Calibration and uncertainty quantification for deep learning-based drought detection by Mengxue Zhang, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Gustau Camps-Valls

“…To address this shortcoming, we introduce methodologies for model calibration and entropy-based uncertainty quantification for deep learning-based drought detection. The calibration algorithm can deal with calibration errors by reducing distributional shifts and alleviating overconfident predictions. …”
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Advancing eHealth in Society 5.0: A Fuzzy Logic and Blockchain-Enhanced Framework for Integrating IoMT, Edge, and Cloud With AI by Joy Dutta, Deepak Puthal

“…We introduce the Health Prediction using Cloud Edge 2.0 (HPCE 2.0) algorithm, which employs fuzzy logic to effectively combine historical Electronic Health Records (EHRs) with real-time IoMT data, providing precise and personalized health severity level predictions. …”
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Hybrid Artificial Neural Network Activation Function to Reduce Water Wastage in Agricultural Irrigation by Baraa H. Jawad, Ola A. Alwesabi, Nibras Abdullah, Ahmed Abed Mohammed

“…The study proposes a hybrid activation function based on the Artificial Neural Network algorithm. This function is used to classify the need for irrigation in various crops and predict the best time of the day for watering. …”
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Promoter Region and Regulatory Elements of IGF and VIP Genes Associated With Reproductive Traits in Chicken by Bosenu Abera, Hunduma Dinka, Hailu Dadi, Habtamu Abera

“…Several in silico tools, such as Neural Network Promoter Prediction (NNPP), Multiple Expectation maximizations for Motif Elicitation (MEME-Suite), GC-Profiles, microsatellite prediction (MISA-web), CLC Genomics, Multiple Association Network Integration Algorithm (GeneMANIA), and Gene Ontology for Motifs (GOMO), were used to characterize the promoter regions and regulatory elements of IGF and VIP genes. …”
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Machine learning developed immune-related exosome signature for prognosis and immunotherapy benefit in bladder cancer by Xiaoting Luo, Yi Luo

“…To investigate the performance of IES in predicting the immunotherapy benefit, three immunotherapy datasets (GSE91061, GSE78220 and IMvigor210) and several predicting scores were used. …”
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Tumor associated neutrophils promote prostate cancer progression by mediating neutrophil trap secretion through PSMA1- NF-κB-HIF-1α signaling axis by Qian Dai, Qian Dai, Hua Wang, Hua Wang, Fang Li, Fang Li, Runchun Huang, Runchun Huang, Chenjun Jiang, Chenjun Jiang, Liuya Yuan, Liuya Yuan, Yayun Wang, Yayun Wang, Xun Li, Xun Li

“…Prostate cancer (PCa) is a common and deadly cancer in men, and despite its low specificity, PSA testing is the main method that is used to predict prognosis. Effective methods for predicting prognosis in clinical practice are lacking. …”
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A method for measuring carbon emissions from power plants using a CNN-LSTM-Attention model with Bayesian optimization by Jiacheng Chen, Li Zheng, Wenyan Che, Li Liu, Hui Huang, Jun Liu, Chang Xing, Penghua Qiu

“…The results indicate that the proposed model achieved the highest performance across every evaluation index for predicting CO2 emissions from coalfired power plants, and the prediction accuracy of the CO2 emission in the future months is also guaranteed.…”
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Artificial Neural Network Approach to Study Imprecisely Defined Nonlinear Systems With Case Studies by Lakshmi Durga Pathipati, Sukanta Nayak

“…A feed-forward neural network (FFNN) is used to generate outputs based on initial guesses for the field variables, and the ANN-LM algorithm optimizes the solution by minimizing the error between predicted and target values. …”
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Cooperative UAV Scheduling for Power Grid Deicing Using Fuzzy Learning and Evolutionary Optimization by Yu-Jun Zheng, Zhi-Yuan Zhang, Jia-Yu Yan, Wei-Guo Sheng

“…The algorithm is fully parallelizable and therefore can solve the problem very efficiently based on GPU parallel acceleration. …”
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Gestational diabetes mellitus and obesity: effects on offspring by O. V. Rozhkova, O. V. Remneva, N. I. Fadeeva, Yu. V. Korenovskiy, S. D. Yavorskaya

“…CHAID method (Chi Squared Automatic Interaction Detection) was used to create an algorithm for predicting adverse perinatal outcomes in GDМ. …”
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Intrusion detection system based on machine learning using least square support vector machine by Pratik Waghmode, Manideep Kanumuri, Hosam El-Ocla, Tanner Boyle

“…This framework utilizes quantum-inspired least square support vector machine (LS-SVM) classifier. This algorithm is used to enhance the classification accuracy in terms of reducing false predictions while minimizing the training time. …”
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A simulated annealing with graph-based search for the social-distancing problem in enclosed areas during pandemics. by Bayram Dundar

“…A greedy random-based algorithm is presented to determine efficiently an initial feasible solution. …”
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Artificial Neural Network Modeling of NixMnxOx based Thermistor for Predicative Synthesis and Characterization by T.D. Dongale, K.G. Kharade, N.B. Mullani, G.M. Naik, R.K. Kamat

“…The accomplished ANN modeling evidences a lower number of hidden neuron architecture exhibiting optimum performance as regards to prediction accuracy. The lower number of hidden neurons signifies a lesser amount of memory required for prediction of different chemical composition. …”
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Interpretable Machine Learning for Thermospheric Mass Density Modeling Using GRACE/GRACE‐FO Satellite Data by Qian Pan, Chao Xiong, ShunZu Gao, Zhou Chen, Artem Smirnov, Chunyu Xu, Yuyang Huang

“…A critical aspect of our work is minimizing the number of input parameters while maintaining high prediction accuracy. And the interpretability analysis of the input parameters using the Shapley additive explanation algorithm has been applied, revealing that the altitude, solar activity index P10.7 and solar zenith angle are the three most influential parameters affecting TMD variations at GRACE satellite. …”
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Efficient Resources Provisioning Based on Load Forecasting in Cloud by Rongdong Hu, Jingfei Jiang, Guangming Liu, Lixin Wang

“…It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. …”
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An Adaptive Robust Event-Triggered Variational Bayesian Filtering Method with Heavy-Tailed Noise by Di Deng, Peng Yi, Junlin Xiong

“…The one-step state prediction probability density function and the measurement likelihood function are modeled as Student’s t-distributions. …”
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Arctic Heatwaves Could Significantly Influence the Isoprene Emissions From Shrubs by Hui Wang, Allison Welch, Sanjeevi Nagalingam, Christopher Leong, Pitchayawee Kittitananuvong, Kelley C. Barsanti, Rebecca J. Sheesley, Claudia I. Czimczik, Alex B. Guenther

“…With a modified algorithm from this study, MEGAN predicts 66% higher isoprene emissions for Arctic willows during an Arctic heatwave. …”
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A recurrent neural network‐based rotor displacement estimation method for eight‐pole active magnetic bearing by Longyuan Fan, Zicheng Liu, Haijiao Wang, Dong Jiang, Yu Chen

“…The input dimensions and the architecture of the neural network are optimised to improve both prediction accuracy and computational complexity. Experimental results validate the effectiveness of the algorithm and demonstrate that the proposed method has high accuracy, robustness and generalisation ability.…”
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Investigation of Vessel Manoeuvring Abilities in Shallow Depths by Applying Neural Networks by Lúcia Moreira, C. Guedes Soares

“…The outcomes achieved with the proposed system have shown excellent accuracy and ability in predicting ship manoeuvring with varying depths of shallow water.…”
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Systematic, computational discovery of multicomponent and one-pot reactions by Rafał Roszak, Louis Gadina, Agnieszka Wołos, Ahmad Makkawi, Barbara Mikulak-Klucznik, Yasemin Bilgi, Karol Molga, Patrycja Gołębiowska, Oskar Popik, Tomasz Klucznik, Sara Szymkuć, Martyna Moskal, Sebastian Baś, Rafał Frydrych, Jacek Mlynarski, Olena Vakuliuk, Daniel T. Gryko, Bartosz A. Grzybowski

“…Moreover, when supplemented by models to approximate kinetic rates, the algorithm can predict reaction yields and identify reactions that have potential for organocatalysis. …”
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