Utilizing the Novel Developed MLP Techniques to Survey Pile Subsidence via Optimization Algorithms by Augustinus Sieck, Graciela Daniels

“…This research aims to develop the Multilayer Perceptron coupled with the Novel Arithmetic Optimization Algorithm and Biogeography-Based Optimization) to find out the optimal number of hidden layers of neurons within MLP. …”
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Hybrid intelligence framework for optimizing shear capacity of lightweight FRP-reinforced concrete beams by Iman Faridmehr, Moncef L. Nehdi, Mohammad Ali Sahraei, Kiyanets Aleksandr Valerievich, Chiara Bedon

“…Leveraging a dataset of 260 experimental FRP-RC beam cases, two distinct Artificial Neural Network (ANN) models were developed using the Levenberg-Marquardt algorithm. …”
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Landslide Displacement Prediction Model Based on Optimal Decomposition and Deep Attention Mechanism by Shuai Ren, Kamarul Hawari Ghazali, Yuanfa Ji, Samra Urooj Khan

“…To address this, this study proposes an advanced forecasting framework integrating the Chebyshev Levy Flight-Sparrow Search Algorithm (CLF-SSA) with Variational Mode Decomposition (VMD) to enhance decomposition accuracy and optimize parameter selection. The trend component is modeled using the Autoregressive Integrated Moving Average (ARIMA) with a grid search strategy, while the periodic component is predicted using a Bidirectional Long Short-Term Memory network with an Attention mechanism (BiLSTM-Attention), which dynamically adjusts the contribution of influencing factors. …”
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A secure and energy-efficient routing using coupled ensemble selection approach and optimal type-2 fuzzy logic in WSN by S. Ambareesh, Pundalik Chavan, S. Supreeth, Rajesh Nandalike, P. Dayananda, S. Rohith

“…Conventional strategies generally goal particular troubles, like strength optimization or improving QoS, frequently failing to provide a holistic answer that effectively balances more than one crucial elements concurrently. …”
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A hybrid deep learning-based approach for optimal genotype by environment selection by Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki, Lizhi Wang

“…Using a new yield dataset containing 93,028 records of soybean hybrids across 159 locations, 28 states, and 13 years, with 5,838 distinct genotypes and daily weather data over a 214-day growing season, we developed two convolutional neural network (CNN) models: one that integrates CNN and fully-connected neural networks (CNN model), and another that incorporates a long short-term memory (LSTM) layer after the CNN component (CNN-LSTM model). …”
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Research on SOC Prediction of Lithium-Ion Batteries Based on OLHS-DBO-BP Neural Network by Genbao Wang, Yejian Xue, Yafei Qiao, Chunyang Song, Qing Ming, Shuang Tian, Yonggao Xia

“…Regarding the issue of the relatively low estimation accuracy of SOC by the backpropagation neural network (BPNN), an enhanced dung beetle optimizer (DBO) algorithm is proposed to optimize the initial weights and thresholds of the BPNN. …”
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Dissolved Oxygen Prediction Based on SOA-SVM and SOA-BP Models by ZHANG Xuekun

“…To improve the accuracy of dissolved oxygen prediction,this paper researches and proposes a prediction method that combines seagull optimization algorithm (SOA) with support vector machine (SVM) and BP neural network,prepares four prediction schemes based on the monthly dissolved oxygen monitoring data of the Jinghong Power Station in Xishuangbanna,a national important water supply source in Yunnan Province,from January 2009 to September 2020,optimizes the key parameters of SVM and weight threshold of BP neural network by SOA to construct SOA-SVM and SOA-BP models,predicts the dissolved oxygen of Jinghong Power Station based on the models,and compares the prediction results with those of SVM and BP models.The results show that:The absolute values of the average relative errors of the SOA-SVM and SOA-BP models for the 4 schemes of dissolved oxygen prediction are between 4.07%~4.98% and 3.85%~4.83%,and that of the average absolute errors are 0.309~0.374 mg/L and 0.294~0.371 mg/L,respectively.With better prediction accuracy than SVM and BP models,they have good prediction accuracy and generalization ability.SOA can effectively optimize the key parameters of SVM and weight threshold of BP neural network.SOA-SVM and SOA-BP models are feasible for dissolved oxygen prediction,which can provide references for related prediction research.…”
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FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images by Liang Zhou, Achin Jain, Arun Kumar Dubey, Sunil K. Singh, Neha Gupta, Arvind Panwar, Sudhakar Kumar, Turki A. Althaqafi, Varsha Arya, Wadee Alhalabi, Brij B. Gupta

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Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems by Joung-Hwan Yoon, Chibuzo Nwabufo Okwuosa, Nnamdi Chukwunweike Aronwora, Jang-Wook Hur

“…Our approach employed image data generated from normal and two defect conditions to train eight deep learning algorithms: four custom convolutional neural networks (CNNs), two variations of VGG-16, and two variations of ResNet-50. …”
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Identification of Road Black Spots Based on the Sliding Window Optimization and Safety Performance Function Development by Shahin Shabani, Jalal Ayoubinejad, Nassir Baradaran Rahmanian

“…Next, a Safety Performance Function is developed to calculate the predicted and expected number of crashes, as well as the Potential Safety Improvement, for each window movement across all selected scenarios within the segment. Subsequently, the average differences are calculated using the analysis of variance, and the window length with the lowest dispersion of difference values from the mean is identified as the optimal length for each segment. …”
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Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network. by Yeping Shi, Yunbo Shi, Haodong Niu, Jinzhou Liu, Pengjiao Sun

“…Response data are classified and regressed using a sparrow search algorithm (SSA)-optimized backpropagation neural network (BPNN). The results show that the sensor has a relative mean deviation of 1.45%. …”
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Personalized learning path optimization based on enhanced deep neural network: higher education teaching model integrating learner behavior and cognitive style by Xiaomei Ding, Huaibao Ding, Fei Zhou, Lihong Zhao

“…To address the above problems, this paper designs an effective model for optimizing personalized learning paths using Enhanced Deep Neural Networks (EDNN). …”
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An Integrated Process-Network Load Balancing in Edge-Assisted Autonomous Vehicles Using Multimodal Applications With Shared Workloads by Sinuk Choi, Pyeongjun Choi, Donghyeon Kim, Jeongho Kwak, Ji-Woong Choi

“…To solve this problem, we leverage Lyapunov optimization to transform the long-term average problem into a slot-by-slot problem. …”
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Unobtrusive Sleep Posture Detection Using a Smart Bed Mattress with Optimally Distributed Triaxial Accelerometer Array and Parallel Convolutional Spatiotemporal Network by Zhuofu Liu, Gaohan Li, Chuanyi Wang, Vincenzo Cascioli, Peter W. McCarthy

“…Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. …”
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Novel Gain-Optimized Two-Step Fusion Filtering Method for Ranging-Based Localization Using Predicted Residuals by Bo Chang, Xinrong Zhang, Na Sun, Hao Ni

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Presenting a robust optimization model for the design of the green loop supply chain network in the power plant by taking into account the disruption in the supply chain by Javad Mohammadghasemi, Seyyed Esmaeil Najafi, Mohammad Fallah, Mohammad Reza Nabatchian

“…Purpose: This paper focuses on modeling a sustainable electricity industry supply chain network under uncertainty. The aim of presenting this supply chain network is to meet customer demands for solar panels to generate clean energy.Methodology: A mixed-integer linear programming model, including facility location, supplier selection, optimal flow allocation, and determination of the optimal price of solar panels in the network, is considered. …”
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Integrating particle swarm optimization with backtracking search optimization feature extraction with two-dimensional convolutional neural network and attention-based stacked bidir... by Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak

“…It is compared against five advanced techniques: particle swarm optimization (PSO), Cat Swarm Optimization (CSO), long short-term memory (LSTM) with convolutional neural networks (LSTM-CNN), support vector regression (SVR), bee swarm algorithm (BSA), ant colony optimization (ACO) and the firefly algorithm (FFA). …”
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Signal-to-event encoding parameter selection for multiple event classification with spiking neural networks by Mateusz Pabian, Dominik Rzepka, Mirosław Pawlak, Mirosław Pawlak, Marek Miśkowicz, Ryszard Sroka

“…This paper studies the problem of optimal event-based signal encoding if data are to be processed by a machine learning model, such as the spiking neural network (SNN). …”
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A Bio-Inspired Data-Driven Locomotion Optimization Framework for Adaptive Soft Inchworm Robots by Mahtab Behzadfar, Arsalan Karimpourfard, Yue Feng

“…Experimental results demonstrate that the framework achieves an average 9.88% reduction in required pressure for efficient movement and a 6.45% reduction for stable locomotion, with the neural network enabling robust adaptation to varying surfaces. …”
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INTELLIGENT TECHNOLOGY FOR OPTIMIZING THE PROJECT-BASED APPROACH TO TEACHING STUDENTS USING LEARNING MANAGEMENT SYSTEMS by Volodymyr Sokol, Mykhaylo Godlevskyi, Mariia Bilova, Roman Tupkalenko

“…To achieve the  goals of the study, it is necessary to solve main tasks, namely: to prepare a training data set using multi-agent modeling and to develop and train a recommendation system that is based on an artificial deep neural network on this data. After completing all the tasks of the work, it is expected that the learning process of students in the learning management system will be optimized in terms of time and resources spent on learning, and the average level of knowledge will be increased.…”
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