Results 41 to 50 of about 33,535 (242)

Deep Contrastive Learning for High‐Throughput Prediction of Drug Resistance Mutations from Sequences

Advanced Science, EarlyView.
This study presents DeepMutDTA, a deep learning framework aimed at predicting mutation‐induced changes in protein‐drug interactions and prioritizing variants potentially linked to drug resistance. Trained on large‐scale data, it incorporates SimSiam‐MuTF, a label‐aware contrastive fine‐tuning strategy that encourages separation between WT and MT ...
Xiaowen Hu, Pan Zhang, Shangqian Wu, Hao Sun, Minwei Li, Sophia Tsoka, Zizhang Sheng, Lei Deng   +7 more
wiley   +1 more source

Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach

Scientific Reports
This study uses Quantum Particle Swarm Optimization (QPSO) optimized Recurrent Neural Networks (RNN), standard RNN, and autoregressive integrated moving average (ARIMA) models to anticipate educational building power demand accurately. Energy efficiency,
Sunawar Khan, Tehseen Mazhar, Tariq Shahzad, Tariq Ali, Muhammad Ayaz, Yazeed Yasin Ghadi, EL-Hadi M. Aggoune, Habib Hamam   +7 more
doaj   +1 more source

Neutrosophic Exponential Ratio-Type Estimator for Finite Population Mean [PDF]

Neutrosophic Sets and Systems
This paper proposes a neutrosophic exponential-type estimator for finite population mean estimation using auxiliary variables. Traditional statistical estimators often fall short when handling vague or uncertain data.
Anisha Taneja, Sachin Malik, Prem Shankar Jha   +2 more
doaj   +1 more source

Solid Harmonic Wavelet Bispectrum for Image Analysis

Advanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown, Mathilda Avirett‐Mackenzie, Carolin Villforth, Georgios Exarchakis   +3 more
wiley   +1 more source

Bagging-based heteroscedasticity-adjusted ridge estimators in the linear regression model

Kuwait Journal of Science
The existence of multicollinearity between independent variables and heteroscedastic error has a colossal impact on the performance of the ordinary least square (OLS) estimator and its covariance matrix.
doaj   +1 more source

Selection of Parameters and Architecture of Multilayer Perceptrons for Predicting Ice Coverage of Lakes

Ekológia (Bratislava), 2017
The ice cover on lakes is one of the most influential factors in the lakes’ winter aquatic ecosystem. The paper presents a method for predicting ice coverage of lakes by means of multilayer perceptrons.
Baklagin Nikolaevich Vyacheslav
doaj   +1 more source

Proving the Efficiency of Alternative Linear Regression Model Based on Mean Square Error (MSE) and Average Width using Aquaculture Data

International Journal of Recent Technology and Engineering, 2019
Multiple linear regressions (MLR) model is an important tool for investigating relationships between several response variables and some predictor variables. This method is very powerful and commonly used in finance, economic, medical, agriculture and many more.
openaire   +1 more source

Overcoming Artificial Structures in Resolution‐Enhanced Hi‐C Data by Signal Decomposition and Multi‐Scale Attention

Advanced Science, EarlyView.
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li, Kelly Yichen Li, Chiara Nicoletti, Stephen Kwok‐Wing Tsui, Pier Lorenzo Puri, Qin Cao, Kevin Y. Yip   +6 more
wiley   +1 more source

Comparative Evaluation of Statistical, Machine‐Learning, and Deep‐Learning Models for Construction Sand and Gravel Price Forecasting: A Synthetic‐Data, Simulation‐Based Benchmark

Engineering Reports
Forecasting construction sand and gravel prices is critical for infrastructure cost control, yet reliable comparisons among model families in the small‐sample, multidriver setting typical of regional markets are lacking.
You Wu, Peng Li, Jing Zhang, Fengsheng Guo, Danshu Hu, Wei He, Zhi Wang   +6 more
doaj   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source