Photovoltaic Generation Prediction of CCIPCA Combined with LSTM by E. Zhu, D. Pi

“…The training speed and convergence speed of LSTM are improved by the dimension-reduced data. …”
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The ethics of AI at the intersection of transgender identity and neurodivergence by Max Parks

“…Drawing on evidence that transgender individuals exhibit higher rates of neurodivergence (Pasterski et al. in Arch Sex Behav 43:387–393, 2014) and on the historical pathologization of both identities (Conrad and Schneider in Deviance and medicalization: from badness to sickness, Temple University Press, Philadelphia, 1992), I focus on two domains, healthcare and language processing, to illustrate how choices in data collection, model training, and algorithm design can perpetuate harmful biases. …”
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On the effect of sampling frequency on the electricity theft detection performance by Fatemeh Soleimani Nasab, Foad Ghaderi

“…To investigate the effect of sampling frequency on the performance of detection methods, we designed a processing framework to evaluate different classification algorithms on versions of a challenging dataset obtained by down‐sampling the original data at various rates. …”
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Research on image generation technology based on deep learning by Li Jinchen

“…Based on the above problems, this paper analyzes the methods of improving and optimizing the mainstream image generation algorithm from the perspectives of improving and optimizing the loss function, improving the space modeling, revising the structure of both the generator and discriminator, while speeding up the training process. …”
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CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley Data by Aulia Arif Wardana, Grzegorz Kołaczek, Parman Sukarno

“…CoAt-Set is compatible with standard machine learning frameworks, offering researchers and practitioners a comprehensive resource for developing, testing, and evaluating CIDS models. It is suitable for various applications, including collective threat intelligence research, analyzing distributed threat patterns, developing machine learning algorithms for distributed systems, and training simulations designed for heterogeneous network environments.…”
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Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping by Oluwatobi Emmanuel Dare, Kennedy Okokpujie, Emmanuel Adetiba, Olabode Idowu-Bismark, Abdultaofeek Abayomi, Raymond Jules Kala, Emmanuel Owolabi, Udeme Christopher Ukpong

“…The model performance was evaluated using mean square error (MSE) and mean absolute error (MAE). 12 different experiments were carried out varying the training parameters of the CGAN architecture to obtain an optimal model. …”
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An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs) by Ran Ni, Yongjie Huang, Lei Wang, Hongjie Chen, Guorui Zhang, Yali Yu, Yinglan Kuang, Yuyan Tang, Xing Lu, Hong Liu

“…The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. …”
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Automated machine learning for predicting perioperative ischemia stroke in endovascularly treated ruptured intracranial aneurysm patients by Yuhang Peng, Ke Bi, Xiaolin Zhang, Ning Huang, Xiang Ji, Weifu Chen, Ying Ma, Yuan Cheng, Yongxiang Jiang, Jianhe Yue

“…Nine machine learning algorithms were evaluated using stratified 10-fold cross-validation. …”
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A bayesian network model for neurocognitive disorders digital screening in Chinese population: development and validation study by Yifan Yu, Shuaijie Zhang, Hongkai Li, Fuzhong Xue

“…Participants were assigned to a training set or a validation set based on their geographic locations. …”
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Prediction of Psychoacoustic Metrics Using Combination of Wavelet Packet Transform and an Optimized Artificial Neural Network by Mehdi POURSEIEDREZAEI, Ali LOGHMANI, Mehdi KESHMIRI

“…The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. The model is optimized using the particle swarm optimization (PSO) and the back propagation (BP) algorithms. …”
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Method for Vocal Fold Paralysis Detection Based on Perceptual and Acoustic Assessment by Rafał HALAMA, Krzysztof SZKLANNY, Danijel KORŽINEK

“…Finally, a classifier is trained using machine learning algorithms from the WEKA (Waikato Environment for Knowledge Analysis) platform. …”
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Improving Cost Contingency Estimation in Infrastructure Projects with Artificial Neural Networks and a Complexity Index by Michael C. P. Sing, Qiuwen Ma, Qinhuan Gu

“…Utilizing a database of 122 infrastructure projects from public works departments totaling HKD 5465 billion (equivalent to USD 701 billion), this study involved training and evaluating seven ML algorithms. Artificial neural networks (ANNs) were identified as the most effective, and the complexity index integration increased the R² for ANN-based single-point estimation from 0.808 to 0.889. …”
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Preliminary Development of a Database for Detecting Active Mounting Behaviors Using Signals Acquired from IoT Collars in Free-Grazing Cattle by Miguel Guarda-Vera, Carlos Muñoz-Poblete

“…A Support Vector Machine (SVM) algorithm is implemented to evaluate the effectiveness of the dataset in detecting active mounts and to compare training performance using both frames. …”
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CatSkill: Artificial Intelligence-Based Metrics for the Assessment of Surgical Skill Level from Intraoperative Cataract Surgery Video Recordings by Binh Duong Giap, PhD, Dena Ballouz, MD, Karthik Srinivasan, MD, MS, Jefferson Lustre, BS, Keely Likosky, BS, Ossama Mahmoud, MD, Shahzad I. Mian, MD, Bradford L. Tannen, MD, JD, Nambi Nallasamy, MD

“…Three CSAMs were computed to analyze 430 cataract surgeries (254 attendings and 176 residents). An ML algorithm was developed to predict surgeon training level using only CSAMs. …”
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Efficacy of cognitive rehabilitation in cognition and brain networks: A randomised clinical trial in patients with multiple sclerosis by E. Lopez-Soley, E. Martinez-Heras, F. Vivo, A. Calvi, S. Alba-Arbalat, L. Romero-Pinel, S. Martínez-Yélamos, C. Ramo-Tello, S. Presas-Rodríguez, E. Munteis, J.E. Martínez-Rodríguez, J. Sastre-Garriga, E. Anglada, E.R. Meza-Murillo, M.J. Arévalo, R. Sánchez-Carrión, R. Pelayo, M. Bernabeu, N. Sola-Valls, M. Hervas, M. Sepulveda, A. Saiz, Y. Blanco, E. Solana, S. Llufriu

“…This multicentre, double-blind, randomised clinical trial compared upward intensity training (active treatment) to low-intensity static training (static treatment). …”
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Protocol for a randomized and a non-randomized controlled trial testing Daily Growth: a personalised ‘ecological momentary intervention’ parenting app for parents and carers of chi... by Elizabeth M. Westrupp, Maria Bates, Kelsie J. Bufton, Tomer B. Berkowitz, Melody Popple, Gabriella L. King, Sara Jones, Jem Stone, Joost J. C. Funke Kupper, John W. Toumbourou, Chandan Karmakar, Sophie S. Havighurst, Christiane E. Kehoe, Maia Angelova, Melissa O’Shea, Leslie Tarrant, Lisa S. Olive, Subhadra Evans, Stefanie Ewald, Julia M. Mikocka, Keri Little, Kylie D. Hesketh, Matthew Fuller-Tyszkiewicz

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Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE by Nan Wei, René Michael Mathy, De-Hua Chang, Philipp Mayer, Jakob Liermann, Christoph Springfeld, Michael T Dill, Thomas Longerich, Georg Lurje, Hans-Ulrich Kauczor, Mark O. Wielpütz, Osman Öcal

“…The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. Results We retrospectively evaluated 202 patients (62.67 ± 9.25 years old) with HCC treated after DEB-TACE. …”
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Induction of Convolutional Decision Trees for Semantic Segmentation of Color Images Using Differential Evolution and Time and Memory Reduction Techniques by Adriana-Laura López-Lobato, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes

“…The first technique is applied to select a representative sample of pixels from an image for the model’s training process, and the second technique is implemented to reduce the number of evaluations in the fitness function considered in the DE process. …”
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ANN-based pulse shape discrimination for background reduction in neutron depth profiling by Hwijoon Jeong, Jinhwan Kim, Byung Gun Park, Kyung Taek Lim

“…Furthermore, explainable artificial intelligence techniques were employed to interpret and evaluate the performance of the algorithm. This underscored the reliability and potential of ANN-based PSD in advancing the high precision of NDP analysis of SSEs.…”
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Multimodal machine learning-based model for differentiating nontuberculous mycobacteria from mycobacterium tuberculosis by Hong-ling Li, Ri-zeng Zhi, Hua-sheng Liu, Mei Wang, Si-jie Yu

“…Optimal algorithm in each model was selected after evaluating the differentiation performance both in training and validation sets. …”
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