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arXiv 2025年

作者： Yu, Yian Wang, Bo Shi, Jian Qing Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom National Center for Applied Mathematics Shenzhen China

Motivated by distinct walking patterns in real-world free-living gait data, this paper proposes an innovative curve-based sampling scheme for the analysis of functional data characterized by a mixture of covariance structures. Traditional approaches often fail to adequately capture inherent complexities arising from heterogeneous covariance patterns across distinct subsets of the data. We introduce a unified Bayesian framework that integrates a nonlinear regression function with a continuous-time hidden Markov model, enabling the identification and utilization of varying covariance structures. One of the key contributions is the development of a computationally efficient curve-based sampling scheme for hidden state estimation, addressing the sampling complexities associated with high-dimensional, conditionally dependent data. This paper details the Bayesian inference procedure, examines the asymptotic properties to ensure the structural consistency of the model, and demonstrates its effectiveness through simulated and real-world examples. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Forecasting the load flow of Engine Driven Pump based on Light Gradient Boosting Machine Model

Forecasting the load flow of Engine Driven Pump based on Lig...

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2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2021

作者： Niu, Wei Song, Xiaogang Wang, Hao Chen, Juan Xi'an Aeronautics Computing Technique Research Institute AVIC Xi'an China Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an China Xi'an Institute of Applied Optics Xi'an China

ISBN: (纸本)9780738146492

The flow rate of aircraft hydraulic system is one of the important parameters to measure the performance of aircraft. The stability of flow rate of hydraulic pipeline is related to the safety of hydraulic system and even aircraft flight. Aiming at the problem that it is difficult to measure the load flow of aircraft hydraulic system, a prediction method based on ensemble learning for the load flow of Engine Driven Pump (EDP) is proposed. According to the different working conditions of EDP, the Light Gradient Boosting Machine (LightGBM) model is established. Through Particle Swarm Optimization algorithm optimizes the super parameters of LightGBM algorithm, and realizes the regression prediction mode. Finally, compared with the multiple linear regression, random forest, experimental results indicate that the proposed model obviously can improve the prediction accuracy. © 2021 IEEE.

关键词： Forecasting

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AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wilfred Blessing N. R Hemalatha Gunasekaran Hariharan B. Jaber Saleh Salim Al Jabri Kavitha S. J Sutherlin Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy.

关键词： Accuracy Magnetic resonance imaging Redundancy Medical services Predictive models Brain modeling Real-time systems Alzheimer's disease Medical diagnostic imaging Overfitting

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GAPPY AE: A NONLINEAR APPROACH FOR GAPPY DATA RECONSTRUCTION USING AUTO-ENCODER

arXiv

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arXiv 2023年

作者： Kim, Youngkyu Choi, Youngsoo Yoo, Byounghyun Center for Artificial Intelligence Korea Institute of Science and Technology Seongbuk-gu Seoul02792 Korea Republic of Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States

We introduce a novel data reconstruction algorithm known as Gappy auto-encoder (Gappy AE) to address the limitations associated with Gappy proper orthogonal decomposition (Gappy POD), a widely used method for data reconstruction when dealing with sparse measurements or missing data. Gappy POD has inherent constraints in accurately representing solutions characterized by slowly decaying Kolmogorov N-widths, primarily due to its reliance on linear subspaces for data prediction. In contrast, Gappy AE leverages the power of nonlinear manifold representations to address data reconstruction challenges of conventional Gappy POD. It excels at real-time state prediction in scenarios where only sparsely measured data is available, filling in the gaps effectively. This capability makes Gappy AE particularly valuable, such as for digital twin and image correction applications. To demonstrate the superior data reconstruction performance of Gappy AE with sparse measurements, we provide several numerical examples, including scenarios like 2D diffusion, 2D radial advection, and 2D wave equation problems. Additionally, we assess the impact of four distinct sampling algorithms - discrete empirical interpolation method, the S-OPT algorithm, Latin hypercube sampling, and uniformly distributed sampling - on data reconstruction accuracy. Our findings conclusively show that Gappy AE outperforms Gappy POD in data reconstruction when sparse measurements are given. © 2023, CC BY-NC-ND.

关键词： Principal component analysis

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Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

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Computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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Computational Protein Science in the Era of Large Language Models (LLMs)

arXiv

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arXiv 2025年

作者： Fan, Wenqi Zhou, Yi Wang, Shijie Yan, Yuyao Liu, Hui Zhao, Qian Song, Le Li, Qing Department of Computing Department of Management and Marketing The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Michigan State University United States Department of Applied Biology and Chemical Technology The Hong Kong Polytechnic University Hong Kong GenBio AI and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Proteins are macromolecules that play essential roles in almost all essential life activities, such as immunity, digestion, disease regulation, etc. Considering the significance of proteins, computational protein science has always been a critical field of scientific research, dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm. In the last few decades, Artificial Intelligence (AI) has made a significant impact in computational protein science, leading to notable, even Nobel-Prize-level successes in various specific protein modeling tasks. However, those previous AI models still meet limitations, such as the difficulty in comprehending the grammar and semantics contained in protein sequences, and the inability to generalize across a wide range of protein modeling tasks. Recently, Large Language Models (LLMs) have emerged as a milestone in AI advances due to their remarkable language processing capability and unprecedented generalization capability. They are capable of promoting comprehensive progress in fields, rather than merely solving individual tasks. As a result, researchers have actively introduced powerful LLM techniques in promoting computational protein science, developing protein Language Models (pLMs) that skillfully grasp the foundational knowledge of proteins and can be effectively generalized to solve a diversity of sequence-structure-function reasoning problems. While witnessing prosperous developments, it’s necessary to present a systematic overview of computational protein science empowered by LLM techniques. First, we summarize existing pLMs into categories based on their mastered protein knowledge, i.e., underlying sequence patterns, explicit structural and functional information, and external scientific languages. Second, we introduce the utilization and adaptation of pLMs, highlighting their remarkable achievements in promoting protein structure prediction, protein function predict

关键词： Semantics

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Explainable Artificial Intelligence in Malignant Lymphoma Classification: Optimized DenseNet121 Deep Learning Approach with Particle Swarm Optimization and Genetic Algorithm.

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IEEE Access 2025年 13卷 98639-98655页

作者： Elwahsh, Haitham Bakhiet, Ali Alirr, Omar Ibrahim Khalifa, Tarek Alsabaan, Maazen Ibrahem, Mohamed I. El-Shafeiy, Engy Applied Science Private University Faculty of Information Technology Amman 11931 Jordan Kafrelsheikh University Faculty of Computers and Information Department of Computer Science Kafrelsheikh 33516 Egypt Badya University Faculty of Computing and Data Science Egypt American University of the Middle East College of Engineering and Technology Egaila 54200 Kuwait King Saud University College of Computer and Information Sciences Department of Computer Engineering P.O.Box 51178 Riyadh 11543 Saudi Arabia Augusta University School of Computer and Cyber Sciences Augusta 30912 GA United States University of Sadat City Faculty of Computers and Artificial Intelligence Department of Computer Science Sadat City 32897 Egypt

One of the forms of cancerous tumors that can be fatal is malignant lymphoma. Histopathological examination of lymphoma tissue images is a diagnostic technique for detecting malignant lymphomas. Differentiating lymphoma subtypes manually is challenging due to their similar morphological features. This paper introduces a cutting-edge methodology for classifying malignant lymphoma using Hematoxylin and Eosin (H&E)-stained biopsy images. It integrates feature reduction using Particle Swarm Optimization (PSO) and model optimization through a Genetic Algorithm (GA) in conjunction with DenseNet121 architecture. The DenseNet121 model, which employs four distinct freezing strategies, extracts features from the images, generating an initial 4096 features per image. These features are subsequently reduced by more than 54.4% using PSO, thereby enhancing the computational efficiency without sacrificing classification accuracy. The trained GA Deep Neural Network (DNN) model achieved a significant score of 96.77% testing accuracy with high precision, recall and F1 scores as well. The results after comparison with other existing state-of-the-art models showed the usefulness of the proposed approach in terms of performance as well as the consumption of resources for medical imaging classification. It is recommended that the combined application of PSO for feature reduction and GA for model optimization can be successfully used for improving accuracy rate of such algorithms while reducing computation time. This technique is expected to be further developed and applied in different tasks of computer aided diagnosis of malignant lymphoma in particular the tasks with requirements for explainable artificial intelligence. © 2013 IEEE.

关键词： Artificial intelligent deep learning genetic algorithm malignant lymphoma optimized DenseNet121 particle swarm optimization

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A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization 1st

A Comparative Study of the Coulomb’s and Franklin’s Laws I...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Ghasemi, Mojtaba Zare, Mohsen Zahedi, Amir Hemmati, Rasul Abualigah, Laith Forestiero, Agostino Department of Electronics and Electrical Engineering Shiraz University of Technology Shiraz Iran Department of Electrical Engineering Faculty of Engineering Jahrom University Fras Jahrom Iran Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Computer Engineering Marquette University MilwaukeeWI United States Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town11800 Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Institute for High Performance Computing and Networking National Research Council of Italy Rende Italy

ISBN: (纸本)9783031314681

Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available at https://***/projects/cfa, https://***/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Charged particles

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Prognostics of rolling element bearing based on combined SSA-VMD and CNN-BiGRU models

Prognostics of rolling element bearing based on combined SSA...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Bangcheng Zhang Jingyuan Song Bo Li Zhi Gao School of Mechatronics Changchun Institute of Engineering and Changchun University of Technology Changchun China Jilin Emergency Management Institute Changchun Institute of Engineering Changchun China School of Computer Science and Computing Changchun Institute of Engineering Changchun China School of Applied Technology Changchun University of Technology Changchun China

In industrial production, rolling element bearings are key rotating components in a wide range of applications. By predicting rolling element bearings, the failure of bearings as well as problems can be detected in time and preventive measures can be taken to improve production efficiency before the failure occurs. For the prediction problem of rolling element bearing, a hybrid model of rolling bearing prediction based on variable mode decomposition optimized by sparrow search algorithm and bidirectional recurrent neural network of convolutional neural network based on attention mechanism is proposed. Firstly, the initial signal is preprocessed with data using variational modal decomposition. Secondly, the signal with threshold value more than 0.5 is selected as the input of convolutional neural network to perform feature extraction. Then, the bidirectional recurrent neural network structure is used to perform the degradation prediction of rolling bearings. Finally, the proposed method is validated using the XJTU-SY dataset and compared with gated recurrent unit, bidirectional recurrent neural network, bidirectional recurrent neural network based on attention and attention-based convolutional neural network, bidirectional recurrent neural network combination model. The experimental results show that the used model has about 30% higher prediction accuracy than the above four models and has some application val.

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XNL-CNN: An Improved Version Of The NL-CNN Model, For Running With TPU Accelerators And Large Image Datasets

XNL-CNN: An Improved Version Of The NL-CNN Model, For Runnin...

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International Symposium on Electrical and Electronics Engineering (ISEEE)

作者： Radu Dogaru Adrian-Dumitru Mirică Ioana Dogaru Dept. of Applied Electronics and Information Eng. Natural Computing Laboratory University “Politehnica” of Bucharest Bucharest Romania Doctoral School of Electronics Telecommunications and Information Technology University “Politehnica” of Bucharest Bucharest Romania

This paper introduces XNL-CNN, an extended version of the previously introduced, compact NL-CNN model. While the NL-CNN was designed with typical Tiny-ML constraints in order to make possible integration of various AI image recognition solutions into low power, low complexity computing platforms, the same stands for its extended version discussed herein. In addition, we consider additional layers to cope with larger image sizes, as found in most actual relevant datasets (e.g. the Cars196 or Flowers104). Moreover, since working with large image sizes requires intensive computational power, TPU accelerators readily available on Kaggle platform were used for training and evaluation of our model. In comparison to other, widely known resources-constrained architectures, such as EfficientNet or MobileNet our model offers the following advantages: i) a larger set of hyper-parameters allowing the user to do better designs (maximal accuracy for minimal resources), adapted for a wide variety of datasets; ii) has a low number of layer primitives, thus making easier their specific design for deployment on various TinyML or EdgeAI platforms, including FPGAS.

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