Combination of gray level features with deep transfer learning for copra classification using machine learning and neural networks by A. Stephen Sagayaraj, T. Kalavathi Devi

“…These concatenated features were evaluated using various machine learning classifiers and neural networks. …”
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Annual global dengue dynamics are related to multi-source factors revealed by a machine learning prediction analysis. by Haoyu Long, Yilin Chen, Jingru Feng, Jian Chen, Xue Zhang, Wenjie Han, Min Kang, Xiangjun Du

“…Feature contribution pattern was different between hyperendemic and non-hyperendemic regions. …”
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Advanced machine learning technique for solving elliptic partial differential equations using Legendre spectral neural networks by Ishtiaq Ali

“…In this work, a novel approach based on a single-layer machine learning Legendre spectral neural network (LSNN) method is used to solve an elliptic partial differential equation. …”
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Unveiling the Spatial Heterogeneity of Urban Vitality Using Machine Learning Methods: A Case Study of Tianjin, China by Fengshuo Sun, Enxu Wang

“…Residential zones demonstrated higher nighttime UV than daytime UV on weekdays, with the opposite pattern observed on weekends. Public service zones maintained a comparable level of UV between the daytime and nighttime on weekdays. …”
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FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING HOLT WINTER'S EXPONENTIAL SMOOTHING METHOD AND EXTREME LEARNING MACHINE METHOD by Rochdi Wasono, Yulia Fitri, M. Al Haris

“…In this study also used the Extreme Learning Machine (ELM) method, apart from being a relatively new method, it has a fast learning speed and has low accuracy. …”
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Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience by Nicholas A. Coles, Bartosz Perz, Maciej Behnke, Johannes C. Eichstaedt, Soo Hyung Kim, Tu N. Vu, Chirag Raman, Julian Tejada, Van-Thong Huynh, Guangyi Zhang, Tanming Cui, Sharanyak Podder, Rushi Chavda, Shubham Pandey, Arpit Upadhyay, Jorge I. Padilla-Buritica, Carlos J. Barrera Causil, Linying Ji, Felix Dollack, Kiyoshi Kiyokawa, Huakun Liu, Monica Perusquia-Hernandez, Hideaki Uchiyama, Xin Wei, Houwei Cao, Ziqing Yang, Alessia Iancarelli, Kieran McVeigh, Yiyu Wang, Isabel M. Berwian, Jamie C. Chiu, Dan-Mircea Mirea, Erik C. Nook, Henna I. Vartiainen, Claire Whiting, Young Won Cho, Sy-Miin Chow, Zachary F. Fisher, Yanling Li, Xiaoyue Xiong, Yuqi Shen, Enzo Tagliazucchi, Leandro A. Bugnon, Raydonal Ospina, Nicolas M. Bruno, Tomas A. D'Amelio, Federico Zamberlan, Luis R. Mercado Diaz, Javier O. Pinzon-Arenas, Hugo F. Posada-Quintero, Maneesh Bilalpur, Saurabh Hinduja, Fernando Marmolejo-Ramos, Shaun Canavan, Liza Jivnani, Stanisław Saganowski

“…However, such conclusions would be premature because other teams exhibited the opposite pattern. Taken together, results illustrate how big team science can be leveraged to understand the promises and limitations of machine learning methods in affective science and beyond.…”
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Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis by Lei Li, Hui Jing, Yuqi Zhao, Shenghua Wu, Boyu Zhu

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The machine psychology of cooperation: can GPT models operationalize prompts for altruism, cooperation, competitiveness, and selfishness in economic games? by Steve Phelps, Yvan I Russell

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Source Analysis of Ozone Pollution in Liaoyuan City’s Atmosphere Based on Machine Learning Models and HYSPLIT Clustering Method by Xinyu Zou, Xinlong Li, Dali Wang, Ju Wang

“…Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O₃) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O₃ concentrations. …”
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Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China by Xiehui Li, Yuting Liu, Lei Wang

“…Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), <i>k</i>-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. …”
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Combining the SHAP Method and Machine Learning Algorithm for Desert Type Extraction and Change Analysis on the Qinghai–Tibetan Plateau by Ruijie Lu, Shulin Liu, Hanchen Duan, Wenping Kang, Ying Zhi

“…For regional desertification control and sustainable development, it is critical to quickly and accurately understand the distribution pattern and spatial and temporal changes of deserts. …”
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Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies by Renata Rozovsky, Maria Wolfe, Halimah Abdul‐waalee, Mariah Chobany, Greeshma Malgireddy, Jonathan A. Hart, Brianna Lepore, Farzan Vahedifard, Mary L. Phillips, Boris Birmaher, Alex Skeba, Rasim S. Diler, Michele A. Bertocci

“…We employed a whole‐brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I/II diagnosis. …”
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Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…Introduction: Urban growth has accelerated in recent decades, therefore, predicting the future growth pattern of the city is very important to prevent environmental, economic, and social problems. …”
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Integrating proteomics and machine learning reveals characteristics and risks of lymph node-independent distant metastasis in colorectal cancer by Chenxiao Zheng, Baiwang Zhu, Yanyu Chen, Numan Shahid, Yiwang Hu, Hajar Mansoor Ahmed Ali Husain, Binbin Ou, Qiongying Zhang, Haobo Jin, Yating Zheng, Peng Li, Yifei Pan, Xiaodong Zhang, Xiaodong Zhang

“…Data-Independent Acquisition Mass Spectrometry (DIA-MS), multi-omics data integration, and machine learning were used to develop a Lymph node-Independent Metastasis Genes (LIMGs) signature to predict synchronous distant metastasis risk in stage I-II CRC patients and validate it in multi-cohort. …”
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Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy by Tobias Schraut, Anne Schützenberger, Tomás Arias-Vergara, Melda Kunduk, Matthias Echternach, Stephan Dürr, Julia Werz, Michael Döllinger

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Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study by Juan Chen, Xiong Chen, Kai Fu, Lan Zhou, Shichao Long, Mengsi Li, Linhui Zhong, Aerzuguli Abudulimu, Wenguang Liu, Deng Pan, Ganmian Dai, Yigang Pei, Wenzheng Li

“…However, the immunological pattern of a high peritumoral TLS (pTLS) density and its clinical potential in hepatocellular carcinoma (HCC) remain poor. …”
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Safety of Information and Communication Technologies in the Context of the Sustainable Development of Human Society by V. A. Artamonov, E. V. Artamonova, L. A. Kulak

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Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches by Yuehjen E. Shao

“…This study proposes the integration of support vector machine (SVM) and artificial neural network (ANN) approaches to recognize the disturbance patterns of the underlying disturbances. …”
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ML-Based Pain Recognition Model Using Mixup Data Augmentation by Raghu M. Shantharam, Friedhelm Schwenker

“…Machine learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. …”
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Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning by Tomoyuki Ito, Thuy Duong Nguyen, Yutaka Saito, Yoichi Kurumida, Hikaru Nakazawa, Sakiya Kawada, Hafumi Nishi, Koji Tsuda, Tomoshi Kameda, Mitsuo Umetsu

“…The clustering analysis of the deep sequencing data from appropriate steps revealed no distinct sequence patterns, but a Bayesian machine learning model trained with the selected deep sequencing data supplied nine clusters with distinct sequence patterns. …”
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