AgrUNet: A Multi-GPU UNet Based Model for Crops Classification by Andrea Miola, Enrico Calore, Sebastiano Fabio Schifano

“…This approach requires high-performance computing systems since DL algorithms are known to be very computing-heavy, and recent multi-GPU HPC systems can boost by one or two orders of magnitude the processing power of classical computing systems based only on CPUs. …”
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Prediction of high-risk pregnancy based on machine learning algorithms by Xinyu Pi, Junzhi Wang, Liangliang Chu, Guochun Zhang, Wenli Zhang

“…With the computational support of an NVIDIA GPU RTX3050Ti, the model demonstrated excellent data processing capabilities, capable of predicting and processing 500 sets of data items per second. …”
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Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms by Ashrafe Alam, Victor R. Prybutok

“…The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). …”
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Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms by Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu

“…Correlation of friction coefficients and wear rates of copper/aluminum-graphite (Cu/Al-graphite) self-lubricating composites with their inherent material properties (composition, lubricant content, particle size, processing process, and interfacial bonding strength) and the variables related to the testing method (normal load, sliding speed, and sliding distance) were analyzed using traditional approaches, followed by modeling and prediction of tribological properties through five different ML algorithms, namely support vector machine (SVM), K-Nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), and least-squares boosting (LSBoost), based on the tribology experimental data. …”
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Machine learning enables legal risk assessment in internet healthcare using HIPAA data by Shixian Liu, Hailing Liu, Siyu Fan, Leming Song, Zeyu Wang

“…The research methods include data collection and processing, construction and optimization of ML models, and the application of a risk assessment framework. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…Sixteen models were evaluated, including traditional classifiers such as Logistic Regression, K-Nearest Neighbors, Decision Trees, ensemble methods that include Random Forest, Gradient Boosting, and advanced techniques such as Extreme Gradient Boosting, Support Vector Machines, Gated Recurrent Units, and Long Short-Term Memory networks. …”
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Application of edge-cloud collaborative intelligence technologies in power grids by Qing HAN, Kunlun GAO, Ting ZHAO, Jiangqi CHEN, Xinyu YANG, Shusen YANG

“…With the continuous development of the Internet of things on electricity (IoTE) and large-scale deployment of intelligent edge devices, an explosively increasing amount of data are being generated at the network edge.The efficient, fast and secure processing and analysis of the massive edge located data brings great challenges for the traditional cloud computing-based intelligence technologies.Instead, edge-cloud collaborative intelligence (ECCI) technologies can significantly outperform the cloud computing-based intelligence in terms of the network bandwidth saving, delay reduction and privacy protection, and therefore have shown a great potential in boosting the development of power grids.To investigate the application of ECCI in power grids, the concept and research progress of ECCI were firstly introduced.The characteristics and advantages of ECCI were summarized and its applicability in the power grids were discussed.Secondly, the key technologies of ECCI applications for power grids were discussed and the solutions based on ECCI technologies for two typical scenes were proposed respectively.Finally, a brief discussion of future work was given.…”
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Neural Stimulation Has a Long-Term Effect on Foreign Vocabulary Acquisition by Achille Pasqualotto, Begüm Kobanbay, Michael J. Proulx

“…There is evidence suggesting that the frontal and temporal cortices are involved in language processing and comprehension, but it is still unknown whether foreign language acquisition recruits additional cortical areas in a causal manner. …”
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Revisiting the Fundamental Planes of Black Hole Activity for Strong Jet Sources by Qing-Chen Long, Ai-Jun Dong, Qi-Jun Zhi, Lun-Hua Shang

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Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization by Yu Li, Jingxiao Zhao, Xiucheng Li, Zhao Xing, Qiqiang Duan, Xiaojun Liang, Xuemin Wang

“…Several ML methods, including Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost), were utilized to predict yield strength (YS) and ultimate tensile strength (UTS) using the aforementioned microstructural features. …”
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Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand by Guangmei Yang, Guangdong Wang, Leping Wan, Xinle Wang, Yan He

“…To improve computational efficiency, we used three algorithms to develop prediction models, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Light Gradient Boosting Machine (LightGBM) algorithms. …”
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Multitask Learning-Based Pipeline-Parallel Computation Offloading Architecture for Deep Face Analysis by Faris S. Alghareb, Balqees Talal Hasan

“…Additionally, both pipeline and parallel processing techniques are utilized to expedite complicated computations, boosting the overall performance of the presented deep face analysis architecture. …”
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Investigation of aromatic compounds and olfactory profiles in cocoa pulp fermentation using yeast-based starters: A Volatilomics and machine learning approach by Haode Chang, Chunhe Gu, Quanmiao Zhang, Wenjing Zhang, Liru Ma, Fei Liu, Zhen Feng

“…The models showed high prediction accuracy, ranging from 0.85 for sourness by Gradient Boost Machine to 0.28 for sweetness by linear regression. …”
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A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms by Hamed Farkhari, Joseanne Viana, Sarang Kahvazadeh, Pedro Sebastiao, Victor P. Gil Jimenez, Rui Dinis

“…Their integration into decision-making processes within 5G telecommunication systems and UAV security is noteworthy. …”
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A fair dividend approach for aggregating wearable sensor data to improve electronic health records. by Turki M Alanazi, Noha Alduaiji, Chahira Lhioui, Rim Hamdaoui, Somia Asklany, Monia Hamdi, Ali Elrashidi, Ghulam Abbas

“…The sequence determination uses balanced linear scheduling, optimizing the structure of sensing operations and increasing WS input availability when interruptions from multiple sensors, thereby boosting operating efficiency. The proposed approach outperforms baseline methods in access time, computational complexity, data utilization, processing time, aggregation ratio, and error rate by 10.18%, 5.19%, 10.57%, 8.48%, and 10.42%, respectively. …”
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Determining parameters of surface defects in the base metal of pipelines using results of complex diagnostics by N.V. Krysko, S.V. Skrynnikov, N.A. Shchipakov, D.M. Kozlov, A.G. Kusyy

“…Finally, the models based on gradient boosting are found to be optimal for all target variables. …”
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Exploring Machine Learning Methods for Aflatoxin M1 Prediction in Jordanian Breast Milk Samples by Abdullah Aref, Eman Omar, Eman Alseidi, Nour Elhuda A. Alqudah, Sharaf Omar

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Mulberry leaf extract reduces damage by regulating the oxidative response, immune response and intestinal flora of largemouth bass (Micropterus salmoides) under chronic heat stress by Bing Fu, Donglai Zhou, Xiaotong Qiu, Xiaotong Qiu, Jieqiu Zheng, Jieqiu Zheng, Qiong Yang, Yang Xiao, Sentai Liao, Qingrong Li, Dongxu Xing

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Multisegment Parallel Coregistration of Sentinel-1 SAR Time-Series Images by Combining OpenMP With MPI by Yonghui Kang, Yonghong Zhang, Hong'an Wu, Jujie Wei, Xiaoxue Sun, Yue Zuo

“…The geometric coregistration process uses OpenMP and MPI for algorithm-level and multitask parallelism, boosting efficiency. …”
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Objective Detection of Newborn Infant Acute Procedural Pain Using EEG and Machine Learning Algorithms by Jean‐Michel Roué, Amir Avnit, Behnood Gholami, Wassim M. Haddad, Kanwaljeet J. S. Anand

“…The five highest ranked features corresponded to EEG electrode pairs: T7‐P4, Fz‐CP5, FC1‐TP10, CP6‐Cz, and Fz‐F3, suggesting involvement of the contralateral temporal gyrus, opercular cortex, thalamus, and bilateral insula in infant pain processing. Preliminary changes in functional connectivity indicate infant pain processing. …”
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