Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning by Yuanxi Han, Liang Li, Siyuan Jiang, Pengpeng Sun, Wenliang Wu, Zhendong Liu

“…After data preprocessing, all models' accuracies and stability improved. Specifically, the logistic regression model was best for jerky identification, with 85.78 %–100.00 % accuracy. …”
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Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains by Joonyeol Yang , Minhyeong Kang , Seulchan Lee, Sanghyun Kim

“…This article proposes the Hybrid A*-guided Model Predictive Path Integral (MPPI) algorithm augmented with traversability estimation to address the challenges of path planning on uneven terrains. …”
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The synergistic effects of circuit training and diet on weight loss, body fat percentage, and BMI in obese Indonesian females: a randomized controlled trial by Moch Yunus, Nanang Tri Wahyudi, Ronal Surya Aditya, Alifia Candra Puriastuti, Daifallah M. Alrazeeni, Gadis Meinar Sari, Farsya Hidayah Septiananda, Lintang Nirmalasari Gemalochaya Manggolono

“…Conclusion: Smartwatch apps have great potential in optimizing cardiovascular fitness training. Future research needs to focus on improving sensor accuracy, refining AI interventions, and long-term user engagement to maximize their impact.…”
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FPGA Acceleration With Hessian-Based Comprehensive Intra-Layer Mixed-Precision Quantization for Transformer Models by Woohong Byun, Jongseok Woo, Saibal Mukhopadhyay

“…It is implemented on a single Xilinx ZCU102 FPGA board, operating at 200MHz with a power consumption of 15.08W during inference on the 110-million-parameter BERT-Base and 345-million-parameter GPT-2 Medium transformer models. Coupled with the proposed algorithm and dataflow optimization, it enables on-chip storage of all necessary parameters, minimizing off-chip memory access. …”
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A Quality Soft Sensing Method Designed for Complex Multi-process Manufacturing Procedures by Kaixiang PENG, Xin QIN, Jiahao WANG, Hui YANG

“…Additionally, an iterative optimization search is performed to determine the final optimal feature subset, thus deriving the auxiliary variable set for modeling. …”
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Bi-Objective Optimization of Techno-Economic and Environmental Performance of CO₂ Capture Strategy Involving Two-Stage Membrane-Based Separation with Recycling by Nobuo Hara, Satoshi Taniguchi, Takehiro Yamaki, Thuy T.H. Nguyen, Sho Kataoka

“…The implemented algorithm combined experimental design, machine learning, genetic algorithms, and Bayesian optimization. …”
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Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…The findings specify a substantial improvement in accuracy in prediction, emphasizing how the model can be used for improving solar power grid integration and energy for improved RE management.…”
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Construction and Application Analysis of an Intelligent Distribution Network Identification System Based on Deep Neural Networks by Yu Ma

“…In addition, the Cross-Validation Grid Search Algorithm (GSA) was used to optimize the hyperparameters to ensure that the model can achieve the optimal performance on different data sets. …”
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A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture by Hussain Mobarak Albarakati, Muhammad Attique Khan, Ameer Hamza, Faheem Khan, Naoufel Kraiem, Leila Jamel, Latifah Almuqren, Roobaea Alroobaea

“…In addition, we proposed a quantum hippopotamus optimization algorithm for the best feature selection. …”
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A robust and statistical analyzed predictive model for drug toxicity using machine learning by Deepak Rawat, Rohit Bajaj, Rachit Manchanda, Ankush Mehta, Prabhu Paramasivam, Suraj Kumar Bhagat, Abinet Gosaye Ayanie

“…Artificial intelligence and machine learning provide a platform to study toxicity prediction more accurately with a reduced time span. An optimized ensembled model is used to contrast the results of seven machine learning algorithms and three deep learning models with regard to state-of-the-art parameters. …”
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Performance of Various Artificial Intelligence Models for Predicting Temperature in an Industrial Building—A Case Study by Johan Roussel, Zoubeir Lafhaj, Pascal Yim, Thomas Danel, Laure Ducoulombier

“…By integrating high-performing predictive models into equipment control systems, it is possible to reduce energy costs, improve thermal comfort, and ensure efficient process stability. …”
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A prediction method for radiation proctitis based on SAM-Med2D model by Ning Zhang, Haifeng Ling, Wenyu Zhang, Mei Zhang

“…Accurate diagnosis are crucial for optimizing treatment strategies and improving patient outcomes. …”
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An ensemble time-embedded transformer model for traffic conflict prediction at RRFB pedestrian crossings by Md Jamil Ahsan, Mohamed Abdel-Aty, B M Tazbiul Hassan Anik, Zubayer Islam

“…For improved performance, these models were then combined using a voting classifier technique. …”
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A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles by Zhijian Chen, Yijun Fang, Jianjun Yin, Shiyu Lv, Farhan Sheikh Muhammad, Lu Liu

“…When compared with other algorithms, such as Faster RCNN, SSD, YOLOv3-tiny, and YOLOv5, the improved model strikes an optimal balance between parameter count, computational efficiency, detection speed, and accuracy, yielding superior results. …”
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Efficient and Stable Learning for Distribution Network Operation: A Model-Based Reinforcement Learning Approach by Dong Yan, Zhan Shi, Xinying Wang, Yiying Gao, Tianjiao Pu, Jiye Wang

“…This study introduces a reward evaluation system to assess the effectiveness of various strategies and proposes an enhanced algorithm based on the Model-based DRL approach. Incorporating a state transition model, the proposed algorithm augments data and enhances dynamic deduction, improving training efficiency. …”
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…The dataset was then split into training and test sets with an 80/20 ratio. Machine-learning models were trained, with hyperparameters optimized via grid search. …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…The results showed that tree-based boosting algorithms consistently outperformed other models in accuracy, precision, F1-score, and ROC-AUC, while the kernel-based approach excelled in recall. …”
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FPCAM: A Weighted Dictionary-Driven Model for Single-Cell Annotation in Pulmonary Fibrosis by Guojun Liu, Yan Shi, Hongxu Huang, Ningkun Xiao, Chuncheng Liu, Hongyu Zhao, Yongqiang Xing, Lu Cai

“…It achieves accurate and efficient cell-type annotation through similarity matrix construction and optimized matching algorithms. To evaluate its performance, we compared FPCAM with state-of-the-art annotation models, including SCSA, SingleR, and SciBet. …”
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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…Additionally, a computational tool for predicting CRC risk based on the optimal model was developed, designed for researchers with programming experience. …”
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Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis by Tengfei Zhou, Tengfei Zhou, Xinming Tian, Wei Wang, Zhe Chu

“…A total of 29 predictive models were developed, comprising 10 optimal models, primarily utilizing logistic regression or machine learning algorithms. …”
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