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![REFERENCE
[1] S. Anita, P. Nagarajan, G. A. Sairam, P. Ganesh, and G. Deepakkumar,
“Fake Job Detection and Analysis Using Machine Learning and Deep Learning
Algorithms,” Rev. GEINTECGESTAO Inov. E Tecnol., vol. 11, no. 2, pp. 642–650,
2021.
[2] B. Alghamdi and F. Alharby, “An intelligent model for online recruitment
fraud detection,” J. Inf. Secur., vol. 10, no. 03, p. 155, 2019.
[3] “Report | Cyber.gov.au.” https://www.cyber.gov.au/acsc/report
(accessed Jun. 19, 2021).
[4] A. Pagotto, “Text Classification with Noisy Class Labels.” Carleton University,
2020. [5] “Employment Scam Aegean Dataset.”
http://emscad.samos.aegean.gr/ (accessed Jun. 19, 2021).
[6] S. Vidros, C. Kolias, G. Kambourakis, and L. Akoglu, “Automatic detection of
online recruitment frauds: Characteristics, methods, and a public dataset,”
Futur. Internet, vol. 9, no. 1, p. 6, 2017.](https://image.slidesharecdn.com/fakejobdetectionppt-250630143533-f85c3b86/85/Fake-Job-Detection-PPT-pptx-using-python-26-320.jpg)
Fake Job Detection PPT.pptx using python
- 1. Fake Job RecruitmentDetection Using Machine Learning Approach
- 2. ABSTRACT • To avoidfraudulent post for job in the internet, an automated tool using machine learning based classification techniques is proposed in the project. • Different classifiers are used for checking fraudulent post in the web and the results of those classifiers are compared for identifying the best employment scam detection model. • It helps in detecting fake job posts from an enormous number of posts. • Two major types of classifiers, such as single classifier and ensemble classifiers are considered for fraudulent job posts detection. • However, experimental results indicate that ensemble classifiers are the best classification to detect scams over the single classifiers.
- 3. INTRODUCTION • Employment scamis one of the serious issues in recent times addressed in the domain of Online Recruitment Frauds (ORF). • In recent days, many companies prefer to post their vacancies online so that these can be accessed easily and timely by the job-seekers. • However, this intention may be one type of scam by the fraud people because they offer employment to job-seekers in terms of taking money from them. • Fraudulent job advertisements can be posted against a reputed company for violating their credibility.
- 4. 4 LITERATURE SURVEY TITLE AUTHORPros Cons DATASET METRICES A Survey of Machine Learning Based Approaches for blood Disease Prediction 2021 Shubham Bind1 Used Realtime NIR Images Less Accuracy NIR Dataset 75 Classifcation of blood’s disease and its stages using machine learning 2022 John MichaelTempleton active NIR patches are extracted The training database consist less image NIR2D 65 Analysis and Prediction of blood's Disease using Machine Learning Algorithms 2022 Sohom Sen Used 30 NIR Images The system was trained with Less type of Disease. BRAINNIR 84 Detection of blood Disease Using Machine Learning 2020 G.Priyadharshini, 2T.Gowtham High Accuracy lowest accuracy with 40%. NIRDATA 90 Predicting Severity Of blood's Disease Using Deep Learning 2020 Srishti Grover Dataset belonging to Multiple category Accuracy Low database with Disease by Name 65
- 5. Existing system • TheExisting system are predicted using classifiers that have been learned. • When detecting fraudulent job postings, the following classifiers are used- • A. Naive Bayes • B. Support Vector Machine • C. Logistic Regression
- 6. Review Spam Detection People frequently share their opinions on the things they buy on online forums. It might be useful to other buyers while they're deciding what to buy. In this setting, spammers can modify reviews for financial advantage, necessitating the development of algorithms to detect spam reviews. This can be done by extracting features from the reviews and using Natural Language Processing to do so (NLP). These features are subjected to machine learning algorithms.
- 7. Fake News Detection In social media, fake news is defined by malicious user accounts and echo chamber effects. Fake news identification is based on three perspectives: how fake news is written, how fake news spreads, and how a user is connected to fake news. To identify fake news, features linked to news content and social context are retrieved, and machine learning algorithms are applied.
- 8. DISADVANTAGES: • Less Featuresconsidered while training the Data which results in Low Accuracy. • Support Vector Machine model Having Less Processing Accuracy compare to the Proposed system. • Also it’s a complex and Time Consuming Process.
- 9. PROPOSED SYSTEM: • Thissystem's main purpose is to identify whether a job posting is genuine or not. • Job seekers will be able to focus entirely on legitimate job openings if fake job postings are identified and deleted. • In this system, we plan to use a Kaggle dataset that contains information on the job, including attributes such as job id, title, location, and department. • Then there's data preprocessing, which involves removing things like trivial spaces, null entries, stopwords, and so on. • The data is provided to the classifier for predictions after it has been preprocessed and cleaned to make it prediction ready.
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- 11. METHODOLOGY The proposedmethod is entirely composed of Artificial Intelligence approaches, which is critical to accurately classify between the real and the fake, instead of using algorithms that are unable to mimic cognitive functions. The three-part method is a combination between Machine Learning algorithms that subdivide into supervised learning techniques, and natural language processing methods. Although each of these approaches can be solely used to classify and detect fake Job, in order to increase the accuracy and be applicable to the social media domain, they have been combined into an integrated algorithm as a method for fake news detection
- 12. ALGORITHM A multilayerperceptron (MLP) Regional Convolutional Neural Network is an artificial neural network, with an input layer, one or more hidden layers, and an output layer. RCNN can be as simple as having each of the three layers; however, in our experiments we have fine- tuned the model with various parameters and number of layers to generate an optimum predicting mode
- 13. Advantages • success indetection of fake Jobs and posts using various Machine learning approaches. • everchanging characteristics and features of fake Jobs in Online networks is posing a challenge in categorization of fake news but proposed system performs with the Better Accuracy of the system.
- 14. MODULES Data Collection Dataset Data Preparation Feature Extraction Implementation of Classifier
- 15. Data Collection DatasetDetails This kaggle dataset contains 17,880 job posting data entries. We must first preprocess this data in order to prepare it for prediction before fitting it into any of the machine learning models or classifiers. Some pre-processing techniques are used on this dataset before it is fitted to any classifier.
- 16. Data Preprocessing Preprocessingdata is the process of transforming raw data into a clean data set. Before running the algorithm, the dataset is preprocessed to check for missing values, noisy data, and other irregularities. It also removes noise and uninformative characters and words from the text, as well as stop-words, extraneous attributes, and extra space.
- 17. Feature Extraction Featureextraction is a step in the dimensionality reduction process, which divides and reduces a large set of raw data into smaller groupings. The fact that these enormous data sets have a large number of variables is the most crucial feature. To process these variables, a large amount of computational power is required. So, by selecting and merging variables into features, feature extraction aids in extracting the best feature from those large data sets, effectively lowering the amount of data.
- 18. Implementation of Classifier Inthis section, proper parameters are used to train classifiers. For predictions, this framework used Multi Layer Perception (MLP) models. MLP has a number of distinguishing characteristics, as a result of which it has gained notoriety and has shown promising experimental results. To partition the data points, MLP constructs a hyper level in authentic input space.
- 19. SOFTWARE REQUIREMENTS FrontEnd – Anaconda IDE Backend – SQL Language – Python 3.8
- 20. HARDWARE REQUIREMENTS: • RAM •Processor • Hard Disk : 4GB and Higher : Intel i3 and above : 500GB: Minimum
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- 25. CONCLUTION • Only reputablebusiness offers will be sent to you. • Several machine learning methods are proposed for detecting employment scams. • In this work, we discuss counter measures. Supervised mechanism is used to demonstrate the utilization of many mechanisms. • Classifiers for detecting job scams. The results of the experiments show that MLP is effective. • The classifier exceeds its peers in classification. The proposed method had a 98 percent accuracy rate. Which is significantly greater than current approaches.
- 26. REFERENCE [1] S. Anita,P. Nagarajan, G. A. Sairam, P. Ganesh, and G. Deepakkumar, “Fake Job Detection and Analysis Using Machine Learning and Deep Learning Algorithms,” Rev. GEINTECGESTAO Inov. E Tecnol., vol. 11, no. 2, pp. 642–650, 2021. [2] B. Alghamdi and F. Alharby, “An intelligent model for online recruitment fraud detection,” J. Inf. Secur., vol. 10, no. 03, p. 155, 2019. [3] “Report | Cyber.gov.au.” https://www.cyber.gov.au/acsc/report (accessed Jun. 19, 2021). [4] A. Pagotto, “Text Classification with Noisy Class Labels.” Carleton University, 2020. [5] “Employment Scam Aegean Dataset.” http://emscad.samos.aegean.gr/ (accessed Jun. 19, 2021). [6] S. Vidros, C. Kolias, G. Kambourakis, and L. Akoglu, “Automatic detection of online recruitment frauds: Characteristics, methods, and a public dataset,” Futur. Internet, vol. 9, no. 1, p. 6, 2017.
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