An Efficient Ad-Click Prediction System using Machine Learning Techniques

Ad-click prediction is a learning problem that is highly related to the multi-billion-dollar ad- promoting the online advertising industry. As the number of internet users in India reached 525 million in 2019, online advertising companies are trying to influence internet usage users for promoting their business. Machine learning is a technique in which systems getting to act without any explicit programming. Currently, machine learning is pervasive today and we can use machine learning models in every research field. The accuracy of the ad-click prediction system impacts business revenue, so it is very important to build a prediction system with fewer false positives and false negatives.in this paper, we proposed an ad-click prediction system based on machine learning techniques. The dataset is taken from Kaggle. The dataset contains nine features. The goal of the model is to evaluate the probability of an online user to click on a given ad. We built a machine learning model based on these features. We applied a voting classifier on the dataset and achieved an accuracy of 98%.We used python language for implementation.

Download Full-text

Predicting Spread, Recovery and Death Due to COVID-19 using a Time-Series Model (Prophet)

AIUB Journal of Science and Engineering (AJSE) ◽

10.53799/ajse.v20i1.152 ◽

2021 ◽

Vol 20 (1) ◽

pp. 71-76

Author(s):

Sk. Golam Mahmud ◽

Mahbub C. Mishu ◽

Dip Nandi

Keyword(s):

Machine Learning ◽

Time Series ◽

Financial Services ◽

Historical Data ◽

Machine Learning Techniques ◽

Effective Vaccine ◽

Python Language ◽

Learning Techniques ◽

Machine Learning Model ◽

The World

The world is facing its biggest challenge since 1920 due to spread of COVID-19 virus. Identified in China in December 2019, the virus has spread more than 200 countries in the world. Scientists have named the virus as Novel Corona Virus (belongs to SARS group virus). The virus has caused severe disruption to our world. Educational institutions, financial Services, government services and many other sectors are badly affected by this virus. More importantly, the virus has causeda massive amount of human deaths around the world and still its infecting people every day. Scientist around the world are trying to find a solution to stop the COVID-19. Their solutions include identifying possible effective vaccine, computeraided modelling to see the pattern of spread etc. Using Machine Learning techniques, it is possible to forecast the spread, death, and recovery due to COVID-19. In this article, we have shown a machine learning model named as Prophet Time Series Analysis to forecast the spread, death, and recovery in different countries. We train the model using the available historical data on COVID-19 from John Hopkins University’s COVID-19 site. Then we forecast spread, death, and recovery for seven days using a well known forecasting model called Prophet. This interval can be increased to see the effect of COVID-19. We chose 145 days of historical data to train the model then we predict effect for seven days (15 June 2020 to 22 June 2020). To verify out result, we compare the predicted value with actual value of spread, death and recovery. The model provides accuracy over 92% in all the cases. Our model can be used to identify the effect of COVID-19 in any countries in the world. The system is developed using Python language and visualization is also possible interactively. By using our system, it will be possible to observe the effect of spread, death and recovery for any countries for any period of time.

Download Full-text

Machine Learning Based Ad-click Prediction System

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9366.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 3646-3648 ◽

Cited By ~ 1

Keyword(s):

Machine Learning ◽

Rapid Growth ◽

Online Advertising ◽

Machine Learning Techniques ◽

Prediction System ◽

Prediction Problem ◽

Targeted Advertising ◽

Novel Approach ◽

Learning Techniques ◽

Accuracy Rates

Online advertising is a gargantuan commerce and has potential for rapid growth. This paper presents novel approach of solving the advertisement prediction problem. The aim of this research is to predict an ad-click through various machine learning techniques and to compare their accuracy rates. This, would help the advertisers use the appropriate technique to increase their overall revenue through targeted advertising. The combination of features used, makes this research unique.

Download Full-text

Leveraging Machine Learning in Financial Fraud Forensics in the Age of Cybersecurity

10.4018/978-1-7998-8386-9.ch010 ◽

2022 ◽

pp. 220-249

Author(s):

Md Ariful Haque ◽

Sachin Shetty

Keyword(s):

Machine Learning ◽

Financial Institutions ◽

Machine Learning Techniques ◽

Cyber Attack ◽

Financial Industry ◽

Security Breaches ◽

Financial Gain ◽

Learning Techniques ◽

Machine Learning Model ◽

Machine Learning Models

Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers' transactions. However, machine learning techniques are often challenging because of financial institutions' confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source e-commerce transaction dataset to illustrate the forensic processes by creating a machine learning model to classify fraudulent transactions. Overall, the chapter focuses on how the machine learning models can help detect and prevent fraudulent activities in the financial sector in the age of cybersecurity.

Download Full-text

Machine Learning Generalisation across Different 3D Architectural Heritage

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9060379 ◽

2020 ◽

Vol 9 (6) ◽

pp. 379 ◽

Cited By ~ 4

Author(s):

Eleonora Grilli ◽

Fabio Remondino

Keyword(s):

Machine Learning ◽

Point Cloud ◽

Machine Learning Techniques ◽

Training Dataset ◽

High Complexity ◽

Architectural Heritage ◽

Learning Techniques ◽

Machine Learning Model ◽

Point Cloud Classification

The use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D heritage data, the diversity of the possible scenarios, and the different classification purposes that each case study might present, makes it difficult to realise a large training dataset for learning purposes. An important practical issue that has not been explored yet, is the application of a single machine learning model across large and different architectural datasets. This paper tackles this issue presenting a methodology able to successfully generalise to unseen scenarios a random forest model trained on a specific dataset. This is achieved looking for the best features suitable to identify the classes of interest (e.g., wall, windows, roof and columns).

Download Full-text

Detecting Suspicious Texts Using Machine Learning Techniques

Applied Sciences ◽

10.3390/app10186527 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6527 ◽

Cited By ~ 1

Author(s):

Omar Sharif ◽

Mohammed Moshiul Hoque ◽

A. S. M. Kayes ◽

Raza Nowrozy ◽

Iqbal H. Sarker

Keyword(s):

Machine Learning ◽

Instant Messaging ◽

Classification Model ◽

Machine Learning Techniques ◽

Text Documents ◽

Digital Platforms ◽

Malicious Activity ◽

Internet Users ◽

Learning Techniques ◽

The Web

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents has been growing enormously in recent years through instant messaging, social networking posts, blogs, online portals and other digital platforms. Unfortunately, the misapplication of technologies has increased with this rapid growth of online content, which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio, or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier ‘tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

Download Full-text

Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/1102083 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Choudhary Sobhan Shakeel ◽

Saad Jawaid Khan ◽

Beenish Chaudhry ◽

Syeda Fatima Aijaz ◽

Umer Hassan

Keyword(s):

Machine Learning ◽

Alopecia Areata ◽

Nearest Neighbor ◽

Autoimmune Disorder ◽

Machine Learning Techniques ◽

Support Vector ◽

Classification Framework ◽

Learning Techniques ◽

Machine Learning Model ◽

Image Set

Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis. We propose a framework pertaining to the classification of healthy hairs and alopecia areata. We used 200 images of healthy hairs from the Figaro1k dataset and 68 hair images of alopecia areata from the Dermnet dataset to undergo image preprocessing including enhancement and segmentation. This was followed by feature extraction including texture, shape, and color. Two classification techniques, i.e., support vector machine (SVM) and k -nearest neighbor (KNN), are then applied to train a machine learning model with 70% of the images. The remaining image set was used for the testing phase. With a 10-fold cross-validation, the reported accuracies of SVM and KNN are 91.4% and 88.9%, respectively. Paired sample T -test showed significant differences between the two accuracies with a p < 0.001 . SVM generated higher accuracy (91.4%) as compared to KNN (88.9%). The findings of our study demonstrate potential for better prediction in the field of dermatology.

Download Full-text

Machine learning techniques to predict daily rainfall amount

Journal Of Big Data ◽

10.1186/s40537-021-00545-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Chalachew Muluken Liyew ◽

Haileyesus Amsaya Melese

Keyword(s):

Machine Learning ◽

Pearson Correlation ◽

Daily Rainfall ◽

Learning Model ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Correlation Technique ◽

Learning Techniques ◽

Machine Learning Model ◽

Extreme Gradient Boosting

AbstractPredicting the amount of daily rainfall improves agricultural productivity and secures food and water supply to keep citizens healthy. To predict rainfall, several types of research have been conducted using data mining and machine learning techniques of different countries’ environmental datasets. An erratic rainfall distribution in the country affects the agriculture on which the economy of the country depends on. Wise use of rainfall water should be planned and practiced in the country to minimize the problem of the drought and flood occurred in the country. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The Pearson correlation technique was used to select relevant environmental variables which were used as an input for the machine learning model. The dataset was collected from the local meteorological office at Bahir Dar City, Ethiopia to measure the performance of three machine learning techniques (Multivariate Linear Regression, Random Forest, and Extreme Gradient Boost). Root mean squared error and Mean absolute Error methods were used to measure the performance of the machine learning model. The result of the study revealed that the Extreme Gradient Boosting machine learning algorithm performed better than others.

Download Full-text

Identification and Implementation Perspective on the Stratification Algorithms in the Prognostication of Heart Disease using Machine Learning Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8663.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1223-1225

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Heart Diseases ◽

Classification Algorithm ◽

Machine Learning Techniques ◽

Data Set ◽

Learning Techniques ◽

Machine Learning Model ◽

Combination Of Classifiers ◽

Great Idea

Analysis of patient’s data is always a great idea to get accurate results on using classifiers. A combination of classifiers would give an accurate result than using a single classifier because one single classifier does not give accurate results but always appropriate ones. The aim is to predict the outcome feature of the data set. The “outcome” can contain only two values that is 0 and 1. 0 means patient doesn’t have heart disease and 1 means patient have heart diseases. So, there is a need to build a classification algorithm that can predict the Outcome feature of the test dataset with good accuracy. For this understanding the data is important, and then various classification algorithm can be tested. Then the best model can be selected which gives highest accuracy among all. The built model can then be given to the software developer for building the end user application using the selected machine learning model that will be able to predict the heart disease in a patient.

Download Full-text

Big Five Personality Prediction from Social Media Data using Machine Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d7946.049420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 2412-2417

Keyword(s):

Machine Learning ◽

Big Five ◽

Machine Learning Techniques ◽

Big Five Personality ◽

Learning Techniques ◽

Machine Learning Model ◽

Personality Prediction ◽

Twitter Users ◽

Bayes Algorithm ◽

Media Data

Personality has been important for a number of types of cooperation; it has useful in predicting job achievement, expert and emotional relationship achievement, and even tendency towards a variety of interfaces. To accurately examine the characters of users, a personality test must be carried out. In numerous areas of online life it is usually impractical to use character research. . We used SVM classification, Random Forest algorithm, Naïve Bayes Algorithm and Logistic regression to comparatively predict the user’s personality accurately. The main goal of the paper is to evaluate the machine learning models using the four parameters- accuracy, precision, recall, f1 score and basing upon these parameters the best machine learning model will be used to classify the big five personality traits of the twitter users.

Download Full-text