Modelo matemático y algoritmo de solución de un flujo con tiempos dependientes de la secuencia // A mathematical model and solution algorithm to a flow shop task with times dependi... by A. Fiol-Zulueta, J. Arzola-Ruíz, A. da Silva-Carvalho

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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…It is required to enhance data processing, improve model generalization by preprocessing and synthesizing data, and optimize model adaptability in changing environments. …”
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A Ｒeinforcement Learning of Cloud Ｒesource Scheduling Algorithm Based on Adaptive Weight by LI Cheng-yan, SUN Wei, TANG Li-min

“…We considered the cloud computing resource scheduling problem，and proposed a multi-objective optimization mathematical model to optimize task completion time and running cost simultaneously. …”
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Optimal distributed generation placement and sizing using modified grey wolf optimization and ETAP for power system performance enhancement and protection adaptation by Nasreddine Bouchikhi, Fethi Boussadia, Riyadh Bouddou, Ayodeji Olalekan Salau, Saad Mekhilef, Chaima Gouder, Sarra Adiche, Abdallah Belabbes

“…The MGWO algorithm is an improved version of the conventional GWO algorithm, which is based on a hierarchical model inspired by the social behavior of grey wolves. …”
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Bio inspired feature selection and graph learning for sepsis risk stratification by D. Siri, Raviteja Kocherla, Sudharshan Tumkunta, Pamula Udayaraju, Krishna Chaitanya Gogineni, Gowtham Mamidisetti, Nanditha Boddu

“…To further improve predictive accuracy, the TOTO metaheuristic algorithm is applied for model fine-tuning. …”
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Implementation of SVM Algorithm to Predict Song Popularity based on Sentiment Analysis of Lyrics by Quiin Latifah Almatin Lubis, Arif Akbarul Huda

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Identification of soil texture and color using machine learning algorithms and satellite imagery by Jiyang Wang

“…For future research, it is recommended to explore the combination of SVR with optimization techniques such as genetic algorithms to further improve the accuracy of soil texture and color predictions.…”
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Progressive filling partitioning and mapping algorithm for Spark based on allocation fitness degree by Chen BIAN, Jiong1 YU, Wei-rong XIU, Bin LIAO, Chang-tian YING, Yu-rong QIAN

“…The job execution mechanism of Spark was analyzed,task efficiency model and Shuffle model were established,then allocation fitness degree (AFD) was defined and the optimization goal was put forward.On the basis of the model definition,the progressive filling partitioning and mapping algorithm (PFPM) was proposed.PFPM established the data distribution scheme adapting Reducers’ computing ability to decrease synchronous latency during Shuffle process and increase cluster the computing efficiency.The experiments demonstrate that PFPM could improve the rationality of workload distribution in Shuffle and optimize the execution efficiency of Spark.…”
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Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour by Xinyi Dong, Ying Dong, Jinming Liu, Siting Wu

“…Compared to regression models built with competitive adaptive reweighted sampling and genetic algorithm for feature wavelength selection, the performance improved significantly, enhancing generalization capability. …”
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Deep learning-based feature selection for detection of autism spectrum disorder by Ibrahim Nafisah, Nermine Mahmoud, Ahmed A. Ewees, Mohamed G. Khattap, Abdelghani Dahou, Safar M. Alghamdi, Ibrahim A. Fares, Mohammed Azmi Al-Betar, Mohammed Azmi Al-Betar, Mohamed Abd Elaziz, Mohamed Abd Elaziz

“…Feature selection is enhanced through an optimized Hiking Optimization Algorithm (HOA) that integrates DynamicOpposites Learning (DOL) and Double Attractors to improve convergence toward the optimal subset of features.ResultsThe proposed model is evaluated using multiple ASD datasets. …”
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Simulation of automatic intrusion detection in university networks by using neural network algorithms by Houdun Xu

“…Using appropriate training methods and optimization algorithms, train and optimize the neural network model to achieve high accuracy and robustness. …”
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Optimization Scheduling of Multiple Heterogeneous Energy Sources by Ying Zhao, Zhiwen Yu, Xiaobin Wang, Jianlin Tang, Xiaoming Lin, Fan Zhang, Bin Qian

“…The study summarizes the mainstream mathematical modeling and optimization algorithms, intelligent optimization techniques, and real-time data processing technologies. …”
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Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data by Simone A. Ludwig

“…It involves selecting a subset of relevant features for use in model construction. Feature selection helps in improving model performance by reducing overfitting, enhancing generalization, and decreasing computational cost. …”
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Research on International Law Data Integrity Guarantee Based on Antiterrorism Prediction Algorithm by Huang Ru Qing

“…In order to improve the quality of international law data, this paper designs a method to ensure the integrity of international law data based on an antiterrorism prediction algorithm. …”
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Application of Particle Swarm Algorithm in Nanoscale Damage Detection and Identification of Steel Structure by Ying Zhang

“…Second, two test functions are used to compare and analyze, which shows that the improved particle swarm algorithm has better optimization performance. …”
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Vehicle Authentication-Based Resilient Routing Algorithm With Dynamic Task Allocation for VANETs by Nagaraju Pacharla, K. Srinivasa Reddy

“…With the goal of reducing task offloading delay and improving enhanced reaction time, a VANET-based task scheduling system is proposed after selecting an optimal route in the VANET. …”
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Algorithm study of digital HPA predistortion using one novel memory type BP neural network by Chun-hui HUANG, Yong-jie WEN

“…Based on the characteristic analysis of the high power amplifier (HPA) in wide-band CMMB repeater stations,a novel neural network was proposed which can respectively process the memory effect and the nonlinear of power amplifier.The novel model based on real-valued time-delay neural networks(RVTDNN) uses the Levenberg-Marquardt (LM) optimization to iteratively update the coefficients of the neural network.Due to the new parameters w⁰in the novel NN model,the modified formulas of LM algorithm were provided.Next，in order to eliminate the over-fitting of LM algorithm,the Bayesian regularization algorithm was applied to the predistortion system.Additionally,the predistorter of CMMB repeater stations based on the indirect learning method was constructed to simulate the nonlinearity and memory effect of HPA.Simulation results show that both the NN models can improve system performance and reduce ACEPR (adjacent channel error power ratio ) by about 30 dB.Moreover,with the mean square error less than 10⁻⁶,the coefficient of network for FIR-NLNNN is about half of that for RVTDNN.Similarly,the times of multiplication and addition in the iterative process of FIR-NLNNN are about 25% of that for RVTDNN.…”
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Comprehensive Analysis of Lightweight Cryptographic Algorithms for Battery-Limited Internet of Things Devices by Nahom Gebeyehu Zinabu, Yihenew Wondie Marye, Kula Kekeba Tune, Samuel Asferaw Demilew

“…In order to synthesize insights into present issues and future goals, it explores cutting-edge methods that improve performance, security, and energy efficiency. …”
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Myocarditis Detection Using Proximal Policy Optimization and Mutual Learning by Asadi Srinivasulu, Sivaram Rajeyyagari

“…To address class imbalance, a proximal policy optimization (PPO)-based algorithm is utilized, significantly improving the training process by preventing abrupt policy shifts and stabilizing them. …”
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Design of digital low-carbon system for smart buildings based on PPO algorithm by Yaohuan Wu, Nan Xie

“…The research results indicate that improving the near-end strategy optimization algorithm can reduce carbon emissions by 2354CO2e, while the lowest operating cost of the model is only 35,000 yuan. …”
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