Human and environmental feature-driven neural network for path-constrained robot navigation using deep reinforcement learning by Nabih Pico, Estrella Montero, Alisher Amirbek, Eugene Auh, Jeongmin Jeon, Manuel S. Alvarez-Alvarado, Babar Jamil, Redhwan Algabri, Hyungpil Moon

“…The representation, combined with weighted features from humans and environmental limitations, is processed through three multi-layer perceptrons (MLP) to calculate the value function and optimal policy, thereby enhancing navigation tasks. …”
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High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction by Md Ashraful Haque, Redwan A. Ananta, Md. Sharif Ahammed, Jamal Hossain Nirob, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Ahmed A. Abd El-Latif, May Almousa, Abdelhamied A. Ateya

“…Among the five different regression machine learning models considered, it was discovered that the Random Forest Regression (RFR) model performed the best in accuracy and achieved the lowest error when predicting gain. …”
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Time-dependent physical unclonable functions by long-lived triplet excitons in carbon dots by Yan-Wei Hu, Qing Cao, Shi-Yu Song, Yuan Sun, Ya-Chuan Liang, Wen-Bo Zhao, Chao-Fan Lv, Chong-Xin Shan, Kai-Kai Liu

“…Abstract Physical unclonable functions (PUFs), relying extensively on the random spatial distribution of block elements, are promising technology for generating unclonable cryptograph. …”
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Impact of Meteorology on PM2.5 and O3 Pollution in Three Provincial Cities in Northeastern Provinces of China by Hui Xu, Hongmei Zhao, Ziwen Zhu, Jiayou Zhang

“…To further understand the effect of meteorology on PM2.5 and O3 in the three northeastern of China, back-propagation neural network (BPNN) and random forest (RF) was used in this study. Firstly, meteorological factors (temperature, relative humidity, precipitation, wind speed, pressure and planetary boundary layer (PBL) height, cloud cover) were put into BPNN and RF models to investigate the influence of weather conditions on PM2.5 and O3 during 2015–2021. …”
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Enhanced Superpixel-Guided ResNet Framework with Optimized Deep-Weighted Averaging-Based Feature Fusion for Lung Cancer Detection in Histopathological Images by Karthikeyan Shanmugam, Harikumar Rajaguru

“…To further refine these features, particle swarm optimization (PSO) and red deer optimization (RDO) techniques are employed within the selective feature pooling layer. The optimized features are classified using various machine learning classifiers, including support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor (KNN), SoftMax discriminant classifier (SDC), Bayesian linear discriminant analysis classifier (BLDC), and multilayer perceptron (MLP). …”
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Long-Term Retrospective Predicted Concentration of PM_2.5 in Upper Northern Thailand Using Machine Learning Models by Sawaeng Kawichai, Patumrat Sripan, Amaraporn Rerkasem, Kittipan Rerkasem, Worawut Srisukkham

“…ML techniques, namely multi-layer perceptron neural network (MLP), support vector machine (SVM), multiple linear regression (MLR), decision tree (DT), and random forests (RF), were used to construct the prediction models. …”
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Modeling forest canopy structure and developing a stand health index using satellite remote sensing by Pulakesh Das, Parinaz Rahimzadeh-Bajgiran, William Livingston, Cameron D. McIntire, Aaron Bergdahl

“…This study generated the leaf area index (LAI) and a novel spatial layer of LCR at site and landscape scales using a combination of satellite data and ground observations. …”
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Study of the probabilistic and temporal characteristics of wireless networks using the CSMA/CA access method by А. S. Leontyev, D. V. Zhmatov

“…The methods employed herein include reliability theory, theory of random processes, queuing theory, and the Laplace–Stieltjes transform.Results. …”
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Machine learning methods for predicting human-adaptive influenza A virus reassortment based on intersegment constraint by Dan-Dan Zeng, Dan-Dan Zeng, Yu-Rong Cai, Sen Zhang, Fang Yan, Tao Jiang, Jing Li

“…Supervised ML models of random forest classifier (rfc) and multiple-layer preceptor (mlp) were developed to predict the human adaption to IAVs.ResultsOur results demonstrated that the frequencies of thymine, cytosine, adenine,and guanine (t, c, a, and g), as well as the content of gc/at were consistently high or low for the segments of PB2, PB1, PA, NP, M1, and NS1 (ribonucleoprotein plus [RNPplus]), between mammalian and avian IAVs or between influenza B viruses (IBVs) and IAVs.RNPplus NC negatively correlated with the NC for HA, NA, and M1 (envelope protein plus [EPplus]). …”
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Downscaling of Soil Moisture Map using Sentinel Radar Satellite Images and Distribution Analysis in the West of Iran by Seyed Hossein Mirmosavi, kohzad Raispour, Muhammad Kamangar

“…By combining the mentioned layers, an educational layer was obtained. Using the soil moisture layer backup (SVM) vector machine classification method, a small scale was obtained and a map with high resolution power was obtained. …”
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NEST HABITAT SELECTION BY WILD TURKEYS IN MINNESOTA by Jan E. Lazarus, William F. Porter

“…Comparisons were made between 18 nests of radio‐tagged turkeys and 50 random sites. Habitat at each nest and random site was examined in 3 concentric areas: a 3 m radius, a 40 m radius (0.5 ha), and a 65 ha area. …”
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Tillage systems and cover crops on soil physical properties after soybean cultivation by Rafael B. Teixeira, Monica C. R. Z. Borges, Cassiano G. Roque, Marcela P. Oliveira

“…The experimental design was randomized blocks with split plots. For the layer of 0.20-0.30 m, millet provided the best results for soil bulk density, macro and microporosity. …”
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Approximate t-Designs in Generic Circuit Architectures by Daniel Belkin, James Allen, Soumik Ghosh, Christopher Kang, Sophia Lin, James Sud, Frederic T. Chong, Bill Fefferman, Bryan K. Clark

“…Unitary t-designs are distributions on the unitary group whose first t moments appear maximally random. Previous work has established several upper bounds on the depths at which certain specific random quantum circuit ensembles approximate t-designs. …”
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Bulk density and water tensions in the soil on corn root production by João A. S. Nunes, Edna M. Bonfim-Silva, Tonny J. A. da Silva

“…An Oxisol collected in the layer of 0-0.2 m was used. The pots were made of PVC (polyvinyl chloride) tube with compacted soil in the middle layer. …”
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Exploration and morphological characteristics evaluation of a tetraploid seedling from periclinal graft chimera Hongrou Huyou (Citrus unshiu + C. aurantium) by JIANG Nan, WANG Gang, ZHANG Xiaoqin, CHEN Xiang, ZHANG Min, ZHANG Chi

“…[Objective] Plant organs are composed of multiple cell types, and organs of dicotyledonous plants normally have three distinct layers of cells, L1, L2 and L3. Layer L1 is the single layer of cells making up the epidermis, and layer L2 is the single cell for sub-epidermal layer, and layer L3 constitutes the rest of the internal cells. …”
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Spatial variability of soil properties in red soil and its implications for site-specific fertilizer management by Fang-fang SONG, Ming-gang XU, Ying-hua DUAN, Ze-jiang CAI, Shi-lin WEN, Xian-ni CHEN, Wei-qi SHI, Gilles COLINET

“…To explore the relationship between soil spatial variability and land management, 256 samples were randomly collected at two depths (surface layer 0–20 cm and subsurface layer 20–40 cm) under different land use types and soil parent materials in Yujiang County, Jiangxi Province, a red soil region of China. …”
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Initial hypotensive therapy of primary glaucoma with the domestic latanoprost generic: efficacy and safety by S. Yu. Petrov, O. M. Kalinina, L. V. Yakubova, S. M. Kosakyan, L. V. Vasilenkova, O. M. Filippova, A. N. Zhuravleva

“…Material and methods. A double-blind, randomized, parallel-group study involved 60 patients (70 eyes) with newly diagnosed POAG, who were randomly divided into 2 groups of equal size. …”
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A Natural Image Pointillism with Controlled Ellipse Dots by Dongxiang Chi

“…At last, the rendering runs layer-by-layer from large size dots to small size dots so as to reserve the detailed parts of the image. …”
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Integrating Physical Unclonable Functions with Machine Learning for the Authentication of Edge Devices in IoT Networks by Abdul Manan Sheikh, Md. Rafiqul Islam, Mohamed Hadi Habaebi, Suriza Ahmad Zabidi, Athaur Rahman Bin Najeeb, Adnan Kabbani

“…However, the proposed APUF exhibited its vulnerability to Multi-Layer Perceptron (MLP) and random forest (RF) modeling attacks, with 95.4% and 95.9% prediction accuracies, gaining successful authentication. …”
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Leveraging environmental DNA (eDNA) to optimize targeted removal of invasive fishes by Jennie J. Wiggins, Vanessa D. Tobias, Erika F. Holcombe, Katie Karpenko, Eric R. Huber, Andrew C. Goodman

“…In the removal phase, we randomly selected sites to sample for loach eDNA, plotted eDNA concentration as a GIS layer to develop heatmaps, and then placed 10 replicate traps at sites with the highest concentrations. …”
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