Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network

Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Bibliografiske detaljer
Main Authors:	Rayhan, Mohammad, Sultana, Samiha, Majid, Annur
Andre forfattere:	Alam, Md. Golam Rabiul
Format:	Thesis
Sprog:	en_US
Udgivet:	Brac University 2020
Fag:	Financial factors Deep learning XGBoost Machine learning
Online adgang:	http://hdl.handle.net/10361/14060

Lignende værker

Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
af: Ahmed, Md. Sabbir, et al.
Udgivet: (2021)

Aﬀective state recognition through analysis of electroencephalogram signals by using extreme gradient boosting
af: khan, Md. Sakib, et al.
Udgivet: (2021)

A comparative study of deep learning methods for automating road condition characterization
af: Ruhi, Zurana Mehrin, et al.
Udgivet: (2021)

Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
af: Tasnim, Anika, et al.
Udgivet: (2022)

A comparative performance analysis of accident anticipation with deep learning extractors
af: Mostak, Alfi Mashab, et al.
Udgivet: (2023)

Prediction on large scale data using extreme gradient boosting
af: Sawon, Md.Tariq Hasan, et al.
Udgivet: (2016)

Investigation assistant with suspect anticipating intelligence
af: Abdullah, Saqueeb, et al.
Udgivet: (2019)

Corn leaf disease detection using deep convolution neural network
af: Rabbi, Rawhatur, et al.
Udgivet: (2023)

Analysis of real-time hostile activitiy detection from spatiotemporal features using time distributed deep convolutional neural networks, recurrent neural networks and attention-based mechanisms
af: Siddique, Labib Ahmed, et al.
Udgivet: (2022)

Flight fare prediction using machine learning
af: Noyon, Md. Shaim Hosan, et al.
Udgivet: (2024)

Prostate cancer detection using deep learning neural network with transfer learning approach
af: Badhon, Ariful Islam Mahmud, et al.
Udgivet: (2022)

A macroeconomic model for forecasting crude oil prices with Feedforward Neural Network Grid Search Experimentation
af: Aunjum, Md. Ragib, et al.
Udgivet: (2021)

Skin cancer detection and classification using multiple optimized deep convolutional neural network
af: Sakir, Adnan, et al.
Udgivet: (2023)

An active-learning based training-schedule for biomedical image segmentation on deep neural networks
af: Hassan, Mehadi, et al.
Udgivet: (2021)

Learning a deep neural network for predicting phishing website
af: Das, Robat, et al.
Udgivet: (2019)

Analysing Facebook user risk using machine learning algorithm
af: Barua, Arnab, et al.
Udgivet: (2021)

Bank loan prediction using machine learning
af: Mahottam, Parthosarothi, et al.
Udgivet: (2023)

Detecting Deepfake images using deep convolutional neural network
af: Dhar, Arpita, et al.
Udgivet: (2022)

Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
af: Bhattacharjee, Ruposri, et al.
Udgivet: (2024)

Bangali Handwritten characters classification using Deep Convolutional Neural Network
af: Sikder, Shihab Uddin, et al.
Udgivet: (2022)

Yoga posture recognition using the deep learning process
af: Islam, Abidul, et al.
Udgivet: (2023)

KDANet: optical recognition for Bangla language using deep neural networks
af: Rabbi, Kazi Kamruzzaman, et al.
Udgivet: (2023)

An efficient deep learning approach to detect skin Cancer
af: Islam, Ashfaqul, et al.
Udgivet: (2022)

Self-learning game bot using deep reinforcement learning
af: Ananto, Azizul Haque
Udgivet: (2018)

Cassava leaf disease classification using deep learning and convolutional neural network ensemble
af: Shahriar, Hasan, et al.
Udgivet: (2022)

Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh
af: Biswas, Sunanda, et al.
Udgivet: (2022)

Predicting crime using deep learning
af: Shihab, Muhammad Nafees, et al.
Udgivet: (2024)

A modern technique to detect potholes by Computer Vision and Deep Learning
af: Saif, Muntasir Mahmud, et al.
Udgivet: (2023)

Damaged road detection using Image Processing and Deep Learning
af: Swadesh, Shimran Mahbub, et al.
Udgivet: (2022)

Deep learning /
af: Goodfellow, Ian
Udgivet: (2016)

Micro-learning modules are linked to enhanced retention of the learners and can be geared toward subconscious knowledge acquisition in an organization
af: Siddique, Noman
Udgivet: (2024)

ROBB: recurrent proximal policy optimization reinforcement learning for optimal block formation in bitcoin blockchain network
af: Dutta, Amit
Udgivet: (2024)

DeepWPD: A deep learning based wavelet packet decomposition to remove Moiré pattern from screen capture images.
af: Khan, Ibrahim
Udgivet: (2023)

Performance analysis of machine learning classi ers for detecting PE malware
af: Azmee, ABM.Adnan, et al.
Udgivet: (2020)

Classification of peripheral blood cell images using deep learning
af: Aadi, Oyshik Ahmed, et al.
Udgivet: (2024)

An implementation and analysis of deep learning models for the detection of wheat diseases
af: Zubair, Ahmad, et al.
Udgivet: (2024)

Document template identification and data extraction using machine learning and deep learning approach
af: Roy, Kaushik, et al.
Udgivet: (2024)

Classiﬁcation of damaged vegetation areas using convolutional neural network over satellite images
af: Haque, Samiha, et al.
Udgivet: (2021)

Classifying insect pests from image data using deep learning
af: Mohsin, Md. Raiyan Bin, et al.
Udgivet: (2022)

Prediction of bipolar disorder from mental episodes using machine learning approach
af: Tasmeem, Sumaiya, et al.
Udgivet: (2023)