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25th International Conference on Distributed computer and Communication Networks, DCCN 2022

作者： Nazarov, Anatoly Phung-Duc, Tuan Paul, Svetlana Lizyura, Olga Institute of Applied Mathematics and Computer Science National Research Tomsk State University 36 Lenina ave. Tomsk634050 Russia Faculty of Engineering Information and Systems University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8573 Japan

ISBN: (纸本)9783031306471

In this paper, we consider single server two-way communication retrial queue with Markov modulated Poisson input. Such system serves two types of calls: incoming and outgoing. We assume that incoming calls arrive according to the input process and retry in case the server is busy upon arrival. If the server is occupied upon arrival, the incoming call joins the orbit and waits for some time before making the next try to receive service. On the other hand, outgoing calls are initiated by server and do not make redial. We present the analysis of the queue using asymptotic diffusion method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Queueing theory

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An Automatic Deep Neural Network Model for Fingerprint Classification

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Intelligent Automation & Soft Computing 2023年第5期36卷 2007-2023页

作者： Amira Tarek Mahmoud Wael AAwad Gamal Behery Mohamed Abouhawwash Mehedi Masud Hanan Aljuaid Ahmed Ismail Ebada Department of Computer Science Faculty of SciencePort Said UniversityPort SaidEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaEgypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI 48824USA Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman University(PNU)P.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaEgypt

The accuracy ofﬁngerprint recognition model is extremely important due to its usage in forensic and securityﬁ***ﬁngerprint recognition system has particular network architecture whereas many other networks achieve higher *** solve this problem in a uniﬁed model,this paper proposes a model that can automatically specify ***,it is called an automatic deep neural net-work(ADNN).Our algorithm can specify the appropriate architecture of the neur-al network used and some signiﬁcant parameters of this *** parameters are the number ofﬁlters,epochs,and *** guarantees the high-est accuracy by updating itself until achieving 99%accuracy then it stops and out-puts the ***,this paper proposes an end-to-end methodology for recognizing a person’s identity from the inputﬁngerprint image based on a resi-dual convolutional neural *** is a complete system and is fully automated whether in the features extraction stage or the classiﬁcation *** goal is to automate thisﬁngerprint recognition system because the more automatic the sys-tem is,the more time and effort it *** model also allows users to react by inputting the initial values of these ***,the model updates itself until itﬁnds the optimal values for the parameters and achieves the best *** advantage of our algorithm is that it can recognize people from their thumb and otherﬁngers and its ability to recognize distorted *** algo-rithm achieved 99.75%accuracy on the publicﬁngerprint dataset(SOCOFing).This is the best accuracy compared with other models.

关键词： Automatic system ﬁngerprint classiﬁcation residual networks deep learning

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data 16

LowResIR-Net: Lightweight Residual Network for ADL Recogniti...

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16th International Conference on ENTERprise information systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care information systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems. © 2025 The Author(s).

关键词： Infrared detectors

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Mobile Application Implementation as Agriculture 4.0 Strategy for Sugarcane Yield Prediction 9

Mobile Application Implementation as Agriculture 4.0 Strateg...

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9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Mawandha, Hangger Gahara Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institut Pertanian Stiper Faculty of Agricultural Technology Yogyakarta55282 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta 11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta 11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta 11480 Indonesia

ISBN: (纸本)9798350304084

information engineering strategies play a significant and transformative role in shaping the landscape of digital agriculture. It drives innovation and efficiency in modern farming practices, with a specific focus on enhancing the value of agricultural commodities, such as sugarcane. The application of mobile technology plays a crucial part in achieving increased sugarcane productivity. Mobile applications, equipped with advanced developer features, offer rapid and user-friendly access to vital information. Traditional methods of estimating sugarcane production relied on manual data recording on paper, followed by data transfer to computer systems, typically managed by sugar factory junior plant officers. This conventional approach presents several inherent weaknesses, including time and effort intensiveness during data recording and entry. Additionally, the potential for errors in calculation and data input is a concern. The storage capacity for paper-based documents is finite, and farmers are often unable to autonomously assess their production potential. The objective of this research is to confront these obstacles by creating a sugarcane production application for Android. This application functions as an information engineering approach focused on forecasting sugarcane yields for plantation owners and their assistants. The development procedure adhered to the systematic waterfall method, following the principles of the Software Development Life Cycle (SDLC) model. Data collection was carried out through observations, while interviews with junior plant officers provided valuable insights into sugarcane estimation techniques. Analysis involved the synthesis of observational and interview data to inform the design of the application's interface and algorithmic system. The resulting application significantly simplifies the process of estimating production potential for farmers, enabling them to access sugarcane productivity data during harvest. Consequently, sugar

关键词： Forecasting

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Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

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computers, Materials & Continua 2021年第1期66卷 315-329页

作者： Abdu Gumaei Mabrook Al-Rakhami Mohamad Mahmoud Al Rahhal Fahad Raddah H.Albogamy Eslam Al Maghayreh Hussain AlSalman College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaizYemen College of Applied Computer Sciences King Saud UniversityRiyadh11362Saudi Arabia

The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the *** of May 30,2020,this disease had infected more than 6 million people globally,with hundreds of thousands of ***,there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare *** study uses gradient boosting regression(GBR)to build a trained model to predict the daily total confirmed cases of *** GBR method can minimize the loss function of the training process and create a single strong learner from weak *** are conducted on a dataset of daily confirmed COVID-19 cases from January 22,2020,to May 30,*** results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR *** results reveal that the GBR model achieves 0.00686 root mean square error,the lowest among several comparative models.

关键词： COVID-19 coronavirus disease SARS-CoV-2 machine learning gradient boosting regression(GBR)method

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Generating synthetic mixed-type tabular data by decoding samples from a latent-space: a case study in healthcare

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Procedia computer science 2024年 246卷 2254-2263页

作者： Jarosław Drapała Jerzy Świątek Department of Computer Science and Systems Engineering Faculty of Information and Communication Technology Wrocław University of Science and Technology Wrocław 50-370 Poland

Medical data are subject to privacy regulations, which severely limit AI specialists who wish to construct decision support systems for medicine. Large amounts of this data are tabular, indicating that they are organized into a table format, where patient records are represented in rows and measured variables in columns. Furthermore, the variables come in different types—some are numerical, while others are categorical. In this work, we introduce a novel method for constructing generators of synthetic tabular data with mixed types. The key point of our approach is the explicit utilization of a latent space to represent the original data. A case study using real medical data is presented.

关键词： Multidimensional scaling Kernel density estimation Random forest regression Support Vector Machine

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Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

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IEEE Access 2023年 11卷 60681-60692页

作者： Khan, Mohammad Ahmar Khan, Ghufran Ahmad Khan, Jalaluddin Anwar, Taushif Ashraf, Zubair Atoum, Ibrahim A. Ahmad, Naved Shahid, Mohammad Ishrat, Mohammad Alghamdi, Abdulrahman Abdullah Dhofar University College of Commerce and Business Administration Salalah211 Oman Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur522502 India Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China GLA University Department of Computer Engineering and Applications Uttar Pradesh Mathura281406 India AlMaarefa University College of Applied Sciences Department of Computer Science and Information Systems Riyadh13731 Saudi Arabia National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Shaqra University College of Computing and Information Technology Shaqra11961 Saudi Arabia

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange's Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority. © 2013 IEEE.

关键词： Data mining

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A novel intelligent approach for color image privacy preservation

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Multimedia Tools and Applications 2024年第33期83卷 79481-79502页

作者： Soualmi, Abdallah Laouamer, Lamri Alti, Adel University Center Cheikh Amoud Ben Moukhtar Illizi33000 Algeria Department of Management Information Systems & amp Production Management College of Business and Economics Qassim University Buraydah Saudi Arabia Faculty of Sciences Computer Science Department LRSD University Ferhat Abbas Sétif 1 Sétif Algeria

Currently, specialists and researchers are focusing their efforts on information security, while frequently exchanged data is increasingly vulnerable to cyberattacks and threats. They use cryptography and watermarking methods to ensure copyright ownership protection, authenticity and integrity by hiding secret data within encrypted images. Most existing watermarking schemes are unable to ensure a good balance between imperceptibility, robustness, time processing, blindness and security. To address these problems, we propose a novel blind watermarking scheme for color images based on chaotic sequence, A-start algorithm and DCT. First, we apply DCT transform on the host image to produce DCT weights. The image block sequences are then slightly updated and scrambled from the obtained DCT weights using A-start algorithm and Arnold chaotic map. Finally, the embedded bits are extracted using the same embedding steps. Optimized image block sequences result in imperceptible, secure and robust watermark bits while reducing processing time. Experimental results show that the proposed approach outperforms other similar works when it comes to imperceptibility exceeding 74 dB and execution time less than 1.5 s for both watermark embedding and extraction. It has exceptional robustness to filtering, rotation and JPEG compression attacks with normalized correlations exceeding 0.8. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

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computers, Materials & Continua 2022年第6期71卷 5751-5764页

作者： Mahmoud Ragab Khalid Eljaaly Maha Farouk SSabir Ehab Bahaudien Ashary S.M.Abo-Dahab E.M.Khalil Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationLuxor University85951Egypt Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based EfficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

关键词： Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning

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MSConv: Multiplicative and Subtractive Convolution for Face Recognition

arXiv

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arXiv 2025年

作者： Zhou, Si Si, Yain-Whar Yuan, Xiaochen Li, Xiaofan Liu, Xiaoxiang Zhang, Xinyuan Lin, Cong Gong, Xueyuan School of Intelligent Systems Science and Engineering Jinan University Guangdong China Department of Computer and Information Science University of Macau China Faculty of Applied Sciences Macau Polytechnic University China

In Neural Networks, there are various methods of feature fusion. Different strategies can significantly affect the effectiveness of feature representation, consequently influencing the model’s ability to extract representative and discriminative features. In the field of face recognition, traditional feature fusion methods include feature concatenation and feature addition. Recently, various attention mechanism-based fusion strategies have emerged. However, we found that these methods primarily focus on the important features in the image, referred to as salient features in this paper, while neglecting another equally important set of features for image recognition tasks, which we term differential features. This may cause the model to overlook critical local differences when dealing with complex facial samples. Therefore, in this paper, we propose an efficient convolution module called MSConv (Multiplicative and Subtractive Convolution), designed to balance the model’s learning of salient and differential features. Specifically, we employ multi-scale mixed convolution to capture both local and broader contextual information from face images, and then utilize Multiplication Operation (MO) and Subtraction Operation (SO) to extract salient and differential features, respectively. Experimental results demonstrate that by integrating both salient and differential features, MSConv outperforms models that only focus on salient features. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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