Brugia malayi excreted/secreted proteins at the host/parasite interface: stage- and gender-specific proteomic profiling. by Sasisekhar Bennuru, Roshanak Semnani, Zhaojing Meng, Jose M C Ribeiro, Timothy D Veenstra, Thomas B Nutman

“…Moreover, this analysis was able to confirm the presence of 274 "hypothetical" proteins inferred from gene prediction algorithms applied to the B. malayi (Bm) genome. …”
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Advanced Technologies for Smart Fertilizer Management in Agriculture: A Review by Jian-Jun Liu, Hao Wu, Imran Riaz

“…Additionally, the study explores key applications of precision fertilization, including data-driven fertilizer recommendations, aerial crop monitoring, real-time nutrient deficiency prediction, automated and optimized fertilization systems, and organic and eco-friendly nutrient management practices. …”
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Federated Learning-Based State of Charge Estimation in Electric Vehicles Using Federated Adaptive Client Momentum by Metin Yilmaz, Eyup Cinar, Ahmet Yazici

“…Traditional Battery Management System (BMS) algorithms can be inadequate for State of Charge (SoC) estimation due to incorrect measurements and unobservable battery characteristics. …”
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Pistachio Classification Based on Acoustic Systems and Machine Learning by Yavuz Türkay, Zekiye Seyma Tamay

“…These features are given as input to a support vector machine algorithm called FITCSVM for classification. FITCSVM is an algorithm that can perform one-class and two-class (binary) classification on low or medium-sized prediction datasets. …”
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Reinforcement learning in artificial intelligence and neurobiology by Tursun Alkam, Andrew H Van Benschoten, Ebrahim Tarshizi

“…In parallel, we discuss how neurobiological findings, especially the role of dopamine in encoding reward prediction errors, have inspired biologically grounded RL models. …”
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State-of-the-Art on IoV-Based Deep Learning Framework for Enhanced Driving Behavior Recognition: Recent Progress, Technology Updates, Challenges, and Future Direction by Hongguang Li, Shafrida Sahrani, Mahidur R. Sarker, Yinglin Xiao

“…However, these advanced algorithms still face several challenges. We analyze relevant literature from 2015 to 2024, to uncover key trends and gaps in the development of applications for identifying dangerous driving behaviors, with a primary focus on optimizing integration to improve the accuracy and real-time capability of driving behavior prediction. …”
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Petri graph neural networks advance learning higher order multimodal complex interactions in graph structured data by Alma Ademovic Tahirovic, David Angeli, Adnan Tahirovic, Goran Strbac

“…The work opens new directions in learning over complex structures, transcending transformer-based and traditional hypergraph-based algorithms. We validate results through theoretical analysis and real-world experiments, while demonstrating superior performance in, e.g., stock market prediction.…”
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Delineating flood susceptibility zones using novel ensemble models – An application of evidential belief function, relative frequency ratio, and Shannon entropy by Samuel Yaw Danso, Yi Ma, Isaac Yeboah Addo

“…Three bivariate statistical algorithms, namely evidential belief function (EBF), relative frequency ratio (RFR), and Shannon entropy (SE) were combined through basic arithmetic operations to produce nine ensemble scenarios. …”
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Linear and Nonlinear Multivariate Classification of Iranian Bottled Mineral Waters According to Their Elemental Content Determined by ICP-OES by J.B. Ghasemi, E. Zolfonoun, R. Khosrokhavar

“…The combinations of inductively coupled plasma-optical emission spectrometry (ICP-OES) and three classification algorithms, i.e., partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM) and soft independent modeling of class analogies (SIMCA), for discriminating different brands of Iranian bottled mineral waters, were explored. …”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…The first section addresses process prediction, showcasing how AI techniques predict welding outcomes using historical data and process parameters, which enhances decision-making prior to actual implementation. …”
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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

“…We utilized support vector machine (SVM), logistic regression (LogR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, linear regression (LR), and feedforward neural network (FNNs) for predicting the cerebral infarction volume ratio. …”
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AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes by José E. Valerio, Guillermo de Jesús Aguirre Vera, Maria P. Fernandez Gomez, Jorge Zumaeta, Andrés M. Alvarez-Pinzon

“…Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. …”
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Evolution of the Swiss pork production systems and logistics: the impact on infectious disease resilience by Francesco Galli, Saskia Perret-Gentil, Antoine Champetier, Rita Lüchinger, Myriam Harisberger, Thibault Kuntzer, Stefan Rieder, Christina Nathues, Beatriz Vidondo, Hartmut Lentz, Vitaly Belik, Salome Dürr

“…For Swiss pig farms, we used prediction and clustering algorithms to classify 9’687 − 11’247 trading farms between 2014 and 2019 by one of eleven production types. …”
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Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods by Bilal Cemek, Yunus Kültürel, Emirhan Cemek, Erdem Küçüktopçu, Halis Simsek

“…This study compares two modeling approaches for predicting soil temperature at various depths: (i) fuzzy logic-based systems, including the Mamdani fuzzy inference system (MFIS) and the adaptive neuro-fuzzy inference system (ANFIS); (ii) supervised machine learning algorithms, such as multilayer perceptron (MLP), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN), along with multiple Linear regression (MLR) as a statistical benchmark. …”
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Enhancing piezoelectric control of cracks in thin solid aluminum plates using finite element data and a neural networks approach by Abdul Aabid, Muneer Baig, Mohammad Abdul Malik

“…In addition, artificial neural networks, specifically feedforward neural networks, are employed for optimal SIF prediction, showcasing the ability to integrate soft computing with mechanical engineering challenges. …”
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Investigation of Underlying Biological Association and Targets between Rejection of Renal Transplant and Renal Cancer by Yinwei Chen, Zhanpeng Liu, Qian Yu, Xu Sun, Shuai Wang, Qingyi Zhu, Jian Yang, Rongjiang Jiang

“…The prognosis model showed excellent performance in prognosis prediction in a ccRCC cohort. In addition, the machine learning algorithms identified 19 rejection-related genes, including PLAC8, involved in ccRCC occurrence. …”
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Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review. by Muhammad Ibtsaam Qadir, Jackson A Baril, Michele T Yip-Schneider, Duane Schonlau, Thi Thanh Thoa Tran, C Max Schmidt, Fiona R Kolbinger

“…The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field. …”
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Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Türkiye... by Yasemin Ayaz Atalan, Hasan Şahin, Abdulkadir Keskin, Abdulkadir Atalan

“…For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. …”
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Chaotic billiards optimized hybrid transformer and XGBoost model for robust and sustainable time series forecasting by Reham H. Mohammed, Asmaa Mohamed El-saieed

“…Abstract Accurate wind speed forecasting plays a key role in supporting renewable energy systems, improving flight safety, and enhancing weather prediction. However, the variability and non-stationary nature of wind patterns make reliable forecasting a difficult task. …”
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Spike Stall Precursor Detection in a Single-Stage Axial Compressor: A Data-Driven Dynamic Modeling Approach by Anish Thapa, Jichao Li, Marco P. Schoen

“…Reducing this margin through active control requires stall precursor detection and mitigation mechanisms. While several algorithms have shown promising results in predicting modal stalls, predicting spike stalls remains a challenge due to their rapid onset, leaving little time for corrective actions. …”
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