An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection by Elsa Pansilvania Andre Manjate, Natsuo Okada, Yoko Ohtomo, Tsuyoshi Adachi, Bernardo Miguel Bene, Takahiko Arima, Youhei Kawamura

“…The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. …”
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The Effect of Transitive Closure on the Calibration of Logistic Regression for Entity Resolution by Yumeng Ye, John Talburt

“…From these experiments the authors concluded that when a supervised ML algorithm is trained to classify a pair of entity references as linked or not linked pair, the evaluation of the model’s performance should take into account the transitive closure of its pairwise linking decisions, not just the pairwise classifications alone. …”
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AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in context by Juan Chen, Jiahui Yao, Wei Chen, Feng Zhang, Heyuan Wang, Xiaoying Xu, Huan Ge, Hongmei Zhou, Jin Cen, Dan Li, Bengui Jiang, Li He, Tingting Fu, Zhengxian Xu, Lei Chu, Shuxia Zhang, Dongmei Yao, Linyi Wei, Liu Huang, Anjing Ge, Cuiping Jin, Zimu Fu, Qin Liu, Xuefeng Yu, Chengmao Zhao, Tengjiao Wang, Lan Zhu

“…Summary: Background: Pelvic floor surface electromyography (sEMG) is widely used to evaluate and treat pelvic floor dysfunctions (PFDs). …”
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Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population by Xia Zhong, Tianen Zhao, Shimeng Lv, Guangheng Zhang, Jing Li, Donghai Liu, Huachen Jiao

“…The patients were separated into a training group (70%) and a validation group (30%). …”
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Postauthorization safety study of the DPP-4 inhibitor saxagliptin: a large-scale multinational family of cohort studies of five outcomes by Qing Liu, Sean Hennessy, Qufei Wu, K Rajender Reddy, Arlene M Gallagher, Kevin Haynes, David J Margolis, Brian L Strom, Vincent Lo Re, M Elle Saine, Craig W Newcomb, Jason A Roy, Serena Cardillo, Stephen E Kimmel, Peter P Reese, Andrea J Apter, Harshvinder Bhullar, Daina B Esposito, Dean M Carbonari

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An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods by Muhammad Junaid Ibrahim, Jaweria Kainat, Hussain AlSalman, Syed Sajid Ullah, Suheer Al-Hadhrami, Saddam Hussain

“…Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. …”
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IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deception by Joseph Bao, Murat Kantaciourglu, Yevgeniy Vorobeychik, Charles Kamhoua

“…A key technical challenge that our approach overcomes is scarcity of device-specific IoT traffic data to effectively train a generator. We address this challenge by leveraging a core generative adversarial learning algorithm for sequences along with domain specific knowledge common to IoT devices. …”
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Detection of sugar beet seed coating defects via deep learning by Abdullah Beyaz, Zülfi Saripinar

“…Using the YOLO algorithm, it is possible to detect and categorize coating defects on sugar beet seeds, thereby enhancing seed quality and production swiftly, and effectively. …”
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Comment on “Odontogenic Tumors: A Challenge for Clinical Diagnosis and an Opportunity for AI Innovation” by Hinpetch Daungsupawong, Viroj Wiwanitkit

“…By incorporating advanced imaging techniques and machine learning algorithms, researchers can improve the accuracy and efficiency of diagnostic processes. …”
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Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations by Yifan Zhu, Fei Guo, Xiaohong Zhang, Wentao Yang

“…Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. …”
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Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions by Qingchun Guo, Zhenfang He, Shanshan Li, Xinzhou Li, Jingjing Meng, Zhanfang Hou, Jiazhen Liu, Yongjin Chen

“…When Bayesian regularization was applied as a training algorithm, the WANN and ANN models accurately reproduced the APIs in both Xi’an and Lanzhou, although the WANN model (R = 0.8846 for Xi’an and R = 0.8906 for Lanzhou) performed better than the ANN (R = 0.8037 for Xi’an and R = 0.7742 for Lanzhou) during the forecasting stage. …”
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Machine learning model for prediction of palliative care phases in patients with advanced cancer: a retrospective study by Junchen Guo, Yunyun Dai, Sishan Jiang, Junqingzhao Liu, Xianghua Xu, Yongyi Chen

“…The Gradient Boosting Decision Tree in the machine learning algorithm to establish a palliative care phase prediction model and evaluated the prediction performance of this model. …”
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Intratumoral Heterogeneity Scores as Predictors of Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules: Insights from Explainable Machine Learning-Based Ter... by Wang Peng BS, Wanyin Qi BS, Yunhua Li BS, Sanhong Zhang BS, Juan Long BS

“…Therefore, this study aimed to develop ternary classification models to classify AIS, MIA, and IAC by leveraging insights from 15 machine-learning algorithms and integrating ITH scores with clinical data. …”
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Identification of immune and major depressive disorder-related diagnostic markers for early nonalcoholic fatty liver disease by WGCNA and machine learning by Yuyun Jia, Yanping Cao, Qin Yin, Xueqian Li, Xiu Wen

“…A predictive model for SS/NASH was developed by evaluating nine machine-learning algorithms with 10-fold cross-validation on the datasets.ResultsFourteen genes strongly linked to both the immune system and the two conditions were identified. …”
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Real-Time Common Rust Maize Leaf Disease Severity Identification and Pesticide Dose Recommendation Using Deep Neural Network by Zemzem Mohammed Megersa, Abebe Belay Adege, Faizur Rashid

“…We used a convolutional neural network (CNN), specifically the ResNet50 model, for this purpose. To evaluate its performance, ResNet50 was compared with other state-of-the-art algorithms, including VGG19, VGG16, and AlexNet, using similar parameters. …”
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Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning by Jiaze Wu, Hao Liang, Changsong Ding, Xindi Huang, Jianhua Huang, Qinghua Peng

“…All of them are fed into different convolutional neural networks (CNN) for training and validation. The receiver-operating characteristic and loss and accuracy curves were used to evaluate and compare the performance of different methods. …”
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Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images by Anand Kumar Gupta, Asadi Srinivasulu, Kamal Kant Hiran, Goddindla Sreenivasulu, Sivaram Rajeyyagari, Madhusudhana Subramanyam

“…The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. …”
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Region search based on hybrid convolutional neural network in optical remote sensing images by Shoulin Yin, Ye Zhang, Shahid Karim

“…The proposed algorithm is evaluated on optical remote sensing images acquired from Google Earth. …”
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Subjective Air Traffic Complexity Analysis Based on Weak Supervised Learning by Weining ZHANG, Weijun PAN, Changqi YANG, Xinping ZHU, Jianan YIN, Jinghan DU

“…Controller subjective evaluation is one of the most important ways to assess air traffic complexity. …”
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Human Action Recognition Method Based on Multi-channel Fusion by Zhiyong TAO, Xijun GUO, Xiaokui REN, Ying LIU, Zemin WANG

“…In the public dataset evaluation, the proposed action recognition model achieves an exceptional accuracy of 98.72% in identifying seven distinct human actions, surpassing the recognition performance of traditional models. …”
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