Forecast Model of TV Show Rating Based on Convolutional Neural Network by Lingfeng Wang

“…Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. …”
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Cooperativity among short amyloid stretches in long amyloidogenic sequences. by Lele Hu, Weiren Cui, Zhisong He, Xiaohe Shi, Kaiyan Feng, Buyong Ma, Yu-Dong Cai

“…The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. …”
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Towards Machine Learning-Driven Catalyst Design and Optimization of Operating Conditions for the Production of Jet Fuel Via Fischer-Tropsch Synthesis by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

“…The random forest ML algorithm was evaluated for predicting CO conversion and C8-C16 selectivity using this dataset. …”
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Challenges of International Trade and Government Governance from the Perspective of Economic Globalization by Dan Ge

“…On the basis of expounding the particle swarm optimization algorithm and GMDH algorithm, the optimization mode, method, and process of GMDH network based on particle swarm optimization are also expounded. …”
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A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles by Yuchang Zhu, Li Ke, Yijing Wei, Xiao Zheng

“…Compared with the opposition learning gray wolf optimizer and particle swarm optimization–gray wolf optimization, the proposed method achieves reductions of 52% and 68%, respectively. Additionally, under dual-frequency superimposed magnetic field excitation, a higher temperature inversion accuracy is achieved compared with that of the particle swarm optimization–gray wolf optimization algorithm, reducing the error from 0.237 K to 0.094 K.…”
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Computational selection of transcriptomics experiments improves Guilt-by-Association analyses. by Prajwal Bhat, Haixuan Yang, László Bögre, Alessandra Devoto, Alberto Paccanaro

“…We demonstrate that: using the selected experiments there is a statistically significant improvement in correlation between genes in the functional category of interest; the selected experiments improve GBA-based gene function prediction; the effectiveness of the selected experiments increases with annotation specificity; our algorithm can be successfully applied to GBA-based pathway reconstruction. …”
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Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning by Nikolaos A. Fountas, Dimitrios E. Manolakos, Nikolaos M. Vaxevanidis

“…The results indicated that the feed rate was the dominant parameter affecting both objectives, namely the main cutting force and surface roughness, while the NSGA-II algorithm was capable of delivering advantageous solutions for enhancing machinability with less than 10% error predictions when comparing simulated and actual machining results.…”
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China’s county-level monthly CO2 emissions during 2013–2021 by Ming Gao, Chaofan Tu, Miaomiao Liu, Jiandong Chen, Xingyu Chen, Hong Zou, Thomas Shiu Tong, Long Chen, Shuke Fu

“…After the main feature variables were identified, a hybrid regression algorithm combining deep neural networks and CatBoost was constructed to generate instrumental variable for predicting CO2 emissions. …”
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Analysis of Communication Compression and Transmission in a Multimedia and Internet of Things Environment Integrating Scene Elements by Jia Jia, Guohua Wu

“…Moreover, this paper proposes a reversible information hiding algorithm for encrypted images based on pixel sorting and grouping prediction and discusses how to improve the prediction accuracy through the histogram of prediction errors. …”
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A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection by Noppadol Maneerat, Athasart Narkthewan, Kazuhiko Hamamoto

“…The ensemble classifier was designed to predict the presence of TB by fusing DCNNs from the winning combination via weighted averaging. …”
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Estimating Economic Insights: A Machine Learning Method for Estimating the Shanghai Stock Exchange by Reza Seifi Majdar, Seyed Hadi Seyed Hatami

“…This work aims to create an accurate hybrid model for predicting stock prices which includes Adaptive Boosting, Slime mould algorithm, and Empirical mode decomposition (EMD) to forecast the stock market values. …”
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Genome-wide association between branch point properties and alternative splicing. by André Corvelo, Martina Hallegger, Christopher W J Smith, Eduardo Eyras

“…Using a Support Vector Machine algorithm, we created a model complemented with polypyrimidine tract features, which considerably improves the prediction accuracy over previously published methods. …”
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Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis. by Leonardo V Castorina, Filippo Grazioli, Pierre Machart, Anja Mösch, Federico Errica

“…In this work, we introduce a novel approach for assessing the generalization capabilities of TCR binding predictors: the Distance Split (DS) algorithm. The DS algorithm controls the distance between training and testing peptides based on both sequence and structure, allowing for a more nuanced evaluation of model performance. …”
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Research on Railway Passenger Volume Forecast Based on the Spline Interpolation and IPSO-Gradient Difference Acceleration Rule by Dingyuan Fan, Fei Yang, Jinghao Ji, Zexi Zhang

“…Finally, taking Beijing as the research object, the Holt exponential smoothing method and the BP neural network are selected to verify the effect of spline interpolation and IPSO-gradient difference acceleration law on prediction accuracy. The research results show that the spline interpolation method has a better prediction effect after processing abnormal passenger traffic data, and the improved particle swarm algorithm also shows better optimization ability and convergence speed when solving the double difference postulate. …”
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Adaptive DBP System with Long-Term Memory for Low-Complexity and High-Robustness Fiber Nonlinearity Mitigation by Mingqing Zuo, Huitong Yang, Yi Liu, Zhengyang Xie, Dong Wang, Shan Cao, Zheng Zheng, Han Li

“…Compared with conventional digital back-propagation and A-DBP based on a gradient-descent algorithm, our proposed method allows substantial complexity reductions of 31.35% and 58.47%, respectively. …”
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Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability by JaeBong Cho, Soonil Jung, Kyungmo Yang, Dohun Kim, WonJong Kim

“…This paper presents an efficient schedule method for maintenance, repair, and overhaul (MRO) tasks for aircraft engines using a constraint programming algorithm. Using data obtained from Korean Air’s MRO maintenance logs, we analyze and predict the optimal scheduling of regular inspections and fault repairs for various engine types. …”
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IMPLEMENTASI METODE RANDOM FOREST DALAM MEMPREDIKSI SINYAL PERGERAKAN SAHAM by MOCH. ANJAS APRIHARTHA, M. HUSNIYADI, TAUFIK NUR ALAM

“…Random forest is a combination algorithm of several decision trees used to solve prediction or classification problems. …”
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Personalized Federated Learning for Heterogeneous Residential Load Forecasting by Xiaodong Qu, Chengcheng Guan, Gang Xie, Zhiyi Tian, Keshav Sood, Chaoli Sun, Lei Cui

“…Based on the principle of generative adversarial network (GAN), the algorithm achieves the balance between privacy and prediction accuracy throughout the game. …”
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A Novel Fault Diagnosis of Induction Motor by Using Various Soft Computation Techniques: BESO-RDFA by Kapu V. Sri Ram Prasad, K. Dhananjay Rao, Guruvulu Naidu Ponnada, Umit Cali, Taha Selim Ustun

“…The established hybrid forecast scheme signifies the combined execution of Bald-Eagle- Search-Optimization (BESO) and Random-Decision-Forest-Algorithm (RDFA), called as BESO-RDFA prediction scheme. …”
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A novel ensemble support vector machine model for land cover classification by Ying Liu, Lihua Huang

“…We then combined finally individual prediction through AdaBoost algorithm to induce the final classification results on this new training set. …”
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