Browsing Scopus İndeksli Yayınlar Koleksiyonu by Subject "Deep learning"
Now showing items 1-18 of 18
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Air quality prediction using CNN plus LSTM-based hybrid deep learning architecture
(Springer Heidelberg, 2022)Air pollution prediction based on variables in environmental monitoring data gains further importance with increasing concerns about climate change and the sustainability of cities. Modeling of the complex relationships ... -
The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things
(Elsevier Ireland Ltd, 2023)Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, ... -
Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review
(Elsevier, 2022)The goal of managing smart cities and societies is to maximize the efficient use of finite resources while enhancing the quality of life. To establish a sustainable urban existence, smart cities use some new technologies ... -
Benchmark Static API Call Datasets for Malware Family Classification
(Institute of Electrical and Electronics Engineers Inc., 2022)Nowadays, malware and malware incidents are increasing daily, even with various antivirus systems and malware detection or classification methodologies. Machine learning techniques have been the main focus of the security ... -
Classification of ADHD using ensemble algorithms with deep learning and hand crafted features
(Institute of Electrical and Electronics Engineers Inc., 2019)Attention Deficit Hyperactivity (ADHD) is a common neurodevelopmental disorder that typically appears in early childhood. Methods developed for diagnosing gives different results at different times. This is a major obstacle ... -
A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder
(Gazi Universitesi, 2023)Denoising is one of the most important preprocesses in image processing. Noises in images can prevent extracting some important information stored in images. Therefore, before some implementations such as image classification, ... -
The effect of data augmentation on ADHD diagnostic model using deep learning
(Institute of Electrical and Electronics Engineers Inc., 2019)Attention Deficit Hyperactivity Disorder (ADHD) is a neuro-behavioral hyperactivity disorder. It is frequently seen in childhood and youth, and lasts a lifetime unless treated. The ADHD classification model should be ... -
Multimodal retrieval with contrastive pretraining
(Institute of Electrical and Electronics Engineers Inc., 2021)In this paper, we present multimodal data retrieval aided with contrastive pretraining. Our approach is to pretrain a contrastive network to assist in multimodal retrieval tasks. We work with multimodal data, which has ... -
Multitype Learning via Multimodal Data Embedding
(Institute of Electrical and Electronics Engineers Inc., 2021)This paper creates a multimodal retrieval system for image and text data in a multi-type learning approach that enables text-to-image, image-to-text, text-to-text, and image-to-image retrievals. As a practical solution, a ... -
MuscleNET: Smart Predictive Analysis for Muscular Activity Using Wearable Sensors
(Institute of Electrical and Electronics Engineers Inc., 2022)Doing weightlifting training at home has become more popular during the pandemic. Unfortunately, exercising without professional help can lead to dangerous injuries such as muscle tearing. It is possible to create a smart ... -
Predatory Conversation Detection Using Transfer Learning Approach
(Springer International Publishing Ag, 2022)Predatory conversation detection on social media can proactively prevent the netizens, including youngsters and children, from getting exploited by sexual predators. Earlier studies have majorly employed machine learning ... -
PREDICTING PATH LOSS DISTRIBUTIONS OF A WIRELESS COMMUNICATION SYSTEM FOR MULTIPLE BASE STATION ALTITUDES FROM SATELLITE IMAGES
(IEEE Computer Society, 2022)It is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems. Optimum positioning of UAVs, serving as base stations, can be done through extensive field measurements or ray ... -
A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain
(Pergamon-Elsevier Science Ltd, 2022)With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due ... -
Random CapsNet forest model for imbalanced malware type classification task
(Elsevier, 2021)Behavior of malware varies depending the malware types, which affects the strategies of the system protection software. Many malware classification models, empowered by machine and/or deep learning, achieve superior ... -
Regression of Large-Scale Path Loss Parameters Using Deep Neural Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2022)Path loss exponent and shadowing factor are among important wireless channel parameters. These parameters can be estimated using field measurements or ray-tracing simulations, which are costly and time-consuming. In this ... -
Smart Stethoscope
(IEEE, 2020)In this study, a device named smart stethoscope that uses digital sensor technology for sound capture, active acoustics for noise cancellation and artificial intelligence (AI) for diagnosis of heart and lung diseases is ... -
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020)Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data ... -
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep Learning
(Springer, 2020)Techniques used for spatio-temporal anomaly detection in an unsupervised settings has attracted great attention in recent years. It has extensive use in a wide variety of applications such as: medical diagnosis, sensor ...