Detecting online recruitment fraud by using machine learning

Detecting online recruitment fraud by using machine learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Bibliographische Detailangaben
Hauptverfasser:	Ghosh, Gitanjali, Tabassum, Hridita, Atika, Afra, Kutubuddi, Zainab
Weitere Verfasser:	Chakrabarty, Amitabha
Format:	Abschlussarbeit
Sprache:	English
Veröffentlicht:	Brac University 2021
Schlagworte:	Machine Learning Fraud Detection Prediction Decision Tree Classifier Logistic Regression algorithm Adaptive Boosting Random Forest Classifier Decision trees Gradient Boost LightGBM Machine learning
Online Zugang:	http://hdl.handle.net/10361/15174

Ähnliche Einträge

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

Predicting the length of stay in a hospital for a particular disease using machine learning algorithms
von: Imam, Saif
Veröffentlicht: (2021)

Early detection of chronic kidney disease using machine learning
von: Abrar, Tahmid, et al.
Veröffentlicht: (2019)

Early grade prediction using profile data
von: Iqbal, Sumaiya, et al.
Veröffentlicht: (2021)

Sentiment analysis for Bengali newspaper headlines
von: Hossain, Mohammad Samman, et al.
Veröffentlicht: (2017)

Predicting stock market movement using sentiment analysis of twitter feed
von: Chakraborty, Pranjal, et al.
Veröffentlicht: (2017)

Credit card fraud detection using machine learning techniques
von: Kabir, Tasmia, et al.
Veröffentlicht: (2021)

Flood prediction using machine learning models
von: Syeed, Miah Mohammad Asif, et al.
Veröffentlicht: (2022)

Heart disease prediction using techniques of classification in machine learning
von: Afrose, Sadia, et al.
Veröffentlicht: (2021)

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

A counseling system to predict the study path for freshmen
von: Usha, Rowshni Tasneem, et al.
Veröffentlicht: (2019)

Unleashing potential: a data-driven exploration of identifying player potentialities through advanced analytics in sports
von: Bhowmik, Prashanta, et al.
Veröffentlicht: (2024)

Prediction of earthquakes: a step towards predicting the unpredictable
von: Chowdhury, Maheen, et al.
Veröffentlicht: (2023)

Modelling and forecasting energy demand of Bangladesh using AI based algorithms
von: Mahmud, Booshra Nazifa, et al.
Veröffentlicht: (2019)

Application of CNN based architectures in detection of distracted drivers
von: Arafin, Irfana, et al.
Veröffentlicht: (2023)

Stock market prediction using ensemble learning
von: Rabbani, Rakin Bin, et al.
Veröffentlicht: (2021)

Building a credit scoring model to assign a reference score based on credit transaction and relevant profile data
von: Islam, Saqib Al, et al.
Veröffentlicht: (2020)

Parking data analysis
von: Ahamed, Kasfi, et al.
Veröffentlicht: (2023)

A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
von: Rahee, Arnob, et al.
Veröffentlicht: (2021)

Machine learning for stress prediction
von: Salehin, Sherajus, et al.
Veröffentlicht: (2023)

Use of machine learning and IoT for monitoring and tracking of livestock
von: Aunindita, Rudaba Farhin, et al.
Veröffentlicht: (2023)

Comparative analysis and implementation of credit risk prediction through distinct machine learning models
von: Turjo, Aquib Abtahi, et al.
Veröffentlicht: (2021)

ML based career suggestive system for informal job sector considering cognitive skills
von: Tonny, Ms. Ayesha Siddika, et al.
Veröffentlicht: (2023)

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

Exploring machine learning techniques for symptom-based detection of livestock diseases
von: Niloy, Mahir Ahmed, et al.
Veröffentlicht: (2024)

Where will you setup your business next?: a machine learning approach to suggest ideal geographical location for new restaurant establishment
von: Shihab, Ibne Farabi, et al.
Veröffentlicht: (2018)

Performance analysis of machine learning algorithms for Malware classification
von: Bushra, Raisa Hasan, et al.
Veröffentlicht: (2023)

Speech emotion detection using supervised, unsupervised and feature selection algorithms
von: Rifat, Abu Nuraiya Mahfuza Yesmin, et al.
Veröffentlicht: (2019)

Heart disease prediction system
von: Amit, Tayab Al Azad, et al.
Veröffentlicht: (2022)

A machine learning approach on classifying orthopedic patients based on their biomechanical features
von: Hasan, Kamrul, et al.
Veröffentlicht: (2018)

A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
von: Farabe, Abdullah Al, et al.
Veröffentlicht: (2021)

Application of machine learning techniques on the context of livestock analysis
von: Islam, Md. Shajedul, et al.
Veröffentlicht: (2018)

SCAMM: detection and prevention of SQL injection attacks using a machine learning approach
von: Alam, Auninda, et al.
Veröffentlicht: (2021)

Intelligent assisted living in pregnancy
von: Zaman, S. M. Tanveer, et al.
Veröffentlicht: (2022)

Automated fabric color prediction
von: Muhtashima, Fawzia, et al.
Veröffentlicht: (2024)

An approach to detect epileptic seizure using XAI and machine learning
von: Bijoy, Emam Hasan, et al.
Veröffentlicht: (2022)

Finding suitable locations for live campaigns using different machine learning techniques
von: Qudar, Mohiuddin Md. Abdul, et al.
Veröffentlicht: (2018)

A feature selection approach to determine obesity using machine learning method
von: Antor, Shahidul Alam, et al.
Veröffentlicht: (2021)

Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning
von: Sarkar, Ripa, et al.
Veröffentlicht: (2024)

Prediction and detection in change of cognitive load for VIP’s by a machine learning approach
von: Rahman, Fahim, et al.
Veröffentlicht: (2021)