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Preprint ARTICLE | doi:10.20944/preprints202304.0645.v1

Automated Word-level Lip Reading using Convolutional Neural Networks with New Turkish Dataset

Nergis Pervan-Akman, Ali Berkol, Hamit Erdem

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Lip Reading; Multiclass Classification; Turkish Lip Reading Dataset; Deep Learning; Convolutional Neural Networks; Lip Detection

Online: 20 April 2023 (10:07:48 CEST)

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Preprint REVIEW | doi:10.20944/preprints202303.0203.v1

Liver Transplant in Patients With Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence

Mihai Dan Pomohaci, Mugur Cristian Grasu, Radu Lucian Dumitru, Mihai Toma, Ioana Gabriela Lupescu

Subject: Medicine And Pharmacology, Gastroenterology And Hepatology Keywords: Hepatocarcinoma; Cirrhosis; Liver transplantation; Liver transplant; Artificial intelligence; Machine learning; Radiomics; Deep learning; Neural networks

Online: 13 March 2023 (02:13:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0050.v1

A Concise Overview of the Vision-based Human Action Recognition Field

Fernando Camarena, Miguel Gonzalez-Mendoza, Leonardo Chang, Ricardo J Cuevas-Ascencio

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: video-based human action recognition; Action Recognition; Deep Learning Methods; handcrafted Methods; Human Action; Overview

Online: 3 February 2023 (01:17:56 CET)

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

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

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

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

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

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

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

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

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

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

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

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

Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments

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

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

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

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Preprint ARTICLE | doi:10.20944/preprints202110.0237.v1

Deep Cross-Project Software Reliability Growth Model using Project Similarity Based Clustering

Kyawt Kyawt San, Hironori Washizaki, Yoshiaki Fukazawa, Kiyoshi Honda, Masahiro Taga, Akira Matsuzaki

Subject: Computer Science And Mathematics, Information Systems Keywords: Software reliability; deep learning; long short-term memory; project similarity and clustering; cross-project prediction

Online: 18 October 2021 (10:33:39 CEST)

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Working Paper ARTICLE

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

Xijia Wei, Zhiqiang Wei, Valentin Radu

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

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

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Preprint ARTICLE | doi:10.20944/preprints202108.0347.v1

A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction

Tahjid Ashfaque Mostafa, Sara Soltaninejad, Tara L. McIsaac, Irene Cheng

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Parkinson’s Disease; Freeze of Gait; Deep Learning; Ensemble Learning; Wearable Sensor Data, Detection and Predication

Online: 16 August 2021 (16:48:14 CEST)

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

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

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

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

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

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

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

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

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

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

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Preprint ARTICLE | doi:10.20944/preprints202011.0591.v2

Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform

Mohammadreza Kaji, Jamshid Parvizian, Hans Wernher van de Venn

Subject: Engineering, Automotive Engineering Keywords: health indicator; performance degradation assessment; deep learning; vibration monitoring; bearing; remaining useful life; digital twin

Online: 11 December 2020 (11:59:20 CET)

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

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

Wang Xi, Guillaume Devineau, Fabien Moutarde, Jie Yang

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

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

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Preprint ARTICLE | doi:10.20944/preprints202009.0458.v1

High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks

Rodrigo Trevisan, Osvaldo Pérez, Nathan Schmitz, Brian Diers, Nicolas Martin

Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: machine learning; deep leaning; physiological maturity; computer vision; plant breeding; Phenology; Glycine max (L.) Merr.

Online: 19 September 2020 (10:08:43 CEST)

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Preprint REVIEW | doi:10.20944/preprints202008.0320.v1

Data Science in Unveiling COVID-19 Pathogenesis and Diagnosis: Evolutionary Origin to Drug Repurposing

Jayanta Kumar Das, Giuseppe Tradigo, Pierangelo Veltri, Pietro H Guzzi, Swarup Roy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Data Science; Machine Learning; Deep Learning; Genomics; COVID-19; Drug Discovery; Image Analysis; Interactomics; Epidemiology

Online: 14 August 2020 (11:01:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201811.0612.v1

A Deep Temporal Convolutional Neural Network for Regional and Teleseismic Detection

Joshua Dickey, Brett Borghetti, William Junek

Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: geophysical signal processing; pattern recognition; temporal convolutional neural networks; seismology; deep learning; nuclear treaty monitoring

Online: 29 November 2018 (03:37:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints201811.0045.v1

Cyber-Physical Attack Detection and Recovery based on RNN in Automotive Brake Systems

Jongho Shin, Youngmi Baek, Jaeseong Lee, Seonghun Lee

Subject: Engineering, Electrical And Electronic Engineering Keywords: Cyber-Physical Systems; Automotive; Cyber-Physical Attack; Integrity; Sensor Attack Detection; Speed Estimation; Deep learning

Online: 2 November 2018 (10:16:50 CET)

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The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literatures. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and cause performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

Preprint ARTICLE | doi:10.20944/preprints201809.0481.v1

Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Zied Tayeb, Juri Fedjaev, Nejla Ghaboosi, Christoph Richter, Lukas Everding, Xingwei Qu, Yingyu Wu, Gordon Cheng, Jörg Conradt

Subject: Computer Science And Mathematics, Computer Science Keywords: Brain-Computer Interfaces, spectrogram-based convolutional neural network model(pCNN), Deep Learning, EEG, LSTM, RCNN

Online: 25 September 2018 (08:58:34 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.1451.v1

Hybrid-3D-Convolutional-Transformer Model for Detecting Stereotypical Motor Movements in Autistic Children During Pre-Meltdown Crisis

Salma Kammoun Jarraya, Marwa Masmoudi

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Autism; Stereotypical Motor Movements; Pre-meltdown Crisis; Hybrid 3D Convolutional and Transformer Model; Deep Learning Approaches

Online: 18 October 2024 (09:20:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0686.v1

ChatGPT: Transforming Healthcare with AI

Fnu Neha, Deepshikha Bhati, Deepak Kumar Shukla, Md Amiruzzaman

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ChatGPT; Large Language Models (LLM); Artificial intelligence (AI); Deep Learning; Digital Health; Healthcare; Health Literacy; Medicine

Online: 9 October 2024 (12:03:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1861.v1

Physics-Informed Neural Network for Solving a 1-Dimensional Solid Mechanics Problem

Vishal Singh, Rajnish Mallick, Sahaj Saxena, Sharanjeet Dhawan, Manoj Sahni, Dineshkumar Harursampath

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Physics-informed Neural Network; Deep Neural Network; Artificial Neural Network; Computational Solid Mechanics; Partial Differential Equation

Online: 24 September 2024 (12:36:03 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.1217.v1

Assessing the Societal Risks of AI’s Rapid Advancement: Innovation or Threat to Safety?

Oma Zaib, Abbas Raza Ali

Subject: Computer Science And Mathematics, Computer Science Keywords: Artificial Intelligence (AI); Deep Fakes; Autonomous Vehicles (AV); Societal Safety; Cyber Security; Regulatory Frameworks

Online: 17 September 2024 (02:55:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.2352.v2

Meta-Connectivity in Urban Morphology: A Deep Generative Approach for Integrating Human-Wildlife Landscape Connectivity in Urban Design

Sheng-Yang Huang, Yuankai Wang, Enriqueta Llabres-Valls, Mochen Jiang, Fei Chen

Subject: Arts And Humanities, Architecture Keywords: urban design; deep generative models; cross-domain translation; landscape connectivity; progressive reasoning; urban ecology

Online: 22 August 2024 (09:13:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1178.v1

Achieving Real-time Prediction of Paroxysmal Atrial Fibrillation Onset by Convolutional Neural Network and Sliding Window on R-R Interval Sequences

Wenjing Chen, Peirong Zheng, Yuxiang Bu, Yuanning Xu, Dakun Lai

Subject: Engineering, Bioengineering Keywords: arrhythmias; paroxysmal atrial fibrillation; electrocardiogram; heart rate variability; deep learning; onset prediction; real-time; monitoring; algorithm

Online: 15 August 2024 (14:29:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.1095.v1

Impact of Image Size and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification

Calimanut-Ionut Cira, Miguel Angel Manso Callejo, Naoto Yokota, Tudor Sălăgean, Ana-Cornelia Badea

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: road classification; aerial orthoimagery; deep learning; image size; image overlap; convolutional neural networks; statistical performance evaluation

Online: 14 July 2024 (03:11:57 CEST)

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Popular geo-computer vision works make use of aerial imager with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near image boundaries. In this work, the impact of three image sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two image overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km² of the Spanish territory that was labelled with binary road information) under twelve different scenarios, each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on a data from novel areas covering approximately 825 km². The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of the performance at distinct levels of the fixed factors (tile size, tile overlap, neural network architecture). Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set.

Preprint ARTICLE | doi:10.20944/preprints202405.2022.v1

Comprehensive Neural Cryptanalysis on Block Ciphers Using Different Encryption Methods

Ongee Jeong, Ezat Ahmadzadeh, Inkyu Moon

Subject: Computer Science And Mathematics, Security Systems Keywords: artificial intelligence; cryptanalysis; block cipher; data encryption standard (DES); advanced encryption standard (AES); SPECK; deep learning

Online: 31 May 2024 (02:37:21 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202405.1971.v1

The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging

Róża Wajer, Adrian Wajer, Natalia Kazimierczak, Justyna Wilamowska, Zbigniew Serafin

Subject: Medicine And Pharmacology, Other Keywords: Cone-Beam Computed Tomography; Deep Learning Model; Image Quality; Noise Reduction; Dental Imaging; Metal Artifact Reduction

Online: 29 May 2024 (11:53:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202405.0659.v1

Facial Pain Expression Detection System using TXQEDA-Deep for Pain Evaluation

Rachid Aliradi, Nidhaleddine Chenni, Mohammed Touami, Ridha Ejbali, Najib Benaoun

Subject: Computer Science And Mathematics, Computer Science Keywords: Accuracy; Pain Assessment; PSPI; SVC; SVM; Multilinear subspace learning; TXQEDA; CNN-Deep Learning; UNBC McMaster dataset

Online: 10 May 2024 (11:52:37 CEST)

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Preprint REVIEW | doi:10.20944/preprints202405.0377.v1

How AI and Robotics Will Advance Interventional Radiology: A Narrative Review and Future Perspectives

Jiaming Zhang, Jiayi Fang, Yanneng XU, Guangyan Si

Subject: Medicine And Pharmacology, Clinical Medicine Keywords: Artificial Intelligence; Robot; Deep Learn; Machine Learn; Convolutional Neural Networks; Interventional Oncology; Interventional Neuroradiology; Interventional Cardiology.

Online: 7 May 2024 (11:41:25 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202405.0021.v1

Turning Features Detection from Aerial Images: Model Development and Application on Florida’s Public Roadways

Richard Boadu Antwi, Michael Kimollo, Eren Erman Ozguven, Thobias Sando, Ren Moses, Maxim A. Dulebenets

Subject: Engineering, Transportation Science And Technology Keywords: Turning lanes; Deep Learning; Roadway Characteristic Index (RCI); Pavement Markings; Machine Learning (ML); Roadway Geometry Features

Online: 1 May 2024 (04:28:21 CEST)

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Preprint REVIEW | doi:10.20944/preprints202404.1084.v1

Plant Synthetic Promoters

Piotr Szymczyk, Małgorzata Majewska

Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: plant synthetic promoter; enhancer; cis-element; trans-factor; orthogonal systems; bidirectional promoters; machine learning; deep learning

Online: 17 April 2024 (07:26:01 CEST)

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Preprint REVIEW | doi:10.20944/preprints202404.0188.v1

Neuromodulation and the Gut-Brain Axis: Therapeutic Mechanisms and Implications for Gastrointestinal and Neurological Disorders

Baha’ Aljeradat, Danisha Kumar, Suliman Abdulmuizz, Mrinmoy Kundu, Yasser F. Almealawy, Dima Ratib Batarseh, Oday Atallah, Michelle Ennabe, Muath Alsarafandi, Albert Alan, Martin Weinand

Subject: Medicine And Pharmacology, Neuroscience And Neurology Keywords: Gut-Brain-Axsis; Neuromodulation; Deep Brain stimulation; Vagus nerve Stimulation; Irritable Bowel Syndrome; Inflammatory Bowel Disease.

Online: 2 April 2024 (10:40:18 CEST)

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Preprint REVIEW | doi:10.20944/preprints202404.0055.v1

Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery

Sarfaraz K. Niazi, Zamara Mariam, Matthias Magoola

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Lead optimization; drug discovery; artificial intelligence; machine learning; deep neural networks; cheminformatics; chemical modifications; drug development

Online: 1 April 2024 (13:35:22 CEST)

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Preprint CASE REPORT | doi:10.20944/preprints202403.1073.v1

Improved Brain Function with Deep Spectrum Biotherapeutics

Iain Strickland, David S Oakley, Anna Parievsky

Subject: Medicine And Pharmacology, Neuroscience And Neurology Keywords: Electroencephalogram (EEG); Event Related Potential (ERP); Advanced &; Deep Biotherapeutics; Photobiomodulation (PBM); Mild Cognitive Impairment (MCI)

Online: 19 March 2024 (05:13:05 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1817.v1

Hybrid Neural Networks for Enhanced Prediction of Remaining Useful Life in Lithium-Ion Batteries

Alireza Rastegarparnah, Mohammed Eesa Asif, Rustam Stolkin

Subject: Engineering, Other Keywords: Deep Learning; Remaining Useful Life; Lithium-Ion Batteries; Battery Management Systems; Recycling and Reuse; Battery Degradation

Online: 25 January 2024 (10:40:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1249.v1

MODEAS – a Counter-UAV System with Optical and Radar Sensors

Alina Lindner, Thomas Müller, Lars Wilko Sommer, Thomas Emter, Angelika Zube, Alexander Kröker

Subject: Engineering, Electrical And Electronic Engineering Keywords: counter-UAV; UAV detection; UAV classification; UAV tracking; optical sensors; radar; artificial intelligence (AI); deep learning

Online: 16 January 2024 (15:31:27 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.2293.v1

Potential of Deep Eutectic Solvents in the Extraction of Organic Compounds From Food Industry By-Products and Agro-Industrial Waste

Maja Molnar, Dajana Gašo Sokač, Mario Komar, Martina Jakovljević Kovač, Valentina Bušić

Subject: Chemistry And Materials Science, Applied Chemistry Keywords: deep eutectic solvents; bioactive organic compounds; food industry by-products; greener sol-vents; agro-industrial waste

Online: 3 January 2024 (09:55:33 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.1362.v1

A Comprehensive Review on Deep Eutectic Solvents and Its Use to Extract Bioactive Compounds of Pharmaceutical Interest

Cátia Ferreira, And Mafalda Sarraguça

Subject: Chemistry And Materials Science, Other Keywords: Green Chemistry; Sustainability; Neoteric Solvents; Deep Eutectic Solvents; Bioactive Compounds; Extraction techniques; Natural products; Pharmaceutical Industry

Online: 19 December 2023 (07:23:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1299.v1

A Deep Sequencing Strategy for Investigation of Virus Variants Within African Swine Fever Virus-infected Pigs

Camille Melissa Johnston, Ann Sofie Olesen, Louise Lohse, Agnete le Maire Madsen, Anette Bøtner, Graham J. Belsham, Thomas Bruun Rasmussen

Subject: Biology And Life Sciences, Virology Keywords: African swine fever virus (ASFV); next-generation sequencing (NGS); deep sequencing; Nanopore sequencing; minority variant analysis

Online: 18 December 2023 (09:08:56 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0011.v1

Enhancing Fingerprint Forensics: A Comprehensive Study of Gender Classification Based on Advanced Data-Centric AI Approaches and Multi-Database Analysis

Assaf Spanier, Dor Steiner, Navon Sahalo, Yoel Abecassis, Dan Ziv, Ido Hefetz, Shimon Kimchi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; fingerprint; Gender classification; Data-Centric AI (DCAI); forensic science; Partial or low-quality fingerprint

Online: 1 December 2023 (08:15:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1888.v1

SkinLesNet: Classification of Skin Lesions Using a Multi-Layer Deep Convolutional Neural Network in Dermoscopy Images

Muhammad Azeem, Kaveh Kiani, Taha Mansouri

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; convolutional neural networks; image analysis; machine learning; ResNetV50; skin lesions; VGG16; cancer; computer vision

Online: 29 November 2023 (11:33:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202310.1519.v1

Continuous Super-Resolution of Climate Data Using Time-aware Implicit Neural Representation

Yang Wang, Hassan A. Karimi, Xiaowei Jia

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Deep Learning; Implicit Neural Representation; Sea Surface Temperature; Super Resolution; Satellite Retrieval Climate Data; Temporal Information

Online: 24 October 2023 (13:26:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.0467.v1

Extruder Machine Gear Fault Detection Using Autoencoder LSTM via Senor Fusion Approach

Joon-Hyuk Lee, Chibuzo Nwabufo Okwuosa, Jang-Wook Hur

Subject: Engineering, Industrial And Manufacturing Engineering Keywords: Anomaly detection; autoencoder; long short-term memory; deep learning; discrete wavelet transform, feature extraction, outlier detection.)

Online: 9 October 2023 (07:34:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.2105.v1

Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification

Tallha Akram, Riaz Junejo, Anas Alsuhaibani, Muhammad Rafiullah, Adeel Akram, Nouf Abdullah Almujally

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: convolutional neural networks; feature selection; transfer learning; feature fusion; gray wolf optimization; deep learning; skin lesion

Online: 31 August 2023 (10:24:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.0555.v1

Postural instability and risk of falls in patients with Parkinson’s disease treated with deep brain stimulation: a stabilometric platform study

Giorgio Leodori, Marco Santilli, Nicola Modugno, Michele D’Avino, Maria Ilenia De Bartolo, Andre Fabbrini, Lorenzo Rocchi, Antonella Conte, Giovanni Fabbrini, Daniele Belvisi

Subject: Medicine And Pharmacology, Neuroscience And Neurology Keywords: deep brain stimulation; fall risk; falls; parkinson’s disease; postural instability; stabilometric platform; movement disorders; postural control.

Online: 7 August 2023 (12:03:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.0258.v1

Autonomously Steering Vehicles Along Unmarked Roads Using Low-Cost Sensing and Computational Systems

Giuseppe DeRose, Austin Ramsey, Justin Dombecki, Nicholas Paul, Chan-Jin Chung

Subject: Computer Science And Mathematics, Robotics Keywords: Autonomous vehicles; self-drive vehicles; deep learning; convolutional neural networks; recurrent neural networks; image histogram matching

Online: 3 August 2023 (08:20:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.1510.v1

Advancing Pig Welfare Assessment: Introducing the SSPD-PER Method for Objective and Reliable Pig Emotion Recognition

Jung Hwan Kim, Savina Jassica Colaco, Alwin Poulose, Suresh Neethirajan, Dong Seog Han

Subject: Biology And Life Sciences, Aquatic Science Keywords: Pig emotion recognition (PER); convolution neural network (CNN); Xception; ResNet; deep neural network; domestic livestock; pigs

Online: 21 July 2023 (11:15:43 CEST)

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Preprint REVIEW | doi:10.20944/preprints202305.1637.v2

Computational Advancements in Cancer Combination Therapy Prediction

Victoria L. Flanary, Jennifer L. Fisher, Elizabeth J. Wilk, Timothy C. Howton, Brittany N. Lasseigne

Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: computational; drug repurposing; drug repositioning; cancer; combination therapy; network biology; machine learning; deep learning; precision oncology

Online: 20 July 2023 (05:02:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.0219.v1

Joint Latency-Oriented, Energy Consumption, and Carbon Emission for Space-Air-Ground Integrated Network with New Designed Power Technology

Yonghao Wang, Bo Li, Jiahao He, Jiaxing Dai, Yuxin Yang, Yidong Liu

Subject: Computer Science And Mathematics, Other Keywords: space-air-ground integrated network; renewable energy; twin delayed deep deterministic policy gradient; latency; energy consumption

Online: 4 July 2023 (11:24:56 CEST)

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Preprint REVIEW | doi:10.20944/preprints202306.2225.v1

A Review of Brain Tumor Diagnosis and Segmentation

Reham Kaifi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Brain tumors; magnetic resonance imaging; computed tomography; computer-aided diagnostic and detection; Deep learning; Machine learning

Online: 30 June 2023 (14:56:31 CEST)

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Preprint REVIEW | doi:10.20944/preprints202306.1124.v1

Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging

Reabal Najjar

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Medical imaging; radiology; artificial intelligence; machine learning; deep learning; convolutional neural networks; computer-aided diagnosis; radiomics.

Online: 16 June 2023 (03:22:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202306.0262.v1

Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network

Isaac Oluwadunsin Ogunrinde, Shonda Bernadin

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Multi-object tracking; DeepSORT; object detection; sensor fusion; deep learning, autonomous vehicles; radars; adverse weather; fog

Online: 5 June 2023 (08:09:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0156.v1

Three-Dimensional Cranio-Facial Landmark Detection in Series of CT Slices, Using Multi-Phased Regression Networks

Soh Nishimoto, Takuya Saito, Hisako Ishise, Toshihiro Fujiwara, Kenichiro Kawai, Masao Kakibuchi

Subject: Medicine And Pharmacology, Dentistry And Oral Surgery Keywords: Multi-phased deep learning; Regression neural network; Coordinate value; Computer assisted tomography (CT); Cranio-facial bone

Online: 10 April 2023 (07:59:58 CEST)

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Preprint REVIEW | doi:10.20944/preprints202303.0116.v1

Explainable Artificial Intelligence (XAI) in Healthcare

Tim Hulsen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: XAI; AI; artificial intelligence; explainable; explainability; machine learning; deep learning; data science; big data; healthcare; medicine

Online: 7 March 2023 (01:43:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0299.v1

Using Ensemble Convolutional Neural Network to Detect Deepfakes Using Periocular Data

David Johnson, Xiaohonh Yuan, Kaushik Roy

Subject: Computer Science And Mathematics, Computer Science Keywords: deepfake detection; CNN; deep neural network; computer vision; scale invariant feature transform; histogram of oriented gradients

Online: 17 February 2023 (06:51:37 CET)

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Preprint DATA DESCRIPTOR | doi:10.20944/preprints202301.0473.v1

Prognosease: A Data Generator for Health Deterioration Prognosis

Tarek BERGHOUT, Mohamed Benbouzid

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Data generator; dataset; deep learning; health index; machine learning; prognosis and health management; remaining useful life

Online: 26 January 2023 (08:37:30 CET)

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Preprint ARTICLE | doi:10.20944/preprints202210.0477.v1

FlowerPhenoNet: Automated Flower Detection from Multi-view Image Sequences using Deep Neural Networks for Temporal Plant Phenotyping Analysis

Sruti Das Choudhury, Samarpan Guha, Aankit Das, Amit Kumar Das, Ashok Samal, Tala Awada

Subject: Computer Science And Mathematics, Analysis Keywords: High Throughput Plant Phenotyping; Deep Neural Network; Flower Detection; Temporal Phenotypes; Benchmark Dataset; Flower Status Report

Online: 31 October 2022 (10:00:24 CET)

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Preprint ARTICLE | doi:10.20944/preprints202210.0448.v1

Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings

Daniel Kvak

Subject: Arts And Humanities, Art Keywords: computational creativity; deep learning; feature extraction; image analysis; machine perception; painting classification; residual networks; transfer learning

Online: 28 October 2022 (09:37:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202208.0197.v1

An Ontological Knowledge Base of Poisoning Attacks on Deep Neural Networks

Majed Altoub, Fahad AlQurashi, Tan Yigitcanlar, Juan Corchado, Rashid Mehmood

Subject: Computer Science And Mathematics, Computer Science Keywords: Deep neural networks; Adversarial Attacks; Poisoning; Backdoors; Trojans; Taxonomy; Ontology; Knowledge Base; Explainable AI; Green AI

Online: 10 August 2022 (09:39:07 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202204.0163.v1

Jointly Optimized Deep Neural Networks to Synthesize Monoenergetic Images from Single-Energy CT Angiography for Improving Classification of Pulmonary Embolism

Matthias A Fink, Constantin Seibold, Hans-Ulrich Kauczor, Rainer Stiefelhagen, Jens Kleesiek

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: artificial intelligence; deep learning; image-to-image translation; dual-energy computed tomography; pulmonary embolism; emergency radiology

Online: 18 April 2022 (09:45:00 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202108.0149.v1

HPV DeepSeq: Ultra-Fast Method of NGS Data Analysis and Visualization Using Automated Workflows and a Customized Papillomavirus Database in CLC Genomics Workbench

Jane Shen-Gunther, Qingqing Xia, Hong Cai, Yufeng Wang

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Bioinformatics; Cervical cancer; Deep Sequencing; Human papillomavirus; HPV genotyping; Metagenome; Next generation sequencing; Taxonomic classification; Virome

Online: 6 August 2021 (08:00:00 CEST)

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Preprint REVIEW | doi:10.20944/preprints202106.0609.v1

Treatment-Free Remission in Chronic Myeloid Leukaemia: Can We Identify Prognostic Factors?

Hilbeen Hisham Saifullah, Claire Marie Lucas

Subject: Medicine And Pharmacology, Immunology And Allergy Keywords: Chronic myeloid leukaemia; chronic phase; Tyrosine kinase inhibitor; Treatment free remission; deep molecular response; BCR-ABL.

Online: 25 June 2021 (08:13:40 CEST)

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Preprint REVIEW | doi:10.20944/preprints202106.0448.v1

Current Knowledge on the Background, Pathophysiology, and Treatment of Levodopa-Induced Dyskinesia – Literature Review.

Michał Jan Hutny, Jagoda Maria Hofman, Aleksandra Klimkowicz-Mrowiec, Agnieszka Gorzkowska

Subject: Medicine And Pharmacology, Immunology And Allergy Keywords: Parkinson’s disease; abnormal involuntary movements; dopaminergic signaling; basal ganglia; spiny projection neurons; neurotransmission; deep brain stimulation

Online: 16 June 2021 (14:17:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202106.0053.v1

Sentiment Analysis Techniques Applied to Raw-Text Data from a CSQ-8 Questionnaire About Mindfulness in Times of Covid-19 to Improve Strategy Generation

Mario Jojoa, Gema Castillo-Sánchez, Begonya Garcia-Zapirain, Isabel De la Torre Diez, Manuel Franco-Martín

Subject: Engineering, Automotive Engineering Keywords: Mindfulness; stress; COVID-1; CSQ-8; Natural Language Processing; Deep Learning; Embedding; IMDB; Swivel; Neural Networks.

Online: 2 June 2021 (08:40:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202105.0605.v1

COVID-19 Diagnosis from chest CT Scans: A Weakly Supervised CNN-LSTM Approach

Mustafa Kara, Zeynep Öztürk, Sergin Akpek, Ayşegül Turupcu

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: deep learning; computed tomography; image classification; COVID-19; medical image analysis; pneumonia; CNN, LSTM, medical diagnosis

Online: 25 May 2021 (10:32:29 CEST)

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Working Paper ARTICLE

Hydrodynamic Forces Exerted on an Oscillating Cylinder at Its Translational Motion in Water Covered by a Compressed Ice

Yury Stepanyants, Izolda Sturova

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Ideal fluid; deep water; ice cover; moving cylinder; hydrodynamic load; added mass; wave resistance; damping coefficient

Online: 21 January 2021 (15:21:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202006.0104.v1

Can Biochar Save Lives? The Impact of Surficial Biochar Treatment on Acute H₂S and NH₃ Emissions During Swine Manure Agitation Before Pump-out

Baitong Chen, Jacek A. Koziel, Myeongseong Lee, Hantian Ma, Zhanibek Meiirkhanuly, Peiyang Li, Andrzej Białowiec, Robert C. Brown

Subject: Environmental And Earth Sciences, Environmental Science Keywords: biochar; hydrogen sulfide; ammonia; livestock manure; agricultural safety; deep pit storage; waste management; air pollution; odor

Online: 7 June 2020 (15:54:48 CEST)

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Hydrogen sulfide and ammonia are always a concern in the livestock industries, especially when farmers try to clear their manure storage pits. Agitation of manure can cause dangerously high concentrations of harmful agents such as H₂S and NH₃ to be emitted into the air. Biochar has the ability to sorb these gases. We hypothesized that applying biochar on top of manure can create an effective barrier to protect farmers and animals from exposure to NH₃ and H₂S. In this study, two kinds of biochar were tested, highly alkaline, and porous (HAP, pH 9.2) biochar made from corn stover and red oak biochar (RO, pH 7.5). Two scenarios of (6 mm) 0.25” and (12 mm) 0.5” thick layers of biochar treatments were topically applied to the manure and tested on a pilot-scale setup, simulating a deep pit storage. Each setup experienced 3-min of agitation using a transfer pump, and measurements of the concentrations of NH₃ and H₂S were taken in real-time and measured until the concentration stabilized after the sharp increase in concentration due to agitation. The results were compared with the control in the following 3 situations: 1. The maximum (peak) flux 2. Total emission from the start of agitation until the concentration stabilized, and 3. The total emission during the 3 min of agitation. For NH₃, 0.5” HAP biochar treatment significantly (p<0.05) reduced maximum flux by 63.3%, overall total emission by 70%, and total emissions during the 3-min agitation by 85.2%; 0.25” HAP biochar treatment significantly (p<0.05) reduced maximum flux by 75.7%, overall, total emission by 74.5%, and total emissions during the 3-min agitation by 77.8%. 0.5” RO biochar treatment significantly reduced max by 8.8%, overall total emission by 52.9%, and total emission during 3-min agitation by 56.8%; 0.25” RO biochar treatment significantly reduced max by 61.3%, overall total emission by 86.1%, and total emission during 3-min agitation by 62.7%. For H₂S, 0.5” HAP biochar treatment reduced the max by 42.5% (p=0.125), overall total emission by 17.9% (p=0.290), and significantly reduced the total emission during 3-min agitation by 70.4%; 0.25” HAP treatment reduced max by 60.6% (p=0.058), and significantly reduced overall and 3-min agitation’s total emission by 64.4% and 66.6%, respectively. 0.5” RO biochar treatment reduce the max flux by 23.6% (p=0.145), and significantly reduced overall and 3-min total emission by 39.3% and 62.4%, respectively; 0.25” RO treatment significantly reduced the max flux by 63%, overall total emission by 84.7%, and total emission during 3-min agitation by 67.4%.

Preprint REVIEW | doi:10.20944/preprints202004.0325.v1

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Dinh C. Nguyen, Ming Dinh, Pubudu N. Pathirana, Aruna Seneviratne

Subject: Medicine And Pharmacology, Epidemiology And Infectious Diseases Keywords: COVID-19; artificial intelligence; AI; blockchain; epidemic; pandemic; machine learning; deep learning; coronavirus; SARS-CoV-2

Online: 19 April 2020 (04:44:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201905.0244.v1

Ensemble of Deep Convolutional Neural Networks for Classification of Early Barrett's Neoplasia Using Volumetric Laser Endomicroscopy

Roger Fonollà, Thom Scheeve, Maarten R. Struyvenberg, Wouter L. Curvers, Albert J. de Groof, Fons van der Sommen, Erik J. Schoon, Jacques J.G.H.M. Bergman, Peter H.N. de With

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Barrett's esophagus; deep learning; volumetric laser endomicroscopy; optical coherence tomography; classification; esophageal adenocarcinoma; glands; machine learning

Online: 20 May 2019 (11:50:09 CEST)

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Preprint REVIEW | doi:10.20944/preprints201805.0011.v1

Changing Trends in Computational Drug Repositioning

Jaswanth K. Yella, Suryanarayana Yaddanapudi, Yunguan Wang, Anil G. Jegga

Subject: Medicine And Pharmacology, Pharmacology And Toxicology Keywords: computational drug repositioning; drug repositioning; drug repurposing; machine learning; deep learning; crowdsourcing; open innovation; drug discovery

Online: 1 May 2018 (12:27:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201706.0012.v3

Road Segmentation on Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields

Teerapong Panboonyuen, Peerapon Vateekul, Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern

Subject: Engineering, Control And Systems Engineering Keywords: deep convolutional neural networks; road segmentation; conditional random fields; landscape metrics; satellite images; aerial images; THEOS

Online: 5 June 2017 (06:39:54 CEST)

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Preprint REVIEW | doi:10.20944/preprints202105.0254.v1

AI-enabled Efficient and Safe Food Supply Chain

Ilianna Kollia, Jack Stevenson, Stefanos Kollias

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; deep neural networks; recurrent LSTM models; attention layers; latent variable extraction; domain adaptation; yield and growth prediction in greenhouses; energy optimization in retail refrigerator systems; verification and recognition of expiry date in retail food packaging.

Online: 11 May 2021 (15:46:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.2033.v1

Hybrid Integent Staging of Age-Related Macular Degeneration for Decision-Making on Patient Management Tactics

Ekaterina A. Lopukhova, Ernest S. Yusupov, Rada R. Ibragimova, Gulnaz M. Idrisova, Timur R. Mukhamadeev, Elizaveta P. Grakhova, Ruslan V. Kutluyarov

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Age-related macular degeneration; optical coherence tomography; staging, computer vision; deep learning; hierarchical classification; semi-supervised learning

Online: 28 October 2024 (10:49:11 CET)

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Preprint REVIEW | doi:10.20944/preprints202410.1058.v1

Advances and Challenges in Automated Drowning Detection and Prevention Systems

Maad Shatnawi, Frdoos Ali Albreiki, Ashwaq Alkhoori, Mariam Alhebshi, Anas Shatnawi

Subject: Engineering, Control And Systems Engineering Keywords: Drowning detection; prevention; rescue; Swimming pool safety; IoT; artificial intelligence: AI; deep learning; machine learning; surveillance technology

Online: 14 October 2024 (13:25:50 CEST)

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Preprint REVIEW | doi:10.20944/preprints202410.0792.v1

State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review

Reshma Ravindran, Ahmed M. Massoud

Subject: Engineering, Electrical And Electronic Engineering Keywords: aerospace; DC-DC converters; deep space missions; high power electric propulsion; modular interleaved converters; satellite power system

Online: 10 October 2024 (12:56:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1852.v1

A Novel Two-Stage Hybrid Model Optimization with FS-FCRBM-GWDO for Accurate and Stable STLF

Eustache Uwimana, Yatong Zhou

Subject: Engineering, Electrical And Electronic Engineering Keywords: Genetic wind-driven optimization algorithm; Short-term load forecasting; Factored conditional deep belief network; Efficiency energy consumption

Online: 26 August 2024 (15:35:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.1300.v1

Parallel PSO for the Efficient Training of Neural Networks Using the GPGPU and Apache Spark in an Edge Computing Environment

Manuel I. Capel, Alberto Salguero-Hidalgo, Juan Antonio Holgado-Terriza

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Apache Spark; Classification recall; Deep Neural Networks; GPU Parallelism; Optimization research; Particle Swarm Optimization (PSO); Predictive Accuracy

Online: 16 July 2024 (08:29:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0478.v1

Gastric Cancer Image Classification: a Comparative Analysis and Feature Fusion Strategies

Andrea Loddo, Marco Usai, Cecilia Di Ruberto

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Computational Pathology; Histopathological Imaging; Gastric Cancer; Convolutional Neural Networks; Machine Learning; Deep Learning; Feature Extraction; Feature Combination

Online: 6 July 2024 (03:16:08 CEST)

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Preprint REVIEW | doi:10.20944/preprints202407.0407.v1

Treating Deep-Seated Tumors With Radio-Dynamic Therapy: Progress and Perspectives

Rongcheng Han, Shengcang Zhu, Siyue Lin

Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: radio-dynamic therapy; reactive oxygen species; one-component RDT systems; mechanisms and its application; deep-seated tumors.

Online: 4 July 2024 (08:36:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202405.0237.v1

Unprecedented High Probe-Reported Polarity of Deep Eutectic Solvents Composed of Lanthanide Salts and Urea

Anushis Patra, Vaishali Khokhar, Siddharth Pandey

Subject: Chemistry And Materials Science, Physical Chemistry Keywords: deep eutectic solvent; hydrated lanthanide salts; Urea; dipolarity/polarizability (π*); pyrene-1-carboxaldehyde; Nile red; aggregates; pyranine

Online: 6 May 2024 (07:15:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0250.v1

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor

Lukas Boborzi, Julian Decker, Razieh Rezaei, Roman Schniepp, Max Wuehr

Subject: Computer Science And Mathematics, Signal Processing Keywords: human activity recognition; inertial sensor; ear; in-ear sensing; vital sign monitoring; wearables; machine learning; deep learning

Online: 3 April 2024 (11:06:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202403.1349.v1

Parkinson's Disease Recognition using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data

Pranita Patil, W. Randolph Ford

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Class bias; DcCNN; decorrelation; deep learning; FE-DcCNN; invariant features; Parkinson’s disease; rs-fMRI image; scanner bias

Online: 22 March 2024 (09:21:12 CET)

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Parkinson’s Disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assist in recognizing the PD with good accuracy. The decorrelation function reduces the non-linear correlation between features and bias in order to learn bias-invariant features. The Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single scanner imbalanced dataset in this study used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new Decorrelated Convolutional Neural Networks (DcCNN) framework by applying decorrelation-based optimization to Convolutional Neural Networks(CNN). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN model performs significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner invariant features without significantly impacting the performance of a model. The obtained dataset is a multi-scanner dataset which leads to scanner bias due to the differences in acquisition protocols and scanners. The multi-scanner dataset is a combination of two datasets, namely PPMI and FTLDNI - frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed Feature Extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multi-scanner dataset for differentiating PD from healthy control, which is superior to the DcCNN model trained on a single scanner imbalanced dataset.

Preprint ARTICLE | doi:10.20944/preprints202402.1165.v1

Improving Generalizability of PET DL Algorithms: List-Mode Reconstructions Improve DOTATATE PET Hepatic Lesion Detection Performance

Xinyi Yang, Michael Silosky, Jonathan Wehrend, Daniel V Litwiller, Muthiah Nachiappan, Scott D Metzler, Debashis Ghosh, Fuyong Xing, Bennett B Chin

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep learning; Convolutional neural network; Gastroenteropancreatic neuroendocrine tumor; GEP-NET; DOTATATE; Positron emission tomography; PET; Liver tumor.

Online: 20 February 2024 (15:38:44 CET)

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Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteopancreatic neuroendocrine tumors (GEP-NETs), and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n=83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n=123 subjects). Set1, the data set from Scanner1 was reconstructed with standard clinical parameters (5 minutes; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-minutes). Set2, data from Scanner2, representing out-of-domain clinical scans, used standard iterative reconstruction (5 minutes; OSEM). Reconstructed datasets were divided into training, validation, and testing datasets in a 60%, 20%, 20% proportion, and a deep neural network was trained with each: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50%, and 75%) of Set1 for training. Scanner1, list-mode 2-minute reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1=0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1=0.755; p-value=0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1=0.478) to 100% training data (F1=0.713; p<0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets, and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.

Preprint ARTICLE | doi:10.20944/preprints202402.1102.v1

Application of Artificial Intelligence in Intelligent Identification Using Neural Networks in Brain Diseases

Behroz Bidabad, Mobin Nazem, Nozar Aghakhani

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: artificial intelligence; functional neural connectivity; functional magnetic resonance imaging (fMRI); deep learning; neural network; epilepsy; parkinson; seizures

Online: 20 February 2024 (08:48:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1037.v1

Simulation and Real-Life Implementation of UAV Autonomous Landing System Based on Object Recognition and Tracking for Safe Landing in Uncertain Environments

Ranjai Baidya, Heon Jeong

Subject: Computer Science And Mathematics, Robotics Keywords: Intelligent autonomous system; Autonomous Landing; Obstacle avoidance; Object detection; Quadrotors; Deep SORT; YOLOv5; Distance Transform; PID control

Online: 12 January 2024 (15:10:20 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1909.v1

DeepFlower: Archival and Retrieval of Videos

Manjunath Aradhya, Jyothi V K, Sharath Kumar, Guru D S, Vinay Kumar

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Keyframe selection; Dimensionality Reduction; ReliefF; Bi-normal separation; Indexing; KD-tree; retrieval of flower video; Deep Flower

Online: 29 November 2023 (16:41:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1411.v1

IMU-Based Fitness Activity Recognition Using CNNs for Time Series Classification

Philipp Niklas Müller, Alexander Josef Müller, Philipp Achenbach, Stefan Peter Göbel

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: activity recognition; inertial measurement unit; deep learning; convolutional neural network; residual neural network; traditional machine learning; study

Online: 22 November 2023 (14:51:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202310.1254.v1

Deep-penetrating Geochemical Prospecting Experiment of Mahuagou Gold Deposit in the Core of Huangling Anticline, Western Hubei

Weihang Zhou, Li Lei, Yin Gong, Demin Liu, Shuyun Xie, Zhijun Chen, Qinglin Xia, Mengqi Wang, Salah Fadlallah Awadelseid, Oraphan Yaisamut

Subject: Environmental And Earth Sciences, Geochemistry And Petrology Keywords: overburden prospecting; deep-penetrating geochemical indicator; chemical form analysis of gold; heat-released mercury survey; Huangling Anticline

Online: 19 October 2023 (09:34:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.1518.v1

Sustainable Bioactive Dyeing of Textiles: A Novel Strategy Using Bacterial Pigments, Natural Antibacterial Ingredients, and Deep Eutectic Solvents

Cláudia Mouro, Ana P. Gomes, Rita V. Costa, Farzaneh Moghtader, Isabel C. Gouveia

Subject: Chemistry And Materials Science, Materials Science And Technology Keywords: Bacterial pigments; Crude gel prodigiosin pigment; Natural-dyeing; Bio-mordant; L-Cysteine; Gel-based deep eutectic solvent

Online: 22 September 2023 (08:20:46 CEST)

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Textile industry is one of the major industries responsible for environmental pollution globally. This is mainly due to the use of synthetic dyes, toxic ingredients and solvents for dy-ing/treatments/protocols of textiles. Replacement of those non ecofriendly ingredients would be a very challenging target to develop novel sustainable textile dying strategies. Using biobased and bioactive pigments, antibacterial ingredients and especially natural solvents are recently pointed out as substances to included in the related formulations. In this study the dyeing ability of the crude gel prodigiosin produced by non-pathogenic bacteria Serratia plymuthica as a natural pigment was investigated on various multifiber fabrics at different condition (temperature and pH) and by using salts (NaOH and Na2S2O4) and alternative mordants (a conventional: FeSO4 and a new bio-mordant: L-Cysteine (L-Cys)) in the recipes with different compositions and especially replacing of organic solvent with a new type of gel-based Choline chloride (ChCl) : Lactic acid (LA) (1 : 2) deep eutectic solvents (DES) as a dyeing medium. For determination of the dying process performances, the color strength (K/S value) of dyed fabric and the dyeing equalization (dE) parameters were evaluated. The fastness properties of the dyed fabrics which displayed as a higher K/S value and good dE were evaluated. The nylon fabrics dyed with 3.0% owf L-Cys at pH = 8.3 improved the color fastness to washing, while the gel-based ChCl : LA (1 : 2) DES dyebath provided a better color fastness to light. Moreover, the nylon fabrics under these conditions exhibited remarkable anti-microbial activity against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). As conclusion, the use of the crude gel prodigiosin pigment revealed an advantageous ability to dye the textile materials, responding to consumers' demand for more ecological and sustainable products. Furthermore, the natural reducing agent L-Cys, never tested before as a bio-mordant, and the use of gel-based DES as a dyeing medium demonstrated enhanced colorimetric and antibacterial properties on dyed nylon with crude gel prodigiosin pigment.

Preprint REVIEW | doi:10.20944/preprints202308.1468.v1

Diagnostic Imaging in Infectious Keratitis: A Review

Maria Cabrera-Aguas, Stephanie L Watson

Subject: Medicine And Pharmacology, Ophthalmology Keywords: infectious keratitis; corneal imaging; in vivo confocal microscopy; optical coherence tomography; artificial intelligence; deep learning; microbial keratitis

Online: 21 August 2023 (11:43:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0596.v2

Feasibility of Proton Dosimetry Overriding Planning CT with Daily CBCT Elaborated through Generative Artificial Intelligence Tools

Matteo Rossi, Gabriele Belotti, Luca Mainardi, Guido Baroni, Pietro Cerveri

Subject: Engineering, Bioengineering Keywords: Deep Learning; image-to-image translation; dosimetry; cycleGAN; CBCT; CT; limited FOV; artifact correction; Hounsfield unit recovery

Online: 5 June 2023 (09:53:36 CEST)

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Radiotherapy commonly utilizes CBCT for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into HU values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow FOV systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent GAN processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm’s ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Preprint REVIEW | doi:10.20944/preprints202306.0243.v1

New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making

Nicolae Iftimia, Raj Pandya, Forest Mahoney

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: AI; artificial intelligence; automation; neural networks; machine learning; deep learning; large language models; biomedical research; imaging; healthcare

Online: 5 June 2023 (05:15:33 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0282.v1

A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks

Alma Guerrero-Sánchez, Edgar Alejandro Rivas-Araiza, Mariano Garduño-Aparicio, Saúl Tovar-Arriaga, Juvenal Rodriguez-Resendiz, Manuel Toledano-Ayala

Subject: Engineering, Electrical And Electronic Engineering Keywords: Artificial intelligence; Neural Networks; Deep learning; Multitasking learning; Solar photovoltaic; Smart grids; Multiple Electrical Disturbances; Power quality

Online: 5 May 2023 (03:12:18 CEST)

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Preprint REVIEW | doi:10.20944/preprints202303.0497.v1

Cyber-attacks in Cyber Physical Microgrid Systems : A comprehensive Review

Sriranga Suprabhath Koduru, Venkata siva Prasad Machina, Sreedhar Madichetty

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Cyber physical systems 1; Cyber attacks 2; Artificial Intelligence 3; Machine learning 4; 8 Deep learning 5

Online: 29 March 2023 (02:48:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202301.0533.v1

Improving Deep Echo State Network with Neuronal Similarity-based Iterative Pruning Merging Algorithm

Qingyu Shen, Hanwen Zhang, Yao Mao

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: reservoir computing; deep echo state network; neuronal similarity-based iterative pruning merging algorithm; chaotic time series forecast

Online: 30 January 2023 (02:34:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0162.v1

MR-Class: A Python Tool for Brain Mr Image Classification Utilizing One-Vs-All DCNNS to Deal With the Open-Set Recognition Problem

Patrick Salome, Francesco Sforazzini, Gianluca Grugnara, Andreas Kudak, Matthias Dostal, Christel Herold-Mende, Sabine Heiland, Jürgen Debus, Amir Abdollahi, Maximilian Knoll

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Content-based image classification; Data curation and preparation; Convolutional neural networks (CNN); Deep learning; Artificial intelligence (AI)

Online: 9 January 2023 (10:59:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints202211.0205.v1

A Novel Algorithm for Histopathological Lung Cancer Detection

Nelson Filipe Miranda Faria, Sofia Campelos, Vítor Carvalho

Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: lung cancer; digital pathology; whole slide imaging; artificial intelligence; deep learning; convolutional neural networks; computer-aided diagnosis

Online: 10 November 2022 (15:06:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202206.0384.v1

Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence

Quan Thanh Huynh, Phuc Hoang Nguyen, Hieu Xuan Le, Lua Thi Ngo, Nhu-Thuy Trinh, Mai Thi-Thanh Tran, Hoan Tam Nguyen, Nga Thi Vu, Anh Tam Nguyen, Kazuma Suda, Kazuhiro Tsuji, Tsuyoshi Ishii, Trung Xuan Ngo, Hoan Thanh Ngo

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep Learning; Smartphone Image; Acne Grading; Acne Object DetectionDeep Learning, Smartphone Image, Acne Grading, Acne Object Detection

Online: 28 June 2022 (10:05:25 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202201.0090.v1

DeHyFoNet: Deformable Hybrid Network for Formula Detection in Scanned Document Images

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

Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: formula detection; Hybrid Task Cascade network; mathematical expression detection; document image analysis; deep neural networks; computer vision

Online: 6 January 2022 (12:56:23 CET)

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Preprint REVIEW | doi:10.20944/preprints202112.0015.v1

Combined Zonation of the African-Levantine-Caucasian Areal of Ancient Hominin: Review and Integrated Analysis of Paleogeographical, Stratigraphic and Geophysical-Geodynamical Data

Lev V. Eppelbaum, Youri Katz

Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: Hydrospheric events; tectonic-geodynamic zonation; paleogeographic reconstructions; ancient man migration; Levantine Corridor; paleomagnetic correlation; deep geodynamic factors

Online: 1 December 2021 (13:06:47 CET)

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