasis. Although driven by a complex combination of intrinsic and extrinsic factors, many chronic diseases such as diabetes, cancer, Inflammatory Bowel Disease among others are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites etc have recently been elucidated, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to pragmatic limitations with respect to parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a systems and community level. In the past decade, various computational biology and machine learning methodologies and approaches have been developed with an aim to fill these existing gaps. Due to the agnostic nature of the tools, they have been applied in various disease contexts to analyze and infer the interactions between the microbiome and host molecular components, and in the case of a few selected tools, on downstream host processes. Generally, most of the tools are enabled by frameworks to statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic microbiome-host interactions and their potential benefit for basic and clinical research. These could include but are not limited to the development of activity and mechanism based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Preprint ARTICLE | doi:10.20944/preprints202009.0088.v1

YOLOv2 for Pigs Detection in Industrial Farming

Akif Quddus Khan, Salman Khan

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: YOLOv2; transfer learning; pig farming; object detection

Online: 4 September 2020 (07:59:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202006.0130.v2

The Impact of COVID-19 Pandemic on the Academic Performance of Veterinary Medical Students

Mohamed Mahdy

Subject: Medicine And Pharmacology, Veterinary Medicine Keywords: academic performance; COVID-19; veterinary; online learning

Online: 21 August 2020 (02:25:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202007.0379.v1

Enhancing Mouth-based Emotion Recognition using Transfer Learning

Valentina Franzoni, Giulio Biondi, Damiano Perri, Osvaldo Gervasi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Transfer Learning; Convolutional Neural Networks; Emotion Recognition

Online: 17 July 2020 (13:58:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202006.0182.v1

Codifying an Approach to Remote Practicals

Andrew Garrard, Joanna Bates, Stephen Beck, Adam Funnell

Subject: Engineering, Control And Systems Engineering Keywords: practical engineering education; remote practicals; blended learning

Online: 14 June 2020 (15:29:04 CEST)

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Preprint REVIEW | doi:10.20944/preprints202005.0408.v1

Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques

Bryan Jordan

Subject: Medicine And Pharmacology, Pharmacology And Toxicology Keywords: drug discovery; machine learning; in silico; pharmacology

Online: 25 May 2020 (04:55:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202005.0336.v1

Extraction of Sea Ice Cover by Sentinel-1 SAR Based on SVM with Unsupervised Generation of Training Data

Xiaoming Li, Yan Sun, Qiang Zhang

Subject: Environmental And Earth Sciences, Oceanography Keywords: synthetic aperture radar; sea ice; machine learning

Online: 21 May 2020 (05:59:34 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202001.0207.v1

Averaging is Probably not the Optimum Way of Aggregating Parameters in Federated Learning

Peng Xiao, Samuel Cheng, Vladimir Stankovic, Dejan Vukobratovic

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: federated learning; federated averaging; mutual information; correlation

Online: 19 January 2020 (04:37:27 CET)

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Preprint ARTICLE | doi:10.20944/preprints201907.0348.v1

Predicting On-Axis Rotorcraft Dynamic Responses Using Machine Learning Techniques

Ryan Jackson, Michael Jump, Peter Green

Subject: Engineering, Control And Systems Engineering Keywords: rotorcraft; machine learning; Gaussian process; flight simulation

Online: 31 July 2019 (04:55:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201907.0309.v1

Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

Oscar Duque-Perez, Carlos Del Pozo-Gallego, Daniel Morinigo-Sotelo, Wagner Fontes Godoy

Subject: Engineering, Electrical And Electronic Engineering Keywords: condition monitoring; bearings; machine learning; current spectra

Online: 28 July 2019 (11:52:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201906.0288.v1

Cancer: Strange Attractors and Complexity

Abicumaran Uthamacumaran

Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: Cancer; Turbulence; Complexity; Machine Learning; Chaos; Reprogramming

Online: 27 June 2019 (11:36:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201906.0105.v1

Morphometry of the Wheat Spike by Analyzing 2D Images

Mikhail Genaev, Evgenii Komyshev, Nikolai Smirnov, Yuliya Kruchinina, Nikolay P. Goncharov, Dmitry Afonnikov

Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: image analysis; machine learning; algorithms; computer vision

Online: 12 June 2019 (12:39:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201904.0091.v5

Preference Neural Network

Ayman Elgharabawy, Mukesh Prasad, Chin-Teng Lin, Ayman Elgharabawy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Preference learning; Multi-label ranking; Neural network; Kendall’s tau; Preference mining

Online: 19 April 2023 (07:43:17 CEST)

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Preprint BRIEF REPORT | doi:10.20944/preprints201902.0257.v2

Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails

Luis Fernando De Mingo Lopez, Clemencio Morales Lucas, NURIA GOMEZ BLAS, Krassimira Ivanova

Subject: Engineering, Control And Systems Engineering Keywords: convolutional neural networks; pattern recognition; machine learning

Online: 12 March 2019 (10:18:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints201812.0132.v1

The Design of Autism Content Management Learning System (ACMLS) Framework

Khaled Ismail, Nazean Jomhari

Subject: Computer Science And Mathematics, Computer Science Keywords: children with autism, caregivers, personalised learning, intervention

Online: 11 December 2018 (13:55:39 CET)

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Preprint ARTICLE | doi:10.20944/preprints201810.0107.v1

Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models

Daniel Buscombe, Paul Grams

Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: multibeam echosounder; backscatter; multi-frequency; machine-learning

Online: 5 October 2018 (16:09:53 CEST)

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We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative machine learning algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of proximity and allowable backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model that doesn't include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF approach therefore may prove to be a powerful `spatially aware' alternative to other discriminative classifiers.

Preprint ARTICLE | doi:10.20944/preprints201804.0030.v1

Allocation of Tutors and Study Centers in Distance Learning Using Geospatial Technologies

Shahid Nawaz Khan, Kamran Mir, Ali Tahir, Arshad Awan, Zaib un Nisa, Syeda Aareeba Gillani

Subject: Social Sciences, Education Keywords: geospatial technologies; distance learning; resource allocation; AIOU

Online: 3 April 2018 (04:29:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201803.0118.v1

Machine Learning for the Design and Development of Biofilm Regulators

Benjamin Stone, Erik Sapper

Subject: Chemistry And Materials Science, Theoretical Chemistry Keywords: machine learning; biochemistry; QSAR; molecules; neural networks

Online: 15 March 2018 (06:37:58 CET)

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Preprint ARTICLE | doi:10.20944/preprints202409.1510.v1

On a New Innovative Steam-Based Method for Teaching Cycloidal Curves in Engineering Higher Education

Szilvia Szilágyi, Attila Körei, Ingrida Vaičiulytė

Subject: Computer Science And Mathematics, Other Keywords: engineering mathematics; cycloidal curves; dynamic geometry software; central trochoids; project-based learning; LEGO 4C learning model; educational robotics

Online: 20 September 2024 (10:48:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0964.v1

Evaluation of Problem-Based Learning Experiences Addressed to Health Promotion in the Fourth Grade of Primary School

Beatriz Mederer-Hengstl, Pilar Núñez-Delgado, Aurora Bueno-Cavanillas

Subject: Public Health And Healthcare, Public, Environmental And Occupational Health Keywords: Health Education; Healthy Habits; Healthy Diet; Active Learning Strategies; Problem-Based Learning; Instrumental Skills; Attitudinal Competences; Primary School.

Online: 15 April 2024 (11:33:23 CEST)

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Preprint REVIEW | doi:10.20944/preprints202311.0532.v1

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

Panagiotis Gkonis, Anastasios Giannopoulos, Panagiotis Trakadas, Xavi Masip-Bruin, Francesco D' Andria

Subject: Engineering, Telecommunications Keywords: IoT; cloud-based operating systems; edge computing; machine learning; federated learning; task offloading; security and privacy; blockchain technology

Online: 8 November 2023 (09:49:07 CET)

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The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things – IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where IoT, edge and cloud converge to form a computing continuum, is also known as IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems architectural approach, including: i) design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; ii) design of efficient task offloading algorithms aimed at optimizing services execution; iii) support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; iv) scalability, flexibility and reliability guarantees to face the expected mobility for IoT systems, and; v) design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges become even more significant towards the new era of sixth generation (6G) networks, which will be based on the integration of various cutting edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented, from an IEC continuum perspective.

Preprint ARTICLE | doi:10.20944/preprints202305.0917.v2

Impact of Varying User Characteristics on The Accuracy of Fall Prediction Algorithms Using Custom Wrist-Worn End Device

Purab Nandi, K R Anupama, Himanish Agarwal, Siddharth Paliwal, Kishan Patel, Vedant Bang, Manan Bharat, Madhen Vyas Guru

Subject: Engineering, Electrical And Electronic Engineering Keywords: Machine learning; Geriartic fall detection; Dataset; Dew Computing; End Device; Feature Extraction; Supervised Machine Learning; Sensor Data Analysis

Online: 1 October 2023 (09:38:25 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.0545.v1

SigML++: Supervised Log Anomaly with Probabilistic Polynomial Approximations

Devharsh Trivedi, Aymen Boudguiga, Nesrine Kaaniche, Nikos Triandopoulos

Subject: Computer Science And Mathematics, Computer Science Keywords: sigmoid function approximation; private machine learning; fully homomorphic encryption; log anomaly detection; supervised machine learning; probabilistic polynomial approximation

Online: 8 September 2023 (04:34:42 CEST)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202209.0380.v1

A Novel Design For A COVID-19 Diagnosis System Using Machine Learning And Drone Technology (COVIDRONE-20)

Asweel KK Mehaboob, Priya Darshini Natarajan, Munawar Peringadi Vayalil, Shamsudheen Marakkar, Mufeeda C. Koyilot, Khader Shameer, Kamlesh K. Yadav

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: UAV Technology; Information Processing; Machine Learning; IR Technology; Covid-19; Multi-modal machine learning; Machine vision; Computer vision

Online: 26 September 2022 (05:46:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202203.0094.v1

Smart Scheduling of Electric Vehicles Based on Reinforcement Learning

Andrei Vizitiu, Daniel Furtuna, Andrei Robu, Stelian Senocico, Petru Cioata, Remus Baltariu, Maria Simona Raboaca, Constantin Filote

Subject: Engineering, Automotive Engineering Keywords: Smart scheduling; Smart Reservations; Reinforcement Learning; Electric vehicle charging; Electric Vehicle Charging Management platform; DQN Reinforcement Learning algorithm

Online: 7 March 2022 (09:20:13 CET)

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Working Paper ARTICLE

Textual Data Distributions: Kullback Leibler Textual Distributions Contrasts on GPT-2 Generated Texts, with Supervised, Unsupervised Learning on Vaccine & Market Topics & Sentiment

Jim Samuel, Ratnakar Palle, Eduardo Correa Soares

Subject: Computer Science And Mathematics, Computer Science Keywords: Textual data distributions; supervised learning; unsupervised learning; Kullback-Leibler divergence; sentiment; textual analytics; text generation; vaccine; stock market

Online: 17 June 2021 (10:03:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202402.1665.v1

Predatory Behaviours in Science

Mario Coccia

Subject: Social Sciences, Library And Information Sciences Keywords: Predatory research field; Science evolution; Science of Science; Social dynamics of science; Transformers; ChatGPT; BERT; Microsoft Copilot:; Large Language Model; Emerging technology; Radical Technology; Natural Language Processing Tool; AI Technology; Deep Learning Architecture; Multi-Head Attention Mechanism. ;

Online: 29 February 2024 (09:06:19 CET)

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Predatory research field in science is when an emerging scientific topic destroys current topics and characterizes a main scientific change. Predatory research field can be a basic driver of scientific and technological change that generates a 'creative destruction' in science and society in contexts of knowledge-based competition and rapid changes. The prediction of proposed theory of predatory research fields is that it destroys with a fast growth other research fields. The theoretical approach is tested here in research fields of large language models (LLM) by analyzing the transformers (a deep learning architecture based on the multi-head attention mechanism) proposed in 2017 and from November 2022 started main applications in generative artificial intelligence with innovations of BERT, ChatGPT, Microsoft Copilot (launched on February 7, 2023) and other natural language processing tools driven by AI technology for engaging conversations, gain insights, automate tasks, etc., etc. Statistical evidence suggests that growth rate of transformer technologies is 80.58%, a high level compared to all other research fields in machine learning (having a growth rate of 13.83%). Moreover, predatory research field of transformers has a destructive power such that all other domains in LLM from 2021 to 2023 have a general reduction of scientific growth. The impact of transformers is much more drastic of previous radical technologies such as CNN having a temporal growth rate of 0.16%, lower than 0.38% by transformers, ceteris paribus. These analysis reveals that transformers have characteristics to generate a radical scientific and technological change in a not-too-distant future. Overall, then, the study suggests that predatory research field on emerging topics and technologies can generate path-breaking innovations and the examination here can clarify the essential elements of the science dynamics for a better theory of scientific and technological change, providing also main implications for knowledge policy to support promising research fields and technologies to guide economic and social change.

Preprint ARTICLE | doi:10.20944/preprints202308.0528.v1

Performance of Lip-sync instead of Speech Imagery, New Combination Signals, Supplement Bond Graph Classifier and Deep Formula Detection as Confidents Extraction and Roots Detection Classifier for EEG and BCI

Ahmad Naebi, Zuren Feng

Subject: Engineering, Bioengineering Keywords: Speech Imagery; Mental Task; Machine Leaning; Feature Extraction; Common spatial pattern (CSP); Filter bank Common Spatial Pattern (FBCSP); Brain – Computer Interface (BCI); Principal Components Analysis (PCA); Feature Selection; Channel Selection; Mutual Information; Lagrange Formula; Deep Learning; SVM Classifier

Online: 7 August 2023 (10:23:13 CEST)

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Nowadays, brain signal processing is performed rapidly in various brain-computer interface (BCI) applications. Most researchers focus on developing new methods for the future or improving the basic implemented models to identify the optimum standalone feature set. Our research focuses on four ideas. One of them introduces future communication models, and the others are for improving old models or methods. These are: 1) new communication imagery model instead of speech imager using the mental task: Due to speech imagery is very difficult, and it is impossible to imagine sound for all of the characters in all of the languages. Our research introduces a new mental task model for all languages that call Lip-sync imagery. This model can use for all characters in all languages. This paper implemented two lip-sync for two sounds, characters or letters. 2) New combination Signals: Selecting an inopportune frequency domain can lead to inefficient feature extraction. Therefore, domain selection is so important for processing. This combination of limited frequency ranges proposes a preliminary for creating Fragmentary Continuous frequency. For the first model, two s intervals of 4 Hz as filter banks were examined and tested. The primary purpose is to identify the combination of filter banks with 4Hz (scale of each filter bank) from the 4Hz to 40Hz frequency domain as new combination signals (8Hz) to obtain well and efficient features using increasing distinctive patterns and decreasing similar patterns of brain activities.3) new supplement bond graph classifier for SVM classifier: When SVM linear uses in very noisy, the performance is decreased. But we introduce a new bond graph linear classifier to supplement SVM linear in noisy data. 4) a deep formula recognition model: it converts the data of the first layer into a formula model (formula extraction model). The main goal is to reduce the noise in the subsequent layers for the coefficients of the formulas. The output of the last layer is the coefficients selected by different functions in different layers. Finally, the classifier extracts the root interval of the formulas, and the diagnosis does based on the root interval. For all of the ideas achieved the results of implementing methods. The results are between 55% to 98%. Less result is 55% for the deep detection formula, and the highest result is 98% for new combination signals.

Preprint ARTICLE | doi:10.20944/preprints202410.1467.v1

Comparative Investigation of Traditional Machine Learning Models and Transformer Models for Phishing Email Detection

René Meléndez, Michal Ptaszynski, Masui Fumito

Subject: Computer Science And Mathematics, Information Systems Keywords: Phishing detection; Phishing emails; Machine Learning; Transformer Models; Traditional 14 Models; Supervised Learning; Text Classification, Cyber threat Mitigation; Cybersecurity

Online: 18 October 2024 (11:18:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.1000.v1

Physics-Informed Neural Networks for the Reynolds Equation with Transient Cavitation Modeling

Faras Brumand-Poor, Florian Barlog, Nils Plückhahn, Matteo Thebelt, Niklas Bauer, Katharina Schmitz

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Hydrodynamic lubrication; Physics-Informed Neural Networks; Average Reynolds Equation with Transient Cavitation; Physics-Informed Machine Learning; Elastohydrodynamic; Machine Learning

Online: 14 October 2024 (11:30:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0243.v1

Artificial Neural Networks and Ensemble Learning for Enhanced Liquefaction Prediction in Smart Cities

Yuxin Cong, Shinya Inazumi

Subject: Engineering, Civil Engineering Keywords: artificial neural networks; bagging technique; ensemble learning; geotechnical engineering; liquefaction prediction; machine learning in geology; smart cities; urban resilience

Online: 3 September 2024 (18:10:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0095.v1

SafeFood4EU: An Interdisciplinary Approach On Education for Sustainable Agri-Food Systems in Europe

Pat Burgess, Kata Körösi, Kiril Mihalev, Aleksandar Yovchev, Milena Dimitrova-Dicheva, Rada Dinkova, Kiril Vassilev, Andreas Schieber, Lea Mehren, Franziska Knuf, Lena Elliger, Georgi Georgiev

Subject: Biology And Life Sciences, Food Science And Technology Keywords: sustainable agri-food systems; higher education; life-long learning; novel interdisciplinary approach; EU project; sustainability; digital learning; project results

Online: 2 July 2024 (02:38:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0138.v1

Extraversion in COVID-19 Coping

Carol Nash

Subject: Social Sciences, Psychology Keywords: extraversion; Big Five; coping; COVID-19; other-directed learning; self-directed learning; Google Scholar; vaccine; public opinion; personal values

Online: 10 April 2023 (03:50:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202303.0510.v1

Machine Learning and Explainable Artificial Intelligence using Counterfactual Explanations for Evaluating Posture Parameters

Carlo Dindorf, Oliver Ludwig, Steven Simon, Stephan Becker, Michael Fröhlich

Subject: Medicine And Pharmacology, Orthopedics And Sports Medicine Keywords: biomechanics; posture; hyperlordosis; hyperkyphosis; machine learning; artificial intelligence; explainable artificial intelligence; human-in-the-loop; confident learning; label errors

Online: 29 March 2023 (14:08:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202203.0188.v1

Structural Damage Prediction of a Reinforced Concrete Frame Under Single and Multiple Seismic Events Using Machine Learning Algorithms

Petros C. Lazaridis, Ioannis E. Kavvadias, Konstantinos Demertzis, Lazaros Iliadis, Lazaros K. Vasiliadis

Subject: Engineering, Civil Engineering Keywords: Seismic Sequence; Machine Learning Algorithms; Repeated Earthquakes; Structural Damage Prediction; Intensity Measures; Damage Accumulation; Machine Learning; Artificial Neural Network

Online: 14 March 2022 (11:57:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints201810.0050.v1

Have you heard the PEPS talk? Towards standardized public engagement practices for scientists (PEPS)

Nina Martin, Radames JB Cordero, Gatien de Broucker

Subject: Biology And Life Sciences, Other Keywords: public engagement on science, science engagement, science communication, public understanding of science, deficit model, informal STEM learning, active learning

Online: 3 October 2018 (13:09:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201804.0065.v1

Haptic Virtual Reality and Immersive Learning for Enhanced Organic Chemistry Instruction

Bosede Iyiade Edwards, Kevin S. Bielawski, Rui F. Prada, Adrian David Cheok

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: virtual reality; immersive learning; haptics; chemistry education; organic chemistry; hydrocarbons; middle school science; introductory chemistry; hands-on learning; gamification

Online: 5 April 2018 (05:59:19 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.1215.v1

Comprehensive Study of IoT Vulnerabilities and Countermeasures

Ian Coston, Eadan Plotnizky, Mehrdad Nojoumian

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Internet of Things; Internet of Battlefield Things; Internet of Medical Things; Internet of Agricultural Things; Artificial Intelligence; Machine Learning; Deep Learning; AI-Driven Security; Cloud Computing; Cloud Security; Cybersecurity; IoT Security; IoT Vulnerabilities; Hardware Security; Software Security; Network Security; Wireless Sensor Networks; Wireless Communication Protocols; IoT Protocols; Intrusion Detection System; Vulnerability Analysis; Attack Mitigation; Penetration Testing

Online: 15 October 2024 (18:15:59 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.2097.v1

Increased Levels of Hsa-miR-199a-3p and Hsa-miR-382-5p in Maternal and Neonatal Blood Plasma in the Case of Placenta Accreta Spectrum

Angelika V. Timofeeva, Ivan S. Fedorov, Anastasia D. Nikonets, Alla M. Tarasova, Ekaterina N. Balashova, Dmitry N. Degtyarev, Gennady T. Sukhikh

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: placenta accreta spectrum; RDS; antenatal corticosteroid therapy; miRNA; deep sequencing; PCR; neonatal complication; blood plasma

Online: 28 October 2024 (13:35:19 CET)

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Despite the increasing number of placenta accreta spectrum (PAS) cases in recent years, its im-pact on neonatal outcomes and respiratory morbidity has not yet been extensively studied. In this context, it is crucial to understand the underlying mechanisms of neonatal complications. Moreover, no study has yet demonstrated the effectiveness of antenatal corticosteroid therapy (CT) for prevention of the respiratory distress syndrome (RDS) in newborns of mothers with PAS at the molecular level. In this regard, miRNA profiling was performed on 160 blood plasma sam-ples from preterm infants (gestational age 33–36 weeks) and their mothers diagnosed with or without PAS. The samples with PAS were categorized into groups: without antenatal RDS prophylaxis, and with CT admin-istered either 2–7 days, 7–14 days, or more than 14 days before delivery. These groups were compared to a control group without PAS in the absence of antenatal CT. Deep sequencing was conducted using the NEB-Next® Multiplex Small RNA Library Prep Set for Illumina® on the NextSeq 500/550 platform, followed by validation by quantitative real-time PCR. A significant increase in hsa-miR-199a-3p and hsa-miR-382-5p levels was observed in the blood plasma of newborns with PAS (placenta accreta and increta) compared to the control group. In maternal blood, hsa-miR-199a-3p levels were markedly higher than hsa-miR-382-5p. The timing of antenatal CT significantly influenced the levels of hsa-miR-199a-3p and hsa-miR-382-5p in neonatal plasma from mothers with placenta accreta or increta. A clear trend toward normalization was observed when CT was administered within 14 days before delivery, particularly in the seven days before delivery, but not beyond 14 days. Among newborns with PAS, higher levels of hsa-miR-382-5p were significantly associated with increased Neomod scale severity scores of 2, 4, and 5 compared to a score of 0. Additional-ly, a direct correlation was found between hsa-miR-382-5p level in neonatal plasma and hsa-miR-199a-3p level in the same sample (r = 0.49; p = 0.0001), oxygen requirements in the NICU (r = 0.41; p = 0.0016), duration of NICU stay (r = 0.31; p = 0.019), and the severity of the newborn’s condition based on the NEOMOD scale (r = 0.36; p = 0.0051). Logistic regression mod-els based on maternal plasma levels of hsa-miR-199a-3p and hsa-miR-382-5p predicted the need for cardiotonic therapy, invasive mechanical ventilation, or high-frequency oscillatory ventila-tion in newborns during the early neonatal period, with a sensitivity of 95-100%. It was con-cluded that elevated circulating levels of miRNAs, hsa-miR-199a-3p and hsa-miR-382-5p, in ma-ternal and fetal blood are crucial in the development of respiratory and cardiac complications in newborns from pregnancies affected by PAS. These miRNAs regulate surfactant synthesis in al-veolar cells, fetal organogenesis via IGF-1, the formation of proper lung tissue architecture, and vascular tone.

Preprint REVIEW | doi:10.20944/preprints202410.1785.v1

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

Momina Liaqat Ali, Zhou Zhang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: YOLO; single stage detection; object detection; performance evaluation; deep neural network; real-time object detection

Online: 23 October 2024 (07:25:08 CEST)

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This paper presents a comprehensive review of the You Only Look Once (YOLO) framework, a transformative one-stage object detection algorithm renowned for its remarkable balance between speed and accuracy. Since its inception, YOLO has evolved significantly, with versions spanning from YOLOv1 to the most recent YOLOv11, each introducing pivotal innovations in feature extraction, bounding box prediction, and optimization techniques. These advancements, particularly in the backbone, neck, and head components, have positioned YOLO as a leading solution for real-time object detection across a variety of domains. In this review, we explore YOLO's diverse applications, including its critical role in medical imaging for COVID-19 detection, breast cancer identification, and tumor localization, where it has significantly enhanced diagnostic efficiency. YOLO's robust performance in autonomous vehicles is also highlighted, as it excels in challenging conditions like fog, rain, and low-light environments, thereby contributing to improved road safety and autonomous driving systems. In the agricultural sector, YOLO has transformed precision farming by enabling early detection of pests, diseases, and crop health issues, promoting more sustainable farming practices. Additionally, we provide an in-depth performance analysis of YOLO models—such as YOLOv9, YOLO-NAS, YOLOv10, and YOLOv11—across multiple benchmark datasets. This analysis compares their suitability for a range of applications, from lightweight embedded systems to high-resolution, complex object detection tasks. The paper also addresses YOLO's challenges, such as occlusion, small object detection, and dataset biases, while discussing recent advancements that aim to mitigate these limitations. Moreover, we examine the ethical implications of YOLO's deployment, particularly in surveillance and monitoring applications, raising concerns about privacy, algorithmic biases, and the potential to perpetuate societal inequities. These ethical considerations are critical in domains like law enforcement, where biased object detection models can have serious repercussions. Through this detailed review of YOLO's technical advancements, applications, performance, and ethical challenges, this paper serves as a valuable resource for researchers, developers, and policymakers looking to understand YOLO’s current capabilities and future directions in the evolving field of object detection.

Preprint COMMUNICATION | doi:10.20944/preprints202410.0431.v1

Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks

Mirosław Łącki

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep neural networks; detection and classification; safety of marine navigation; image processing; object detection techniques

Online: 8 October 2024 (12:00:28 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.1975.v1

Biomarkers: The Key To Enhancing Dbs Treatment For Psychiatric Conditions

Guillermo Jose Bazarra Castro, Vicente Casitas, Carlos Martinez-Macho, Alejandra Madero Pohlen, Amelia Alvarez-Sala, Enrique Barbero Pablos, José Fernández Alén, Cristina V. Torres

Subject: Medicine And Pharmacology, Neuroscience And Neurology Keywords: Deep Brain Stimulation (DBS); Psychosurgery; Depression; Obsessive-Compulsive Disorder (OCT); Biomarkers; Diffusion tensor imaging (DTI)

Online: 25 September 2024 (12:46:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1851.v1

From Single Shot to Structure: End-to-End Network based Deflectometry for Specular Free-Form Surface Reconstruction

M.Hadi Sepanj, Saed Moradi, Amir Nazemi, Claire Preston, Anthony M. D. Lee, Paul Fieguth

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Deflectometry, Specular Surface Reconstruction, Deep Neural Networks, Single-Shot Measurement, Data Simulation, Variational Autoencoder (VAE)

Online: 25 September 2024 (11:35:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0233.v1

High-Efficiency Extraction of Flavonoids from Flos Sophorae Immaturus Using Ultrasound-Assisted Deep Eutectic Solvent: Optimization and Antioxidant Activity

Xinrui Yang, Ziyan Zhang, Yifeng Zhang

Subject: Chemistry And Materials Science, Food Chemistry Keywords: Flos Sophorae Immaturus; deep eutectic solvent; ultrasound-assisted extraction; flavonoid; response surface methodology; antioxidant activity

Online: 5 August 2024 (11:25:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202405.0047.v1

The “Galenic Question”: A solution Based on Historical Sources and Mathematical Analysis of Texts

Fernando La Greca, Liberato De Caro, Emilio Matricciani

Subject: Arts And Humanities, History Keywords: classical Greek; deep– language variables; Galen of Pergamum; history of medicine; statistical analysis; linguistics

Online: 1 May 2024 (07:20:05 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202402.1634.v1

Enhancing Legal Sentiment Analysis: A CNN-LSTM Document-Level Model

Bolanle Abimbola, Enrique De La Cal Marin, Qing Tan

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Convolutional neural networks; deep neural networks; long short-term memory; sentimental analysis; recurrent neural networks

Online: 28 February 2024 (13:48:43 CET)

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Preprint ARTICLE | doi:10.20944/preprints202310.0329.v1

Marine Cultural Target Detection for Underwater Robotics Based on Deep Aggregation Network with Deformation Convolution

Yutuo Yang, Wei Liang, Daoxian Zhou, Yinlong Zhang, Gaofei Xu

Subject: Computer Science And Mathematics, Robotics Keywords: autonomous underwater vehicle (AUV); underwater target detection; deformable convolution; multi-scale deep aggregation; attention mechanism

Online: 6 October 2023 (11:51:19 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.1788.v1

Characteristics and Research Significance of Micro-Nanoparticles in Geothermal Fluids in the Central Area of Shandong Province

Lei Zuo, Peng Zhang, Yaqin Wang, Rui Liu, Guangxi Ma

Subject: Environmental And Earth Sciences, Water Science And Technology Keywords: geothermal fluids; micro-nanoparticles; TEM; NTA; deep hidden geothermal resources; central area of Shandong Province

Online: 27 September 2023 (05:44:12 CEST)

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Preprint REVIEW | doi:10.20944/preprints202309.1715.v1

Percutaneous Deep Venous Arterialization for Limb Salvage of No Option Patients with Chronic Limb-Threatening Ischemia

Stavros Spiliopoulos, Efstathia Davoutis, Nikolaos-Achilleas Arkoudis, Kaji Sritharan, Symeon Lechareas

Subject: Medicine And Pharmacology, Cardiac And Cardiovascular Systems Keywords: chronic limb-threatening ischemia; peripheral arterial disease; endovascular treatment; tibial vessels; percutaneous deep venous arterialization

Online: 26 September 2023 (04:44:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.0642.v1

Combination of Physics-Informed Neural Networks and Single-Relaxation-Time Lattice Boltzmann Method for Solving Inverse Problems in Fluid Mechanics

Zhixiang Liu, Yuanji Chen, Ge Song, Wei Song, Jingxiang Xu

Subject: Computer Science And Mathematics, Mathematics Keywords: physics-informed neural networks; deep neural networks; lattice Boltzmann method; fluid mechanics; inverse problem; PDEs

Online: 11 September 2023 (07:35:30 CEST)

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Preprint REVIEW | doi:10.20944/preprints202307.0847.v1

Quorum Sensing: Unravelling the Intricacies of Microbial Communication for Biofilm Formation, Biogeochemical Cycling, and Biotechnological Applications

Edamkue Israel, Selvarajan Ramganesh, Akebe Luther King Abia, Chioma Blaise Chikere

Subject: Environmental And Earth Sciences, Environmental Science Keywords: quorum sensing; microbial communication; biofilm formation; biogeochemical cycling; biotechnological applications; marine environment; deep‐sea environment

Online: 13 July 2023 (05:26:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202306.0830.v1

Wireless Device with Energy Management for Closed-Loop Deep Brain Stimulation (CLDBS)

Tiago Matheus Nordi, Gabriel Augusto Ginja, Rodrigo Gounella, Erich Talanoni Fonoff, Eduardo Colombari, Melkzedekue M. Alcântara Moreira, Jose A. Afonso, Vitor Monteiro, Joao L. Afonso, João Paulo Carmo

Subject: Engineering, Electrical And Electronic Engineering Keywords: closed-loop deep brain stimulation (CLDBS); neurostimulatio; implantable devices; internet of things (IoT); energy management

Online: 12 June 2023 (11:11:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.1245.v1

The Impact of Irrationals on the Range of Arctan Activation Function for Deep Learning Models

Talya Tümer-Sivri, Nergis Pervan-Akman, Ali Berkol

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep neural networks; Activation functions; Multiclass classification; Time-Series prediction; Reuters data; Energy trade value

Online: 17 May 2023 (11:11:21 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202206.0230.v1

How the First Year of the COVID-19 Pandemic Impacted Patients’ Care in the Vascular Surgery Divisions of the Southern Regions of the Italian Peninsula

Eugenio Martelli, Giovanni Sotgiu, Laura Saderi, Massimo Federici, Giuseppe Sangiorgi, Matilde Zamboni, Allegra Rosa Martelli, Giancarlo Accarino, Giuseppe Bianco, Francesco Bonanno, Umberto Marcello Bracale, Enrico Cappello, Giovanni Cioffi, Giovanni Colacchio, Adolfo Crinisio, Salvatore De Vivo, Carlo Patrizio Dionisi, Loris Flora, Giovanni Impedovo, Francesco Intrieri, Luca Iorio, Gabriele Maritati, Piero Modugno, Mario Monaco, Giuseppe Natalicchio, Vincenzo Palazzo, Fernando Petrosino, Francesco Pompeo, Raffaele Pulli, Davide Razzano, Maurizio Ruggero Ruggieri, Carlo Ruotolo, Paolo Sangiuolo, Gennaro Vigliotti, Pietro Volpe, Antonella Biello, Pietro Boggia, Michelangelo Boschetti, Enrico Maria Centritto, Flavia Condò, Lucia Cucciolillo, Amodio Salvatore D'Amodio, Mario De Laurentis, Claudio Desantis, Daniela Di Lella, Giovanni Di Nardo, Angelo Disabato, Ilaria Ficarelli, Angelo Gasparre, Antonio Nicola Giordano, Alessandro Luongo, Mafalda Massara, Vincenzo Molinari, Andrea Padricelli, Marco Panagrosso, Anna Petrone, Serena Pisanello, Roberto Prunella, Michele Tedesco, Alberto Maria Settembrini

Subject: Medicine And Pharmacology, Hematology Keywords: COVID-19; carotid stenosis; abdominal aortic aneurysm; chronic limb-threatening ischemia; amputation; deep venous thrombosis

Online: 16 June 2022 (04:40:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202206.0166.v1

The Long-Term Efficacy, Prognostic Factors, Safety, and Hospitalization Costs following Denervation and Myotomy of the Affected Muscles and Deep Brain Stimulation in 94 Patients with Spasmodic Torticollis

Zhiqiang Cui, Tong Chen, Jian Wang, Zhiqi Mao, Longsheng Pan, Zhipei Ling, Jianning Zhang, Xuemei Li

Subject: Medicine And Pharmacology, Neuroscience And Neurology Keywords: spasmodic torticollis (ST); deep brain stimulation (DBS); denervation and myotomy (DAM); hospitalization cost; prognostic factors

Online: 13 June 2022 (04:12:34 CEST)

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Surgical methods for treating spasmodic torticollis include denervation and myotomy (DAM) of the affected muscles, and deep brain stimulation (DBS). This study reports on the long-term efficacy, prognostic factors, safety, and hospitalization costs following these two procedures. We collected data from 94 patients with spasmodic torticollis, of whom 41 and 53 were treated with DAM and DBS, respectively, from June 2008 to December 2020 at the Chinese People’s Liberation Army General Hospital. We used the Tsui scale and the global outcome score of the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) to evaluate the preoperative and postoperative clinical condition in all patients. We also determined the cost of hospitalization, prognostic factors, and serious adverse events following the two surgical procedures. The mean follow-up time was 68.83 months (range = 13–116). Both resection surgery and DBS showed good results in terms of Tsui (Z = -5.103, P = 0.000; Z = -6.210, P = 0.000) and TWSTRS scores (t = 8.762, P = 0.000; Z = -6.308, P = 0.000). Compared with the DAM group, the preoperative (47.71, range 24–67.25) and postoperative (18.57, range 0–53) TWSTRS scores in the DBS group were significantly higher (Z = -3.161, P = 0.002). We found no correlation between prognostic factors and the patient age, gender, or disease duration for either surgical procedure. However, prognostic factors were related to the length of the postoperative follow-up period in the DBS surgery group (Z = -2.068, P = 0.039; Z = -3.287, P = 0.001). The mean hospitalization cost in the DBS group was 6.85 times that found in the resection group (Z = -8.284, P = 0.000). The total complication rate was 4.26%. We found, both resection surgery and DBS, showed good results in the patients with spasmodic torticollis. Although DBS had a greater impact on the quality of life and the degree of pain relief, it was more expensive. Prognostic factors were related to the length of the postoperative follow-up period in patients who underwent DBS surgery.

Preprint ARTICLE | doi:10.20944/preprints202204.0279.v1

Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments

Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: object detection; challenging environments; low-light; image enhancement; complex environments; deep neural networks; computer vision

Online: 28 April 2022 (09:42:37 CEST)

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Working Paper ARTICLE

Sensor-fusion for Smartphone Location Tracking using Hybrid Multimodal Deep Neural Networks

Xijia Wei, Zhiqiang Wei, Valentin Radu

Subject: Computer Science And Mathematics, Computer Science Keywords: Indoor Localization; Sensor Fusion; Multimodal Deep Neural Network; Multimodal Sensing; WiFi Fingerprinting; Pedestrian Dead Reckoning

Online: 13 October 2021 (12:14:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202106.0294.v1

Combining Business Model Innovation and Model-based Analysis to Tackle the Deep Uncertainty of Societal Transitions – A Case Study on Industrial Electrification and Power Grid Management

Juliana Zapata Riveros, Matthias Speich, Mirjam West, Silvia Ulli-Beer

Subject: Business, Economics And Management, Accounting And Taxation Keywords: Prosumer concepts; technology change; business strategies; system dynamics; decentralization; photovoltaic; deep uncertainty; low-carbon transitions

Online: 10 June 2021 (13:26:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202103.0165.v1

Combining Business Model Innovation and Model-Based Analysis to Tackle the Deep Uncertainty of Societal Transitions – a Case Study on Industrial Electrification and Power Grid Management

Juliana Zapata Riveros, Matthias Speich, Mirjam West, Silvia Ulli-Beer

Subject: Business, Economics And Management, Accounting And Taxation Keywords: Prosumer concepts; technology change; business strategies; system dynamics; decentralization; photovoltaic; deep uncertainty; low-carbon transitions

Online: 4 March 2021 (14:14:46 CET)

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Preprint ARTICLE | doi:10.20944/preprints202011.0039.v1

Generative Model for Skeletal Human Movements based on conditional DC-GAN applied to pseudo-images

Wang Xi, Guillaume Devineau, Fabien Moutarde, Jie Yang

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: generative model; human movement; conditional Deep Convolutional Generative Adversarial Network; GAN; spatio-temporal pseudo-image

Online: 2 November 2020 (12:55:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints202410.2588.v1

Combining CNNs and Symptom Data for Improved Monkeypox Virus Detection

Emir Oncu

Subject: Biology And Life Sciences, Virology Keywords: monkeypox; convolutional neural network; virus; machine learning; prediction

Online: 31 October 2024 (14:20:09 CET)

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The zoonotic disease known as monkeypox, which is related to smallpox, can be difficult to diagnose since it resembles other illnesses that might cause the same symptoms. In this study, we present an accurate technique for monkeypox detection based on convolutional neural networks (CNNs). Our method improves diagnosis reliability by having the CNN model incorporate a dataset of nine important symptoms. For creating CNN-based monkeypox detection system, the researchers used a collection of high-definition photos and nine symptoms. The model was constructed using TensorFlow's Keras API and Python 3.10, classifying lesions based on a probabilistic score. A likelihood greater than 50% on CNN indicated that the case was really monkeypox. If not, five or more symptoms suggested that monkeypox may be present with metrics including accuracy, precision, recall, and F1-score. The study developed a CNN model to predict monkeypox from lesion images and symptom analysis. The model, built with layers including convolutional, pooling, and fully connected, achieved high accuracy and low loss, with metrics like ROC AUC (0.84), precision (0.78), and recall (0.90). The model effectively differentiates between monkeypox and other conditions. Tables illustrate the model's predictions based on various symptom combinations, highlighting its capability to assess monkeypox risk with high confidence. The study developed a CNN model integrating patient symptoms and skin lesion images for accurate monkeypox diagnosis. With a balanced accuracy of 82%, F1-Score of 84%, and sensitivity and specificity of 80% and 85%, respectively, the model effectively distinguishes monkeypox from similar illnesses. It demonstrated resilience despite limited high-resolution images, emphasizing the potential of CNNs in medical diagnosis. The model's performance can be further improved by incorporating additional data and advanced techniques, enhancing early detection in future outbreaks.

Preprint ARTICLE | doi:10.20944/preprints202410.1607.v1

New Proposal for Image Processing and Machine Learning Techniques to Detect the Pest Aleurothrixus floccosus in Citrus Trees in the Pedregal Arequipa-Perú Region

Manuel Alejandro Valderrama Solis, Javier Valenzuela Nina, German Alberto Echaiz Espinoza, Daniel Domingo Yanyachi Aco Cardenas, Juan Moises Mauricio Villanueva, Andrés Ortiz Salazar, Elmer Rolando Llanos Villarreal

Subject: Biology And Life Sciences, Food Science And Technology Keywords: Machine learning; Aleurothrixus floccosus; clustering; segmentation; bounding box

Online: 21 October 2024 (17:18:25 CEST)

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