Metric learning unveiling disparities: A novel approach to recognize false trigger images in wildlife monitoring by Rui Zhu, Enting Zhao, Chunhe Hu, Jiangjian Xie, Junguo Zhang, Huijian Hu

“…This study proposes a metric learning-based method for false-triggered image recognition. …”
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A Deep-learning based Model with Intra- and Inter-Well Constraints for Intelligent Identification of Stratigraphic Layers by Jinghua Yang, Bin Gong, Hu Huang, Heng Zhao, Haoqiang Wu, Chen Liu, Shifan Zhang, Hui Li

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Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning by Mohammed Alghamdi, Hanan Abdullah Mengash, Mohammed Aljebreen, Mohammed Maray, Abdulbasit A. Darem, Ahmed S. Salama

“…The comprehension of automatic product identification becomes of major importance for both social and economic improvement since it is more reliable and time-consuming than manual function. Product detection through images is a complex task in the domain of CV. …”
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Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain by Shangmin Chen, Yongshan Gao, Lin Du, Mengzhen Min, Lei Xie, Liping Li, Xiaodong Chen, Zhigang Zhong

“…Unique features for the pain model were functional limitation, SPPB, WBC, chronic disease score, life satisfaction, platelets, cooking fuel, and pain quantity, while marital status, age, depressive symptoms, cognitive function, hearing, rainy days, tidiness, and sleep duration were exclusive to the non-pain model. …”
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Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm by Ayuba John, Ismail Fauzi Bin Isnin, Syed Hamid Hussain Madni, Farkhana Binti Muchtar

“…First, PCA is combined with the AdaBoost ensemble machine learning algorithm, which acts as stagewise additive modelling to compensate for PCA's deficiency in feature selection in network traffic by minimizing the exponential loss function. …”
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Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning by Yuanxi Han, Liang Li, Siyuan Jiang, Pengpeng Sun, Wenliang Wu, Zhendong Liu

“…Additionally, the artificial neural network with the hyperbolic tangent activation function had optimal training performance, exceeding 90.00 % accuracy. …”
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Machine learning-based prediction of pervaporation permeation using physicochemical properties of permeant-membrane and process conditions by Muhammad Mujiburohman, Marwen Elkamel, Farzad Hourfar, Ali Elkamel

“…This study proposes a machine learning approach in terms of artificial neural network (ANN) to predict the permeation flux of PV, as a function of physicochemical properties of permeant-membrane material and process conditions. …”
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A Dual-Source Deep Learning Model for Mapping Wet Anabranches in Braided Rivers on the Tibetan Plateau by Xiaolu Liu, Xiaoyi Ma, Shuai Qin, Tang Liu, Chenghu Zhou

“…Additionally, a multicomposite loss function was designed to enhance the model's performance. …”
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Disorders of the central nervous system cognitive and mnestic functions in genetic arterial hypertension and with the use of beta-blockers of different generations by A. O. Svitlytskyi, A. A. Yehorov, O. V. Melnikova, T. A. Hrekova

“…To comparatively analyze the effect of β-blockers from different generations on cognitive and memory functional parameters of the central nervous system in rats with genetically determined hypertension. …”
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Machine learning-based characterization of stemness features and construction of a stemness subtype classifier for bladder cancer by Heping Qiu, Xiaolin Deng, Jing Zha, Lihua Wu, Haonan Liu, Yichen Lu, Xinji Zhang

“…The function of the classifier gene was validated through experiments. …”
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Cross-Domain Transfer Learning Architecture for Microcalcification Cluster Detection Using the MEXBreast Multiresolution Mammography Dataset by Ricardo Salvador Luna Lozoya, Humberto de Jesús Ochoa Domínguez, Juan Humberto Sossa Azuela, Vianey Guadalupe Cruz Sánchez, Osslan Osiris Vergara Villegas, Karina Núñez Barragán

“…We applied transfer learning (TL) and trained on 50, 70, and 100 <inline-formula><math display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>m resolution patches from the MEXBreast database, achieving accuracies of 98.32%, 99.27%, and 89.17%, respectively. …”
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Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

“…The intersection of DEGs and ARGs was analyzed for Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. …”
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Optimizing HVAC&R System Efficiency and Comfort Levels Using Machine Learning-Based Control Methods by Suroor M. Dawood, Raad Z. Homod, Alireza Hatami

“…The HVAC&R system control trouble is defined as a specific Markov Decision Processes (MDPs) model. The reward function balances the ability to increase energy conservation while preserving the interior comfort requirements of occupants. …”
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Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis by Xingren Liu, Junmei Song, Shujin Guo, Yi Liao, Jun Zou, Liqing Yang, Caiyu Jiang

“…Results Two distinct clusters were identified, showing significant differences in lung function parameters and fibrosis-related gene expression. …”
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Identification of Crucial Genes Associated With MYCN‐Driven Neuroblastoma Based on Single‐Cell Analysis and Machine Learning by Jiasi Zhang, Yichen Lei, Yaqin Wang, Wen Yu, Xiaoyan Zhao, Yongbing Zhu, Dedong Zhang, Siying Liu, Aiguo Liu

“…Key genes were then identified using machine learning based random survival forest (RSF) and nomogram analyses. …”
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High Incidence of Burst Suppression during Propofol Sedation for Outpatient Colonoscopy: Lessons Learned from Neuromonitoring by Jamie Bloom, David Wyler, Marc C. Torjman, Tuan Trinh, Lucy Li, Amy Mehta, Evan Fitchett, David Kastenberg, Michael Mahla, Victor Romo

“…The Patient State Index (PSi™) generated by the Masimo SedLine® Brain Root Function monitor (Masimo Corp., Irvine, CA) was used to assess the depth of sedation. …”
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Deep Reinforcement Learning With Dueling DQN for Partial Computation Offloading and Resource Allocation in Mobile Edge Computing by Ehzaz Mustafa, Junaid Shuja, Faisal Rehman, Abdallah Namoun, Mazhar Ali, Abdullah Alourani

“…Furthermore, we implement a long short-term memory (LSTM) network with distinct advantage-value layers to enhance both short-term action selection and long-term system cost estimation, as well as improve the temporal learning capacity of the model. Finally, we propose an innovative adaptive cost-weighting mechanism within the reward function to dynamically balance competing objectives, including energy consumption, latency, and bandwidth utilization. …”
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Time Series Remote Sensing Image Classification with a Data-Driven Active Deep Learning Approach by Gaoliang Xie, Peng Liu, Zugang Chen, Lajiao Chen, Yan Ma, Lingjun Zhao

“…Then, we define a new loss function with the aim of improving the deep model’s performance. …”
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Role of the amygdala opioid system in the effects of stress on the post-learning sleep patterns of male Wistar rats by Mehdi Graily-Afra, Farideh Bahrami, Zahra Bahari, Hedayat Sahraei, Zeinab Shankayi, Ali Gharib

“…Through naloxone injection in the Baso Lateral Amygdala (BLA), this study intends to shed light on the interactions between stress, learning, and sleep, as well as the function of the opioid system and its impact on Brain-Derived Neurotrophic Factor (BDNF) production in the hippocampus.Materials and Methods: Male Wistar rats (n=77) in eleven groups were implanted with electroencephalogram (EEG) and electromyography (EMG) recording electrodes, and the BLA area was bilaterally cannulated. …”
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HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios by Houkui Zhou, Chang Chen, Zhongyi Xia, Qifeng Ding, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu, Tao He

“…With the rise of deep learning technology, intelligent garbage detection provides a new idea for garbage classification management. …”
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