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Preprint ARTICLE | doi:10.20944/preprints202308.0148.v1

Konstantine Arkoudas

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: GPT-4; LLM; AI; reasoning; inference

Online: 2 August 2023 (04:10:57 CEST)

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Preprint REVIEW | doi:10.20944/preprints202303.0344.v1

Strategies for the Management of Spike Protein-Related Pathology

Matthew T.J. Halma, Cristof Plothe, Theresa Lawrie

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Long Covid; Covid-19 vaccine; thrombosis; clots; inflammation; therapeutics

Online: 20 March 2023 (04:37:02 CET)

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Preprint ARTICLE | doi:10.20944/preprints202104.0710.v2

Estimation of Oxygen Cylinder Availability and Classification of Its Types, Suitability Using Machine Learning and Data Analysis

Anurag Sinha

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: covid-19, machine learning, oxygen cylinder.

Online: 6 May 2021 (14:58:50 CEST)

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This is break direction on oxygen sources and conveyance methodologies for COVID-19 treatment. It has been adjusted from WHO and UNICEF's specialized details and direction for oxygen treatment gadgets, which is important for the WHO clinical gadget specialized arrangement, 1 and depends on current information on the circumstance in China and different nations where cases have been distinguished. This direction is proposed for wellbeing office chairmen, clinical leaders, acquisition officials, arranging officials, biomedical architects, foundation engineers and strategy producers. It portrays how to: measure oxygen interest, to distinguish oxygen sources that are accessible, and select suitable flood sources to best react to COVID-19 patients' requirements, particularly in low-and-center pay nations. WHO will refresh these suggestions as new data opens up. Coronavirus pandemic spurred fake interest for oxygen gas chambers for clinical use - both at emergency clinics and inquisitively, for home use by patients. A few patients and surprisingly sound people investigate the conceivable outcomes and likely benefits of utilizing oxygen from chambers for private utilization. Be that as it may, this isn't continuously protected, and sufficient safety measures are to be taken, bombing which there can be fatalities. This paper investigates the significance of keeping up satisfactory degrees of oxygen levels appropriate for human utilization. It advises the clinical use and the advantages and disadvantages of putting away oxygen chambers at home. The investigation likewise addresses lawful and administrative perspectives. The investigation's discoveries can help people settle on an educated choice on the protected use regarding oxygen gas. Further, it cautions on the expanded significance of guidelines and limiting access and use. This paper aims at designing an oxygen level monitoring technique in an oxygen cylinder. The amount of oxygen present inside the oxygen cylinder is very vital information when such cylinder is in use for supply of oxygen to a critical patient. The amount of oxygen present inside the cylinder is measured by the pressure at the outlet nozzle. The pressure is measured using a high precision MEMS Pressure Sensor. The output of the MEMS pressure sensor is voltage of the order milli. An amplifier is used to amplify this milli volt signal. A microcontroller is used in cascade to process the signal and display the pressure of oxygen cylinder.

Preprint BRIEF REPORT | doi:10.20944/preprints202303.0422.v1

GPT-4 vs. GPT-3.5: A Concise Showdown

Anis Koubaa

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ChatGPT; GPT-4; GPT-3.5; GPT performance; GPT limitations; OpenaAI; NLP

Online: 24 March 2023 (02:23:41 CET)

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Preprint ARTICLE | doi:10.20944/preprints202308.0148.v2

GPT-4 Can't Reason

Konstantine Arkoudas

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: GPT-4; LLM; AI; reasoning; inference

Online: 7 August 2023 (07:13:00 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201811.0546.v4

An Overview of Convolutional Neural Network: Its Architecture and Applications

Shadman Sakib, Nazib Ahmed, Ahmed Jawad Kabir, Hridon Ahmed

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Convolutional Neural Network (CNN), Deep learning, Architecture, Applications

Online: 14 February 2019 (10:01:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints201912.0028.v1

Diversity-Generated Image Inpainting with Style Extraction

Weiwei Cai, Zhanguo Wei

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; generative adversarial networks; image inpainting; diversity inpainting

Online: 3 December 2019 (12:04:01 CET)

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Preprint REVIEW | doi:10.20944/preprints202007.0230.v1

Machine Learning: A Review of Learning Types

Shagan Sah

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

Online: 11 July 2020 (04:46:12 CEST)

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Preprint REVIEW | doi:10.20944/preprints202005.0234.v1

Epidemiological Challenges in Pandemic Coronavirus Disease (COVID-19): Role of Artificial Intelligence

Abhijit Dasgupta, Abhisek Bakshi, Srijani Mukherjee, Kuntal Das, Soumyajeet Talukdar, Pratyayee Chatterjee, Sagnik Mondal, Puspita Das, Subhrojit Ghosh, Archisman Som, Pritha Roy, Rima Kundu, Akash Sarkar, Arnab Biswas, Karnelia Paul, Sujit Basak, Krishnendu Manna, Chinmay Saha, Satinath Mukhopadhyay, Nitai P. Bhattacharyya, Rajat K. De

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: SIRD; Twitter; GHSI; Pre-symptomatic; EHR; Contact tracing; On-line survey; qRT-PCR; X-ray; CT/HRCT; CNN; Autoencoder; Drug affinity; CPI; and Inflation.

Online: 14 May 2020 (11:25:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201908.0073.v1

Self-Supervised Contextual Keyword and Keyphrase Retrieval with Self-Labelling

Prafull Sharma, Yingbo Li

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: contextual keyword extraction; BERT; word embedding; LSTM; transformers; Deep Learning

Online: 6 August 2019 (09:17:36 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/preprints202304.0827.v2

ROSGPT: Next-Generation Human-Robot Interaction with ChatGPT and ROS

Anis Koubaa

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Human-Robot Interaction; ROS; ROS2; ChatGPT; Large Language Model; Robot Operating System

Online: 25 April 2023 (03:42:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201701.0097.v1

Physical Intelligence and Thermodynamic Computing

Robert L. Fry

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Carnot cycle; causality; distinguishability; entropy; intelligent processes; questions

Online: 22 January 2017 (05:23:08 CET)

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Preprint REVIEW | doi:10.20944/preprints202303.0438.v1

Exploring ChatGPT Capabilities and Limitations: A Critical Review of the NLP Game Changer

Anis Koubaa, Wadii Boulila, Lahouari Ghouti, Ayyub Alzahem, Shahid Latif

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ChatGPT; Review; Technical Novelties; Taxon-omy; Applications

Online: 27 March 2023 (03:36:47 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0257.v1

Predictive Analytics of COVID-19 Using Information, Communication and Technologies

Parikshit N. Mahalle, Nilesh P. Sable, Namita P. Mahalle, Gitanjali R. Shinde

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: COVID-19; predictive analytics; forecasting models; Machine Learning Method; prediction; epidemic; pandemic

Online: 16 April 2020 (05:54:13 CEST)

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Working Paper ARTICLE

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Mohamed Loey, Florentin Smarandache, Nour Eldeen M. Khalifa

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: 2019 novel coronavirus; COVID-19; SARS-CoV-2; Deep Transfer Learning; Convolutional Neural Network; Machine Learning; GAN

Online: 7 April 2020 (10:59:04 CEST)

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The coronavirus (covid-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest x-ray images is presented. The lack of benchmark datasets for covid-19 especially in chest x-rays images is the main motivation of this research. The main idea is to collect all the possible images for covid-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of the virus from the available x-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the covid-19, normal, pneumonia bacterial, and pneumonia virus. The dataset is divided into 90% for the GAN and the training and the validation phase, while 10% used in the testing phase. The GAN helps in generating more images from the original dataset to be 30 times larger than the originally collected dataset. The GAN also help in overcoming the overfitting problem and made the proposed model more robust. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models prove it is efficiency according to validation, testing accuracy, and performance measurements such as precision, recall, and F1 score. Three case scenarios are tested through the paper, the first scenario which includes 4 classes from the dataset, while the second scenario includes 3 classes and the third scenario includes 2 classes. All the scenarios include the covid-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes 2 classes(covid-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthen the obtained results through the research. Finally, this research may be considered one of the first trails to use GAN and deep transfer models together to help in detecting coronaviruses (covid-19) within the absence of a benchmark dataset around the world, especially in x-rays chest images.

Preprint REVIEW | doi:10.20944/preprints201911.0278.v1

Machine Learning for Clinical Decision-Making: Challenges and Opportunities

Sergio Sanchez-Martinez, Oscar Camara, Gemma Piella, Maja Cikes, Miguel Angel Gonzalez Ballester, Marius Miron, Alfredo Vellido, Emilia Gomez, Alan Fraser, Bart Bijnens

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Machine learning; clinical decision-making; personalized medicine; digital health

Online: 24 November 2019 (13:26:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202004.0311.v1

COVID-19 Outbreak Prediction with Machine Learning

Sina F. Ardabili, Amir Mosavi, Pedram Ghamisi, Filip Ferdinand, Annamaria R. Varkonyi-Koczy, Uwe Reuter, Timon Rabczuk, Peter M. Atkinson

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: COVID-19; coronavirus disease; coronavirus; SARS-CoV-2; model; prediction; machine learning

Online: 19 April 2020 (01:47:10 CEST)

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Preprint REVIEW | doi:10.20944/preprints201909.0019.v1

Trends in Virtual and Augmented Reality Research: A Review of Latest Eye Tracking Research Papers and Beyond

Jicheng Li, Roghayeh Barmaki

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Virtual Reality; Augmented Reality; eye tracking; deep learning; gaze estimation; collaborative computing

Online: 2 September 2019 (09:18:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202005.0151.v1

COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images

Laboni Sarker, Md. Mohaiminul Islam, Tanveer Hannan, Zakaria Ahmed

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; CNN; DenseNet; COVID-19; transfer learning

Online: 9 May 2020 (04:54:34 CEST)

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Preprint REVIEW | doi:10.20944/preprints201908.0152.v1

List of Deep Learning Models

Amir Mosavi, Sina Faizollahzadeh ardabili, Annamária R. Várkonyi-Kóczy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; machine learning model; convolutional neural networks (CNN); recurrent neural networks (RNN); denoising autoencoder (DAE); deep belief networks (DBNs); long short-term memory (LSTM); review; survey; state of the art

Online: 13 August 2019 (09:32:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202402.1299.v1

Advancements in Quantum Computing and AI May Impact PQC Migration Timelines

Dr. Robert Campbell, Dr. Whitfield Diffie, Charles Robinson

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Post-quantum cryptography (PQC), PQC transition timelines, hybrid quantum-classical computing, artificial intelligence, machine learning, deep learning, Grover's Adaptive Search (GAS), Harrow-Hassidim-Lloyd (HHL) Algorithm

Online: 22 February 2024 (11:54:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202307.2142.v2

Llama 2: Early Adopters' Utilization of Meta's New Open-Source Pretrained Model

Konstantinos I. Roumeliotis, Nikolaos D. Tselikas, Dimitrios K. Nasiopoulos

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: llama 2; llama2; llama 2 projects; llama 2 model architecture; llama 2 fine-tuning

Online: 2 August 2023 (04:30:51 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201607.0064.v1

Modeling, Simulation and Optimization of agricultural greenhouse microclimate by the application of artificial intelligence and / or fuzzy logic

Didi Faouzi, Nacereddine Bibi-Triki

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Greenhouse , microclimate , Modelling , fuzzy controller , Optimization , Solar Energy , Energy saving , Climate Model ,Greenhouse effect , Temperature

Online: 21 July 2016 (09:44:16 CEST)

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Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

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/preprints201807.0488.v1

Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data

Pratik Vyas, Diptangshu Pandit

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: heart rate variability; machine learning; abnormality detection; window shifting; high accuracy prediction

Online: 25 July 2018 (14:22:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0257.v2

Data Analytics: COVID-19 Prediction Using Multimodal Data

Parikshit N. Mahalle, Nilesh P. Sable, Namita P. Mahalle, Gitanjali R. Shinde

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: COVID-19; Predictive Analytics; Machine Learning; Prediction; Pandemic

Online: 14 May 2020 (09:03:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201611.0017.v1

Empirical Prediction of Turnovers in NFL Football

Joel Bock

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; unbalanced data; predicting rare events; NFL football; sports analytics

Online: 2 November 2016 (07:07:06 CET)

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Preprint REVIEW | doi:10.20944/preprints202007.0479.v1

Past, Present, and Future of Face Recognition: A Review

Insaf Adjabi, Abdeldjalil Ouahabi, Amir Benzaoui, Abdelmalik Taleb-Ahmed

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Face Recognition; Face Analysis; Face Database; Deep Learning

Online: 21 July 2020 (11:13:45 CEST)

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Preprint REVIEW | doi:10.20944/preprints201908.0166.v1

Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review

Sina Faizollahzadeh ardabili, Amir Mosavi, Majid Dehghani, Annamária R. Várkonyi-Kóczy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; deep learning; big data; hydrology; climate change; global warming; hydrological model; earth systems

Online: 15 August 2019 (05:50:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201607.0085.v1

Garments Texture Design Class Identification Using Deep Convolutional Neural Network

S.M. Sofiqul Islam, Emon Kumar Dey, Md. Nurul Ahad Tawhid, B. M. Mainul Hossain

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: CNN; Deep Learning; AlexNet; VGGNet; Texture Descriptor; Garment Categories; 13 Garment Trend Identification; Design Classification for Garments.

Online: 27 July 2016 (15:39:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202301.0521.v1

Feasibility Study on Utilization of the Artificial Intelligence GPT-3 in Public Health

David Jungwirth, Daniela Haluza

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ChatGPT; GPT-3; OpenAI; chatbots; digital health; artificial intelligence; automation; technological advancement; human-AI interaction; collaboration; open science

Online: 28 January 2023 (07:56:35 CET)

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Preprint ARTICLE | doi:10.3390/sci2030048

Statistics and Machine Learning Experiments in Poetry

Ovidiu Calin

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: entropy; Kullback–Leibler relative entropy; recurrent neural networks; learning

Online: 2 July 2020 (00:00:00 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202002.0197.v1

Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Rafidah Md Noor, Ng Seong Yik, Raenu Kolandaisamy, Ismail Ahmedy, Mohammad Asif Hossain, Kok-Lim Alvin Yau, Wahidah Md Shah, Tarak Nandy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Support Vector Machine; Support Vector Regression; Machine learning; Prediction; Urban Smart Bus

Online: 15 February 2020 (14:33:23 CET)

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Working Paper ARTICLE

Design Of a Dynamic and Self-Adapting System, Supported With Artificial Intelligence, Machine Learning and Real-Time Intelligence For Predictive Cyber Risk Analytics in Extreme Environments- Cyber Risk in the Colonisation of Mars

Petar Radanliev

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial intelligence; machine learning; real-time probabilistic data; for cyber risk; super forecasting; red teaming;

Online: 12 April 2021 (12:18:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202007.0745.v1

Early Detection of The Advanced Persistent Threats Attacks Using Performance Analysis of Deep Learning

Javad Hassannataj Joloudari, Mojtaba Haderbadi, Amir Mashmool, Mohammad GhasemiGol, Shahaboddin Shamshirband, Amir Mosavi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: APT-attack; detection and classification; feature extraction; machine learning; C5.0 decision tree; Bayesian network; deep learning

Online: 31 July 2020 (11:01:59 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.1723.v1

Truth-O-Meter: Collaborating with LLM in Fighting its Hallucinations

Boris A. Galitsky

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Large Language Model; hallucination; fact-checking; multiple inconsistent sources

Online: 25 July 2023 (13:09:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202307.0563.v1

ChatGPT and the Generation of Digitally Born “Knowledge”: How Does a Generative AI Language Model Interpret Cultural Heritage Values?

Dirk H.R. Spennemann

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: artificial intelligence; ChatGPT; construction of values; cultural heritage; cultural relativism; digitally born content; heritage management; falsified references; language model; machine learning

Online: 10 July 2023 (09:41:40 CEST)

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Preprint REVIEW | doi:10.20944/preprints201908.0154.v1

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Saeed Nosratabadi, Amir Mosavi, Ramin Keivani, Sina Faizollahzadeh ardabili, Farshid Aram

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; machine learning; smart cities; urban sustainability; cities of future; internet of things (IoT); data science; big data

Online: 13 August 2019 (10:00:34 CEST)

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Preprint ARTICLE | doi:10.3390/sci2010013

Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks

Marios Zachariou, Neofytos Dimitriou, Ognjen Arandjelović

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; computer vision; Cycle-GAN; image reconstruction

Online: 12 March 2020 (00:00:00 CET)

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Preprint ARTICLE | doi:10.20944/preprints201903.0039.v1

Handwritten Digit Recognition using Convolutional Neural Network in Python with Tensorflow and Observe the Variation of Accuracies for Various Hidden Layers

Fathma Siddique, Shadman Sakib, Md. Abu Bakr Siddique

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Handwritten digit recognition, Convolutional Neural Network (CNN), Deep learning, MNIST dataset, Epochs, Hidden Layers, Stochastic Gradient Descent, Backpropagation

Online: 4 March 2019 (10:32:36 CET)

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Preprint ARTICLE | doi:10.20944/preprints202304.0827.v3

ROSGPT: Next-Generation Human-Robot Interaction with ChatGPT and ROS

Anis Koubaa

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Human-Robot Interaction; ROS; ROS2; ChatGPT; Large Language Model; Robot Operating System

Online: 26 April 2023 (03:54:36 CEST)

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Preprint ARTICLE | doi:10.3390/sci2010018

Classification of Ancient Roman Coins by Denomination Using Colour, a Forgotten Feature in Automatic Ancient Coin Analysis

Yuanyuan Ma, Ognjen Arandjelović

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: colour words; hue histogram; colour representation; machine learning; computer vision

Online: 24 March 2020 (00:00:00 CET)

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Preprint REVIEW | doi:10.20944/preprints202011.0043.v1

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Roberto Sánchez-Reolid, María T. López, Antonio Fernández-Caballero

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Electrodermal activity; Stress detection; Machine learning; Scoping review

Online: 2 November 2020 (13:37:08 CET)

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Preprint REVIEW | doi:10.20944/preprints202003.0309.v1

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Amir Mosavi, Pedram Ghamisi, Yaser Faghan, Puhong Duan, Shahab Shamshirband

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

Online: 20 March 2020 (07:13:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints202006.0031.v3

A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

Mohammad Rahimzadeh, Abolfazl Attar, Seyed Mohammad Sakhaei

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep learning; Convolutional Neural Network; Coronavirus; COVID-19; radiology; CT scan; Medical image analysis; Automatic medical diagnosis; lung CT scan dataset

Online: 5 September 2020 (03:36:20 CEST)

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Preprint ARTICLE | doi:10.3390/sci2010008

Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis

Jessica Cooper, Ognjen Arandjelović

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: numismatics; Roman; Rome; deep learning; computer vision

Online: 2 March 2020 (00:00:00 CET)

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Preprint ARTICLE | doi:10.20944/preprints201912.0154.v1

Weighting the Key Features Affecting Supplier Selection using Machine Learning Techniques

Ahmad Abdulla, George Baryannis, Ibrahim Badi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: supplier selection; machine learning; decision tree

Online: 11 December 2019 (12:21:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202005.0106.v1

Benchmark Synthetic Training Data for Artificial Intelligence-based Li-ion Diagnosis and Prognosis

Matthieu Dubarry, David Beck

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Machine Learning; Training Data; alawa; AI

Online: 7 May 2020 (06:20:09 CEST)

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Preprint REVIEW | doi:10.20944/preprints201908.0203.v1

Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

Sina Ardabili, Amir Mosavi, Annamária R. Várkonyi-Kóczy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; deep learning; ensemble models

Online: 20 August 2019 (08:41:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.1271.v1

Exploring Ethical Boundaries: Can ChatGPT Be Prompted to Give Advice on How to Cheat in University Assignments?

Dirk H.R. Spennemann

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: academic misconduct; artificial intelligence; ChatGPT; ethics; language model; machine learning

Online: 17 August 2023 (10:32:04 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0195.v1

LLMs and AI: Understanding Its Reach and Impact

Anand Gokul

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: llm; impact; society; ai; large-language-model; transformer; natural language processing; nlp

Online: 4 May 2023 (05:15:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201901.0281.v1

Wind Turbine Maintenance Cost Reduction by Deep Learning Aided Drone Inspection Analysis

ASM Shihavuddin, Xiao Chen, Vladimir Fedorov, Nicolai Andre Brogaard Riis, Anders Nymark Christensen, Kim Branner, Anders Bjorholm Dahl, Rasmus Reinhold Paulsen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Wind Energy; Wind Turbine; Drone Inspection; Damage Detection; Deep Learning; Convolutional Neural Network (CNN)

Online: 28 January 2019 (15:50:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0258.v1

Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection

Md. Mahbubur Rahman, Mostofa Kamal Nasir, Nur A-Alam, Saikat Islam Khan, Shahab Band, Iman Dehzangi, Amin Beheshti, Hamid Alinejad Rokny

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Skin cancer; Deep learning; Hybrid feature extractor; Local binary pattern; Feature extraction

Online: 18 January 2022 (12:43:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints201807.0119.v1

Implementation of Regional-CNN and SSD Machine Learning Object Detection Architectures for the Real Time Analysis of Blood Borne Pathogens in Dark Field Microscopy

Daniel Fleury, Angelica Fleury

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Convolutional Neural Network,Single Shot Detector, Regional Convolutional Neural Network, Machine Learning, Visualization-Localization

Online: 6 July 2018 (14:38:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201811.0546.v1

An Overview of Convolutional Neural Network: Its Architecture and Applications

Shadman Sakib, Nazib Ahmed, Ahmed Jawad Kabir, Hridon Ahmed

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Convolutional Neural Network (CNN), Deep learning, Architecture, Applications

Online: 20 November 2018 (15:25:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints201805.0102.v2

What Do We Learn from Word Associations? Evaluating Machine Learning Algorithms for the Extraction of Contextual Word Meaning in Natural Language Processing

Epaminondas Kapetanios, Saad Alshahrani, Anastasia Angelopoulou, Mark Baldwin

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; algorithms; natural language processing, deep learning, vector space models, semantic similarity, distributional semantics, latent semantic analysis, word2vec

Online: 10 May 2018 (05:56:56 CEST)

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Preprint REVIEW | doi:10.20944/preprints201708.0003.v1

De-Anonymizing Authors of Electronic Texts: A Survey on Electronic Text Stylometry

Mahmoud Khonji, Youssef Iraqi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: stylometry; author identification; author verification; authorprofiling; stylistic inconsistency; text analysis; supervised learning; unsupervised learning; classification; forensics

Online: 2 August 2017 (12:38:17 CEST)

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Electronic text stylometry is a collection of forensics methods that analyze the writing styles of input electronic texts in order to extract information about authors of the input electronic texts. Such extracted information could be the identity of the authors, or aspects of the authors, such as their gender, age group, ethnicity, etc. This survey paper presents the following contributions: 1) A description of all stylometry problems in probability terms, under a unified notation. To the best of our knowledge, this is the most comprehensive definition to date. 2) A survey of key methods, with a particular attention to data representation (or feature extraction) methods. 3) An evaluation of 23,760 feature extraction methods, which is the most comprehensive evaluation of feature extraction methods in the literature of stylometry to date. The importance of this evaluation is two fold. First, identifying the relative effectiveness of the features (since, currently, many are not evaluated jointly; e.g. syntactic n-grams are not evaluated against k-skip n-grams, and so forth). Second, thanks to our generalizations, we could evaluate novel grams, such as what we name compound grams. 4) The release of our associated Python feature extraction library, namely Fextractor. Essentially, the library generalizes all existing n-gram based feature extraction methods under the "at least l-frequent, dir-directed, k-skipped n-grams'', and allows grams to be diversely defined, including definitions that are based on high-level grammatical aspects, such as POS tags, as well as lower-level ones, such as distribution of function words, word shapes, etc. This makes the library, by far, the most extensive in this domain to date. 5) The construction, evaluation, and release of the first dataset for Emirati social media text. This evaluation represents the first evaluation of author identification against Emirati social media texts. Interestingly, we find that, when using our models and feature extraction library (Fextractor), authors could be identified significantly more accurately than what is reported with similarly sized datasets. The dataset also contains sub-datasets that represent other languages (Dutch, English, Greek and Spanish), and our findings are consistent across them.

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/preprints202004.0252.v1

A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images

Mohamed Loey, Florentin Smarandache, Nour Eldeen M. Khalifa

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: COVID-19; 2019 novel coronavirus; SARS-CoV-2; Deep Transfer Learning; Convolutional Neural Network; Machine Learning; CGAN

Online: 16 April 2020 (05:23:06 CEST)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202103.0194.v1

scikit-activeml: A Library and Toolbox for Active Learning Algorithms

Daniel Kottke, Marek Herde, Tuan Pham Minh, Alexander Benz, Pascal Mergard, Atal Roghman, Christoph Sandrock, Bernhard Sick

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Active Learning, Classification, Machine Learning, Python, Github, Repository, Open Source

Online: 5 March 2021 (21:14:20 CET)

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Preprint ARTICLE | doi:10.20944/preprints201611.0092.v2

A Semi-Formal Multi-Policy Secure Model for Semantic Spatial Trajectories

Xingang Wang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: semantic spatial trajectory; role based access control; Bell-Lapadula model; multi-policy; Web Ontology Language

Online: 17 November 2016 (15:19:51 CET)

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Preprint REVIEW | doi:10.20944/preprints201903.0033.v1

Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals

Yasmin Elsahar, Sijung Hu, Kaddour Bouazza-Marouf, David Kerr, Annysa Mansor

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: augmentative and alternative communication; assistive technologies; sensing modalities; signal processing; voice communication; machine learning; mobile health; speech disability

Online: 4 March 2019 (10:14:44 CET)

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Preprint ARTICLE | doi:10.20944/preprints201906.0051.v1

Machine Learning Techniques for Detecting Identifying Linguistic Patterns in News Media

A Samuel Pottinger

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: NLP, news media, bias, neural networking, LSTM, information retrieval, filter bubble

Online: 6 June 2019 (13:15:28 CEST)

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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/preprints202107.0269.v1

The Snooker Algorithm for Ultrasonic Imaging of Fatigue Cracks in order to use Parameter-Spaces to Aid Machine Learning

Channa Nageswaran

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Fatigue cracks; ultrasonic inspection; machine learning; imaging; techniques

Online: 12 July 2021 (14:36:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201903.0115.v1

On to the Next Chapter for Crop Breeding: Convergence with Data Science

Elhan S. Ersoz, Nicolas F. Martin, Ann E. Stapleton

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; agroclimactic modelling; crop breeding and genetics; GxE

Online: 11 March 2019 (07:59:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints201901.0311.v1

Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks

Mounir Bensalem, Sandeep Kumar Singh, Admela Jukan

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning

Online: 30 January 2019 (10:16:14 CET)

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Preprint REVIEW | doi:10.20944/preprints201908.0179.v1

Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research

Sina Faizollahzadeh Ardabili, Amir Mosavi, Annamária R. Várkonyi-Kóczy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: biofuels; deep learning; big data; machine learning models; biodiesel

Online: 17 August 2019 (03:48:28 CEST)

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Working Paper ARTICLE

Reach on Waste Classification and Identification by Transfer Learning and Lightweight Neural Network

Xiujie Xu, Xuehai Qi, Xingjian Diao

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: waste classification; transfer learning; deep learning; recognition classification

Online: 23 February 2020 (14:01:01 CET)

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Preprint ARTICLE | doi:10.3390/sci2030060

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Jian Zhang, Rahul Soangra, Thurmon E. Lockhart

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: locomotion; machine learning; support vector machines; activity classification; activity of daily life (ADL)

Online: 18 July 2020 (00:00:00 CEST)

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Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter

γ

—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

Preprint ARTICLE | doi:10.20944/preprints202301.0474.v1

Artificial Intelligence and Ten Societal Megatrends: A GPT-3 Case Study

Daniela Haluza, David Jungwirth

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ChatGPT; GPT-3; global trends; OpenAI; chatbots; digital health; artificial intelligence; automation; technological advancement; human-AI interaction; collaboration

Online: 26 January 2023 (08:37:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints201908.0019.v1

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

Muhammad Zubair Asghar, Fazli Subhan, Muhammad Imran, Fazal Masud Kundi, Shahab Shamshirband, Amir Mosavi, Peter Csiba, Annamária R. Várkonyi-Kóczy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: emotion classification; machine learning classifiers; ISEAR dataset; data mining; performance evaluation; data science; opinion-mining

Online: 2 August 2019 (08:49:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201905.0090.v1

A new Deductive Method with Intelligence Using Pseudorandom Number for Solving Coin-Weighing Puzzles: Review of Inductive and Deductive Methods

Yoshiyasu Takefuji, Ren Yamada

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: intelligence; inductive methods; deductive methods; pseudorandom number; artificial intelligence; Prolog; Otter; Z3; deep learning; ensemble methods; automated reasoning; coin-weighing puzzles

Online: 8 May 2019 (10:03:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201911.0148.v2

Designing Strategic Games With Preestablished Nash Equilibrium Through Artificial Inference and Global Learning

Hime A. e Oliveira Jr.

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Nash equilibria; game and mechanism design; simulated annealing; fuzzy ASA; artificial inference; global machine learning

Online: 18 November 2019 (07:34:00 CET)

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Preprint REVIEW | doi:10.20944/preprints202008.0215.v1

Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Analysis

Sejuti Rahman, Sujan Sarker, Abdullah Al Miraj, Ragib Amin Nihal, A. K. M. Nadimul Haque, Abdullah Al Noman

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: COVID-19; deep learning; radiography; automated detection; medical imaging; SARS-CoV-2

Online: 8 August 2020 (18:24:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201908.0068.v1

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

Husein Perez, Joseph H.M. Tah, Amir Mosavi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; convolutional neural networks (CNN); transfer learning; class activation mapping (CAM); building defects; structural-health monitoring

Online: 6 August 2019 (04:18:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202002.0318.v1

FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification

Robert D. Chambers, Nathanael C. Yoder

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: activity recognition; time series classification; neural; networks; deep learning; machine learning; CNNs; LSTMs; many-to-many

Online: 23 February 2020 (11:00:10 CET)

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Working Paper ARTICLE

COVID19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?

Muhammad Saqib, Saeed Anwar, Abbas Anwar, Lars Petersson, Nabin Sharma, Michael Blumenstein

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: COVID-19; Deep learning; Convolutional neural network; Computed Tomography; X-Rays; Classification; Detection

Online: 14 June 2020 (17:51:12 CEST)

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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 REVIEW | doi:10.20944/preprints202304.0158.v1

ChatGPT: A Brief Narrative Review

Bulbul Gupta, Tabish Mufti, Shahab Saquib Sohail, Dag Øivind Madsen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ChatGPT; OpenAI; Artificial Intelligence (AI); Machine Learning (ML); Large Language Models (LLM)

Online: 10 April 2023 (08:40:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.1550.v1

CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics

Yining Luo, Yingfa Chen, Zhen Zhang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Computational fluid dynamics; Deep learning; Partial differential equations; Neural operators

Online: 22 September 2023 (11:14:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201908.0202.v1

Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology

Tarahom Mesri Gundoshmian, Sina Ardabili, Amir Mosavi, Annamária R. Várkonyi-Kóczy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: combine harvester; hybrid machine learning; ANFIS; response surface methodology (RSM); artificial intelligence in agriculture; radial basis function (RBF)

Online: 20 August 2019 (08:03:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0051.v1

Semi-supervised Adversarial Variational Autoencoder

Ryad Zemouri

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Variational autoencoder; Adversarial learning; Deep feature consistent; Data generation

Online: 2 August 2020 (18:11:27 CEST)

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Preprint REVIEW | doi:10.20944/preprints202111.0044.v1

Deep Reinforcement Learning For Trading - A Critical Survey

Adrian Millea

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep reinforcement learning; model-based RL; hierarchy; trading; cryptocurrency; foreign exchange; stock market; risk; prediction; reward shaping

Online: 2 November 2021 (10:57:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints201711.0193.v1

A Quantum Hybrid PSO Combined with Fuzzy K-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection

Abdullah M. Iliyasu, Chastine Fatichah

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: computational intelligence; quantum hybrid intelligent systems; quantum machine learning; medical image processing; disease diagnosis; Fuzzy k-NN; Quantum-behaved PSO; cervical smear images; cancer detection

Online: 30 November 2017 (07:21:00 CET)

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Preprint ARTICLE | doi:10.20944/preprints202103.0644.v1

Stress Classification of ECG-Derived HRV Features Extracted from Wearable Devices

Kayisan Mary Dalmeida, Giovanni Luca Masala

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: stress; wearable device; machine learning; smart watch; heart rate variability; electrocardiogram

Online: 25 March 2021 (16:24:00 CET)

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Working Paper ARTICLE

Software Quality Assurance Testing Methodologies in IoT

Mehreen Sirshar, Komal Naeem, Mahnoor Khan, Tanzeela Akbar

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Internet of Things (IoT); Quality Assurance; Testing; Artificial Intelligence (AI)

Online: 9 December 2019 (07:39:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202309.1528.v1

Will the Age of Generative Artificial Intelligence Become an Age of Public Ignorance?

Dirk H.R. Spennemann

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: artificial intelligence; ChatGPT; ethics; knowledge generation; language models; machine learning; strategic foresight

Online: 22 September 2023 (08:20:13 CEST)

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Preprint REVIEW | doi:10.20944/preprints201912.0149.v1

Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances

Amir Mosavi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: damage detection; machine learning; principal component analysis; composites; micromechanics of damage; continuum damage mechanics

Online: 11 December 2019 (04:50:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0367.v1

Image Classification Using Deep and Classical Machine Learning on Small Datasets: A Complete Comparative

Gonzalo Miranda, Clemente Rubio-Manzano

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial Intelligence; Deep Learning; Image Classification; Machine Learning; Predictive Models; Small Datasets; Supervised Learning

Online: 25 January 2022 (08:24:17 CET)

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Preprint ARTICLE | doi:10.20944/preprints202209.0215.v1

Skin Cancer Classification and Comparison of Pretrained Models Performance using Transfer Learning

Subroto Singha, Priyangka Roy

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: pretrained model; transfer learning; skin cancer; deep learning; ISIC 2020

Online: 15 September 2022 (03:02:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202210.0238.v1

From Twitter to Aso-Rock: A Natural Language Processing Spotlight for Understanding Nigeria 2023 Presidential Election

Olusola Olabanjo, Ashiribo Wusu, Mauton Asokere, Rebecca Padonu, Olufemi Olabanjo, Oluwafolake Ojo, Oseni Afisi, Olusegun Folorunso, Manuel Mazzara, Benjamin Aribisala

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: NLP; NLU; Twitter; Sentiment Analysis; Opinion Mining; Nigeria; Election; Machine Learning; BERT; LSTM; SVM

Online: 17 October 2022 (12:01:42 CEST)

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Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as a tool for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Past studies have established people’s opinion elections using social media posts. The advent of state-of-the-art algorithms for unstructured text processing implies tremendous progress in natural language processing and understanding. Aim: In this work, a Natural Language framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Raw datasets concerning discourse around Nigeria 2023 elections from Twitter of 2,059,113 18 dimensions were collected. Sentiment analysis was performed on the preprocessed dataset using three different machine learning models namely: Long Short-Term Memory (LSTM) Recurrent Neural Network, Bidirectional Encoder Representations from Transformers (BERT) and Linear Support Vector Classifier (LSVC) models. Personal tweet analysis of the three candidates provided insight on their campaign strategies and personalities while public tweet analysis established the public’s opinion about them. The performance of the models was also compared using accuracy, recall, false positive rate, precision and F-measure. Results: LSTM model gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2% , 87.6% and 82.9% respectively; the BERT model gave an accuracy, precision, recall, AUC and f-measure of 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively while the LSVC model gave an accuracy, precision, recall, AUC and f-measure of 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively. Conclusion: The experimental results show that sentiment analysis and other Natural Language Processing tasks can aid in the understanding of the social media space. Results also revealed the leverage of each aspirant towards winning the election. We conclude that sentiment analysis can form a general basis for generating insights for election and modeling election outcomes.

Working Paper ARTICLE

Multimodal Hate Speech Detection in Greek Social Media

Konstantinos Perifanos, Dionysis Goutsos

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Multimodal Machine Learning; Deep Learning; Hate Speech Detection

Online: 15 March 2021 (13:46:27 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints202307.2035.v1

Children of AI: A Protocol for Managing the Born-Digital Ephemera Spawned by ChatGPT

Dirk H.R. Spennemann

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: audio-visual archiving; ChatGPT; cultural heritage digital ephemera; publications ethics

Online: 31 July 2023 (04:15:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202110.0020.v1

Artificial Intelligence in News Media: Current Perceptions and Future Outlook

Mathias-Felipe de-Lima-Santos, Wilson Ceron

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: journalism; artificial intelligence; computer science; machine learning; computer vision; NLP.

Online: 1 October 2021 (13:33:47 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201808.0350.v1

Personalized E-Learning System Architecture Using Data Mining Approach

Samina Kausar, Huahu Xu, Iftikhar Hussain, Wenhau Zhu, Misha Zahid

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: big data; clustering; data mining; educational data mining; e-learning; profile learning

Online: 20 August 2018 (10:38:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202109.0199.v1

Big Five Personality Detection Using Deep Convolutional Neural Networks

Waiel Tinwala, Shristi Rauniyar

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

Online: 13 September 2021 (09:59:25 CEST)

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Working Paper ARTICLE

Assessing the Effectiveness of an Advertising Campaign on Instagram Based on Advertising Cost and Sentiment Analysis: Case of CSR Campaign

Ali Ranjbar Ferdos, Mohammad Taghi Taghavifard, Iman Raeesi Vanani

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Data Science; Advertising Campaign; Effectiveness; Evaluating, Social Media; Digital Marketing; Sentiment Analysis; Instagram; Machine Learning

Online: 11 August 2021 (08:44:07 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201906.0104.v1

Cycle-consistent Generative Adversarial Networks (CycleGANs) for the Non-Parallel Creation of Fake Voice Media

Daniel Fleury, Angelica Fleury

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep Learning, Generative Adversarial Networks (GANs), Machine Learning, Autoencoders, Voice Conversion, Ethics, CycleGANs

Online: 12 June 2019 (11:17:52 CEST)

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Computer Science and Mathematics