A non-optically active lake salinity dataset by satellite remote sensing by Mingming Deng, Ronghua Ma, Lixin Wang, Minqi Hu, Kun Xue, Zhigang Cao, Junfeng Xiong, Zhengyang Yu

“…Conventional function models based on salinity tracers or single lakes have low regional applicability, while machine learning algorithms can effectively capture the nonlinear relationship between radiance and salinity, providing large-scale inversion opportunities. …”
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Using Pleiades Satellite Imagery to Monitor Multi-Annual Coastal Dune Morphological Changes by Olivier Burvingt, Bruno Castelle, Vincent Marieu, Bertrand Lubac, Alexandre Nicolae Lerma, Nicolas Robin

“…However, ongoing improvements on the stereo matching algorithms and spatial resolution of the satellite sensors (e.g., Pleiades Neo) highlight the growing potential of Pleiades images as a cost-effective alternative to other mapping techniques of coastal dune topography.…”
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Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validation by Emi Kalita, Abdullah Mana Alfarwan, Houssam El Aouifi, Houssam El Aouifi, Ashima Kukkar, Sadiq Hussain, Tazid Ali, Silvia Gaftandzhieva

“…Predicting the student’s academic performance also helps to identify at-risk students and explore the possibility of providing intervention techniques.MethodsIn this paper, a deep learning model using a Bi-LSTM network is introduced to predict second term GPA.ResultsThe model had an average accuracy of 88.23% and was statistically better than traditional machine learning algorithms such as CatBoost, XGBoost, Hist Gradient Boosting, and LightGBM for accuracy, precision, recall, or F1-score metrics. …”
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PREACT-digital: study protocol for a longitudinal, observational multicentre study on digital phenotypes of non-response to cognitive behavioural therapy for internalising disorder... by Christine Knaevelsrud, Sebastian Burchert, Till Langhammer, Leona Hammelrath, Annette Brose, Manuel Heinrich, Pavle Zagorscak

“…Predictive analyses focus on classification of non-response using basic algorithms (ie, logistic regression and gradient boosting) for straightforward interpretability and advanced methods (LSTM, DSEM) to capture complex temporal and hierarchical patterns. …”
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A Novel Self-Attention-Enabled Weighted Ensemble-Based Convolutional Neural Network Framework for Distributed Denial of Service Attack Classification by Shravan Venkatraman, S. Kanthimathi, K. S. Jayasankar, T. Pranay Jiljith, R. Jashwanth

“…Traditional approaches, such as single Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) algorithms like Decision Trees (DTs) and Support Vector Machines (SVMs), struggle to extract the diverse features needed for precise classification, resulting in suboptimal performance. …”
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EDITORIAL: ARTIFICIAL INTELLIGENCE AND ITS TRANSFORMATIVE IMPACT ON SCIENTIFIC PUBLISHING by Abubakar Munir

“…Some AI tools, like ScholarOne and Editorial Manager, have already started using machine learning algorithms to recommend reviewers and detect probable conflicts of interest, making an efficient and unbiased review process possible (2). …”
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Numerical Background-Oriented Schlieren for Phase Reconstruction and Its Potential Applications by Shiwei Liu, Yichong Ren, Haiping Mei, Zhiwei Tao, Shuran Ye, Xiaoxuan Ma, Ruizhong Rao

“…However, even in degraded conditions, the extracted optical flow fields preserve structural features correlated with the underlying shock patterns, indicating potential for BOS-based target recognition. …”
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Smart diabetes management: remote monitoring and predictive health insights by K.S. Smelyakov, I.A. Lurin, K.V. Misiura, A.S. Chupryna, T.V. Tyzhnenko, O.D. Dolhanenko, V.M. Repikhov

“…Newer CGM technologies like Dexcom and Freestyle Libre enable continuous, non-invasive monitoring of glucose levels, producing time series data essential for detecting patterns and predicting dangerous fluctuations (hypo- or hyperglycemia). …”
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Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review by Vishal Gaurav, Chander Grover, Mehul Tyagi, Suman Saurabh

“…In healthcare, AI encompasses various subfields, including machine learning, deep learning, natural language processing, and expert systems. …”
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Molecular biomarkers in salivary diagnostic materials: Point-of-Care solutions — PoC-Diagnostics and -Testing by Ziyad S. Haidar

“…AI-driven algorithms now process and analyze salivary proteomic data with remarkable accuracy, identifying patterns and biomarkers associated with diseases such as oral cancer at an early stage. …”
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Collective behavior quantification on human odor effects against female Aedes aegypti mosquitoes-Open source development. by Abdul Halim Poh, Mahmoud Moghavvemi, Cherng Shii Leong, Yee Ling Lau, Alireza Safdari Ghandari, Alexlee Apau, Faisal Rafiq Mahamd Adikan

“…The average distance of the blobs within the frames against time forms a spectra where behavioral patterns can be observed directly, whether any collective effect is observed. …”
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Informational Approaches in Modelling Social and Economic Relations: Study on Migration and Access to Services in the European Union by Florentina-Loredana Dragomir-Constantin, Camelia Madalina Beldiman, Monica Laura Zlati

“…The applied methodology includes attribute distribution analysis, identification of hidden patterns through clustering algorithms (K-Means and Expectation-Maximisation) and training of classifiers using regression decision trees with linear leaf models (M5P) corresponding to interdependent data processing and integration modules, exploratory analysis module, machine learning and decision-making modules, oriented to support public policies through explainable scenarios and predictive-evaluative structures. …”
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Data augmentation of time-series data in human movement biomechanics: A scoping review. by Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt

“…However, the field faces challenges such as limited large-scale data sets, high data acquisition costs, and restricted participant access that hinder the development of robust algorithms. Additional issues include variability in sensor placement, soft tissue artifacts, and low diversity in movement patterns. …”
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Impact of occupancy behavior on building energy efficiency: What’s next in detection and monitoring technologies? by Wenjie Song, John Calautit

“…Particular attention is paid to data-driven methods, including probabilistic models such as Hidden Markov Models (HMMs), classical machine learning algorithms such as Support Vector Machines (SVMs) and K-Nearest Neighbors (KNN), and deep learning architectures such as Convolutional Neural Networks (CNNs), all of which have demonstrated high accuracy in both laboratory and real-world settings. …”
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Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems by Natalya Denissova, Serik Nurakynov, Olga Petrova, Daniker Chepashev, Gulzhan Daumova, Alena Yelisseyeva

“…Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. …”
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Investigating the Impact of Earnings Management and Internal Control on the Financial Performance of Banks Using a Spatial Artificial Intelligence Approach by Wisam Fadhil Hanoon, Parviz Piri, Ali Ashtab

“…This study utilizes the statistical population of 44 banks from Iraq and 22 banks from Iran during 2010-2023 using the combined methods of deep learning, machine learning and spatial metrics. Deep neural networks identified different patterns in the two countries and deep learning algorithms determined the relative importance of the variables. …”
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Transformer attention fusion for fine grained medical image classification by Danyal Badar, Junaid Abbas, Raed Alsini, Tahir Abbas, Wang ChengLiang, Ali Daud

“…This model uses self-attention mechanics to improve spatial connections between single scales and cross-attention to automatically match feature patterns across multiple scales, thereby developing a comprehensive information structure. …”
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Study on the calculation of surface residual deformation in goaf based on the Gudermann time function by Haiguang Du, Wenchang Wang, Zhilin Dun, Lianwei Ren, Yakun Nan

“…In order to comprehensively and effectively grasp the dynamic evolution process of surface movement and deformation, and realize the scientific and accurate residual deformation calculation of each key point of the surface in goaf, the Gudermann time function for dynamic anticipation is established by introducing the Gudermann function and optimizing it, the spatial and temporal characteristics of this function in the subsidence prediction are analyzed, and the influence law of parameter changes on the surface subsidence, subsidence velocity and subsidence acceleration curve patterns of the Gudermann time function is discussed, and the method of extracting the optimal values of the function parameters by Simulated Annealing Algorithm (SAA) is presented. …”
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Analyzing Patient Complaints in Web-Based Reviews of Private Hospitals in Selangor, Malaysia, Using Large Language Model–Assisted Content Analysis: Mixed Methods Study by Muhammad Hafiz Sulaiman, Nora Muda, Fatimah Abdul Razak

“…Fake reviews were filtered out using natural language processing and machine learning algorithms trained on yelp.com validated datasets. …”
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Large-area OLED substrate printing path planning method based on multi-head GAT imitation learning to solve partitioned integer programming by Jiacong Xiong, Jiankui Chen, Yiqun Li, Xiao Yue, Yu Fu, Zhouping Yin

“…Therefore, the travel path of the printhead module must be planned to minimize the number of print cycles required to complete the pattern. This involves a challenging multi-objective optimization process. …”
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