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Preprint REVIEW | doi:10.20944/preprints202308.1777.v1

A Comprehensive Analysis of the Latest Automatic Applications of Artificial Intelligence in the Diagnosis of Cardiovascular Diseases

Carlos Polanco, Rosalinda Posadas-Sánchez, Gilberto Vargas-Alarcón

Subject: Public Health And Healthcare, Health Policy And Services Keywords: Artificial intelligence; cardiology; deep learning; diagnosis; machine learning; neural networks

Online: 28 August 2023 (10:28:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202311.2003.v1

Deep machine learning of the MobileNet, Efficient and Inception models

Monika Rybczak, Krystian Kozakiewicz

Subject: Engineering, Industrial And Manufacturing Engineering Keywords: artificial intelligence; deep learning; CNN; Mobilenet; EfficientNetB0; Inception

Online: 30 November 2023 (15:39:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints202310.1146.v1

A Proposal for a Consolidated Structural Model of the CagY Protein of Helicobacter pylori

Mario Angel López-Luis, Eva Elda Soriano-Pérez, José Carlos Parada-Fabián, Rogelio Maldonado-Rodríguez, Javier Torres, Alfonso Méndez-Tenorio

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: bioinformatics; structural biology; CagY protein; T4SS; deep learning

Online: 18 October 2023 (09:56:34 CEST)

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Preprint REVIEW | doi:10.20944/preprints202305.1299.v1

Could Deep-sea Fisheries Contribute to the Food Security of Our Planet? Pros and Cons

Elkhan Richard Sadik-Zada, Mattia Ferrari, Alicia Gonzalez

Subject: Social Sciences, Political Science Keywords: undernourishment; food security; deep-sea fisheries; uncertainties

Online: 18 May 2023 (08:21:58 CEST)

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Preprint SHORT NOTE | doi:10.20944/preprints202004.0132.v1

Zeiform Mouthbrooding in the Deepsea – Dispelling a Myth

Ralf Britz, G. David Johnson, Kevin Conway

Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: Mouthbrooding; deep water fish; Zeiforms; Parazen

Online: 8 April 2020 (12:57:36 CEST)

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Preprint REVIEW | doi:10.20944/preprints202302.0405.v1

The Promise of Explainable Deep Learning for Omics Data Analysis: Adding New Discovery Tools to AI

Mariangela Santorsola, Francesco Lescai

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Explainability; Deep Learning; Artificial Intelligence; Genomics; Transcriptomics

Online: 23 February 2023 (09:32:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints202310.1190.v1

Causal Fuzzy Deep Learning Algorithm for the Differentiation of Gliosarcoma From Glioblastoma

Usef Faghihi, Cyrus Kalantarpour, Ismaila Baldé, Amir Saki

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Cancer; Gliosarcoma; fuzzy logic; deep Learning algorithms; GPT

Online: 19 October 2023 (03:54:57 CEST)

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Working Paper ARTICLE

On the Theory of Deep Learning: A Theoretical Physics Perspective (Part I)

Alejandro Chinea Manrique de Lara

Subject: Physical Sciences, Thermodynamics Keywords: Deep Learning; Thermodynamics; Learning and Generalization; Diophantine equations

Online: 13 October 2020 (14:32:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201810.0218.v1

Deep Learning: A Review

Rocio Vargas, Amir Mosavi, Ramon Ruiz

Subject: Computer Science And Mathematics, Information Systems Keywords: deep learning; machine learning; applied deep learning

Online: 10 October 2018 (11:37:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202301.0406.v1

Estimating the Sustainability of AI Models Based on Theoretical Models and Experimental Data

Ralf Gitzel, Marie Platenius-Mohr, Andreas Burger

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: AI; Sustainability; Energy efficiency; Deep learning; Neural networks

Online: 23 January 2023 (09:38:57 CET)

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Preprint REVIEW | doi:10.20944/preprints202108.0030.v1

Assessing the Performance of Artificial Intelligence Systems for the Screening of Diabetic Retinopathy: A Systematic Review and Meta-Analysis

Ryan Sadjadi

Subject: Medicine And Pharmacology, Ophthalmology Keywords: Artificial Intelligence; Neural Networks; Deep Learning; Diabetic Retinopathy; Diabetes Mellitus

Online: 2 August 2021 (12:26:52 CEST)

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Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and one of the leading causes of blindness globally. Due to the progressive nature of the disease, earlier detection and timely treatment can lead to substantial reductions in the incidence of irreversible vision-loss. Artificial intelligence (AI) screening systems have offered clinically acceptable and quicker results in detecting diabetic retinopathy from retinal fundus and optical coherence tomography (OCT) images. Thus, this systematic review and meta-analysis of relevant investigations was performed to document the performance of AI screening systems that were applied to fundus and OCT images of patients from diverse geographic locations including North America, Europe, Africa, Asia, and Australia. A systematic literature search on Medline, Global Health, and PubMed was performed and studies published between October 2015 and January 2020 were included. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and AI-based investigations were mandatory for studies inclusion. The abstracts, titles, and full-texts of potentially eligible studies were screened against inclusion and exclusion criteria. Twenty-one studies were included in this systematic review; 18 met inclusion criteria for the meta-analysis. The pooled sensitivity of the evaluated AI screening systems in detecting diabetic retinopathy was 0.93 (95% CI: 0.92-0.94) and the specificity was 0.88 (95% CI: 0.86-0.89). The included studies detailed training and external validation datasets, criteria for diabetic retinopathy case ascertainment, imaging modalities, DR-grading scales, and compared AI results to those of human graders (e.g., ophthalmologists, retinal specialists, trained nurses, and other healthcare providers) as a reference standard. The findings of this study showed that the majority AI screening systems demonstrated clinically acceptable levels of sensitivity and specificity for detecting referable diabetic retinopathy from retinal fundus and OCT photographs. Further improvement depends on the continual development of novel algorithms with large and gradable sets of images for training and validation. If cost-effectiveness ratios can be optimized, AI can become a financially sustainable and clinically effective intervention that can be incorporated into the healthcare systems of low-to-middle income countries (LMICs) and geographically remote locations. Combining screening technologies with treatment interventions such as anti-VEGF therapy, acellular capillary laser treatment, and vitreoretinal surgery can lead to substantial reductions in the incidence of irreversible vision-loss due to proliferative diabetic retinopathy.

Preprint REVIEW | doi:10.20944/preprints202106.0295.v1

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence - A Narrative Review

Janne Cadamuro

Subject: Medicine And Pharmacology, Immunology And Allergy Keywords: machine learning; deep learning; neural network; tricorder; laboratory medicine; extraanalytics

Online: 10 June 2021 (13:30:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202401.0931.v1

Applying YOLO-V8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

Daniel de Amaral Da Silva, Emannuel Diego Gonçalves De Freitas, Haynna Fernandes Abud, Danielo G. Gomes

Subject: Biology And Life Sciences, Food Science And Technology Keywords: Deep learning; Seed quality; X-ray imaging

Online: 11 January 2024 (12:20:53 CET)

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Preprint ARTICLE | doi:10.20944/preprints202209.0070.v1

Density and Velocity Analysis of the Planetary Nebula NGC 6853

Robert Hrubý

Subject: Physical Sciences, Astronomy And Astrophysics Keywords: Nebula; NGC 6853; Stellar; Planetary nebula; Deep space; remnant

Online: 5 September 2022 (14:08:07 CEST)

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Preprint REVIEW | doi:10.20944/preprints202404.0220.v1

From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks

Marcelo Kauffman

Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: deep learning; machine learning; protein design; biotechnology

Online: 2 April 2024 (15:14:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.0199.v1

EvacuAI: An analysis of Escape Routes in Indoor Environments with the Aid of Reinforcement Learning

Anna Carolina Rosa, Mariana Cabral Falqueiro, Rodrigo Bonacin, Fábio Lúcio Lopes De Mendonça, Geraldo Pereira Rocha Filho, Vinícius Pereira Gonçalves

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; deep reinforcement learning; transfer learning; fire; evacuation

Online: 4 July 2023 (10:38:05 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202206.0043.v1

LumbarNet: A Deep Learning Network for the Automated Detection of Lumbar Spondylolisthesis From X-Ray Images

Giam Minh Trinh, Hao-Chiang Shao, Kevin Li-Chun Hsieh, Ching-Yu Lee, Hsiao-Wei Liu, Chen-Wei Lai, Sen-Yi Chou, Pei-I Tsai, Kuan-Jen Chen, Fang-Chieh Chang, Meng-Huang Wu, Tsung-Jen Huang

Subject: Medicine And Pharmacology, Orthopedics And Sports Medicine Keywords: deep learning; lumbarnet; lumbar spine; spondylolisthesis; u-net

Online: 3 June 2022 (10:15:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202205.0121.v1

Evolution of Brains and Computers: The Roads not Taken

Ricard Sole, Luis Seoane

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Evolution; brains; deep learning; embodiment; neural networks; artificial intelligence; neurorobotics

Online: 9 May 2022 (14:12:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202402.0286.v1

Fast Fake – easy to train Face Swap model

Tomasz Walczyna, Zbigniew Piotrowski

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: face deep-fake, face swap, image deep-fake

Online: 5 February 2024 (12:29:12 CET)

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Preprint REVIEW | doi:10.20944/preprints202002.0239.v1

Opening the Black Box: Interpretable Machine Learning for Geneticists

Christina B. Azodi, Jiliang Tang, Shin-Han Shiu

Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: interpretable machine learning; deep learning; predictive biology

Online: 17 February 2020 (04:12:20 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1851.v1

Analyzing the Influence of Diverse Background Noises on Voice Transmission: A Deep Learning Approach to Noise Suppression

Alberto Nogales, Javier Caracuel Cayuela, Álvaro J. García-Tejedor

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Speech enhancement; Noise suppression; Deep learning; Variational autoencoders

Online: 29 November 2023 (06:25:59 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints201706.0074.v1

Microindolinone A, a Novel 4,5,6,7-Tetrahydroindole, from the Deep-Sea-Derived Actinomycete Microbacterium sp. MCCC 1A11207

Siwen Niu, Ting-Ting Zhou, Chun-Lan Xie, Gai-Yun Zhang, Xian-Wen Yang

Subject: Chemistry And Materials Science, Medicinal Chemistry Keywords: deep-sea; actinomycete; Microbacterium sp.; indole

Online: 16 June 2017 (04:40:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202105.0636.v1

A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies

Massimo De Maria, Lorenza Fiumi, Mauro Mazzei, Bik Oleg V.

Subject: Engineering, Automotive Engineering Keywords: cultural heritage; environment; deep learning; artificial intelligence; neural network.

Online: 26 May 2021 (13:06:34 CEST)

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Preprint REVIEW | doi:10.20944/preprints202108.0258.v1

Automatic Segmentation of Pelvic Cancers using Deep Learning: State-of-the-Art Approaches and Challenges

Reza Kalantar, Gigin Lin, Jessica M Winfield, Christina Messiou, Susan Lalondrelle, Matthew D Blackledge, Dow-Mu Koh

Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: Deep Learning; Pelvic Cancer Segmentation; Radiology; Radiation Oncology; Radiotherapy Planning

Online: 11 August 2021 (12:17:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202102.0471.v1

Crack Propagation Modeling of Strengthening of RC Deep Beams With CFRP Plates

Shahriar Shahbazpanahi, Hogr Karim, Wrya Abdullah, Amir Mosavi

Subject: Chemistry And Materials Science, Biomaterials Keywords: CFRP; concrete; deep beam; propagation

Online: 22 February 2021 (13:14:36 CET)

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Preprint ARTICLE | doi:10.20944/preprints202305.1420.v1

An Efficient and Mild Method for the Alkylation of p-Cresol with tert-Butyl Alcohol

Qi Wu, Dejin Zhang, Shu Sun, Chengcheng Liu, Cong Wang

Subject: Chemistry And Materials Science, Chemical Engineering Keywords: Alkylation reaction; deep eutectic solvent; mild; response surface methodology

Online: 19 May 2023 (09:52:53 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/preprints202211.0488.v1

Recognition of Micro-Expressions using the Spatiotemporal Capsule Network

Muhammad Mugees Asif, Sana Asif, Rabia Hussain, Imtnan Khalid

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Capsule network; differential features; deep learning; micro-expression recognition; spatiotemporal features

Online: 28 November 2022 (03:12:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202111.0080.v1

The application of Artificial Intelligence to Ophthalmology: A bibliometric study (2000-2021)

Han Wang, Xiaoshu Zhou, Wencai Du, Lina Huang

Subject: Medicine And Pharmacology, Ophthalmology Keywords: Artificial Intelligence; Ophthalmology; Bibliometric Study; CiteSpace; Machine Learning; Deep Learning.

Online: 3 November 2021 (13:39:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0208.v1

Deep Unsupervised Machine Learning for Early Diabetes Risk Prediction using Ensemble Feature Selection and Deep Belief Neural Networks

Olusola Olabanjo, Ashiribo Wusu, Manuel Mazzara

Subject: Physical Sciences, Biophysics Keywords: Deep belief network; Diabetes; Prediction; Risk Factors; Deep Learning

Online: 12 January 2023 (03:54:15 CET)

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Preprint ARTICLE | doi:10.20944/preprints201812.0253.v1

Deep Learning for Joint MIMO Detection and Channel Decoding

Taotao Wang, Lihao Zhang, Soung Chang Liew

Subject: Computer Science And Mathematics, Information Systems Keywords: deep learning; MIMO detection; channel decoding; deep neural network

Online: 20 December 2018 (13:08:27 CET)

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Preprint ARTICLE | doi:10.20944/preprints202404.0705.v1

A Comparative Study of PP-LiteSeg, Dual Attention Network, DeeplabV3p and Asymmetric Neural Network for Rooftop Detection in UAV Images

Zeenat Khadim Hussain, Jiang Congshir, Yuan Xin Xin, Muhammad Raza e Mustafa

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Deep Learning; Remote Sensing; PaddlePaddle; Rooftop; Semantic Segmentation; EISeg; Asymmetric Neural Network; PP-LiteSeg; Deeplab3; Dual Attention Network

Online: 10 April 2024 (12:10:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201806.0195.v1

Impact of Shear Zone on Rockburst in the Deep Neelum-Jehlum Hydropower Tunnel: A Numerical Modeling Approach

Abdul Muntaqim Naji, Hafeezur Rehman, Zaka Emad, HanKyu Yoo

Subject: Engineering, Civil Engineering Keywords: deep tunnel, high stresses, shear zone, rockburst, energy released

Online: 12 June 2018 (14:12:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0329.v1

Redundant Space Manipulator Autonomous Guidance for In-Orbit Servicing via Deep Reinforcement Learning

Matteo D’Ambrosio, Lorenzo Capra, Andrea Brandonisio, Stefano Silvestrini, Michèle Roberta Lavagna

Subject: Engineering, Aerospace Engineering Keywords: Deep Reinforcement Learning; 7-DoF space manipulator; motion synchronization; In-Orbit Servicing

Online: 4 April 2024 (17:28:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.1664.v2

Dissolving the Self, Embracing the Earth: A Philosophical Exploration of Psychedelics, Self-Transcendence, and Environmental Connection

Muzamil Hussain

Subject: Psychology, Social Sciences Keywords: Psychedelics; Ego Dissolution; Self-Transcendence; Interconnectedness; Deep Ecology; Environmental Philosophy; Consciousness Studies

Online: 26 April 2024 (12:50:11 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/preprints202312.1044.v1

Improving Breast Tumor Multi-Classification from High-Resolution Histological Images with the Integration of Feature Space Data Augmentation

Nadia Brancati, Maria Frucci

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep Learning; Whole Slide Image; Augmentation; Classification; Feature Space

Online: 14 December 2023 (10:05:41 CET)

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Preprint REVIEW | doi:10.20944/preprints202302.0249.v2

Improvement of Algan Homoepitaxial Tunnel Junction Deep-UV Light-Emitting Diodes by Controlling the Growth of N-Type Algan and Polycrystalline Mgzno/Al Electrodes

Kengo Nagata, Taichi Matsubara, Yoshiki Saito, Keita Kataoka, Tetsuo Narita, Kayo Horibuchi, Maki Kushimoto, Shigekazu Tomai, Satoshi Katsumata, Yoshio Honda, Tetsuya Takeuchi, Hiroshi Amano

Subject: Physical Sciences, Optics And Photonics Keywords: AlGaN; tunnel junction; light-emitting diode; deep-ultraviolet; MgZnO

Online: 17 February 2023 (07:14:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1758.v1

Introduction of Deep Learning Based-IR Image Analysis for Marginal Reflex Distance-1 Measurement Method, Simultaneously Capture Images and Compute the Result: Clinical Validation Study

Bokeun Song, Hyeokjae Kwon, Sunje Kim, Yooseok Ha, Sang-Ha Oh, Seung-Han Song

Subject: Medicine And Pharmacology, Ophthalmology Keywords: Blepharoplasty; Deep learning; Machine learning; Eye movement measurements

Online: 28 November 2023 (10:19:49 CET)

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Preprint REVIEW | doi:10.20944/preprints202105.0364.v1

Automated Tracking Systems for the Assessment of Farmed Poultry

Suresh Neethirajan

Subject: Engineering, Automotive Engineering Keywords: Poultry behaviour; target tracking; deep learning; precision livestock farming; poultry production systems.

Online: 16 May 2021 (22:43:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.1699.v1

Deep Supervised Hashing by Fusing Multiscale Deep Features

REDAOUI ADIL, BELLOULATA Kamel, BELALIA Amina

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Image retrieval, Deep learning, Multi-scale feature, Deep supervised hashing .

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

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Preprint ARTICLE | doi:10.20944/preprints202309.1202.v1

Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism

Konstantinos Mountzouris, Isidoros Perikos, Ioannis Hatzilygeroudis

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: speech emotion recognition; deep learning; Deep Belief Network; deep neural network; Convolutional Neural Network; LSTM; attention mechanism

Online: 19 September 2023 (08:24:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201912.0260.v1

A New Potential of the Deep Convective Clouds as the Calibration Target for a Geostationary UV/VIS Hyperspectral Spectrometer

Ye Eun Lee, Myoung-Hwan Ahn, Mina Kang

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: GEMS; UV; VIS; hyperspectral data; deep convective cloud; vicarious calibration; OMI; TROPOMI

Online: 19 December 2019 (13:14:55 CET)

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As one of GEO-constellation for environmental monitoring in the next decade, Geostationary Environment Monitoring Spectrometer (GEMS) is designed to observe the Asia Pacific region to provide the information on the atmospheric chemicals, aerosol and cloud properties. For the continuous monitoring of the sensor performance after its launch in early 2020, here we suggest deep convective clouds (DCCs) as a possible target for the vicarious calibration of GEMS, the first UV/VIS hyperspectral sensor onboard a geostationary satellite. Tropospheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) are used as a proxy of GEMS, and a conventional DCC detection approach applying the thermal threshold test is used for the DCC detection based on the collocations with Advance Himawari-8 Imager (AHI) onboard Himawari-8 geostationary satellite. DCCs are frequently detected over the GEMS observation area on average over 200 pixels in a single observation scene. Considering the spatial resolution of GEMS, 3.5 km×7 km which is similar to TROPOMI, and its temporal resolution (8 times a day), availability of DCCs for vicarious calibration of GEMS is expected to be sufficient. Inspection of the DCC reflectivity spectra estimated from the OMI and TROPOMI data also shows a promising result. Even though, their observation geometry and sensor characteristics are quite a different, the estimated DCC spectra agree quite a well within a known uncertainty range with comparable spectral features. When the DCC detection is further improved by applying both visible and infrared tests, the variability of DCC reflectivity from the TROPOMI data is reduced by half, from 10% to 5%. This is mainly due to the efficient screening of cold thin cirrus with the visible test and of bright warm clouds with the infrared test. The precise DCC detection is also expected to contribute to the accurate characterization of the cloud reflectivity, which will be further investigated.

Preprint ARTICLE | doi:10.20944/preprints202202.0070.v1

Two-Dimensional Stress Analysis of Isotropic Deep Beams Using the Finite Difference Method

Valentin Fogang

Subject: Engineering, Civil Engineering Keywords: Finite difference method; additional nodes; Airy stress function; displacement potential function; deep beam of varying thickness; layered beam; deep beam having openings; skew edge; buckling analysis

Online: 4 February 2022 (10:48:03 CET)

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This paper presents an approach to the two-dimensional analysis of elastic isotropic deep beams using the finite difference method (FDM). Deep beams are subjected to in-plane loading and present a shear span to height ratio of less than 2.50; consequently, Euler-Bernoulli beam theory and Timoshenko beam theory do not apply. Deep beams analysis is generally conducted using numerical methods such as the finite element method and to a lesser extent the FDM; the strut-and-tie model and the stress field method are also widely utilized. Analytical approaches usually make use of the Airy stress function, where stresses are formulated in terms of the stress function; however, the exact solution of this function satisfying all of the boundary conditions can hardly be found, even for simple cases. In this paper, deep beams were analyzed using the FDM. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected nodes of the structure. Therefore, the deep beam was discretized with a two-dimensional grid, and additional nodes were introduced at the boundaries and at positions of discontinuity (openings, brutal change of material properties, non-uniform grid spacing), the number of additional nodes corresponding to the number of boundary conditions at the node of interest. The introduction of additional nodes allowed us to apply the governing equations at boundary nodes and satisfy the boundary and continuity conditions. An Airy stress function approach and a displacement potential function approach were considered in this study whereby strong formulations of equations (equilibrium, kinematic, and constitutive) were set. Stress and stability analyses were carried out with this model; furthermore, deep beams of varying stiffness, layered beams, and beams having openings were analyzed. For slender beams, the results obtained with the Airy stress function approach showed good agreement with those of the Euler-Bernoulli beam theory, and for deep beams, the shapes of stress distributions were in good agreement with a proper understanding of the behavior of structures. On the other hand, the displacement potential function approach delivered unsatisfactory results, probably due to the use of an inefficient equation solver; a more powerful tool will be needed in future research for this purpose.

Preprint CONCEPT PAPER | doi:10.20944/preprints202202.0057.v2

Can Artificial Intelligence be Used to Improve Productivity by Automating Elements of the User Experience Design Processes?

Nural Choudhury

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial Intelligence; Deep Learning; Design Processes; Graphic Design; User Experience

Online: 3 July 2023 (13:57:49 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/preprints202309.1676.v1

Varietal Differences in the Root Systems of Rice (Oryza sativa L.) under Drip Irrigation with Plastic-Film Mulch

JUNFA WANG, Oluwasegun Olamide Fawibe, Akihiro Isoda

Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: drip irrigation; root; varietal difference; deep root; plastic-film mulch

Online: 25 September 2023 (10:13:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.1663.v1

Chemical Constituents of the Deep-Sea-Derived Penicillium citrinum W17 and Their Anti-Inflammatory and Anti-Osteoporotic Bioactivities

Yong Zhang, Chun-Lan Xie, Yuan Wang, Xi-Wen He, Ming-Min Xie, You Li, Kai Zhang, Zheng-Biao Zou, Long-He Yang, Xian-Wen Yang, Ren Xu

Subject: Chemistry And Materials Science, Medicinal Chemistry Keywords: deep-sea; fungus; Penicillium citrinum; polyketides; anti-osteoporosis; anti-inflammation.

Online: 25 September 2023 (09:36:29 CEST)

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Preprint REVIEW | doi:10.20944/preprints202312.1229.v1

Decision Support Systems in Silvicultural Practices and the Essential Prioritization of Ecosystem Services: A Review

Neelesh Yadav, Shrey Rakholia, Reuven Yosef

Subject: Environmental And Earth Sciences, Sustainable Science And Technology Keywords: Decision Support System; Climate Resilience; Ecosystem Services; Deep Neural Networks; Sustainability

Online: 18 December 2023 (10:44:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints202111.0344.v1

Extraction of the Relations between Significant Pharmacological Entities in Russian-Language Internet Reviews on Medications

Alexander Sboev, Anton Selivanov, Ivan Moloshnikov, Roman Rybka, Artem Gryaznov, Sanna Sboeva, Gleb Rylkov

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: pharmacological text corpus; automatic relation extraction; natural language processing; deep learning

Online: 19 November 2021 (10:40:10 CET)

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Preprint ARTICLE | doi:10.20944/preprints202205.0134.v1

Will Artificial Intelligence Become Conscious? Can Thermodynamics Explain the Evolution of Intellect?

Eva Deli

Subject: Computer Science And Mathematics, Computer Science Keywords: deep neural networks; artificial intelligence; intelligent computation; orthogonal transformation; entropy; Carnot cycle

Online: 10 May 2022 (10:05:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202101.0408.v1

Deep Learning Model for Detection of Hypoalbuminemia Using Electrocardiography

Joon-myoung Kwon, Soo Youn Lee, Yoon-Ji Lee, Yong-Yeon Jo, Min-Seung Jung, Yong-Hyeon Cho, Jae-Hyun Shin, Jang-Hyeon Ban, Kyung-Hee Kim, Jinsik Park, Byung-Hee Oh

Subject: Medicine And Pharmacology, Immunology And Allergy Keywords: Electrocardiography; Albumins; Deep Learning; Artificial Intelligence

Online: 21 January 2021 (09:03:32 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0591.v1

Use of Natural Deep Eutectic Solvents (NADES) for the Selective Fractionation of Bioactive Low Molecular Weight Carbohydrates

Ignacio Jiménez Amezcua, Manuel Ignacio López Martínez, A.I. Ruiz-Matute, María Luz Sanz

Subject: Chemistry And Materials Science, Analytical Chemistry Keywords: Natural Deep Eutectic Solvents (NADES); ketoses; aldoses; fractionation; low molecular weight carbohydrates

Online: 9 November 2023 (07:24:45 CET)

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Preprint ARTICLE | doi:10.20944/preprints202304.0079.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; Multi-class classification; Time-Series prediction; Reuters data; Energy trade value

Online: 6 April 2023 (08:55:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201802.0023.v1

HIVEC: A Hierarchical Approach for Vector Representation Learning of Graphs

Ayushi Patel, Ramakrishna R Mathiraj, Mausam Jai, Krishnakant Singh, Naveen Sivadasan, Vineeth N Balasubramanian

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; graph kernels; unsupervised learning

Online: 4 February 2018 (10:52:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints201904.0232.v1

A Machine Learning Approach for the Discovery of Ligand-specific Functional Mechanisms of GPCRs

Ambrose Plante, Derek M. Shore, Giulia Morra, George Khelashvili, Harel Weinstein

Subject: Biology And Life Sciences, Biophysics Keywords: functional selectivity; biased ligands; molecular dynamics; deep neural networks; sensitivity analysis; pharmacological efficacy

Online: 22 April 2019 (10:54:24 CEST)

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Preprint REVIEW | doi:10.20944/preprints202308.1029.v1

Advances in the Development of Representation Learning and Its Innovations against COVID-19

Peng Li, Md Parvej Mosharaf, Chenghao Zhang, Shufang Guo, Jing Zhang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: representation learning; graph embedding; graph neural network; deep learning; COVID-19; healthcare

Online: 14 August 2023 (10:43:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0127.v1

Contour-Based Metric for the Evaluation of Image Defogging without a Ground Truth

Gerard DeMas-Giménez, Pablo García-Gómez, Josep R. Casas, Santiago Royo

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Image defogging; visual enhancement evaluation; edge detection; deep neural networks; autonomous systems

Online: 8 September 2022 (15:37:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202002.0180.v1

Which Attributes Matter the Most for Loan Origination? A Neural Attention Approach

Antonios Alexos, Sotirios Chatzis

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; neural attention; loans; loan origination; machine learning

Online: 14 February 2020 (02:45:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints202206.0267.v1

Investigating the Relationship between Facial Mimicry and Empathy

Yevgeniya Kovalchuk, Elizabeta Budini, Robert Cook, Andrew Walsh

Subject: Social Sciences, Psychology Keywords: affective computing; empathy; facial mimicry; facial recognition technology; deep learning

Online: 20 June 2022 (10:08:13 CEST)

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Facial expressions play a key role in interpersonal communication when it comes to negotiating our emotions and intentions, as well as interpreting those of others. Research has shown that we can connect to other people better when we exhibit signs of empathy and facial mimicry. However, the relationship between empathy and facial mimicry is still debated. Among the factors contributing to the difference in results across existing studies is the use of different instruments for measuring both empathy and facial mimicry, as well as often ignoring the differences across various demographic groups. This study first looks at the differences in empathetic abilities of people across different demographic groups based on gender, ethnicity and age. The empathetic ability is measured based on the Empathy Quotient capturing a balanced representation of both emotional and cognitive empathy. Using statistical and machine learning methods, the study then investigates the correlation between the empathetic ability and facial mimicry of subjects in response to images portraying different emotions displayed on a computer screen. Unlike the existing studies measuring facial mimicry using electromyography, this study employs a technology detecting facial expressions based on video capture and deep learning. This choice was made in the context of increased online communication during and post the COVID-19 pandemic. The results of this study confirm the previously reported difference in the empathetic ability between females and males. However, no significant difference in the empathetic ability was found across different age and ethnic groups. Furthermore, no strong correlation was found between empathy and facial reactions to faces portraying different emotions shown on a computer screen. Overall, the results of this study can be used to inform the design of online communication technologies and tools for training empathy team leaders, educators, social, and health care providers.

Preprint REVIEW | doi:10.20944/preprints202210.0096.v1

The Role of Artificial Intelligence in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Mohammad Madani, Mohammad Mahdi Behzadi, Sheida Nabavi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial intelligence; Breast cancer; Deep learning; Histopathology; Imaging modality; Mam-mography

Online: 8 October 2022 (07:01:42 CEST)

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Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in early-stage of cancer, from whom all survived cancer. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually that leads to increase the risk of wrong decisions for cancer detection. Thus, utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and availability of datasets obtained from various imaging modalities have opened an opportunity to surpass limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Preprint REVIEW | doi:10.20944/preprints202306.2249.v1

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Oussama Kerdjidj, Yassine Himeur, Shahab Saquib Sohail, Abbes Amira, Fodil Fadli, Shadi Atalla, Wathiq Mansoor, Abigail Copiaco, Mohammad Daradkeh, Amjad Gawanmeh, Sami Miniaoui, Diana Wasfi Dawoud

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Indoor localization; Wireless signal techniques; Computer vision techniques; Deep and transfer learning; Hybrid techniques

Online: 30 June 2023 (12:00:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202312.1851.v1

Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images

Moh Afandi, Peter Ardhianto, Jun-Dai Lin

Subject: Public Health And Healthcare, Nursing Keywords: parkinson; deep learning; classification; mobilenet; neurodegenerative

Online: 26 December 2023 (02:49:57 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints202307.1896.v1

Pre-training of Multi-order Acoustic Simulation for Replay Voice Spoofing Detection

Changhwan Go, Nam In Park, Oc-Yeub Jeon, Chanjun Chun

Subject: Computer Science And Mathematics, Signal Processing Keywords: voice spoofing; acoustic configuration; deep learning

Online: 28 July 2023 (10:14:32 CEST)

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Voice spoofing attempts to break into a specific automatic speaker verification (ASV) system by forging the user’s voice, and can be used through methods, such as text-to-speech (TTS), voice conversion (VC), and replay attacks. Recently, deep learning-based voice spoofing countermeasures have been developed. however, the problem with replay is that it is difficult to construct a large number of datasets because it requires a physical recording process. To overcome these problems, this study proposes a pre-training framework based on multi-order acoustic simulation for replay voice spoofing detection. Multi-order acoustic simulation utilizes existing clean signal and room impulse response (RIR) datasets to generate audios, which simulate the various acoustic configurations of the original and replayed audios. The acoustic configuration refers to factors, such as the microphone type, reverberation, time delay, and noise that may occur between a speaker and microphone during the recording process. We assume that a deep learning model trained on an audio that simulates the various acoustic configurations of the original and replayed audios can classify the acoustic configurations of the original and replay audios well. To validate this, we performed pre-training to classify the audio generated by the multi-order acoustic simulation into 3 classes: clean signal, audio simulating the acoustic configuration of the original audio, and audio simulating the acoustic configuration of the replay audio. We also set the weights of the pre-training model to the initial weights of the replay voice spoofing detection model using the existing replay voice spoofing dataset and then performed fine-tuning. To validate the effectiveness of the proposed method, we evaluated the performance of the conventional method without pre-training and proposed method using an objective metric, i.e., the accuracy. As a result, the conventional method achieved 92.94% accuracy and proposed method achieved 98.16% accuracy.

Preprint ARTICLE | doi:10.20944/preprints202303.0048.v1

A Deep Learning Method for Predicting the Minimum Inhibitory Concentration of Antimicrobial Peptides against Escherichia coli using Multi-Branch-CNN and Attention

Jielu Yan, Bob Zhang, Mingliang Zhou, Francois-Xavier Campbell-Valois, Shirley W.I. Siu

Subject: Biology And Life Sciences, Immunology And Microbiology Keywords: Minimum Inhibitory Concentrations; Deep Learning; Regression; Antimicrobial peptides; Drug Discovery

Online: 3 March 2023 (01:29:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202305.1358.v1

Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet

Zhu Liang, Weiping Peng, Wei Liu, Houzan Huang, Jiaming Huang, Kangming Lou, Guochao Liu, Kaihua Jiang

Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: Landslide Susceptibility; Information Value; Logistic regression; Machine learning; Deep learning; GIS

Online: 19 May 2023 (02:28:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202306.0240.v1

Forecasting PM Levels Using Machine Learning Models in the Arctic: A Comparative Study

Paolo Fazzini, Marco Montuori, Antonello Pasini, Alice Cuzzucoli, Ilaria Crotti, Emilio Fortunato Campana, Francesco Petracchini, Srdjan Dobricic

Subject: Environmental And Earth Sciences, Pollution Keywords: deep learning; PM10; environmental forecasting; chaotic time series; Arctic

Online: 5 June 2023 (05:04:24 CEST)

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Preprint BRIEF REPORT | doi:10.20944/preprints202207.0419.v1

A Simple Convolutional Neural Network for Precise and Automated Identification of COVID-19

Zhenyi Zhu

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: computer vision; deep learning; CoughNet model

Online: 27 July 2022 (10:01:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202202.0354.v1

A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph

Élisson da Silva Rocha, Patricia Takako Endo

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Dental segmentation; deep learning; panoramic radiograph

Online: 28 February 2022 (10:15:46 CET)

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Preprint ARTICLE | doi:10.20944/preprints201811.0509.v1

Recognition of Urban Sound Events using Deep Context-Aware Feature Extractors and Handcrafted Features

Theodoros Giannakopoulos, Stavros Perantonis

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: audio recognition; context-aware; deep learning

Online: 20 November 2018 (16:32:16 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.1990.v2

Multilingual Sentiment Analysis Using Deep Learning: Survey

Almas Bibi, Uswa Ihsan, Humaira Ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: sentiment; deep learning; multilingual

Online: 3 January 2024 (03:13:11 CET)

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Preprint ARTICLE | doi:10.20944/preprints202309.0280.v1

FPL: False Positive Loss

Ali Akbar Kiaei, Mahnaz Boush, Danial Safaei, Sadegh Abadijou, Nima Baselizadeh, Ali Fayzi, Reza Bahadori, Nader Salari

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: loss function; deep learning

Online: 6 September 2023 (03:29:46 CEST)

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When training deep neural networks tasks, the most popular choices are cross-entropy loss. On the other hand, in general speaking, a decent loss function can take on shapes that are considerably more flexible and ought to be adapted for different activities and datasets. In most of the classification tasks, generally if the true class is not correctly recognized by the network (top1), that class is placed among the five classes with the highest probability (top5). This shows that the network does not necessarily recognize the correct class with a low probability, but a class similar to it (such as 3 vs. 8 in mnist) assigns a higher probability and this causes a mistake in that task. Accordingly, we proposed a loss function deals with the error of class that the neural network incorrectly recognized as correct, in addition to the correct class error. We call our proposed loss as False Positive Loss (FPL), with the intention of viewing and designing loss functions not only through the utilization of true class but also through the utilization of the value of false positive classes. One of the core properties of our proposed loss is full adaptability, which makes False Positive Loss be fully capable of getting reformulated by using other widely used loss functions formulas based on the task or the need of the users. Extensive experimental results demonstrate that our suggested loss function outperforms other well-known losses on a variety of tasks and datasets. As can be observed, the performance of our False Positive Loss is superior to that of the cross-entropy loss when it comes to tasks involving 2D picture classification. We have compared our loss with cross entropy as the most common classification loss function on some models (such as ResNet-18, ResNet-50 and Efficientnet-V2) through classification known as a basic computer vision task. with both random or pre-trained initial weights. As a result, in some cases the models with our loss outperform the same tasks with cross entropy from the viewpoint of some metric (i.e. accuracy and FP). For example, the resnet-50 on cifar-10 dataset with random initialization indicated a top1 accuracy of 94.93 with cross entropy and 95.25 with our loss, while for top5 accuracy the results are 99.86 and 99.87, respectively.

Preprint ARTICLE | doi:10.20944/preprints202202.0015.v1

MRI-GAN: MRI and Tissues Transfer Using Generative Adversarial Networks

Afifa Khaled

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep learning; Machine learning

Online: 1 February 2022 (13:34:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints201908.0001.v1

Texture Segmentation: An Objective Comparison between Traditional and Deep-Learning Methodologies

Cefa Karabag, Jo Verhoeven, Naomi Rachel Miller, Constantino Carlos Reyes-Aldasoro

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: texture; segmentation; deep learning

Online: 1 August 2019 (03:30:51 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202312.1456.v1

Model for identification of electrical appliance and determination of patterns using high-resolution WSN for the efficient home energy consumption based on Deep Learning

Fernando Ulloa-Vásquez, Victor Heredia-Figueroa, Cristóbal Espinoza-Iriarte, José Tobar-Ríos, Fernanda Aguayo-Reyes, Dante Carrizo, Luis García-Santander

Subject: Engineering, Electrical And Electronic Engineering Keywords: Deep Learning, AMR, Smart-Meter, Smart-Socket, HEMS, Smart-Cities, ILM.

Online: 19 December 2023 (12:16:24 CET)

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Preprint ARTICLE | doi:10.20944/preprints202309.0085.v1

The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild

Panagiotis Derekas, Panagiota Spyridonos, Aristidis Likas, Athanasia Zampeta, George Gaitanis, Ioannis Bassukas

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; semantic segmentation; U‐Net; actinic keratosis; cutaneous cancerization field; skin lesions; clinical photography

Online: 4 September 2023 (04:23:19 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202012.0046.v1

Eruption of Mount Ile Lewotolok on November 29, 2020: A Historical Volcanic and Tectonic Seismicity Assessment

Andri Wibowo

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: deep; eruption; precursor; seismicity; tectonic; volcano

Online: 2 December 2020 (07:59:11 CET)

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Preprint ARTICLE | doi:10.20944/preprints202404.0832.v1

Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning

Rodrigo Olivares, Omar Salinas, Camilo Ravelo, Ricardo Soto, Broderick Crawford

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: biomimetic optimization algorithm; deep q–learning; cyber SOC; security information event management

Online: 12 April 2024 (08:28:04 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202301.0579.v1

A General SBML Compatible Hybrid Modeling Framework: Combining Biochemical Mechanisms with Deep Neural Networks for Systems Biology Applications

José Pinto, João R. C. Ramos, Rafael S. Costa, Rui Oliveira

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: hybrid modeling; deep neural networks; deep learning; SBML; systems biology; computational modeling

Online: 31 January 2023 (08:51:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202105.0641.v1

Instance Segmentation of Microscopic Foraminifera

Thomas Haugland Johansen, Steffen Aagaard Sørensen, Kajsa Møllersen, Fred Godtliebsen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: foraminifera; instance segmentation; object detection; deep learning

Online: 26 May 2021 (13:33:54 CEST)

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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)

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Preprint ARTICLE | doi:10.20944/preprints202305.1115.v1

A Real-Time Lightweight Detection Algorithm for Deck Crew and the Use of Fishing Nets based on Improved YOLOv5s Network

Wang Jiaming, Yin Xiangbo, Li Guodong

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: YOLOv5s; deck crew detection; fishing net detection; deep learning model lightweight

Online: 16 May 2023 (07:18:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202205.0248.v1

COVID-19 Real Time Live Tweet Sentimental Analysis Using Deep Learning Methods, Its Effect on Phases of COVID-19 Waves

Ubio Obu, Shruti Thakur

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: COVID-19; sentiment analysis; deep learning

Online: 19 May 2022 (03:42:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202312.0976.v1

A High-Resolution Aftershock Catalog for the 2014 Ms6.5 Ludian (China) Earthquake Using Deep Learning Methods

Jun Li, Ming Hao, Zijian Cui

Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: deep learning; 2014 Ludian earthquake; aftershocks catalog ; ;

Online: 13 December 2023 (11:31:20 CET)

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Preprint ARTICLE | doi:10.20944/preprints202309.1836.v1

Adipose Stromal Cell Spheroids for Cartilage Repair: A Promising Tool for Unveiling the Critical Maturation Point

Azzurra Sargenti, Simone Pasqua, Marco Leu, Laura Dionisi, Giuseppe Filardo, Brunella Grigolo, Daniele Gazzola, Spartaco Santi, Carola Cavallo

Subject: Biology And Life Sciences, Cell And Developmental Biology Keywords: adipose stromal cells; spheroids; chondrogenesis; mass density; deep imaging

Online: 27 September 2023 (05:23:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202110.0371.v1

A Novel Approach to the Holistic 3D Characterization of Weld Seams – Paving the Way for Deep Learning-Based Process Monitoring

Maximilian Schmoeller, Christian Stadter, Michael Karl Kick, Christian Geiger, Michael Friedrich Zaeh

Subject: Engineering, Industrial And Manufacturing Engineering Keywords: non-destructive testing; weld seam contour; microfocus computed tomography; laser beam welding; Deep Learning

Online: 25 October 2021 (15:54:02 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202003.0158.v1

Energy Demand Forecasting Using Deep Learning: Application to the French Grid

Alejandro J. del Real, Fernando Dorado, Jaime Durán

Subject: Engineering, Energy And Fuel Technology Keywords: energy; demand; forecasting; deep; learning; machine; convolutional; artificial; neural; networks

Online: 10 March 2020 (03:40:31 CET)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202003.0337.v1

A Beginner's Tutorial of Restricted Boltzmann Machines

Yiping Cheng

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Restricted Boltzmann machines; artificial intelligence; deep learning

Online: 23 March 2020 (05:51:32 CET)

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Preprint ARTICLE | doi:10.20944/preprints202308.0882.v1

Deep Learning-Based Dose Predictor for Glioblastoma – Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

Robert Poel, Amith J. Kamath, Jonas Willmann, Nicolaus Andratschke, Ekin Ermis, Daniel Matthias Aebersold, Peter Manser, Mauricio Reyes

Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: Radiotherapy; Dose Prediction; Deep Learning; Quality Assurance; VMAT; Glioblastoma

Online: 11 August 2023 (08:01:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0077.v1

Deep Learning to Near-Surface Humidity Retrieval from Multi-sensor Remote Sensing Data over the China Seas

Rongwang Zhang, Weihao Guo, Xiaofeng Li, Xin Wang

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: near-surface humidity; remote sensing; deep learning; China Seas

Online: 5 July 2022 (13:46:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202001.0283.v3

Improving the Learning of Self-driving Vehicles Based on Real Driving Behavior using Deep Neural Network Techniques

Nayere Zaghari, Mahmood Fathy, Seyed Mahdi Jameii, Mohammad Sabokrou, Mohammad Shahverdy

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Autonomous vehicle; Self-driving; Real Driving Behavior; Deep Neural Network; LSV-DNN

Online: 30 November 2020 (11:16:54 CET)

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Preprint REVIEW | doi:10.20944/preprints202402.0320.v1

Research Progress on the Application of Crop Yield Calculation based on Image Analysis Technology

Feng Yu, Ming Wang, Jun Xiao, Qian Zhang, Jinmeng Zhang, Xin Liu, Yang Ping, Rupeng Luan

Subject: Computer Science And Mathematics, Computer Science Keywords: remote sensing images; visible light images; machine learning; deep learning; biomass; yield calculation

Online: 6 February 2024 (03:52:45 CET)

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Preprint ARTICLE | doi:10.20944/preprints202404.1344.v1

Enhanced Sequence-to-Sequence Deep Transfer Learning for day-ahead electricity load forecasting

Vasileios Laitsos, Georgios Vontzos, Apostolos Tsiovoulos, Dimitrios Bargiotas, Lefteri H. Tsoukalas

Subject: Electrical And Electronic Engineering, Engineering Keywords: Deep Transfer Learning; Electricity Load Forecasting; Multilayer Perceptron; Convolutional Neural Network; Ensemble Deep Learning; Multi-task Deep Learning; Exploratory Data Analysis

Online: 19 April 2024 (15:58:08 CEST)

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Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Preprint ARTICLE | doi:10.20944/preprints201902.0233.v1

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

Saptarshi Sengupta, Sanchita Basak, Pallabi Saikia, Sayak Paul, Vasilios Tsalavoutis, Frederick Ditliac Atiah, Vadlamani Ravi, Richard Alan Peters II

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep neural network architectures; supervised learning; unsupervised learning; testing neural networks; applications of deep learning; evolutionary computation

Online: 26 February 2019 (04:02:00 CET)

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Preprint ARTICLE | doi:10.20944/preprints202305.0708.v1

Framework for Representing, Building and Reusing Novel State-of-the-art 3D Object Detection Models in Point Clouds Targeting Self-Driving Applications

António Linhares Silva, Pedro Oliveira, Dalila Durães, Duarte Fernandes, Rafael Névoa, João Monteiro, Pedro Melo-Pinto, José Machado, Paulo Novais

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Autonomous Driving; Deep learning Methods; LiDAR Sensing Technology; 3D Object Detection

Online: 10 May 2023 (08:21:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202401.1254.v1

Stereoselective Catalytic Synthesis of Bioactive Compounds in Natural Deep Eutectic Solvents (NADES): A Survey across the Catalytic Spectrum

Elisabete P. Carreiro, Hans-Jurgen Federsel, Gesine J Hermann, Anthony Burke

Subject: Chemistry And Materials Science, Organic Chemistry Keywords: Natural deep eutectic solvents (NADES), asymmetric catalysis; organocatalysis; biocatalysis; metal based catalysis

Online: 17 January 2024 (11:24:15 CET)

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Preprint ARTICLE | doi:10.20944/preprints202207.0131.v2

Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings

Samuel Boudet, Agathe Houzé de l'Aulnoit, Laurent Peyrodie, Romain Demailly, Denis Houzé de l'Aulnoit

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: fetal heart rate; maternal heart rate; cardiotocogram; gated recurrent unit; deep learning

Online: 22 July 2022 (03:08:59 CEST)

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We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a clinical alert system to assist the practitioner. Three models were developed and used to detect (i) FSs on the MHR channel (the FSMHR model), (ii) the MHR and FSs on the Doppler FHR sensor (the FSDop model), and (iii) FSs on the scalp ECG channel (the FSScalp model). The FSDop model was the most useful because FSs are far more frequent on the Doppler FHR channel. All three models were based on a multilayer, symmetric, GRU and were trained on data recorded during the first and second stages of delivery. The FSMHR and FSDop models were also trained on antepartum recordings. The training dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. In an initial evaluation of routine clinical practice, 30 fully annotated recordings for each sensor type (mean duration: 5 h for MHR and Doppler sensors, and 3 h for the scalp ECG sensor) were analyzed. The sensitivity, positive predictive value (PPV) and accuracy were respectively 62.20%, 87.1% and 99.90% for the FSMHR model, 93.1%, 95.6% and 99.68% for the FSDop model, and 44.6%, 87.2% and 99.93% for the FSScalp model. We built a second test dataset with a more solid ground truth by selecting 45 periods (lasting 20 min, on average) on which the Doppler FHR and scalp ECG signals were recorded simultaneously. Using scalp ECG data, the experts estimated the true FHR value more reliably and thus annotated the Doppler FHR channel more precisely. The models achieved a sensitivity of 53.3%, a PPV of 62.4%, and an accuracy of 97.29%. In comparison, two experts (blinded to the scalp ECG data) respectively achieved a sensitivity of 15.7%, a PPV of 74.3%, and an accuracy of 96.91% and a sensitivity of 60.7%, a PPV of 83.5% and an accuracy of 98.24%. Hence, the models performed at expert level (better than one expert and worse than the other), although a well-trained expert with good knowledge of FSs could probably do better in some cases. The models and datasets have been included in the Fetal Heart Rate Morphological Analysis open-source MATLAB toolbox and can be used freely for research purposes.

Working Paper ARTICLE

Detection of Coronavirus Disease (COVID-19) Based on Deep Features and Support Vector Machine

Prabira Kumar Sethy, Santi Kumari Behera, Pradyumna Kumar Ratha, Preesat Biswas

Subject: Engineering, Electrical And Electronic Engineering Keywords: coronavirus; COVID-19; diagnosis; deep features; SVM

Online: 22 April 2020 (05:58:22 CEST)

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The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images. For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset for training and validation. The deep features from the fully connected layer of CNN model are extracted and fed to SVM for classification purpose. The SVM classifies the corona affected X-ray images from others. The methodology consists of three categories of Xray images, i.e., COVID-19, pneumonia and normal. The method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people. SVM is evaluated for detection of COVID-19 using the deep features of different 13 number of CNN models. The SVM produced the best results using the deep feature of ResNet50. The classification model, i.e. ResNet50 plus SVM achieved accuracy, sensitivity, FPR and F1 score of 95.33%,95.33%,2.33% and 95.34% respectively for detection of COVID-19 (ignoring SARS, MERS and ARDS). Again, the highest accuracy achieved by ResNet50 plus SVM is 98.66%. The result is based on the Xray images available in the repository of GitHub and Kaggle. As the data set is in hundreds, the classification based on SVM is more robust compared to the transfer learning approach. Also, a comparison analysis of other traditional classification method is carried out. The traditional methods are local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM. In traditional image classification method, LBP plus SVM achieved 93.4% of accuracy.

Preprint REVIEW | doi:10.20944/preprints202308.2166.v1

Artificial Intelligence in the Interpretation of Videofluoroscopic Swallowing Studies: Implications and Advances for Speech Pathologists

Anna M Girardi, Elizabeth A Cardell, Stephen P Bird

Subject: Public Health And Healthcare, Primary Health Care Keywords: dysphagia; artificial intelligence; videofluoroscopic swallowing study; deep learning; machine learning; imaging; speech pathology

Online: 31 August 2023 (10:42:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.1109.v1

Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism

Vasileios Skaramagkas, Iro Boura, Cleanthi Spanaki, Emilia Michou, Georgios Karamanis, Zinovia Kefalopoulou, Manolis Tsiknakis

Subject: Engineering, Bioengineering Keywords: Parkinson’s disease; Bi-LSTM with attention; in the wild detection; accelerometer; deep learning

Online: 15 August 2023 (08:21:28 CEST)

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