Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…Abstract Background In the hyper-arid environment of the United Arab Emirates (UAE), understanding rainfall patterns is essential for effective water resource management, agricultural planning, and ecological conservation. …”
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Transforming tabular data into images via enhanced spatial relationships for CNN processing by Hameedah A. Alenizy, Jawad Berri

“…This transformation enables CNNs to process tabular data efficiently by leveraging automated feature extraction and enhanced pattern recognition. The NCTD algorithm was extensively evaluated and compared with traditional machine learning algorithms and existing methods on ten benchmark datasets. …”
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Improving cancer detection through computer-aided diagnosis: A comprehensive analysis of nonlinear and texture features in breast thermograms. by Hamed Khodadadi, Shima Nazem

“…Besides, to optimize feature selection and reduce redundancy, a metaheuristic optimization technique called Non-Dominated Sorting Genetic Algorithm (NSGA III) is applied. The proposed method utilizes various machine learning algorithms, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Pattern recognition Network (Pat net), and Fitting neural Network (Fit net), for classification. ten-fold cross-validation ensures robust performance evaluation. …”
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Combination of Music, Artificial Intelligence, and Brainwaves for the Promotion of Mental Well-Being by Georgios Karachalios, Evina Makaroni

“…The algorithms used can offer valuable insights into how different auditory stimuli affect human psychology, creating a dynamic framework for study across both psychology and neuroscience. …”
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Analysis of Gene Expression Omnibus high-throughput sequencing data for the determination of microribonucleic acids in the blood plasma of patients with glioblastomas by A. A. Pushkin, D. Yu. Gvaldin, N. N. Timoshkina, E. E. Rostorguev, L. Yu. Vladimirova, E. A. Dzenkova

“…Determination of significant miRNAs using machine learning algorithms of the R 4.0.4 project. …”
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An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates by J. Hu, C. Miao, J. Su, Q. Zhang, J. Gou, J. Gou, Q. Sun, Q. Sun

“…Building upon the improved inverse distance weighting interpolation method used in our previous dataset CHM_PRE V1, we integrated a machine learning algorithm – light gradient boosting machine (LGBM) – to incorporate precipitation-related covariates in a data-driven manner. …”
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Geographical features and management strategies for microplastic loads in freshwater lakes by Huike Dong, Ruixuan Zhang, Xiaoping Wang, Jiamin Zeng, Lei Chai, Xuerui Niu, Li Xu, Yunqiao Zhou, Ping Gong, Qianxue Yin

“…To address this gap, our study utilizes Machine Learning (the random forest algorithm), combined with number-to-mass transformation techniques to generate a global prediction. …”
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Detailed Time-resolved Spectral and Temporal Investigations of SGR J1550–5418 Bursts Detected with Fermi Gamma-Ray Burst Monitor by Mustafa Demirer, Ersin Göğüş, Yuki Kaneko, Özge Keskin, Sinem Şaşmaz, Shotaro Yamasaki

“…Subsequently, we obtained nonoverlapping time segments with varying lengths based on their spectral evolution patterns, employing a machine learning algorithm called k -means clustering. …”
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Population status and impact of climate change on the distribution of vulnerable multipurpose plant Hippophae rhamnoides ssp. turkestanica for conservation in Trans-Himalaya, India by Shiv Paul, Shiv Paul, Khilendra Singh Kanwal, Anil Kumar, Sher Singh Samant, Sher Singh Samant, Indra Dutt Bhatt, Rakesh Chandra Sundriyal, Rakesh Chandra Sundriyal, Swaran Lata

“…Therefore, the current study aims to assess the population status and predict highly suitable areas for Trans-Himalaya species under changing climatic conditions. The machine learning algorithm showed that Bio_6 (minimum temperature of the coldest month), elevation, and slope were the best suitable variables for the habitat prediction along with the CMIP6 project’s MIROC6 and CMCC-ESM2 climate change models to identify the potential distribution area of the species for the future under the SSP245 (middle of the way) and SSP585 (fossil-fueled development) scenarios, respectively. …”
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Artificial Intelligence for Unstructured Data Processing by Yohanes Bowo Widodo, Febrianti Widyahastuti, Mohammad Narji, Sondang Sibuea

“…By using deep learning models and advanced algorithms, AI can identify patterns and relationships in complex data, thereby providing deeper insights for better decision making. …”
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Proactive Data Placement in Heterogeneous Storage Systems via Predictive Multi-Objective Reinforcement Learning by Suchuan Xing, Yihan Wang

“…The framework’s ability to proactively adapt to evolving access patterns while maintaining computational efficiency makes it particularly suitable for large-scale machine learning and scientific computing environments where data placement critically impacts overall system performance.…”
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Application of pso for solving problems of invariant comparison of two-dimensional closed curve by Dmitry Borisovich Abramov, Sergey Olegovich Baranov, Sergey Vladimirovich Leykhter

“…The developed algorithms can be used in bioinformatics and biometrics systems, classification of images and objects, machine vision systems, for pattern recognition and object tracking systems.…”
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Wavelet filterbank‐based EEG rhythm‐specific spatial features for covert speech classification by Sukanya Biswas, Rohit Sinha

“…With the motivation of deriving more discriminative features, each channel data has been decomposed into distinct bands focussing on the five basic EEG rhythms using the discrete wavelet transform (DWT)‐based signal decomposition algorithm. Following that, for each band, the multi‐class common spatial pattern (CSP) features are computed using joint approximate diagonalisation. …”
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Massive unsourced multiple access scheme based on block sequence codebook and compressed sensing by Jing ZHANG, Lin MA, Chulong LIANG, Hongxu GAO

“…A massive unsourced multiple access scheme based on block sequence codebook and compressed sensing was proposed for sporadic burst scenario in massive machine type communication (mMTC).Firstly, a large-capacity spreading codebook generation scheme was designed according to a specific shift pattern, thus the codebook space was expanded.Secondly, the sparse structure of uplink signal was combined with multi-carrier technology to support overlapping transmission of multi-user data on some subcarriers, thus the spectral efficiency was improved.Finally, a multi-carrier CS-MUD model was established, and a group orthogonal matching pursuit algorithm based on codebook sequence blocks was designed to achieve the joint detection of active users and their uplink data.Simulation results show that the proposed scheme can effectively reduce the bit error rate of massive random access.…”
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Sparse Feature-Weighted Double Laplacian Rank Constraint Non-Negative Matrix Factorization for Image Clustering by Hu Ma, Ziping Ma, Huirong Li, Jingyu Wang

“…As an extension of non-negative matrix factorization (NMF), graph-regularized non-negative matrix factorization (GNMF) has been widely applied in data mining and machine learning, particularly for tasks such as clustering and feature selection. …”
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Mitigating malicious denial of wallet attack using attribute reduction with deep learning approach for serverless computing on next generation applications by Amal K. Alkhalifa, Mohammed Aljebreen, Rakan Alanazi, Nazir Ahmad, Sultan Alahmari, Othman Alrusaini, Ali Alqazzaz, Hassan Alkhiri

“…The deep learning (DL) model, a part of the machine learning (ML) technique, has developed as an effectual device in cybersecurity, permitting more effectual recognition of anomalous behaviour and classifying patterns indicative of threats. …”
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Integrating structured and unstructured data for livestock price forecasting: a sustainability study from South Korea by Yifan Zhu, Yifan Zhu, Yifan Zhu, Tserenpurev Chuluunsaikhan, Jong-Hyeok Choi, Aziz Nasridinov

“…SASD framework, which systematically decomposes complex livestock price time series into trend, seasonal, and residual components, improving the forecasting accuracy by isolating seasonal patterns and irregular fluctuations. Additionally, we develop a Korean-language sentiment lexicon using an improved Term Frequency–Inverse Document Frequency (ITF-IDF) algorithm, enabling morpheme-level sentiment analysis for better sentiment extraction in Korean contexts. …”
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Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré Fringes by Dajie Yu, Junbo Liu, Chuan Jin, Yuyang Li, Kairui Zhang, Ji Zhou

“…Overlay accuracy is a fundamental indicator of a photolithography machine performance. Misalignment between the mask and wafer is the main factor affecting overlay accuracy. …”
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Associations of accelerometer-measured physical activity, sedentary behaviour, and sleep with next-day cognitive performance in older adults: a micro-longitudinal study by Mikaela Bloomberg, Laura Brocklebank, Aiden Doherty, Mark Hamer, Andrew Steptoe

“…Physical behaviour (time spent in moderate-to-vigorous physical activity [MVPA], light physical activity [LPA], and sedentary behaviour [SB]) and sleep characteristics (overnight sleep duration, time spent in rapid eye movement [REM] sleep and slow wave sleep [SWS]) were extracted from accelerometers, with sleep stages derived using a novel polysomnography-validated machine learning algorithm. We used linear mixed models to examine associations of physical activity and sleep with next-day cognitive performance, after accounting for habitual physical activity and sleep patterns during the study period and other temporal and contextual factors. …”
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Whole genome resequencing reveals genetic diversity, population structure, and selection signatures in local duck breeds by Pengwei Ren, Yongdong Peng, Liu Yang, Muhammad Zahoor Khan, Yadi Jing, Chao Qi, Zhansheng Liu, Shuer Zhang, Nenzhu Zheng, Meixia Zhang, Xiang Liu, Zhiming Zhu, Mingxia Zhu

“…Conclusions This study, utilizing genome sequencing data and machine learning algorithms, provides a comprehensive evaluation of the genetic resources of Shandong’s local duck breeds. …”
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