Object Ontologies as a Priori Models for Logical-Probabilistic Machine Learning by D. N. Gavrilin, A.V. Mantsivoda

“…Logical-probabilistic machine learning (LPML) is an AI method able to explicitly work with a priori knowledge represented in data models. …”
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Precision Agriculture: Utilizing Machine Learning for Accurate Plant Disease Diagnosis by Divya Banti, Tandan Gajendra

“…Real time real data on crop conditions and stress factors is of great help to early detection of diseases through remote sensing. …”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…Machine learning algorithms were trained to classify hearing impairment thresholds and predict pure tone average values. …”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…In order to surpass these limitations, a new approach by using state-of-the-art machine learning algorithms such as Extreme Gradient Boosting (XGBoost) combined with Fuzzy Inference Systems (FIS) presents a new perspective towards improved accuracy and comprehensibility in fault detection. …”
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Promising methods of prenatal diagnostics based on passive sensors and machine learning by A. A. Ivshin, V. M. Vorobyova, N. A. Malyshev

“…It is noteworthy that technologies employing passive sensors for continuous and long-term monitoring of fetal vital signs, in conjunction with machine learning algorithms for data analysis and interpretation are of particular interest. …”
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Enhanced‐Resolution Learning‐Based Direction of Arrival Estimation by Programmable Metasurface by Nawel Meftah, Badreddine Ratni, Mohammed Nabil El Korso, Shah Nawaz Burokur

“…While traditional DOA estimation methods rely on antenna arrays and complex algorithms, recent progress achieved in the design and implementation of metasurfaces has proved their effectiveness as promising alternatives. …”
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Stain Normalization of Histopathological Images Based on Deep Learning: A Review by Chuanyun Xu, Yisha Sun, Yang Zhang, Tianqi Liu, Xiao Wang, Die Hu, Shuaiye Huang, Junjie Li, Fanghong Zhang, Gang Li

“…However, color variations caused by differences in tissue preparation and scanning devices can lead to data distribution discrepancies, adversely affecting the performance of downstream algorithms in tasks like classification, segmentation, and detection. …”
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Deep learning-based automated tool for diagnosing diabetic peripheral neuropathy by Qincheng Qiao, Juan Cao, Wen Xue, Jin Qian, Chuan Wang, Qi Pan, Bin Lu, Qian Xiong, Li Chen, Xinguo Hou

“…Methods This study is based on data from two independent clinical centers. Various popular deep learning (DL) models have been trained and evaluated for their performance in CCM image segmentation using DL-based image segmentation techniques. …”
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Rapid literature mapping on the recent use of machine learning for wildlife imagery by Nakagawa, Shinichi, Lagisz, Malgorzata, Francis, Roxane, Tam, Jessica, Li, Xun, Elphinstone, Andrew, Jordan, Neil R., O'Brien, Justine K., Pitcher, Benjamin J., Van Sluys, Monique, Sowmya, Arcot, Kingsford, Richard T.

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Advancing Particle Tracking: Self-Organizing Map Hyperparameter Study and Long Short-Term Memory-Based Outlier Detection by Max Klein, Niklas Dormagen, Lukas Wimmer, Markus H. Thoma, Mike Schwarz

“…However, when the experimental setup involves high-speed, high-density particles that are indistinguishable and follow complex or unknown flow fields, matching particles between images becomes significantly more challenging. Reliable PTV algorithms are crucial in such scenarios. Previous work has demonstrated that the Self-Organizing Map (SOM) machine learning approach offers superior outcomes on complex-plasma data compared with traditional methods, though its performance is sensitive to hyperparameter calibration, which requires optimization for specific flow scenarios. …”
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Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations by Tomasz Blachowicz, Sara Bysko, Szymon Bysko, Alina Domanowska, Jacek Wylezek, Zbigniew Sokol

“…Unlike contemporary machine learning techniques, TSM relies on a simple and interpretable algorithm designed to process data from standard industrial automation systems. …”
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Abnormal event detection based on local topology and l_1/2norm regularize by Qing YU, Ken CHEN, Meng LI, Fei LI

“…A new dictionary learning method was proposed by introducing a local topology term to describe structural information of video events and using the l_1/2norm as the sparsity constraint to the representation coefficients based on the traditional analysis dictionary learning method.In feature extraction,a histogram of interaction force(HOIF) containing rich motion information and a histogram of oriented gradient(HOG) containing texture information were merged.Then,the improved dictionary was used to train the feature data.Finally,the reconstruction error of the testing sample under the dictionary was used to determine whether the testing sample was an abnormal sample.Experiments on UMN show the high performance of the algorithm.Compared with the state-of-the-art algorithms,the analysis dictionary classification algorithm based on local topology and l_1/2norm has made more effective detection on the abnormal events in the crowd.…”
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Deep Learning-Based Pulmonary Nodule Screening: A Narrative Review by Abhishek Mahajan, Ujjwal Agarwal, Rajat Agrawal, Aditi Venkatesh, Shreya Shukla, K S. S. Bharadwaj, M L. V. Apparao, Vivek Pawar, Vivek Poonia

“…Artificial intelligence algorithms have recently demonstrated remarkable progress in medical imaging, especially with deep learning techniques such as convolutional neural networks (CNNs). …”
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Investigation into fatigue micro-crack identification of steel bridge decks based on acoustic emission detection technology. by Li Jiaqing, Song Fei, Xiao Zidong, Zhu Longji, Chen Lan, Wei Zheliang

“…To address the non-stationary nature of acoustic emission (AE) signals during crack initiation and propagation, this study combines the K-singular value decomposition (K-SVD) dictionary learning algorithm with convolutional neural networks (CNN) to enhance AE signal processing and fatigue crack detection. …”
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Efficient Detection of Mind Wandering During Reading Aloud Using Blinks, Pitch Frequency, and Reading Rate by Amir Rabinovitch, Eden Ben Baruch, Maor Siton, Nuphar Avital, Menahem Yeari, Dror Malka

“…These methods are often cumbersome, uncomfortable for participants, and invasive, requiring specialized, expensive equipment that disrupts the natural learning environment. To overcome these challenges, a new algorithm has been developed to detect mind wandering during reading aloud. …”
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Unveiling smart contract vulnerabilities: Toward profiling smart contract vulnerabilities using enhanced genetic algorithm and generating benchmark dataset by Sepideh HajiHosseinKhani, Arash Habibi Lashkari, Ali Mizani Oskui

“…Many of them, such as rule-based methods, machine learning techniques, and neural networks, also struggle to detect complex vulnerabilities due to limited data availability. …”
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Interactive Mitigation of Biases in Machine Learning Models for Undergraduate Student Admissions by Kelly Van Busum, Shiaofen Fang

“…Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of bias in AI systems used in education are nuanced and can affect university reputation and student retention rates motivating a need for the development of fair AI systems. (3) Methods and Dataset: Admissions data over six years from a large urban research university was used to create AI models to predict admissions decisions. …”
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ML Auditing and Reproducibility: Applying a Core Criteria Catalog to an Early Sepsis Onset Detection System by Markus Schwarz, Ludwig Christian Hinske, Ulrich Mansmann, Fady Albashiti

“…The AUC change of 1.45% indicates resilience of the self-attention deep learning model to input data manipulation. An algorithmic error is most likely responsible for the missing lead time to sepsis onset metric. …”
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Machine learning applications in the analysis of sedentary behavior and associated health risks by Ayat S Hammad, Ayat S Hammad, Ali Tajammul, Ismail Dergaa, Ismail Dergaa, Ismail Dergaa, Maha Al-Asmakh, Maha Al-Asmakh

“…The review highlights the utility of various ML approaches in classifying activity levels and significantly improving the prediction of sedentary behavior, offering a promising approach to address this widespread health issue.ConclusionML algorithms, including supervised and unsupervised models, show great potential in accurately detecting and predicting sedentary behavior. …”
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Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

“…Using a set of classical machine learning algorithms for predictive modelling (Random Forest, Gradient Boosting, XGBoost, Decision Tree, AdaBoost, Neural Networks, Extra Tree Classifier) and state of art methods such as sequential-attention based Tabent transfer learning, we explore the best performing models which are effective to predict the cognitive status of the subjects given certain clinical and other characteristics. …”
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