Introducing neuromodulation in deep neural networks to learn adaptive behaviours. by Nicolas Vecoven, Damien Ernst, Antoine Wehenkel, Guillaume Drion

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Fast autoscaling algorithm for cost optimization of container clusters by José María López, Joaquín Entrialgo, Manuel García, Javier García, José Luis Díaz, Rubén Usamentiaga

“…In this paper we present FCMA (Fast Container to Machine Allocator), a resource allocation algorithm designed to calculate a suitable allocation of the resources of a cluster in autoscaling operations, to minimize cluster deployment costs. …”
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SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks by Sneh Pandya, Purvik Patel, Brian D Nord, Mike Walmsley, Aleksandra Ćiprijanović

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Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey by Robert Chab, Fei Li, Sanjeev Setia

“…In this survey, we provide a comprehensive classification of GPU task scheduling approaches, categorized by their underlying algorithmic techniques and evaluation metrics. We examine traditional methods—including greedy algorithms, dynamic programming, and mathematical programming—alongside advanced machine learning techniques integrated into scheduling policies. …”
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Computerized Interactive Gaming via Supporting Vector Machines by Y. Jiang, J. Jiang, I. J. Palmer

“…This paper describes a supporting vector machine-based artificial intelligence algorithm as one of the possible solutions to the problem of random data processing and the provision of interactive indication for further actions. …”
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Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port by MA Zhutong, XIANG Long, YAN Ke

“…A predictive model of water demand for scouring siltation was constructed, which combined adaptive particle swarm optimization (APSO) algorithm with support vector machine (SVM) and optimized the model parameters of the SVM through the APSO algorithm, enhancing the prediction accuracy of the APSO-SVM model. …”
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Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port by MA Zhutong, XIANG Long, YAN Ke

“…A predictive model of water demand for scouring siltation was constructed, which combined adaptive particle swarm optimization (APSO) algorithm with support vector machine (SVM) and optimized the model parameters of the SVM through the APSO algorithm, enhancing the prediction accuracy of the APSO-SVM model. …”
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An Improved SOGI-Higher-Order Sliding Mode Observer-Based Induction Motor Speed Estimation by Enyam Kobena Badu, Effah Francis Boafo, Hammond Desmond

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A MACHINE LEARNING DISTRACTED DRIVING PREDICTION MODEL by Samira AHANGARI, Mansoureh JEIHANI, Abdollah DEHZANGI

“…In this study, we use a Bayesian Network classifier as a robust machine learning algorithm on our trained data (80%) and tested (20%) with the data collected from a driving simulator, in which the 92 participants drove six scenarios of handheld calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). …”
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The prediction of karst-collapse susceptibility levels based on the ISSA-ELM integrated model by Jiaxin Wang, Ying Yang, Xian Yang, Yulong Lu, Yang Liu, Da Hu, Yongjia Hu

“…To address the limitations of conventional prediction methods, in this study, we introduce an enhanced predictive model, the improved sparrow search algorithm-optimized extreme learning machine (ISSA-ELM), for accurate karst-collapse susceptibility assessment. …”
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Machine learning-driven condition monitoring for predictive maintenance by Mahliyo Aliyeva

“…Leveraging sensor data integration and machine learning frameworks, real- time monitoring of machine health status enables the prediction of mechanical wear and prevention of unforeseen downtime. …”
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Predicting Employee Turnover Using Machine Learning Techniques by Adil Benabou, Fatima Touhami, My Abdelouahed Sabri

“…This study aims to identify the most effective machine learning model for predicting employee attrition, thereby providing organizations with a reliable tool to anticipate turnover and implement proactive retention strategies.Objective: This study aims to address the challenge of employee attrition by applying machine learning techniques to provide predictive insights that can improve retention strategies.Methods: Nine machine learning algorithms are applied to a dataset of 1,470 employee records. …”
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Fault detection in beam structure using adaptive immune based approach by Sasmita Sahu, Shakti P. Jena

“…Later one such method has been developed in the concepts of adaptive immune based technique (Adaptive Clonal Section Algorithm-ACSA) which is the combination of an artificial immune (Clonal Selection Algorithm) and Regression Analysis (RA). …”
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A Learning Probabilistic Boolean Network Model of a Manufacturing Process with Applications in System Asset Maintenance by Pedro Juan Rivera Torres, Chen Chen, Sara Rodríguez González, Orestes Llanes Santiago

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A Dynamic Neural Network Optimization Model for Heavy Metal Content Prediction in Farmland Soil by Kun Cao, Cong Zhang, Liangliang Li, Shuaifeng Li

“…The weights and bias of the output layer of the RBFNN were generated using an adaptive dynamic genetic optimization algorithm (ADGOA), and the center point of the hidden layer of the RBFNN was determined using an efficient density peak clustering algorithm (EDPC). …”
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Deep Learning-Augmented Evolutionary Strategies for Intelligent Global Optimization by Absalom El-Shamir Ezugwu, Olaide Nathaniel Oyelade, Jeffrey Ovre Agushaka, Apu Kumar Saha

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Towards a Scalable and Adaptive Learning Approach for Network Intrusion Detection by Alebachew Chiche, Million Meshesha

“…While machine learning algorithm is used to construct a classifier model, knowledge-based system makes the model scalable and adaptive. …”
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Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory by Naoki Masuyama, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota

“…To tackle this problem, this paper proposes a privacy-preserving continual federated clustering algorithm. In the proposed algorithm, an adaptive resonance theory-based clustering algorithm capable of continual learning is used as a base clusterer. …”
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Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM by Yinuo Wang, Huanqi Zheng, Hua Wang, Yucheng Zhou

“…The multi-strategy improved black-winged kite algorithm then optimizes key parameters of the successive variational mode decomposition (SVMD) and hybrid kernel extreme learning machine (HKELM). …”
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Research on Adaptive Drilling Control Technology Based on Coal Rock Traits During the Drilling Process by Bin Liang, Guang Li, Guangpeng Shan

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