A Review of Generative Design Using Machine Learning for Additive Manufacturing by Parankush Koul

“…The scalability and predictability of artificial intelligence (AI) models make handling huge data easy and enable scale-up of production without compromising quality. …”
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A data-driven state identification method for intelligent control of the joint station export system by Guangli Xu, Yifu Wang, Zhihao Zhou, Yifeng Lu, Liangxue Cai

“…Compared with the traditional hydraulic calculation modified (THCM) models and other machine learning algorithms, the PSO-GWO-BP model has significant advantages in prediction accuracy. …”
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Artificial Intelligence in Hand Surgery: The Future is Upon Us by Bryan S. Crook, MD, Eoghan T. Hurley, MBBS, PhD, Marc J. Richard, MD, Suhail K. Mithani, MD, Tyler S. Pidgeon, MD

“…The resulting impact of these technologies, including machine learning algorithms and large language models, has yet to be fully realized, but has already begun improving patient care, offloading administrative burden, and disrupting clinical research. …”
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Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images by Huo Li, Jing Qin, Zhongzhuan Li, Rong Ouyang, Zhixin Chen, Shijiang Huang, Shufen Qin, Qiliang Huang

“…Meta-regression identified center, reference standard, and tile size as major sources of heterogeneity, with no significant differences observed between internal and external performance. Overall, DL algorithms demonstrate excellent sensitivity in detecting MSI-H; however, their lower specificity in external validation suggests overfitting and highlights the need for algorithm standardization to improve generalizability and clinical utility.…”
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Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection by Shuangle Guo, Yongxia Li, Jianguang Zhang, Yue Liu, Tian Tian, Mengchen Guo

“…These two drawbacks of TR-LDA significantly reduce the performance of feature selection algorithms based on it. To overcome these drawbacks, this paper proposes the sparse LDA with constant between-class distance (SLDA-CBD) model to select relavant features. …”
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PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS by Raghda Azad Hasan, Ibrahim Ahmed Saleh

“…The model applies the basic algorithms (Random Forest (RF), Decision Tree (DT), Extra Tree) and the learning model ensemble (Adaboost, xgboost ,Stack, Voting, bagging) and metrics (accuracy, recall, F1 score, accuracy) to measure the prediction performance of the models and a comparison was made between the proposed model algorithms. …”
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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…Machine learning (ML) prediction model can be an effective tool for mortality prediction, enabling the timely identification of high-risk patients and improving outcomes.MethodsA total of 3,050 acute upper gastrointestinal bleeding (AUGIB) patients were included in our research from the MIMIC-IV database, among which 625 patients were classified as AVGIB and 2,425 patients were categorized as ANGIB. …”
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An artificial intelligence platform for predicting postoperative complications in metastatic spinal surgery: development and validation study by Weihao Jiang, Juan Zhang, Weiqing Shi, Xuyong Cao, Xiongwei Zhao, Bin Zhang, Haikuan Yu, Shengjie Wang, Yong Qin, Mingxing Lei, Yuncen Cao, Boyu Zhu, Yaosheng Liu

“…The remaining 125 patients from another tertiary hospital were served as external validation cohort to externally validate the model. The machine learning algorithms employed in this study includes logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), neural network (NN), and k-nearest neighbor (KNN). …”
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History Matching of a Channelized Reservoir Using a Serial Denoising Autoencoder Integrated with ES-MDA by Sungil Kim, Baehyun Min, Seoyoon Kwon, Min-gon Chu

“…Regarding the loss as artificial noise, this study designs a serial denoising autoencoder (SDAE) composed of two neural network filters, utilizes this machine learning algorithm for relieving noise effects in the process of ensemble smoother with multiple data assimilation (ES-MDA), and improves the overall history matching performance. …”
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Revolutionising English language education:empowering teachers with BERT-LSTM-driven pedagogical tools by Sheik Hameed Nagoor Gani, Vijayakumar Selvaraj, Sahidul Islam Md, Sugadev Thalapathy, Mohamed Jalaludeen Abdulkadhar, Kanmani Kalimuthu

“…Abstract In the field of English language education, state-of-the-art NLP techniques based on deep learning models such as BERT and LSTM can be regarded as breakthroughs in terms of education. …”
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Breast cancer diagnosis with MFF-HistoNet: a multi-modal feature fusion network integrating CNNs and quantum tensor networks by Tariq Mahmood, Tanzila Saba, Amjad Rehman

“…The MFF-HistoNet algorithms reveal the benchmark performance, achieving impressive accuracy of 98.8% at the image level and 98.4% at the patient level under 100 × magnification and 98.1% and 98.9% under 40 × magnification, outperforming existing models and reducing resource requirements. …”
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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

“…Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. …”
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Efficient electromagnetic transient simulation for DFIG-based wind farms using fine-grained network partitioning by Jiale Yu, Haoran Zhao, Yibao Jiang, Bing Li, Linghan Meng, Futao Yang

“…This paper first establishes the decoupling algorithm for core electrical equipment of DFIG-based WFs. …”
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MUFFNet: lightweight dynamic underwater image enhancement network based on multi-scale frequency by Dechuan Kong, Dechuan Kong, Yandi Zhang, Xiaohu Zhao, Yanqiang Wang, Lei Cai

“…A Multi-Scale Joint Loss framework facilitates dynamic network optimization.ResultsExperimental results demonstrate that MUFFNet outperforms existing state-of-the-art models while consuming fewer computational resources and aligning enhanced images more closely with human visual perception.DiscussionThe enhanced images generated by MUFFNet exhibit better alignment with human visual perception, making it a promising solution for improving underwater robotic vision systems.…”
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Diagnostic Accuracy of <i>Mycobacterium tuberculosis</i> Antigen-Based Skin Tests (TBSTs) for Tuberculosis Infection Compared with TST and IGRA: A Network Meta-Analysis by Li Peng, Weijie Ma, Lei Zhong, Jiaru Yang, Hanxin Wu, Liangyu Zhu, Xun Huang, Rui Yang, Bingxue Li, Weijiang Ma, Xinya Wu, Jieqin Song, Suyi Luo, Fukai Bao, Aihua Liu

“…Data were synthesized with frequentist random-effects network meta-analyses, a single-group rate meta-analysis algorithm, and a bivariate mixed-effects logistic regression model. …”
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Utilization of fine needle aspiration cytology at Kamuzu central hospital. by Shiraz Khan, George Liomba, Nora E Rosenberg, Christopher Stanley, Cocxilly Kampani, Bal Mukunda Dhungel, Mina C Hosseinipour

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Understanding levels of best practice: An empirical validation. by Huy P Phan, Bing H Ngu, Hui-Wen Wang, Jen-Hwa Shih, Sheng-Ying Shi, Ruey-Yih Lin

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Masks-to-Skeleton: Multi-View Mask-Based Tree Skeleton Extraction with 3D Gaussian Splatting by Xinpeng Liu, Kanyu Xu, Risa Shinoda, Hiroaki Santo, Fumio Okura

“…In our method, a skeleton is modeled as a graph whose nodes store positions and radii while its adjacency matrix encodes branch connectivity. …”
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Analysis of Deep Learning Techniques for Vehicle Detection and Reidentification Using Data from Multiple Drones and Public Datasets by FELIPE P.A. EUPHRÁSIO, RAFAEL M. DE ANDRADE, ELCIO H. SHIGUEMORI, LIANGRID L. SILVA, MOISÉS JOSÉ S. FREITAS, NATHAN AUGUSTO Z. XAVIER, ARGEMIRO S.S. SOBRINHO

“…Overall, the techniques were able to distinguish vehicles of different models and adapted to the data captured by drones.…”
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A Spatial Crowdsourcing Engine for Harmonizing Volunteers’ Needs and Tasks’ Completion Goals by Maite Puerta-Beldarrain, Oihane Gómez-Carmona, Liming Chen, Diego López-de-Ipiña, Diego Casado-Mansilla, Felipe Vergara-Borge

“…This work addresses the task allocation problem in spatial crowdsensing with altruistic participation, tackling challenges like declining engagement and user fatigue from task overload. Unlike typical models relying on financial incentives, this context requires alternative strategies to sustain participation. …”
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