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Proceedings of the 4th International Conference on computer, Artificial Intelligence and Control Engineering

作者： Chengyue Li Yankai Chen Department of Mathematics School of Mathematics and Computer Science Shantou University Shantou Guangdong China Mathematics and Applied Mathematics (Sino US Cooperative Actuarial Science and Risk Management) Ningbo University Ningbo Zhejiang China

ISBN: (纸本)9798400712647

We extracted four key metrics from the "Wimbledon_featured_***" dataset: scoring variance, cumulative winning serves, unforced errors, and physical *** first assigned reasonable weights to these four indicators through AHP-CRITIC, and then summed them up to obtain a momentum calculation model, which was used to calculate the momentum value of each scoring point. Momentum magnitudes of athletes and opponents were then compared, setting fluctuations as binary variables (“0” for defense and “1” for offense). Streaks were quantified using differences in scoring data. The chi-square test was then used to confirm this significant relationship between streaks and momentum fluctuations (p<0.05). The prediction model we developed used the scoring data as the input variable and the magnitude of momentum as the output variable. We first compared the accuracy of the four models, neural network, Markov chain and GBDT, and found that GBDT has 99.8% accuracy and neural network has only 69.9% accuracy, so we chose GBDT as the prediction *** model reflects the cumulative score and service opportunity are important indicators. When our model is used for the dataset “2023-wimbledon-1701”, the accuracy of the model is very high. The sensitivity analysis shows that the model has 68.6% accuracy after excluding key scoring data. A women's tennis tournament data is applied to our model with 95.4% accuracy, which shows the strong generalization ability of our model.

关键词： AHP-CRITIC

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Can a k-Lucas Number be a Product of Two Fermat Numbers? 4th

Can a k-Lucas Number be a Product of Two Fermat Numbers?

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4th International Conference on Class Groups of Number Fields and Related Topics, ICCGNFRT 2021 and 5th International Conference on Class Groups of Number Fields and Related Topics, ICCGNFRT 2022

作者： Gueye, Alioune Rihane, Salah Eddine Togbé, Alain UFR of Applied Sciences and Technology University Gaston Berger of Saint-Louis Saint-Louis Senegal Department of Mathematics National Higher School of Mathematics P.O. Box 75 Sidi Abdellah Mahelma16093 Algeria Department of Mathematics Statistics and Computer Science Purdue University Northwest 1401 S U.S. 421 WestvilleIN46391 United States

ISBN: (纸本)9789819769100

Let k≥2. A generalization of the well-known Lucas sequence is the k-Lucas sequences. For this sequence, the first k terms are 0,…,0,2,1 and each term afterwards is the sum of the preceding k terms. In this paper, we show that there is no k-Lucas written as product of two Fermat numbers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Random number generation

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ROOM DIMENSION AND BOUNDARY CONDITION INFERENCE USING ROOM TRANSFER FUNCTIONS 10

ROOM DIMENSION AND BOUNDARY CONDITION INFERENCE USING ROOM T...

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10th Convention of the European Acoustics Association, EAA 2023

作者： Xia, Yuanxin Engsig-Karup, Allan P. Jeong, Cheol-Ho Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. Lyngby2800 Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby2800 Denmark

ISBN: (纸本)9788888942674

The estimation of the absorption coefficients of the boundary surfaces in a room is important in room acoustic engineering. This research presents a machine learning method learns from simulated data to estimate the room dimensions and frequency-dependent absorption coefficients. We employ multi-task convolutional neural networks for inferring the frequency-dependent absorption coefficients and the dimensions of the room from transfer functions calculated by wave-based room acoustic methods. The proposed method provides reasonably accurate estimation of the boundary conditions and dimensions. © 2023 Xia et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Boundary conditions

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Optimal Experimental Design to Estimate Insulin Response in Type 2 Diabetes

Optimal Experimental Design to Estimate Insulin Response in ...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Engell, Sarah Ellinor Bengtsson, Henrik Benam, Karim Davari Fougner, Anders Lyngvi Jorgensen, John Bagterp Novo Nordisk A/S BagsværdDK-2880 Denmark Technical University of Denmark Department of Applied Mathematics and Computer Science LyngbyDK-2800 Kgs. Denmark Department of Engineering Cybernetics Trondheim Norway

ISBN: (纸本)9798350335446

In late-stage type 2 diabetes, automated titration algorithms provide a promising alternative to the current standard-of-care. Many published methods rely on personalized dose-response models to predict a safe and effective insulin dose. In this case study, we address the challenge of how to collect an informative data set to ensure practical identifiability of such models. We apply optimal experimental design to enhance the performance of a published titration algorithm. For a 24-hour experiment, we solve an optimization problem to select the size of three meals and the hourly fast-acting insulin infusion rate. In simulation, we demonstrate how the optimized protocol improves the safety of the algorithm's dose-predictions. The results indicate that optimal experimental design has the potential to improve model-based algorithms and may be used as a qualitative tool when planning clinical experiments. © 2023 IEEE.

关键词： Insulin

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Detection of Mobile LoRa Jammers

Detection of Mobile LoRa Jammers

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Virtual Conference on Communications (VCC), IEEE

作者： Bartlomiej Jozef Bleszynski Charalampos Orfanidis Xenofon Fafoutis Department of Applied Mathematics and Computer Science Technical University of Denmark (DTU) Denmark

Low Power Wide Area Networks (LPWANs) plat-forms (LoRa, NB-IoT, Sigfox) came to add a missing piece to the Internet of Things (IoT) ecosystem, namely long range communication in low power. LPWAN platforms are characterized by low data rate and long transmission time. LoRa specifically, has a data rate from 27 to 0.3 kbps and the transmission time of a single packet might be more than 1.5 seconds for some cases. These characteristics render LPWANs vulnerable to jamming attacks as a malicious user can perform a jamming attack from long range and the long transmission time is allowing a large time window to perform the attack. Moreover, if the jamming node is mobile (i.e., attached on a vehicle or an Unmanned Aerial Vehicle (UAV)), the current countermeasures proposed by the literature will not be relevant anymore. In this paper we investigate if it is possible to detect a mobile LoRa jammer based on the impact of the Doppler effect and based on a combination of signal strength and the entropy of the transmitted data. The results, obtained by utilizing commercial hardware, reveal that we could detect a potential mobile jammer that follows a deceptive jamming strategy with random payload, but further investigation is required for more sophisticated jamming attacks.

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computer-aided diagnosis of breast cancer from mammogram images using deep learning algorithms

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Journal of Electrical Systems and Information Technology 2024年第1期11卷 1-16页

作者： Dada, Emmanuel Gbenga Oyewola, David Opeoluwa Misra, Sanjay Department of Mathematical Sciences University of Maiduguri Maiduguri Nigeria Department of Mathematics and Computer Science Federal University Kashere Gombe Nigeria Department of Computer Science and Communication Østfold University College Halden Norway Department of Applied Data Science Institute for Energy Technology Halden Norway

Even though accurate detection of dangerous malignancies from mammogram images is mostly dependent on radiologists' experience, specialists occasionally differ in their assessments. computer-aided diagnosis provides a better solution for image diagnosis that can help experts make more reliable decisions. In medical applications for diagnosing cancerous growths from mammogram images, computerized and accurate classification of breast cancer mammogram images is critical. The deep learning approach has been widely applied in medical image processing and has had considerable success in biological image classification. The Convolutional Neural Network (CNN), Inception, and EfficientNet are proposed in this paper. The proposed models attain better performance compared to the conventional CNN. The models are used to automatically classify breast cancer mammogram images from Kaggle into benign and malignant. Simulation results demonstrated that EfficientNet, with an accuracy between 97.13 and 99.27%, and overall accuracy of 98.29%, perform better than the other models in this paper.

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Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Models

Evaluating the Influence of Temporal Context on Automatic Mo...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Javier García Ciudad Morten Mørup Birgitte Rahbek Kornum Alexander Neergaard Zahid Department of Neuroscience University of Copenhagen Copenhagen Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In human sleep staging models, augmenting the temporal context of the input to the range of tens of minutes has recently demonstrated performance improvement. In contrast, the temporal context of mouse sleep staging models is typically in the order of tens of seconds. While long-term time patterns are less clear in mouse sleep, increasing the temporal context further than that of the current mouse sleep staging models might still result in a performance increase, given that the current methods only model very short term patterns. In this study, we examine the influence of increasing the temporal context in mouse sleep staging up to 15 minutes in three mouse cohorts using two recent and high-performing human sleep staging models that account for long-term dependencies. These are compared to two prominent mouse sleep staging models that use a local context of 12 s and 20 s, respectively. An increase in context up to 28 s is observed to have a positive impact on sleep stage classification performance, especially in REM sleep. However, the impact is limited for longer context windows. One of the human sleep scoring models, L-SeqSleepNet, outperforms both mouse models in all cohorts. This suggests that mouse sleep staging can benefit from more temporal context than currently used.

关键词： Biological system modeling Transformers Mice Data models Rapid eye movement sleep Engineering in medicine and biology Context modeling

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： El-Sheikh, Mohamed M. Bayoumy, Aya Ahmed, Nada M. Mohamed Eldeeb, Amany Rabea, Fatma I. Nawar, Amira A. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

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computers, Materials & Continua 2022年第7期72卷 243-259页

作者： S.Karthik Robin Singh Bhadoria Jeong Gon Lee Arun Kumar Sivaraman Sovan Samanta A.Balasundaram Brijesh Kumar Chaurasia S.Ashokkumar Department of ECE College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalaniChennai600026India Department of CSE Birla Institute of Applied Sciences(BIAS)BhimtalUttarakhand263136India Division of Applied Mathematics Wonkwang University460Iksan-DaeroIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India Indian Institute of Information Technology(IIIT) LucknowUttar Pradesh226002India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India

Data is always a crucial issue of concern especially during its prediction and computation in digital *** paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data *** also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the *** paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and *** method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from *** proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown *** scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic *** also demonstrates the generative function forKalman-filer based prediction model and its *** paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.

关键词： Bayesian learning model kalman filter machine learning data accuracy prediction

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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