Probabilistic Entropy EMD Thresholding for Periodic Fault Signal Enhancement in Rotating Machine by Jian Dang, Rong Jia, Hua Wu, Xingqi Luo, Diyi Chen

“…However, for rotating machine with poor working environment, the components attributed to noise might have higher amplitudes, which restrict the efficiency of noise reduction in current EMD-based denoising methods. Therefore, a probabilistic entropy EMD thresholding algorithm for periodic fault signal enhancement in rotating machine is proposed in this paper. …”
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Integrating machine learning and sentiment analysis in movie recommendation systems by Amany M. Sarhan, Hager Ayman, Mariam Wagdi, Bassant Ali, Aliaa Adel, Rahf Osama

“…Abstract The fast growth of the film business, along with an ever-increasing number of movie options, has highlighted the need for better recommendation algorithms. This study investigates the application of sentiment analysis in a movie recommendation system with the goal of improving the user experience. …”
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Random Forest-Based Retrieval of XCO₂ Concentration from Satellite-Borne Shortwave Infrared Hyperspectral by Wenhao Zhang, Zhengyong Wang, Tong Li, Bo Li, Yao Li, Zhihua Han

“…Currently, most global shortwave infrared CO₂ retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. …”
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Methodological Integration of Machine Learning and Geospatial Analysis for PM10 Pollution Mapping by Kalid Hassen Yasin, Muaz Ismael Yasin, Anteneh Derribew Iguala, Tadele Bedo Gelete, Erana Kebede

“…The study contributes valuable insights for implementing scalable pollution prediction systems in resource-constrained urban environments while acknowledging interpretability challenges inherent to complex ML models. • Preprocessing of spatial data from various sources, incorporating the handling of missing/abnormal data, analysis, and normalization • Implementation of the three ML algorithms with rigorous hyperparameter tuning, model validation, and performance assessment • Mapping PM10 Hotspots on the Gradient Direction and Distance from the City Center…”
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OBJECT DETECTION IN COMPUTER VISION SYSTEMS: A VISUAL SALIENCY BASED APPROACH by V. A. Kachurka, K. .. Madani, C. .. Sabourin, V. A. Golovko, P. A. Kachurka

“…Experimental results show viability and efficiency of this approach as compared with state-of-art algorithms, and predict its usability on the broader class of tasks - applied variations of eye fixation problem.…”
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Prospects for Legal Regulation of Quantum Communication by A. Minbaleev, S. Zenin, K. Evsikov

“…The authors note that their predictions about the cryptographic strength of encryption algorithms are based solely on modern knowledge about the capabilities of quantum computing and do not take into account its hidden potential, for example, in terms of cryptanalysis information systems based on a machine learning model generated by a quantum computer. …”
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Robust InceptionV3 with Novel EYENET Weights for Di-EYENET Ocular Surface Imaging Dataset: Integrating Chain Foraging and Cyclone Aging Techniques by Muhammad Ahmad Khan, Saif Ur Rehman Khan, Hafeez Ur Rehman, Suliman Aladhadh, Ding Lin

“…Abstract Predicting diabetic types from ocular surface eye images is a challenging task due to subtle variations in features and the potential overlap in presentations among different diabetic types. …”
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The effects of limb position and grasped load on hand gesture classification using electromyography, force myography, and their combination. by Peyton R Young, Kihun Hong, Eden J Winslow, Giancarlo K Sagastume, Marcus A Battraw, Richard S Whittle, Jonathon S Schofield

“…An emerging alternative control technique is force myography (FMG) which uses pattern recognition algorithms to predict hand gestures from the axial forces present at the skin's surface created by contractions of the underlying muscles. …”
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An Optimised CNN Hardware Accelerator Applicable to IoT End Nodes for Disruptive Healthcare by Arfan Ghani, Akinyemi Aina, Chan Hwang See

“…Addressing the challenges posed by constrained dataset sizes, compute-intensive AI algorithms, and hardware limitations, the approach presented in this paper leverages efficient image augmentation and pre-processing techniques to enhance both prediction accuracy and the training efficiency. …”
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Model Input-Output Configuration Search With Embedded Feature Selection for Sensor Time-Series and Image Classification by Anh Tuan Hoang, Zsolt Janos Viharos

“…Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. Traditional machine learning algorithms rely on well-defined input and output variables; however, there are scenarios where the separation between the input and output variables and the underlying, associated input and output layers of the model are unknown. …”
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Research on RF Intensity Temperature Sensing based on 1D-CNN by DING Meiqi, GUI Lin, WANG Ziyi, SHANG Disen, QIAN Min, LI Qiankun

“…【Results】The experimental results show that the RMSE of the prediction model based on 1D-CNN reaches the order of 10^-3, while the RMSE of the traditional algorithms is usually in the order of 10^-1. …”
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Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques by Juswin Sajan John, Boppuru Rudra Prathap, Gyanesh Gupta, Jaivanth Melanaturu

“…Building on existing literature, discussions with psychologists and other mental health practitioners, we developed a taxonomy of 27 distinctive markers spread across 4 label categories; aiming to create a preliminary screening tool leveraging textual data.The core objective is to identify the most suitable model for this complex task, encompassing comprehensive evaluation of various machine learning and deep learning algorithms. we experimented with support vector machines (SVM), random forest (RF) and long short-term memory (LSTM) algorithms incorporating various feature combinations involving Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA). …”
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Keypoints-Based Multi-Cue Feature Fusion Network (MF-Net) for Action Recognition of ADHD Children in TOVA Assessment by Wanyu Tang, Chao Shi, Yuanyuan Li, Zhonglan Tang, Gang Yang, Jing Zhang, Ling He

“…Existing video-based action recognition algorithms focus on object or interpersonal interactions, they may overlook ADHD-specific behaviors. …”
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Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review by Sunshine A. De Caires, Chaney St Martin, Melissa A. Atwell, Fuat Kaya, Glorious A. Wuddivira, Mark N. Wuddivira

“…Hybrid approaches combining geostatistics with ML algorithms (e.g., RF, Boost, SVM, ANN) demonstrate promise in addressing spatial uncertainty, while RS data enhances covariate enrichment and near-real-time applications. …”
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Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review by Jade Basem, Racheed Mani, Scott Sun, Kevin Gilotra, Neda Dianati-Maleki, Reza Dashti

“…In the focus of cryptogenic strokes, there is promising research elucidating likely underlying cardiac causes and thus, improved treatment options and secondary stroke prevention. While many algorithms still require a larger knowledge base or manual algorithmic training, AI/ML in neurocardiology has the potential to provide more comprehensive healthcare treatment, increase access to equitable healthcare, and improve patient outcomes. …”
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Clustering and classification of early knee osteoarthritis using machine-learning analysis of step-up and down test kinematics in recreational table tennis players by Ui-jae Hwang, Kyu Sung Chung, Sung-min Ha

“…Supervised learning models achieved high performance in classifying EOA status, with Random Forest, gradient boosting, and decision tree algorithms achieving 100% classification accuracy (AUC = 1.000) on the test dataset. …”
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Identifying the NEAT1/miR-26b-5p/S100A2 axis as a regulator in Parkinson's disease based on the ferroptosis-related genes. by Taole Li, Jifeng Guo

“…According to the five machine algorithms, 4 features (S100A2, GNGT1, NEUROD4, FCN2) were screened and used to create a PD diagnostic model. …”
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Optimizing Artificial Neural Networks Using Mountain Gazelle Optimizer by Muhammed Abdulhamid Karabiyik, Bahaeddin Turkoglu, Tunc Asuroglu

“…Effectively adjusting these parameters is essential to minimize the error between predicted and actual outputs. While traditional training algorithms, such as gradient-based methods, have been widely used, they often face challenges like premature convergence and stagnation in local optima. …”
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Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning by Zakwan AlArnaout, Chamseddine Zaki, Yehia Kotb, Mouhammad AlAkkoumi, Nour Mostafa

“…We propose a system model that integrates wearable devices equipped with photoplethysmography (PPG) sensors, transmitting data to a smartphone and then to a cloud server. Two novel algorithms are developed to segment and label features periodically, predicting drowsiness levels based on HRV derived from PPG signals. …”
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Dynamic programming for home appliance scheduling with renewable energy integration by Iqra Rafiq, Anzar Mahmood, Ubaid Ahmed, Imran Aziz, Zafar Ali Khan

“…This study proposes an energy cost minimization model, which is solved using a single Knapsack algorithm combined with dynamic programming (DP). The Knapsack problem is used to schedule appliances and ensure that users and producers receive the maximum benefits in terms of cost minimization and peak-to-average ratio (PAR) reduction. …”
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