A Framework for User Traffic Prediction and Resource Allocation in 5G Networks by Ioannis Konstantoulas, Iliana Loi, Dimosthenis Tsimas, Kyriakos Sgarbas, Apostolos Gkamas, Christos Bouras

“…This framework consists of a hybrid approach utilizing a Long Short-Term Memory (LSTM) network or a Transformer architecture for user traffic prediction in base stations, as well as a Convolutional Neural Network (CNN) to allocate users to base stations in a realistic scenario. The models show high accuracy in the tasks performed, especially in the user traffic prediction task, where the models show an accuracy of over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99</mn><mo>%</mo></mrow></semantics></math></inline-formula>. …”
Get full text

Adjusting for principal components can induce collider bias in genome-wide association studies. by Kelsey E Grinde, Brian L Browning, Alexander P Reiner, Timothy A Thornton, Sharon R Browning

“…Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.…”
Get full text

Heart Disease Prediction Using Ensemble Tree Algorithms: A Supervised Learning Perspective by Enoch Sakyi-Yeboah, Edmund Fosu Agyemang, Vincent Agbenyeavu, Akua Osei-Nkwantabisa, Priscilla Kissi-Appiah, Lateef Moshood, Lawrence Agbota, Ezekiel N. N. Nortey

“…Adaptive Boosting stands out as the highest performer, achieving an average testing accuracy of 93.70%, precision of 93.71%, recall of 93.70%, and F1 score of 93.69%, along with the highest AUC score of 0.9708, across all competing models considered in the study. …”
Get full text

A Picking Point Localization Method for Table Grapes Based on PGSS-YOLOv11s and Morphological Strategies by Jin Lu, Zhongji Cao, Jin Wang, Zhao Wang, Jia Zhao, Minjie Zhang

“…More specifically, the network PGSS-YOLOv11s is composed of an original backbone of the YOLOv11s-seg, a spatial feature aggregation module (SFAM), an adaptive feature fusion module (AFFM), and a detail-enhanced convolutional shared detection head (DE-SCSH). …”
Get full text

Evaluating and Optimizing MySejahtera App Analytics for Sustainable Digital Government Services by Adawiyah Ashar, Nur Fazidah Elias, Ruzzakiah Jenal, Meng Chun Lam, Maheshwara Rao Appannan, Siti Aishah Iskandar

“…Furthermore, this comprehensive study endeavor meticulously proposes a variety of optimization strategies that are fundamentally rooted in the best practices that have been observed and documented in the MySejahtera application, and these strategies encompass a wide array of enhancements to its key feature insights, emphasizing key feature insights, frequent updates, response to app reviews, user engagement, emerging technology adoption, geographical, demographic, and language diversity, enhanced security and data privacy, optimize technical specifications, observing performance issues, and strategizing organizational efforts to address the dynamic requirements of citizens that can evolve in tandem with the needs of the user base and the technological landscape. …”
Get full text

Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia by Clara S. Vetter, Clara S. Vetter, Clara S. Vetter, Annika Bender, Dominic B. Dwyer, Dominic B. Dwyer, Dominic B. Dwyer, Max Montembeault, Anne Ruef, Katharine Chisholm, Lana Kambeitz-Ilankovic, Linda A. Antonucci, Stephan Ruhrmann, Joseph Kambeitz, Marlene Rosen, Theresa Lichtenstein, Anita Riecher-Rössler, Rachel Upthegrove, Raimo K. R. Salokangas, Jarmo Hietala, Christos Pantelis, Christos Pantelis, Rebekka Lencer, Rebekka Lencer, Eva Meisenzahl, Stephen J. Wood, Stephen J. Wood, Paolo Brambilla, Paolo Brambilla, Stefan Borgwardt, Peter Falkai, Peter Falkai, Alessandro Bertolino, Nikolaos Koutsouleris, Nikolaos Koutsouleris, Nikolaos Koutsouleris, PRONIA Consortium

“…Their diagnostic value compared to regional GMV was assessed in a stepwise analysis using a series of linear support vector machines within a nested cross-validation framework and stacked generalization, all models were externally validated in an independent sample (NPAT=71, NHC=74), SCN feature importance was assessed, and the derived risk scores were analyzed for differential relationships with clinical variables.ResultsWe found that models trained on SCNs were able to classify patients with schizophrenia and combining SCNs and regional GMV in a stacked model improved training (balanced accuracy (BAC)=69.96%) and external validation performance (BAC=67.10%). …”
Get full text

A Nomogram Based on Laboratory Data, Inflammatory Bowel Disease Questionnaire and CT Enterography for Activity Evaluation in Crohn&rsquo;s Disease by Zhang H, Shen Y, Cao B, Zheng X, Zhao D, Hu J, Wu X

“…However, there was no significant difference in AUC between the two models in the validation set (P = 0.206). IBDQ + clinical outperformed clinical (AUC 0.808), clinical outperformed IBDQ (AUC 0.746), and IBDQ outperformed radiomic signature (AUC 0.688). …”
Get full text

Enhanced classification of tinnitus patients using EEG microstates and deep learning techniques by Zahra Raeisi, Abolfazl Sodagartojgi, Fahimeh Sharafkhani, Amirsadegh Roshanzamir, Hossein Najafzadeh, Omid Bashiri, Alireza Golkarieh

“…Additionally, pre-trained models (VGG16, ResNet50, Xception) were used with a novel feature-to-image transformation approach for validation. …”
Get full text

TS2GNet: A temporal–spatial–spectral multidomain guided network for classifying hyperspectral tree species using multiseason satellite imagery by Kaijian Xu, Henghui Han, Shuzhou Wang, Ping Zhao, Jun Geng, Hailan Jiang, Anxin Ding

“…TS2GNet employs a dual-stream architecture that focus on dynamic interactions between spatial and spectral domains, while also incorporating temporal modeling and SHSI feature-domain guidance. We evaluate our method on eight dominant tree species in the Ta-pieh Mountains and compare its performance with six state-of-the-art (SOTA) deep learning-based hyperspectral classification methods. …”
Get full text

Deep learning-based lung cancer classification of CT images by Mohammad Khalid Faizi, Yan Qiang, Yangyang Wei, Ying Qiao, Juanjuan Zhao, Rukhma Aftab, Zia Urrehman

“…Pretrained on the LUNA16 and LUNA16-K datasets, which consist of annotated CT scans from thousands of patients, DCSwinB was evaluated using ten-fold cross-validation. The model demonstrated superior performance, achieving 90.96% accuracy, 90.56% recall, 89.65% specificity, and an AUC of 0.94, outperforming existing models such as ResNet50 and Swin-T. …”
Get full text

Enhanced YOLO and Scanning Portal System for Vehicle Component Detection by Feng Ye, Mingzhe Yuan, Chen Luo, Shuo Li, Duotao Pan, Wenhong Wang, Feidao Cao, Diwen Chen

“…Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. …”
Get full text

Protecting digital assets using an ontology based cyber situational awareness system by Tariq Ammar Almoabady, Yasser Mohammad Alblawi, Ahmad Emad Albalawi, Majed M. Aborokbah, S. Manimurugan, Ahmed Aljuhani, Hussain Aldawood, P. Karthikeyan

“…Ontology development was employed to represent knowledge systematically and enable semantic correlation of threats. Feature mapping enriched datasets with contextual threat information.ResultsThe proposed dual-algorithm framework demonstrated superior performance, achieving 95% accuracy, a 99% F1 score, and a 94.60% recall rate. …”
Get full text

基于IMU信号的人工智能上肢多关节运动状态识别系统构建——卒中后人工智能运动功能评估与检测系统建设前导研究 Construction of an Artificial Intelligence Upper Limb Multi-Joint Motion State Recognition System Based on IMU Signals—A Pre... by 程相鑫1，张烁1，杜松骏1，刘子阳1，周宏宇2，3，贾伟丽2，3，李子孝2，3，刘涛1 (CHENG Xiangxin1, ZHANG Shuo1, DU Songjun1, LIU Ziyang1, ZHOU Hongyu2,3, JIA Weili2,3, LI Zixiao2,3, LIU Tao1 )

“…The results demonstrated that the motion state recognition system designed in this study performed well in multi-joint state decoding of the upper limb. …”
Get full text

Predicting stroke with machine learning techniques in a sub-Saharan African population by Benjamin Segun Aribisala, Deirdre Edward, Godwin Ogbole, Onoja M. Akpa, Segun Ayilara, Fred Sarfo, Olusola Olabanjo, Adekunle Fakunle, Babafemi Oluropo Macaulay, Joseph Yaria, Joshua Akinyemi, Albert Akpalu, Kolawole Wahab, Reginald Obiako, Morenikeji Komolafe, Lukman Owolabi, Godwin Osaigbovo, Akinkunmi Paul Okekunle, Arti Singh, Philip Ibinaye, Osahon Osawata, Adeniyi Sunday, Ijezie Chukwuonye, Carolyn Jenkins, Hemant K. Tiwari, Okechukwu Ogah, Ruth Y. Laryea, Daniel T. Lackland, Oyedunni Arulogun, Omotolani Ajala, Rufus Akinyemi, Bruce Ovbiagele, Steffen Sammet, Mayowa Owolabi

“…Results: Our results showed that the 16 features-based classification (maximum AUC of 82.32%) had a slightly better performance than the 11 feature-based (maximum AUC 81.17%) algorithm. …”
Get full text

Using deep learning for ultrasound images to diagnose chronic lateral ankle instability with high accuracy by Masamune Kamachi, Kohei Kamada, Noriyuki Kanzaki, Tetsuya Yamamoto, Yuichi Hoshino, Atsuyuki Inui, Yuta Nakanishi, Kyohei Nishida, Kanto Nagai, Takehiko Matsushita, Ryosuke Kuroda

“…The important features were visualized using occlusion sensitivity, a method for visualizing areas that are important for model prediction. …”
Get full text

Breast Cancer Detection Using Mammography: Image Processing to Deep Learning by Shahzad Ahmad Qureshi, Aziz-Ul-Rehman, Lal Hussain, Touseef Sadiq, Syed Taimoor Hussain Shah, Adil Aslam Mir, Muhammad Amin Nadim, Darnell K. Adrian Williams, Tim Q. Duong, Qurat-Ul-Ain Chaudhary, Natasha Habib, Asrar Ahmad, Syed Adil Hussain Shah

“…Evaluating a deep learning architecture based on self-extracted features for classification tasks demonstrated outstanding performance. …”
Get full text

Analysis and validation of serum biomarkers in brucellosis patients through proteomics and bioinformatics by Xiao Li, Bo Wang, Xiaocong Li, Juan He, Yue Shi, Rui Wang, Dongwei Li, Ding Haitao, Ding Haitao

“…Differential expression analysis was performed to identify proteins with altered expression, while Weighted Gene Co-expression Network Analysis (WGCNA) was applied to detect co-expression modules associated with clinical features of brucellosis. …”
Get full text

Protection of the plasma facing components in the WEST tokamak, progress and development in view of ITER by M. Houry, M-H. Aumeunier, Y. Corre, X. Courtois, R. Mitteau, TH. Loarer, L. Dubus, E. Gauthier, J. Gerardin, V. Gorse, E. Grelier, A. Juven, PH. Malard, V. Moncada, Q. Tichit, S. Vives, J. Gaspar, the WEST Team

“…The aim is to protect the PFCs from damage during experimental campaigns, whilst enabling the expansion of the operational domain toward long duration and high power performances. With nearly 35 years of operation of Tore Supra and now WEST, CEA’s magnetic fusion research institute, the IRFM, has deployed a system combining thermal instrumentation, modeling of the heat transfer and photonic emission, signal processing and understanding of the physics of plasma-wall interaction to provide an optimized and controlled protection of the PFCs in metallic environment (with tungsten, bore, copper and stainless steel materials). …”
Get full text

A Deep Learning-Based Probabilistic Approach for Non-Destructive Testing of Aircraft Components Using Laser Ultrasonic Data by Adriano Liso, Cosimo Patruno, Angelo Cardellicchio, Pierfrancesco Ardino, Nicola Gallo, Giuseppe del Prete, Valerio Dentico, Veronica Vespini, Sara Coppola, Pietro Ferraro, Vito Reno

“…We show that training deep learning-based models as autoencoders makes it possible to extract features that can be used to discern defective areas from non-defective ones in the US C-scan maps. …”
Get full text

Mechanical properties and energy absorption of CoCrNi functionally graded TPMS cellular structures by Zhaoyi Wang, Junxian Zhou, Yunzhuo Lu, Dongming Li, Deyu Yue, Xu Zhang, Bingzhi Chen

“…Additionally, based on experimental validation, the Johnson-Cook constitutive model for SLM-ed CoCrNi was successfully developed and applied to finite element analysis (FEA) predictions. …”
Get full text