Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization by Huaitao Shi, Yangyang Li, Peng Zhou, Shenghao Tong, Liang Guo, Baicheng Li

“…The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. …”
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Variable-Speed Hydropower Control and Ancillary Services: A Remedy for Enhancing Grid Stability and Flexibility by Cagatay Cebeci, Max Parker, Luis Recalde-Camacho, David Campos-Gaona, Olimpo Anaya-Lara

“…The control system proposed integrates a machine-side controller, a Frequency Support Controller (FSC), a Virtual Synchronous Machine (VSM), a Vector Current Controller (VCC) for the grid-side converter, a turbine governor for regulating turbine speed, and a DC-link controller. …”
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Short-term prediction network for short-wave MUF based on model-data dual-driven by Junbing LI, Youjun ZENG, Xiaoping ZENG, Guojun LI, Chenxi BAI

“…Predicting the maximum available frequency of short-wave communication presents the challenges of low prediction accuracy of classical prediction model methods and difficulty in obtaining training set data for machine learning prediction methods.To address this issue, a model-data dual-driven bidirectional gated recurrent unit (BiGRU) network for short-term prediction of MUF was proposed.On the model-driven, a large-scale dataset generated by the classical MUF prediction model was used as the model-driven training set, and a preliminary network was obtained after joint learning of the 2D CNN and the BiGRU network.On the data-driven, the preliminary network was trained twice using a small-scale measured dataset to obtain the final network CNN-BiGRU-NN.The simulation results show that the proposed network has reduced average root mean squared error (RMSE) at both daily and momentary scales compared with the GRU network, LSTM network and VOACAP model.…”
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Efficient and Explainable Bearing Condition Monitoring with Decision Tree-Based Feature Learning by Trong-Du Nguyen, Thanh-Hai Nguyen, Danh-Thanh-Binh Do, Thai-Hung Pham, Jin-Wei Liang, Phong-Dien Nguyen

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Torque and Stator Flux Ripples Minimization for Direct Torque Control of PMSM by using Space Vector Modulation by Omar Ouledali, Abdelkader Meroufel, Patrice Wira, Said Bentouba

“…This paper presents a Direct Torque Control (DTC) strategy for the Permanent Magnets Synchronous Machine (PMSM). The principle relies on a Space Vector Modulation (SVM) technique that uses hysteresis comparators for the determination of the voltage module and angle, the proposed control method is simple to implement. …”
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Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis by Ziyang Li, Hong Wang, Jianing Song, Jiale Gong

“…Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT). Time–frequency features were extracted using the Multitaper method, followed by multidimensional reduction techniques. …”
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IT diagnostics of Parkinson's disease based on voice markers and decreased motor activity by U. V. Vishniakou, X. Yiwei

“…The objectives of the article to propose the method for complex recognition of Parkinson's disease using machine learning, based on markers of voice analysis and changes in patient movements on known data sets. …”
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Systematic Review on Algorithmic Trading by David Jukl, Jan Lansky

“…Tools such as Rayyan, NVivo, MS Excel and Zotero support the screening, coding and qualitative synthesis of findings.Results: AI methods, especially machine learning (used in 50% of the studies) and sentiment analysis (20%), significantly improve predictive accuracy and profitability. …”
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Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea by Daniele Padovano, Arturo Martinez-Rodrigo, José M. Pastor, José J. Rieta, Raul Alcaraz

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Online-Identification of Electromagnetic Parameters of an Induction Motor by V. K. Tytiuk, M. L. Baranovskaya, O. P. Chorny, E. V. Burdilnaya, V. V. Kuznetsov, K. N. Bogatyriov

“…The objective of the paper is to develop a method of online-identification of the electromagnetic parameters of an induction motor making it possible to implement accurate regulator adjustment of the frequency control system in terms of operational changes in the driving motor parameters. …”
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Acoustic Noise of Induction Motor Drive with Voltage-Source Inverter by Random Space Vector PWM: Simulation and Experimentation Analysis by Bouyahi Henda, Adel Khedher

“…Therefore, Random SVPWM (RSVPWM) is a new switching method applied for power converters. For the control of the three-phase inverter, three different RSVPWM approaches are suggested: Random Switching Frequency (RSF), Random Zero Vector (RZV) and Random Pulse Position (RPP). …”
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A Safe and Efficient Brain–Computer Interface Using Moving Object Trajectories and LED-Controlled Activation by Sefa Aydin, Mesut Melek, Levent Gökrem

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Alzheimer’s disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study by Juan Wang, Juan Wang, Jiamei Zhao, Xiaoling Chen, Xiaoling Chen, Bowen Yin, Xiaoli Li, Xiaoli Li, Ping Xie, Ping Xie

“…Moreover, the AD group had increased frequency coupling in the frontal and central regions. …”
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Estimation the pH of CO2-saturated NaCl solutions using gene expression programming: Implications for CO2 sequestration by Mohammad Rasool Dehghani, Parirokh Ebrahimi, Moein Kafi, Hamed Nikravesh, Yousef Kazemzadeh

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Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model by Naila Aslam, Furqan Rustam, Ernesto Lee, Patrick Bernard Washington, Imran Ashraf

“…LSTM and GRU are stacked where the GRU is trained on the features extracted by LSTM. Utilizing term frequency-inverse document frequency, word2vec, and bag of words (BoW) features, several machine learning and deep learning approaches and a proposed ensemble model are investigated. …”
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The 2024–2025 seismic sequence in the Santorini-Amorgos region: Insights into volcano-tectonic activity through high-resolution seismic monitoring by Ioannis Fountoulakis, Christos P. Evangelidis

“…In this study, a detailed seismic catalog is presented, generated for operational monitoring using machine-learning-based phase picking and high-precision relocation methods. …”
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Drunk Driver Detection Using Multiple Non-Invasive Biosignals by Sang Hyuk Kim, Hyo Won Son, Tae Mu Lee, Hyun Jae Baek

“…This study aims to decrease the number of drunk drivers, a significant social problem. Traditional methods to measure alcohol intake include blood alcohol concentration (BAC) and breath alcohol concentration (BrAC) tests. …”
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Global forecasting models for dengue outbreaks in endemic regions: a systematic review by Agung Sutriyawan, Mursid Rahardjo, Martini Martini, Dwi Sutiningsih, Cheerawit Rattanapan, Nur Faeza Abu Kassim

“…The present study corroborates the superior efficacy of machine learning-based forecasting models, particularly random forest, in forecasting dengue cases compared to conventional statistical methods. …”
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RanViz: Ransomware Visualization and Classification Based on Time-Series Categorical Representation of API Calls by Vhuhwavho Mokoma, Avinash Singh

“…The system incorporates ML models based on API call frequency, temporal interval, and sequence to classify unknown samples as either benign or ransomware. …”
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A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence by Abdullah Alabdulatif

“…Traditional IDS methods, often based on static signatures and rule-based systems, are no longer sufficient to detect and respond to complex and evolving attacks. …”
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