Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models by Salwa Sahnoun, Rihab Souissi, Sirine Chiboub, Aziza Chabchoub, Mohamed Khalil Baazaoui, Ahmed Fakhfakh, Faouzi Derbel

“…This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a real Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor. …”
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Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography Angiography by Bo Zhao, Jianjun Peng, Ce Chen, Yongyan Fan, Kai Zhang, Yang Zhang

“…TransCHD incorporates a Contextual Representation Learning (CRL) module and a Spatially-Aware Feature (SAF) module, enabling both local feature extraction and global contextual awareness within a unified architecture. …”
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Rapid Discrimination of Aging Year of Chenpi Based on Hyperspectral Images by Cheng LIU, Lulu ZHAO, Songbin ZHOU, Yisen LIU, Tingyou WANG

“…A lightweight 1D-Rep network was utilized to develop a classification model enhanced by a feature band selection technique using multi-layer gradient-weighted class activation mapping (M-Grad-CAM). …”
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Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data by Cristian Constantin Volovăț, Călin Gheorghe Buzea, Diana-Ioana Boboc, Mădălina-Raluca Ostafe, Maricel Agop, Lăcrămioara Ochiuz, Ștefan Lucian Burlea, Dragoș Ioan Rusu, Laurențiu Bujor, Dragoș Teodor Iancu, Simona Ruxandra Volovăț

“…Two convolutional neural network backbones—ResNet-50 and EfficientNet-B0—were fused with fully connected layers processing tabular data. …”
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Simulating Intraday Electricity Consumption with ForGAN by Ralf Korn, Laurena Ramadani

“…Sparse data and an unknown conditional distribution of future values are challenges for managing risks inherent in the evolution of time series. This contribution addresses both aspects through the application of ForGAN, a special form of a generative adversarial network (GAN), to German electricity consumption data. …”
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Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment by Sikha S. Bagui, Germano Correa Silva De Carvalho, Asmi Mishra, Dustin Mink, Subhash C. Bagui, Stephanie Eager

“…In an era marked by the rapid growth of the Internet of Things (IoT), network security has become increasingly critical. …”
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CNN-ELM-BASED DEEP LEARNING FRAMEWORK FOR KNEE OSTEOARTHRITIS CLASSIFICATION FROM RADIOGRAPHIC IMAGES by V Srividhya, P Jega Juliet, N Neelima, Senthil Kumar Seeni

“…This paper proposes a deep learning framework for automated Knee OsteoArthritis (KOA) severity classification from radiographic images, using a hybrid custom Convolutional Neural Network and Extreme Learning Machine (CNN-ELM) architecture. …”
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Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN by Simanto Saha, Mathias Baumert, Alistair Mcewan

“…This study proposed a motor imagery (MI) classification pipeline featuring a 1−dimensional convolutional neural network (1D-CNN) with different time/frequency feature representation techniques. …”
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SHIFA: SBERT-Based Healthcare Information Focused Arabic Question Answering by Rahaf Alruwaithi, Sarah AlHumoud

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A hypergraph convolution-based intelligent healthcare platform for aging population management by Wenjie Li, Bing Hou, Xiao-xiao Wang

“…These data are integrated into a dynamic medical knowledge graph, and analyzed using HGCN and hierarchical feature learning to facilitate health condition monitoring and inter-institutional collaboration.ResultsExperimental evaluations demonstrated the platform’s effectiveness, achieving an 87.26% accuracy and a 0.831 F1-score in disease risk prediction. …”
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Multi-Class Skin Cancer Classification Using a Hybrid Dynamic Salp Swarm Algorithm and Weighted Extreme Learning Machines with Transfer Learning by Ramya Panneerselvam, Sathiyabhama Balasubramaniam

“…GoogleNet is a pre-trained network model used with the hybrid model, which helps converge faster with the optimization process. …”
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Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning by Wajahat Nawaz, Kevin Albert, Philippe Jouvet, Rita Noumeir

“…Our method integrates color and depth data using deep convolutional neural networks with a late feature fusion scheme. We train and evaluate our model on a dataset of 153 patients with respiratory illnesses, 86 of whom have ARD of varying severity levels. …”
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Pure data correction enhancing remote sensing image classification with a lightweight ensemble model by Huaxiang Song, Hanglu Xie, Yingying Duan, Xinyi Xie, Fang Gan, Wei Wang, Jinling Liu

“…This strategy effectively corrects feature distributions across remote sensing data, significantly improving the performance of Convolutional Neural Networks and Vision Transformers beyond traditional data augmentation techniques. …”
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GeoMM-SSL: Integrating Geospatial Object Relations in Multimodal Self-Supervised Learning for Semantic Segmentation of Remote Sensing Images by Yang Liu, Tong Zhang, Yanru Huang

“…The proposed framework includes a teacher-student framework with residual gated guidable attention units as the backbone, a multihead graph attention network that encodes prior knowledge of geospatial object relations, a multimodal representation fusion module that facilitates mutual learning between visual features of remote sensing images and topological features of geospatial object relations, and a multilevel loss function that performs multiple levels of evaluation, enabling the model to learn the data representation at the pixel, object, and global levels. …”
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An interpretable machine learning model for predicting myocardial injury in patients with high cervical spinal cord injury by Jiaqi Li, Jiaqi Li, Bingyu Zhang, Bingyu Zhang, Ye Liao, Ye Liao, Liqin Wei, Qinfeng Huang, Qinfeng Huang, Lijun Lin, Lijun Lin, Jiaxin Chen, Jiaxin Chen, Hui Chen, Hui Chen

“…Four machine learning (ML) models—logistic regression, gradient boosting machine (GBM), neural network (NeuralNetwork), and adaptive boosting (AdaBoost)—were constructed to predict myocardial injury, and model performance was evaluated using the area under the curve (AUC), F1 score, and average precision (AP). …”
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Conveyor belt deviation identification algorithm based on anchor point positioning and cross-layer correction by Zhe WANG, Zhe FU, Pengjun CAO, Qing LI, Gaoxiang ZHANG

“…To address the problems of information loss and inaccurate extraction of conveyor belt edge lines in the traditional convolutional neural network (CNN) methods for conveyor belt edge line detection due to the difficulty in constructing long-distance dependency relationships between pixels, a conveyor belt deviation recognition algorithm based on the DETR (Detection Transformer) encoder-decoder network structure with anchor point positioning and cross-layer correction is proposed. …”
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Identification of biomarkers and immune microenvironment associated with heart failure through bioinformatics and machine learning by Jingyun Jin, Jingyun Jin, Shuyan Qin, Qiang Fu, Changzhi Yu, Hongjin Wu, Hongjin Wu, Hongjin Wu

“…Differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify key genes. …”
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An automatic classification of breast cancer using fuzzy scoring based ResNet CNN model by Nisha Y., Jagadeesh Gopal

“…So, this research introduces a hybrid DL model for improving prediction performance andreducing time consumption compared to the machine learning (ML)model.Describing a pre-processing method utilizing statistical co-relational evaluation to improve the classifier’s accuracy.The features are then extracted from the Region of Interest (ROI) images using the wrapping technique and a fast discrete wavelet transform (FDWT). …”
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Recovering 3D Basin Basement Relief Using High-Precision Magnetic Data Through Particle Swarm Optimization and Back Propagation Algorithm by Shen Yan, Xinjun Zhang, Zhongda Shang, Kai Wang, Yixin Ma

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A Deep Learning Model for Psychological Support in Student Entrepreneurship by Tao Yang, Wendong Xu, Lingbo Wang, Morozova Olga Petrovna

“…The model innovatively combines fully embedded feature engineering and high-low order parallel network structure, thus improving the recognition ability and evaluation accuracy of psychological characteristics. …”
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