Prediction of Therapeutic Response and Prognosis in Ovarian Cancer Patients With Plasma Circulating Biomarkers by Haixia Cheng, Guangwen Yuan, Leilei Liang, Tiantian Wang, Jiarun Zhu, Hongying Yang, Zhendiao Zhou, Pei Wang, Qianqian Song, Yuchen Jiao, Mei Liu, Lingying Wu

“…ABSTRACT Background To assess whether changes in TP53 mutations and copy number alterations (CNA) in plasma circulating tumor DNA (ctDNA) can predict treatment response and prognosis in platinum‐resistant recurrent ovarian cancer (PROC) patients. …”
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Identification of cancer-associated fibroblast signature genes for prognostic prediction in colorectal cancer by Wei Jin, Yuchang Lu, Jingen Lu, Zhenyi Wang, Yixin Yan, Biao Liang, Shiwei Qian, Jiachun Ni, Yiheng Yang, Shuo Huang, Changpeng Han, Haojie Yang

“…The expression trends of CD177 and CCDC78 were consistent with our predicted results.ConclusionThe CAFs risk model accurately predicted prognosis, immune cell infiltration, and stromal estimates. …”
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Cuproptosis-related lncRNA predicts prognosis and immune pathways in osteosarcoma patients by LIAO Jun, FENG Yanbin, XI Deshuang, ZONG Shaohui

“…Objective To identify cuproptosis-related lncRNAs (CRLs) and use them to construct models to predict survival in osteosarcoma (OS) patients. Methods RNA-seq data of OS patients were downloaded from the TARGET database along with relevant clinical information. …”
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PREDICTION OF VERTICAL IMPACTION OF LOWER WISDOM TOOTH ACCORDING ORTHOPANTOMOGRAPHY OF LOWER JAW by A.M. Hohol, A.I. Pankevych, I.A. Kolisnyk, D.S. Machulenko, Ya.A. Hohol

“…To substantiate the algorithm for predicting the vertical retention of third lower molar in order to improve treatment tactics for the preservation or removal of the tooth which based on the obtained search data and the results of our own clinical observations and it is planned in the future. …”
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Damage prediction of rear plate in Whipple shields based on machine learning method by Chenyang Wu, Xiangbiao Liao, Lvtan Chen, Xiaowei Chen

“…The results demonstrate that the training and prediction accuracies using the Random Forest (RF) algorithm significantly surpass those using Artificial Neural Networks (ANNs) and Support Vector Machine (SVM). …”
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Monitoring land-use changes and predicting their spatio-temporal trends in Hamedan City by Naser Shafiei Sabet, Faranak Feyzbabaei cheshmeh sefidi

“…The CA-MARKOV model also identifies how land-use changes in Hamedan and simulates and predicts land use and its changes in 2050.Material and methods: In this study, after obtaining satellite images of TM, ETM, and OLI sensors, preprocessing steps including various radiometric and geometric corrections were performed on the images.Then, the classification of satellite images was done using Google Earth software and the vector support algorithm. …”
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Machine learning and molecular docking prediction of potential inhibitors against dengue virus by George Hanson, Joseph Adams, Daveson I. B. Kepgang, Luke S. Zondagh, Lewis Tem Bueh, Andy Asante, Soham A. Shirolkar, Maureen Kisaakye, Hem Bondarwad, Olaitan I. Awe

“…The detailed interactions, toxicity, stability, and conformational changes of selected compounds were assessed through protein-ligand interaction studies, molecular dynamics (MD) simulations, and binding free energy calculations.ResultsWe implemented a robust three-dataset splitting strategy, employing the Logistic Regression algorithm, which achieved an accuracy of 94%. The model successfully predicted 18 known DENV inhibitors, with 11 identified as active, paving the way for further exploration of 2683 new compounds from the ZINC and EANPDB databases. …”
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Video-fluoroscopic swallowing study scale for predicting aspiration pneumonia in Parkinson's disease. by Satoshi Tomita, Tomoko Oeda, Atsushi Umemura, Masayuki Kohsaka, Kwiyoung Park, Kenji Yamamoto, Hiroshi Sugiyama, Hideyuki Sawada

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Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF) by Haytham Elmousalami, Felix Kin Peng Hui, Aljawharah A. Alnaser

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A Nomogram for Predicting Survival in Patients with SARS-CoV-2 Omicron Variant Pneumonia Based on Admission Data by Yang Y, Li D, Nie J, Wang J, Huang H, Hang X

“…Risk analysis was performed using clinical symptoms, laboratory findings, and chest CT imaging features. A predictive algorithm was developed using Cox multivariate analysis.Results: The high-risk group had a shorter survival duration than the low-risk group. …”
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Short-Term Photovoltaic Power Combined Prediction Based on Feature Screening and Weight Optimization by Liqing Geng, Yadong Yang, Genghuang Yang, Yongfeng Zheng, Xiaocong Liu

“…Firstly, K-means is used to cluster the photovoltaic power; Secondly, CEEMDAN is used to decompose photovoltaic power and wavelet decomposition is used to decompose irradiance, and sample entropy and K-means are used to reconstruct each component of photovoltaic power into high, intermediate, and low frequency terms; Then, Spearman’s correlation coefficient is used to calculate the correlation between each meteorological factor and the decomposed irradiance component and the high, intermediate, and low frequency terms of photovoltaic power, and the feature selection is carried out; Then, CNN-BiLSTM-Attention is used to predict the high frequency term, LSTM is used to predict the intermediate frequency and low frequency terms, and the results are superimposed to obtain the preliminary prediction value; Finally, the dung beetle algorithm is used to optimize the weights of the initial prediction values of the training set of high, intermediate, and low frequency terms, and the optimal weight is substituted into the test set to obtain the final prediction result. …”
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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. …”
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Early prediction of cognitive impairment in adults aged 20 years and older using machine learning and biomarkers of heavy metal exposure by Ali Nabavi, Farimah Safari, Mohammad Kashkooli, Sara Sadat Nabavizadeh, Hossein Molavi Vardanjani

“…Machine learning can integrate multi-factorial data to predict cognitive outcomes. Objective: To develop and validate machine learning models for early prediction of cognitive impairment risk using demographics, clinical factors, and biomarkers of heavy metal exposure. …”
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Identifying leptospirosis hotspots in Selangor: uncovering climatic connections using remote sensing and developing a predictive model by Muhammad Akram Ab Kadir, Rosliza Abdul Manaf, Siti Aisah Mokhtar, Luthffi Idzhar Ismail

“…Conclusions This GIS-based study identified a primarily sporadic occurrence of leptospirosis in Selangor with minimal spatial clustering. The LGBM algorithm effectively predicted leptospirosis hotspots based on the analysed hydroclimatic factors. …”
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Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study by Sanghwan Kim, Sowon Jang, Borham Kim, Leonard Sunwoo, Seok Kim, Jin-Haeng Chung, Sejin Nam, Hyeongmin Cho, Donghyoung Lee, Keehyuck Lee, Sooyoung Yoo

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Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP... by Sana Qaiyum, Kashif Irshad, Mohamed E. Zayed, Salem Algarni, Talal Alqahtani, Asif Irshad Khan

“…Consequently, Emperor Penguins Colony (EPC) optimization algorithm was also employed for selecting the essential features, which reduces the data dimensionality and assists the predictive algorithm to focus on important features in its training phase. …”
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Risk Factors and Predictive Model for Ischemic Complications in Endovascular Treatment of Intracranial Aneurysms: Insights From a Large Patient Cohort by Jianwen Jia, Zeping Jin, Mirzat Turhon, Yixin Lin, Xinjian Yang, Yang Wang, Yunpeng Liu

“…Our study aimed to identify the risk factors for ischemic complications after the interventional treatment of IAs and to make an individualized prediction of the occurrence of ischemic complications, providing important reference guidance for clinicians. …”
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Advanced Machine Learning Methodology for Earthquake Magnitude Forecasting Using Comprehensive Seismic Data by Subhieh El-Salhi, Bashar Igried, Sari Awwad

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The Role of Artificial Intelligence in Criminal Investigations in India by Kumar Navin

“…Predictive policing algorithms can be used to predict crime hotspots, while facial recognition systems can help identify suspects in a matter of minutes rather than hours and days taken in the manual methods. …”
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A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections by Yazan Alatoom, Abdallah Al-Hamdan

Subjects: “…machine learning for delay prediction…”
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