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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rajanidi, Saikrishna Saminathan, R. Philip, Sebin Beebi Palanisamy, T. Thangavel, Senthil Kumar Kandukuru, Tirumala Kumar Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Coimbatore India

ISBN: (纸本)9798350370249

Human Activity Recognition (HAR) is a crucial component in the development of intelligent systems, ranging from healthcare monitoring to smart environments. While existing HAR systems achieve promising results, limitations arise in handling a diverse range of activities and differentiating each activity accurately. This research addresses these challenges by proposing models to enhance activity recognition accuracy. The paper presents a comprehensive exploration of HAR using a diverse set of machine learning and deep learning models, aimed at improving accuracy and real-world applicability. This approach involves using the HAR dataset from the UCI ML repository which is a popular benchmark dataset for the same. The pipeline includes data preprocessing, handling outliers, and adding noise to better approximate real-world scenarios. To tackle the inherently complex and dynamic nature of human behavior, a wide variety of techniques from Machine Learning models like SVM with K-Means clustering to sequential models in Deep Learning were implemented. Impressive accuracy scores of 93-94 percent were achieved using linear models like multinomial logistic regression and SVM with K-Means clustering. The sequential Deep Learning models like LSTM and GRU produced a decent accuracy of 80-85 percent, but lesser than their Machine Learning counterparts primarily because of limited data samples. The best-performing model in the study was found to be the multinomial logistic regression model. All the models have been extensively evaluated using different metrics and techniques. The analysis revealed some powerful insights regarding the data of each activity and how to further improve the classification, for the sake of better activity recognition. © 2024 IEEE.

关键词： GRU Human Activity Recognition (HAR) LSTM Multinomial Logistic Regression SVM with K-Means

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Dynamical analysis,control,boundedness,and prediction for a fractional-order financial risk system

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Chinese Physics B 2024年第11期33卷 223-235页

作者： Kehao Yang Song Zheng Tianhu Yu Aceng Sambas Muhamad Deni Johansyah Hassan Saberi-Nik Mohamad Afendee Mohamed School of Data Science Zhejiang University of Finance & EconomicsHangzhou 310018China Department of Mathematics Luoyang Normal UniversityLuoyang 471934China Faculty of Informatics and Computing Universiti Sultan Zainal AbidinBesut Campus22200TerengganuMalaysia Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaTasikmalayaJawa Barat46196Indonesia Department of Mathematics Universitas PadjajdaranJatinangorKabupaten Sumedang 45363Indonesia Department of Mathematics and Statistics University of NeyshaburNeyshabur ***Iran

This paper delves into the dynamical analysis,chaos control,Mittag–Leffler boundedness(MLB),and forecasting a fractional-order financial risk(FOFR)system through an absolute function *** this end,the FOFR system is first proposed,and the adomian decomposition method(ADM)is employed to resolve this fractional-order *** stability of equilibrium points and the corresponding control schemes are assessed,and several classical tools such as Lyapunov exponents(LE),bifurcation diagrams,complexity analysis(CA),and 0–1 test are further extended to analyze the dynamical behaviors of *** the global Mittag–Leffler attractive set(MLAS)and Mittag–Leffler positive invariant set(MLPIS)for the proposed financial risk(FR)system are ***,a proficient reservoir-computing(RC)method is applied to forecast the temporal evolution of the complex dynamics for the proposed system,and some simulations are carried out to show the effectiveness and feasibility of the present scheme.

关键词： FOFR system dynamical analysis control boundedness forecasting

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An Interpretable Machine Learning Model for Meningioma Grade Prediction

An Interpretable Machine Learning Model for Meningioma Grade...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Golbaf, Ali Veljanoski, Damjan Chawda, Prutha Gaudl, Swen Hanemann, C. Oliver Ifeachor, Emmanuel School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom Peninsula Schools of Medicine University of Plymouth University Plymouth United Kingdom

Accurate preoperative prediction of meningioma grade is crucial for enhancing the clinical management of these tumours. In this study, we developed a non-invasive machine learning (ML) model to predict meningioma grade using clinical features and radiomics features from preoperative MRI scans, focusing on interpretability to improve clinical adoption of such models. A dataset of 94 patients from The Cancer Imaging Archive (TCIA) was analysed. Clinical features and radiomics features from T1-weighted contrast-enhanced (T1C) and T2-weighted Fluid Attenuated Inversion Recovery (T2 FLAIR) scans were utilised. Two feature subsets were constructed: one using radiomics features alone and the other combining clinical and radiomics features. Feature selection was performed using a modified Least Absolute Shrinkage and Selection Operator (LASSO) technique. Four ML models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB), were developed. SHapley Additive exPlanations (SHAP) was employed to address the blackbox nature of ML models by providing radiomics overall feature importance scores and model interpretation. Results using the clinical-radiomics subset showed that the SVM outperformed others (test AUC: 0.83), indicating its reliability for predicting meningioma grade. SHAP highlights discriminative radiomics features and their interaction with clinical features, thereby enhancing the clinical adoption of such models. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Logistic regression

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Numerical Simulation Study of Oil-Water Emulsion Separation in an Ultrasonic Field:Effect of Coupling between Acoustic and Flow Field Parameters

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China Petroleum Processing & Petrochemical Technology 2024年第1期26卷 116-125页

作者： Wang Heping Lin Yinchao Li Yanggui Zhang Xiaohang Wu Yi Key Laboratory of Engineering Mathematics and Advanced Computing of Nanchang Institute of Technology School of SciencesNanchang Institute of TechnologyNanchang 330099People’s Republic of China Dongguan Key Laboratory of Kunpeng Computing School of Computer ScienceGuangdong University of Science&TechnologyDongguan 523083China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016China

In this study,the separation and coalescence of oil-in-water emulsions are explored in an ultrasonic field using the lattice Boltzmann *** simulating the propagation of ultrasonic waves,this study focuses on examining the effects of acoustic wave frequency,the ratio of oil to water components,and the aspect ratio of the boundary on the emulsification and separation processes of oil-water *** following conclusions are drawn.①Frequency affects the speed of oil droplet separation,leading to an increase in droplet size over *** droplets are found near the source,while smaller droplets are distributed throughout the wave web.②As the boundary aspect ratio increases,the emulsification efficiency of the droplets weakens,and the system takes longer to stabilize.③Emulsions with a higher component of oil can better resist acoustic waves.④At the same acoustic frequency,longer wavelength ultrasonic fields promote the formation of uniformly distributed,smaller oil droplets,which is beneficial to the storage of *** numerical simulation results offer insights for optimizing conditions for oil-in-water separation and serve as a numerical reference for the study of oil-in-water emulsion separation in ultrasonic environments.

关键词： lattice Boltzmann method phase separation ultrasound field

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Fuzzy Filter Design for Affine Systems with Sensor Faults:A Dynamic Event-Triggered Approach

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Journal of Systems Science & Complexity 2022年第5期35卷 1761-1784页

作者： CHENG Jun WU Yuyan WU Zhengguang LI Kezan School of Mathematics and Statistics Center for Applied Mathematics of GuangxiGuangxi Normal Univer-sityGuilin 541006China School of Mathematics and Statistics Guangxi Normal UniversityGuilin 541006China School of Information Science and Engineering Chengdu UniversityChengdu 610106China Institute of Cyber-Systems and Control Zhejiang UniversityHangzhou 310027China School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China

This study addresses the issue of dynamic event-triggered-based filtering for fuzzy affine *** alleviate the utilization of constraint bandwidth resources and improve the efficiency of the signals exchange,a dynamic event-triggered protocol is forwarded to regulate the trigger instants with objective system ***,the nonhomogeneous Markov process is proposed to characterize the dynamic behaviors of sensor faults,where the time-varying transition probabilities belong to a convex polytope ***,the validity and applicability of devised filter design methodology for fuzzy affine systems are displayed via two practical models.

关键词： Dynamic event-triggered protocol fuzzy affine systems nonhomogeneous Markov process sensor fault

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Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems

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Communications in Computational Physics 2023年第2期33卷 596-627页

作者： Sidi Wu Aiqing Zhu Yifa Tang Benzhuo Lu State Key Laboratory of Scientific and Engineering Computing National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China.

With the remarkable empirical success of neural networks across diverse scientific disciplines,rigorous error and convergence analysis are also being developed and ***,there has been little theoretical work focusing on neural networks in solving interface *** this paper,we perform a convergence analysis of physics-informed neural networks(PINNs)for solving second-order elliptic interface ***,we consider PINNs with domain decomposition technologies and introduce gradient-enhanced strategies on the interfaces to deal with boundary and interface jump *** is shown that the neural network sequence obtained by minimizing a Lipschitz regularized loss function converges to the unique solution to the interface problem in H2 as the number of samples *** experiments are provided to demonstrate our theoretical analysis.

关键词： Elliptic interface problems generalization errors convergence analysis neural networks.

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Detection and Classification of AI-Generated Text 15

Detection and Classification of AI-Generated Text

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gupta, Shreya Gupta, Deepa Indian Institute of Technology Delhi Department of Mathematics Delhi India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The advent of Large Language Models (LLMs), has largely transformed global interactions with artificial intelligence. These models, though useful in tasks ranging from translation to text generation, have now permeated everyday life, thereby raising concerns about their potential misuse, particularly in educational institutions. Despite measures like access restrictions implemented by educational institutions, AI based plagiarism has continued to grow, with existing plagiarism detection tools exhibiting limited efficacy in curbing the problem. This paper addresses the need for better AI detection algorithms, focusing on the detection of subtler forms of plagiarism facilitated by LLMs. Leveraging insights from existing studies, the research explores the efficacy of various boosting based machine learning models and dimensionality reduction techniques in improving the accuracy of detecting AI-augmented essays. © 2024 IEEE.

关键词： AI in Education ChatGPT Natural Language Processing plagiarism detection

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Constructing Ensembles of Automatically Designed Relocation Rules for the Container Relocation Problem 13

Constructing Ensembles of Automatically Designed Relocation ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Durasević, Marko Dumić, Mateja Gil-Gala, Francisco Javier University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia School of Applied Mathematics and Informatics J. J. Strossmayer University of Osijek Osijek Croatia University of Oviedo Department of Computing Gijon Spain

ISBN: (纸本)9798350308365

Automated design of heuristics with genetic programming (G P) has, in recent years, become an intensively researched research area. One of the most recent applications of this methodology is to generate relocation rules (RRs) for the container relocation problem (CRP). CRP is an important combinatorial optimisation problem that is found in ship ter-minals and warehouses. RRs are simple constructive heuristic methods that provide a good solution quickly, thus representing an alternative to computationally expensive exact or metaheuris-tic methods. Even though the RRs designed by GP provide an improvement over existing manually designed rules, they have limited performance. An efficient way to improve the performance of RRs generated by GP is to use ensemble learning. In this study, we apply ensemble learning on RRs generated by GP for CRP to improve the performance of individual rules. We investigate how different ensemble sizes and combination methods affect the quality of the results, as well as which rules are selected to form ensembles. The experimental study shows that ensembles constructed out of automatically designed RRs significantly improve performance compared to the individual rules. © 2024 IEEE.

关键词： Combinatorial optimization

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Designing Relocation Rules with Genetic Programming for the Online Container Relocation Problem 13

Designing Relocation Rules with Genetic Programming for the ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

ISBN: (纸本)9798350308365

The container relocation problem (CRP) in shipping terminals is becoming increasingly important due to the growing amount of transferred goods. Until now, the most commonly investigated problem variant has been the offline CRP, in which the order in which the containers need to be retrieved is known beforehand. However, in many real-world situations this is not the case, which is modelled using the online CRP variant. In this variant, not all information is available from the beginning, but rather, it becomes available as the problem is being solved. Unfortunately, many traditional metaheuristic solution methods can not be applied to such a problem variant, which prompts the application of problem-specific heuristics called relocation rules (RRs). However, RRs are challenging to design manually, which prompted the application of genetic programming (GP) to design them automatically. Since GP was used only to design RRs for the offline problem variant, we apply GP to design relocation rules for the online variant in this study. The performance of GP is investigated under different levels of information availability to measure its performance. The results demonstrate that GP can evolve RRs that perform better than existing manually designed ones. Furthermore, the results show that in certain cases, rules generated for one level of information availability perform well for other levels, demonstrating that the evolved rules exhibit a good generalisation capability. © 2024 IEEE.

关键词： Genetic programming

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An Enhanced Optical-Flow Based Attendance Tracking System for Campus Environment 4

An Enhanced Optical-Flow Based Attendance Tracking System fo...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ram, S Karthik Somasundaram, K. Tangavel, Senthil Kumar Venkatesa, M.G. Kolandapalayam, Selvanayaki Reddy, N Manjunadh Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Amrita School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India Ashland University Shanmugam Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

Manual attendance marking consumes significant time during classes, as it involves calling out students' names individually and recording their presence either physically or digitally. Although efforts to automate this process have been made, such as using biometric data like fingerprints, these methods are still time-consuming due to the need for students to queue up for scanning. A computer vision-based attendance system offers a more efficient and seamless alternative by leveraging advanced face recognition algorithms to identify students or employees automatically. This approach uses convolutional neural networks (CNNs) to extract facial features from images and match them with pre-registered data. A pipeline combining the Multi-task Cascaded Convolutional Networks (MTCNN) for face detection and alignment with the FaceNet model for recognition and classification is employed to ensure high accuracy. The system marks attendance in the backend, eliminating the need for manual intervention. Additionally, the model's performance is compared with traditional recognition and classification algorithms to demonstrate its superior efficiency and reliability, making it an ideal solution for automating attendance systems. © 2025 IEEE.

关键词： Convolutional neural networks

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