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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

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2024 IEEE international conference on systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Yang, Jinhong Zhang, Jia College of Computer Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9781665410205

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions. © 2024 IEEE.

关键词： Scheduling algorithms

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4th international conference on Numerical Computations: Theory and algorithms, NUMTA 2023

4th International Conference on Numerical Computations: Theo...

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4th international conference on Numerical Computations: Theory and algorithms, NUMTA 2023

ISBN: (纸本)9783031812439

The proceedings contain 105 papers. The special focus in this conference is on Numerical Computations: Theory and algorithms. The topics include: Resource Allocation via Bayesian Optimization: an Efficient Alternative to Semi-Bandit Feedback;multi-Objective and Multiple Information Source Optimization for Fair & Green Machine Learning;extended Optimal Control Problem for Practical Application;explainable Process Deviance Discovery with Data-Efficient Deep Learning;line Search Stochastic Gradient Algorithm with A-priori Rule for Monitoring the Control of the Variance;a Machine Learning Approach to Speed up the Solution of the Distributor’s Pallet Loading Problem;combined First- and Second-Order Directions for Deep Neural Networks Training;constrained Global Optimization by Smoothing;the Unreasonable Effectiveness of Optimal Transport Distance in the Design of Multi-Objective Evolutionary Optimization algorithms;an Improved Modified Jaya Optimization Algorithm: Application to the Solution of Nonlinear Equation systems;GPU Acceleration of the Enhanced Jaya Optimization Algorithm for Solving Large systems of Nonlinear Equations;effective Resistance Based Community Detection in Complex Networks;a Comparison of Formulations for Aircraft Deconfliction;optimal Recombination Problem in Genetic Programming for Boolean Functions;heuristics with Local Improvements for Two-Processor Scheduling Problem with Energy Constraint and Parallelization;numerical Analysis of Optimal Control of Assets and Liabilities by a Bank;optimal Control for Stochastic Multi-agent systems With the Use of Parallel Hybrid Genetic Algorithm;DC Optimization in Adversarial Sparse Support Vector Machine;A First-Order Optimality Condition in Nonsmooth Generalized Semi-infinite Programming (GSIP);miniaturisation of Binary Classifiers Through Sparse Neural Networks;Price Forecasting for Bitcoin: Linear Regression and SVM Approaches;inside the Box: 0–1 Linear Programming Under Interval Uncertainty;machine Learn

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Comparative Analysis and Practical Implementation of Machine Learning algorithms for Phishing Website Detection 9

Comparative Analysis and Practical Implementation of Machine...

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9th international conference on Computer Science and engineering, UBMK 2024

作者： Najjar-Ghabel, Samad Yousefi, Shamim Habibi, Payam University of Mohaghegh Ardabili Department of Computer Engineering Ardabil Iran

ISBN: (纸本)9798350365887

Phishing, characterized by fraudulent attempts to obtain sensitive information through deceptive communications, poses a significant cybersecurity threat. The rapid increase in internet usage and the proliferation of online services have made phishing attacks more prevalent and sophisticated. Machine learning algorithms have emerged as powerful tools in identifying and mitigating these threats by analyzing vast amounts of data and detecting patterns indicative of phishing activities. Various studies have examined and compared machine learning-based methods for phishing website detection, but they often lack practical implementation and do not comprehensively compare criteria and classification algorithms on specific datasets. This paper aims to enhance phishing website detection by assessing the performance of six distinct classification algorithms: k-Nearest Neighbors, Naive Bayes, Random Forest, Bernoulli Naive Bayes, Support Vector Machine, and Decision Tree. Utilizing the 'Phishing Websites' dataset from the UCI Machine Learning Repository, the classifiers are evaluated based on F-Score, accuracy, precision, and recall. Among these algorithms, Random Forest demonstrates superior performance, achieving an accuracy of 96.7%, precision of 96.1%, recall of 98.2%, and an F-Score of 97.1%. These results underscore the robustness and efficacy of Random Forest in phishing website detection tasks. While other algorithms also show promise, especially under specific conditions, further research is needed to explore the potential improvements from combining multiple algorithms and examining additional features. This direction offers a promising avenue for enhancing phishing website detection systems in future studies. © 2024 IEEE.

关键词： Phishing

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EEG-Based Classification for Autism Spectrum Disorder and Emotional State Recognition - A Critical Evaluation 4

EEG-Based Classification for Autism Spectrum Disorder and Em...

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4th international conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Sarvalingam, Parameswaran Vajravelu, Ashok Asick Ali, M. Bin Abdul Jamil, Muhammad Mahadi Ahammed, Syed Riyaz Selvam, Janani Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Malaysia K S R College of Engineering Department of Mathematics Tiruchengode India Nitte University Mangalore India Nitte University Department of Electronics and Communication Engineering Mangalore India Lincoln University College Faculty of Civil and Built Environment Engineering Malaysia

ISBN: (纸本)9798331529635

EEG signal processing has seen rapid advances in recent years due to innovations in machine learning, deep learning, and data preprocessing techniques. This review examines major studies from 2020 to 2024, exploring preprocessing methods, sampling rates, data samples, and model accuracy metrics across various EEG applications. Through a year-wise analysis, highlight the progression in signal processing methods, the impact of higher sampling rates, and the rise of sophisticated architectures like hybrid neural networks and graph-based models. Identify notable trends, including improved accuracy rates and efficiency gains with adaptive algorithms and the integration of automated artifact removal in preprocessing pipelines. This review also contrasts the advantages and limitations of recent technologies, providing a comprehensive outlook on current capabilities and future directions in EEG signal processing. © 2024 IEEE.

关键词： Adaptive algorithms

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An Efficient Technique for Feature Selection in Learning to Rank Using NSGA-II

An Efficient Technique for Feature Selection in Learning to ...

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2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Amala, K.J. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Feature Selection (FS) is essential for optimizing Learning to Rank (LTR) models by determining the vital choice of attributes from a complex dataset. This study examines feature selection strategies within the framework of learning to rank, specifically emphasizing a multi-objective evolutionary algorithm utilizing the listwise approach. Conventional ranking algorithms frequently experience issues with superfluous or unnecessary features, undermining their efficacy. The suggested method employs a combination of instance selection, evolutionary optimization, and ensemble techniques to choose feature subsets. The methodology seeks to reduce the feature count while enhancing ranking precision, assessed via the Normalized Discounted Cumulative Gain (NDCG) metric. Experiments were performed using LETOR 4.0 dataset (MQ2007-list and MQ2008-list), evaluating proposed method against established listwise ranking algorithms like ListMLE, ListNet, RankCosine and p-ListMLE. The findings indicate that, although there are performance discrepancies relative to current algorithms, the suggested method attains substantial feature reduction up to 60 percent while maintaining competitive ranking accuracy. © 2024 IEEE.

关键词： Optimization algorithms

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Deep Learning-Based Optimization of Dolphin Echolocation for Skin Lesion Image Classification 2

Deep Learning-Based Optimization of Dolphin Echolocation for...

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2nd IEEE international conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Anbuselvan, N. Anandan, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

The healthcare industry has benefited greatly from the recent developments in Machine Learning (ML), especially deep learning (DL). This motivates the study of how to best employ AI, and deep learning (DL) algorithms in particular, to identify skin lesions in dermoscopic pictures. To do this, the model employs an Inventive Dolphin Echolocation Optimization Deep Learning driven Skin Lesion Detection and Classification (IDEODL-SLDC) technique to analyse these images and improve upon a previously developed Dolphin Echolocation Optimizer (IDEO). In addition, the SqueezeNet method can be used to obtain feature vectors. The dermoscopy images are also classified using IDEO and a deep wavelet neural network (DWNN) approach. The IDEODL-SLDC method is improved on the reference dataset after undergoing extensive testing. The experimental results demonstrated that the IDEODL-SLDC model exhibited superior performance compared to alternative approaches across different dimensions of model classifier classes. © 2024 IEEE.

关键词： Deep learning Dermoscopy Dolphin Echolocation Optimization Image classification Skin cancer

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Performance Analysis and Predictive Modeling of MPI Collective algorithms in Multi-Core Clusters: A Comparative Study 17

Performance Analysis and Predictive Modeling of MPI Collecti...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Reddy, Manideep Raya V.S.R.S. Raju, Sanjay Ramapogu Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Lab Department of Computer Science and Engineering Surathkal Mangalore India

ISBN: (纸本)9798331531195

Efficient communication is the foundation of parallel computing systems, enabling seamless coordination across multiple processors for optimal performance. At the core of this communication lies the Message Passing Interface, a crucial framework designed to facilitate data exchange between processors through collective operations. However, these MPI operations often face challenges, including fluctuating process counts, varying message sizes, and increased communication overhead. These issues can significantly impact execution times and scalability, leading to potential bottlenecks in large-scale systems. To address these concerns, this paper provides an in-depth evaluation of key MPI collective algorithms - Flat Tree, Chain, and Binary Tree - by examining their performance under varying configurations. By analyzing execution times and communication overhead, the study reveals the trade-offs inherent in each algorithm, offering insights into strategies for reducing communication costs. Through this analysis, we aim to provide valuable guidance to improve the efficiency and scalability of parallel computing, particularly in high-performance systems where communication efficiency is critical. © 2025 IEEE.

关键词： Scalability

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VARIATIONAL AUTOENCODERS FOR EXTEROCEPTIVE PERCEPTION IN REINFORCEMENT LEARNING-BASED COLLISION AVOIDANCE 43

VARIATIONAL AUTOENCODERS FOR EXTEROCEPTIVE PERCEPTION IN REI...

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ASME 2024 43rd international conference on Ocean, Offshore and Arctic engineering, OMAE 2024

作者： Larsen, Thomas Nakken Barlaug, Eirik Runde Rasheed, Adil Department of Engineering Cybernetics NTNU O.S. Bragstadsplass 2 Trondheim7034 Norway

ISBN: (纸本)9780791887783

Modern control systems are increasingly turning to machine learning algorithms to augment their performance and adaptability. Within this context, Deep Reinforcement Learning (DRL) has emerged as a promising control framework, particularly in the domain of marine transportation. Its potential for autonomous marine applications lies in its ability to seamlessly combine path-following and collision avoidance with an arbitrary number of obstacles. However, current DRL algorithms require disproportionally large computational resources to find near-optimal policies compared to the posed control problem when the searchable parameter space becomes large. To combat this, our work delves into the application of Variational AutoEncoders (VAEs) to acquire a generalized, low-dimensional latent encoding of a high-fidelity range-finding sensor, which serves as the exteroceptive input to a DRL agent. The agent’s performance, encompassing path-following and collision avoidance, is systematically tested and evaluated within a stochastic simulation environment, presenting a comprehensive exploration of our proposed approach in maritime control systems. Copyright © 2024 by ASME.

关键词： Marine applications

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An improved multi-omics data clustering algorithm based on graph regularization constraints 3

An improved multi-omics data clustering algorithm based on g...

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3rd international conference on Biomedical and Intelligent systems, IC-BIS 2024

作者： Zhang, Shunqin Kong, Wei College of Information Engineering Shanghai Maritime University 1550 Haigang Ave. Shanghai201306 China

ISBN: (纸本)9781510681279

Accurate clustering of cell type classification by integrating multi-omics data is necessary for subsequent data analysis. However, related algorithms face difficulties in dealing with noise and missing values present in multi-omics data. The network-based integrative clustering algorithm (NIC) can overcome the problem of data heterogeneity but does not fully utilize the spatial structure information in the data. To solve this problem, this study proposes a new algorithm-graph regularization-based NIC (GR-NIC) to improve the integration accuracy, remove redundant features, and preserve the geometric structure of the data by fusing single-cell multi-omics data, single-cell sequencing data (scRNA-seq or scATACseq) and histone modification data. To verify the effectiveness of the GR-NIC algorithm, this paper compares three stateof- the-art multi-omics clustering algorithms through five datasets. The results show that GR-NIC can select features that are more representative of the structure of the data and improve the accuracy of clustering. This new approach is effectively validated on mouse brain and peripheral blood sample datasets and shows significant improvements. © 2024 SPIE.

关键词： Clustering algorithms

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A Comprehensive Analysis of Intrusion Detection System using Machine Learning and Deep Learning algorithms

A Comprehensive Analysis of Intrusion Detection System using...

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2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Varaprasad, R. Prabhu Chakkaravarthy, A. Veeresha, M. Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Dr. K.V. Subba Reddy Institute of Technology Department of Computer Science and Engineering Kurnool India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

The Internet of Things (IoT) plays an essential character in both domestic and corporate sectors by facilitating connectivity and automation through a network of devices and sensors. IoT wearable gadgets, sensors, and embedded devices are widely utilized for data collection and transmission. However, privacy and security are challenging tasks to be achieved while the standard implementation of monitoring and security operations is not possible because of large number of IoT devices, their heterogeneity and technical specification limitations. In order to solve this issue, Intrusion Detection System (IDS) is introduced to secure the network against various vulnerabilities and threats. To identify intrusion attacks accurately, Machine learning (ML) and Deep Learning (DL) algorithms namely, Random Forest (RF), Logistic Regression (LR), Decision Tree (DL), AdaBoost, XGboost, Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and Long Short-Term Memory (LSTM), Deep Neural Network (DNN) are used. This research aims to identify the strengths, relationship, and challenges of the ML and DL-based prediction approaches. © 2024 IEEE.

关键词： Network intrusion

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