Leader selection based Multi-Objective Flow Direction Algorithm (MOFDA): A novel approach for engineering design problems by Nima Khodadadi, Mohammad Ehteram, Hojat Karami, Mohammad H. Nadimi-Shahraki, Laith Abualigah, Seyedali Mirjalili

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autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil by Hugo Rodrigues, Marcos Bacis Ceddia, Gustavo Mattos Vasques, Sabine Grunwald, Ebrahim Babaeian, André Luis Oliveira Villela

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Predicting indoor temperature of solar green house by machine learning algorithms: A comparative analysis and a practical approach by Wenhe Liu, Tao Han, Cong Wang, Feng Zhang, Zhanyang Xu

“…This study focuses on a solar greenhouse located at the experimental base of Shenyang Agricultural University in Shenyang, Liaoning Province, to develop multi-step temperature prediction models based on machine learning algorithms. The research employs five algorithms: Random Forest (RF), Multiple Linear Regression (MLR), Support Vector Regression (SVR), Long Short-Term Memory Recurrent Neural Network (LSTM), and Gated Recurrent Unit (GRU) for temperature prediction. …”
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DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s by Zhaomei Qiu, Weili Wang, Xin Jin, Fei Wang, Zhitao He, Jiangtao Ji, Shanshan Jin

“…By refining the YOLOv5s algorithm, a novel, lightweight model termed DCS-YOLOv5s has been introduced for the simultaneous identification of tuber buds and defects. …”
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Blind Recognition Algorithm of Multi-Carrier Composite Modulation Signal Based on Multi-Dimensional Time-Frequency Superimposed Spectrum by Shoubin Wang, Huan Li, Xiaolong Zhang, Hao Jiang, Lei Shen

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Enhanced wombat optimization algorithm for multi-objective optimal power flow in renewable energy and electric vehicle integrated systems by Karthik Nagarajan, Arul Rajagopalan, Mohit Bajaj, Valliappan Raju, Vojtech Blazek

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Algorithm for the development of a multicomponent pharmaceutical substance of plant origin with antimicrobial action: from science search to dosage form by V. A. Filatov, O. Yu. Kulyak, E. I. Kalenikova

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Soliton Solutions of Generalized Third Order Time-Fractional KdV Models Using Extended He-Laplace Algorithm by Mubashir Qayyum, Efaza Ahmad, Sidra Afzal, Saraswati Acharya

“…In this research, the He-Laplace algorithm is extended to generalized third order, time-fractional, Korteweg-de Vries (KdV) models. …”
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Enhanced Dynamic Network DEA: A Novel Algorithm for Sustainable Development Efficiency Assessment in “Internet Plus Logistics” Sector by Yingli Wang, Guobin Qiu, Jing Wang, Qi Sun

“…This study introduces the enhanced dynamic network DEA, an innovative algorithmic extension of the foundational data envelopment analysis (DEA), to assess the sustainable development efficiency of Jiangxi Province’s “Internet Plus Logistics” sector from 2002 to 2016. …”
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Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures by Mohsin Ali, Li Chen, Qadir Bux Alias Imran Latif Qureshi, Deema Mohammed Alsekait, Adil Khan, Kiran Arif, Muhammad Luqman, Diaa Salama Abd Elminaam, Amir Hamza, Majid Khan

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Tuberculosis Detection using Gray Level Co-Occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN) Algorithms by Fuad Anwar, Mohtar Yunianto*, Rahmanisya Fani Aisha Putri

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A novel fixed-point based two-step inertial algorithm for convex minimization in deep learning data classification by Kobkoon Janngam, Suthep Suantai, Rattanakorn Wattanataweekul

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Performance Analysis of SpectralNet Algorithm on Fashion MNIST and KMNIST Datasets: A Study on Clustering Efficiency and Resource Utilization by Mujeeb Ur Rehman, Zenab Bibi

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An aggressive approach to hyperopia management: a method of maximum use of adaptive reserves intrinsic for the algorithms of natural refractive development by N. Yu. Kushnarevich

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Design and evaluation of a new tent-shaped transfer function using the Polar Lights Optimizer algorithm for feature selection by Zaynab Ayham Almishlih, Omar Saber Qasim, Zakariya Yahya Algamal

“… This research aims to develop a new transfer function to transform continuous space to binary space using the Polar Lights Optimizer (PLO) algorithm for the feature selection problem. …”
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A stratified two-stage tumor molecular profiling algorithm to identify clinically actionable molecular alterations in pancreatic cancer☆ by S. Hussung, D. Akhoundova, C. Pistoni, D. Lenggenhager, A. Töpfer, C. Pauli, B. Pestalozzi, C. Britschgi, M. Zoche, M. Rechsteiner, H. Moch, A. Weber, R. Fritsch

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A Study on Deep Learning Performances of Identifying Images’ Emotion: Comparing Performances of Three Algorithms to Analyze Fashion Items by Gaeun Lee, Seoyun Yi, Jongtae Lee

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Comparison of ResNet-50, EfficientNet-B1, and VGG-16 Algorithms for Cataract Eye Image Classification by Ilham Santoso, Ayub Michaelangelo Manurung, Egia Rosi Subhiyakto

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Creating a Newer and Improved Procedural Content Generation (PCG) Algorithm with Minimal Human Intervention for Computer Gaming Development by Lazaros Lazaridis, George F. Fragulis

“…Previous research has demonstrated the effectiveness of PCG in generating various game elements, such as levels and weaponry, with unique attributes across different playthroughs. …”
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Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River by CHEN Haitao, ZHAO Zhijie

“…The Muskingum model plays an important role in river flood simulation,and its simulation accuracy relies on the optimal selection of parameters.To address the current challenges in parameter calibration for the Muskingum model,such as complex solution processes and low accuracy,the use of the Harris Hawks optimization (HHO) algorithm was proposed to optimize its parameters.HHO algorithm has a wide range of global search capabilities,with fewer parameters to be adjusted.Taking Luohe River,a tributary of the Yellow River,as the research object,the generalized nonlinear Muskingum model was used to simulate the flood in the Yiyang-Baimasi section of the river.The parameters were optimized by employing the HHO algorithm,particle swarm optimization (PSO) algorithm,and ant colony optimization (ACO) algorithm,respectively.The results show that the generalized nonlinear Muskingum model based on the HHO algorithm achieved high simulation accuracy in the Yiyang-Baimasi section of the Luohe River,with a Min.SSD of 1 237 and the flood peak error (DPO) of only 5,outperforming those obtained through optimization using PSO algorithm and ACO algorithm.The results are suitable for application in flood forecasting in the Yiyang-Baimasi section of the Luohe River.…”
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