PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…To diagnose PD, the proposed method uses two different data sets. Algorithms for machine learning are also capable of helping in producing specific details from such data. …”
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A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data by Xiao Zhang, Yuxin Tao, Chenglong Zhang

“…In conclusion, the key contribution of this study is the proposal of a hybrid deep learning framework that expands the applications of emerging deep learning algorithms and ensemble learning in the field of evapotranspiration estimation, which enables a more precise matching between irrigation water extraction and actual crop needs. …”
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Transforming nano grids to smart grid 3.0: AI, digital twins, blockchain, and the metaverse revolutionizing the energy ecosystem by Herman Zahid, Adil Zulfiqar, Muhammad Adnan, Muhammad Sajid Iqbal, Anwar Shah, Usman Abbasi, Salah Eldeen Gasim Mohamed

“…This study systematically analyzes the application of advanced algorithms and technologies across all hierarchical subsystems—nanogrid 3.0, microgrid 3.0, VPP 3.0, and Smart Grid 3.0. …”
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Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort by Kyndaron Reinier, Harpriya S. Chugh, Audrey Uy-Evanado, Elizabeth Heckard, Marco Mathias, Nichole Bosson, Vinicius F. Calsavara, Piotr J. Slomka, David A. Elashoff, Alex A.T. Bui, Sumeet S Chugh

“…We will use conventional approaches (diagnosis code algorithms) and artificial intelligence (natural language processing, deep learning) to define patient phenotypes and biostatistical and machine learning approaches for analysis. …”
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Using baseline MRI radiomics to predict the tumor shrinkage patterns in HR-Positive, HER2-Negative Breast Cancer by Lijia Wang, Yongchen Wang, Li Yang, Jialiang Ren, Qian Xu, Yingmin Zhai, Tao Zhou

“…Radiomics features were extracted and analyzed using machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). …”
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Evaluation of Four Rapid Tests for Detection of Hepatitis B Surface Antigen in Ivory Coast by Bamory Dembele, Roseline Affi-Aboli, Mathieu Kabran, Daouda Sevede, Vanessa Goha, Aimé Cézaire Adiko, Rodrigue Kouamé, Emile Allah-Kouadio, Andre Inwoley

“…The diagnostic performance (sensitivity and specificity) was calculated in comparison to the reference sequential algorithms of two EIA tests (Dia.Pro HBsAg® one version ULTRA and Monolisa™ HBsAg ULTRA). …”
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The TYPHOON Stellar Population Synthesis Survey. II. Pushing Full Spectral Fitting to the Limit in the Nearby Grand Design Barred Spiral M83 by Eva Sextl, Rolf-Peter Kudritzki, Fabio Bresolin, Kathryn Grasha, Hye-Jin Park, Qian-Hui Chen, Andrew J. Battisti, Mark Seibert, Barry F. Madore, Jeffrey A. Rich

“…We confirm our results by using alternative fitting algorithms and stellar libraries.…”
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Automatic Detection for Mining Subsidence Areas Using the CBAM-Enhanced VGG-UNet Model With Long Time Series InSAR Interferograms by Kegui Jiang, Keming Yang, Mengting Gao, Liuguo Zhu, Chuang Jiang

“…The rapid advancements in remote sensing technology and deep learning algorithms have enabled significant breakthroughs in monitoring and accurately identifying mining subsidence. …”
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Machine Learning for Non-Destructive Prediction of Sunflower Leaf Area by Joao Everthon da Silva Ribeiro, Antonio Gideilson Correia da Silva, Pablo Henrique de Almeida Oliveira, Josiana Micarla da Silva Oliveira, Alessandra Nunes da Silva, John Victor Lucas Lima, Ivan Euzebio da Silva, Ester Dos Santos Coelho, Isaque de Oliveira Leite, Elania Freire da Silva, Toshik Iarley da Silva, Lindomar Maria da Silveira, Aurelio Paes Barros Junior

“…Therefore, the present study aimed to develop and compare linear regression models and machine learning algorithms for the non-destructive prediction of leaf area in four sunflower cultivars (BRS 323, Altis 99, Sany 66, and BRS 442). …”
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Molecular Identification of Elvers (<I>Anguilla</I> spp.) from River Estuaries in Central Java, Indonesia Using DNA Barcoding Based on <I>mt</I>DNA CO1 Sequences by Iffan Nadhif Falah, Ratih Ida Adharini, Susana Endah Ratnawati

“…MEGA was used to construct the phylogenetic trees via Neighbour Joining (NJ) algorithms using the Kimura 2-parameter model with uniform rates and obtained by 1000 bootstraps replication. …”
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A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection by U. Sumalatha, K. Krishna Prakasha, Srikanth Prabhu, Vinod C. Nayak

“…The review focuses on the overview of unimodal and multimodal fingerprint biometric systems and the importance of fusion, advancements in data acquisition, preprocessing, feature extraction, matching algorithms, performance metrics, indexing, template protection, and addressing attacks in enhancing system security and reliability. …”
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Urban Expansion and Land Use Transformations in Midnapore City (2003–2024): Implications for Sustainable Development by Rakesh Ranjan Thakur, Debabrata Nandi, Anoop Kumar Shukla, Subhasmita Das, Sasmita Chand, Pankaj Singha, Roshan Beuria, Chetan Sharma

“…The study employs Landsat 5 and 8 imagery with 30 m spatial resolution which were processed through Maximum Likelihood Classifier (MLC) algorithms. The results were attained through ArcGIS 10.2.2 and ERDAS IMAGINE 2014 software, with ground-truth validation using data from 117, 111, and 116 points for 2024, 2014, and 2003, respectively. …”
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Recovering Pulsar Periodicity from Time-of-arrival Data by Finding the Shortest Vector in a Lattice by Dotan Gazith, Aaron B. Pearlman, Barak Zackay

“…Identifying such a periodicity from a discrete set of arrival times is a difficult algorithmic problem, In particular when the pulsar is in a binary system. …”
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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

“…The system captures glucose-related optical signals, which are analyzed using various machine learning algorithms, including a novel Convolutional Neural Network&#x2013;Attention Hybrid Model (CNN-AHM). …”
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microRNA-183–5p induces cell density-dependent apoptosis through the regulation of Presenilin 2 by Yuki Yabuuchi, Yosuke Matsuno, Kai Yazaki, Wei Zhen Ting, Kengo Nishino, Sosuke Matsumura, Kenya Kuramoto, Kazufumi Yoshida, Masashi Matsuyama, Takumi Kiwamoto, Yuko Morishima, Nobuyuki Hizawa

“…PSEN2 is predicted to be targeted by microRNA-183–5p (miR-183–5p) by several algorithms. We verified miR-183–5p directly regulates PSEN2 expression and induces apoptosis. …”
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Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches by Md. Atiqur Rahman, Israt Jahan, Maheen Islam, Taskeed Jabid, Md Sawkat Ali, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam, Md. Hasanul Ferdaus, Md Mostofa Kamal Rasel, Mahmuda Rawnak Jahan, Shayla Sharmin, Tanzina Afroz Rimi, Atia Sanjida Talukder, Md. Mafiul Hasan Matin, M. Ameer Ali

“…This fact highlights the need for accurate machine learning algorithms (MLAs) for analyzing, monitoring, and diagnosing sleep disorders. …”
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A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications by Mujaheed Abdullahi, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Yahia Baashar, Abdussalam Ahmed Alashhab, Afroza Afrin

“…The findings show that Support Vector Machines (SVM) is the most effective learning algorithms for the detection and adaptation of CD in regression and classification tasks using time-series data. …”
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Single-cell multiomics reveals simvastatin inhibits pan-cancer epithelial-mesenchymal transition via the MEK/ERK pathway in XBP1+ mast cells by Sen Lin, Huimin Zhang, Ruiqi Zhao, Zhulin Wu, Weiqing Zhang, Mengjiao Yu, Bei Zhang, Lanyue Ma, Danfei Li, Lisheng Peng, Weijun Luo

“…A prognostic model was established using WGCNA and 12 machine learning algorithms to identify potential mast cell targets. …”
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The relationship between the CYP2C19*17 genetic polymorphism and the efficacy and safety of diazepam in patients with alcohol withdrawal syndrome by V. Yu. Skryabin, M. S. Zastrozhin, E. A. Grishina, K. A. Ryzhikova, V. V. Shipitsyn, I. V. Barna, T. E. Galaktionova, A. S. Sorokin, E. A. Bryun, D. A. Sychev

“…The aim of our study was to study the relationship between the CYP2C19 genetic polymorphism and the efficacy and safety of diazepam in patients with alcohol withdrawal syndrome in order to develop algorithms for optimizing the therapy of diazepam to reduce the risk of dose-dependent adverse drug reactions and pharmacoresistance.Materials and methods. …”
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Identifying emphysema risk using brominated flame retardants exposure: a machine learning predictive model based on the SHAP methodology by Qihang Xie, Haoran Qu, Jianfeng Li, Rui Zeng, Wenhao Li, Rui Ouyang, Chengxiang Zhang, Siyu Xie, Siyu Xie, Ming Du

“…Eight machine learning algorithms, including lightGBM, MLP, DT, KNN, RF, SVM, Enet, and XGBoost, were applied to build and evaluate the model. …”
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