Intelligent Processing Methods by Veronika V. Tolmanova, Denis A. Andrikov

“…These methods, together with modern algorithms and computer models, allow extracting valuable information from huge volumes of raw data, as well as analyzing and forecasting various phenomena and trends. …”
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Transcriptome signature for multiple biotic and abiotic stress in barley (Hordeum vulgare L.) identifies using machine learning approach by Bahman Panahi

“…Machine learning models, specifically Random Forest and C4.5, were optimized and evaluated using a 10-fold cross-validation approach. The C4.5 algorithm demonstrated superior accuracy in predicting stress-responsive signatures. …”
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Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. by Mabirizi, Vicent, Kawuma, Simon, Kyarisiima, Addah, Bamutura, David, Atwiine, Barnabas, Nanjebe, Deborah, Oyesigye, Adolf Mukama

“…This is evidenced by some models and algorithms with ≥90% prediction accuracy. From the literature, most of the proposed methods are trained and tested on pre-trained deep learning models like VGG16, VGG19, ResNet, Inception_V3, and ReNet. …”
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Identification and validation of TUBB, CLTA, and FBXL5 as potential diagnostic markers of postmenopausal osteoporosis by Yue Tan, Yujing Wang, Qin Zhu, Yan Xue, Xuhao Ji, Zhenkun Li, Jiawen Shen, Chengming Sun, Shiqi Ren, Chenlin Zhang, Jianfeng Chen

“…Additionally, we intersected the clusters to identify differentially expressed genes (DEGs) and analyzed potential diagnostic markers for PMOP using support vector machine recursive feature elimination (SVM-RFE), LASSO, and random forest (RF) algorithms, which were subsequently validated in the GSE56116 dataset. …”
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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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Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images by Abdallah Waleed Ali, Sefer Kurnaz

“…The findings underscore the significant potential of optimized machine learning approaches in predicting extreme events, such as wildfires, and improving fire management strategies. …”
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Adaptative Pressure Estimation and Control Architecture for Integrated Electro-Hydraulic Brake System by Zhenhai Gao, Yi Yang, Guoying Chen, Liang Yuan, Jianguang Zhou, Jie Zhang

“…The fusion strategy is mainly based on the prediction performance of two algorithms under different vehicle speeds, pressures, and ABS states. …”
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Critical Review and Benchmark Proposal on FE Modeling for Patch Loading Resistance of Slender Steel Plate Girders in Launched Bridges by Marck Anthony Mora Quispe

“…It provides a comprehensive description of an FE model that accurately predicts both the load–displacement behavior and the patch loading resistance. …”
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LoD2 Building Reconstruction from Stereo Satellite Imagery using Deep Learning and Model-Driven Approach by Rojgar Qarani Ismael, Haval Sadeq

“…The suggested methodology is tested on the GeoEye-1 satellite imagery dataset for Erbil City, which is validated with ground truth data. The proposed algorithm presented promising results, it is shown that the model can predict building heights for ridge and eave to a mean absolute error of 0.70 m, and in the occluded area was approximately 1.0 m. …”
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Bird Species Detection Net: Bird Species Detection Based on the Extraction of Local Details and Global Information Using a Dual-Feature Mixer by Chaoyang Li, Zhipeng He, Kai Lu, Chaoyang Fang

“…Currently, most deep learning algorithms focus on designing local feature extraction modules while ignoring the importance of global information. …”
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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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Nasopharyngeal Carcinoma Subtype Discovery via Immune Cell Scores from Tumor Microenvironment by Yanbo Sun, Yun Liu, Hanqi Chu

“…Machine learning models, including random forest (RF) and support vector machine (SVM) algorithms, were constructed to predict the immune subtype. …”
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A Machine Learning Framework for Student Retention Policy Development: A Case Study by Sidika Hoca, Nazife Dimililer

“…For the case study, various machine learning algorithms—including Support Vector Classifier, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Artificial Neural Network, Random Forest, Classification and Regression Trees, and Categorical Boosting—were trained for dropout prediction using data available at the end of the students’ second semester. …”
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Plausible Vehicle Trajectory Augmentation With Hybrid Machine Learning by Junyeop Park, Hyeonsoo Jang, Byeongju Kang, Yunhyoung Hwang

“…A crucial factor in achieving these outcomes is the accurate prediction of surrounding vehicle trajectories, which relies on the effective application of artificial intelligence and machine learning algorithms. …”
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Doodle.AI: Redefining creative play with human-AI competition in real-time doodle recognition by Ishan Panta, Biplov Paneru, Bishwash Paneru, Bipul Thapa

“…The participants draw random words as they compete with an artificial intelligence model, which predicts drawings in real time. The AI component not only adds challenge and thrill but also demonstrates the capability of machine learning in interactive gaming. …”
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Modeling Estuarine Algal Bloom Dynamics with Satellite Data and Spectral Index-Based Classification by Mayya Podsosonnaya, Maria J. Schreider, Sergei Schreider

“…This can be achieved by implementation of processing algorithms and indices on multi-spectral satellite images. …”
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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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Analytical Methods and Determinants of Frequency and Severity of Road Accidents: A 20-Year Systematic Literature Review by Carlos M. Ferreira-Vanegas, Jorge I. Vélez, Guisselle A. García-Llinás

“…This information is crucial for projecting further research on RTA analysis and developing computational and data visualization tools oriented to the automation of RTA predictions based on intelligent systems.…”
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Association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and mortality among hypertension patients by Xiaozhou Su, Huiqing Rao, Chunli Zhao, Jiehua Wu, XianWei Zhang, Donghua Li

“…NHHR was classified as an important variable in the prediction model, with the random survival forest (rsf) algorithm showing superior performance. …”
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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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