Machine learning opportunities to predict obstetric haemorrhages by Yu. S. Boldina, A. A. Ivshin

“…Early preventive measures based on the OH prediction allow to profoundly reduce the rate of female mortality and morbidity as well as prevent the economic costs of patient intensive care, blood transfusion, surgical treatment and long-term hospitalization. …”
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Development of Ai-Based Crop Quality Grading Systems using Image Recognition by Dusi Prerna, Sharma Pooja

“…It also integrate Convolutional Neural Networks (CNN), Transfer Learning, Support Vector Machines (SVM) and Random Forest algorithms to label crop images into pre defined categories. …”
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Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…Fault diagnostics in internal combustion engines (ICEs) is vital for optimal operation and avoiding costly breakdowns. This paper reviews methodologies for ICE fault detection, including model-based and data-driven approaches. …”
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Sleep Posture Recognition Method Based on Sparse Body Pressure Features by Changyun Li, Guoxin Ren, Zhibing Wang

“…Conventional methods depend on a costly professional medical apparatus that is challenging to adapt for domestic use. …”
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Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks by Favour Ibude, Abayomi Otebolaku, Jude E. Ameh, Augustine Ikpehai

“…In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. …”
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Predicting and Mitigating Delays in Cross-Dock Operations: A Data-Driven Approach by Amna Altaf, Adeel Mehmood, Adnen El Amraoui, François Delmotte, Christophe Lecoutre

“…This paper investigates the effectiveness of deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLPs), and Recurrent Neural Networks (RNNs), in predicting late arrivals of trucks. …”
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TinyML and IoT-enabled system for automated chicken egg quality analysis and monitoring by Omoy Kombe Hélène, Martin Kuradusenge, Louis Sibomana, Ipyana Issah Mwaisekwa

“…The web-based program provides real-time feedback on egg quality, utilizing a Convolutional Neural Network (CNN) algorithm. Our system, implemented on Arduino Nano 33 BLE Sense, demonstrated remarkable performance with a TinyML classification F1-Score of 97.4 % and an accuracy rate of 95.79 %, paving the way for a more precise and efficient method of egg quality monitoring. …”
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Pellet Roasting Management System Based on Deep Learning and Internet of Things by Weixing Liu, Liyan Zhang, Jiahao Wang, Yiming Yang, Jie Li, Zhijie Zhang

“…According to the working principle and technological characteristics of the grate-rotary kiln at all stages, this paper designs the management system of firing pellets based on convolutional neural network (CNN) and IoT technology, so as to realize automatic recognition of image data obtained by the perceptual layer and make an intelligent analysis of it. …”
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A secured accreditation and equivalency certification using Merkle mountain range and transformer based deep learning model for the education ecosystem by Sumathy Krishnan, Surendran Rajendran, Mohammad Zakariah

“…TCRN employs Bi-GRU to retain long-term academic trends, Depth-wise separable convolutions (DSC) to concentrate on course-specific information, and BERT to capture global semantic context. …”
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A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke by Gayatri Thakre, Rohini Raut, Chetan Puri, Prateek Verma

“…This research investigates the use of hybrid deep learning models, such as recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs), to improve stroke prediction accuracy. …”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…Reinforcement learning models optimize power distribution by learning from historical patterns and adapting to changes in energy usage in real time. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate detailed analysis of spatial and temporal data to better predict energy usage. …”
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DNS over HTTPS Tunneling Detection System Based on Selected Features via Ant Colony Optimization by Hardi Sabah Talabani, Zrar Khalid Abdul, Hardi Mohammed Mohammed Saleh

“…Ant Colony Optimization (ACO) is integrated with machine learning algorithms such as XGBoost, K-Nearest Neighbors (KNN), Random Forest (RF), and Convolutional Neural Networks (CNNs) using CIRA-CIC-DoHBrw-2020 as the benchmark dataset. …”
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Enhanced Mamba model with multi-head attention mechanism and learnable scaling parameters for remaining useful life prediction by Fugang Liu, Shenyang Liu, Yuan Chai, Yongtao Zhu

“…Secondly, a learnable scaling parameter is introduced into the Residual block to adjust the output, and a multi-head attention mechanism is innovatively integrated into the Mamba block to operate on the data processed by the convolutional layer, thereby enhancing the expressiveness and accuracy of the model. …”
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Application of Artificial Intelligence in Prosthodontics in the 21st century by Lavanya V, Keerthivasan MS, Venkatakrishnan CJ, Tamizhesai BV, Anandh V

“…In removable prosthodontics convolutional neural networks CNNs have enabled accurate classification of partially edentulous arches and prediction of facial aesthetics. …”
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Harnessing Spatial-Frequency Information for Enhanced Image Restoration by Cheol-Hoon Park, Hyun-Duck Choi, Myo-Taeg Lim

“…We utilize a multi-branch/domain module that integrates latent features from the frequency and spatial domains using the discrete Fourier transform (DFT) and complex convolutional neural networks. Furthermore, we introduce a multi-scale pooling attention mechanism that employs average pooling along the row and column axes. …”
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Two-stage Mamba-based diffusion model for image restoration by Lei Liu, Luan Ma, Shuai Wang, Jun Wang, Silas N. Melo

“…DFNN regulates the information flow, enabling each depthwise convolutional layer to focus on the details of image, thus learning more effective local structures for image restoration. …”
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Investigation of deep learning approaches for automated damage diagnostics in fiber metal laminates using Detectron2 and SAM by Sanjeev Kumar, Stefan Bosse, Chirag Shah

“…Accurate detection, segmentation, and characterization of these damages are crucial for improved safety and reduced maintenance costs. This study proposes an automated approach to detect, segment, reconstruct, and characterize the damages in FML plates using state-of-the-art deep learning models: the Segment Anything Model (SAM) and the Mask Region-based Convolutional Neural Network (Mask R-CNN) implemented by the Detectron2 framework. …”
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AHN-YOLO: A Lightweight Tomato Detection Method for Dense Small-Sized Features Based on YOLO Architecture by Wenhui Zhang, Feng Jiang

“…Convolutional neural networks (CNNs) are increasingly applied in crop disease identification, yet most existing techniques are optimized solely for laboratory environments. …”
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Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees by Cheng Shen, Yuewei Liu

“…Traditional defect detection methods predominantly rely on manual inspection, which suffers from low efficiency and high costs. Some machine learning algorithms and artificial intelligence models for defect detection, such as Convolutional Neural Networks (CNNs), present outstanding performance, but they are often data-dependent and cannot provide guarantees for new test samples. …”
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Study on lightweight strategies for L-YOLO algorithm in road object detection by Ji Hong, Kuntao Ye, Shubin Qiu

“…However, traditional algorithms often suffer from large parameter sizes and high computational costs, limiting their applicability in resource-constrained environments. …”
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