Performance comparison of ICP algorithms. by Mengyuan Yan (14200322)

“…We propose an improved LiDAR odometry and mapping with sliding window (LOAM-SLAM) algorithm enables real-time dynamic mapping, while an optimized iterative closest point (ICP) algorithm achieves high-precision point cloud registration and colorization. …”

High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization by Stephanos Karafiludis (12902591)

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High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization by Stephanos Karafiludis (12902591)

Subjects:

High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization by Stephanos Karafiludis (12902591)

Subjects:

High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization by Stephanos Karafiludis (12902591)

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Pre-optimization iteration process. by Meilin Zhu (688698)

“…Firstly, from the perspective of data-driven, it crawls the historical data of driving speed through Baidu map big data platform, and uses a BP neural network optimized by genetic algorithm to predict the driving speed of vehicles in different periods. …”

Supplementary Information for Machine Learning-Driven Optimization of Hardness and Toughness in High-Entropy Alloy Coatings Based on Composition and Descriptor by Kehong Zhong (21210977)

“…The supplementary material provides comprehensive details of the study "Machine Learning-Driven Optimization of Hardness and Toughness in High-Entropy Alloy Coatings Based on Composition and Descriptor", including the algorithmic workflow, experimental results, and dataset descriptions.…”

Event-driven data flow processing. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

MultiCRISPR-EGA: Optimizing Guide RNA Array Design for Multiplexed CRISPR Using the Elitist Genetic Algorithm by Yangyu Zhang (4609117)

“…Recognizing that more stable gRNAs, characterized by lower minimum free energy (MFE), have prolonged activity and thus higher efficacy, we developed MultiCRISPR-EGA, a graphical user interface (GUI)-based tool that employs the Elitist Genetic Algorithm (EGA) to design optimized single-promoter-driven multiplexed gRNA arrays. …”

Bagging Model Algorithm Flow. by Wen Yu (384130)

Tailoring stiffness distribution of tendon-driven continuum finger from manipulation force vector by Shunya Yamamoto (1678501)

“…The proposed method employs a genetic algorithm to optimize the thickness distribution of the continuum finger driven by a single motor, resulting in a highly versatile and cost-effective solution. …”

An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid by Mohib Hussain (21344850)

Data Sheet 1_AI-driven optimization of dynamic vibration absorbers with hydraulic amplifier and mechanical inerter integration.pdf by Ahmed Shamseldin (19759437)

UAV paths for the Priority-Driven scenario (sensor colors indicate priority, yellow high to blue low). by Bashir Olaniyi Sadiq (22478011)

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Run times of two algorithms. by Gourab Saha (8987405)

Optimized post-calibration amplitude and phase shifts for 4-channel PTX in safe mode, for two different regularization parameters. by Maryam Arianpouya (12299222)

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Performance of models by different algorithms. by Yong Zhang (5893)

“…This model offers innovative technical solutions and data-driven support for the clinical early identification of high-risk populations, with the potential to optimize and refine MASLD prevention and control strategies.…”

Image 2_AI-driven innovation in antibody-drug conjugate design.jpeg by Heather A. Noriega (21604514)

“…This review is organized into six sections: (1) the progression from traditional modeling approaches to AI-driven design of individual ADC components; (2) the application of deep learning (DL) to antibody structure prediction and identification of optimal conjugation sites; (3) the use of AI/ML models for forecasting pharmacokinetic properties and toxicity profiles; (4) emerging generative algorithms for antibody sequence diversification and affinity optimization; (5) case studies demonstrating the integration of computational tools with experimental pipelines, including systems that link in silico predictions to high-throughput validation; and (6) persistent challenges, including data sparsity, model interpretability, validation complexity, and regulatory considerations. …”

Image 1_AI-driven innovation in antibody-drug conjugate design.jpeg by Heather A. Noriega (21604514)

“…This review is organized into six sections: (1) the progression from traditional modeling approaches to AI-driven design of individual ADC components; (2) the application of deep learning (DL) to antibody structure prediction and identification of optimal conjugation sites; (3) the use of AI/ML models for forecasting pharmacokinetic properties and toxicity profiles; (4) emerging generative algorithms for antibody sequence diversification and affinity optimization; (5) case studies demonstrating the integration of computational tools with experimental pipelines, including systems that link in silico predictions to high-throughput validation; and (6) persistent challenges, including data sparsity, model interpretability, validation complexity, and regulatory considerations. …”

Discovered processes for variants 1 and 2: A) The process model was discovered using the heuristic miner algorithm for variant 1 in by Mohammad Bakhtiari (11838264)