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MLP-FOX: A Metaheuristic Approach-Based Training Multilayer Perceptron for data Classification

MLP-FOX: A Metaheuristic Approach-Based Training Multilayer ...

引用

cognitive, Green and Ubiquitous computing (IC-CGU), international conference on

作者： Swagatika Mohapatra Madhumita Panda Partha Pratim Sarangi Bhabani Shankar Prasad Mishra Nikhil Parihar Biswajit Sahoo School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Computer Science & Engineering Trident Academy of Technology Bhubaneswar India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Training a Multilayer Perceptron (MLP) using metaheuristic optimization algorithms serves as a benchmark problem for demonstrating the effectiveness of emerging metaheuristic optimizers. In this study, we explore the efficacy of a novel metaheuristic optimizer namely, Fox algorithm, to optimize the weights and biases of an MLP. However, backpropagation algorithm is a traditional and well-known gradient-based optimizer is commonly used in training MLP, may become trap in local minima in many cases due to complex multimodal training error curves. This issue can diminish the classification accuracy. To overcome this problem, stochastic optimization algorithms play a vital role in improving error convergence and generalization capability of the MLP. In our work, exhaustive experimentation and analysis has been performed using five standard classification medical datasets to show the effectiveness of the Fox algorithm. The experimental results compare with backpropagation (generalized delta rule) and three recently proposed metaheuristic optimizers to evaluate its capability to converge to optimal solutions and enhance the overall performance of the MLP. Based on error convergence rate, accuracy, and F1-score, the proposed approach reveals promising classification performance compared to competitors.

关键词： Training Metaheuristics Sociology Multilayer perceptrons Benchmark testing Ubiquitous computing Classification algorithms

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A Novel Estimation Method for Satellite Network Traffic data Based on GCN-HMM

A Novel Estimation Method for Satellite Network Traffic Data...

引用

ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Xiyuan Liu Jiale Wang Xinrui Wang Le Bu Jiajie Pu Xidian University Xi'an China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Traffic measurements are essential to the maintenance and operation of satellite networks. The complexity and overhead of global measurement are particularly challenging because satellite networks are large-scale and highly dynamic. One potential approach is to estimate global network traffic data from partial traffic measurements. However, most existing estimation techniques focus on low-rank linear decomposition, which is not a precise estimator. It's unable to represent the complex nonlinear spatio-temporal relationship that exists in large-scale, highly dynamic traffic data. This paper proposes a novel estimation method for satellite network traffic data. We introduce the Graph-Clustering module to group nodes in the network adaptively. On different time slices, different groups were sampled or estimated according to the HMM and round-robin mechanism. Our model performs much better than baseline approaches, according to experiments conducted on small, medium, and large-scale satellite network datasets. With a 76.5% rise, it offers significant benefits in recovering error measurements, especially for larger networks. The methodology exhibits potential for implementation in real-world systems.

关键词： Satellites Convolution Network topology Measurement uncertainty Hidden Markov models Estimation Telecommunication traffic Spatial databases Maintenance Satellite communications

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Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits

Dynamic Hypergraph-Enhanced Prediction of Sequential Medical...

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ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Wangying Yang Zitao Zheng Shi Bo Zhizhong Wu Bo Zhang Yuanfang Yang University of Southern California Los Angeles USA New Jersey USA Boston University Boston USA University of California Berkeley Berkeley USA Texas Tech University Lubbock USA Southern Methodist University Dallas USA

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

This study introduces a pioneering Dynamic Hypergraph Networks (DHCE) model designed to predict future medical diagnoses from electronic health records with enhanced accuracy. The DHCE model innovates by identifying and differentiating acute and chronic diseases within a patient's visit history, constructing dynamic hypergraphs that capture the complex, high-order interactions between diseases. It surpasses traditional recurrent neural networks and graph neural networks by effectively integrating clinical event data, reflected through medical language model-assisted encoding, into a robust patient representation. Through extensive experiments on two benchmark datasets, MIMIC-III and MIMIC-IV, the DHCE model exhibits superior performance, significantly outpacing established baseline models in the precision of sequential diagnosis prediction.

关键词： Recurrent neural networks Computational modeling MIMICs Predictive models data models Sequential diagnosis History Electronic medical records Medical diagnostic imaging Diseases

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Multi-Modal Feature Fusion with Fuzziness based Semi-Supervised Learning for Alzheimer’s Disease Diagnosis

Multi-Modal Feature Fusion with Fuzziness based Semi-Supervi...

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computing, Applications and Systems (COMPAS), ieee international conference on

作者： Umme Habiba Bushra Fahmida Chowdhury Priya M. Jamshed Alam Patwary Department of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh Institute of Information & Communication Technology Chittagong University of Engineering & Technology Chattogram Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Alzheimer’s disease is a prominent kind of dementia that is caused by a slow decline of cognitive abilities, including memory loss, impaired speech and diminished reactivity. The condition primarily affects brain regions responsible for cognition, memory and language and typically becomes apparent after the age of 60, with a greater risk among older individuals. To meet the requirement for early identification, researchers have explored various machine-learning techniques. To enhance the accuracy of diagnosing Alzheimer’s disease, researchers have employed supervised and unsupervised learning approaches derived from machine learning. While supervised learning relies on labelled data, obtaining sufficient labelled samples can be time-consuming and requires specialized assistance. The focus is on multiclass classification, harnessing MRI and PET data. SGLDM and SIFT feature extraction algorithms are utilized to enable the fusion of features from diverse perspectives, resulting in a robust classification model. This research introduces a novel strategy for AD diagnosis, combining multimodal feature fusion with a fuzziness-based-semi-supervised learning method. The method leverages both labeled and mislabeled data to enhance the model’s reliability. The proposed method demonstrated notable enhancements in the classifier’s performance compared to existing approaches on the dataset.

关键词： Machine learning algorithms Magnetic resonance imaging Supervised learning Machine learning Semisupervised learning Feature extraction Reliability Medical diagnosis Alzheimer's disease Unsupervised learning

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On Modeling User-Aware Proactive Communication in Conversational Recommendation Systems*

On Modeling User-Aware Proactive Communication in Conversati...

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ieee international conference on e-Science and Grid computing

作者： Amarnath Gupta San Diego Supercomputer Center University of California San Diego La Jolla USA

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

Despite tremendous advances in large language models and conversational recommendation systems, these system are unable to handle many complex recommendation tasks. We present a new recommendation task called business ideation and show that the baseline performance of LLM-only recommendation is far from satisfactory. A part of the reason is neither the LLM not the recommendation has a proper user model and does not have the mechanisms to effective elicit a user model via interactions. We propose borrowing insights from decades of human communication research may bring in new ideas into the development of LLM-centric recommenders and present a research agenda to advance new generation systems.

关键词： Large language models Oral communication Benchmark testing data models Recommender systems Business

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Hybrid IoT Programming with data Flow Diagrams and mruby

Hybrid IoT Programming with Data Flow Diagrams and mruby

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ieee international Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Kazuaki Tanaka Sota Ogura R. Krishnamoorthy Faculty of Information Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India

ISBN: (数字)9798331522728

ISBN: (纸本)9798331522735

IoT is increasingly being used in different areas of society. IoT is used in society by collecting values acquired by sensor devices through networks and analysing the data. IoT software is needed to use the IoT, but efficient software development methods are desired because software for an increasingly complex society tends to become more complex. This research aims to improve the efficiency of IoT software and develop an intuitive method for developing IoT *** paper proposes a development methodology that combines a data-flow-based programming approach with a procedural programming language. When the focus is on data, data flow programming is intuitive and efficient. On the other hand, when the focus is on data processing, it is better to use a procedural programming language because data is processed sequentially[1].In the hybrid IoT programming environment proposed in this paper, the code of a procedural programming language is generated from the description by data flow programming, and the program is executed by a micro *** products of this research are available as open source software[2], [3].

关键词： Hands Computer languages Codes Programming data processing Hybrid power systems Open source software Software development management Programming environments

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Research on Neural Network-based Multi-channel Interactive Vision Platform for Space Robots

Research on Neural Network-based Multi-channel Interactive V...

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ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Yanyu Liu School of Electronic Information Engineering Beihai Vocational College Beihai China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

This study aims to develop a multi-channel interactive visual platform for space robots based on neural networks, with the objective of enhancing operational flexibility and autonomy. The research initially explores the implementation of gesture and voice interaction channels, including gesture recognition using Kinect and data gloves, as well as a voice recognition system. Through the application of artificial neural networks and BP neural network techniques, precise gesture and voice recognition capabilities are achieved. Subsequently, a multi-channel information fusion strategy is proposed, optimizing the processing of multi-channel input information through a management framework and fusion methods. The software and hardware structure of the platform, along with the control targets, are thoroughly detailed to provide a foundation for practical application. Finally, experimental validation demonstrates the effectiveness of the multi-channel interactive technology and analyzes its performance in real-world operations. The results reveal that the platform exhibits significant practicality and reliability in complex space environments, offering new directions and technological support for the future development of space robotics.

关键词： Visualization Speech recognition Gesture recognition Artificial neural networks data gloves Robot sensing systems Software Hardware Space exploration Software reliability

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A large model method for fine-tuning segmentation of all abdominal tumors based on the AdaLoRA approach

A large model method for fine-tuning segmentation of all abd...

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ieee international conference on Signal and Image Processing (ICSIP)

作者： Hongliang Wang Hu Li Lijun Fu Xiaozhou Liu Jing Xu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Stomatological Hospital Affiliated to China Medical University Shenyang China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Medical image segmentation is an important task in the field of medical image processing, and deep learning-based methods have made significant progress in this area. However, traditional segmentation models have limitations when dealing with complex and diverse medical images, and they lack interactivity. To overcome these limitations, the SAM adapter is introduced into the SAM algorithm to provide greater flexibility and scalability. We also introduce an object detection interactive algorithm to enhance the interactivity of the model. The SAM adapter allows for customized model adjustments to adapt to different medical image segmentation tasks and datasets. By finetuning the SAM adapter using the AdaLORA method, we propose the AdaSAMed algorithm, which enables researchers to conveniently develop adaptive and accurate segmentation models, thus promoting further advancement in medical image segmentation of gastrointestinal tumors.

关键词： Learning systems Image segmentation Adaptation models Scalability Object detection Robustness Gastrointestinal tract

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Koopman Theory Inspired Neural Network for State of Charge Estimation

Koopman Theory Inspired Neural Network for State of Charge E...

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ieee international conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Vedika Gadia Adarsh Jaju P V Subrahmanyam Debashisha Jena EEE Dept. NITK Surathkal India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Koopman theory offers a potent framework for mode-by-mode analysis of system dynamics. Inspired by this theory, this work introduces a deep-learning framework utilizing autoencoders and a customized loss function for time series prediction. The work demonstrates the effectiveness of the model through practical application on state of charge (SoC) estimation of a battery. Model based SoC estimation techniques like Extended Kalman Filter(EKF) require complex models and technical data to estimate the SoC of a battery. The proposed model is able to predict the state of charge upto 10 time stamps in the future. This model is able to generalize the system dynamics such that a model trained for T time stamps is seen to give RMSE lesser than 0.01 for all tested temperatures at a future time stamp lesser than T. These findings demonstrate the superior performance of the proposed Koopman-inspired neural network(KoNN) compared to the traditional time series estimation technique EKF, Multivariate Linear Regression(MVLR), Extra Tree Regression(ETR), and Neural Network(NN) showcasing its versatility for various predictive tasks.

关键词： System dynamics Time series analysis Neural networks Estimation Predictive models Reliability theory data models

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Paving the Way for an Urban Intelligence OpenStack-Based Architecture

Paving the Way for an Urban Intelligence OpenStack-Based Arc...

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international conference on Smart computing (SMARTCOMP)

作者： Giuseppe Tricomi Luca D'Agati Francesco Longo Giovanni Merlino Antonio Puliafito Stefano Silvestri CINI: National Interuniversity Consortium for Informatics Rome Italy Department of Engineering University of Messina Italy Institute for High Performance Computing and Networking of ICAR National Research Council (CNR) Naples Italy

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

This paper describes a novel architecture aiming to create an open-source template for implementing a platform enabling support for the deployment and integration of services and workflows that compose, manage, and continuously evolve a complex system: the Urban Intelligence. More specifically, the proposed Smart City IT architecture is an evolution of a concrete urban intelligence architecture meant mostly to support data scientist activities and, obviously, the exploitation of Smart City services by citizens and users. The proposed solution, due to its open-source nature, is fully replicable and improves its ancestor by provisioning new characteristics: i) managing facilities of urban Cyber-Physical System via exploitation of a combination of Administrator/data scientist workflows and IoT management platform, ii) the definition of workflow directly on the Edge (even through the exploitation of FaaS paradigm directly on IoTs), and iii) enhancement of Smart City infrastructure security via the reduction of external attack surface. The prototype of the proposed IT solution has been implemented leveraging open-source frameworks and technologies belonging to the OpenStack ecosystem.

关键词： Smart cities Distributed ledger Scalability data security Ecosystems Prototypes Computer architecture

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