Machine Learning Techniques for Classification of Stress Levels in Traffic by Amanda Trojan Fenerich, Egídio José Romanelli, Rodrigo Eduardo Catai, Maria Teresinha Arns Steiner

“… The aim of this study is to apply Machine Learning techniques for predicting and classifying the stress level of people commuting from home to work and also to evaluate the performance of prediction models using feature selection. …”
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High-Throughput cDNA Screening Utilizing a Low Order Neural Network Filter by Guyang Matthew Huang, James Farkas, Leroy Hood

“…Evaluation of the detections with two other dissimilar computer prediction algorithms yielded strong transmembrane predictions for 15 of the detections, while 8 of the detections resulted in a definitive negative result. …”
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Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators by Katia Benamara, Hocine Amimeur, Yanis Hamoudi, Maher G. M. Abdolrasol, Umit Cali, Umit Cali, Taha Selim Ustun

“…A comparative analysis of the optimization results has been conducted, focusing on the outcomes obtained with the GWO algorithm. The findings reveal a notable reduction in steady-state error, signifying improved stability, and an overall enhancement in the wind power system’s performance. …”
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Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR by Mingjun Wang, Xuezhi Zhang, Feng Jing, Mei Gao

“…Compared to the BBRv1 algorithm, Ad-BBR achieved a 17% increase in throughput and a 30% improvement in RTT fairness, along with a 13% reduction in the retransmission rate and an approximate 20% decrease in latency.…”
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A simplification method for large-scale urban point clouds considering diversity of terrain object features by Shen Shen, Yonghong Wu, Hongwei Zhang, Jiankai Lu, Hong Fan

“…Comparative experimental results indicate that compared to baseline algorithms that consider geometric features on the test dataset, the proposed method achieves about 16.5-min processing time for 13-million-point dataset, significantly faster than other algorithms. …”
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Explainable light-weight deep learning pipeline for improved drought stress identification by Aswini Kumar Patra, Aswini Kumar Patra, Lingaraj Sahoo

“…Sensor-based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis that aims to identify drought stress. …”
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A Framework of Recommendation System for Unmanned Aerial Vehicle Autonomous Maneuver Decision by Qinzhi Hao, Tengyu Jing, Yao Sun, Zhuolin Yang, Jiali Zhang, Jiapeng Wang, Wei Wang

“…Through rigorous computer-based testing, we validated the effectiveness of established recommendation algorithms within our framework. Notably, the prioritized experience replay deep deterministic policy gradient (PER-DDPG) algorithm, employing dense rewards and continuous actions, demonstrated superior performance, achieving a 69% success rate in confrontational scenarios against a versatile expert algorithm after 1000 training iterations, marking an 80% reduction in training time compared to conventional reinforcement learning methods. …”
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Lossless Hyperspectral Image Compression in Comet Interceptor and Hera Missions with Restricted Bandwith by Kasper Skog, Tomáš Kohout, Tomáš Kašpárek, Antti Penttilä, Monika Wolfmayr, Jaan Praks

“…These results contribute valuable knowledge to the behavioral characteristics of both algorithms and provide insight for entities planning on using either algorithm on board planetary missions.…”
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Integrated Time Series Analysis, Clustering, and Forecasting for Energy Efficiency Optimization and Tariff Management by Anderson Jhones Passos Nascimento, Menaouar Berrehil El Kattel, Jose Antonio Fernandes de Macedo, Fernando Luiz Marcelo Antunes

“…Additionally, the BIRCH algorithm was evaluated in combination with dimensionality reduction methods, such as Principal Component Analysis (PCA) and Autoencoders, to enhance clustering performance. …”
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Artificial Intelligence in Revolutionizing Kidney Care and Beyond: Kid-AI Revolution by Kounaina Khan, Farhan Zameer, Pratheek Jain, Ravi KR, Vidya Niranjan, Manoj S, Ravish H, Subrahmanya Padyana

“…This review provides a comprehensive overview of AI applications in renal pathology, focusing on computer vision algorithms for kidney structure segmentation, specific pathological changes, diagnosis, treatment, and prognosis prediction based on images along with the role of machine learning (ML) and deep learning (DL) in addressing global public health issues related to various nephrological conditions. …”
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Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks by Chamaka Karunanayake, Miyuru B. Gunathilake, Upaka Rathnayake

“…Results revealed that the LM training algorithm outperforms the other tests algorithm in developing the prediction model. …”
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Retrieval of Cloud Ice Water Path from FY-3F MWTS and MWHS by Fuxiang Chen, Hao Hu, Fuzhong Weng, Changjiao Dong, Xiang Fang, Jun Yang

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Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer by Lei Shen, Kang Hou, Jifeng Zhang, Xiaodong Li

“…The oncoPredict package predicted candidate chemotherapy agents.ResultsBased on screening and identification results, we established three anoikis-related genes: LEP, HAMP, and FAM43B, as the prognostic prediction genes of CRC. …”
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Prospective Validation and Usability Evaluation of a Mobile Diagnostic App for Obstructive Sleep Apnea by Pedro Amorim, Daniela Ferreira-Santos, Marta Drummond, Pedro Pereira Rodrigues

“…Current guidelines recommend the development of clinical prediction algorithms in screening prior to PSG. A recent intuitive and user-friendly tool (OSABayes), based on a Bayesian network model using six clinical variables, has been proposed to quantify the probability of OSA. …”
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REST Network: An Ensemble Deep Learning Approach for EV Charging Load Forecasting in Artificial Port Supply Chains by Elham Abdullah Alghamdi, Monagi Hassan Alkinani, Abdulwahab Ali Almazroi, Mohammed Alqarni, Eman Ali Aldhahri, Nasir Ayub

“…Traditional forecasting algorithms can fail to address logistics’ complicated, variable charge patterns and uneven data. …”
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Improving Soil Heavy Metal Lead Inversion Through Combined Band Selection Methods: A Case Study in Gejiu City, China by Ping He, Xianfeng Cheng, Xingping Wen, Yi Cao, Yu Chen

“…To construct a preliminary Pb content prediction model, the initial selection of spectral bands utilized methods including CARS (Competitive Adaptive Reweighted Sampling), GA (Genetic Algorithm), MI (mutual information), SPA (Successive Projections Algorithm), and WOA (Whale Optimization Algorithm). …”
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An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition by Zongying Liu, Shaoxi Li, Jiangling Hao, Jingfeng Hu, Mingyang Pan

“…However, these algorithms have their own limitations and their prediction accuracy still has space to improve. …”
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Adaptive Real-Time Transmission in Large-Scale Satellite Networks Through Software-Defined-Networking-Based Domain Clustering and Random Linear Network Coding by Shangpeng Wang, Chenyuan Zhang, Yuchen Wu, Limin Liu, Jun Long

“…Existing research primarily emphasizes traffic prediction and scheduling using spatiotemporal models and machine learning. …”
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Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning by Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang

“…Preceding forecasting algorithms primarily focused on time series analysis, often overlooking influential factors such as economic conditions. …”
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High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks by Haoyi Tan, Yukun Teng, Guangcun Shan

“…Subsequently, two machine learning regression algorithms—Random Forest and CatBoost, were employed to predict the iodine adsorption capabilities of MOF materials. …”
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