Machine Learning-Based Anomaly Prediction for Proactive Monitoring in Data Centers: A Case Study on INFN-CNAF by Andrea Asperti, Gabriele Raciti, Elisabetta Ronchieri, Daniele Cesini

“…We evaluate several methods, including Long Short-Term Memory, Random Forest, and various neural networks, assessing their Accuracy and sensitivity in distinguishing normal from anomalous behaviors. …”
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Strategy-Switch: From All-Reduce to Parameter Server for Faster Efficient Training by Nikodimos Provatas, Iasonas Chalas, Ioannis Konstantinou, Nectarios Koziris

“…However, the abundance of available data presents a challenge when training neural networks on a single node. Consequently, various distributed training methods have emerged. …”
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Dual Domain Swin Transformer based Reconstruction method for Sparse-View Computed Tomography by Jonas Van der Rauwelaert, Caroline Bossuyt, Jan Sijbers

“…Two architectures are tested: a long one using neural networks in both domains of the residual refinement block and a short one using a network exclusively in the sinogram domain. …”
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Application of Soft Computing Paradigm to Large Deformation Analysis of Cantilever Beam under Point Load by Yanmei Cui, Yong Hong, Naveed Ahmad Khan, Muhammad Sulaiman

“…Since finding an exact solution to such nonlinear models is difficult task, this paper focuses on developing soft computing technique based on artificial neural networks (ANNs), generalized normal distribution optimization (GNDO) algorithm, and sequential quadratic programming (SQP). …”
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Random features and polynomial rules by Fabián Aguirre-López, Silvio Franz, Mauro Pastore

“…Random features models play a distinguished role in the theory of deep learning, describing the behavior of neural networks close to their infinite-width limit. In this work, we present a thorough analysis of the generalization performance of random features models for generic supervised learning problems with Gaussian data. …”
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Relieve Adversarial Attacks Based on Multimodal Training by Lai Hongjie

“…However, the emergence of adversarial attacks has exposed shortages of neural networks, forcing people to confront their limitations and further increasing concerns about the security of deep learning models. …”
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Real-Time Football Match Prediction Platform by An Zhongqi

“…The platform employs machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, combined with feature engineering techniques, to generate accurate predictions. …”
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Fast and accurate deep learning scans for signatures of natural selection in genomes using FASTER-NN by Sjoerd van den Belt, Nikolaos Alachiotis

“…Abstract Deep learning classification models based on Convolutional Neural Networks (CNNs) are increasingly used in population genetic inference for detecting signatures of natural selection. …”
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Analyzing the Application of Machine Learning in Anemia Prediction by Li Yuxi

“…This paper examines decision trees, random forests, support x'ector machines, and neural networks. emphasizing their efficacy in identifying patterns and risk factors associated with anemia. …”
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Automatic classification of mobile apps to ensure safe usage for adolescents. by Hanadi Hakami

“…This work introduces an innovative approach utilizing Deep Learning techniques, specifically Attentional Convolutional Neural Networks (A-CNNs), for classifying M-APPs. The goal is to secure adolescent mobile usage by predicting the potential negative impact of M-APPs on adolescents. …”
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An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model by Fengying Ma, Jingyao Zhang, Wei Chen, Wei Liang, Wenjia Yang

“…The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. …”
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Mathematical Analysis of the Prey-Predator System with Immigrant Prey Using the Soft Computing Technique by Naveed Ahmad Khan, Muhammad Sulaiman, Jamel Seidu, Fahad Sameer Alshammari

“…The proposed algorithm uses a function approximating ability of Legendre polynomials based on Legendre neural networks (LeNNs), global search ability of the whale optimization algorithm (WOA), and a local search mechanism of the Nelder–Mead algorithm. …”
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Random Frequency Division Multiplexing by Chanzi Liu, Jianjian Wu, Qingfeng Zhou

“…We take full account of the great power of deep neural networks (DNN) to detect the signal as it is an underdetermined equation. …”
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Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification by Cuijie Zhao, Hongdong Zhao, Guozhen Wang, Hong Chen

“…Due to the high dimensionality of spectral features, limited samples of ground truth, and high nonlinearity of hyperspectral data, effective classification of HSI based on deep convolutional neural networks is still difficult. This paper proposes a novel deep convolutional network structure, namely, a hybrid depth-separable residual network, for HSI classification, called HDSRN. …”
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Forecasting the Cell Temperature of PV Modules with an Adaptive System by Giuseppina Ciulla, Valerio Lo Brano, Edoardo Moreci

“…In this work an alternative method, based on the employment of artificial neural networks (ANNs), was proposed to predict the operating temperature of a PV module. …”
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Enhanced classification of medicinal plants using deep learning and optimized CNN architectures by Hicham Bouakkaz, Mustapha Bouakkaz, Chaker Abdelaziz Kerrache, Sahraoui Dhelim

“…To address this issue, a deep learning-based framework is proposed in the research for classifying images related to medicinal plants using convolutional neural networks (CNNs). In this framework, a CNN architecture with residual and inverted residual block configurations is selected, and a set of data augmentation is applied to improve the dataset. …”
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CNN-Trans-SPP: A small Transformer with CNN for stock price prediction by Ying Li, Xiangrong Wang, Yanhui Guo

“…In this paper, we propose a simple yet effective fusion model that leverages the strengths of both transformers and convolutional neural networks (CNNs). The CNN component is employed to extract local features, while the Transformer component captures temporal dependencies. …”
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A Hybrid Machine Learning Framework for Soccer Match Outcome Prediction: Incorporating Bivariate Poisson Distribution by Chen Zhong An

“…The author utilizes a comprehensive dataset from top European leagues (2014-2022) and employ models including Bivariate Poisson Distribution, Naive Bayes, Neural Networks, Support Vector Machines, Random Forests, and Gradient Boosting. …”
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Analyzing the customer purchase data of an online shopping store by data mining: A real case study in Iran by Nima Moradi, Mosayeb Jalilian

“…., Random Forest, gradient-boosted trees, K-Nearest Neighbor (KNN), Naïve Bayes, Kernel Naïve Bayes, and Neural Networks) and clustering approaches have been applied to discover the knowledge and patterns. …”
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Iono–Magnonic Reservoir Computing With Chaotic Spin Wave Interference Manipulated by Ion‐Gating by Wataru Namiki, Daiki Nishioka, Yuki Nomura, Takashi Tsuchiya, Kazuo Yamamoto, Kazuya Terabe

“…Utilizing the strong nonlinearity resulting from chaos, the reservoir shows good computational performance in completing the Mackey–Glass chaotic time‐series prediction task, and the performance is comparable to that exhibited by simulated neural networks.…”
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