Advancements in Hematologic Malignancy Detection: A Comprehensive Survey of Methodologies and Emerging Trends by Rajashree Nambiar, Ranjith Bhat, Balachandra Achar H V

“…Methodologically, we organize the literature by categorizing the malignancy types—leukemia, lymphoma, and multiple myeloma—and particularizing the preprocessing steps, feature extraction techniques, network architectures, and ensemble strategies employed. …”
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Document Relevance Filtering by Natural Language Processing and Machine Learning: A Multidisciplinary Case Study of Patents by Raj Bridgelall

“…These models include extreme gradient boosting, random forest, and support vector machines; a deep artificial neural network; and three natural language processing methods: latent Dirichlet allocation, non-negative matrix factorization, and k-means clustering of a manifold-learned reduced feature dimension. …”
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O2O Recycling Closed-Loop Supply Chain Modeling Based on Classification Process considering Environmental Index by Shidi Miao, Di Liu, Junfeng Ma

“…This mode is capable of integrating upstream and downstream resources on the network platform, creating a recycling and processing mode for the entire industry chain of waste sorting, and developing a circular development mode featuring “resources-products-waste-renewable resources” to realize the recycling of resources. …”
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Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods by Ruixiang Kan, Mei Wang, Tian Luo, Hongbing Qiu

“…Gait is considered a valuable biometric feature, and it is essential for uncovering the latent information embedded within gait patterns. …”
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Scene Text Detection and Recognition Using Maximally Stable Extremal Region by Golda Jeyasheeli P, Athinarayanan B, Manish T, Mohamad Umar M

“…Our CRNN architecture consists of convolutional and recurrent layers, which enable us to capture both spatial and temporal features of the text. The methodology is evaluated on various benchmark datasets and has obtained good results with accuracy of 96% when compared to existing methods. …”
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State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…However, in cases where the input data exhibit limited variation over time and consist of low-dimensional features, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) may tend toward overfitting. …”
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PIABC: Point Spread Function Interpolative Aberration Correction by Chanhyeong Cho, Chanyoung Kim, Sanghoon Sull

“…We compare our method—based on pixel-wise, physical correction, and densely interpolated PSF at pre-processing—with post-processing networks, including deformable convolutional neural networks (CNNs) that enhance image quality without modeling degradation. …”
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Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning by Ilya Larin, Alexander Karabelsky

“…Studies of C2C12 cells will reveal more about aspects of muscle differentiation by using neural networks. This work focuses on analyzing the applicability of the latent space to extract morphological features. …”
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Rapid discovery of Transglutaminase 2 inhibitors for celiac disease with boosting ensemble machine learning by Ibrahim Wichka, Pin-Kuang Lai

“…In this study, we utilized data from approximately 1100 TG2 inhibition assays to develop ligand-based molecular screening techniques using ensemble machine-learning models and extensive molecular feature libraries. Various classifiers, including tree-based methods, artificial neural networks, and graph neural networks, were evaluated to identify primary systems for predictive analysis and feature significance assessment. …”
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Research on short-term traffic flow prediction based on the PCC-IGA-LSTM model by Junxi Zhang, Shiru Qu, Yang Bi, Lijing Ma

“…To effectively address the spatial–temporal feature mining problem in short-term traffic flow prediction for complex road networks, a new method that combined the Pearson correlation coefficient (PCC) and improved genetic algorithm to optimize the long short-term memory model (IGA-LSTM) was constructed. …”
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Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

“…Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. …”
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A Fast Image Encryption Scheme Based on a Four-Dimensional Variable-Parameter Hyperchaotic Map and Cyclic Shift Strategy by Guidong Zhang, Yanhao Zhao, Yanpei Zheng, Yulin Shen, Jun Huang

“…Digital images are widely transmitted over untrusted networks, raising severe challenges to the security of confidential and personal data. …”
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A comparative analysis of variants of machine learning and time series models in predicting women’s participation in the labor force by Rasha Elstohy, Nevein Aneis, Eman Mounir Ali

“…This study proposes a hybrid machine-learning model that integrates principal component analysis (PCA) for feature extraction with various machine learning and time-series models to predict women’s employment in times of crisis. …”
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EDGE DETECTION TECHNIQUE BASED ON BILATERAL FILTERING AND ITERATIVE THRESHOLD SELECTION ALGORITHM AND TRANSFER LEARNING FOR TRAFFIC SIGN RECOGNITION by Milind PARSE, Dhanya PRAMOD

“…The performance of the proposed method is evaluated and compared with existing edge detection methods. …”
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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. …”
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An In-depth Investigation of OBIA Classification with High-Resolution Imagery: Unravelling the Explanations Behind Deep Learning and Machine Learning by E. O. Yilmaz, T. Kavzoglu

“…The present study evaluates the use of OBIA-based classification in conjunction with deep learning and machine learning classifiers. …”
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Audio–Visual Synchronization and Lip Movement Analysis for Real-Time Deepfake Detection by Muhammad Javed, Zhaohui Zhang, Fida Hussain Dahri, Asif Ali Laghari, Martin Krajčík, Ahmad Almadhor

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An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security by J. Maruthupandi, S. Sivakumar, B. Lakshmi Dhevi, S. Prasanna, R. Karpaga Priya, Shitharth Selvarajan

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A Robust U-Net-Based Approach for Accurate Brain Tumor Segmentation Using Multimodal MRI Data by Mohammad Talal Ghazal

“…A completely automated brain tumor segmentation method is proposed, leveraging U-Net-based deep convolutional networks. This approach underwent rigorous evaluation on the Multimodal Brain Tumor Image Segmentation BraTS-19 dataset a widely recognized medical image analysis dataset featuring multimodal MRI scans of brain tumors, including glioblastoma, anaplastic astrocytoma, and lower-grade glioma, coupled with corresponding manual tumor segmentations. …”
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Deep learning-based multi-criteria recommender system for technology-enhanced learning by Latifat Salau, Hamada Mohamed, Yunusa Simpa Abdulsalam, Hassan Mohammed

“…The model captures both low-order feature interactions using factorization machines and high-order dependencies through deep neural networks, enabling more adaptive recommendations. …”
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