Artificial Neural Network (ANN) Approach to Predict Tensile Properties of Longitudinally Placed Fiber Reinforced Polymeric Composites including Interphase by Sagar Chokshi, Piyush Gohil, Vijay Parmar, Vijaykumar Chaudhary

“…Machine Learning has become prevalent nowadays for predicting data on the mechanical properties of various materials and is widely used in various polymeric applications. …”
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Novel transfer learning based bone fracture detection using radiographic images by Aneeza Alam, Ahmad Sami Al-Shamayleh, Nisrean Thalji, Ali Raza, Edgar Anibal Morales Barajas, Ernesto Bautista Thompson, Isabel de la Torre Diez, Imran Ashraf

“…Initially, the spatial features are extracted from bone X-ray images using a transfer model, MobileNet, and then input into a tree-based light gradient boosting machine (LGBM) model for the generation of class probability features. Several machine learning (ML) techniques are applied to the subsets of newly generated transfer features to compare the results. …”
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Do indigenous people get left behind? An innovative methodology for measuring the unmeasurable economic conditions and poverty from the poorest region of Luzon, Philippines by Emmanuel A. Onsay, Jomar F. Rabajante

“…This work puts forth fresh approaches to quantify the incalculable multifaceted poverty and socioeconomic conditions: (i) a thorough statistical analysis using diagnostic and descriptive analytics to examine socioeconomic situations; (ii) combining sophisticated econometrics and predictive analytics to measure multidimensional poverty; and (iii) integrating machine learning to model socioeconomic situations and prescriptive analytics to develop policy. …”
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Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation by F. (. Rouholahnejad, J. Gottschall

“…Our study highlights the potential enhancement in wind resource assessment by means of machine learning methods, specifically random forest. Future research may explore extending the random forest methodology for higher heights, benefiting a new generation of offshore wind turbines, and investigating cluster wakes in the North Sea through a multinational network of floating lidars, contingent on data availability.…”
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An online intelligent electronic medical record system via speech recognition by Xin Xia, Yunlong Ma, Ye Luo, Jianwei Lu

“…On the data sets from real clinical scenarios, our proposed algorithm significantly outperforms other machine learning algorithms. Furthermore, compared to traditional electronic medical record systems that rely on keyboard inputs, our system is much more efficient, and its accuracy rate increases with the increasing online time of the proposed system. …”
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Gesture Recognition System Based on Time-Frequency Point Density of sEMG by Qiang Wang, Yao Chen, Chunhua Sheng, Shuaidi Song

“…It is usually realized by extracting the characteristics of different finger movements and then using machine learning or deep learning algorithms to classify and recognize them. …”
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A Multiplex High-Resolution Melting (HRM) assay to differentiate Fusarium graminearum chemotypes by Lovepreet Singh, Milton T. Drott, Hye-Seon Kim, Robert H. Proctor, Susan P. McCormick, J. Mitch Elmore

“…Multiplex HRM analysis produced unique melting profiles for each chemotype, and was validated on a panel of 80 isolates. We applied machine learning-based linear discriminant analysis (LDA) to automate the classification of chemotypes from the HRM data, achieving a prediction accuracy of over 99%. …”
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Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty sets by Farzaneh MansooriMooseloo, Maghsoud Amiri, Mohammad Taghi Taghavi Fard, Mostafa Hajiaghaei-Keshteli

“…Therefore, the aim of this study is to design and optimize the biomass-to-bioethanol supply chain network using data-driven robust optimization methods and disjunctive uncertainty sets.Methodology: The methodology of this study is a multi-methodology approach based on mathematical modeling and machine learning algorithms. Initially, uncertainty sets for the non-deterministic model parameter were created using K-means and SVC methods. …”
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Monitoring Soil Salinity in Arid Areas of Northern Xinjiang Using Multi-Source Satellite Data: A Trusted Deep Learning Framework by Mengli Zhang, Xianglong Fan, Pan Gao, Li Guo, Xuanrong Huang, Xiuwen Gao, Jinpeng Pang, Fei Tan

“…These variables are then integrated into various machine learning models—such as Ensemble Tree (ETree), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and LightBoost—as well as deep learning models, including Convolutional Neural Networks (CNN), Residual Networks (ResNet), Multilayer Perceptrons (MLP), and Kolmogorov–Arnold Networks (KAN), for modeling. …”
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CINNAMON-GUI: Revolutionizing Pap Smear Analysis with CNN-Based Digital Pathology Image Classification [version 1; peer review: 2 approved] by Luca Zammataro

“…Background Medical imaging has seen significant advancements through machine learning, particularly convolutional neural networks (CNNs). …”
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Post-processing enhances protein secondary structure prediction with second order deep learning and embeddings by Sotiris Chatzimiltis, Michalis Agathocleous, Vasilis J. Promponas, Chris Christodoulou

“…Accurate PSSP can be instrumental in inferring protein tertiary structure and their functions. Machine Learning and in particular Deep Learning approaches show promising results for the PSSP problem. …”
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Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-informer method by Jiajie Zhen, Ming Huang, Shuang Li, Kai Xu, Qianghu Zhao

“…However, current machine learning models for predicting the position and attitude deviations of shield machines encounter significant challenges in achieving reliable long-term forecasting during shield tunneling. …”
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Experience-based food insecurity in Bangladesh: Evidence from Household Income and Expenditure Survey 2022 by Faria Rauf Ria, Md. Muhitul Alam, Md. Azad Uddin, Mohaimen Mansur, Md. Israt Rayhan

“…A classification tree, a popular machine learning method, is also applied to explore important interactions among these determinants. …”
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Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data by Xiangqian Li, Qiongyan Peng, Ruoque Shen, Wenfang Xu, Zhangcai Qin, Shangrong Lin, Si Ha, Dongdong Kong, Wenping Yuan

“…This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020. …”
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A Novel Active Learning Technique for Fetal Health Classification Based on XGBoost Classifier by Kaushal Bhardwaj, Niyati Goyal, Bhavika Mittal, Vandna Sharma, Shiv Naresh Shivhare

“…The application of machine learning algorithms in monitoring fetal health helps to improve the chances of timely intervention and better outcomes in the event of any possible health issues in fetuses. …”
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Advancements in Liposomal Nanomedicines: Innovative Formulations, Therapeutic Applications, and Future Directions in Precision Medicine by Izadiyan Z, Misran M, Kalantari K, Webster TJ, Kia P, Basrowi NA, Rasouli E, Shameli K

“…The integration of artificial intelligence and machine learning in optimizing liposomal designs promises to revolutionize personalized medicine, paving the way for innovative strategies in disease detection and therapeutic interventions. …”
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Global research trends on biomarkers for cancer immunotherapy: Visualization and bibliometric analysis by Yuan Qiao, Dong Xie, Zhengxiang Li, Shaohua Cao, Dong Zhao

“…Furthermore, “artificial intelligence” and “machine learning” have become the most important research hotspot over the last 2 y, which will help us to identify useful biomarkers from complex big data and provide a basis for precise medicine for malignant tumors.…”
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APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions by Eva Viesi, Ugo Perricone, Patrick Aloy, Rosalba Giugno

“…Moreover, the interactivity between biological entities can be represented through combined feature vectors that can be given as input to a machine learning (ML) model to capture the underlying interaction. …”
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Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review by Abid Ali, Hans-Peter Kaul

“…At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. …”
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Feature Representations Using the Reflected Rectified Linear Unit (RReLU) Activation by Chaity Banerjee, Tathagata Mukherjee, Eduardo Pasiliao Jr.

“…Deep Neural Networks (DNNs) have become the tool of choice for machine learning practitioners today. One important aspect of designing a neural network is the choice of the activation function to be used at the neurons of the different layers. …”
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