Motor Bearing Failure Identification Using Multiple Long Short-Term Memory Training Strategies by Youcef ATMANI, Ammar Mesloub, Said Rechak

“…In the context of condition-based maintenance of rotating machines in manufacturing systems, the early diagnosis of possible faults related to rolling elements of the bearing is mainly based on techniques from artificial intelligence, namely, Machine Learning (ML) and Deep Learning (DL). …”
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Characterising Payload Entropy in Packet Flows—Baseline Entropy Analysis for Network Anomaly Detection by Anthony Kenyon, Lipika Deka, David Elizondo

“…A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. …”
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Real-time bearing fault classification of induction motor using enhanced inception ResNet-V2 by Karan Kumar K, Srihari Mandava

“…Detecting defects in bearings is crucial for the safe operation of these machines. However, it is challenging to use Deep Learning techniques to identify bearing defects when the machine is not under load. …”
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Short-term prediction of trimaran load based on data driven technology by Haoyun Tang, Rui Zhu, Qian Wan, Deyuan Ren

“…Due to the complex flow interference by side hulls, load prediction has been one of the obstacles to achieve structural health monitoring and intelligent navigation of trimarans. According to the machine learning theory, a short-term prediction method towards trimaran loads is studied by using an optimized data-driven model. …”
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An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy by Zhaopeng He, Tielin Shi, Xu Chen

“…Compared to the single SSAE model and shallow machine learning models, the proposed method significantly improved both the prediction accuracy and reliability.…”
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Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy by Seyed Mostafa Mirhassani, Alireza Zourmand, Hua-Nong Ting

“…Second, different vowel classes contain complementary information for age estimation. Mel-frequency cepstral coefficients are computed for each group and single layer feed-forward neural networks based on self-adaptive extreme learning machine are applied to the features to make a primary decision. …”
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Evaluation of Floods and Landslides Triggered by a Meteorological Catastrophe (Ordu, Turkey, August 2018) Using Optical and Radar Data by Sultan Kocaman, Beste Tavus, Hakan A. Nefeslioglu, Gizem Karakas, Candan Gokceoglu

“…The results presented here show that considering the occurrence frequency and immense damages after such events, the use of novel remote sensing technologies and spatial analysis methods is unavoidable for disaster mitigation efforts and for the monitoring of environmental effects. …”
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Novel transfer learning based acoustic feature engineering for scene fake audio detection by Ahmad Sami Al-Shamayleh, Hafsa Riasat, Ala Saleh Alluhaidan, Ali Raza, Sahar A. El-Rahman, Diaa Salama AbdElminaam

“…We propose a novel transfer learning method, which initially extracts mel-frequency cepstral coefficients (MFCC) and then class prediction probability value features. …”
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Energy Efficiency Improvement of Hydraulic Indirect Elevator by Łukasz Stawiński, Andrzej Kosucki, Justyna Skowrońska, Piotr Malenta

“…The EHD system uses a frequency converter with an energy recovery module to control the speed of the car in both directions and the return of potential energy during the lowering cycle. …”
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Fault detection in beam structure using adaptive immune based approach by Sasmita Sahu, Shakti P. Jena

“…Here dynamic analysis of structure has been conducted as the forward method to find out the modal natural frequencies related with the damage. …”
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Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques by Soraya Khanmohmmadi, Toktam Khatibi, Golnaz Tajeddin, Elham Akhondzadeh, Amir Shojaee

“…Previous studies have focused on traditional machine learning (ML) methods such as K-Nearest Neighbors (KNNs), Support Vector Machines (SVMs), and ensemble learning methods for sleep disorders analysis. …”
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Reliable Evolutionary Field Optimization-Based Energy Management Strategy for Mitigating Hydrogen Consumption of Hybrid Generation System by Shahr Alshahr, Ahmed Fathy, Ahmed Alshahir, Hossam Hassan Ali

“…The proposed strategy is contrasted with other metaheuristic approaches such as Kepler optimization algorithm, walrus optimization algorithm, and golf optimization algorithm in addition to traditional strategies like classical proportional-integral, hybrid frequency decoupling state machine, equivalent consumption minimization strategy, and EEMS. …”
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Theoretical and Experimental Study of the Effects of Impact Drilling Parameters on the Properties of Surrounding Rock Damage by Zhi Liu, Wanrong Wu, Qianhua Hao, Peng Sun, Yuangen Ding

“…. × 200 mm) containing central axial holes formed either by impact or nonimpact drilling methods were tested in uniaxial compression to failure on an Instron 1346 universal testing machine to investigate their mechanics and damage properties. …”
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Balancing Efficiency and Efficacy: A Contextual Bandit-Driven Framework for Multi-Tier Cyber Threat Detection by Ibrahim Mutambik, Abdullah Almuqrin

“…In response to the rising volume and sophistication of cyber intrusions, data-oriented methods have emerged as critical defensive measures. …”
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CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer by Najamuddin NAJAMUDDIN, Usman Ullah SHEIKH, Ahmad Zuri SHA’AMERI

“…Underwater acoustic target classification has become a key area of research for marine vessel classification, where machine learning (ML) models are leveraged to identify targets automatically. …”
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Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks by Haithem Ben Chikha, Alaa Alaerjan, Randa Jabeur

“…When compared with traditional machine learning approaches reported in recent studies, our DRN-based method shows significantly improved classification accuracy and robustness. …”
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Incidence of Helicobacter pylori in Type l Diabetes Mellitus Patients and Association with Certain HLA Alleles by Samir S. Aljawahiry, Naer A. M. Alkaabawi, Alaa A. Jawad

“…Materials and Methods: Several 140 patients diagnosed with type 1 diabetes mellitus (T1DM), as well as 60 healthy individuals (nondiabetic) were enrolled in this study. …”
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Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness by Hui Li, Hui Li, Hui Li, Linghui Dong, Linghui Dong, Linghui Dong, Wenlong Su, Wenlong Su, Ying Liu, Ying Liu, Zhiqing Tang, Zhiqing Tang, Xingxing Liao, Xingxing Liao, Junzi Long, Junzi Long, Xiaonian Zhang, Xinting Sun, Hao Zhang, Hao Zhang, Hao Zhang, Hao Zhang

“…Patients were followed up for 6 months. A combination of machine learning and SHapley Additive exPlanations (SHAP) were used to develop predictive model and interpret the results.ResultsThe results indicated significant abnormalities in low-frequency spectral power, microstate parameters, and amplitudes of MMN and P3a in MCS and UWS. …”
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Harnessing Data-mining Algorithms to Model and Evaluate Factors Influencing Distortion Product Otoacoustic Emission Variations in a Mining Industry by Sajad Zare, Reza Esmaeili, Mojtaba Nakhaei pour

“…Aim: The aim of this study was to gauge the condition of otoacoustic emissions (OAEs) in workers, followed by modeling and estimating the weight of factors affecting changes in their emissions. Methods: The present study comprises two main phases. …”
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Design and Development of Gorilla Optimized Deep Resilient Architecture for Prediction of Agro-Climatic Changes to Increase the Crop–Yield Production by Deepa Devarashetti, S. S. Aravinth

“…Climatic data and services are crucial for agriculturalists to withstand the rising frequency of strong meteorological conditions, which negatively impact crop yields. …”
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