Machine Learning-Driven Customer Segmentation: A Behavior-Based Approach for F&B Providers by Jacint JUHASZ

“…This study explores behavior-based customer segmentation by integrating Recency, Frequency, and Monetary value (RFM) analysis with the K-Means++ clustering algorithm. …”
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Bridging Neuroscience and Machine Learning: A Gender-Based Electroencephalogram Framework for Guilt Emotion Identification by Saima Raza Zaidi, Najeed Ahmed Khan, Muhammad Abul Hasan

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Power management in isolated microgrids using machine learning-based robust model predictive control by Chou-Yi Hsu, Amit Ved, Hannah Jessie Rani R, Zayd Ajsan Balsem, Nora Rashid Najem, Abhayveer Singh, P․Sasi Kiran, Ankita Aggarwal, Satish Kumar Samal, Alireza Kamranfar

“…Simulations conducted in MATLAB/Simulink under three different scenarios (normal, 30 % uncertainty, and 50 % uncertainty) demonstrate the effectiveness of the proposed controller. The proposed method achieves a frequency deviation error of 0.02 pu. under normal conditions, 0.03 pu. with 30 % uncertainty, and 0.05 pu. with 50 % uncertainty. …”
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Non-Invasive Monitoring of Cerebral Edema Using Ultrasonic Echo Signal Features and Machine Learning by Shuang Yang, Yuanbo Yang, Yufeng Zhou

“…There is a pressing clinical demand for a real-time, non-invasive cerebral edema monitoring method. Ultrasound methods are prime candidates for such investigations due to their non-invasive nature. …”
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Machine Learning-Based Image Pattern Recognition Using Histogram of Oriented Gradient for Islanding Detection by Kumaresh Pal, Kumari Namrata, Ashok Kumar Akella, Manoj Gupta, Pannee Suanpang, Aziz Nanthaamornphong

“…In this paper, a novel machine learning islanding detection method (IDM) based on image classification utilizing the histogram of oriented gradient (HOG) feature is proposed. …”
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Machine learning-based voice assistant: optimizing the efficiency of speech conversion for people with speech disorders by M.H. Antor, N.V. Chudinovskikh, M.V. Bachurin, A.A. Shurpikov, N.A. Khlebnikov, B.A. Bredikhin

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Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning by Navita, Pooja Mittal, Yogesh Kumar Sharma, Anjani Kumar Rai, Sarita Simaiya, Umesh Kumar Lilhore, Vimal Kumar

“…The dataset (imbalance) collected from both feet is passed to the preprocessing phase (for balancing data using the SMOTE method), followed by the feature extraction phase to extract features related to time, frequency, spatial, and temporal features domains that are highly effective for detecting and assigning severity levels of PD. …”
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Flood Risk Forecasting: An Innovative Approach with Machine Learning and Markov Chains Using LIDAR Data by Luigi Bibbò, Giuliana Bilotta, Giuseppe M. Meduri, Emanuela Genovese, Vincenzo Barrile

“…This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. …”
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Analysis of Calving Cow Posture Recognition, Behavioral Changes, and Influencing Factors Based on Machine Vision by Yuning An, Yifeng Song, Hehao Jiang, Yuan Wang, Na Liu, Xia Li, Zhalaga Zhang, Xiaoping An

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Landslide Susceptibility Analysis Based on Dataset Construction of Landslides in Yiyang Using GIS and Machine Learning by Chengxun Hou, Huanhua Liu, Xuan Wang, Jinqi Hu, Youde Tang, Xunwen Yao

“…In combination with the information value method, this model was applied to assess landslide susceptibility and rainfall-induced landslide risks in Yiyang City, demonstrating its validity. …”
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An efficient smart phone application for wheat crop diseases detection using advanced machine learning. by Awais Amir Niaz, Rehan Ashraf, Toqeer Mahmood, C M Nadeem Faisal, Muhammad Mobeen Abid

“…The application utilizes sophisticated machine learning techniques, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, combined with feature extraction methods such as Count Vectorization (CV) and Term Frequency-Inverse Document Frequency (TF-IDF). …”
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Application of the modal control method to an asynchronous drive by Yu. M. Kulinich, D. A. Starodubtsev

“…The development of power electronics and information technology facilitates the development and implementation of variable frequency electric drives for both traction motors and auxiliary machines of an electric locomotive. …”
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A study on forest fire risk assessment in jiangxi province based on machine learning and geostatistics by Jinping Lu, Mangen Li, Yaozu Qin, Niannan Chen, Lili Wang, Wanzhen Yang, Yuke Song, Yisu Zheng

“…This study integrated multiple factors, including topography, climate, vegetation, and human activities, and employed machine learning models, specifically random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN), to predict forest fire occurrence in Jiangxi. …”
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Replicating Current Procedural Terminology code assignment of rhinology operative notes using machine learning by Christopher P. Cheng, Ryan Sicard, Dragan Vujovic, Vikram Vasan, Chris Choi, David K. Lerner, Alfred‐Marc Iloreta

“…Text was preprocessed and then vectorized using CountVectorizer (CV), term frequency‐inverse document frequency, and Word2Vec. …”
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Nonlinear signal processing, spectral, and fractal based stridor auscultation: A machine learning approach by VIMAL RAJ, A RENJINI, M S SWAPNA, S SREEJYOTHI, S SANKARARAMAN

“… The work reported in the paper analyses the adventitious stridor breath sound (ST) and the normal bronchial breath sound (BR) using spectral, fractal, and nonlinear signal processing methods. The forty breath sound signals are subjected to power spectral density (PSD) and wavelet analyses to understand the temporal evolution of the frequency components. …”
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A Machine Learning Tool for Determining the Required Sample Size for GEV Fitting in Climate Applications by R. J. Matear, P. Jyoteeshkumar Reddy

“…Achieving such quantities will require extensive climate downscaling simulations, potentially aided by ML‐based downscaling methods to increase the ensemble size.…”
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Real-Time Acoustic Measurement System for Cutting-Tool Analysis During Stainless Steel Machining by Tom Salm, Kourosh Tatar, José Chilo

“…The proposed system employs low-cost, high-frequency microphones and advanced signal processing—featuring analog/digital filtering, oversampling, signal conditioning, PLL-based synchronization, and feature extraction (ZCR, RMS)—to capture acoustic emissions during machining. …”
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Investigating the relationship between groundwater level variations and geologic drought factor using MODFLOW and wavelet theory (case study: Southern Iran) by Mehrdad Donyadideh, Alireza Nikbakht Shahbazi, Narges Zohrabi, Hossein Fathian, Ali Afroos

“…Sentinel-2 satellite imagery processing is utilized to derive the normalized difference water index (NDWI) as a standardized indicator of water structure changes and land cover, establishing a framework for identifying drought-prone areas. Machine learning classification and Earth Object methods generate a refined land structure layer. …”
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The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports. by Guomei Cui, Chuanjun Wang

“…This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. …”
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Photoplethysmography Feature Extraction for Non-Invasive Glucose Estimation by Means of MFCC and Machine Learning Techniques by Christian Salamea-Palacios, Melissa Montalvo-López, Raquel Orellana-Peralta, Javier Viñanzaca-Figueroa

“…Two variants of the MFCC feature extraction methods are evaluated along with three machine learning techniques for the development of an effective regression function for the estimation of glucose concentration. …”
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