A lightweight deep-learning model for parasite egg detection in microscopy images by Wenbin Xu, Qiang Zhai, Jizhong Liu, Xingyu Xu, Jing Hua

“…Abstract Background Intestinal parasitic infections are still a serious public health problem in developing countries, and the diagnosis of parasitic infections requires the first step of parasite/egg detection of samples. Automated detection can eliminate the dependence on professionals, but the current detection algorithms require large computational resources, which increases the lower limit of automated detection. …”
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Extraction of Optimal Measurements for Drowsy Driving Detection considering Driver Fingerprinting Differences by Yifan Sun, Chaozhong Wu, Hui Zhang, Yijun Zhang, Shaopeng Li, Hongxia Feng

“…Finally, we selected measurements calculated by IDBCPs that can distinguish drowsy driving to constitute individual drivers’ optimal drowsiness-detection measurement set. To verify the advantages of IDBCPs, the measurements calculated by UCPs and IDBCPs were, respectively, used to build driver-specific drowsiness-detection models: DF_U and DF_I based on the Fisher discriminant algorithm. …”
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CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer Perceptron by Zhenhua Gan, Dongyu He, Peishu Wu, Baoping Xiong, Nianyin Zeng, Fumin Zou, Feng Guo, Qin Bao, Fengyan Zhao

“…The gray-level of the fluorescent probe in detection image was obtained as the data set acquired by the microarray scanner at different exposure time. …”
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Rapid detection and quantification of Nile Red-stained microplastic particles in sediment samples by Masashi Tsuchiya, Tomo Kitahashi, Yosuke Taira, Hitoshi Saito, Kazumasa Oguri, Ryota Nakajima, Dhugal J. Lindsay, Katsunori Fujikura

“…This means that our method can efficiently detect MPs as small as 100 µm found in deep-sea sediments. …”
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A dataset to train intrusion detection systems based on machine learning models for electrical substationsZenodo by Esteban Damián Gutiérrez Mlot, Jose Saldana, Ricardo J. Rodríguez, Igor Kotsiuba, Carlos Gañán

“…In summary, the dataset addresses the critical need for high-quality, targeted data for tuning IDS at electrical substations and contributes to the advancement of secure and reliable power distribution networks.…”
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Augmenting precision medicine via targeted RNA-Seq detection of expressed mutations by Dan Li, Jianying Li, Donald J. Johann, Daniel Butler, Guangchun Chen, Jonathan Foox, Binsheng Gong, Wendell Jones, David P. Kreil, Rebecca Kusko, Paweł P. Łabaj, Anne Bergstrom Lucas, Christopher E. Mason, Christopher Mozsary, Natalia Novoradovskaya, Carlos Pabón-Peña, Bohu Pan, Todd A. Richmond, Roberta Maestro, Sayed Mohammad Ebrahim Sahraeian, Andreas Scherer, Hagen U. Tilgner, James C. Willey, Pierre R. Bushel, Joshua Xu

“…In this study, we conducted targeted RNA-seq on a reference sample set for expressed variant detection to explore its potential capability to complement DNA variant results or detect variants independently. …”
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Unsupervised Anomaly Detection With Variational Autoencoders Applied to Full‐Disk Solar Images by Marius Giger, André Csillaghy

“…To alleviate the data bottleneck of loosely annotated data sets, unsupervised deep learning has become an important strategy, with anomaly detection being one of the most prominent applications. …”
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Enhanced anomaly traffic detection framework using BiGAN and contrastive learning by Haoran Yu, Wenchuan Yang, Baojiang Cui, Runqi Sui, Xuedong Wu

“…Experimental results show that the method proposed in this paper performs well on multiple traffic data sets and significantly improves the accuracy and efficiency of anomaly detection. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…In addition, to assess the model's generalization ability, a stratified sampling method was used, selecting 20% of the images from the dataset as the test set. The results showed that the improved model could maintain a high detection accuracy in complex and variable scenes, with mAP50 and mAP50:95 increasing by 1.7% and 1.2%, respectively, compared to the original model. …”
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RHLS: A Robust Hybrid Level Set Model Using Global-Local Signed Energy-Based Pressure Force for Medical Image Segmentation by M. Almasganj, E. Fatemizadeh

“…Medical image segmentation often encounters significant challenges due to noise and intensity inhomogeneity. While Level Set Models (LSMs) are widely used for segmentation, their effectiveness in these scenarios remains limited. …”
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Detection of Tomato Leaf Pesticide Residues Based on Fluorescence Spectrum and Hyper-Spectrum by Jiayu Gao, Xuhui Yang, Simo Liu, Yufeng Liu, Xiaofeng Ning

“…After comparison, the quantitative model of data based on fluorescence spectrum for pesticide residue detection in tomato leaves proved to have a better effect, and the qualitative model showed higher accuracy in discrimination. …”
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Automatic Edge Detection From Point Clouds Collected by Terrestrial Laser Scanners by Anh Thu Thi Phan, Thi Ngoc Huynh, Mai Pham Huynh

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Deceptive Maneuvers: Subverting CNN-AdaBoost Model for Energy Theft Detection by Santosh Nirmal, Pramod Patil

“…Evasion attacks (EA) attempt to evade detection by misclassifying input data during testing. …”
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Relationships between hand grip strength and gait parameters measured using a foot-mounted sensor in non-laboratory settings in older women by Takuma Inai, Tomoya Takabayashi

“…To develop practical tools for early detection, it is important to understand how hand grip strength relates to gait parameters in non-laboratory settings. …”
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PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…To diagnose PD, the proposed method uses two different data sets. Algorithms for machine learning are also capable of helping in producing specific details from such data. …”
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Barriers and facilitators of fetal heart monitoring with a mobile cardiotocograph (iCTG) device in underserved settings: An exploratory qualitative study from Tanzania. by Dorkasi L Mwakawanga, Sanmei Chen, Yhuko Ogata, Minami Suzuki, Yuryon Kobayashi, Miyuki Toda, Naoki Hirose, Yoko Shimpuku

“…<h4>Background</h4>Fetal monitoring in low-resource settings is often inadequate. A mobile cardiotocograph fetal monitoring device is a digital innovation that could ensure the safety of pregnant women at high risk and their fetuses through early detection and management of fetal distress. …”
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FedNDA: Enhancing Federated Learning with Noisy Client Detection and Robust Aggregation by Tuan Dung Kieu, Charles Fonbonne, Trung-Kien Tran, Thi-Lan Le, Hai Vu, Huu-Thanh Nguyen, Thanh-Hai Tran

“… Federated Learning is a novel decentralized methodology that enables multiple clients to collaboratively train a global model while preserving the privacy of their local data. Although federated learning enhances data privacy, it faces challenges related to data quality and client behavior. …”
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Intrusion detection model of random attention capsule network based on variable fusion by Xinglan ZHANG, Shenglin YIN

“…In order to enhance the accuracy and generalization of the detection model,an intrusion detection model of random attention capsule network with variable fusion was proposed.Through dynamic feature fusion,the model could better capture data features.At the same time,random attention mechanism was used to reduce the dependence on training data and make the model more generalization.The model was validated on NSL-KDD and UNSW-NB15 datasets.The experimental results show that the accuracy of the model on the two test sets is 99.49% and 98.60% respectively.…”
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Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing by Bertram Richter, Lisa Ulbrich, Max Herbers, Steffen Marx

“…Hence, cleaning the raw measurement data in a pre-processing stage is key for successful subsequent data evaluation and damage detection on engineering structures. …”
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Enhancing lung cancer detection through integrated deep learning and transformer models by Revathi Durgam, Bharathi Panduri, V. Balaji, Adil O. Khadidos, Alaa O. Khadidos, Shitharth Selvarajan

“…The reasons behind the usage of the transformer and deep learning classifiers for the detection of lung cancer include accuracy, robustness along with the capability to handle and evaluate large data sets and much more. …”
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