Novelty Detection in Autonomous Driving: A Generative Multi-Modal Sensor Fusion Approach by Hafsa Iqbal, Haleema Sadia, Abdulla Al-Kaff, Fernando Garcie

“…The MSF framework fuses both proprioceptive (wheel odometry) and exteroceptive (LiDAR point-clouds) sensory inputs. A novel 3-Dimensional Dynamic Variational Auto-Encoder (3D-DVAE) model is employed to learn attention-focused distributions from point-clouds in an unsupervised manner. …”
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Postprocessing of convection permitting precipitation forecast using UNets by Marcos Esquivel-González, Albano González, Juan Carlos Pérez, Juan Pedro Díaz

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Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals by Simfukwe C, An SSA, Youn YC

“…The processed PSD data, representing 19 scalp regions, were then input into a Random Forest (RF) machine learning classifier to identify distinctive EEG patterns across the groups. …”
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New strategies of beneficial development of in-situ combustion in nearly abandoned heavy oil reservoirs by ZHENG Yiqiong, ZHANG Tao, LIU Haiying, RUAN Conghui, ZOU Shuai

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Evaluating Geostatistical and Statistical Merging Methods for Radar–Gauge Rainfall Integration: A Multi-Method Comparative Study by Xuan-Hien Le, Naoki Koyama, Kei Kikuchi, Yoshihisa Yamanouchi, Akiyoshi Fukaya, Tadashi Yamada

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Integrative in Silico modeling for mTOR inhibition: From ridge classifiers to descriptor-free deep neural networks by Seyed Alireza Khanghahi, Hadi Kamkar, Seyedehsamaneh Shojaeilangari, Abdollah Allahverdi, Parviz Abdolmaleki

“…Using a variety of quantitative structure-activity relationship (QSAR) models, we present a comprehensive comparison of deep learning (DL) and classical machine learning (ML) techniques for modeling mTOR inhibitor activity. …”
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An Operational Status Assessment Model for SF₆ High-Voltage Circuit Breakers Based on IAR-BTR by Ningfang Wang, Yujia Wang, Yifei Zhang, Ci Tang, Chenhao Sun

“…The selected dataset is then normalized according to the input feature matrix. Secondly, conventional risk factors are extracted using traditional association rule mining techniques. …”
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Comparison of DNA and RNA extraction efficiency from blood by SU Xinglei, LU Ping, PENG Junjie, WANG Zimin, SONG Ping, HAN Da

“…When comparing three samples, the cfDNA yields from larger plasma input volumes was 3.98-fold, 2.38-fold, and 3.82-fold higher than those from smaller input volumes, respectively. …”
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Drosophila Toxicogenomics: genetic variation and sexual dimorphism in susceptibility to 4-Methylimidazole by Katelynne M. Collins, Elisabeth Howansky, Sarah C. Macon-Foley, Maria E. Adonay, Vijay Shankar, Richard F. Lyman, Nestor Octavio Nazario-Yepiz, Jordyn K. Brooks, Rachel A. Lyman, Trudy F. C. Mackay, Robert R. H. Anholt

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SIMULATION OF STATIC AND DYNAMIC LOSSES IN MOSFET KEYS by V. P. Babenko, V. K. Bityukov, V. V. Kuznetsov, D. S. Simachkov

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Getting it right: suppression and leveraging of noise in robust decision-making by Rishikesh P. Bhalerao

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Environmental signal integration by a modular AND gate by J Christopher Anderson, Christopher A Voigt, Adam P Arkin

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Research on Grid-Connected Speed Control of Hydraulic Wind Turbine Based on Enhanced Chaotic Particle Swarm Optimization Fuzzy PID by Yujie Wang, Yang Cao, Zhong Qian, Jianping Xia, Xuhong Kang, Yixian Zhu, Yanan Yang, Wendong Zhang, Shaohua Chen, Guoqing Wu

“…Simulation results show that when the swash plate angle factor varies within a specific range, the variable motor speed is only related to the quantitative pump speed. When the input speed of the quantitative pump changes in a step from 400 to 500 r/min, the enhanced CPSO fuzzy PID control approach reduces ascension time by 40% and 76%, and settling time by 80% and 76%, compared to the fuzzy PID and PSO fuzzy PID control approaches, respectively. …”
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Vocational High School (SMK) Students Accounting Competence Prediction Model by Using Astin I-E-O Model by Defi Sri Harwati, Heri Yanto

“…This research aims to know the descriptive of input, environment, and outcome; analyze the influence of input to outcome; analyze the influence of environment to outcome; analyze the influence of input to environement; and analyze the role of environment in mediating the influence of input to outcome. …”
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Classification of Incidental Carcinoma of the Prostate Using Learning Vector Quantization and Support Vector Machines by Torsten Mattfeldt, Danilo Trijic, Hans‐Werner Gottfried, Hans A. Kestler

“…Tumour vascularization (angiogenesis) and epithelial texture were investigated by quantitative stereology. Learning vector quantization (LVQ) and support vector machines (SVM) were used for the purpose of prediction of tumour category from a set of 10 input variables (age, Gleason score, preoperative PSA value, immunohistochemical scores for proliferation and p53‐overexpression, 3 stereological parameters of angiogenesis, 2 stereological parameters of epithelial texture). …”
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Dynamic UAV data fusion and deep learning for improved maize phenological-stage tracking by Ziheng Feng, Jiliang Zhao, Liunan Suo, Heguang Sun, Huiling Long, Hao Yang, Xiaoyu Song, Haikuan Feng, Bo Xu, Guijun Yang, Chunjiang Zhao

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Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks by Andrzej Liebert, Dominique Hadler, Chris Ehring, Hannes Schreiter, Luise Brock, Lorenz A. Kapsner, Jessica Eberle, Ramona Erber, Julius Emons, Frederik B. Laun, Michael Uder, Evelyn Wenkel, Sabine Ohlmeyer, Sebastian Bickelhaupt

“…Quantitative metrics were used to evaluate the different input protocols. …”
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Dissection of a Krox20 positive feedback loop driving cell fate choices in hindbrain patterning by Yassine X Bouchoucha, Jürgen Reingruber, Charlotte Labalette, Michel A Wassef, Elodie Thierion, Carole Desmarquet‐Trin Dinh, David Holcman, Pascale Gilardi‐Hebenstreit, Patrick Charnay

“…The stochasticity of the activation process leads to a graded input–output response until saturation is reached. …”
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Prediction model for chemical explosion consequences via multimodal feature fusion by Yilin Wang, Beibei Wang, Yichen Zhang, Jiquan Zhang, Yijie Song, Shuang-Hua Yang

“…The model utilizes molecular descriptors derived from the Simplified Molecular Input Line Entry System (SMILES) and Gaussian16 software, combined with leakage condition parameters, as input features to investigate the quantitative relationship between these factors and explosion consequences. …”
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Triangular Mesh Surface Subdivision Based on Graph Neural Network by Guojun Chen, Rongji Wang

“…In particular, using the general input mesh, the algorithm in this paper was compared to neural subdivision through quantitative experiments. …”
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