Search Results - novel stack deep conventional autoencoder

Multimodal Deep Learning for Alzheimer’s Disease Diagnosis: Integrating Neuroimaging and Genetic Data

Authors: Md Jubaer Rahman Romana Nourin Nipa

Source: Journal of Computer Science and Technology Studies; Vol. 7 No. 2; 284-293; 2709-104X

Index Terms: Deep Learning, Alzheimer’s Disease, Alzheimer's Disease Neuroimaging Initiative (ADNI), Genetic Data, cognitively normal (CN)., info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion

URL: https://al-kindipublisher.com/index.php/jcsts/article/view/9204
https://al-kindipublisher.com/index.php/jcsts/article/view/9204/7882
https://al-kindipublisher.com/index.php/jcsts/article/view/9204/7882

Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network.

Authors: Sahani, Mrutyunjaya Choudhury, Sasmita Siddique, Marif Daula et al.

Source: Engineering Applications of Artificial Intelligence. Oct2024:Part A, Vol. 136, pN.PAG-N.PAG. 1p.

Subject Terms: *FIELD programmable gate arrays, *SOLAR temperature, *SOLAR power plants, *STATISTICAL correlation, *ENERGY shortages

SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising.

Authors: Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam

Source: IET Image Processing (Wiley-Blackwell); Dec2019, Vol. 13 Issue 14, p2778-2789, 12p

From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?

Authors: Hsu, Honda Lee, Kun-Hua Karmakar, Riya et al.

Source: Diagnostics (2075-4418); Nov2025, Vol. 17 Issue 21, p2718, 48p

Subject Terms: BREAST cancer, DIAGNOSTIC imaging, MACHINE learning, DEEP learning, CLINICAL trials

Autoencoder-based confidence score for item preknowledge detection.

Authors: Pan, Yiqin

Source: Behaviormetrika; Jul2025, Vol. 52 Issue 2, p317-342, 26p

Subject Terms: AUTOENCODERS, DEEP learning, PRIOR learning, SIMULATION methods & models, DETECTION algorithms, SIGNAL detection, TEST design

Multimodal Deep Learning for Alzheimer’s Disease Diagnosis: Integrating Neuroimaging and Genetic Data

Authors: null Md Jubaer Rahman null Romana Nourin Nipa

Source: Journal of Computer Science and Technology Studies. 7:284-293

EEG emotion recognition across subjects based on deep feature aggregation and multi-source domain adaptation.

Authors: Lin, Kunqiang Li, Ying He, Yiren et al.

Source: Cognitive Neurodynamics; 11/24/2025, Vol. 20 Issue 1, p1-18, 18p

An Integrated Hybrid Deep Learning Framework for Intrusion Detection in IoT and IIoT Networks Using CNN-LSTM-GRU Architecture.

Authors: Afraji, Doaa Mohsin Abd Ali Lloret, Jaime Peñalver, Lourdes

Source: Computation; Sep2025, Vol. 13 Issue 9, p222, 28p

Subject Terms: INTRUSION detection systems (Computer security), DEEP learning, ELECTRONIC data processing, INTERNET security, INTERNET of things

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays.

Authors: Arifeen, Murshedul Petrovski, Andrei Hasan, Md Junayed et al.

Source: Machines; Dec2024, Vol. 12 Issue 12, p894, 15p

Subject Terms: SOLAR cells, RENEWABLE energy sources, PHOTOVOLTAIC power systems, SOLAR energy, AUTOENCODERS

A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization.

Authors: Duceac, Mădălina Buzea, Călin Gheorghe Pleșea-Condratovici, Alina et al.

Source: Diagnostics (2075-4418); Jul2025, Vol. 15 Issue 13, p1628, 40p

Subject Terms: ARTIFICIAL neural networks, ENSEMBLE learning, CLINICAL decision support systems, AUTOENCODERS, BLENDED learning

Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review.

Authors: Zhen Zhao Joon Huang Chuah Khin Wee Lai et al.

Source: Frontiers in Computational Neuroscience; 2/6/2023, Vol. 17, p1-16, 16p

Subject Terms: DEEP learning, ALZHEIMER'S disease, MACHINE learning, DIAGNOSIS, MAGNETIC resonance imaging, CONVOLUTIONAL neural networks

Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.

Authors: Shobayo, Olamilekan Saatchi, Reza

Source: Diagnostics (2075-4418); May2025, Vol. 15 Issue 9, p1072, 31p

Subject Terms: ARTIFICIAL neural networks, PATTERN recognition systems, ARTIFICIAL intelligence, TRANSFORMER models, GENERATIVE adversarial networks

MCRIP-UNet: A modified U-Net architecture for semantic segmentation of images for breast cancer detection.

Authors: Praveena, Pothina Kumar, N. Suresh

Source: Onkologia i Radioterapia; 2025, Vol. 19 Issue 6, p1-11, 11p

Subject Terms: IMAGE segmentation, EARLY detection of cancer, MULTISENSOR data fusion, COMPUTER-assisted image analysis (Medicine), CELL segmentation, DEEP learning

Modeling Coagulant Dosage in Water Treatment Plant Using Explainable Deep Learning Model–Based SHapley Additive exPlanations (SHAP).

Authors: Fouchal, Amel Merabet, Khaled Kisi, Ozgur et al.

Source: Water Conservation Science & Engineering; 10/3/2025, Vol. 10 Issue 3, p1-31, 31p

Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals.

Authors: Sahani, Mrutyunjaya Rout, Susanta Kumar Dash, Pradipta Kishor

Source: IEEE Transactions on Biomedical Circuits & Systems; Jun2021, Vol. 16 Issue 3, p595-605, 11p

Deep Learning-Based Early Detection of Lung Diseases Using Light Attention Visual Geometry Networks.

Authors: Christiyana, C. Callins Senthilselvi, A. Elangovan, D. et al.

Source: Cognitive Computation; Dec2025, Vol. 17 Issue 6, p1-32, 32p

Deep Learning and Attention-Based Methods for Human Activity Recognition and Anticipation: A Comprehensive Review.

Authors: Shuvo, Md Rezowan Mekala, M S Elyan, Eyad

Source: Cognitive Computation; Dec2025, Vol. 17 Issue 6, p1-28, 28p

A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.

Authors: Kim, Seonghyuk Park, HyunWook Park, Sung-Hong

Source: Biomedical Engineering Letters; Nov2024, Vol. 14 Issue 6, p1221-1242, 22p

Prediction of diseases using transformers for gene expression dataset.

Alternate Title: Predicción de enfermedades mediante transformadores para conjuntos de datos de expresión génica. (Spanish)
Predição de doenças usando transformadores para conjunto de dados de expressão gênica. (Portuguese)

Authors: Mercy, Immaculate Chidambaram, M.

Source: Revista Colombiana de Ciencias Químico-Farmacéuticas; 2025, Vol. 54 Issue 3, p591-620, 30p

Subject Terms: GENE expression, TRANSFORMER models, CANCER prognosis, MEDICAL research, PROGNOSIS, DEEP learning

A survey on deep learning based reenactment methods for deepfake applications.

Authors: Dhanyalakshmi, Ramamurthy Popirlan, Claudiu‐Ionut Hemanth, Duraisamy Jude

Source: IET Image Processing (Wiley-Blackwell); 12/11/2024, Vol. 18 Issue 14, p4433-4460, 28p

Subject Terms: HUMAN mechanics, RESEARCH personnel, DEEP learning