OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. by Greg Finak, Jacob Frelinger, Wenxin Jiang, Evan W Newell, John Ramey, Mark M Davis, Spyros A Kalams, Stephen C De Rosa, Raphael Gottardo

“…We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. …”
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Controllable Blind AC FDIA via Physics-Informed Extrapolative AVAE by Siliang Zhao, Wuman Luo, Qin Shu, Fangwei Xu

“…Current machine learning-based approaches struggle to effectively control state estimation errors and are confined to the data distribution of training sets. To address these limitations, we propose the physics-informed extrapolative adversarial variational autoencoder (PI-ExAVAE) for generating controllable and stealthy false data injections. …”
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Combining Feature Dimensionality Reduction and NGO-CNN-BiLSTM′s Network Abnormal Traffic Detection Method in Recruitment Intelligence Platform by YUAN Xiaopeng, MIAO Sirui, ZHANG Qiaojun

“…Then, the convolution kernel of CNN is optimized by NGO to obtain the optimal convolution kernel, and the abnormal traffic was detected by BiLSTM. The CIC-IDS-2017 data set is used to analyze the training and test samples of the intrusion detection network model, and the accuracy and training time are improved compared with other methods, which confirms the feasibility and effectiveness of this method.…”
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Multi release software reliability modelling incorporating fault generation in detection process and fault dependency with change point in correction process by Sujit Kumar Pradhan, Anil Kumar, Vijay Kumar

“…These models are validated on two actual medium-sized software system data sets. The results show that the proposed models fit the data set more accurately than the existing SRGMs. …”
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Design of novel intelligent electronic trap for early detection and monitoring of tomato crops pest Tuta Absoluta using Deep learning by Yaser M. Abid Alasady, Eduardo Pérez, Sebastián Ventura

“…In this research, a new data set collected and published for the first time, novel electronic trap designed with intelligent system to detect pests in tomato crop, and monitor the spread of the pest periodically and continuously based on the collected dataset. …”
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Anomaly Detection and Remaining Useful Life Prediction for Turbofan Engines with a Key Point-Based Approach to Secure Health Management by Yuntao Duan, Tao Zhang, Dunhuang Shi

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A Model‐Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO Prediction by Weixun Rao, Youmin Tang, Yanling Wu, Xiaojing Li

“…With this strategy, we designed an optimal observational array in the tropical Pacific for the El Niño‐Southern Oscillation (ENSO) prediction using the multiple historical simulation data sets from Coupled Model Intercomparison Project Phase 6 and reanalysis data sets. …”
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An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation by Xiaofei Dai, Guodong Cheng, Lu Yang, Yali Wang, Zhongkun Li, Shuqing Han, Jifang Liu

“…For moving-state windowed data, the InceptionTime network was modified with YOLOConv1D and SeparableConv1D modules plus Dropout, which significantly reduced model parameters and helped mitigate overfitting risk, enhancing generalization on the test set. …”
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MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attack by Meng Yue, Silin Peng, Wenzhi Feng

“…Abstract A new type of Collusive Interest Flooding Attack (CIFA), Improved Collusive Interest Flooding Attack (I‐CIFA), which originates from CIFA with a stronger concealment, higher attack effect, lower attack cost, and wider attack range in Named Data Networking (NDN). In order to detect this attack, the present study explores new detection features and establishes a sample set of attack features with different granularities, and accordingly, the Pearson coefficient is used to validate the correlation between the proposed features and the network states. …”
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Enhancing intrusion detection systems: Innovative deep learning approaches using CNN, RNN, DBN and autoencoders for robust network security by Yakub HOSSAIN, Zannatul FERDOUS, Tanzillah WAHID, Md. Torikur RAHMAN, Uttam Kumar DEY, Mohammad Amanul ISLAM

“…Using the models, these models are trained and tested only on the NSL-set. KDD data, which is a widely accepted benchmark for evaluating IDS performance. …”
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Integrative analysis of bulk and single-cell RNA sequencing data reveals increased arachidonic acid metabolism in osteoarthritic chondrocytes by Kan Wu, Zhaoqian Zhong, Li Chen, Haihua Luo, Aolin Jiang, Linlin Tao, Yong Jiang

“…However, the metabolic regulation of chondrocytes in OA remains to be investigated.MethodsBulk RNA sequencing (RNA-seq) data and single-cell RNA sequencing (scRNA-seq) data of human knee cartilage were downloaded from public databases. …”
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Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images by Jialin Shi, Ruolin Zhang, Zongyao Yang, Zhixian Chen, Zhixin Hao, Li Huo, Ji Wu, Qiang Sun, Yali Xu

“…This retrospective study included 512 patients with breast cancer bone metastases from Peking Union Medical College Hospital. The data type is whole-body bone scan image. For our study, the ratio of training set, validation set and test set is about 6:2:2. …”
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A Systematic Review on Subjective Cognitive Complaints: Main Neurocognitive Domains, Myriad Assessment Tools, and New Approaches for Early Detection by Felipe Webster-Cordero, Lydia Giménez-Llort

“…More importantly, the detection of subtle cognitive changes, such as subjective cognitive complaints (SCCs), an understudied phenomenon, is critical for early detection and preventive interventions. …”
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Detecting 3D Salinity Anomalies from Soil Sampling Points: A Case Study of the Yellow River Delta, China by Zhoushun Han, Xin Fu, Jianing Yu, Hengcai Zhang

“…The experimental results from the Yellow River Delta data set show that 3D-SSAS can effectively identify the 3D structure of salinity-anomaly areas, which are highly correlated with the geographical distribution mechanism of soil salinity. …”
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A novel approach in diagnosing knee osteoarthritis for content based image retrieval in big data analytics and medical images by Pinar Gundogan Bozdag, Hurşit Burak Mutlu, Mucahit Karaduman, Muhammed Yildirim, M. Attique Khan, Shrooq Alsenan, Jamel Baili, Yunyoung Nam

“…On the contrary, this effectiveness decreases when large data sets are accessed. Content-based Image retrieval (CBIR) methods are used in large data sets. …”
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A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All‐Sky Imagers by Daniel Okoh, Claudio Cesaroni, Babatunde Rabiu, Kazuo Shiokawa, Yuichi Otsuka, Samuel Ogunjo, Aderonke Akerele, John Bosco Habarulema, Bruno Nava, Yenca Migoya‐Orué, Punyawi Jamjareegulgarn, Adeniran Seun, Ogechi Adama, George Ochieng, James Ameh, Adero Awuor, Paul Baki

“…This study presents a novel bootstrapping convolutional neural network (CNN) approach to optimize automated EPB detection on ASI images for operational space weather monitoring applications, and overcoming challenges related to image variability and imbalanced data sets. …”
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Heuristically enhanced multi-head attention based recurrent neural network for denial of wallet attacks detection on serverless computing environment by Sarah A. Alzakari, Mohammad Alamgeer, Abdullah M. Alashjaee, Monir Abdullah, Khalid Nazim Abdul Sattar, Asma Alshuhail, Ahmad A. Alzahrani, Abdulwhab Alkharashi

“…Eventually, the improved secretary bird optimizer algorithm (ISBOA)-based hyperparameter choice process is accomplished to optimize the detection results of the MHA-BiGRU model. A comprehensive set of simulations was conducted to demonstrate the promising results of the MHARNN-DoWAD method. …”
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Machine learning based analysis of leucocyte cell population data by Sysmex XN series hematology analyzer for the diagnosis of bacteremia by Sadashi Horie, Mizuki Yamashita, Kenta Matsumura, Yoji Uejima, Makito Kaneda, Kentaro Nagaoka, Kentaro Tamura, Kenu Harada, Yoshihiro Yamamoto, Isao Kitajima, Chihaya Imai, Hideki Niimi

“…Recently, cell population data (CPD) from the Sysmex XN-series hematology analyzer has attracted attention as a new method for the early diagnosis of bacteremia, but its usefulness in clinical practice remains unclear. …”
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Antimicrobial resistance detection in Southeast Asian hospitals is critically important from both patient and societal perspectives, but what is its cost? by Tamalee Roberts, Nantasit Luangasanatip, Clare L Ling, Jill Hopkins, Risara Jaksuwan, Yoel Lubell, Manivanh Vongsouvath, H Rogier van Doorn, Elizabeth A Ashley, Paul Turner

“…Financial costs for setting up and running a microbiology laboratory were estimated using a top-down approach based on resource and cost data obtained from three clinical laboratories in the Mahidol Oxford Tropical Medicine Research Unit network. …”
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Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model by Yu Xia, Ao Shen, Tianci Che, Wenbo Liu, Jie Kang, Wei Tang

“…Subsequently, a maize seed image was extracted to create an image of a single maize seed, which was then divided to establish the data set. An enhanced YOLOv5s–ShuffleNet–CBAM model was ultimately developed. …”
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