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Preprint ARTICLE | doi:10.20944/preprints202409.1664.v1

Stabilization of Percolation Probability in Supercritical Regimes: A Measure Theoretic Approach

Richard Murdoch Montgomery

Subject: Computer Science And Mathematics, Mathematics Keywords: Keywords: Percolation probability, supercritical regime, measure theory, Monotone Convergence Theorem, Fatou's Lemma, lattice models, infinite systems, statistical physics, compact balls, vanishing corrections.

Online: 20 September 2024 (16:06:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1647.v1

Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19

Asmik Nalmpatian, Christian Heumann, Stefan Pilz

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: mortality modeling; covid impact; multi-populational; cross-country; generalized additive models; partial APC plots; APC; machine learning; excess mortality

Online: 20 September 2024 (15:07:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1617.v1

CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction

Cangqing Wang, Minghao Liu, Mingxiu Sui, Yi Nian, Zhejie Zhou

Subject: Computer Science And Mathematics, Computer Science Keywords: Meta-reinforcement learning; theoretical analysis; generalization bound; convergence guarantee

Online: 20 September 2024 (12:03:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1615.v1

Comparative Study of Time Series Analysis Algorithms Suitable for Short-term Forecasting in Implementing Demand Response Based on AMI

Myung-joo Park, Hyo-sik Yang

Subject: Computer Science And Mathematics, Computer Science Keywords: Smart Grid, Internet of Things, SCADA, AMI, Demand Response, ARIMA, SARIMA, LSTM, SVM, Short-term Forecasting

Online: 20 September 2024 (11:28:54 CEST)

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Preprint REVIEW | doi:10.20944/preprints202302.0123.v2

A Glimpse of Ocean of Abundant Discoveries: Two-Way Cross Family Analysis of In-Silico Ranked 2nd Order Unexplored, ETC-1922159 Affected, Synergistic Combinations in CRC Cells

Shriprakash Sinha

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: WNT; NF-κ B, Ion Channels; Anthrax toxin recep- tors; Poliovirus receptors; Porcupine inhibitor ETC-1922159; Sensitivity analysis; Colorectal cancer; Unknown biological hypotheses; Combinatorial search space; Support vector ranking

Online: 20 September 2024 (11:03:20 CEST)

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Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. For example, Brancati et al.1 observe that mutations in poliovirus receptor related protein 4 (PVRL4), encoding cell adhesion molecule nectin-4, causes Ectodermal dysplasia-syndactyly syndrome. Interaction with cad- herins also implies an influence of nectin-4 on Wnt signaling, which plays a relevant role in limb development (Brancati et al.1). However, not much work has been done to explore the relation of Wnts and PVR family. In CRC cells treated with ETC-1922159, both were found up regulated. In a recent unpublished work in Open Science Framework, Sinha2, we had the opportunity to rank these unknown biological hypotheses for both up and down regulated genes at 2nd order level after drug administration. The search engine alloted high nu- merical valued rankings to some combinations of PVR-WNT, thus indicating a possibility of high combinatorial synergy also. The in-silico derived influences can be represented graphically as - PVR w.r.t WNT with PVR <- WNT9A; and WNT w.r.t PVR with WNT-7B/9A <- PVR and WNT4 <- PVRL2; In the light of the recent findings of PVR with IFN (interferon) and the known interactions between IFN and Wnts, there might be a possibilty to explore the bridge of PVR, IFN and WNTs. The 3 fold (PVR - IFN; IFN - WNT; WNT - PVR), 2 way cross family analysis might shed light on the possible combinations that might be of import. Here, we present a 2-way cross family analysis of multiple, such in-silico ranked 2nd order synergistic combinations, after ETC-1922159 treatment of CRC cells. Via this 2-way cross family analysis, we are able to discover through majority voting, the combinations that might of interest to biologists and also derive plausible influences of components of combinations among themselves. Note that these form biological hypotheses which indicate whether a particular combination and the direction of influence within the combination, exist synergistically in CRC cells. Wet lab tests will indicate the veracity of these combinations and if proven true, will lead to further study of mechanism between the components.

Preprint ARTICLE | doi:10.20944/preprints202409.1500.v1

Insider Threat Detection Form Irregular Login Patterns with Metaheuristic Optimized AdaBoost

Luka Jovanovic, Žaklina Spalević, Nebojsa Bacanin, Milan Simić, Filip Marković

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: AdaBoost; legal frameworks; cyber security; Crayfish optimization algorithm; insider threat; Metaheuristics

Online: 20 September 2024 (10:58:43 CEST)

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

ON A NEW INNOVATIVE STEAM-BASED METHOD FOR TEACHING CYCLOIDAL CURVES IN ENGINEERING HIGHER EDUCATION

Szilvia Szilágyi, Attila Körei, Ingrida Vaičiulytė

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

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

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Preprint ARTICLE | doi:10.20944/preprints202409.1562.v1

Ulam Stability of Second Order Periodic Linear Differential Equations with Periodic Damping

Jin Li, Xiaofang Xia, Chuanfang Zhang, Renlian Chen

Subject: Computer Science And Mathematics, Mathematics Keywords: Ulam stability; periodic coefficient; linear differential equation; minimal Ulam constant

Online: 20 September 2024 (09:44:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202202.0254.v3

Minimum Statistical Uncertainty as Bayesian Network Model Selection Principle

Grigoriy Gogoshin, Andrei Rodin

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Bayesian Networks; probabilistic networks; conditional independence; model selection criteria; mutual information; sampling error; statistical uncertainty; MDL; BIC; AIC; BD; tRNA; APOE

Online: 20 September 2024 (09:42:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1433.v1

Feature Coding and Graph via Transformer: Different Granularities Classification for Aircrafts

Jianghao Rao, Senlin Qin, Zongyan An, Jianlin Zhang, Qiliang Bao, Zhenming Peng

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: fine-grained; aircraft recognition; label divisions; swin-transformer

Online: 20 September 2024 (03:47:37 CEST)

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Against the background of the sky, imaging and perception of aircraft are cru- cial. Among these, the location of aircrafts is not a very difficult problem in general, because that deep learning algorithms and object detection models based on convolutional neural network(CNN) are ever-evolving. In order to have a better identification of various aircrafts, or to accurately locate different types of flying targets in the sky, it becomes significant to distinguish and recognize different types of aircrafts. One question is that, distinguishing and recognizing between sub-categories of aircrafts pose great challenges. Although fine-grained recognition focuses on exploring and studying such problems, aircrafts under different sub-categories and granularities lead us to rethink the application of features. We noticed that features in swin-transformer demonstrates the under- standing and processing of images, fully showcasing the encoding and indexing of information. Through this research and proposed approach, we discovered a better understanding of features encoding and use, which are inspired by the feature encoding and computation in swin-transformer. In our paper, our ap- proach has achieved effects on features encoded graphically, manifested from the architecture design and convolutional neural network computation, and outper- forms other famous fine-graiend classification models on this issue. Not only approach we proposed has demonstrated superior performance in fine-grained aicraft classification, but also the mechanisms of the feature encoding under different sample space partitions is revealed for this issue, which provides a re- search for the representation of aircraft fineness characteristics. The relationship between representation orientation of aircraft feature information under various grained divisions, shows that the recognition of different categories according to man-made, such as aircraft, can be achieved by means of the feature represen- tations studied in this paper, for more specific defined classification tasks and various man-made targets partition criteria, which may influences the princi- ple of design for calculation and feature extraction in fine-grained classification models.

Preprint CONCEPT PAPER | doi:10.20944/preprints202409.1388.v1

Automating Portfolio Replication with Stock Market Algorithms

Srinivas Raju Gottimukkala

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: financial; portfolios; S&; P 500; stakeholders; stock

Online: 20 September 2024 (03:35:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0643.v2

Entrop-Hesitationistic-Fuzz-TOPSIS: A Novel Entropy Based Hesitant Intuitionistic Fuzzy TOPSIS for Web Service Optimization

Paul Aazagreyir, Peter Appiahene, James Ami-Narh, William Leslie Brown-Acquaye

Subject: Computer Science And Mathematics, Computer Science Keywords: Web service optimization; Quality of Service (QoS); Hesitant Intuitionistic Fuzzy Linguistic Terms; TOPSIS; Entropy Objective weights

Online: 20 September 2024 (03:32:19 CEST)

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In the fast-evolving landscape of web services, success relies on optimizing efficiency, reliability, and user satisfaction. Decision-making in real-world applications demands a blend of quantitative (numerical values of quality of Service factors) and qualitative data of experts’ ratings of the quality of service factors. This paper proposes an optimization technique that integrates Hesitant and Intuitionistic Fuzzy Linguistic Terms with TOPSIS, leveraging Entropy Objective weights. There is limited research on Hesitant Intuitionistic Fuzzy Linguistic terms and the need to model fuzziness, haste and intuition in data precisely from experts rating. Thus, this article introduces an Entropy-based model tailored for web service optimization. Using the WS-DREAM dataset, quantitative data was extracted and objective weights were determined through the Entropy algorithm and qualitative evaluations from five experts, employing Hesitant Intuitionistic Fuzzy Linguistic Terms, were integrated. The process involved computing criteria weights and experts' assessment of Quality of Service factors for four services (S1, S2, S3, and S4) to form the Fuzzy Decision Matrix using modified Online output software. Applying the proposed technique to a web service optimization problem validated its effectiveness. The results indicated optimal selection of S1 as the best web service, supported by Coefficient Index values (0.785). This study introduces a novel approach, Entrop – Hesitationistic - Fuzz-TOPSIS, for web service optimization, integrating quantitative and qualitative data through an Entropy-based model. The effectiveness of the proposed technique, validated through application on the WS-DREAM dataset, underscores its potential in addressing real-world decision-making challenges. The optimal selection of S1 as the best web service suggests practical utility. The study encourages further exploration of this technique's applicability to diverse Multiple Criteria Decision Making (MCDM) problems, opening avenues for future research and application.

Preprint REVIEW | doi:10.20944/preprints202409.1369.v1

Comprehensive Review Analysis and Countermeasures for Cybersecurity Threats: DDoS, Ransomware, and Trojan Horse Attacks

Naveesen Ravichandran, Tahrhunraj Tewaraja, Vishendraa Rajasegaran, Sri Sharvesh Kumar, Siva Kumar Livekha Gunasekar, Siva Raja Sindiramutty

Subject: Computer Science And Mathematics, Computer Science Keywords: Cybersecurity; DDoS Attacks; Ransomware; Trojan Horse; Countermeasures

Online: 20 September 2024 (03:17:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1543.v1

PerFuSIT: Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming

Konstantina Chrysafiadi, Maria Virvou

Subject: Computer Science And Mathematics, Other Keywords: Fuzzy Logic; Intelligent Tutoring System; tutoring strategy; personalization; computer programming

Online: 20 September 2024 (03:13:02 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1563.v1

Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings

David Cabezuelo, Izar Lopez-Ramirez, June Urkizu, Ander Goikoetxea

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: power consumption prediction; machine learning models; predictive analytics; feature analysis; random forest; support vector machine (SVM); deep neuronal networks (DNN)

Online: 20 September 2024 (02:59:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1518.v1

Comparison of Large Language Models Versus Traditional Information Extraction Methods for Real World Evidence of Patient Symptomatology in Acute and Post-Acute Sequelae of SARS-CoV-2

Vedansh Thakkar, Greg M. Silverman, Abhinab Kc, Nicholas E. Ingraham, Emma Jones, Samantha King, Christopher J. Tignanelli

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Information Extraction; Natural Language Processing; Large Language Models; Electronic Health Records

Online: 20 September 2024 (02:48:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1555.v1

State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer

Mingfei Wan, Daoguang Liu, Zhigui Liu

Subject: Computer Science And Mathematics, Robotics Keywords: quadruped robots; invariant extended Kalman filter; state estimates; non-stationary terrain

Online: 19 September 2024 (20:17:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1522.v1

A Three-Phase Novel Angular Perturbation Technique for Metaheuristic-Based School Bus Routing Optimization

Moses Karema, Fullgence Mwakondo, Kevin Tole

Subject: Computer Science And Mathematics, Mathematics Keywords: School bus Problem; Vehicle Routing Problem; Combinatorial optimization; NP-Hard; 13

Online: 19 September 2024 (15:55:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1557.v1

Generative Artificial Intelligence in Corporate America: A Review of Artificial Intelligence Use, Benefits, Challenges, Regulation and Cyber Security Issues

Joseph Ashley-Osuzoka, Lord Nyame, Eunice Marfo-Ahenkorah, Abigail Abrahams, Godswill Ashong, Daniel Aboagye

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: artificial intelligence; generative AI; corporate strategy; research management; business management; threats; business performance; information technology; cyber security and regulation

Online: 19 September 2024 (15:53:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1530.v1

Mathematical Models for Removal of Pharmaceutical Pollutants in Rehabilitated Treatment Plants

Irina Meghea

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: mathematical model; Navier-Stokes equations; pharmaceutical pollutants; infiltration through membranes; flow through porous media; rehabilitation of existing cleaning plants

Online: 19 September 2024 (12:45:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1524.v1

Ramsey Approach to Vector Fields

Edward Bormashenko

Subject: Computer Science And Mathematics, Discrete Mathematics And Combinatorics Keywords: Ramsey theory; vector fields; complete graph; ring-like vector systems

Online: 19 September 2024 (12:03:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1497.v1

How to Build a Software Quantum Simulator

Gilberto Javier Díaz, Luiz Angelo Steffenel, Carlos Jaime Barrios, Jean Francois Couturier

Subject: Computer Science And Mathematics, Computer Science Keywords: Quantum Computing; High-Performance Computing; Parallel Computing

Online: 19 September 2024 (10:54:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1446.v1

Leveraging Large Language Models to Enhance an Intelligent Agent with Multifaceted Capabilities

Sree Ganesh Thottempudi, Sagar Borra

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: large language models; retrieval augmented generation

Online: 19 September 2024 (10:01:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1364.v1

Detection of Aspergillus Flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms

Cristian Cruz-Carrasco, Josefa Díaz-Álvarez, Francisco Chávez de la O, Abel Sánchez-Venegas, Juan Villegas-Cortez

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Agriculture; Deep Learning; Hyperspectral; imaging Artificial Neural Network; Precision agriculture

Online: 19 September 2024 (08:48:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1532.v2

User-Cooperative Dynamic Resource Allocation for Backscatter-Aided Wireless-Powered MEC Network

Huaiwen He, Chenghao Zhou, Feng Huang, Hong Shen, Yihong Yang, Shangsong Liang

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Mobile edge computing (MEC); Wireless Power Transfer (WPT); User Cooperation (UC); backscatter communication (Backcom); Lyapunov optimization

Online: 19 September 2024 (03:41:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1381.v1

Designing Personalized Learning Paths for Foreign Language Acquisition Using Big Data: Theoretical and Empirical Analysis

Yina Xia, Seong-Yoon Shin, Kwang-Seong Shin

Subject: Computer Science And Mathematics, Computer Science Keywords: Personalized Learning; Big Data Analytics; Language Acquisition; Educational Technology; Learner Engagement

Online: 18 September 2024 (11:37:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1400.v1

An Innovative Way of Studying Plane Curves Based on Educational Robotics at University Level

Szilvia Szilágyi, Attila Körei, And Ingrida Vaičiulytė

Subject: Computer Science And Mathematics, Robotics Keywords: educational robotics; STEM-based education; LEGO robotics; central trochoids; drawing devices; higher education

Online: 18 September 2024 (11:34:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1294.v1

Huan Xiao

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Riemann zeta function; Riemann hypothesis

Online: 18 September 2024 (11:12:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1353.v1

Machine Learning Discoveries of Interleukin-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells

Shriprakash Sinha

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: ; Interleukin(IL),PorcupineinhibitorETC-1922159,Sensitivityanalysis, Colorectal cancer. ;

Online: 18 September 2024 (07:13:19 CEST)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202409.1236.v1

UE4 Redundant Asset Detection Method Based on Pointer Analysis

Tingzhen Liu

Subject: Computer Science And Mathematics, Software Keywords: program analysis; static analysis; pointer analysis; data flow analysis; flow sensitivity; game develop

Online: 18 September 2024 (05:05:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1287.v1

A Game-Theoretic Model of Global AI Development Race: Dynamics of Domination and Technological Catch-up

Richard Murdoch Montgomery

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial Intelligence; Technological Race; Game Theory; Monte Carlo Simulation; Technological Domination; Catch-up Dynamics; National Development Strategies; Global Competition; Innovation Policy; Technological Leapfrogging

Online: 18 September 2024 (04:48:13 CEST)

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Preprint CONCEPT PAPER | doi:10.20944/preprints202409.1320.v1

Evaluating the Impact of Fed and Domestic Monetary Policies on Long-Term Government Bond Yields

Srinivas Raju Gottimukkala

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Federal Open Market Committee; Financial; Ordinary Least Squared; US Federal Reserve

Online: 18 September 2024 (03:46:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1325.v1

Exploring the Impact of Crypto-Ransomware on Critical Industries: Case Studies and Solutions

Austin Yee Meng Jun, Bryan Alexander Jinu, Lay Kah Seng, Muhamad Haris Firdaus Bin Zainol Maharaiq, Wadthanak Khongsuwan, Bryan Thong Kai Junn, Aidan Au Wen Hao, Siva Raja Sindiramutty

Subject: Computer Science And Mathematics, Computer Science Keywords: Crypto Ransomware; Cybersecurity; Ransomware Attack; Cryptocurrency; Data Encryption

Online: 18 September 2024 (03:22:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1142.v1

Effect of Virtual Reality Technology in Table Tennis Teaching: A Multi-Center Controlled Study

Tingyu Ma, Wenhao Du, Qiufen Zhang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: virtual reality; table tennis; sports education; skill acquisition; learning motivation; training effectiveness

Online: 18 September 2024 (02:26:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1334.v1

Trends and Challenges in Data Analytics and Machine Learning Using Call Detail Records in Telecom Systems

James Barrett, Phil Legg, Jim Smith, Tim Barnes, Charles Boyle

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: call detail records; big data; mobile networks; machine learning

Online: 17 September 2024 (13:30:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1300.v1

The Straight Proof To Riemann Hypothesis

Sameer Subedi

Subject: Computer Science And Mathematics, Mathematics Keywords: Riemann Xi function; Upper Incomplete Gamma Function; Riemann Hypothesis

Online: 17 September 2024 (11:31:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1310.v1

Comparison of CNN-Based Architectures for Detection of Different Object Classe

Nataliya Bilous, Vladyslav Malko, Marcus Frohme, Alina Nechyporenko

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: deep learning; object detection; neural network; YOLO; SSD; EfficientDet

Online: 17 September 2024 (11:08:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0460.v4

On Duality Principles and Concerned Convex Dual Formulations Applied to a Non-Linear Plate Theory and Related Models

Fabio Botelho

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: duality principle; non-linear plate model; convex dual approximate formulation

Online: 17 September 2024 (10:46:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1283.v1

Smooth Attention: Improving Image Semantic Segmentation

Boris Kriuk, Fedor Kriuk, Karthik Praveen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Semantic Segmentation; Attention Mechanism; Spatial Relationships; Computer Vision; Deep Learning

Online: 17 September 2024 (06:14:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1197.v1

Intelligent Fault Diagnosis using Deep Learning Algorithms: A Comparative Analysis of MLP, CNN, RNN and LSTM

Vijyalakshmi K, Amuthakkannan Rajakannu, Mohsina Kamarudden, Ramachandran KP, Sri Rajkavin A V

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Condition monitoring; Machine Fault Simulator; unbalancing; Multilayer Perceptron (MLP); Convolutionary Neural Network (CNN); Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM)

Online: 17 September 2024 (05:13:59 CEST)

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Health management in industrial systems is more critical in maintenance management and it plays an important role in productivity, fault diagnosis, safety, efficiency, and economy in manufacturing industries. Early detection of faults in machinery may increase the effectiveness of maintenance actions and will avoid unwanted consequences in process operations and maintenance. Existing fault diagnosis methods have limitations such as insufficient accuracy, slow detection rate, and handling large and complex data sets. In this digital age, Industry 4.0 techniques have been applied across all fields, including the condition monitoring of machines. This research addresses the gaps in traditional fault diagnosis by using deep learning, a modern AI technique effective for diagnosing faults in various machines. Deep learning algorithms Multilayer Perceptron (MLP), Coevolutionary Neural Network (CNN), and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) are tested for fault diagnosis using vibration datasets collected from Spectra Quest Machinery Fault Simulator (SQMFS). In this research work, NI-DAQ (National Instruments- Data Acquisition) system, accelerometer, and LabVIEW software are used to collect vibration signals. Preprocessing of the signals has been done using a sampling strategy, shuffling, standardization, and reshaping data augmentation. The result shows that MLP accuracy in the prediction of fault is 0.9, CNN reached 0.95, and RNN and LSTM with 0.57 and 0.45 respectively. The high performance of CNN is due to its ability to effectively capture spatial patterns in vibration data which is crucial for fault diagnosis in rotating machinery followed by MLP due to its faster convergence during training. However, when scaling the data, MLP outperformed CNN, demonstrating superior adaptability to increased data complexity and volume. Due to the need for larger datasets and temporal patterns in the vibration data, which RNN and LSTM are designed to handle, they resulted in a lower accuracy. This study shows that CNN has given better results than other deep learning algorithms MLP, RNN, and LSTM in fault diagnosis of rotating machinery. Future research could explore the application of these techniques to different types of machinery and fault conditions.

Preprint ARTICLE | doi:10.20944/preprints202409.1208.v1

Efficient Hybrid Inference for LLMs: Reward-Based Token Modelling with Selective Cloud Assistance

Adarsh MS, Jithin VG, Ditto PS

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: llm; hybrid-llm; reward-token

Online: 17 September 2024 (05:12:30 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/preprints202409.1229.v1

Exploring NLP Challenges and Opportunities Forlanguages with Extensive Character Sets: A Casestudy on Nepali

Basab Jha

Subject: Computer Science And Mathematics, Computer Science Keywords: NLP; Language Processing

Online: 17 September 2024 (02:55:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1241.v1

The Trail-Making-Test in Virtual Reality (TMT-VR): The Effects of Interaction Modes and Gaming Skills on Cognitive Performance of Young Adults

Evgenia Giatzoglou, Panagiotis Vorias, Ryan Kemm, Irene Karayianni, Chrysanthi Nega, Panagiotis Kourtesis

Subject: Computer Science And Mathematics, Computer Science Keywords: Virtual Reality; Trail Making Test; Cognitive Assessment; Eye-Tracking; Head Movement; Usability; User Experience; Acceptability; Gaming Skills; Human-Computer Interaction

Online: 16 September 2024 (16:44:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1146.v1

Quaternion and Biquaternion Representations of Proper and Improper Transformations in Non-cartesian Reference Systems

Andrzej Katrusiak, Hien Quy Le

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: rotations; proper and improper symmetry; general transformation; non-Cartesian system; crystal lattice; quaternion; biquaternion; computations optimization; Caylay table

Online: 16 September 2024 (12:19:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1141.v1

Discrete Logarithms

Lukasz Matysiak

Subject: Computer Science And Mathematics, Security Systems Keywords: algorithm; congruence; discrete logarithm; protocol

Online: 16 September 2024 (11:46:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1219.v1

Improvement of Electric Fish Optimization Algorithm for Standstill Label Combined with Levy Flight Strategy

Wangzhou Luo, Hailong Wu, Jiegang Peng

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: Electric Fish Optimization Algorithm; Meta-heuristic Algorithm; Levy Flight; Standstill Label; Local Optimum

Online: 16 September 2024 (10:53:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1123.v2

Visual Imitation Learning from One-Shot Demonstration for Multi-Step Robot Pick-and-Place Tasks

Shuang Lu, Christian Haerdtlein, Johannes Schilp

Subject: Computer Science And Mathematics, Robotics Keywords: Visual imitation learning; Robot; One-shot demonstration

Online: 16 September 2024 (10:03:54 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.1193.v1

Exploring IoT and Blockchain: A Comprehensive Survey on Security, Integration Strategies, Applications and Future Research Directions

Muath A. Obaidat, Majdi Rawashdeh, Mohammad Alja’afreh, Meryem Abouali, Kutub Thakur, Ali Karime

Subject: Computer Science And Mathematics, Security Systems Keywords: Blockchain integration; I T security; Security; privacy; cyber-attacks; smart devices; distributed ledger technologv

Online: 16 September 2024 (10:02:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1178.v1

KoMPT: A Multimodal Emotion Recognition Model Integrating KoELECTRA, MFCC, and Pitch with Multimodal Transformer

MoungHo Yi, KeunChang Kwak, JuHyun Shin

Subject: Computer Science And Mathematics, Computer Science Keywords: KoELECTRA; MFCC; Pitch; Cross Modal Attention; Multimodal Transformer

Online: 16 September 2024 (09:59:26 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.0889.v2

A Survey on Real-Time Metaverse: Challenges and Opportunities

Mohsen Hatami, Qian Qu, Yu Chen, Hisham Kholidy, Erik Blasch, Erika Ardiles-Cruz

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Metaverse; Real-Time Metaverse; blockchain; Artificial intelligence (AI); Virtual reality (VR), Augmented Reality (AR); 5G networks

Online: 16 September 2024 (09:36:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1103.v1

PEDRO: Privacy-Enhancing Decision suppoRt tOol

Paul Van Schaik, Karen V Renaud

Subject: Computer Science And Mathematics, Security Systems Keywords: privacy-enhancing tool; technology adoption; step model; PET decision support tool

Online: 16 September 2024 (09:21:00 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.2370.v2

Tailoring Deep Learning for Real-Time Brain-Computer Interfaces: From Offline Models to Calibration-Free Online Decoding

Martin Wimpff, Jan Zerfowski, Bin Yang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Motor imagery; Electroencephalography; Deep Learning; Online decoding; Domain adaptation; Calibration-free; Mutual learning

Online: 15 September 2024 (07:32:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1095.v1

IRB-YOLO: An efficient Vatica Segmentation Model based on Inverted Residual Block for Drone Camera

Caili Yu, Yanheng Mai, Caijuan Yang, Jiaqi Zheng, Yongxin Liu, Chaoran Yu

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: precision agriculture; instance segmentation; drone; deep learning; computer vision

Online: 14 September 2024 (10:44:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1085.v1

Large Language Models in Entrepreneurship: A Survey

Yutong Li, Yuechen Yang, Chaoran Zhao, Chunling Cao

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Large Language Models; Entrepreneurship

Online: 14 September 2024 (10:37:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1129.v1

Chinese Event Trigger Recommendation Model for High-Accuracy Applications

Jiashun Duan, Xin Zhang

Subject: Computer Science And Mathematics, Computer Science Keywords: event detection; event trigger recommendation; human–machine collaboration; span model; boundary smoothing; natural language processing

Online: 14 September 2024 (09:44:37 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.0857.v2

The Extended Mind & Body in Extended Realities: A Scoping Review of XR Applications and Risks in the Metaverse

Panagiotis Kourtesis

Subject: Computer Science And Mathematics, Computer Science Keywords: Extended Mind; XR; VR; AR; MR; Metaverse; BCI; ethics; data privacy; embodiment

Online: 14 September 2024 (08:58:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1118.v1

Cervical Cancer Prediction based on Imbalanced Data using Machine Learning Algorithms

Madalina Maria Muraru, Zsuzsa Simó, László Barna Iantovics

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: cervical cancer; cancer; artificial intelligence; sampling methods; unbalanced datasets; 34 classification methods; prediction methods; K-Nearest Neighbours; Logistic Regression; Random 35 Forest

Online: 14 September 2024 (08:33:58 CEST)

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Cervical Cancer affects a large part of female population that makes the prediction of this disease based on Machine Learning (ML) by outmost importance. ML algorithms can be integrated in complex intelligent agent-based systems that can offer decision support to the resident medical doctors or even to experienced medical doctors. For instance can be mentioned the situation when an experienced medical doctor diagnose a case but he/she needs expertise support that is related to another medical specialty. Data imbalance is frequent in healthcare data and has a negative influencing effect in making predictions using ML algorithms. Cancer data generally and cervical cancer data particularly are frequently imbalanced. Based on this fact the study of data imbalance impact on diverse state-of-the-art ML prediction algorithms is important. This research subject is also motivated by the fact that in many research are presented experimental evaluations of algorithms without characterization of the data on that they have been applied. Such characterizations could give clear indication to other researchers regarding the applicability of the algorithms on their specific data. Specifically, if the data have the respective characteristics than are expectable the same performance evaluation results like those in the reported research. For the study we chosed a messy real-life Cervical Cancer dataset available in a recognized data repository included a large amount of missing and noisy values. To identify the best imbalanced technique for this medical dataset, it is compared the performance of eleven important resampling methods combined with the following state-of-the-art ML models: K-Nearest Neighbours (with k values of 2 and 3), binary Logistic Regression, and Random Forest, that are frequently applied in prediction types of researches in healthcare. The studied resampling methods includes seven undersampling methods namely Condensed Nearest Neighbour, Tomek Links, Edited Nearest Neighbours, Repeated Edited Nearest Neighbours, All K-Nearest Neighbours, NearMiss, Neighbourhood Cleaning Rule, and Instance Hardness Threshold, and four oversampling methods namely Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Approach for Imbalanced Learning SMOTE, Support Vector Machine SMOTE and Borderline SMOTE. In the case of the dataset for the confidence interval with 95% confidence level was between 9.23 and 16.22, while the imbalance ratio is 12.73. The obtained results show that resampling methods help the learning models to improve the classification ability of cervical cancer. The applied oversampling techniques generally showed better results than undersampling methods. In the case of Logistic Regression classifier had the highest impact on balanced techniques, while Random Forest had promising performance, even before balancing techniques and KNN2 was generally better than KNN3.

Preprint ARTICLE | doi:10.20944/preprints202409.1096.v1

A Review of IoT-Enabled Smart Energy Hub Systems: Rising, Applications, Challenges, and Future Prospects

Ruchit Parekh, Bishoy Sedhom, Sanjeevikumar Padmanaban, Abdelfattah A. Eladl

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Smart Energy Hub; Internet of Things; Cybersecurity Strategies; Smart Meters; Blockchain

Online: 14 September 2024 (07:40:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1012.v1

The Energy Consumption Problem in Cryptocurrency Mining and AI: Analysis, Future, and Breakthrough Mathematical Solutions

Asset Durmagambetov

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: energy consumption; cryptocurrency; artificial intelligence; data compression; Riemann Hypothesis; cryptography; algorithm acceleration

Online: 14 September 2024 (05:24:03 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.1014.v1

A Systematic Literature Review with Meta – Analysis of Predictive Modelling of Rift Valley Fever Outbreaks in East Africa: Machine Learning and Time Series Approaches

Damaris Felistus Mulwa, Benedicto Kazuzuru, Gerald Misinzo, Benard Bett

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: machine learning; time series; outbreak; inclusion criteria; PICOS; PRISMA

Online: 14 September 2024 (05:11:33 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1089.v1

Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4, and Logistic Regression: A Data-Driven Approach

Olamilekan Shobayo, Sidikat Adeyemi-longe, Olusogo Popoola, Bayode Ogunleye

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: FinBERT Model; Logistic Regression; FinBERT; Optuna; Timeseries cross validation

Online: 13 September 2024 (16:56:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202406.1808.v3

Energy Efficiency Evaluation of Artificial Intelligence Algorithms

Kalin Penev, Alexander Gegov, Olufemi Isiaq, Raheleh Jafari

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: green computing; software energy efficiency; sustainable and responsible artificial intelligence; Free Search; Bremermann's limit

Online: 13 September 2024 (16:55:04 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1098.v1

CS-Eval—A Concise Benchmark for Evaluating the Security Risks of Large Language Models

Yu Zhang, Yongbing Gao, Lidong Yang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Large language models (LLMs); security risks; security benchmark; Multi-Security Hazard Dataset (MSHD)

Online: 13 September 2024 (15:46:19 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1093.v1

Development Of A Real-Time Object Detection Model For The Detection Of Secretly Cultivated Plants

Ali Yılmaz, Yüksel Yurtay, Nilüfer Yurtay

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: YOLO Series Algorithm, DETR Architecture, Vision Transformers (ViT), Object Detection, FPN (Feature Pyramid Network)

Online: 13 September 2024 (14:55:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202402.1566.v2

Weather Forecasting Using Machine Learning Techniques: Rainfall and Temperature Analysis

Adil Hussain, Ayesha Aslam, Sajib Tripura, VIneet Dhanawat, Varun Shinde

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: rainfall prediction, temperature prediction, ensemble classifier, rain prediction, weather prediction

Online: 13 September 2024 (11:43:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1042.v1

Lung and Colon Cancer Detection Using Deep AI Model

Nazmul Shahadat, Ritika Lama, Anna Nguyen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: 1D CNN; Squeeze-and-Excitation networks; RCN; lightweight model; lung and colon cancer detection; lung cancer detection; colon cancer detection; cancer detection; histopathological images; image classification; deep learning

Online: 13 September 2024 (11:07:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1076.v1

An Overview of Current and New Data Quality Dimensions under a Common Framework

Russell Miller, Harvey Whelan, Michael Chrubasik, David Whittaker, Paul Duncan, João Gregório

Subject: Computer Science And Mathematics, Computer Science Keywords: data quality; data model; data quality dimensions; data traceability; confidence in data; data metrology; data uncertainty; data structures; big data; IoT

Online: 13 September 2024 (10:39:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1046.v1

Enhancing Parameters Tuning of Overlay Models with Ridge Regression: Addressing Multicollinearity in High-Dimensional Data

Aris Magklaras, Christos Gogos, Panayiotis Alefragis, Alexios Birbas

Subject: Computer Science And Mathematics, Other Keywords: Overlay Modeling; Photolithography; Parameters Tuning; EUV lithography; Semiconductor Manufacturing; Yield Results

Online: 13 September 2024 (08:24:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1067.v1

Rare Visual Token Enhancement Improves Registration and Few-Shot Change Detection in Remote Sensing Data

Adrien CHAN-HON-TONG

Subject: Computer Science And Mathematics, Computer Science Keywords: self-supervised; remote sensing; registration; change detection

Online: 13 September 2024 (07:16:01 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1631.v2

Solving NP-Complete Problems Efficiently

Frank Vega

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: complexity classes; Boolean formula; graph; completeness; polynomial time

Online: 13 September 2024 (05:21:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0957.v1

Homogenization of a Thermoelastic Bristly Structure Immersed in a Thermofluid

Sergey Sazhenkov, Elena Sazhenkova

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: compressible thermofluid; thermoelastic solid; Stokes–Fourier equations; linear thermoelasticity equations; homogenization; periodic structure; biotechnology; bionics

Online: 13 September 2024 (04:22:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1002.v1

Dynamics and Forms of Products of Twin, Cousin and Sexy Primes

Ahmet F. Gocgen, Emin M. Buyukyayla

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: number theory; cousin primes; product of cousin primes; prime products

Online: 12 September 2024 (14:09:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0989.v1

Application of the Generalized Double Reduction Method to the (1+1)-Dimensional Kaup-Boussinesq (K-B) System: Exploiting Lie Symmetries and Conservation Laws

Molahlehi Charles Kakuli

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: Generalized double reduction method; conservation laws; Kaup-Boussinesq system; Lie symmetry analysis, exact solutions

Online: 12 September 2024 (13:03:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1005.v1

A Comparative Analysis of Machine Learning and Deep Learning Techniques for Accurate Market Price Forecasting

Olamilekan Shobayo, Sidikat Adeyemi-longe, Olusogo Popoola, Obinna Okoyeigbo

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Hybrid Model; LSTM; Logistic Regression, Recurrent Neural Network, Support Vector Regression, FinBERT)

Online: 12 September 2024 (12:55:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0983.v1

Enhancing Supply Chain Efficiency with Time Series Analysis and Deep Learning Techniques

Jun Sun, Shuwen Zhou, Xiaoan Zhan, Jiang Wu

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Time Series Analysis; Machine Learning Models; Deep Learning Forecasting; Supply Chain; LSTM

Online: 12 September 2024 (12:29:33 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1004.v1

Dynamic Bayesian Networks, Elicitation and Data Embedding for Secure Environments

Kieran Drury, Jim Q. Smith

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Bayesian networks; dynamic Bayesian networks; decision support systems; expert judgement; elicitation; model libraries; missing data; crime intervention; causality

Online: 12 September 2024 (11:39:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0991.v1

Existence and Stability for Fractional Differential Equations with a Regularized ψ–Hilfer Fractional Derivative

Wenchang He, Yuhang Jin, Luyao Wang, Jia Mu

Subject: Computer Science And Mathematics, Mathematics Keywords: regularized ψ -Hilfer fractional derivative; existence; fixed point; Ulam-Hyers-Rassias stability; semi-Ulam-Hyers-Rassias stability

Online: 12 September 2024 (11:35:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0891.v2

Area-Time Efficient High-Radix Modular Inversion Algorithm and Hardware Implementation for ECC over Prime Fields

Yamin Li

Subject: Computer Science And Mathematics, Computer Science Keywords: computer security; elliptic curve cryptography; modular inversion; hardware; Verilog HDL; FPGA; cost performance evaluation

Online: 12 September 2024 (11:34:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0982.v1

Breast Cancer Classification Using Fine-Tuned SWIN Transformer Model on Mammographic Images

Olamilekan Shobayo, Oluwatosin Tanimola, Olusogo Popoola, Obinna Okoyeigbo

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Breast cancer; Deep Learning; Transformer models

Online: 12 September 2024 (11:34:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0998.v1

A Consumer Preferences. An Influence of Society on the Individual

Lukasz Matysiak, Julia Fidler

Subject: Computer Science And Mathematics, Other Keywords: factorization; magma; preference; product; society

Online: 12 September 2024 (11:33:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1161.v4

A Note on Oppermann's Conjecture

Frank Vega

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: prime gaps; prime numbers; square root; inequality

Online: 12 September 2024 (10:56:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0460.v3

On Duality Principles and Concerned Convex Dual Formulations Applied to a Non-Linear Plate Theory and Related Models

Fabio Botelho

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: duality principle; non-linear plate model; convex dual approximate formulation

Online: 12 September 2024 (09:20:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0955.v1

Generative AI in Academics

Brittney Schultz

Subject: Computer Science And Mathematics, Computer Science Keywords: generative AI; GitHub Copilot; Education; Computer Science; OOP

Online: 12 September 2024 (08:17:07 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.0920.v1

A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics

Pengfei Hu, Jinxing Wu, Yanying Wu, Yi Zhang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: hyperspectral; crop economy; non-destructive detection; classification

Online: 12 September 2024 (07:37:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0885.v1

Machine Learning Discoveries of DNA Repair-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells

Shriprakash Sinha

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: DNA repair; Porcupine inhibitor ETC-1922159; Sensitivity analysis; Colorectal cancer

Online: 12 September 2024 (02:52:34 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.0857.v1

The Extended Mind & Body in Extended Realities: A Scoping Review of XR Applications and Risks in the Metaverse

Panagiotis Kourtesis

Subject: Computer Science And Mathematics, Computer Science Keywords: Extended Mind; XR; VR; AR; MR; Metaverse; BCI; ethics; data privacy; embodiment

Online: 11 September 2024 (17:11:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0908.v1

Machine Learning Discoveries of ABC Transporter-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells

Shriprakash Sinha

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: ATP-bindingcassette(ABC)transporters; Porcupineinhibitor ETC-1922159; Sensitivity analysis; Colorectal cancer

Online: 11 September 2024 (17:09:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0923.v1

Ensemble based Machine Learning Algorithm for Loan Default Risk Prediction

Abisola Akinjole, Olamilekan Shobayo, Jumoke Popoola, Obinna Okoyeigbo, Bayode Ogunleye

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Credit default prediction; deep learning; ensemble learning; machine leaning

Online: 11 September 2024 (16:42:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0890.v1

Derivation of Analytical Equations for the Fundamental Period of Framed Structures Using Machine Learning and SHAP Values

Ioannis Karampinis, Konstaninos Morfidis, Lazaros Iliadis

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: fundamental period; masonry infilled framed structures; machine learning; explainability; SHAP; analytical equations

Online: 11 September 2024 (16:23:56 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.0889.v1

A Survey on Real-Time Metaverse: Challenges and Opportunities

Mohsen Hatami, Qian Qu, Yu Chen, Hisham Kholidy, Erik Blasch, Erika Ardiles-Cruz

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Metaverse; Real-Time Metaverse; blockchain; Artificial intelligence (AI); Virtual reality (VR), Augmented Reality (AR); 5G networks

Online: 11 September 2024 (16:21:25 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0855.v1

Machine Learning Discoveries of Bcl-X Synergy in etc-1922159 Treated Colorectal Cancer Cells

Shriprakash Sinha

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: BCL; PorcupineinhibitorETC-1922159; Sensitivity analysis; Colorectal cancer

Online: 11 September 2024 (16:21:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0925.v1

Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy

Kadhim Kamal Al-Barazanchi, Ali Hussein Al-Timemy, Zahid Mohamed Kadhim

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: bag of features; ensemble subspace KNN; muscle ultrasound; diabetic peripheral neuropathy; speeded up robust features

Online: 11 September 2024 (16:19:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0541.v2

The Distribution of Prime Numbers Based on the Proof of Riemann’s Hypothesis and the Properties of the Numbers 3, 6, and 9

seyed kazem mousavi

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: prime numbers; Riemann's hypothesis; three numbers 3; 6; 9

Online: 11 September 2024 (15:29:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0891.v1

Area-Time Efficient High-Radix Modular Inversion Algorithm and Hardware Implementation for ECC over Prime Fields

Yamin Li

Subject: Computer Science And Mathematics, Computer Science Keywords: computer security; elliptic curve cryptography; modular inversion; hardware; Verilog HDL; FPGA; cost performance evaluation

Online: 11 September 2024 (15:27:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0876.v1

Two-Step Fifth-Order Efficient Jacobian-Free Iterative Method for Solving Nonlinear Systems

Alicia Cordero, Javier G. Maimó, Antmel Rodríguez-Cabral, And Juan R. Torregrosa

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: Nonlinear systems; Iterative processes; Convergence order; Efficiency

Online: 11 September 2024 (14:14:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0897.v1

Advanced ENF Region Classification Using UniTS-SinSpec: A Novel Approach Integrating Sinusoidal Activation Function and Spectral Attention

Yujin LI, Tianliang LU, Gaojun ZENG, Kai ZHAO, Shufan PENG

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: electric network frequency; ENF classification; sinusoidal activation function; spectral attention; UniTS-SinSpec model

Online: 11 September 2024 (12:39:30 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.0826.v1

Factors Impacting the Adoption and Acceptance of ChatGPT in Educational Settings: A Narrative Review of Empirical Studies

Mousa Al-kfairy

Subject: Computer Science And Mathematics, Information Systems Keywords: ChatGPT in education; intention to use; acceptance; narrative review; Information Systems theoriess

Online: 11 September 2024 (10:59:44 CEST)

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This narrative review synthesizes findings from empirical studies to explore the factors influencing the adoption of ChatGPT in higher education. The review highlights several critical determinants, both confirmed and debatable, of ChatGPT acceptance among students and educators. Previous studies have predominantly utilized theories such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Diffusion of Innovations (DoI) theory. Confirmed factors include performance expectancy, effort expectancy, social influence, facilitating conditions, and trust, which significantly predict user attitudes and intentions toward adopting ChatGPT. Perceived usefulness and ease of use are core components influencing adoption positively. However, some factors have shown inconsistent impacts. While generally critical, effort expectancy and facilitating conditions vary based on context. Social influence, demographic characteristics (age, gender), extrinsic motivation, and technology readiness exhibit inconsistent effects, suggesting that intrinsic motivations may be more critical. Data collection primarily used online surveys with structured questionnaires, varying widely in sample sizes from small pilot studies to large-scale surveys and covering diverse academic disciplines. Practical implications suggest enhancing perceived usefulness and ease of use through training and technical support, building trust by addressing privacy concerns, and leveraging social influence to drive adoption. The review underscores the need to refine technology acceptance models to incorporate broader psychological and contextual factors, integrate intrinsic motivation theories, and adopt a systems thinking approach. Addressing limitations such as potential selection bias and the quality and scope of existing studies, future research can develop robust models and provide deeper insights into ChatGPT adoption dynamics in higher education, supporting its effective integration to enhance learning outcomes and foster educational innovation.

Preprint ARTICLE | doi:10.20944/preprints202409.0834.v1

BBPE-AE: A Byte Pair Encoding-Based Auto Encoder for Password Guessing

Samane Ghafari, Leila Safari, Mohsen Afsharchi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Password Guessing; Cybersecurity; Password Strength Assessment; Autoencoder Network; Long Short-Term Memory (LSTM) Networks; Byte-level Byte Pair Encoding

Online: 11 September 2024 (10:49:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0871.v1

Enhanced Multi-Scale Attention-Driven 3D Human Reconstruction from Single Image

Yong Ren, Mingquan Zhou, Pengbo Zhou, Shibo Wang, Yangyang Liu, Guohua Geng, Kang Li, Xin Cao

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: enhancing multi-scale attention; single image; normal map; human reconstruction

Online: 11 September 2024 (10:34:23 CEST)

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Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes 1 from a single image has long been a challenging task. While deep learning networks enable us to 2 approximate the shape of unseen sides, capturing the texture details of the non-visible side remains 3 difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) 4 to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles 5 on the model’s surface. However, we have identified challenges with existing normal prediction 6 networks when dealing with complex scenes, such as a lack of focus on local features and insufficient 7 modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced 8 Multi-scale Attention-driven Single Image 3D Human Reconstruction. This approach incorporates 9 a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate 10 features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention 11 mechanisms by adaptively adjusting the weights between features, enabling the network to more 12 effectively leverage information across various scales.Furthermore, we have improved the feature 13 fusion method to better integrate representations from different scales. This enhanced feature fusion 14 allows the network to more comprehensively understand both fine details and global structures 15 within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention 16 mechanism and feature fusion method, optimizing the network’s training process and enhancing the 17 quality of reconstruction results.Our network demonstrates significant improvements in performance 18 for 3D human model reconstruction. Experimental results show that our method exhibits greater 19 robustness to challenging poses compared to traditional single-scale approaches.

Computer Science and Mathematics