Etiology of Late-Onset Alzheimer’s Disease, Biomarker Efficacy, and the Role of Machine Learning in Stage Diagnosis by Manash Sarma, Subarna Chatterjee

“…This paper examines the predominant use of deep learning (DL) and convolutional neural networks (CNNs) in Alzheimer’s disease (AD) diagnosis utilizing various types of biomarker data. …”
Get full text

Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm by Hanqing Hu, Tianmu Tian, Chengjing Liu, Xueyuan Bai

“…And compared with convolutional networks (CNN) class, long short-term memory (LSTM) class and Transformer class prediction methods, it performs better in terms of accuracy and stability, providing a more reliable basis for the operation and promotion of online movies.…”
Get full text

Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

“…Various machine learning methods were evaluated, including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Support Vector Regression (SVR), k-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting Machines, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Gaussian Processes, as well as ensemble algorithms such as XGBoost, LightGBM, and CatBoost. …”
Get full text

Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap). by Reyhaneh Abbasi, Peter Balazs, Maria Adelaide Marconi, Doris Nicolakis, Sarah M Zala, Dustin J Penn

“…This study aims to 1) determine the most efficient USV detection tool among the existing methods, and 2) develop a classification model that is more generalizable than existing methods. …”
Get full text

Application of Artificial Intelligence Virtual Image Technology in Photography Art Creation Under Deep Learning by Qiong Yao

“…In the experimental section, the proposed cGANs + VAEs model is systematically compared with traditional Deep Convolutional GANs (DCGAN) and Pix2Pix models through empirical analysis. …”
Get full text

The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

“…The objective is to develop an innovative deep learning (DL) model that integrates a convolutional neural network (CNN) with a gated recurrent unit (GRU) to enhance forecasting precision for day-ahead applications. …”
Get full text

A Novel Transformer-Based Object Detection Method With Geometric and Object Co-Occurrence Prior Knowledge for Remote Sensing Images by Nan Mo, Ruixi Zhu

“…Last, we design a graph convolutional reference module with co-occurrence prior knowledge to improve the inferential ability of the detector. …”
Get full text

Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition by Sk Mahmudul Hassan, Kumar Sekhar Roy, Ruhul Amin Hazarika, Mehbub Alam, Mithun Mukherjee

“…To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. …”
Get full text

Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos by Lisa Boucret, Floris Chabrun, Magalie Boguenet, Pascal Reynier, Pierre-Emmanuel Bouet, Pascale May-Panloup

“…Abstract Time-lapse imaging and deep-learning algorithms are promising tools to assess the most viable embryos and improve embryo selection in IVF laboratories. …”
Get full text

Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions by Ömer Akgüller, Larissa M. Batrancea, Mehmet Ali Balcı, Gökhan Tuna, Anca Nichita

“…This study examines the effectiveness of Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE) models in detecting anomalies within occupational accident data from the Mining of Coal and Lignite (NACE05), Manufacture of Other Transport Equipment (NACE30), and Manufacture of Basic Metals (NACE24) sectors. …”
Get full text

PlantCareNet: an advanced system to recognize plant diseases with dual-mode recommendations for prevention by Muhaiminul Islam, AKM Azad, Shifat E. Arman, Salem A. Alyami, Md Mehedi Hasan

“…The proposed architecture utilizes a convolutional neural network (CNN) to examine images of plant leaves, with the final block flattened and subsequently forwarded to Dense-100 and ultimately Dense-35 for the precise classification of various plant diseases. …”
Get full text

Automated assessment of simulated laparoscopic surgical skill performance using deep learning by David Power, Cathy Burke, Michael G. Madden, Ihsan Ullah

“…We employ a 3-dimensional convolutional neural network (3DCNN) with a weakly-supervised approach to classify the experience levels of surgeons. …”
Get full text

Full-Scale Piano Score Recognition by Xiang-Yi Zhang, Jia-Lien Hsu

“…Sheet music is one of the most efficient methods for storing music. Meanwhile, a large amount of sheet music-image data is stored in paper form, but not in a computer-readable format. …”
Get full text

Using Deep Learning (CNN, RNN, LSTM, GRU) methods for the prediction of Protein Secondary Structure by Ezgi Çakmak, İhsan Hakan Selvi

“…The goal of this study is to compare the results generated by predictive models that were created using the four most frequently utilized deep learning methods: convolutional neural networks (CNN), recurrent neural networks (RNN), long short term memory networks (LSTM), and gated recurrent units (GRU). …”
Get full text

An Effective Feature Extraction Method for Tomato Leafminer - Tuta Absoluta (Meyrick) (Lepidoptera: Gelechiidae) Classification by Tahsin Uygun, Serhat Kiliçarslan, Cemil Közkurt, Mehmet Metin Ozguven

“…Using a hybrid approach, features were extracted through Convolutional Neural Networks (CNNs) with transfer learning and classified using traditional machine learning techniques. …”
Get full text

A Destination Prediction Network Based on Spatiotemporal Data for Bike-Sharing by Jian Jiang, Fei Lin, Jin Fan, Hang Lv, Jia Wu

“…This candidate set is then used to build the DPNst model: a long short-term memory network learns the user’s behavior; a convolutional neural network learns the spatial relationships between the origin and the candidate destinations; and a fully connected neural network learns the external features. …”
Get full text

Machine Learning in Acute Stroke Neuroimaging. A Systematic Literature Review by D. Matuliauskas, I. Stražnickaitė, A. Samuilis, D. Jatužis

“…The training set sizes consisted of minimum 28 CT scans, maximum – 24214, mean – 1279, median – 153, standard deviation – ±5006.7. Most popular software used in the studies were Brainomix (n=12, 20% of studies) and RAPID (n=12, 20%), 6 studies (10%) used convolutional neural networks, and 6 studies did not iden- tify the model or name of software used. …”
Get full text

Boosting Degradation Representation Learning for Blind Image Super-Resolution by YUAN Jiang, MA Ji, ZHOU Dengwen

“…In most convolutional neural networks-based super-resolution (SR) methods, the degradation assumptions are fixed and known (e.g., bicubic degradation). …”
Get full text

TrioConvTomatoNet-BiLSTM: An Efficient Framework for the Classification of Tomato Leaf Diseases in Real Time Complex Background Images by S. Ledbin Vini, P. Rathika

“…Abstract Tomatoes are the most valuable vegetable worldwide that suffer from leaf diseases, which affect long-term tomato protection. …”
Get full text

Integrating Deep Learning and Transcriptomics to Assess Livestock Aggression: A Scoping Review by Roland Juhos, Szilvia Kusza, Vilmos Bilicki, Zoltán Bagi

“…The main developments include convolutional neural network (CNN)-based object detection and pose estimation systems, together with the transcriptomic identification of molecular pathways that link to aggression and stress. …”
Get full text