Penerapan Deep Convolutional Generative Adversarial Network Untuk Menciptakan Data Sintesis Perilaku Pengemudi Dalam Berkendara by Michael Stephen Lui, Fitra Abdurrachman Bachtiar, Novanto Yudistira

“…DCGAN terdiri dari dua neural network bernama generator dan discriminator yang membentuk hubungan ­zero-sum game. …”
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Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin by Marco Bianchini, Mohamed Tarhouni, Matteo Francioni, Marco Fiorentini, Chiara Rivosecchi, Jamila Msadek, Abderrazak Tlili, Farah Chouikhi, Marina Allegrezza, Giulio Tesei, Paola Antonia Deligios, Roberto Orsini, Luigi Ledda, Maria Karatassiou, Athanasios Ragkos, Paride D'Ottavio

“…A set of 33 environmental variables (topography, soil, and bioclimatic) was screened using Pearson correlation matrices, and predictive models were built using four algorithms: MaxEnt, Random Forest, XG Boost, and Neural Network. Results revealed increasing temperatures and declining precipitation in both regions, confirming Mediterranean climate trends. …”
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Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grassland by Wuhua Wang, Jiakui Tang, Na Zhang, Xuefeng Xu, Anan Zhang, Yanjiao Wang, Kaihui Li, Yidan Wang

“…This study utilized machine learning models such as Random Forest (RF), AdaBoost, BP-Neural Network (BPNN), and Stacking Ensemble, combining them with topographic and meteorological data, MODIS reflectance data, and grassland height measurement data. …”
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Identification and preliminary validation of biomarkers associated with mitochondrial and programmed cell death in pre-eclampsia by Rong Lin, Rong Lin, XiaoYing Weng, XiaoYing Weng, Liang Lin, Liang Lin, XuYang Hu, XuYang Hu, ZhiYan Liu, ZhiYan Liu, Jing Zheng, Jing Zheng, FenFang Shen, FenFang Shen, Rui Li, Rui Li

“…Their performance was assessed through nomogram and artificial neural network models. Biomarkers were subjected to localization, functional annotation, regulatory network analysis, and drug prediction. …”
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Jaringan Syaraf Tiruan Perambatan Balik untuk Klasifikasi Covid-19 Berbasis Tekstur Menggunakan Orde Pertama Berdasarkan Citra Chest X-Ray by Muchtar Ali Setyo Yudono, Eki Ahmad Zaki Hamidi, Jumadi Jumadi, Abdul Haris Kuspranoto, Aryo De Wibowo Muhammad Sidik

“…The feature extraction used is based on the first-order texture, and the classification used is a backpropagation neural network. The classification system in this study resulted in an average classification accuracy of 94.17% for the normal class and 77.5% for Covid -19. …”
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Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning by Taojing Ran, Wei Huang, Xianzheng Qin, Xingran Xie, Yingjiao Deng, Yundi Pan, Yao Zhang, Ling Zhang, Lili Gao, Minmin Zhang, Dong Wang, Yan Wang, Qingli Li, Chunhua Zhou, Duowu Zou

“…This study developed a method that combines hyperspectral imaging (HSI) technology and a convolutional neural network (CNN) to conduct a cytological diagnosis of PanNETs. …”
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Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance by Younes Sadat-Nejad, Younes Sadat-Nejad, Marlee M. Vandewouw, Marlee M. Vandewouw, R. Cardy, J. Lerch, J. Lerch, J. Lerch, M. J. Taylor, M. J. Taylor, A. Iaboni, C. Hammill, B. Syed, J. A. Brian, J. A. Brian, E. Kelley, E. Kelley, E. Kelley, M. Ayub, J. Crosbie, J. Crosbie, R. Schachar, R. Schachar, S. Georgiades, R. Nicolson, E. Anagnostou, E. Anagnostou, A. Kushki, A. Kushki

“…We integrated cortical thickness, surface area, and cortical/subcortical volume, with a measure of single-participant structural covariance using a graph neural network approach.ResultsOur findings suggest two large clusters, which differed in measures of adaptive functioning (χ2 = 7.8, P = 0.004), inattention (χ2 = 11.169, P < 0.001), hyperactivity (χ2 = 18.44, P < 0.001), IQ (χ2 = 9.24, P = 0.002), age (χ2 = 70.87, P < 0.001), and sex (χ2 = 105.6, P < 0.001).DiscussionThese clusters did not align with existing diagnostic labels, suggesting that brain structure is more likely to be associated with differences in adaptive functioning, IQ, and ADHD features.…”
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Loss of MEF2C function by enhancer mutation leads to neuronal mitochondria dysfunction and motor deficits in mice by Ali Yousefian-Jazi, Suhyun Kim, Jiyeon Chu, Seung-Hye Choi, Phuong Thi Thanh Nguyen, Uiyeol Park, Min-gyeong Kim, Hongik Hwang, Kyungeun Lee, Yeyun Kim, Seung Jae Hyeon, Hyewhon Rhim, Hannah L. Ryu, Grewo Lim, Thor D. Stein, Kayeong Lim, Hoon Ryu, Junghee Lee

“…Methods Convolutional neural network was used to identify an ALS-associated SNP located in the intronic region of MEF2C (rs304152), residing in a putative enhancer element. …”
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Inflammation biomarkers and neurobehavioral performance in rural adolescentsKey PointsRelevance by Beemnet Amdemicael, Kun Yang, Briana N.C. Chronister, Caroline Mackey, Xin Tu, Sheila Gahagan, Danilo Martinez, Harvey Checkoway, David R. Jacobs, Jr., Jose Suarez-Torres, Suzi Hong, Jose R. Suarez-Lopez

“…Cytokines can alter neural network activity to induce neurocognitive changes. …”
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Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis by Xiao-Lei Li, Aibibanmu Aizezi, Yan-Peng Li, Yan-Hong Li, Fen Liu, Qian Zhao, Xiang Ma, Dilare Adi, Yi-Tong Ma

“…The machine learning models based on the seven metabolites all had good accuracy in distinguishing DCM [All area under the curve (AUC) > 0.900], and the artificial neural network (ANN) model performed the most consistently (AUC: 0.919 ± 0.075). …”
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Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder by Yilu Zhao, Zhao Fu, Eric J. Barnett, Ning Wang, Kangfuxi Zhang, Xuping Gao, Xiangyu Zheng, Junbin Tian, Hui Zhang, XueTong Ding, Shaoxian Li, Shuyu Li, Qingjiu Cao, Suhua Chang, Yufeng Wang, Stephen V. Faraone, Li Yang

“…The convolutional neural network (CNN) model, using variants with genome-wide P values less than E-02 (5516 SNPs), demonstrated the best performance with mean squared error (MSE) equals 0.012 (Accuracy = 0.83; Sensitivity = 0.90; Specificity = 0.75) in the validation dataset, 0.081 in an independent test dataset (Acc = 0.61, Sensitivity = 0.81; Specificity = 0.26). …”
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Predicting Insemination Outcome in Holstein Dairy Cattle using Deep Learning by Mohammad Alishahi, Mahdi Ravakhah

“…In the problem of predicting the results of artificial insemination of livestock, the presented LSTM neural network model shows the best performance based on the stated evaluation criteria, and then the XGBoost-based classifier has better performance than MLP.…”
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Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective coho... by Stefanie Aeschbacher, David Conen, Diederick E Grobbee, Raphael Twerenbold, Thomas Lung, Theo Rispens, Jakob Kjellberg, Lorenz Risch, Martin Risch, Marianna Mitratza, Harald Renz, Spiros Denaxas, Billy Franks, Diederick Grobbee, Martina Rothenbühler, Janneke Wijgert, Santiago Montes, Richard Dobson, Hans Reitsma, Christian Simon, Titia Leurink, Charisma Hehakaya, Patricia Bruijning, Kirsten Grossmann, Ornella C Weideli, Marc Kovac, Fiona Pereira, Nadia Wohlwend, Corina Risch, Dorothea Hillmann, Daniel Leibovitz, Vladimir Kovacevic, Andjela Markovic, Paul Klaver, Timo B Brakenhoff, George S Downward, Ariel Dowling, Maureen Cronin, Brianna M Goodale, Brianna Goodale, Ornella Weideli, Regien Stokman, Hans Van Dijk, Eric Houtman, Jon Bouwman, Kay Hage, Lotte Smets, Marcel van Willigen, Maui Chodura, Niki de Vink, Tessa Heikamp, Timo Brakenhoff, Wendy van Scherpenzeel, Wout Aarts, Alison Kuchta, Antonella Chiucchiuini, Steve Emby, Annemarijn Douwes, George Downward, Nathalie Vigot, Pieter Stolk, Duco Veen, Daniel Oberski, Amos Folarin, Pablo Fernandez Medina, Eskild Fredslund

“…The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO.Conclusion Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. …”
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Adverse events in the nervous system associated with blinatumomab: a real-world study by Wen Gao, Jingwei Yu, Yifei Sun, Zheng Song, Xia Liu, Xue Han, Lanfan Li, Lihua Qiu, Shiyong Zhou, Zhengzi Qian, Xianhuo Wang, Huilai Zhang

“…The reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence interval progressive neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) algorithms were utilized for data mining. …”
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AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection by Zhiyang Chen, Tingting Xie, Shuting Chen, Zhenhui Li, Su Yao, Xuanjun Lu, Wenfeng He, Chao Tang, Dacheng Yang, Shaohua Li, Feng Shi, Huan Lin, Zipei Li, Anant Madabhushi, Xiangtian Zhao, Zaiyi Liu, Cheng Lu

“…We trained a deep neural network and a random forest model to segment tumor regions and locate CD8+ TILs in H&E and CD8-stained whole-slide images. …”
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Data-Driven Bearing Fault Diagnosis for Induction Motor by Aqib Raqeeb, Fahim Shah, Zaheer Alam, Subhashree Choudhury, Bilal Khan, R. Palanisamy

“…Our convolutional neural networks-based approach is compared to traditional methods such as support vector machine, nearest neighbors, and artificial neural networks. …”
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Asymmetric representation of symmetric semantic information in the human brain by Jiaxin Wang, Kiichi Kawahata, Antoine Blanc, Naoya Maeda, Shinji Nishimoto, Satoshi Nishida

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Are conventional methods sufficient to calculate growth parameters of Pontastacus leptodactylus (Eschscholtz, 1823)? A case study of artificial intelligence from Keban Dam Lake by Benzer Semra, Benzer Recep

“…These measurements were analyzed using both the conventional length–weight relationship method and artificial neural networks. The results obtained using artificial neural networks and conventional methods were compared, and the analysis was based on MAPE and R2 performance criteria. …”
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Artificial intelligence in the service of entrepreneurial finance: knowledge structure and the foundational algorithmic paradigm by Robert Kudelić, Tamara Šmaguc, Sherry Robinson

“…The results demonstrate a high representation of artificial neural networks, deep neural networks, and support vector machines across almost all identified topic niches. …”
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The Relationship Between Traffic Flow Forecasting and Traffic Accident Forecasting and the Possible Combination Points by Wu Haoyu

“…This paper emphasizes the importance of neural networks for traffic flow forecasting and traffic accident forecasting and introduces the commonly used neural networks. …”
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