Search results for "LSTM"
showing 10 items of 16 documents
Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
2020
&ldquo
RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process
2021
The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acquired in the design of a SS for a given process to a similar one. This has been approached as a transfer learning problem from a source to a target domain. The implementation of a transf…
Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment
2022
The present era is facing the industrial revolution. Machine-to-Machine (M2M) communication paradigm is becoming prevalent. Resultantly, the computational capabilities are being embedded in everyday objects called things. When connected to the internet, these things create an Internet of Things (IoT). However, the things are resource-constrained devices that have limited computational power. The connectivity of the things with the internet raises the challenges of the security. The user sensitive information processed by the things is also susceptible to the trusability issues. Therefore, the proliferation of cybersecurity risks and malware threat increases the need for enhanced security in…
Toward Optimal LSTM Neural Networks for Detecting Algorithmically Generated Domain Names
2021
Malware detection is a problem that has become particularly challenging over the last decade. A common strategy for detecting malware is to scan network traffic for malicious connections between infected devices and their command and control (C&C) servers. However, malware developers are aware of this detection method and begin to incorporate new strategies to go unnoticed. In particular, they generate domain names instead of using static Internet Protocol addresses or regular domain names pointing to their C&C servers. By using a domain generation algorithm, the effectiveness of the blacklisting of domains is reduced, as the large number of domain names that must be blocked g…
Reāllaika laikrindu analīze prognozēšanai un anomāliju detektēšanai
2021
Šajā darbā tiek aprakstīts laikrindu anomāliju noteikšanas modeļa izstrādes process un tā realizācija. Darbs tiek balstīts uz temperatūras mērījumu sensoru datiem. Anomāliju noteikšanas modeļa izstrādes ietvaros tiek apskatītas sekojošas tēmas - simulāciju veidošanda, laikrindu analīze, laikrindu priekšapstrāde, laikrindu klasterēšana, laikrindu prognozējošo modeļu izveide, anomāliju noteikšana un modeļu ansambļa izveide. Darba mērķis ir apskatīt dažāda tipa modeļus, metodes un to apvienojumus, lai izveidotu robustu anomāliju noteikšanas modeļu ansambli. Darba rezultātā tika izveidots laikrindu anomāliju noteikšanas modeļu ansamblis, kura pamatā ir četri modeļi - LightGBM, LSTM, Holt-Winter…
Dziļo neironu tīkla lietojums portfeļa konstrukcijas optimizācijā
2019
Samazinoties skaitļošanas jaudas izmaksām un pieaugot pētniecībai, neironu tīklu popularitāte pēdējos gados strauji augusi, un to pielietojumam tiek atrastas jaunas vietas, kas iepriekš nav bijušaspraktiskipieejamas. DarbātiekizmantotarekurentuneironatīklustruktūraMarkovitza optimālā portfeļa kontekstā, lai optimizētu riska un kapitāla ienesīguma attiecību ieguldījumu portfeļos. Izmantojotpēdējodesmitgaduikmēnešadatustiekdemonstrēts,kadziļoneironutīklu struktūrasuzrādalabākusniegumukāvienmērīgisabalansētsieguldījumuportfelisunklasiskās finanšu literatūras metodes, sasniedzot augstāku absolūto ienesīgumu un Sharpe koeficientu trenēšanasuntestakopās.
Deep learning for agricultural land use classification from Sentinel-2
2020
[ES] En el campo de la teledetección se ha producido recientemente un incremento del uso de técnicas de aprendizaje profundo (deep learning). Estos algoritmos se utilizan con éxito principalmente en la estimación de parámetros y en la clasificación de imágenes. Sin embargo, se han realizado pocos esfuerzos encaminados a su comprensión, lo que lleva a ejecutarlos como si fueran “cajas negras”. Este trabajo pretende evaluar el rendimiento y acercarnos al entendimiento de un algoritmo de aprendizaje profundo, basado en una red recurrente bidireccional de memoria corta a largo plazo (2-BiLSTM), a través de un ejemplo de clasificación de usos de suelo agrícola de la Comunidad Valenciana dentro d…
Malware Detection in Internet of Things (IoT) Devices Using Deep Learning
2022
Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is prep…
Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series
2020
L'analyse prédictive permet d'estimer les tendances des évènements futurs. De nos jours, les algorithmes Deep Learning permettent de faire de bonnes prédictions. Cependant, pour chaque type de problème donné, il est nécessaire de choisir l'architecture optimale. Dans cet article, les modèles Stack-LSTM, CNN-LSTM et ConvLSTM sont appliqués à une série temporelle d'images radar sentinel-1, le but étant de prédire la prochaine occurrence dans une séquence. Les résultats expérimentaux évalués à l'aide des indicateurs de performance tels que le RMSE et le MAE, le temps de traitement et l'index de similarité SSIM, montrent que chacune des trois architectures peut produire de bons résultats en fon…
Akciju cenu prognozēšana, izmantojot Relatīvo Spēka Indeksu un LSTM neironu tīklus
2020
Neironu tīkli akciju tirgus prognozēšanā sāk spēlēt arvien lielāku lomu. Darbā tiks skatīta relatīvā spēka indeksa (RSI) ietekme akciju cenu prognozēšanā, izmantojot garas īslaicīgās atmiņas (LSTM) neironu tīklus. Mērķis ir noskaidrot, vai RSI, LSTM neironu tīklu ievadē, spēj uzlabot nākotnes akciju cenas prognozi.