A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis by Shuaibu Abdullahi Hudu, Ahmed Subeh Alshrari, Esra’a Jebreel Ibrahim Abu-Shoura, Amira Osman, Abdulgafar Olayiwola Jimoh

“…This paper reviews how AI-driven models improve diagnostic accuracy, predict patient outcomes, and contribute to effective disease management. …”
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L-Shaped-Sensor-Array-Based Localization and Tracking Method for 3D Maneuvering Target by Xing Zhang, Xue Wang

“…Thirdly an autoregressive (AR) particle filter (PF) algorithm is realized to predict the locations in the next moment. …”
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Comparative review of intelligent structural safety in building seismic risk mitigation utilizing an integrated artificial intelligence controller by Normaisharah Mamat, Rawad Abdulghafor, Sherzod Turaev, Fitri Yakub

“…The research highlights the influence of integrated AI controllers on control systems, examining several AI controllers, including machine learning algorithms, neural networks, and evolutionary algorithms concerning structural safety. …”
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Feature selection and aggregation for antibiotic resistance GWAS in Mycobacterium tuberculosis: a comparative study by Kirill Reshetnikov, Daria Bykova, Daria Bykova, Konstantin Kuleshov, Konstantin Kuleshov, Konstantin Chukreev, Egor Guguchkin, Alexey Neverov, Gennady Fedonin

“…For some antibiotics, the resistance of a particular isolate can be reliably predicted by identifying specific mutations, while for other antibiotics the knowledge of resistance mechanisms is limited. …”
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Assessment of a Hyperspectral Remote Sensing Model Performance for Particulate Phosphorus in Optically Shallow Lake Water by Banglong Pan, Wuyiming Liu, Zhuo Diao, Qianfeng Gao, Lanlan Huang, Shaoru Feng, Juan Du, Qi Wang, Jiayi Li, Jiamei Cheng

“…This study aims to propose an algorithm to accurately predict the PP concentration and to assess the performance of the model. …”
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Nondestructive On-Site Detection of Soybean Contents Based on An Electrothermal MEMS Fourier Transform Spectrometer by Donglin Wang, Peng Wang, Huan Liu, Hongqiong Liu, Jicheng Zhang, Weiguo Liu, Huikai Xie

“…More than 300 samples of soybeans are collected, 80% of which are used to establish a prediction model using chemometrics methods. The standard error is no more than 3% when using the prediction model to test the remaining 20% samples.…”
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Azo Dyes and the Microbial World: Synthesis, Breakdown, and Bioactivity by Valery M. Dembitsky, Alexander O. Terent’ev

“…Using mathematical algorithms and the PASS program, researchers predict new potential applications based on their structure–activity relationships. …”
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A Hybrid GRA-TOPSIS-RFR Optimization Approach for Minimizing Burrs in Micro-Milling of Ti-6Al-4V Alloys by Rongkai Tan, Abhilash Puthanveettil Madathil, Qi Liu, Jian Cheng, Fengtao Lin

“…Moreover, the GRA-TOPSIS-RFR method significantly outperforms existing optimization and prediction models, with the integration of the RFR model enhancing prediction accuracy by 42.6% compared to traditional linear regression approaches. …”
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The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection by Dong Dai, Zhenyu Wang, Hao Huang, Xu Mao, Yehong Liu, Hao Li, Du Chen

“…For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R² = 0.9846, MSE = 0.2768, MAE = 0.3986). …”
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Disrupted fetal carbohydrate metabolism in children with autism spectrum disorder by Serena B. Gumusoglu, Brandon M. Schickling, Donna A. Santillan, Lynn M. Teesch, Mark K. Santillan

“…Untargeted principle components analysis of all metabolites did not reveal group differences, while targeted biomarker assessment (using only Fructose 6-phosphate, D-Mannose, and D-Fructose) by a Random Forest algorithm generated an area under the curve (AUC) = 0.766 (95% CI: 0.612–0.896) for ASD prediction. …”
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Assessment and application of melting-layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model by R. Mangla, R. Mangla, M. Borderies, P. Chambon, A. Geer, J. Hocking

“…<p>Because of their high sensitivity to hydrometeors and high vertical resolutions, spaceborne radar observations are emerging as an undeniable asset for numerical weather prediction (NWP) applications. The EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) NWP SAF (Satellite Application Facility for Numerical Weather Prediction) released an active sensor module within version 13 of the RTTOV (Radiative Transfer for TIROS Operational Vertical Sounder) software with the goal of simulating both active and passive microwave instruments within a single framework using the same radiative transfer assumptions. …”
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Utilizing Principal Singular Vectors for 2D DOA Estimation in Single Snapshot Case with Uniform Rectangular Array by Yuntao Wu, Xiaobing Pei, Hing Cheung So

“…Using the principal singular vectors of the observed data matrix, an iterative procedure based on the linear prediction property, and weighted least squares is proposed for finding the DOAs with lower computational complexity. …”
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Improved Mixture Cure Model Using Machine Learning Approaches by Huina Wang, Tian Feng, Baosheng Liang

“…When combined with mixture cure models, they can effectively improve the performance of mixture cure models, distinguish between susceptible and non-susceptible individuals, and accurately predict the influencing factors and their magnitude of incidence and latency.…”
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Regression-based investigation of latency in automated networks by Urooj Khan, Tariq Soomro, M.N. Brohi

“…Regression analysis of such a system has predicted that the performance gap will persist with varying network nodes and increasing time limits. …”
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A comparison of several intrusion detection methods using the NSL-KDD dataset by hazem salim abdullah

“…This research investigates the way to classify and predict cyber-attacks on the NSL-KDD dataset using intrusion detection methods the investigation contrasts the capabilities of various algorithms, including RNN, MLP, CNN-LSTM, and ANN, in recognizing attacks. …”
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Optimising Daily Fantasy Sports Teams with Artificial Intelligence by Beal Ryan, Norman Timothy J., Ramchurn Sarvapali D.

“…Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. …”
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Estimation of CO₂ Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach by Seval Ene Yalçın

“…The findings of this study indicate that the aforementioned models can be effectively implemented to predict transport emissions, concluding that they have valuable and practical applications in this field.…”
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Integrated metabolomic-lipidomic profiling reveals novel biomarkers and therapeutic targets for alcohol use disorder with cognitive impairment by Li Shi, Li Shi, Li Shi, Xiaofang Chen, Bojie Zhou, Bojie Zhou, Shanghao Yang, Shanghao Yang, Qinglin Ou, Qinglin Ou, Xuhui Zhou, Xuhui Zhou

“…AUD-CI patients exhibited 49 unique lipid alterations, notably 70% reduction of phosphatidylcholine PC (42:4) versus HCs (p=0.002), strongly correlated with synaptic protein markers (r=0.82, p&lt;0.001).ConclusionOur findings characterize a dysregulated liver-gut-brain metabolic axis in AUD-CI pathogenesis, highlighting the mTORC1-sphingolipid pathway as a promising therapeutic target. …”
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Continual Learning With Neuromorphic Computing: Foundations, Methods, and Emerging Applications by Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad, Osman Hasan, Muhammad Shafique

“…It also covers optimization techniques including SNN operations reduction, weight quantization, and knowledge distillation. …”
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Utility of positron emission tomography–computed tomography in the evaluation of fever of unknown origin in a resource-limited tropical nation by Sohini Das, Sowmya Sathyendra, Julie Hephzibah, Reka Karuppusami, Karthik Gunasekaran, Nylla Shanthly, Angel Miraclin, Ramya Iyadurai

“…Sensitivity, specificity, and positive and negative predictive value of PET-CT scans were 76.9% (20/26), 33.3% (2/6), 83% (20/24), and 25% (2/8), respectively. …”
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