Development of an artificial intelligence-based multimodal diagnostic system for early detection of biliary atresia by Ya Ma, Yuancheng Yang, Yuxin Du, Luyang Jin, Baoyu Liang, Yuqi Zhang, Yedi Wang, Luyu Liu, Zijian Zhang, Zelong Jin, Zhimin Qiu, Mao Ye, Zhengrong Wang, Chao Tong

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Application of data mining to extract knowledge about the occurrence of fistulas after palatoplasty by Patrick Pedreira Silva, Elvio Gilberto da Silva, Vinicius Santos Andrade, Telma Vidotto de Sousa Brosco, Gabriela Aparecida Prearo, Maria Inês Pegoraro-Krook, Jeniffer de Cassia Rillo Dutka

“…Two models for detecting the outcome of surgery were induced using data mining techniques (Decision Tree and Apriori). …”
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Employee Turnover Prediction Model Based on Feature Selection and Imbalanced Data Handling by Yuan Fang, Zhongqiu Zhang

“…The dataset underwent rigorous preprocessing and exploratory data analysis (EDA) to identify key patterns and relationships. …”
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Passive indoor human daily behavior detection method based on channel state information by Xiaochao DANG, Yaning HUANG, Zhanjun HAO, Xiong SI

“…The daily behavior detection of indoor human based on CSI is developing rapidly in the field of WSN.At present,most of the research is still in the environment of 2.4 GHz,so the detection rate,robustness and overall performance still need to be improved.In order to solve this problem,a passive indoor human behavior detection method HDFi (Human Detection with Wi-Fi) based on CSI signal was proposed.The method was used to detect the indoor human daily behavior in a 5 GHz band environment,which was divided into three steps:data acquisition,data processing,feature extraction,online detection.Firstly,the experiment collected typical daily behavioral data in complex laboratory and relatively empty meeting room.Secondly,the amplitude and phase data with more obvious features were extracted and processed by low-pass filtering to obtain a set of stable and noise-free data,and then the fingerprint database was established effectively.Finally,in the real-time detection stage,the collected data features were classified by SVM algorithm to extract more stable eigenvalues,and a classification model of indoor human daily behavior detection was established,and then matched the data in the fingerprint database.The experimental results show that the proposed method has the characteristics of high efficiency,high precision and good robustness,and the method does not need any testing personnel to carry any electronic equipment,so it has high practicability.…”
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Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature by Taotao LIU, Yu FU, Kun WANG, Xueyuan DUAN

“…Considering the problems of traditional intrusion detection methods limited by the class imbalance of datasets and the poor representation of selected features, a detection method based on VAE-CWGAN and fusion of statistical importance of features was proposed.Firstly, data preprocessing was conducted to enhance data quality.Secondly, a VAE-CWGAN model was constructed to generate new samples, addressing the problem of imbalanced datasets, ensuring that the classification model no longer biased towards the majority class.Next, standard deviation, difference of median and mean were used to rank the features and fusion their statistical importance for feature selection, aiming to obtain more representative features, which made the model can better learn data information.Finally, the mixed data set after feature selection was classified through a one-dimensional convolutional neural network.Experimental results show that the proposed method demonstrates good performance advantages on three datasets, namely NSL-KDD, UNSW-NB15, and CIC-IDS-2017.The accuracy rates are 98.95%, 96.24%, and 99.92%, respectively, effectively improving the performance of intrusion detection.…”
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Iterative segmentation and classification for enhanced crop disease diagnosis using optimized hybrid U-Nets model by Malathi Chilakalapudi, Sheela Jayachandran

“…Our research proposes a new framework instigated and developed to improve crop disease detection and classification by multifaceted analysis. …”
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Evidence that cultural groups differ in their abilities to detect fake accents: a follow up by Jonathan R. Goodman, Robert A. Foley

“…We recently reported that cultural group membership may be a predictor of the likelihood that an individual will detect a faked accent in a recording. Here, we present follow-up data to our original study using a larger data set comprised of responses from the across the world. …”
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Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning by Vânia Guimarães, Inês Sousa, Miguel Velhote Correia

“…In this study, we investigated whether using gait and dual tasks could help detect cognitive impairment after stroke. Methods We analyzed gait and neuropsychological data from 47 participants who were part of the Ontario Neurodegenerative Disease Research Initiative. …”
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A genetic programming approach with adaptive region detection to skin cancer image classification by Kunjie Yu, Jintao Lian, Ying Bi, Jing Liang, Bing Xue, Mengjie Zhang

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RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection by Ismail Keshta, Pallavi Sagar Deshpande, Mohammad Shabaz, Mukesh Soni, Mohit kumar Bhadla, Yasser Muhammed

“…Abstract Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). …”
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Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge by Amro Abdrabo

“…The study is the first to assess the applicability of one-class classification for anomaly detection on the short-term structural health data of the Z24 bridge.…”
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Characterising Payload Entropy in Packet Flows—Baseline Entropy Analysis for Network Anomaly Detection by Anthony Kenyon, Lipika Deka, David Elizondo

“…The accurate and timely detection of cyber threats is critical to keeping our online economy and data safe. …”
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Hybrid Deep Learning-Based Security Model for Robust Intrusion Detection in IoT Networks by Patil Jayashri J., Solanki Ramkumar

“…Training and validation of the model was done using the IoT23 dataset, which is a thorough set of real-world, labeled network data covering various malware attacks, including Mirai, Gafgyt, Tsunami, and Torii. …”
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Enhancing Medicare Fraud Detection With a CNN-Transformer-XGBoost Framework and Explainable AI by Mohammad Balayet Hossain Sakil, Md Amit Hasan, Md Shahin Alam Mozumder, Md Rokibul Hasan, Shafiul Ajam Opee, M. F. Mridha, Zeyar Aung

“…On the Medicare dataset, the framework achieved an F1-score of 0.95 on the training set and 0.92 on the test set, with an AUC-ROC of 0.98 and 0.97, respectively, outperforming state-of-the-art models such as LightGBM and CatBoost. …”
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Federated Learning-Assisted Coati Deep Learning-Based Model for Intrusion Detection in MANET by S. Faizal Mukthar Hussain, S. M. H. Sithi Shameem Fathima

“…Abstract MANET is a set of self-arranged, wirelessly connected nodes. …”
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Boosting Malware Detection with AlexNet and Optimized Neural Networks Using the Grasshopper Algorithm by Mohammed Aswad

“…To combat such nefarious software that can steal data and do a number of other privatively outcomes, you need to be very vigilant and also train all our artificial intelligent tools not just to find the malware per se but all the countless other ways in which meddlers might find their way into your computer or set off some enormously disruptive chain reaction (or series thereof). …”
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Human Activity Detection Events Through Human Eye Reflection using Bystander Analyzer by P. Nagalakshmi

“…Using the pixel-based Kruskal methodology and this method, the input data set’s minimal weight is precisely determined. …”
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Optimizing Attendance System: Integrating Liveness Detection and Deep Learning for Reliable Face Recognition by Joseph Teguh Santoso, Eko Sediyono, Kristoko Dwi Hartomo, Irwan Sembiring

“…Meanwhile, deep learning is used to analyze and process facial photos correctly by learning from large amounts of data and recognizing facial features in depth. The study data set consists of 1300 photographs of professional school instructors taken with official authority. …”
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Solar Cell Defects Detection Based on Photoluminescence Images and Upgraded YOLOv5 Model by Gengcong Xu, Jinhua Huang, Weidong Gong, Jiahui Teng

“…At the same time, five data enhancement methods such as Mosaic, Mixup, HSV transformation, Gaussian noise, and rotation transformation are introduced to improve the representativeness of the data set and enhance the detection ability of the model. …”
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Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing by Övünç Öztürk, Tuğba Özacar, Bora Canbula

“…The accuracy reported was achieved on a dedicated test set using real reflectogram data from actual construction sites, distinguishing this study from prior work relying primarily on synthetic data. …”
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