Enhancing SAR-ATR Systems’ Resistance to S2M Attacks via FUA: Optimizing Surrogate Models for Adversarial Example Transferability by Xiaying Jin, Shuangju Zhou, Chenyu Wang, Mingxin Fu, Quan Pan, Yang Li

“…Finally, Architecture modification phase modifies the activation functions and skip connections of the model architecture with the parameters fixed. Experimental results demonstrate that FUA can outperform SOTA methods and significantly improve the S2M transferability across various adversarial attack algorithms. …”
Get full text

AI driven automation for enhancing sustainability efforts in CDP report analysis by Ramya Rangarajan, Tamilarasi Kathirvel Murugan, Logeswari Govindaraj, Venyaa Venkataraman, Krithik Shankar

“…The hybrid model consists of two main components: LSTM networks for predictive modeling of emission trends and GA for optimization of supply chain processes. …”
Get full text

Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved... by Hossein MolaAbasi, Satinder Kaur Brar, Amin Tanhadoust, Sepideh Nasrollahpour, Omolbanin Ataee, Moncef L. Nehdi

“…Through a tuning process, the BPNN model found the optimal architecture and accurately predicted the unconfined compressive strength of cement-zeolite improved sand systems. …”
Get full text

Advanced Cotton Boll Segmentation, Detection, and Counting Using Multi-Level Thresholding Optimized with an Anchor-Free Compact Central Attention Network Model by Arathi Bairi, Uma N. Dulhare

Get full text

Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling by He Wang, Weiquan Huang, Sindri Magnússon, Tony Lindgren, Chen Chen, Junyu Wu, Yanjie Song

“…This increased demand for complex and diverse imaging products requires addressing multi-objective optimization in practice. To this end, we propose a multi-objective agile Earth observation satellite scheduling problem (MOAEOSSP) model and introduce a reinforcement learning-based multi-objective grey wolf optimization (RLMOGWO) algorithm. …”
Get full text

Link Prediction in Social Networks Using the HTOA by Foad Asef, Vahid Majidnezhad, Mohammad-Reza Feizi-Derakhshi

“…HTOA, inspired by the physical phenomenon of heat transfer, identifies an optimal subset of topological features to feed XGBoost machine learning model. …”
Get full text

An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis by Jixin Liu, Liwei Deng, Yue Cao, Chenglin Wen, Zhihuan Song, Mei Liu, Xiaowei Cui

“…Initially, an adaptive golden jackal optimization (GJO) algorithm is employed to refine important CYCBD parameters. …”
Get full text

LSTM-ANN-GA A HYBRID DEEP LEARNING MODEL FOR PREDICTIVE MAINTENANCE OF INDUSTRIAL EQUIPEMENT by Farouk Noumich, Abouchabaka Jaafar, Amrani Ayoub

“…The proposed hybrid model incorporates two deep learning architectures: long short-term memory (LSTM) and artificial neural networks (ANN), with a genetic algorithm (GA) applied as an optimization method to simultaneously optimize the parameters of the model structure. …”
Get full text

Spatial Downscaling of TRMM Precipitation Data Using an Optimal Subset Regression Model with NDVI and Terrain Factors in the Yarlung Zangbo River Basin, China by Jinping Liu, Wanchang Zhang, Ning Nie

“…The objective of this study was to develop a reliable statistical downscaling algorithm to produce high quality, high spatial resolution precipitation products from Tropical Rainfall Monitoring Mission (TRMM) 3B43 data over the Yarlung Zangbo River Basin using an optimal subset regression (OSR) model combined with multiple topographical factors, the Normalized Difference Vegetation Index (NDVI), and observational data from rain gauge stations. …”
Get full text

Optimization of grading rings for 1000 kV dry-type air-core shunt reactor based on hybrid RBFNN–Kriging surrogate model by Yiqin Liu, Liang Xie, Dongyang Li, Yunpeng Liu, Kexin Liu, Gang Liu

“…First, the sparrow search algorithm is used to optimize the hyperparameters of the RBFNN. …”
Get full text

Deep learning framework based on ITOC optimization for coal spontaneous combustion temperature prediction: a coupled CNN-BiGRU-CBAM model by Xuming Shao, Wenhao Liu, Gang Bai, Yan Chen, Yu Liu, Jiahe Guang

“…Based on these variables, a deep learning framework combining an Improved Tornado Optimization with Coriolis force (ITOC) strategy and a CNN-BiGRU-CBAM model is proposed. …”
Get full text

Optimizing photocatalytic dye degradation: A machine learning and metaheuristic approach for predicting methylene blue in contaminated water by Yunus Ahmed, Keya Rani Dutta, Sharmin Nahar Chowdhury Nepu, Meherunnesa Prima, Hamad AlMohamadi, Parul Akhtar

“…This work points out the possibility of taking complete advantage of advanced machine learning algorithms along with metaheuristics optimization in improving photocatalytic processes, hence opening a bright avenue for real applications in water treatment.…”
Get full text

Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț

“…These results demonstrate that hybrid models combining deep learning and traditional ML techniques can improve predictive accuracy. …”
Get full text

Steganographic model to conceal the secret data in audio files utilizing a fourfold paradigm: Interpolation, multi-layering, optimized sample space, and smoothing by Daffa Tristan Firdaus, Ntivuguruzwa Jean De La Croix, Tohari Ahmad, Didacienne Mukanyiligira, Louis Sibomana

“…To address these limitations, this study offers valuable insights to guide researchers in developing high-performing audio steganography models. The proposed method seeks to improve stego audio quality by implementing a smoothing-based technique and optimizing the sample space through linear interpolation, followed by a multi-layering process. …”
Get full text

An efficient patient’s response predicting system using multi-scale dilated ensemble network framework with optimization strategy by Nalini Manogaran, Nirupama Panabakam, Durai Selvaraj, Koteeswaran Seerangan, Firoz Khan, Shitharth Selvarajan

“…The Repeated Exploration and Exploitation-based Coati Optimization Algorithm (REE-COA) is employed to select the features. …”
Get full text

Optimizing EV charging stations and power trading with deep learning and path optimization. by Qing Zhu

“…Path optimization, using the Dijkstra algorithm, minimized travel times for EV users by 11.4%. …”
Get full text

QPSO-Based Adaptive DNA Computing Algorithm by Mehmet Karakose, Ugur Cigdem

“…In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). …”
Get full text

Comparison of clustering methods and conventional approaches for geological fracture analysis: A case study in northern Shiraz, Iran by HAJAR KAZEMI, KOUROS YAZDJERDI, ABDOLMAJID ASADI, MOHAMMAD REZA MOZAFARI

Get full text

Book Recommendation Using Collaborative Filtering Algorithm by Esmael Ahmed, Adane Letta

“…Moreover, using hyperparameter tuning with SVD also has an improvement on model performance compared with the existing SVD algorithm.…”
Get full text

Intelligent Classification Method for Rail Defects in Magnetic Flux Leakage Testing Based on Feature Selection and Parameter Optimization by Kailun Ji, Ping Wang, Yinliang Jia

“…Three key innovations drive this research: (1) A dynamic PSO algorithm incorporating adaptive learning factors and nonlinear inertia weight for precise RBF parameter optimization; (2) A hierarchical feature processing strategy combining mutual information selection with correlation-based dimensionality reduction; (3) Adaptive model architecture adjustment for small-sample scenarios. …”
Get full text