African buffalo algorithm: Training the probabilistic neural network to solve classification problems by Mohammed Alweshah, Lobna Rababa, Mohammed Hashem Ryalat, Ammar Al Momani, Mohamed F. Ababneh

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Classification of Intraoral Photographs with Deep Learning Algorithms Trained According to Cephalometric Measurements by Sultan Büşra Ay Kartbak, Mehmet Birol Özel, Duygu Nur Cesur Kocakaya, Muhammet Çakmak, Enver Alper Sinanoğlu

“…This study aimed to evaluate deep learning algorithms trained utilizing actual cephalometric measurements for the classification of intraoral clinical photographs. …”
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Retrospective Clinical Trial to Evaluate the Effectiveness of a New Tanner–Whitehouse-Based Bone Age Assessment Algorithm Trained with a Deep Neural Network System by Meesun Lee, Young-Hun Choi, Seul-Bi Lee, Jae-Won Choi, Seunghyun Lee, Jae-Yeon Hwang, Jung-Eun Cheon, SungHyuk Hong, Jeonghoon Kim, Yeon-Jin Cho

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Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer by Kaimin Hu, Yinan Wu, Yajing Huang, Meiqi Zhou, Yanyan Wang, Xingru Huang

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Quantifying the impact of precision errors on quantum approximate optimization algorithms by Gregory Quiroz, Paraj Titum, Phillip Lotshaw, Pavel Lougovski, Kevin Schultz, Eugene Dumitrescu, Itay Hen

“…Here, we examine the effect of analog precision errors on QAOA performance from the perspective of both algorithmic training and performance guarantees. Leveraging cumulant expansions, we recast the faulty QAOA as a control problem in which precision errors are expressed as multiplicative control noise and derive bounds on the performance of QAOA. …”
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Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review by Jade Basem, Racheed Mani, Scott Sun, Kevin Gilotra, Neda Dianati-Maleki, Reza Dashti

“…While many algorithms still require a larger knowledge base or manual algorithmic training, AI/ML in neurocardiology has the potential to provide more comprehensive healthcare treatment, increase access to equitable healthcare, and improve patient outcomes. …”
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An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images by Zhong-Yan Ma, Hai-lin Zhang, Fa-jin Lv, Wei Zhao, Dan Han, Li-chang Lei, Qin Song, Wei-wei Jing, Hui Duan, Shao-Lei Kang

“…Conclusion The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. …”
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A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups by A. Jayanth Balaji, Binoy B. Nair, D. S. Harish Ram, Kuruvachan Kalluvelil George

“…Eight deep learning algorithms trained using eleven mini-batch combinations, for two forecasting horizons resulted in 176 workflows. …”
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System and application of video surrveillance based on edge computing by Sanming PAN, Mingqiang YUAN

“…With the popularization of video surveillance systems and the diversification of their applications,traditional data processing methods using cloud computing to handle all the computations are more and more unsustainable.Edge computing pre-processes video data nearby,and performs well in terms of bandwidth,storage,and latency,but edge-cloud collaboration is still required to improve overall performance.The architecture of edge-cloud collaborative video surveillance system was proposed,then an open edge node design scheme and algorithm training and inferencing ideas were proposed.Finally,taking forest fire prevention and smart poles ＆amp; towers as examples,the application realization and value of edge computing in video surveillance services were illustrated.…”
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Integrating Machine Learning and Educational Robotics by Charles Soares Pimentel, Fábio Ferrentini Sampaio

“…The scripted activities focused on the mathematical calculations involved in the algorithm's training and classification phases, enhancing students' understanding of machine learning (ML). …”
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Image Defogging Algorithm Based on Sparse Representation by Di Fan, Xinyun Guo, Xiao Lu, Xiaoxin Liu, Bo Sun

“…Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. …”
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Domain Adaptation in Application to Gravitational Lens Finding by Hanna Parul, Sergei Gleyzer, Pranath Reddy, Michael W. Toomey

“…In this work, we assess the performance of three domain adaptation (DA) techniques—adversarial discriminative DA, Wasserstein distance guided representation learning (WDGRL), and supervised domain adaptation (SDA)—in enhancing lens-finding algorithms trained on simulated data when applied to observations from the Hyper Suprime-Cam Subaru Strategic Program. …”
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Clays Are Not Created Equal: How Clay Mineral Type Affects Soil Parameterization by P. Lehmann, B. Leshchinsky, S. Gupta, B. B. Mirus, S. Bickel, N. Lu, D. Or

“…Clay mineral‐informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes.…”
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A Wi-Fi RSS-RTT Indoor Positioning Model Based on Dynamic Model Switching Algorithm by Xu Feng, Khuong An Nguyen, Zhiyuan Luo

“…Our algorithm employs a machine learning weighted model selection algorithm trained on raw Wi-Fi received signal strength (RSS), raw Wi-Fi round-trip time (RTT) data, statistical RSS and RTT measures, and access point line-of-sight information. …”
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Simulation of automatic intrusion detection in university networks by using neural network algorithms by Houdun Xu

“…Using appropriate training methods and optimization algorithms, train and optimize the neural network model to achieve high accuracy and robustness. …”
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Using machine learning algorithms (supervised) to generate automatically labeled dataset for detecting digital dating abuse from text messages by Tania Roy, Thomas Maranzatto, Zachary Loomas

“…This poster explores using machine learning algorithms trained on human-annotated datasets to label more extensive crowd-sourced datasets and generate a larger training dataset for abuse detection algorithms. …”
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Water Segmentation from SAR Images with the Presence of Speckle Noise by R. Larionov, A. Sennikov, V. Khryashchev

“…U-ResNet-34, SegFormer_b5 and SegNeXt_l neural networks are used as segmentation algorithms. Training using balanced batch and augmentation invariance was used to improve the quality of the algorithms and the highest Dice value was equal 0.90. …”
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A novel indoor localization method using passive phase difference fingerprinting based on channel state information by Xiaochao Dang, Jiaju Ren, Zhanjun Hao, Yili Hei, Xuhao Tang, Yan Yan

“…During the online phase, the algorithm trains a back-propagation neural network using the fingerprint data and determines the modelled mapping relationship between the fingerprint data and the physical localization after carrying out the phase difference correction and the principal component analysis–based dimensionality reduction. …”
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Design a PID Controller for Suspension System by Back Propagation Neural Network by M. Heidari, H. Homaei

“…The best results were obtained by the BPN by Levenberg-Marquardt algorithm training with 10 neurons in the one hidden layer. …”
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A selective approach to human age estimation: a case study of cranial suture closure methods by Shirobokov I.G.

“…This study analyzes a documented series of 130 skulls using the Meindl-Lovejoy method to compare of several age estimation algorithms trained on reference samples with varying demographic profiles. …”
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