Optimization of upper respiratory tract cancer diagnosis method based on exhaled breath gas analysis by D. E. Kulbakin, E. L. Choinzonov, I. K. Fedorova, E. V. Obkhodskaya, A. V. Obkhodsky, V. O. Tskhay, E. A. Smolina, E. O. Rodionov, D. V. Podolko, V. I. Sachkov, V. I. Chernov

“…To evaluate diagnostic accuracy of sensory gas analysis device and artificial neural network for examination of exhaled gas samples from patients with oropharyngeal, laryngeal, laryngopharyngeal cancer and to establish the optimal conditions for sampling.Materials and methods. …”
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Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection by R. Hari Prasada Rao, Agam Das Goswami

“…Further, the implemented methods have certain limitations including scalability, robustness, data availability, and false detection rate.MethodsTo overcome the limitations in the existing techniques, this research proposes the Cnidaria Herd Optimization (CHO) algorithm-enabled Bi-directional Long Short-Term Memory (CHSTM) model for effective lung nodule detection. …”
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Optimized Molecular Detection of Cryptosporidium Within the Water-Soil-Plant-Food Nexus: Advancing Surveillance in Agricultural Systems by Robyn Marijn Schipper, Loandi Richter-Mouton, Lise Korsten

“…This study optimized DNA extraction and detection of Cryptosporidium in environmental samples and evaluated their practical use in agriculture. …”
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Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture by Haining Gao, Haoyu Wang, Hongdan Shen, Shule Xing, Yong Yang, Yinlin Wang, Wenfu Liu, Lei Yu, Mazhar Ali, Imran Ali Khan

“…To address the low accuracy in chatter detection caused by the limitations of both one-dimensional temporal and two-dimensional image modal information, this study proposes a multi-modal denoised data-driven milling chatter detection method using an optimized hybrid neural network architecture. …”
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Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT by Sepehr Sharifi, Soulmaz Gheisari

“…In this study, after collecting and preparing data, the improved support vector machine with grasshopper optimization algorithm is used as a proposed method to detect anomaly-based intrusion in the Internet of Things. …”
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Evolutionary Artificial Intelligence Algorithm for Optimizing Step Phase Detection Based on Foot-Mounted Triaxial Accelerometer Data by P. A. Khmarskiy

“…Experiments involving walking along a closed square path confirmed the high accuracy and robustness of the proposed method: the match between the optimized and reference trajectories demonstrates the practical applicability of the approach for precise gait reconstruction under different conditions. …”
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Advanced bearing fault detection at varying rotational speeds using PSO-optimized SVM and CDET feature selection by Hongxu Chai, Xiaoshi Ma, Feng Zhu, Yandong Hu

“…The outcomes demonstrate that specific features can effectively differentiate various bearing conditions at different speeds, highlighting the efficacy of the recommended approach compared to other fault detection methods.…”
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A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement by Kun Wang, Ling Han, Juan Liao

“…For the problems of landslide detection in remote sensing images, deep learning and playback method is adopted. …”
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A Model for Detecting <i>Xanthomonas campestris</i> Using Machine Learning Techniques Enhanced by Optimization Algorithms by Daniel-David Leal-Lara, Julio Barón-Velandia, Lina-María Molina-Parra, Ana-Carolina Cabrera-Blandón

“…To address this issue, this study developed a model that combines fuzzy logic and neural networks, optimized with intelligent algorithms, to detect symptoms of this foliar disease in 15 essential crop species under different environmental conditions using images. …”
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A novel approach for breast cancer detection using a Nesterov accelerated adam optimizer with an attention mechanism by Abeer Saber, Tamer Emara, Samar Elbedwehy, Esraa Hassan

“…Traditional disease detection methods often involve manual feature extraction from images, a process requiring extensive expertise from specialists and pathologists. …”
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Optimizing oropharyngeal swabbing techniques: the relationship between force applied and SARS-CoV-2 detection sensitivity by Melcher, Peter, Pietsch, Corinna, Bergs, Sandra, Youssef, Yasmin, Rahden, Paul, Hepp, Pierre, Henkelmann, Ralf

“…Conclusion: These findings indicate that while applying greater force during sample collection increases the number of collected cells, it does not improve the sensitivity of SARS-CoV-2 detection and can even lead to poorer results. Further research should focus on optimizing swab design to improve sample quality and the number of cells obtained.…”
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SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing by Gilberto Alvarado-Robles, Isac Andres Espinosa-Vizcaino, Carlos Gustavo Manriquez-Padilla, Juan Jose Saucedo-Dorantes

“…To address these issues, this study proposes the Supervised Dynamic Kernel U-Net (SDKU-Net), a novel architecture designed to enhance shadow detection in complex remote sensing scenarios. SDKU-Net integrates dynamic kernel adjustment, a combined loss function incorporating Focal and Tversky Loss, and optimizer switching to effectively tackle class imbalance and improve segmentation quality. …”
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An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP-Based DRDoS Attack Detection by V. Sreenivasulu, C. V. Ravikumar

“…Moreover, the deep stack autoencoder (DSA) model is used to perform intrusion detection in the attack validation phase. The detection performance of DSA is increased by training using the fractional lyrebird optimization algorithm (FLOA); FLOA provides a more effective, reliable, and scalable optimization strategy for training DSAs than traditional algorithms. …”
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Task-Decoupled Learning Strategies for Optimized Multiclass Object Detection From VHR Optical Remote Sensing Imagery by Guangyao Zhou, Guanqun Wang, Yu Huang, Wenzhi Wang, Zhi Qu, Chenghan Yin

“…Object detection in remote sensing imagery poses significant challenges due to the vast scale variations and long-tail class distributions inherent in these scenarios. …”
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Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review by Olga Adriana Caliman Sturdza, Florin Filip, Monica Terteliu Baitan, Mihai Dimian

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Optimizing Multiparametric MRI Protocols for Prostate Cancer Detection: A Comprehensive Assessment Aligned with PI‐RADS Guidelines by Mohammad Hossein Jamshidi, Ali Fatemi, Aida Karami, Sepehr Ghanavati, Durjoy D. Dhruba, Mohammad H. Negarestanian

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TMBO-AOD: Transparent Mask Background Optimization for Accurate Object Detection in Large-Scale Remote-Sensing Images by Tianyi Fu, Hongbin Dong, Benyi Yang, Baosong Deng

“…To address this, we propose a novel framework for accurate object detection, termed transparent mask background optimization for accurate object detection (TMBO-AOD), which incorporates a clear focus module and an adaptive filtering framework. …”
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Bi directional sparse attention recurrent autoencoder based intrusion detection for VANET security with tuna swarm optimization by R. Gopi, V. Thiruppathy Kesavan, Md. Jakir Hossen, Nor Hidayati Binti Abdul Aziz

“…The evolutionary based optimization effectively tuned the hyper parameters of the DL model which will improve the detection accuracy. …”
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A Lightweight YOLOv8s Algorithm for Ceiling Fan Blade Defect Detection With Optimized Pruning and Knowledge Distillation by Qinyuan Huang, Chen Fan, Yuqi Sun, Jiaxiong Huang, Wengziyang Jiang

“…We propose a lightweight algorithm for detecting surface defects on ceiling fan blades using YOLOv8s, incorporating optimizations and enhancements in model pruning and knowledge distillation. …”
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Optimizing Heart-Kidney Interaction for Cancer Detection Through Physiological Process Simulation as a Decision Support System by Najmeh Sadat Jaddi, Salwani Abdullah, Zalinda Othman, Yazrina Yahya, Mohd Zakree Ahmad Nazri, Say Leng Goh, Fatemeh Alvankarian, Jafar Alvankarian

“…Additionally, the HK algorithm&#x2019;s effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare.…”
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