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5th International Conference on Research, Implementation, and Education of Mathematics and science, ICRIEMS 2018

作者： Novianto, M.A. Suhartono Prastyo, D.D. Suharsono, A. Setiawan Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Generalized Space-Time Autoregressive (GSTAR) is a statistics model that usually applied for forecasting data that have both spatial and temporal dependency. The monthly tourist arrival data in some locations are example of spatio-temporal data. Most of previous researches in GSTAR model only focused on stationary data. Otherwise, tourist arrival data in Indonesia mostly contain trend, seasonal, and some extreme values caused by interventions or outliers. The objective of this study is to apply and develop GSTAR model for forecasting spatio-temporal data with trend, seasonal, and interventions or outliers. This model is then known as GSTAR with exogeneous variables or GSTARIX model. Then, the forecast accuracy of GSTARIX model are compared to VAR with exogenous variables or VARIX model. Monthly data about number of tourist arrivals to Jakarta, Surakarta, Surabaya, and Denpasar are used as case study. Moreover, two methods are used for parameter estimation, i.e. Ordinary Least Square (OLS) and Generalized Least Square (GLS). The criteria for selecting the best model is Root Mean Square Error (RMSE). The results showed that the best model for forecasting tourist arrivals in each location are different. The best model for forecasting number of tourist arrivals to Jakarta and Surabaya is GSTARIX with OLS method or GSTARIX-OLS. Whereas, the best model for Denpasar and Surakarta data are VARIX and GSTARIX-GLS, respectively. © 2018 Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI

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Scientific Reports 2025年第1期15卷 1-14页

作者： Hussain, Sayyed Shahid Shah, Pir Masoom Dawood, Hussain Degang, Xu Alshamayleh, Ahmad Khan, Muhammad Adnan Ghazal, Taher M. School of Automation Central South University Changsha 410083 China School of Computer Science and Engineering Central South University Changsha 410010 China Department of Computer Science Bacha Khan University Charsadda Pakistan School of Computing Skyline University College Sharjah United Arab Emirates Department of Data Science and Artificial Intelligence Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13120 South Korea Riphah School of Computing and Innovation Riphah International University Lahore Campus Lahore 54000 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Networks and Cybersecurity Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

Parkinson’s disease ranks as the second most prevalent neurological disorder after Alzheimer’s disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson’s disease (PD) detection using MR images. However, CNN models generally focus on local features while prone to capture global representations. On the other hand, the vision transformer (ViT) excels at capturing global features through its self-attention mechanism, but it compromises local feature representations. Additionally, the varying magnitude of MR data poses a challenge for ViT, potentially leading to the gradient vanishing problem. To address these limitations, this paper proposed a novel framework that combines the Swin-Transformer and CNN to capture both local and global features effectively. To mitigate the gradient vanishing issue in ViT, we used skipped connections and cosine attention mechanism in VIT that preserves the output distribution regardless of input magnitude variations. The proposed model comprises three primary blocks: Transformer-block, convolutional block, and dense-block. The input image is processed concurrently by the cosine transformer and convolutional block. Subsequently, the extracted features from both blocks are concatenated and fed to the dense block for decision-making. The proposed model achieved promesing results of 96%, 97%, 95%, and 95% in terms of accuracy, sensitivity, specificity, and area under the curve, respectively. © The Author(s) 2025.

关键词： CNN Cosine similarity MRI Parkinson’s disease Swin-transformer

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Bayesian Mixture Generalized Extreme Value Regression with Double-Exponential CAR Frailty for Dengue Haemorrhagic Fever in Pamekasan, East Java, Indonesia 3

Bayesian Mixture Generalized Extreme Value Regression with D...

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3rd International Conference on Statistics, Mathematics, Teaching, and Research 2019, ICSMTR 2019

作者： Rantini, D. Iriawan, N. Irhamah Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

There is a lot of research on infectious diseases. One of the dangers is caused by dengue hemorrhagic fever (DHF) viruses. Almost all the world there are dengue viruses that spread. In health science, this research continues to increase over time. It has been widely studied by medical circles, but from a statistical point of view, patient survival can be investigated probabilistically. The survival time of DHF patients could refer to the time of arrival of DHF patients to the hospital with all the health conditions of the patient to be discharged from the hospital. It is because the patient has been declared medically cured or further improved from the initial hospital admission. In this case, survival analysis will be appropriate to be implemented, where the event of the study is DHF patients who are discharged from the hospital due to recovery. DHF patient survival data is found following the multimodal Generalized Extreme Value distribution so that a mixture model would be employed. There is a lot of transmission media for diseases transmitted by the DHF virus, but the fastest transmission medium occurs in a short time, called the Aedes Aegypti Mosquito. This transmission of the DHF virus by mosquitoes can occur between adjacent neighboring regions. This article brings together two fields of research, namely survival models with spatial random effects. The Bayesian analysis will be used as a model parameter estimation approach, whereby unmeasured autocorrelations between intersecting regions will be captured through the conditional autoregressive (CAR). Two different distributions approach for these unmeasured autocorrelations will be given, i.e. the Normal CAR and the Double Exponential CAR. Four predictor variables, namely sex, age, hematocrit level, and platelet count in each DHF patient are used to explain the variability of the survival. The Cox Proportional Hazard regression is employed to find out the significant influence of those variables on the surviva

关键词： Hospitals

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Distinguishing unitary gates on the IBM quantum processor

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science China(Information sciences) 2019年第7期62卷 210-216页

作者： Shusen LIU Yinan LI Runyao DUAN School of Data and Computer Science Sun Yat-sen University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney Centrum Wiskunde & Informatica and Research Center for Quantum Software Institute for Quantum Computing

An unknown unitary gate, which is secretly chosen from several known ones, can always be distinguished perfectly. In this paper, we implement such a task on IBM's quantum processor. More precisely,we experimentally demonstrate the discrimination of two qubit unitary gates, the identity gate and the 2/3π-phase shift gate, using two discrimination schemes — the parallel scheme and the sequential scheme. We program these two schemes on the ibmqx4, a 5-qubit superconducting quantum processor via IBM cloud,with the help of the QSI modules. We report that both discrimination schemes achieve success probabilities at least 85%.

关键词： quantum experiment IBM quantum computer software-aided quantum experiment sequential schemes for unitary discrimination parallel schemes for unitary discrimination

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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

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Neural computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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Note on a New Construction of Kantorovich Form q-Bernstein Operators Related to Shape Parameter λ

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Computer Modeling in Engineering & sciences 2022年第3期130卷 1479-1493页

作者： Qingbo Cai Resat Aslan Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingSchool of Mathematics and Computer ScienceQuanzhou Normal UniversityQuanzhou362000China Department of Mathematics Faculty of Sciences and ArtsHarran UniversitySanlıurfa63300Turkey

The main purpose of this paper is to introduce some approximation properties of a Kantorovich kind q-Bernstein operators related to B′ezier basis functions with shape parameterλ∈[−1,1].Firstly,we compute some basic results such as moments and central moments,and derive the Korovkin type approximation theorem for these ***,we estimate the order of convergence in terms of the usual modulus of continuity,for the functions belong to Lipschitz-type class and Peetre’s K-functional,***,with the aid of Maple software,we present the comparison of the convergence of these newly defined operators to the certain function with some graphical illustrations and error estimation table.

关键词： q-calculus (λ,q)-Bernstein polynomials order of convergence Lipschitz-type function Peetre’s K-functional

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Enhancing CNN-based network intrusion detection through hyperparameter optimization

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Intelligent Systems with Applications 2025年 26卷

作者： Kaissar, Antanios Nassif, Ali Bou Soudan, Bassel Injadat, MohammadNoor College of Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science & AI Faculty of Information Technology Zarqa University Zarqa Jordan

This research investigates the optimization of hyperparameters in Convolutional Neural Networks (CNNs) to enhance the performance of Network Intrusion Detection Systems (NIDS). Four distinct optimization techniques, including Grid Search (GS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), are rigorously examined. Comprehensive experiments employ the UNSW-NB15 and CSE-CIC-IDS2018 datasets to determine the optimal hyperparameter configurations for each technique. Subsequently, detailed experiments evaluate the efficiency of the optimized models in detecting network attacks. The findings consistently reveal that optimized CNN models outperform their non-optimized counterparts across all optimization techniques. Notably, GWO emerges as the top-performing technique, achieving remarkable detection performance on both datasets, with 97.08 % accuracy, 96.95 % precision, 97.21 % recall, and 97.08 % F1 score on the UNSW-NB15 dataset, and 96.37 % accuracy, 96.13 % precision, 96.59 % recall, and 96.36 % F1 score on the CSE-CIC-IDS2018 dataset. Furthermore, hyperparameter optimization significantly reduces training and testing times. The GWO-optimized model achieved a reduction of >11 % in training time and 6.14 % in testing time on the UNSW-NB15 dataset. On the CSE-CIC-IDS2018 dataset, the GA-optimized model provided the best improvements, reducing training and testing times by 9.63 % and 8.61 %, respectively. In comparison to existing CNN models trained on the same datasets, the GWO-optimized CNN model consistently excels in all performance metrics, without the need for complex hybrid Deep Learning models. These results underscore the value of systematic hyperparameter optimization in enhancing CNN-based NIDS, with GWO standing out as a compelling technique for achieving optimal model configurations. © 2025 The Author(s)

关键词： Particle swarm optimization (PSO)

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Transforming Finger Vein Template in Multi-instance Scenario 10

Transforming Finger Vein Template in Multi-instance Scenario

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10th International Conference on Information and Communication Technology, ICoICT 2022

作者： Cai, Shuxian Yau, Wei Chuen Ong, Thian Song Teng, Jackson Horlick School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia Multimedia University Faculty of Information Science and Technology Melaka Malaysia

ISBN: (数字)9781665481656

ISBN: (纸本)9781665481656

Unlike single biometric systems, multi-instance biometric systems can reduce the risk of user privacy leakage. Meanwhile, maintaining biometric security also requires template protection. However, there is still limited study focusing on template protection in multi-instance biometrics. To address such issues, this paper proposes a method for multi-instance finger vein template protection. In this work, the vein features of different finger instances are extracted at feature fusion and image fusion levels using Gabor filter method, respectively. For feature transformation, the Gabor feature blocks are transformed with Bloom filters to extract the finger vein template for secure storage and verification. When analyzing the performance, the experiments are mainly conducted with the constant key scenario to be compared with performance of the baseline Gabor feature extraction method and the genuine key scenario. It is shown that this proposed method effectively improves system performance, with equal error rate (EER) as low as 5.00% in constant key scenario, while maintaining zero error rate in genuine key scenario. Besides, the proposed method is also examined for the properties of unlinkability and irreversibility as required for a template protection method. © 2022 IEEE.

关键词： Gabor filters

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Effect of copper nanoparticle volume fraction on flow in a 3D lid-driven cavity with phase change materials using molecular dynamics simulation

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Sawaran Singh, Narinderjit Singh Hassan, Waqed H. Thiab, Rifqa F. Al-zahy, Younis Mohamed Atiah Salahshour, Soheil Hekmatifar, M. Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia University of Warith Al-Anbiyaa Kerbala 56001 Iraq Department of Civil Engineering College of Engineering University of Kerbala Kerbala 56001 Iraq College of Health and Medical Techniques Al-Zahraa University for Women Karbala 56100 Iraq Department of Physics College of Education Misan University Maysan Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Background: Phase Change Materials are substances characterized by specific properties, including defined melting points and substantial latent heat of fusion. Effective heat transfer management is vital in modern industries, as it supports essential processes across various sectors. Methods: This study investigates the effect of copper nanoparticle volume fraction on flow behavior and thermal dynamics in a 3D lid-driven cavity. This enclosure was a controlled environment filled with phase change materials, designed to optimize thermal energy management. The system configuration comprised a wavy bottom wall and an adaptable upper wall, allowing for dynamic adjustments during the simulation. The results show that as the volume ratio of copper nanoparticles increased from 1 % to 3 %, the steady heat transfer process in the simulated nanoparticles also increased. Increasing the volume ratio from 1 % to 3 % resulted in a decrease in the maximum density of nanoparticles, which decreased from 0.0152 to 0.0146 atom/Å³. Additionally, this increase led to a rise in thermal conductivity from 1.26 to 1.45 W/m·K and in heat flux from 8.26 to 9.95 W/m². Significant Findings: The study demonstrates that optimizing the volume fraction of Cu-NPs in PCMs can significantly enhance thermal conductivity and heat flux, offering potential improvements in thermal energy storage systems. © 2025 The Authors

关键词： Density Heat flux Molecular dynamics simulation Phase change material Thermal conductivity

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Sequences analysis and phylogenetic of leukemia DNA 2

Sequences analysis and phylogenetic of leukemia DNA

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2nd International Seminar on science and Technology 2020, ISST-2 2020

作者： Lusiyanti, D. Nacong, N. Irawan, M.I. Mathematics Study Program Science Faculty Tadulako University Indonesia Dapertment of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya Indonesia

DNA is one part of a biological sequence other than RNA and protein. DNA is a nucleic acid that stores all the unique biological information of every living thing and several viruses. Someone who has an illness can be seen in the structure of the DNA which can then be compared with other DNA structures. This process is part of sequence analysis which is the core of bioinformatics. This study aims to analyze the results of sequences alignment of cancer. The sequence was obtained from DNA leukemia taken from GenBank (***). In the sequence analysis, the most basic thing is the sequence alignment. Alignment results can provide information about the level of similarity in the structure of the two sequences. This study will apply the Super Pairwise Alignment method that is implemented in JAVA-based programming languages. Alignment results are carried out by giving different values of gap penalty and gap extend. The results of the study obtained different alignment values, this proves that there is a significant effect of the selection of core parameters used in alignment. © Published under licence by IOP Publishing Ltd.

关键词： DNA

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