iYOLOV7-TPE-SS: Leveraging Improved YOLO Model With Multilevel Hyperparameter Optimization for Road Damage Detection on Edge Devices by Haniah Mahmudah, Aulia Siti Aisjah, Syamsul Arifin, Catur Arif Prastyantyo

“…The research investigates how leveraging multilevel hyperparameter optimization using the TPE-SS model improves system performance where road damage is detected. …”
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A New Meta-heuristic Algorithm based on Multi-criteria Decision Making to Solve Community Detection Problem by Vahid Baradaran, Amir Hossein Hosseinian, Reza Derakhshani

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Optimization of saccharification culture process and analysis of volatile flavor components of Qingchuan Huangjiu by REN Xiaohu, ZHU Ying, CHEN Lei, YANG Anting, GONG Hucheng, TIAN Shulin, BIAN Minghong, XU Qiang, HAN Baolin

“…The physicochemical indexes of Qingchuan Huangjiu were determined by national standard method, and the volatile flavor components were detected by GC-MS. …”
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Optimization of a Method for the Determination of a Mustard Gas Biomarker in Human Blood Plasma by Liquid Chromatography–Mass Spectrometry by А. V. Braun, I. V. Rybalchenko, V. N. Fateenkov, М. А. Ponsov, Y. V. Stavitskay, D. V. Oreshkin

“…A method for the determination of a mustard gas biomarker (an S-hydroxyethylthioethyl adduct with albumin) in blood plasma was optimized with the use of HPLC with tandem mass-spectrometric detection. …”
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GC-Faster RCNN: The Object Detection Algorithm for Agricultural Pests Based on Improved Hybrid Attention Mechanism by Bolun Guan, Yaqian Wu, Jingbo Zhu, Juanjuan Kong, Wei Dong

“…In addition, experiments have also shown that the GC-Faster RCNN detection method can reduce interference from multiple scales and high similarity between categories, improving detection performance.…”
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A green and robust LC–MS/MS bioanalytical method for sulopenem etzadroxil and probenecid: Optimization, validation, and pharmacokinetic application by Niloufer Tasnim Khazi, Kumaraswamy Gandla, Lalitha Repudi

“…The method exhibited excellent linearity (10–400 ng/mL), low limits of detection (LOD: ∼3 ng/mL), and quantification (LOQ: ∼9 ng/mL) for both analytes, with recovery rates > 93 % and %CVs < 15 %. …”
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Optimal Labeling Dose, Labeling Time, and Magnetic Resonance Imaging Detection Limits of Ultrasmall Superparamagnetic Iron-Oxide Nanoparticle Labeled Mesenchymal Stromal Cells by Anders Bruun Mathiasen, Louise Hansen, Tina Friis, Carsten Thomsen, Kishore Bhakoo, Jens Kastrup

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On Optimally Selecting Candidate Detectors with High Predicted Radio Signals from Energetic Cosmic Ray-Induced Extensive Air Showers by Tudor Alexandru Calafeteanu, Paula Gina Isar, Emil Ioan Slușanschi

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Enhanced AUV Autonomy Through Fused Energy-Optimized Path Planning and Deep Reinforcement Learning for Integrated Navigation and Dynamic Obstacle Detection by Kaijie Zhang, Yuchen Ye, Kaihao Chen, Zao Li, Kangshun Li

“…Autonomous Underwater Vehicles (AUVs) operating in dynamic, constrained underwater environments demand sophisticated navigation and detection fusion capabilities that traditional methods often fail to provide. …”
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Optimization and Benchmarking of Image Segmentation for Improved Landmark Detection in Lower Limb X-Rays and Accurate Coronal Plane Alignment of the Knee Classification by Sebastian Amador Sanchez, Ashkan Zarghami, Philippe van Overschelde, Jef Vandemeulebroucke

“…We contrasted image segmentation against optimized heatmap, coordinate, and segmentation-guided coordinate regression methods. …”
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Improved Optimized Minimum Generalized L_p/L_q Deconvolution and Application to Bearing Fault Detection by Na Yang, Zhigang Pan, Yuanbo Xu

“…Locating the fault-induced cyclic impulses from corrupted vibration signals is a key step in detecting bearing fault characteristics. Recently, a novel deconvolution technique named the optimized minimum generalized L_p/L_q deconvolution (OMGD) was proposed and has been validated as a useful technique to highlight the periodic impulses related to bearing faults. …”
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Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept study by Myrthe van den Broek, M. Claire Greene, Anthony F. Guevara, Sandra Agondeze, Erimiah Kyanjo, Olivier Irakoze, Rosco Kasujja, Brandon A. Kohrt, Mark J. D. Jordans

“…This proof-of-concept study evaluated an optimization strategy for the Community Case Detection Tool (CCDT) aimed at improving community-level mental health detection and help-seeking among children aged 6–18 years. …”
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Optimization Identification of wild mustard (Sinapis arvensis L.) seeds in rapeseed seed lots by morphological, chemical and Molecular methods by Bita Oskouei, Leila Sadeghi, Fatemeh Doorooshi

“…The present study was conducted to optimize the identification and detection of wild mustard seeds in rapeseed lots by morphological, chemical and molecular methods. …”
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A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization by Mădălina Duceac (Covrig), Călin Gheorghe Buzea, Alina Pleșea-Condratovici, Lucian Eva, Letiția Doina Duceac, Marius Gabriel Dabija, Bogdan Costăchescu, Eva Maria Elkan, Cristian Guțu, Doina Carina Voinescu

“…<b>Methods:</b> A dataset of 977 patients from a Romanian neurosurgical center was used. …”
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A Novel Diagnostic Framework with an Optimized Ensemble of Vision Transformers and Convolutional Neural Networks for Enhanced Alzheimer’s Disease Detection in Medical Imaging by Joy Chakra Bortty, Gouri Shankar Chakraborty, Inshad Rahman Noman, Salil Batra, Joy Das, Kanchon Kumar Bishnu, Md Tanvir Rahman Tarafder, Araf Islam

“…Artificial intelligence-based solutions are putting great importance on identifying the disease efficiently, where deep learning with medical imaging is highly being utilized to develop disease detection frameworks. In this work, a novel and optimized detection framework has been proposed that comes with remarkable performance that can classify the level of Alzheimer’s accurately and efficiently. …”
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Research on Anti-Submarine Warfare Method of Unmanned Aerial Vehicle Cluster Based on Area Coverage and Distributed Optimization Control by Yongzhao Yan, Yi Liu, Ying Bi, Qian Wang, Huazhen Cao, Ziping Yu, Kangkang Li, Bo Wang, Xiaoping Ma

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Development of paper based colorimetric method using pigment from red dragon fruit for determination of Cu and Fe by Rimadani Pratiwi, Raspati Dewi Mulyaningsih, Aliya Nur Hasanah

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Encrypted traffic classification method based on convolutional neural network by Rongna XIE, Zhuhong MA, Zongyu LI, Ye TIAN

“…Aiming at the problems of low accuracy, weak generality, and easy privacy violation of traditional encrypted network traffic classification methods, an encrypted traffic classification method based on convolutional neural network was proposed, which avoided relying on original traffic data and prevented overfitting of specific byte structure of the application.According to the data packet size and arrival time information of network traffic, a method to convert the original traffic into a two-dimensional picture was designed.Each cell in the histogram represented the number of packets with corresponding size that arrive at the corresponding time interval, avoiding reliance on packet payloads and privacy violations.The LeNet-5 convolutional neural network model was optimized to improve the classification accuracy.The inception module was embedded for multi-dimensional feature extraction and feature fusion.And the 1*1 convolution was used to control the feature dimension of the output.Besides, the average pooling layer and the convolutional layer were used to replace the fully connected layer to increase the calculation speed and avoid overfitting.The sliding window method was used in the object detection task, and each network unidirectional flow was divided into equal-sized blocks, ensuring that the blocks in the training set and the blocks in the test set in a single session do not overlap and expanding the dataset samples.The classification experiment results on the ISCX dataset show that for the application traffic classification task, the average accuracy rate reaches more than 95%.The comparative experimental results show that the traditional classification method has a significant decrease in accuracy or even fails when the types of training set and test set are different.However, the accuracy rate of the proposed method still reaches 89.2%, which proves that the method is universally suitable for encrypted traffic and non-encrypted traffic.All experiments are based on imbalanced datasets, and the experimental results may be further improved if balanced processing is performed.…”
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Improved Isolation Optimizes Downstream Application of Extracellular Vesicles Derived from <i>Mycobacterium tuberculosis</i> by Wenjing Wang, Yue Hou, Jingfang Zhang, Zhaogang Sun, Hong Sun

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Multi-objective optimization of heat transfer performance of power battery cold plate based on bionic spider web flow channel by Junfu Qiao, Jinqin Guo, Yongwei Li

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