What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection by Lu Liang, Jacob Daniels

“…While confirming that the latest research tendency is deep learning, regression is still a viable option for studies with limited effort in parameter tuning and method selection, especially considering its computational efficiency and simplicity. …”
Get full text

Efficient guided inpainting of larger hole missing images based on hierarchical decoding network by Xiucheng Dong, Yaling Ju, Dangcheng Zhang, Bing Hou, Jinqing He

“…Abstract When dealing with images containing large hole-missing regions, deep learning-based image inpainting algorithms often face challenges such as local structural distortions and blurriness. …”
Get full text

Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence by Yi-Ming Chen, Tzu-Hung Hsiao, Ching-Heng Lin, Yang C. Fann

“…Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. Deep learning techniques have significantly advanced variant calling, pathogenicity prediction, splicing analysis, and MHC-peptide binding predictions in genetics. …”
Get full text

Polarity-JaM: an image analysis toolbox for cell polarity, junction and morphology quantification by Wolfgang Giese, Jan Philipp Albrecht, Olya Oppenheim, Emir Bora Akmeriç, Julia Kraxner, Deborah Schmidt, Kyle Harrington, Holger Gerhardt

“…Advances in fluorescence microscopy and deep learning algorithms open up a wealth of unprecedented opportunities to characterise various aspects of cell polarity, but also create new challenges for comprehensible and interpretable image data analysis workflows to fully exploit these new opportunities. …”
Get full text

MLDFNet: A Multilabel Dual-Flow Network for Change Detection in Bitemporal Remote Sensing Images by Daniyaer Sidekejiang, Panpan Zheng, Liejun Wang

“…With the development of deep learning (DL) in recent years, numerous remote sensing image change detection (CD) networks have emerged. …”
Get full text

Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching Networks by Liting Jiang, Feng Wang, Wenyi Zhang, Peifeng Li, Hongjian You, Yuming Xiang

“…Stereo matching, a critical step of binocular 3-D reconstruction, has fully shifted to deep learning due to its strong feature representation of remote sensing images. …”
Get full text

Breast cancer classification based on hybrid CNN with LSTM model by Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey, Yasser Fouad

“…Medical image analysis methods and computer-aided diagnosis can enhance this process, providing training and assistance to less experienced clinicians. Deep Learning (DL) models play a great role in accurately detecting and classifying cancer in the huge dataset, especially when dealing with large medical images. …”
Get full text

Synchronization-based graph spatio-temporal attention network for seizure prediction by Jie Xiang, Yanan Li, Xubin Wu, Yanqing Dong, Xin Wen, Yan Niu

“…In recent years, a large number of studies have been conducted using deep learning models on epileptic open electroencephalogram (EEG) datasets with good results, but due to individual differences there are still some subjects whose seizure features cannot be accurately captured and are more difficult to differentiate, with poor prediction results. …”
Get full text

IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT by Jayapradha J, Su-Cheng Haw, Naveen Palanichamy, Kok-Why Ng, Senthil Kumar Thillaigovindhan

“…In recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. …”
Get full text

Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. by Gülcan Gencer, Kerem Gencer

“…This paper offers a hybrid SE (Squeeze-and-Excitation)-Enhanced Hybrid Model for detecting retinal disorders from OCT images, including DME, Drusen, and CNV, using artificial intelligence and deep learning.<h4>Methods</h4>The model integrates SE blocks with EfficientNetB0 and Xception architectures, which provide high success in image classification tasks. …”
Get full text

Advances in antiviral strategies targeting mosquito-borne viruses: cellular, viral, and immune-related approaches by Ayyaz Khan, Zakirullah, Shahid Wahab, Seong-Tshool Hong

“…Additionally, it explores immunomodulatory therapies to enhance antiviral responses and emphasizes the potential of drug repurposing, bioinformatics, artificial intelligence, and deep learning in identifying novel antiviral candidates. …”
Get full text

Modeling and designing enhancers by introducing and harnessing transcription factor binding units by Jiaqi Li, Pengcheng Zhang, Xi Xi, Liyang Liu, Lei Wei, Xiaowo Wang

“…Here we propose the concept of transcription factor binding unit (TFBU) to modularly model enhancers by quantifying the impact of context sequences surrounding TFBSs using deep learning models. Based on this concept, we develop DeepTFBU, a comprehensive toolkit for enhancer design. …”
Get full text

Medium-term load forecasting with Power Market Survey: GEPCO case study by Sheeza Mariam Nawaz, Ubaid Ahmed, Adil Amin, Syed Afraz Hussain Shah, Anzar Mahmood

“…The time horizon for MTLF ranges from a few weeks to one year and it has applications in energy management and planning. The deep-learning networks (DLNs), proposed in recent years, have a black-box nature, which reduces the interpretability of the results. …”
Get full text

A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), na&#x00EF;ve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
Get full text

IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced by Zhaoyang Han, Linlin Zhang, Qingyan Meng, Chongchang Wang, Wenxu Shi, Maofan Zhao

“…Currently, the development of deep learning has had a positive impact on remote sensing image change detection tasks, but many current methods still face challenges in effectively processing global and local features, especially in the task of building change detection in high-resolution images containing complex scenes. …”
Get full text

Prediction of mechanical characteristics of shearer intelligent cables under bending conditions. by Lijuan Zhao, Dongyang Wang, Guocong Lin, Shuo Tian, Hongqiang Zhang, Yadong Wang

“…The research shows that the TCN-BiLSTM-SEAttention model demonstrates outstanding predictive capability under complex operating conditions, providing a novel approach for improving cable management and equipment safety through optical fiber monitoring technology in the intelligent development of coal mines, highlighting the potential of deep learning in complex mechanical predictions.…”
Get full text

Deep empirical neural network for optical phase retrieval over a scattering medium by Huaisheng Tu, Haotian Liu, Tuqiang Pan, Wuping Xie, Zihao Ma, Fan Zhang, Pengbai Xu, Leiming Wu, Ou Xu, Yi Xu, Yuwen Qin

“…The DENN might shed new light on the applications of deep learning in physics, information science, biology, chemistry and beyond.…”
Get full text

Bilingual hate speech detection on social media: Amharic and Afaan Oromo by Teshome Mulugeta Ababu, Michael Melese Woldeyohannis, Emuye Bawoke Getaneh

“…In this work, a Bilingual hate speech detection for Amharic and Afaan Oromo languages were conducted using four different deep learning classifiers (CNN, BiLSTM, CNN-BiLSTM, and BiGRU) and three feature extraction (Keras word embedding, word2vec, and FastText) techniques. …”
Get full text

Reducing lead requirements for wearable ECG: Chest lead reconstruction with 1D-CNN and Bi-LSTM by Kazuki Hebiguchi, Hiroyoshi Togo, Akimasa Hirata

“…This study aims to develop a deep learning model capable of reconstructing complete 12-lead ECG waveforms using a minimal number of chest leads, thereby optimizing lead configurations for wearable ECG systems. …”
Get full text

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network by Guimei Yin, Jie Yuan, Yanjun Chen, Guangxing Guo, Dongli Shi, Lin Wang, Zilong Zhao, Yanli Zhao, Manjie Zhang, Yuan Dong, Bin Wang, Shuping Tan

“…Abstract Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. …”
Get full text