Multi-objective: hybrid particle swarm optimization with firefly algorithm for feature selection with Leaky ReLU by Ashish Kumar Singh, Anoj Kumar

“…Abstract High-dimensional datasets often pose challenges due to the presence of numerous irrelevant and redundant features, which can compromise the performance of machine learning models. This study proposes a novel optimization algorithm, LR-GPSOFA, designed to improve feature selection by enhancing computational efficiency and classification accuracy. …”
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Finite Impulse Response Filter Design Using Fuzzy Logic-Based Diversity-Controlled Self-Adaptive Differential Evolution by K. Mohaideen Abdul Kadhar, S. Rengarajan, S. Tamilselvi, N. Karuppiah, Praveen Kumar Balachandran, A. Thamilmaran, C. Dhanamjayulu, Baseem Khan

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AI augmented edge and fog computing for Internet of Health Things (IoHT) by Deepika Rajagopal, Pradeep Kumar Thimma Subramanian

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Adltformer Team-Training with Detr: Enhancing Cattle Detection in Non-Ideal Lighting Conditions Through Adaptive Image Enhancement by Zhiqiang Zheng, Mengbo Wang, Xiaoyu Zhao, Zhi Weng

“…The Adltformer and Detr team-training (AT-Detr) method is employed to preprocess the low-light cattle dataset for image enhancement, ensuring that the enhanced images are more compatible with the requirements of machine vision systems. The experimental results demonstrate that the AT-Detr algorithm achieves superior detection accuracy, with comparable runtime and model complexity, reaching 97.5% accuracy under challenging illumination conditions, outperforming both Detr alone and sequential image enhancement followed by Detr. …”
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2CA-R²: A Hybrid MAC Protocol for Machine-Type Communications by Sergio Javier-Alvarez, Pablo Hernandez-Duran, Miguel Lopez-Guerrero, Luis Orozco-Barbosa

“…What distinguishes this proposal is that the contention stage is controlled by a conflict–resolution algorithm known as Adaptive-2C. The protocol was evaluated using a model based on a Markov chain and computer simulations. …”
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Pemodelan Sistem Monitoring Kualitas Udara Pintar Berbasis Internet of Things dengan Pendekatan Machine Learning by Eddy Prasetyo Nugroho, Ani Anisyah, Deva Shofa Al Fathin, Muhammad Nur Yasin Amadudin, Muhammad Satria Ramadhani, Yosafat

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Adaptive Beam Tracking in 5G/6G mmWave Networks: A Clustered Federated Learning Approach by Amjad Ali, Yevgeni Koucheryavy

“…However, mmWave signals are highly susceptible to attenuation and blockage, necessitating directional beamforming antennas and efficient beam tracking algorithms. Traditional machine learning-based approaches, such as centralized learning (CL) and federated learning (FL), face significant challenges. …”
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Intrusion Detection System for Network Security Using Novel Adaptive Recurrent Neural Network-Based Fox Optimizer Concept by R. Manivannan, S. Senthilkumar

“…In the proposed approach, the fox algorithm (FOX) is utilized for the adjustment of hyperparameters in the ARNN model. …”
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A synergistic approach using digital twins and statistical machine learning for intelligent residential energy modelling by Ahmad Almadhor, Shtwai Alsubai, Natalia Kryvinska, Nejib Ghazouani, Belgacem Bouallegue, Abdullah Al Hejaili, Gabriel Avelino Sampedro

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Machine Learning-Assisted Mitigation of Optical Multipath Interference in PAM4 IM-DD Transmission Systems by Wenxin Cui, Jiahao Huo, Jin Zhu, Jianlong Tao, Peng Qin, Xiaoying Zhang, Haolin Bai

“…In this scheme, KNN-aided SVM serves as a soft decision algorithm that adapts the decision threshold to signal amplitude fluctuations, improving the decision accuracy for MPI-affected PAM4 signals. …”
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A Hybrid Automata-Driven Machine Learning Framework for Real-Time Energy Optimization in Smart Buildings by Rikame Rajashri, Ranjan Mritunjay Kr., Jadhav Monali, Wankhede Shrushti, Bankar Sakshi, Bachhav Pranjal

“…This study presents a hybrid approach that combines Finite Automata (FA) with sophisticated Machine Learning (ML) algorithms and Artificial Intelligence (AI) to minimize energy consumption in smart buildings. …”
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Integrated intrusion detection design with discretion of leading agent using machine learning for efficient MANET system by K. S. Nirmala Bai, Dr M.V. Subramanyam

“…At first, the IDS is performed in the network using Adaptive Ensemble Tree Learning (AETL) based classification of typical nodes and malicious intrusions. …”
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Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas by Yue Hu, Xin Cao, Hongyi Chen, Daoying Geng, Kun Lv

“…The logical regression, random forest, support vector machine (SVM) and adaptive boosting algorithms were used to establish models. …”
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Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis by Gaddam Advitha, Allada Nagasai Varaprasad, Koti Vennela Khushi, Pullabhotla Vijay, Sukanta Nayak

“…This research delves into the utilization of Machine Learning (ML) algorithms, specifically Random Forest (RF) and XGBoost, to predict AQIs in Gujarat. …”
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Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Abdul Hameed Kalifullah, Arife Tugsan Isiacik Colak, Md Redzuan Zoolfakar

“…This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. …”
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An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions by Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa

“…The model integrates the Gaussian mutation and shrink mechanism-based moth flame optimization (GMSMFO) algorithm with an extreme learning machine (ELM). GMSMFO enhances population diversity and avoids local optima through Gaussian mutation (GM), while the shrink mechanism (SM) improves exploration–exploitation balance. …”
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Integrating an artificial neural network with genetic, particle swarm optimization algorithms, and fuzzy rule to optimize the surface roughness in dry turning AISI 1045 steel by Abdulmajeed Dabwan, Mohammed K Almatani, Mohammed Alqahtani, Husam Kaid, Khaled N Alqahtani, Mustafa M Nasr, Adham E Ragab

“…This research aims to propose optimization methods including a hybrid artificial neural network (ANN) with genetic algorithms (GA) particle swarm optimization (PSO), and Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict and optimize surface roughness during dry machining of AISI 1045 steel. …”
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Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction by Zhengbiao Hu, Shuangshan Bu, Kai Wang, Qianqian Cao, Huanhuan Zheng, Jie Yang, Shanshan Chen, Yuemeng Wu, Wenkai Ren, Chenlei He

“…In this study, multiple machine learning algorithms were used to develop a non-invasive and improved model for the diagnosis of IgAN. …”
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Machine Learning Classification of 3D Intracellular Trafficking Using Custom and Imaris-Derived Motion Features by Oleg Kovtun

“…The incorporation of the Imaris track features streamlined diffusion classification and enhanced adaptability across diverse volumetric imaging modalities. …”
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Development and validation of an explainable machine learning prediction model of hemorrhagic transformation after intravenous thrombolysis in stroke by Yanan Lin, Yan Li, Yayin Luo, Jie Han

“…We utilized the Random Forest (RF), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GauNB) algorithms to develop ML-HT models. …”
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