Search | Preprints.org

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Supplementary Files| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints202405.0980.v1

Data-Driven AI Model for Turbomachinery Compressor Aerodynamics Enabling Rapid Approximation of 3D Flow Solutions

Marcel Aulich, Georgios Goinis, Christian Voß

Subject: Engineering, Mechanical Engineering Keywords: AI for 3D CFD; turbomachinery; compressor design; aerodynamic optimization; transformer network; deep neural network

Online: 15 May 2024 (10:24:19 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202403.0778.v1

Comparison of the Efficiency of Deep Eutectic and Organic Solvents on the Extraction of Phytochemicals from Cannabis sativa L.

Getrude G Kanyairita, Desmond G Mortley, Melissa Boersma, Willard E Collier

Subject: Chemistry And Materials Science, Applied Chemistry Keywords: phytochemical; deep eutectic solvent; terpenes; cannabinoids; organic solvents; extraction; industrial hemp; Cannabis sativa L.

Online: 13 March 2024 (12:14:13 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202311.1836.v1

From Hemp Waste to Bioactive Nanofiber Composites: Deep Eutectic Solvents and Electrospinning in Upcycling Endeavors

Cláudia Mouro, Ana P. Gomes, Isabel C. Gouveia

Subject: Chemistry And Materials Science, Biomaterials Keywords: Hemp; agro-waste; deep eutectic solvents; electrospinning; gel-based blends; waste valorization; nanocomposite gels

Online: 29 November 2023 (09:47:16 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202305.1182.v1

Low-Noise Amplifier and Neurostimulator in submicron CMOS for Closed-Loop Deep-Brain Stimulation (CLDBS)

Tiago Matheus Nordi, Rodrigo Henrique Gounella, Gabriel Augusto Ginja, Márcio Luis Amorim, Maximiliam Luppe, João Navarro Junior, Erich Talamoni Fonoff, Eduardo Colombari, Joao Paulo Carmo

Subject: Medicine And Pharmacology, Neuroscience And Neurology Keywords: CMOS; Closed Loop Deep-Brain Stimulation (CLDBS); Low Noise Amplifier (LNA); Neurostimulation; Implantable Devices

Online: 17 May 2023 (04:27:59 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202208.0495.v1

Deep Learning Approaches to Automatic Chronic Venous Disease Classification

Marina Barulina, Aschat Sanbaev, Sergey Okunkov, Ivan Ulitin, Ivan Okoneshnikov

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: chronic venous disease; deep leaning; data mining; Resnet50; DeiT; automatic classification; automatic CEAP classification

Online: 29 August 2022 (12:46:56 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202207.0452.v1

Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR images

Khushboo Verma, Satwant Kumar, David Paydarfar

Subject: Medicine And Pharmacology, Neuroscience And Neurology Keywords: Stroke; automatic segmentation; deep neural networks; 3D-UNet; lesion studies; vascular neurology; precision medicine

Online: 29 July 2022 (07:37:34 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202202.0155.v1

Dynamic Alloying of Steels in the Super-Deep Penetration Mode

Yulia Usherenko, Viktors Mironovs, Sergey Usherenko, Vjaceslavs Lapkovskis, Andrei Shishkin

Subject: Chemistry And Materials Science, Metals, Alloys And Metallurgy Keywords: powder metallurgy; dynamic alloying; heat treatment; super-deep penetration; alloys; composite materials; mechanical properties

Online: 11 February 2022 (03:19:55 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202110.0319.v1

Deep Learning-based Automatic Monitoring of Pigs Physico-temporal Activities at Different Greenhouse Gas Concentrations

Anil Bhujel, Elanchezhian Arulmozhi, Byeong Eun Moon, Hyeon Tae Kim

Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: YOLOv4; Faster RCNN; Deep-SORT; pig posture detection; object tracking; greenhouse gas; animal welfare

Online: 21 October 2021 (23:06:30 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202108.0284.v1

Climate Change, Uncertainty and Policy

Jeroen Hopster

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Climate change; Scientific uncertainty; Moral uncertainty; Deep uncertainty; Risk; IPCC; Storylines; Probability; Expected utility

Online: 13 August 2021 (08:26:29 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202105.0429.v1

Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks

Chayan Mondal, Md. Kamrul Hasan, Md. Tasnim Jawad, Aishwariya Dutta, Md. Rabiul Islam, Md. Abdul Awal, Mohiuddin Ahmad, Salem A. Alyami, Mohammad Ali Moni

Subject: Medicine And Pharmacology, Other Keywords: Acute lymphoblastic leukemia; Deep convolutional neural networks; Ensemble image classifiers; C-NMC-2019 dataset.

Online: 19 May 2021 (07:42:23 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202103.0302.v2

Deep Learning for Wave Energy Converter Modeling Using Long Short Term Memory

Seyed Milad Mousavi, Majid Ghasemi, Mahsa Dehghan Manshadi, Amir Mosavi

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Searaser; Flow-3D; Prediction; Long short term memory; deep neural network; Root mean error.

Online: 13 April 2021 (09:51:25 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202007.0571.v1

Migration of Natural Hydrogen from Deep-seated Sources in the São Francisco Basin, Brazil

Frederic Victor Donze, Laurent Truche, Parisa Sheraki Namin, Nicolas Lefeuvre, Elena Bazarkina

Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: native hydrogen; deep-seated source of natural hydrogen; H2 seepage; radiolysis; serpentinization; draining faults

Online: 24 July 2020 (05:23:26 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202001.0015.v1

Evaluation of Recommendation System for Sustainable E-Commerce: Accuracy, Diversity and Customer Satisfaction

Qinglong Li, Ilyoung Choi, Jaekyeong Kim

Subject: Business, Economics And Management, Business And Management Keywords: expectancy disconfirmation theory; customer satisfaction; e-commerce personalized service; recommendation system; deep neural network

Online: 2 January 2020 (05:34:39 CET)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints201805.0463.v1

Is There a Right to Get a Right Diagnosis Whenever Possible? Ethical and Social Issues in Dealing with Undiagnosed Rare Diseases

Sabina Gainotti, Deborah Mascalzoni, Virginie Bros Facer, Carlo Petrini, Giovanna Floridia, Marco Roos, Marco Salvatore, Domenica Taruscio

Subject: Medicine And Pharmacology, Other Keywords: undiagnosed rare diseases; diagnostic odyssey; NGS; deep phenotyping; genomic matchmaking; secondary findings; patient involvement

Online: 31 May 2018 (09:35:32 CEST)

Show abstract| Download PDF| Share

The time required to reach a correct diagnosis is one of the most important problems for rare disease (RD) patients. Diagnostic delay can be intolerably long, to the point that it is usually described as a “diagnostic odyssey” and, sometimes, a diagnosis might never occur. The International Rare Disease Research Consortium proposed, as ultimate goal for 2017-2027, to enable all people with a suspected RD to be diagnosed within one year if the disorder is known, and to enter a globally coordinated diagnostic and research pipeline for the unsolved cases. In-depth analysis of the genotype through next generation sequencing, together with a standardized in-depth phenotype description and sophisticated high-throughput approaches, have been applied as diagnostic tools to increase the chance of a timely and accurate diagnosis. This approach is very fruitful as, according to the Orphanet database, from 2010 to March 2017 more than 600 new RDs have been described and about 3600 genes linked to RDs have been identified. However, combination of -omics and phenotype data and international sharing of this information raise ethical concerns. Values to be assessed include not only patient autonomy but also family implications, beneficence, non-maleficence, justice, solidarity and reciprocity, which must be respected and promoted and, at the same time, balanced among each other. In this work we suggest that, to maximise patients involvement in the search for a diagnosis and identification of new causative genes, undiagnosed patients should have the possibility to: 1) actively participate in the description of their phenotype; 2) choose the level of visibility of their profile in matchmaking databases; 3) express their preferences regarding return of new findings, in particular which level of Variant of Unknown Significance (VUS) significance should be considered relevant to them. The quality of the relation between individual patients and physicians, and between the patient community and the scientific community is critically important for making the best use of available data and combining efforts in the search for matches with similar cases worldwide that will help to solve unsolved cases. The contribution of patients to collecting and coding data comprehensively is critical for efficient use of data downstream of data collection.

Preprint ARTICLE | doi:10.20944/preprints201804.0207.v1

Failure Characteristics Induced by Unloading Disturbance and Corresponding Mechanical Mechanism of the Coal Seam Floor in Deep Mining

Li Jiazhuo, Xie Guangxiang, Wang Lei

Subject: Engineering, Civil Engineering Keywords: deep mining; coal seam floor; unloading disturbance; space–time difference; stress shell; mechanical mechanism

Online: 16 April 2018 (11:29:05 CEST)

Show abstract| Download PDF| Share

Failure characteristics induced by unloading disturbance and the corresponding mechanical mechanism of the coal seam floor are important theoretical bases for water-bursting prevention from the floor of the coal seam and rock burst alarm in deep mining. However, the existing two-dimensional ground-pressure-control theory based on shallow mining cannot sufficiently guide deep-mining practices. In this study, the redistribution of mining-induced stress field in rocks surrounding the longwall face and mechanical behaviors of strata in deep mining are investigated through a combination of numerical simulation, physical simulation, and field measurement. Results demonstrate that mining-induced stress fields in the floor of the longwall face differ in space and time. Vertical stress unloading from top to bottom of the floor and horizontal stress unloading are relatively low. A concentration zone of high horizontal stress exists at stope boundaries. The critical yield load of rock stratum in the floor is determined through thin plate yield theory. Under the combined effect of concentrated high horizontal and vertical resilience stresses, strata in the floor fracture from seam to seam if the load increases to the minimum critical buckling value. Fractured strata slide along the fracture surface, which leads to floor heave. The stope floor shows evident time-delay progressive failure characteristics. The stress shell in the stope floor in deep mining is found to be a sensitive mechanical parameter that produces three-dimensional ground-pressure behavior in the floor. This ground-pressure behavior in the stope floor is controlled by the existence of the corresponding stress shell and effects induced by its space–time evolution. This study provides theoretical basis for the dynamic control of a hazard-inducing environment in engineering and minimizing or altering disaster-occurrence conditions during the construction engineering of the coal seam floor.

Preprint ARTICLE | doi:10.20944/preprints201704.0061.v1

Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images

Mi Zhang, Xiangyun Hu, Like Zhao, Ye Lv, Min Luo, Shiyan Pang

Subject: Engineering, Electrical And Electronic Engineering Keywords: semantic segmentation; deep convolutional neural networks; manifold ranking; single stream optimization; high resolution image

Online: 11 April 2017 (10:27:10 CEST)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Supplementary Files| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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/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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Share

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)

Show abstract| Download PDF| Supplementary Files| Share

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)

Show abstract| Download PDF| Supplementary Files| Share

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)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202409.1262.v1

Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model

Le-Yi Wang, Yiming Wang, Xiaoyu Hu, Hui Wang, Ruilin Zhou

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: convection parameterization; diffusion model; generative model; machine learning

Online: 16 September 2024 (16:48:27 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints202409.0988.v1

Machine Learning Model Trained with Finite Element Modeling Can Predict the Risk of Osteoarthritis: Data from the Osteoarthritis Initiative

Mika E. Mononen, Mimmi K. Liukkonen, Mikael J. Turunen

Subject: Physical Sciences, Applied Physics Keywords: knee; cartilage; finite element analysis; machine learning; osteoarthritis

Online: 12 September 2024 (12:26:21 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints202409.0875.v1

Predicting Industrial Property Prices with Explainable Artificial Intelligence

Tris Kee, Winky Ho

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Industrial property prices; Machine learning; Explainable Artificial Intelligence

Online: 11 September 2024 (08:48:15 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202409.0847.v1

TMining: Detecting Fake News with Machine Learning and Explainable AI

Maksym Lupei, Myroslav Shliakhta

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Text Mining; Fake News; Model Explanation; Machine Learning

Online: 11 September 2024 (07:32:27 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202409.0810.v1

Assessing Stream Flows and Dynamics of the Athabasca River Basin Using Machine Learning

Sue Kamal, Junye Wang, M. Ali Akber Dewan

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: river flow model; machine learning; modeling; and simulation

Online: 10 September 2024 (13:35:34 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202409.0632.v1

Impeding Digital Transformation by Establishing a Continuous Process of Competence Reconfiguration: Developing the New Construct and Measurements for Sustained Learning

Sandra Starke, Iveta Ludviga

Subject: Social Sciences, Behavior Sciences Keywords: Digital transformation; digital competences; sustained learning; knowledge management

Online: 9 September 2024 (09:17:27 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202409.0544.v1

Fair Federated Learning

Amirreza Talebi

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Fairness; Federated learning; Stackelberg games; Incentive mechanism design

Online: 6 September 2024 (12:28:45 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202409.0366.v1

A Machine Learning Pipeline for Predicting Wine Quality from Viticulture Data: Development and Implementation

Don Kulasiri, Sarawoot Somin, Samantha KumaraPathirannahalage

Subject: Biology And Life Sciences, Food Science And Technology Keywords: machine learning; Wine Quality; Viticulture; Modeling; Pipeline; software

Online: 4 September 2024 (13:12:26 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints202408.2111.v1

Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning

Elijah Kiplimo, Stuart N. Riddick, Mercy Mbua, Aashish Upreti, Abhinav Anand, Daniel J. Zimmerle

Subject: Environmental And Earth Sciences, Pollution Keywords: methane; quantification; metal-oxide; sensor; calibration; machine learning

Online: 29 August 2024 (11:59:16 CEST)

Show abstract| Download PDF| Share

Quantifying methane emissions is essential for meeting near-term climate goals and typically done using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas productions sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm with maximum values of 105.9 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios show overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher price trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deploying, and allow for timely and accurate tracking of methane emissions.

Preprint ARTICLE | doi:10.20944/preprints202408.2125.v1

Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems

Fabio Liberti, Davide Berardi, Barbara Martini

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Privacy; Federated Machine Learning; Edge Computing; Ubiquitous Intelligence

Online: 29 August 2024 (09:18:48 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202406.0184.v2

The Rise and Transfer of the River Chief System: A Review of Chinese Water Governance and Its Potential to Transfer to the Global Community

David Dolowitz

Subject: Social Sciences, Government Keywords: River Chief; Water Management; Policy Transfer; Learning China

Online: 28 August 2024 (12:34:12 CEST)

Show abstract| Download PDF| Share

Preprint COMMUNICATION | doi:10.20944/preprints202408.2008.v1

Behavioral Coding of Captive African Elephants (Loxodonta africana): Utilizing DeepLabCut and Create ML for nocturnal activity tracking.

Silje Marquardsen Lund, Jonas Nielsen, Frej Gammelgård, Maria Gytkjær Nielsen, Trine Hammer Jensen, Cino Pertoldi

Subject: Biology And Life Sciences, Behavioral Sciences Keywords: machine learning; nocturnal behavior; computer vision; captive elephants

Online: 28 August 2024 (09:13:23 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202408.1937.v1

Soil Salinity Inversion Based on a Stacking Integrated Learning Algorithm

Haili Dong, Fei Tian

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: soil salinity; machine learning; stacking; remote sensing inversion

Online: 27 August 2024 (11:48:58 CEST)

Show abstract| Download PDF| Share

Soil salinization is an essential risk factor for agricultural development as well as for food security, and how to obtain regional soil salinity information more reliably remains a priority problem to be solved. To improve the accuracy of the inversions for soil salinity, a new inversion model for soil salinity based on stacking integrated algorithm for learning was submitted for this work which took the prediction results of several basic models as new features and then trained a secondary model to fuse the prediction results of basic models. We compared and analyzed it against four machine learning regression models, namely, random forest (RF), back propagation neural network, support vector regression, and convolutional neural network. Findings indicated the stacking integrated learning regression model fitted better and had good stability, on the test set, the stacking integrated learning regression model showed a relative increase of 8.16% in R2, a relative decrease of 13.95% in RMSE, and a relative increase of 6.47% in RPD when compared to the RF model, which was the single most effective machine learning regression model, and the stacking model was able to achieve soil salinity inversion more accurately. The soil salinity in the oasis areas of the Manas River Basin tended to decrease from north to south in 2016 to 2020 from a spatial point of view, and it was reduced in April from a temporal point of view. The percentage of pixels with a high soil salinity content of 2.75–2.8 g/kg in the study area had decreased by 19.64% in April 2020 compared to April 2016. The innovatively constructed stacking integrated learning regression model improved the accuracy of soil salinity estimation on the basis of the superior results obtained in the training of the single optimal machine learning regression model. As a consequence, this model can provide technological backup for a fast monitoring and inversion of soil salinity as well as prevention and containment of salinization.

Preprint REVIEW | doi:10.20944/preprints202408.1736.v1

The Future of Footwear: A survey on Smart Shoe

D.K. Chaturvedi, Nisha Verma

Subject: Engineering, Electrical And Electronic Engineering Keywords: Artificial Intelligence; Machine Learning; Smart shoes; sensors; IoTs

Online: 23 August 2024 (13:40:04 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202408.1199.v1

Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients

Xiaolei Lu, Chenye Qiao, Hujun Wang, Yingqi Li, Congxiao Wang, Yingpeng Wang, Shuyan Qie

Subject: Medicine And Pharmacology, Clinical Medicine Keywords: Isokinetic Dynamometry; Gait Deviation Index; Machine Learning; Stroke

Online: 19 August 2024 (17:56:16 CEST)

Show abstract| Download PDF| Share

Background: Three-dimensional gait analysis plays a crucial role in the rehabilitation assessment of post-stroke hemiplegic patients. However, the data generated from such analysis is often complex and difficult to interpret in clinical practice, requiring extensive time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. Objective: This study aims to investigate the use of isokinetic muscle strength data obtained from the Biodex system, combined with machine learning techniques, to predict the GDI in hemiplegic patients. Methods: A cohort of 150 post-stroke hemiplegic patients was evaluated. The peak torque, peak torque/body weight, maximum work of repeated actions, coefficient of variation, average power, total work, acceleration time, deceleration time, range of motion, and average peak torque for both flexor and extensor muscles on the affected side were measured at three angular velocities (60°/s, 90°/s, and 120°/s) using the Biodex System 4 Pro. GDI was calculated using data from a Vicon three-dimensional motion capture system. The study employed four machine learning models—Lasso regression, random forest (RF), support vector regression (SVR), and BP neural networks—to model and validate the data. Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Results: The RF model outperformed others in predicting GDI, with an MSE of 16.18, an R² of 0.89, and an MAE of 2.99. In contrast, the Lasso regression model yielded an MSE of 22.29, an R² of 0.85, and an MAE of 3.71. The SVR model had an MSE of 31.58, an R² of 0.82, and an MAE of 7.68, while the BP neural network exhibited the poorest performance with an MSE of 50.38, an R² of 0.79, and an MAE of 9.59. SHAP analysis identified the maximum work of repeated actions of the extensor muscles at 60°/s and 120°/s as the most critical features for predicting GDI, underscoring the importance of muscle strength metrics at varying speeds in rehabilitation assessments. Conclusion: This study highlights the potential of machine learning techniques in analyzing complex clinical data. The developed GDI prediction model, based on isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application.

Preprint ARTICLE | doi:10.20944/preprints202408.0483.v1

Deep Learning for Generating Time-of-Flight Camera Artifacts

Tobias Müller, Tobias Schmähling, Stefan Elser, Jörg Eberhardt

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Time-of-flight; learning-based simulation; domain transfer

Online: 7 August 2024 (17:57:53 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202408.0160.v1

Post-Pandemic Imaginations of Remote Work Futures in Africa: Learning from an Environment, Health, and Safety Perspective

Bob Offei Manteaw, Yaw Agyeman Boafo, Bernard Ayittah, Cephas Adorliyah

Subject: Public Health And Healthcare, Public, Environmental And Occupational Health Keywords: Remote work; Environment; health and safety; pandemic; learning

Online: 2 August 2024 (11:51:35 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202407.2620.v1

Using an Ensemble of Machine Learning Algorithms to Predict Economic Recession

Leakey Omolo, Nguyet Nguyen

Subject: Business, Economics And Management, Econometrics And Statistics Keywords: Economic crisis; machine learning; ensemble model; recession probability

Online: 31 July 2024 (18:56:20 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202407.2441.v1

Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach

Eduardo Maekawa, Mariana Martins, Carina Akemi Nakamura, Ricardo Araya, Tim J Peters, Pepijn Van De Ven, Marcia Scazufca

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Bayesian network; machine learning; anxiety; older population; clinician

Online: 30 July 2024 (13:42:56 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202407.2247.v1

Enhancing Greenhouse Efficiency: Integrating IoT and Reinforcement Learning for Optimised Climate Control

Manuel Platero-Horcajadas, Sofia Pardo-Pina, José-María Cámara-Zapata, Jose A Brenes, Francisco-Javier Ferrández-Pastor

Subject: Computer Science And Mathematics, Other Keywords: smart agriculture; reinforcement learning; IoT; greenhouse energy management

Online: 30 July 2024 (09:21:59 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202407.2013.v1

Application of a Novel Groundwater Storage Analysis Tool to Assess Drought Impacts in a Groundwater-Driven Basin in the Klamath Watershed, Oregon, USA

Daniel Shepard, Norman L Jones, Gustavious P. Williams

Subject: Engineering, Civil Engineering Keywords: drought; climate change; groundwater; remote sensing; machine learning

Online: 25 July 2024 (07:35:19 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202407.1894.v1

An Examination of the Physical Proximity of Learning in Shaping Students’ Mastery of Technology-Enhanced Mathematics and Science Concepts

ONYANCHA Valentine MORAGWA

Subject: Arts And Humanities, Humanities Keywords: physical proximity; virtual learning; technology; design; sampling; questionnaire

Online: 24 July 2024 (14:44:00 CEST)

Show abstract| Download PDF| Supplementary Files| Share

The fast-paced advancements in the technological space and its application have influenced greatly its usage in teaching especially in science and mathematics subjects. The overall objective of this study was to examine the physical proximity of learning in shaping students’ mastery of technology-enhanced mathematics and science concepts. Consequently, the study investigates how technology can promote teaching presence toward the mastery of mathematics and science concepts and how the latter can be grasped by the learners using physical proximity strategies. This research study used a mixed-method research design and was conducted in Kayonza District (Eastern Province) and Gasabo District (Kigali), Rwanda. The participants were postgraduate students at the University of Rwanda, College of Education undertaking a master degree in chemistry education, mathematics education, physics education and biology education under the Africa Centre of Excellence for Innovative Teaching and Learning Mathematics and Science. A sample size of 88 students was selected from both Remera and Rukara campuses. Participants were in a range of 21-40 years old. The study used both interviews and questionnaires to collect empirical data. Qualitative data was analyzed using thematic analysis while quantitative data was analyzed using R software. The study reveals that a virtual lab is a vital tool in promoting teaching presence regarding the mastery of mathematics and science concepts. The use of virtual labs seems to foster instructional design and organization from teachers. Moreover, virtual labs seem to facilitate meaningful discourses, interactions between learners and between the learners and the teacher. The findings show also that virtual visualizing seems also to play a role in direct instruction where the teacher offers guidance and direction to learners on various subjects or issues of contention that may need the clarity. Thus 87.6% of the respondents affirm that technology integration in learning is efficient in bridging the gap of physical proximity among learners and teachers through various virtual instruments and platforms. The study also demonstrates that various forms of technology that can be used to bring mathematics and science concepts to the learners’ physical proximity including WhatsApp, Microsoft Teams, Google Meet, Zoom and Webex. The study therefore recommends that visualization should be integrated in the curricula, training of teachers and students to maximize physical proximity components and sharing of best practices among teachers and students.

Preprint ARTICLE | doi:10.20944/preprints202407.1211.v1

Leveraging Big Data and AI for Personalized Learning Opportunities, Challenges, and Ethical Considerations

Budee U Zaman

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: AI; Education systems; learning experiences; ethical and effective

Online: 16 July 2024 (07:33:36 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202407.0874.v1

Image Databases with Features Augmented with Singular Point Shapes for Enhanced Machine Learning

Nikolay Metodiev Sirakov, Adam Bowden

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Image; Vector Field; Features; Augmentatiion; Classification; Machine Learning

Online: 11 July 2024 (05:14:01 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202407.0535.v1

Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation

Manoranjan Kumar, Yash Agrawal, Sirisha Adamala, Pushpanjali ., Subbarao A.V.M, V.K. Singh, Ankur Srivastava

Subject: Engineering, Marine Engineering Keywords: Ensembled machine learning, Reference evapotranspiration, Decision tree, XGBoost

Online: 5 July 2024 (12:09:35 CEST)

Show abstract| Download PDF| Share

Preprint REVIEW | doi:10.20944/preprints202407.0263.v1

A Review of Passenger Counting in Public Transport Concepts based on Image Processing and Machine Learning

Aleksander Radovan, Goran Đambić, Branko Mihaljević

Subject: Engineering, Electrical And Electronic Engineering Keywords: public transport; passenger counting; image processing; machine learning

Online: 3 July 2024 (10:45:15 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202406.2011.v1

Comparative Analysis Of The Responses Of Various Bromodomain Inhibitors In Cancer Cell Lines Utilizing Various Machine Learning Models

Gizem Tutkun, Muharrem Okan Cakir, Ugur Bilge

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; prediction methods; cancer; ic50; r programming

Online: 29 June 2024 (05:59:44 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202406.1933.v1

Machine Learning Insights into Digital Payment Behaviors and Fraud Prediction

Xu zhang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Digital payments; machine learning models; forecasting; financial services

Online: 27 June 2024 (11:35:26 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202406.0478.v2

Can a Transparent Machine Learning Algorithm Predict Better than Its Black-Box Counterparts? A Benchmarking Study using 110 Diverse Datasets

Ryan A. Peterson, Max McGrath, Joseph E. Cavanaugh

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

Online: 27 June 2024 (11:07:41 CEST)

Show abstract| Download PDF| Supplementary Files| Share

We developed a novel machine learning (ML) algorithm with the goal of producing transparent models (i.e. understandable-by-humans) while also flexibly accounting for nonlinearity and interactions. Our method is based on ranked sparsity, and allows for flexibility and user-control in varying the shade of the opacity of black-box machine learning methods. The main tenet of ranked sparsity is that an algorithm should be more skeptical of higher-order polynomials and interactions \textit{a~priori} compared to main effects, and hence the inclusion of these more complex terms should require a higher level of evidence. In this work, we put our new ranked sparsity algorithm (as implemented in the open-source R package, `sparseR`) to the test in a predictive model "bakeoff" (i.e. a benchmarking study of ML algorithms applied "out-of-the-box," that is, with no special tuning). Algorithms were trained on a diverse set of simulated and real-world data sets from the Penn Machine Learning Benchmarks database, addressing both regression and binary classification problems. We evaluate the extent to which our human-centered algorithm can attain predictive accuracy that rivals popular black-box approaches such as neural networks, random forests, and support vector machines, while also producing more interpretable models. Using out-of-bag error as a meta-outcome, we describe the properties of data sets in which human-centered approaches can perform as well as or better than black-box approaches. We find that interpretable approaches predicted optimally or within 5% of the optimal method in most real-world data sets. We provide a more in-depth comparison of the performances of random forests to interpretable methods for several case studies, including exemplars in which algorithms performed similarly, and several cases when interpretable methods underperformed. This work provides a strong rationale for including human-centered transparent algorithms such as ours in predictive modeling applications.

Preprint ARTICLE | doi:10.20944/preprints202406.1153.v1

Distilling Knowledge from Multiple Foundation Models for Zero-shot Image classification

Siqi Yin, Lifan Jiang

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Zero-shot learning; Multi-method integration; Image classification

Online: 17 June 2024 (12:11:58 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202406.1093.v1

The ESG Determinants of Mental Health Index Across Italian Regions: A Machine Learning Approach

Emanuela Resta, Giancarlo Logroscino, Silvio Tafuri, Preethymol Peter, Chiara Noviello, Alberto Costantiello, Angelo Leogrande

Subject: Business, Economics And Management, Econometrics And Statistics Keywords: Mental Health Index; Machine Learning; ESG; Regional Inequalities

Online: 17 June 2024 (08:35:30 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202406.0700.v2

Quantifying the Cross-Sectoral Intersecting Discrepancies Within Multiple Groups Using Latent Class Analysis Towards Fairness

Yingfang Yuan, Kefan Chen, Mehdi Rizvi, Lynne Baillie, Wei Pang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Machine Learning; AI for Social Science; AI bias

Online: 12 June 2024 (11:18:25 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202406.0355.v1

Dictionary Learning with the K-SVDAlgorithm for Recovery of Highly Textured Images

Alexander Köhler, Michael Breuß, Shima Shabani

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: image recovery; dictionary learning; sparse representation; textured images

Online: 10 June 2024 (09:18:33 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202406.0549.v1

Bridging Artificial Intelligence and Neurological Signals (BRAINS): A Novel Framework for EEG-Based Image Generation

Mateo Sokač, Leo Mršić, Mislav Balković, Maja Brkljačić

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; EEG; image generation; EEG to image

Online: 10 June 2024 (07:59:10 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints202406.0330.v1

Phenolic Acid – β-cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine-Learning-based QSAR Study

Kweeni Iduoku, Marvellous Ngongang, Jayani Kulathunga, Amirreza Deghighi, Gerardo Casanola-Martin, Senay Simsek, Bakhtiyor Rasulev

Subject: Chemistry And Materials Science, Food Chemistry Keywords: flavors; machine learning; β -cyclodextrin; binding affinity; QSAR

Online: 6 June 2024 (07:49:28 CEST)

Show abstract| Download PDF| Supplementary Files| Share

Preprint ARTICLE | doi:10.20944/preprints202406.0127.v1

A Fair Contribution Measurement Method for Federated Learning

Peng Guo, Yanqing Yang, Wei Guo, Yanping Shen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Federated Learning; Shapley Value; contribution measurement; Non-IID

Online: 4 June 2024 (08:51:44 CEST)

Show abstract| Download PDF| Share

Preprint ARTICLE | doi:10.20944/preprints202405.1615.v1

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Aymen Zayed, Nidhameddine Belhadj, Khaled Ben Khalifa, Mohamed Hedi Bedoui, Carlos Alberto Valderrama Sakuyama

Subject: Public Health And Healthcare, Public Health And Health Services Keywords: EEG signals; feature selection; machine learning; drowsiness detection

Online: 24 May 2024 (08:32:09 CEST)

Show abstract| Download PDF| Share

Search Results

3673 articles found