Statistical Approach to Research on the Relationship Between Kp/Dst Geomagnetic Indices and Total GPS Position Error by Mario Bakota, Igor Jelaska, Serdjo Kos, David Brčić

“…GNSS data from a globally distributed network of 14 IGS stations were analyzed for September 2017, featuring significant geomagnetic activity. …”
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Harnessing dual variational autoencoders to decode microbe roles in diseases for traditional medicine discovery by Qing Ye, Yaxin Sun, Yaxin Sun, Yaxin Sun

“…Then, these different sources of information are fused to provide rich and powerful feature support for subsequent prediction tasks. Finally, the Hadamard product operation and a deep neural network are used to predict MDAs. …”
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Suitability Assessment of Remotely Sensed Urban Air Quality Data by Zixin Zhang, Bin Zou, Shenxin Li

“…Specifically, taking Changsha city as a case, four evaluation indices—R², RMSE, uncertainty, and high deviation rate (HDR)—were employed to evaluate the credibility of remotely sensed data at national and dense ground-based stations, then analyze spatial variations of credibility and develop a Recursive Feature Elimination–Cross-Validation Random Forest (RFECV-RF) model to improve local fitting accuracy. …”
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Ensemble of deep learning and IoT technologies for improved safety in smart indoor activity monitoring for visually impaired individuals by Mesfer Al Duhayyim

“…Furthermore, the ResNet50 model is employed for feature extraction to capture complex spatial patterns in visual data. …”
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Cooperative Intelligent Transport Systems: The Impact of C-V2X Communication Technologies on Road Safety and Traffic Efficiency by Jingwen Wang, Ivan Topilin, Anastasia Feofilova, Mengru Shao, Yadong Wang

“…The advancement of intelligent road transport represents a promising direction in the evolution of transportation systems, aimed at improving road safety and reducing traffic accidents. …”
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ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images by Jing Zhang, Hao Zhou, Kunyu Liu, Yuguang Xu

“…These experiments demonstrate the superior performance of the ED-Swin Transformer model in cassava classification networks.…”
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Building Diagnostics Options for Existing Buildings – Innovative Methods by Zsolt Szurös, Rita Rebeka Ordog, Kitti Ajtayné Károlyfi, Dóra Szalai, Ádám Bukovics

“…In the second phase of the study, the possibilities of the analysis of point clouds and image processing-based survey options for structural diagnostic purposes will be explained and presented. The evaluation of the three damage detection methodologies (Geometrical features, Color and intensity information, Combined Method) highlights their complexity, technological requirements, cost, practical applicability, and accuracy. …”
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Utilization of Single-Pulse Transcranial-Evoked Potentials in Neurological and Psychiatric Clinical Practice: A Narrative Review by Hilla Fogel, Noa Zifman, Mark Hallett

“…<i>Conclusions:</i> TEP is a useful measurement of mechanisms underlying neuronal networks. It may be utilized in several clinical applications. …”
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Decoding vocal indicators of stress in laying hens: A CNN-MFCC deep learning framework by Suresh Neethirajan

“…This study leverages advanced Convolutional Neural Networks (CNNs) combined with Mel Frequency Cepstral Coefficients (MFCCs) to decode intricate vocalization patterns in laying hens experiencing acute environmental stress. …”
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A New Hybrid ConvViT Model for Dangerous Farm Insect Detection by Anil Utku, Mahmut Kaya, Yavuz Canbay

“…By utilizing a dataset comprising images of 15 distinct insect species, the suggested approach combines the strengths of traditional convolutional neural networks (CNNs) with vision transformer (ViT) architectures. …”
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Machine learning-based analysis of defensive strategies in basketball using player movement data by Jianlong Li

“…Automatic annotations were evaluated based on accuracy, precision, recall, and F1 score and compared with manual annotations. …”
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Leveraging deep learning for robust EEG analysis in mental health monitoring by Zixiang Liu, Juan Zhao

“…Conventional methods for EEG-based mental health evaluation often depend on manually crafted features or basic machine learning approaches, like support vector classifiers or superficial neural networks. …”
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Interpretable artificial intelligence (AI) for cervical cancer risk analysis leveraging stacking ensemble and expert knowledge by Priyanka Roy, Mahmudul Hasan, Md Rashedul Islam, Md Palash Uddin

“…Generative artificial intelligence methods, such as variational autoencoders and generative teaching networks, were evaluated but showed suboptimal performance. …”
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Mechanistic role of miR-375 in regulating PDPK1 to promote progression of small bowel neuroendocrine tumors: a silico analysis by Tao Ren, Lu Zhou, Zhenlong Li, Mingmei Pan, Xueqiong Han

“…XGBoost algorithm was used to screening feature miRNAs. Module genes in SBNETs-related pathways were selected to construct regulated network for feature miRNAs. …”
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MODELS FOR DEVELOPMENT OF LEADERSHIP QUALITIES IN STUDENT YOUTH: DOMESTIC AND FOREIGN EXPERIENC by Lavrentieva Olena, Krupskyi Oleksandr

“…It has been pointed out that most models of leadership development have several common features, namely: a) emphasis on both academic knowledge and personal development of leadership qualities of pupils / students; b) ensuring a high level of interaction in the “teacher-student” and “student-student” systems; c) experiential learning; d) reflective learning; e) collaborative learning; e) creation of a comfortable and emotionally rich educational environment; f) activation of study and cognitive activities of pupils/students via project activities and networking; g) operating the mutual evaluation and feedback. …”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

“…Machine learning models including Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boost Classifier (GBC), Multilayer Perceptron (MLP), and 1D Convolutional Neural Network (1DCNN) were developed and evaluated. Although the performance differences among the models were subtle (within a range of 0.001), the SVM classifier outperformed the others, achieving a recall of 0.9979 and an AUC of 0.9998. …”
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An explainable unsupervised learning approach for anomaly detection on corneal in vivo confocal microscopy images by Ningning Tang, Qi Chen, Yunyu Meng, Daizai Lei, Li Jiang, Yikun Qin, Xiaojia Huang, Fen Tang, Shanshan Huang, Qianqian Lan, Qi Chen, Lijie Huang, Rushi Lan, Xipeng Pan, Huadeng Wang, Fan Xu, Wenjing He

“…To address these limitations, we propose a Transformer-based unsupervised anomaly detection method for IVCM images, capable of identifying corneal abnormalities without prior knowledge of specific disease features.MethodsOur method consists of three submodules: an EfficientNet network, a Multi-Scale Feature Fusion Network, and a Transformer Network. …”
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Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms by Shuhui Hua, Chuan Li, Yuanlong Wang, YiZhi Liang, Shanling Xu, Jian Kong, Hongyan Gong, Rui Dong, Yanan Lin, Xu Lin, Yanlin Bi, Bin Wang

“…Among ten ML models, the Neural Network (NN) incorporating total bilirubin showed the best predictive performance for POD. …”
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Automated Detection of Aberrant Episodes in Epileptic Conditions: Leveraging EEG and Machine Learning Algorithms by Uddipan Hazarika, Bidyut Bikash Borah, Soumik Roy, Manob Jyoti Saikia

“…Our approach creates and evaluates support vector machine, random forest classifier, and long short-term memory network machine learning models to classify seizures using EEG inputs. …”
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Mitigating Sinkhole Attacks in MANET Routing Protocols using Federated Learning HDBNCNN Algorithm by Sherril Sophie Maria Vincent

“…By combining progressive ML methods through decentralized network structure, hence the study expressively provides a secure communication in MANETs and contributes a scalable and effective results through evaluating its performance metrics.  …”
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