Weight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique by Tran-Hieu Nguyen, Anh-Tuan Vu

“…A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. …”
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Enhancing Fuzzy C-Means Clustering with a Novel Standard Deviation Weighted Distance Measure by Ahmed Husham Mohammed, Marwan Abdul Hameed Ashour

“…It was proven  through the experimental results that  the proposed distance measure Weighted Euclidean distance  had the advantage over improving the work of the HFCM algorithm through the criterion (Obj_Fun, Iteration, Min_optimization, good fit clustering and overlap) when (c = 2,3) and according to the simulation results, c = 2 was chosen to form groups for the real data, which contributed to determine the best objective function (23.93, 22.44, 18.83) at degrees of fuzzing (1.2, 2, 2.8), while according to the degree of fuzzing (m = 3.6), the objective function for Euclidean Distance (ED) was the lowest, but the criteria were (Iter. = 2, Min_optimization = 0 and )  which confirms that (WED) is the best.…”
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Study on the parameters of the matrix NTRU cryptosystem by LI Zichen, WU Qinghao, SONG Jiashuo, PENG Haipeng

“…With the rapid development of quantum computers， post-quantum cryptography has emerged as a prominent area of research in cryptography.ObjectivesIn order to avoid the decryption failure in matrix NTRU as NTRU， the Matrix NTRU algorithm was optimized.MethodsBased on the method of constraining the parameter space in congruent cryptographic algorithms， a method for optimal selection of the parameter space of matrix NTRU cryptographic regimes was proposed. …”
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Novel Fast Super Twisting for Dynamic Performance Enhancement of Double-Fed Induction-Generator-Based Wind Turbine: Stability Proof and Steady State Analysis by Belgacem Kheira, Atig Mebarka, Abdelli Houaria, Mezouar Abdelkader

“…To overcome these shortcomings and optimize the efficiency and performance of this technique, a new method is suggested for the super-twisting algorithm (STA). …”
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SC-PA: A spot-checking model based on Stackelberg game theory for improving peer assessment by Jia Xu, Panyuan Yang, Teng Xiao, Pin Lv, Minghe Yu, Ge Yu

“…In particular, to ensure the utilities of both of the teachers and students, an algorithm that computes the optimal allocation plan for review resources is carefully designed based on Stackelberg equilibrium. …”
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Sperm swarm optimization for many objective power flow problems with enhanced performance evaluation in power systems by Wulfran Fendzi Mbasso, Ambe Harrison, Pradeep Jangir, Idriss Dagal, Hossam Kotb, Njimboh Henry Alombah, Raman Kumar, Aseel Smerat, Emmanuel Fendzi Donfack, Saad F. Al-Gahtani, Z. M. S. Elbarbary

“…Abstract This research presents an advanced optimization framework motivated from biological sources using the Sperm Swarm Optimization (SSO) algorithm to specifically deal with the Many-Objective Optimal Power Flow (MaO-OPF) problem in power systems. …”
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Exploring optimal combinations of multi-frequency polarimetric SAR observations to estimate forest above-ground biomass by Yongjie Ji, Fuxiang Zhang, Wangfei Zhang, Lei Zhao, Kunpeng Xu, Jianmin Shi, Guoran Huang, Qian Jing, Lu Wang, Feifei Yang

“…Taking advantage of available X-, C-, L-, and P-band quad-polarimetric SAR images of airborne or spaceborne for the test site located at Genhe national forest scientific field station, we used a Genetic Algorithm and Support Vector Regression optimization algorithm (GA-SVR) to explore the sensitivity of polarimetric observations at various frequencies to forest AGB and effectiveness of AGB retrievals using single-frequency, dual-frequency, triple-frequency, and quad-frequency SAR observation combinations. …”
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A correlation-based binary particle swarm optimization method for feature selection in human activity recognition by Huaijun Wang, Ruomeng Ke, Junhuai Li, Yang An, Kan Wang, Lei Yu

“…In existing works, for simplification purposes, feature selection algorithms are mostly based on the assumption of feature independence. …”
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Combining non-Monotone trust rregion method with a new adaptive radius for unconstrained optimization problems by Seyed Hamzeh Mirzaei, Ali Ashrafi

“…Purpose: One of the most effective methods for solving unconstrained optimization problems is the trust region method. …”
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Cross-Domain Edge Computing Offloading Strategy for Delay-Optimized in Low Earth Orbit Satellite Network by YANG Guisong, HUANG Yechao, HE Xingyu

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Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network by Kefei Cheng, Xiaoyong Guo, Xiaotong Cui, Fengchi Shan

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Current state and prospects of development of energy-optimal control systems for 2ES6 electric locomotives by S. G. Istomin, K. I. Domanov, A. P. SHATOKHIN, I. N. Denisov

“…The researchers show that the most feasible way to build real-time dynamic models of energy-optimal locomotive motion for such smart system is to use data from the automated workstation of a freight locomotives motion recorder and auto-drive, as this is the data that contains accurate geographic coordinates to synchronise measurements on trips in a particular section.Discussion and conclusion. …”
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Energy Efficiency Optimization for UAV Distribution and Resource Allocation in NOMA and Multi-UAV Assisted Wireless Networks by Yuan Ren, Jieyu Wang, Yulong Xiao, Xuewei Zhang, Fan Jiang, Junxuan Wang, Jing Jiang, Guangyue Lu

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Design of intelligent optimization of sports strategy and training decision support system based on deep reinforcement learning by Hua Xu, Bing Lin, Long Liu

“…The data is preprocessed by a sliding window average filter algorithm to eliminate noise and outliers. The system adopts the DQN (Deep Q-Network) architecture and applies dual DQN technology to improve model stability. …”
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Adaptive optimization decision system for plate-fin heat Exchangers: An integrated approach to enhancing efficiency and performance by Na Sun, Shuai Zhang, Nan Li, Zijian Li, Meng He, Zhengchun Shen, Ke Wang, Xiaoyong Guo, Wen-Quan Tao

“…The GSA module uses the Sobol method to evaluate the impact of design variables on performance. The optimization module employs the Newton-Raphson-based optimizer (NRBO) and the multi-strategy improved grey wolf optimization algorithm (MIGWO). …”
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Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks by Saad Said Alqahtany, Asadullah Shaikh, Ali Alqazzaz

“…To test the new FS technique and decide on an optimal set of features based on the accuracy achieved and the feature taking filters, the most recent FS approach relies on the NF-ToN-IoT dataset. …”
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Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…This study highlights the importance of integrating Machine Learning and statistical analysis methods for the effective modeling and optimization of LPBF processes. The findings contribute significantly to the literature and serve as a valuable reference for future research aimed at improving LPBF process efficiency and performance.…”
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Enhancing Efficiency and Reducing the Carbon Footprint of Cloud-Based Healthcare Applications through Optimal Data Preprocessing by El Aziz Btissam, Eddabbah Mohammed, Laaziz Yassin

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HiGMA-DADCN: Hirudinaria granulosa multitropic algorithm optimised double attention enabled deep convolutional neural network for psoriasis classification by Soumya C S, Jayanna H S

“…The HiGMA algorithm plays a crucial role in identifying and extracting the most relevant regions of affected skin through optimal segmentation. …”
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Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…In addition, we developed a novel hybrid feature selection model that combined correlation filtering with mutual information, Cross-Validation (CV) along with Recursive Feature Eliminatio (RFE) (R, and stability selection to identify the most impactful features. Moreover, This study develops the proposed EffiXNet, a more refined version of EfficientNet augmented with self-attention mechanisms, dynamic convolutions, improved normalization methods, and Sparrow Search Optimization Algorithm for hyperparameter optimization. …”
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