Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network by Ning Yuan, Donghai Guan, Shengrong Li, Li Zhang, Qi Zhu

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Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy by Young Kwang Chae, Emma Yu, Na Hyun Kim, Min Jeong Kim, Leeseul Kim, Hyung-Gyo Cho, Yeonggyeong Park, Yoonhee Choi, Seung Pyo Daniel Hong

“…These protein signatures are derived from patient serum samples based on mass spectrometry and act as biomarkers to predict response to immunotherapy. Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). …”
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Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis by Lu Xing, Tao Wu, Li Yu, Nian Zhou, Zhao Zhang, Yunjing Pu, Jinnan Wu, Hong Shu

“…This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms. …”
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Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis. by Jessica L Nielson, Shelly R Cooper, John K Yue, Marco D Sorani, Tomoo Inoue, Esther L Yuh, Pratik Mukherjee, Tanya C Petrossian, Jesse Paquette, Pek Y Lum, Gunnar E Carlsson, Mary J Vassar, Hester F Lingsma, Wayne A Gordon, Alex B Valadka, David O Okonkwo, Geoffrey T Manley, Adam R Ferguson, TRACK-TBI Investigators

“…Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. …”
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…By detecting and addressing faults early, systems can maintain optimal performance levels. Machine Learning (ML) in Solar Photovoltaic (PV) systems refers to the application of algorithms and statistical models that enable computers to perform specific tasks without using explicit instructions, relying instead on patterns and inference. …”
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Data Mining Techniques for Early Detection and Classification of Plant Diseases: An Optimization-Based Approach by Wagh Swapnil, Sharma Ruchi

“…The model proposed uses the state-of-art algorithms including the decision trees, support vector machines and the deep learning techniques in the feature extraction and pattern recognition as well as binary classification. …”
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Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence by Karthik Seetharam, Sirish Shrestha, Partho P Sengupta

“…Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. …”
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Calculation Model of Multi-roll Straightening Process Based on Bilinear Hardening and Power Hardening by ZHU Xiaoyu, CHENG Zixing, WANG Xiaogang, HAN Peisheng

“…Using these relationships and curvature integration, a mathematical model for the multi-roll straightening process was developed. A least squares algorithm was employed to solve the nonlinear equations iteratively, enabling accurate straightening calculations. …”
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ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYSIS IN CRIME PREVENTION AND COMBAT by George-Marius ȚICAL

“…In an interconnected world where data volume is growing exponentially, traditional investigative methods are often overwhelmed. Machine learning algorithms, neural networks, and natural language processing enable the rapid analysis of unstructured data, enhancing the ability to predict and prevent crimes. …”
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Integrating AI into neurosurgical decisions: a new Frontier in medicine by Kashif Munir

“…AI does best in the preoperative stage of planning where medical data depicted by MRI and CT scans are processed through machine learning and deep-learning algorithms that help improve diagnostic accuracy and prognosis. …”
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Nitrogen content estimation of apple trees based on simulated satellite remote sensing data by Meixuan Li, Xicun Zhu, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…Correlation coefficient method and partial least squares regression were used to screen sensitive bands for apple tree nitrogen content. Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN) algorithms were used to construct and screen the optimal models for apple tree nitrogen content estimation.ResultsResults showed that visible light, red edge, near-infrared, and yellow edge bands were sensitive bands for estimating apple tree nitrogen content. …”
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Unraveling the oxidative stress landscape in diabetic foot ulcers: insights from bulk RNA and single-cell RNA sequencing data by Jialiang Lin, Linjuan Huang, Weiming Li, Haijun Xiao, Mingmang Pan

“…Drug prediction highlighted Thymoquinone and Erlotinib as potential therapeutic candidates. Machine learning algorithms (SVM-RFE, LASSO and RF) identified BCL2 and 和FOXP2 as candidate hub DORGs for DFU diagnosis. …”
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Unsupervised Learning With Hybrid Models for Detecting Electricity Theft in Smart Grids by Ali Jaber Almalki

“…By fusing supervised learning models (Random Forest) with unsupervised learning algorithms (Isolation Forest, One-Class Support Vector Machine (SVM), Local Outlier Factor (LOF), and Density-Based Spatial Clustering of Applications with Noise(DBSCAN)), this study presents a unique hybrid technique for identifying power theft. …”
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<b>The investigation of commonalities in human brain semantic representations across people and across languages</b><br> by Augusto Buchweitz

“…These patterns allow computer algorithms to identify the brain activity associated with a specific word or picture. …”
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Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models by Haoyuan Chen, Emil Constantinescu, Vishwas Rao, Cristiana Stan

“…Abstract The Madden–Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather patterns. In spite of the improvement in MJO predictions made by machine learning algorithms, such as neural networks, most of them cannot provide the uncertainty levels in the MJO forecasts directly. …”
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Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…To further improve the model’s performance, the sparrow search algorithm (SSA) is employed for parameter optimization, ensuring the best configuration of the CNN-LSTM-Attention framework. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…These enhanced features are then used as inputs for advanced machine-learning algorithms. Unlike traditional models, this feature extraction enhances classification accuracy and robustness. …”
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The Nexus of UG-ESs in the Chinese Loess Plateau using CL-CA and Ecological Assessment Models by Liang Youjia, Su Zichong, Liu Lijun

“…To address long-term spatiotemporal dependencies in grid neighborhood interactions, this study enhances land-use simulation accuracy using a method combining machine learning algorithms and cellular automata (CL-CA) to model competitive relationship between urban growth and other land-use types during 2000-2050, and then, ESs supply was simulated with ecological assessment models under three landuse scenarios: business as usual, ecological priority, and economic priority. …”
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Artificial Intelligence in Cardiovascular Diagnosis: Innovations and Impact on Disease Screenings by Amber Ahmad, Sahil Ahmad, Rida Ahmad, Jahnavi Bodi, Abdulla Mohamed, Ahmad Wasim

“…Materials and methods: Various AI models as well as algorithms, such as machine learning (ML) and deep learning (DL) algorithms, have shown good results in the detection of diseases like heart failure, atrial fibrillation, coronary artery disease, and valvular heart disease. …”
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EDITORIAL: ARTIFICIAL INTELLIGENCE AND ITS TRANSFORMATIVE IMPACT ON SCIENTIFIC PUBLISHING by Abubakar Munir

“…Some AI tools, like ScholarOne and Editorial Manager, have already started using machine learning algorithms to recommend reviewers and detect probable conflicts of interest, making an efficient and unbiased review process possible (2). …”
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