PREDIKSI PRODUKTIVITAS JAGUNG DI INDONESIA SEBAGAI UPAYA ANTISIPASI IMPOR MENGGUNAKAN JARINGAN SARAF TIRUAN BACKPROPAGATION by Anjar Wanto

“…This algorithm is able to predict data well, especially data that is maintained for a certain period of time. …”
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A novel deep learning model for predicting marine pollution for sustainable ocean management by Michael Onyema Edeh, Surjeet Dalal, Musaed Alhussein, Khursheed Aurangzeb, Bijeta Seth, Kuldeep Kumar

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Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach by Iancu-Bogdan Teodoru, Zakaria Owusu-Yeboah, Mircea Aniculăesi, Andreea Vasilica Dascălu, Florian Hörtkorn, Alessia Amelio, Irina Lungu

“…Random Forest emerged as the optimal algorithm, providing robust and accurate UCS predictions. …”
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MMPred: a tool to predict peptide mimicry events in MHC class II recognition by Filippo Guerri, Filippo Guerri, Valentin Junet, Valentin Junet, Judith Farrés, Xavier Daura, Xavier Daura, Xavier Daura

“…We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”
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AI-Driven Transcriptome Prediction in Human Pathology: From Molecular Insights to Clinical Applications by Xiaoya Chen, Huinan Xu, Shengjie Yu, Wan Hu, Zhongjin Zhang, Xue Wang, Yue Yuan, Mingyue Wang, Liang Chen, Xiumei Lin, Yinlei Hu, Pengfei Cai

“…Machine learning algorithms and deep learning models excel in extracting meaningful features from diverse biomedical modalities, enabling tools like PathChat and Prov-GigaPath to improve cancer subtyping, therapy response prediction, and biomarker discovery. …”
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A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis by Chen Zhang, Jie He, Yinhai Wang, Xintong Yan, Changjian Zhang, Yikai Chen, Ziyang Liu, Bojian Zhou

“…The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. …”
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Enhancing Stroke Prediction with Logistic Regression and Support Vector Machine Using Oversampling Techniques by Syamsul Risal, Fajar Apriyadi, A. Sumardin, Andini Dani Achmad, Annisa Nurul Puteri

“…This study compares the performance of Logistic Regression (LR) and Support Vector Machine (SVM) algorithms combined with different oversampling methods—SMOTE, Borderline-SMOTE, ADASYN, Random Over Sampling (ROS), and Random Under Sampling (RUS)—on a stroke prediction dataset. …”
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Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy by Guangzong Li, Yuesen Zhang, Di Li, Manhong Zhao, Lin Yin

“…The Extra Trees model demonstrated the highest predictive accuracy. The top three predictors were a history of hypertension, serum albumin level, and total calcified volume.ConclusionThe total volume of IAC is a critical imaging biomarker for predicting MT outcomes in patients with anterior circulation AIS. …”
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Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism by Adel Binbusayyis, Mohemmed Sha

“…Traditional techniques have limitations in accuracy and error rates, necessitating advancements in prediction techniques. To enhance prediction accuracy, a proposed smart city system utilizes the Household Energy Consumption dataset, employing deep learning algorithms. …”
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Solar radiation prediction: A multi-model machine learning and deep learning approach by C Vanlalchhuanawmi, Subhasish Deb, Md. Minarul Islam, Taha Selim Ustun

“…Focusing on five input variables—solar irradiance, dew point, temperature, relative humidity, and wind speed—this study evaluates the predictive performance of 13 data-driven models, comprising ten machine learning (ML) and three deep learning (DL) algorithms. …”
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Machine learning-based prediction of diabetic peripheral neuropathy: model development and clinical validation by Meng Sun, Xingling Sun, Fei Wang, Li Liu

“…Nine machine learning models were developed and compared for DPN risk prediction.ResultsStochastic Gradient Boosting (SGBT) demonstrated the best performance (training AUC: 0.933, 95% CI: 0.921–0.946; testing AUC: 0.811, 95% CI: 0.776–0.843). …”
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Machine learning-driven SLC prognostic signature for glioma: predicting survival and immunotherapy response by Jianghua Lin, Xiao Yang, Kaijun Zhao, Yu’e Liu, Yu’e Liu

“…The model demonstrated superior predictive performance compared to existing glioma prognostic models. …”
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Spatio-Temporal Aware Collaborative Service Ranking Prediction in IoT-Enabled Edge Computing by Yuze Huang, Xiao Chen, Wenhui Zhang, Qianxi Li, He Li

“…The results demonstrate that our approach achieves higher accuracy in prediction compared to other baseline algorithms.…”
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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait by Anam Naz, Hikmat Ullah Khan, Tariq Alsahfi, Mousa Alhajlah, Bader Alshemaimri, Ali Daud

“…In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. …”
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HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION by Syaiful Anam, M. Rafael Andika Putra, Zuraidah Fitriah, Indah Yanti, Noor Hidayat, Dwi Mifta Mahanani

“…The number of claims plays an important role the profit achievement of health insurance companies. Prediction of the number of claims could give the significant implications in the profit margins generated by the health insurance company. …”
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Predicting anemia management in dialysis patients using open-source machine learning libraries by Takahiro Inoue, Norio Hanafusa, Yuki Kawaguchi, Ken Tsuchiya

“…Performance metrics were compared across models, including XGBoost and LightGBM, to identify the most accurate algorithms. Results LightGBM and XGBoost outperformed logistic regression in predicting ESA and iron dosage changes, achieving high accuracy (e.g., area under the curve (AUC) = 0.86 for iron dosing). …”
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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. …”
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A portable retina fundus photos dataset for clinical, demographic, and diabetic retinopathy prediction by Chenwei Wu, David Restrepo, Luis Filipe Nakayama, Lucas Zago Ribeiro, Zitao Shuai, Nathan Santos Barboza, Maria Luiza Vieira Sousa, Raul Dias Fitterman, Alexandre Durao Alves Pereira, Caio Vinicius Saito Regatieri, Jose Augusto Stuchi, Fernando Korn Malerbi, Rafael E. Andrade

“…To validate the utility of mBRSET, state-of-the-art deep models, including ConvNeXt V2, Dino V2, and SwinV2, were trained for benchmarking, achieving high accuracy in clinical tasks diagnosing diabetic retinopathy, and macular edema; and in fairness tasks predicting education and insurance status. The mBRSET dataset serves as a resource for developing AI algorithms and investigating real-world applications, enhancing ophthalmological care in resource-constrained environments.…”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…To address these limitations, the integration of digital technologies, such as computational modeling and machine learning (ML), has become increasingly important.MethodsThis paper proposes a hybrid multiscale modeling framework that integrates molecular dynamics (MD) simulations, finite element methods (FEM) from continuum mechanics, and supervised ML algorithms—including deep neural networks and gradient boosting regressors—to enable accurate and efficient prediction of material properties across scales. …”
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Feature Selection for Hypertension Risk Prediction Using XGBoost on Single Nucleotide Polymorphism Data by Lailil Muflikhah, Tirana Noor Fatyanosa, Nashi Widodo, Rizal Setya Perdana, Solimun, Hana Ratnawati

“…This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. …”
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