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2024 International Conference on information technology Research and Innovation, ICITRI 2024

作者： Ilmi, Happid Ridwan Puspitaningtyas, Mery Oktaviyanti Wardani, Yulita Ayu Puspita, Kartika Alfebi, Fadlan Hamid Adimas, Denada Ragil Muhidin Hermawan, Ady Manik, Lindung Parningotan Universitas Nusa Mandiri Faculty of Information Technology Jakarta Indonesia Research Center for Data and Information Sciences Badan Riset dan Inovasi Nasional Jakarta Indonesia

ISBN: (纸本)9798350376210

This study investigates the use of machine learning, specifically linear regression, to estimate software vulnerabilities in open-source GitHub projects. A dataset comprising 30 PHP projects was developed through static analysis to generate 53 features, including metrics related to the size and complexity of the codebase. Feature selection was performed using Pearson correlation, with features exhibiting a correlation above 0.75 with the target variable being included in the model. The study highlights the importance of the number of paths and executable statements in predicting vulnerabilities, suggesting that the complexity of execution paths and the number of executable statements significantly influence the presence of software vulnerabilities. The model's performance was evaluated using Mean Squared Error (MSE) and R-squared metrics, with improvements observed following hyperparameter tuning and regularization. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

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International Journal of Intelligent Computing and Cybernetics 2023年第3期16卷 629-646页

作者： Haoning Pu Zhan Wen Xiulan Sun Lemei Han Yanhe Na Hantao Liu Wenzao Li Chengdu University of Information Technology ChengduChina Education Department of Sichuan Province Education Informatization and Big Data CenterChengduChina Network and Data Security Key Laboratory of Sichuan Province UESTCChengduChina

Purpose–The purpose of this paper is to provide a shorter time cost,high-accuracy fault diagnosis method for water *** pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing *** the time-consuming empirical mode decomposition(EMD)method and the more efficient classification provided by the convolutional neural network(CNN)method,a novel classification method based on incomplete empirical mode decomposition(IEMD)and dual-input dual-channel convolutional neural network(DDCNN)composite data is proposed and applied to the fault diagnosis of water ***/methodology/approach–This paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient(MFCC)and a neural network model of ***,the sound signal is decomposed by IEMD to get numerous intrinsic mode functions(IMFs)and a residual(RES).Several IMFs and one RES are then extracted by MFCC ***,the obtained features are split into two channels(IMFs one channel;RES one channel)and input into ***–The Sound dataset for Malfunctioning Industrial Machine Investigation and Inspection(MIMII dataset)is used to verify the practicability of the *** results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the *** with EMD,51.52% of data preprocessing time,67.25% of network training time and 63.7%of test time are saved and also improve *** limitations/implications–This method can achieve higher accuracy in fault diagnosis with a shorter time ***,the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research ***/value–This method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.

关键词： Fault identification Neural nets DDCNN IEMD MFCC

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BrainFTFCN: Synergistic feature fusion of temporal dynamics and network connectivity for brain age prediction 46

BrainFTFCN: Synergistic feature fusion of temporal dynamics ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Ma, Zihan Yang, Ruonan Ding, Zhenqing Hu, Jianlong Zhang, Heng He, Xiaowei Ren, Yudan Northwest University School of Information Science & Technology Xi'an China Northwest University School of Network and Data Center Xi'an China

ISBN: (纸本)9798350371499

Using neuroimaging-derived data for age estimation serves as a prominent approach in comprehending the normal pace of brain development and mechanisms underlying cognitive declines due to aging and neurological diseases. Despite the promise of resting-state functional magnetic resonance imaging (rs-fMRI) for brain age prediction, previous deep learning models have prioritized capturing either the temporal dynamics via time courses (TCs) or the inherent network topology revealed by functional network connectivity (FNC). These fragmented models neglect the complementary information available by synergistically integrating both. To address this, we introduced BrainFTFCN, a novel feature fusion network that synergistically integrates TCs and FNC for enhanced brain age prediction and model interpretability. BrainFTFCN uniquely combines a Temporal Attention Autoencoder (TAAE) to model evolving activity patterns within TCs and a Functional Connectivity Graph Attention Network (FCGAT) to capture spatial relationships embedded within FNC. The fused features were then fed into a support vector regression model for final age prediction. BrainFTFCN's efficacy shone on Cam-CAN dataset, outperforming state-of-the-art models by 28.21% in mean absolute error (MAE) and demonstrating consistent improvement across other metrics. Ablation studies solidified the critical role of multi-feature integration in boosting prediction. Notably, the most crucial brain regions and discriminative FNC can be easily unveiled via LASSO regression and GNNExplainer respectively, together unlocking biological interpretability and highlighting the model's potential for uncovering valid aging biomarkers. © 2024 IEEE.

关键词： Support vector regression

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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

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Neural Computing and Applications 2024年第30期36卷 18727-18759页

作者： Kassaymeh, Sofian Al-Betar, Mohammed Azmi Rjoubd, Gaith Fraihat, Salam Abdullah, Salwani Almasri, Ammar Software Engineering Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Artificial Intelligence Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Management Information Systems Department Al-Balqa Applied University Amman Jordan

This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter (c1) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Global optimization

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Construction of a Security Framework Model for Medical information Sharing Based on Distributed Networks

Construction of a Security Framework Model for Medical Infor...

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2024 International Conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Liu, Wei Li, Wei Huang, Wenjing Gao, Yong Information and Data Center Union Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan430022 China

ISBN: (纸本)9798350369212

This study aims to construct a distributed network-based medical information sharing security framework to address the current security challenges of medical information sharing. Analyzing existing models, the system design provides a detailed description of the distributed network architecture and explores current security challenges. Research has found that the system performs well under normal workloads, but its availability slightly decreases in high concurrency scenarios. Future research should focus on performance optimization, security reinforcement, and user experience. Overall, this security framework provides the foundation for medical information sharing systems, and future research will promote their further development in practical applications. © 2024 IEEE.

关键词： blockchain distributed network medical information security model medical information sharing platform

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Multi Party Secure Android Cash Register

Multi Party Secure Android Cash Register

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2024 International Conference on Artificial Intelligence, Computer, data Sciences, and Applications, ACDSA 2024

作者： Cimen, Keziban Gokturk, Mehmet E Data Elektronik A.Ş R&d Center İstanbul Turkey Institute of Information Technology Gtu Gebze Turkey

ISBN: (纸本)9798350394528

As businesses prioritize integration with applications like ERP and inventory tracking, the Android Cash Register POS device emerges as a transformative solution, aligning with modern POS trends. Developed with a focus on usercentered design principles, the Android Cash Register stands out as the first of its kind in the market. It ensures a decade-long usability, encompassing both hardware and software aspects. This study focuses on multipary software enabled Cash Register POS device's compatibility with digital transformations its collaborative development, and its potential for increased market share internationally. Key features of such a POS device include remote control capabilities, compliance with high-level financial regulations, and the unique combination of being a Payment Recording Device (ECR) with EFTPOS capabilities. The study introduces a Multi-Stakeholder Android Cash Register connected to a Secure Remote Software Distribution Network, offering a distinctive product globally. The hardware design incorporates security measures like MESH and Electronic Seal to safeguard against physical impact and tampering. Security factors, including signing mechanisms like Sign2, are discussed, highlighting the enhanced protection against tampering attacks and the utilization of robust hash algorithms. The study also focuses on Android's role in the payment sector, addressing security vulnerabilities related to internet access and NFC. In short, importance of a multi-party store model is emphasized, allowing only approved applications to run on the secure Android POS device. The proposed Android Cash Register represents a significant leap, offering a secure, integrated, and technologically advanced solution to meet evolving business needs in the digital era, all within a concise framework. © 2024 IEEE.

关键词： Cash registers

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A Review of Knowledge Graph in Traditional Chinese Medicine:Analysis,Construction,Application and Prospects

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Computers, Materials & Continua 2024年第12期81卷 3583-3616页

作者： Xiaolong Qu Ziwei Tian Jinman Cui Ruowei Li Dongmei Li Xiaoping Zhang School of Information Science and Technology Beijing Forestry UniversityBeijing100083China National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical SciencesBeijing100700China Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing Forestry UniversityBeijing100083China

As an advanced data science technology,the knowledge graph systematically integrates and displays the knowledge framework within the field of traditional Chinese medicine(TCM).This not only contributes to a deeper comprehension of traditional Chinese medical theories but also provides robust support for the intelligent decision systems and medical applications of *** this backdrop,this paper aims to systematically review the current status and development trends of TCM knowledge graphs,offering theoretical and technical foundations to facilitate the inheritance,innovation,and integrated development of ***,we introduce the relevant concepts and research status of TCM knowledge ***,we conduct an in-depth analysis of the challenges and trends faced by key technologies in TCM knowledge graph construction,such as knowledge representation,extraction,fusion,and reasoning,and classifies typical knowledge graphs in various subfields of ***,we comprehensively outline the current medical applications of TCM knowledge graphs in areas such as information retrieval,diagnosis,question answering,recommendation,and knowledge ***,the current research status and future directions of TCM knowledge graphs are concluded and *** believe this paper contributes to a deeper understanding of the research dynamics in TCM knowledge graphs and provides essential references for scholars in related fields.

关键词： Systematic review traditional Chinese medicine knowledge graph deep learning medical applications

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A circuit area optimization of MK-3 S-box

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Cybersecurity 2025年第1期8卷 61-71页

作者： Yanjun Li Weiguo Zhang Yiping Lin Jian Zou Jian Liu Information Industry Information Security Evaluation Center The 15th Research Institute of China Electronic Technology Group CorporationBeijing 100083China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China Beijing Electronic Science and Technology Institute No.7 Fufeng RoadFengtai DistricBeijing 100083China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

In MILCOM 2015,Kelly et *** the authentication encryption algorithm MK-3,which applied the 16-bit *** paper aims to implement the 16-bit S-box with less circuit ***,we classifed the irreducible polynomials over F_(2n)into three *** we compared the logic gates required for multiplication over the fnite feld constructed by the three types of irreducible *** to the comparison result,we constructed the composite felds, F_((2^(4))^(2))and F_((2^(8))^(2)).Based on the isomorphism of fnite felds,the operations over F_(2)^(16)can be conducted over F_((2^(8))^(2)).Similarly,elements over F28 can be mapped to the corresponding elements over F_((2^(4)))^(2).Next,the SAT solver was used to optimize the operations over smaller feld F_(2)^(4).At last,the architecture of the optimized MK-3 S-box was worked *** with the implementation proposed by the original designer,the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.

关键词： irreducible operations optimization

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Bifurcation analysis of a diffusive radio-dependent model with spatial memory

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International Journal of Biomathematics 2024年第8期17卷 309-347页

作者： Qi An Xingyu Guo Xuebing Zhang College of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing 210044 P.R.China Jiangsu International Joint Laboratory on System Modeling and Data Analysis Center for Applied Mathematics of Jiangsu Province Nanjing University of Information Science and Technology Nanjing 210044 P.R.China

This is an Open Access article published by World Scientific Publishing *** is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0(CC BY-NC-ND)License,which permits use,distribution and reproduction,provided that the original work is properly cited,the use is non-commercial and no modifications or adaptations are *** cognitive abilities of animals,such as memory,have a significant impact on their movement in *** this paper,we consider a radio-dependent model with memorybased diffusion under the conditions of Neumann *** stability of a positive equilibrium and the existence of the Turing-Hopf bifurcation induced by memory diffusion and memory delay are carried out in ***,our findings indicate that with a relatively short average memory period,the large memory diffusion can stabilize an otherwise unstable *** addition,the third-order truncated normal form for the Turing-Hopf bifurcation restricted to the central manifold is derived,which can reveal the generation of some steady-state and time-periodic solutions with spatial *** coefficients within the normal form are systematically determined through matrix operations,and these results can also be applied to other models with memory ***,leveraging the theoretical findings,we elucidated the intricate spatial-temporal dynamics and their associated parameter scopes caused by Turing-Hopf bifurcation through numerical simulations.

关键词： Spatial memory normal form Turing-Hopf bifurcation spatio-temporal patterns periodic solutions

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