An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.

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The Study of Noise Effect on CNN-Based Deep Learning from Medical Images

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.3.1036 ◽

2021 ◽

Vol 11 (3) ◽

pp. 202-207

Author(s):

Kittipat Sriwong ◽

◽

Kittisak Kerdprasop ◽

Nittaya Kerdprasop

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Medical Images ◽

Research Work ◽

Health Science ◽

Learning Performance ◽

Machine Learning Techniques ◽

Disease Symptoms ◽

Deep Learning Algorithm ◽

Learning Techniques

Currently, computational modeling methods based on machine learning techniques in medical imaging are gaining more and more interests from health science researchers and practitioners. The high interest is due to efficiency of modern algorithms such as convolutional neural networks (CNN) and other types of deep learning. CNN is the most popular deep learning algorithm because of its prominent capability on learning key features from images that help capturing the correct class of images. Moreover, several sophisticated CNN architectures with many learning layers are available in the cloud computing environment. In this study, we are interested in performing empirical research work to compare performance of CNNs when they are dealing with noisy medical images. We design a comparative study to observe performance of the AlexNet CNN model on classifying diseases from medical images of two types: images with noise and images without noise. For the case of noisy images, the data had been further separated into two groups: a group of images that noises harmoniously cover the area of the disease symptoms (NIH) and a group of images that noises do not harmoniously cover the area of the disease symptoms (NNIH). The experimental results reveal that NNIH has insignificant effect toward the performance of CNN. For the group of NIH, we notice some effect of noise on CNN learning performance. In NIH group of images, the data preparation process before learning can improve the efficiency of CNN.

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Credit Card Fraud Detection Using Autoencoder Model in Unbalanced Datasets

Journal of Advances in Mathematics and Computer Science ◽

10.9734/jamcs/2019/v33i530192 ◽

2019 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

M. A. Al-Shabi

Keyword(s):

Logistic Regression ◽

Deep Learning ◽

Efficient Algorithm ◽

Credit Card ◽

Learning Algorithm ◽

Credit Cards ◽

Reconstruction Error ◽

Insurance Companies ◽

Deep Learning Algorithm ◽

Unbalanced Dataset

Fraudulent credit card transaction is still one of problems that face the companies and banks sectors; it causes them to lose billions of dollars every year. The design of efficient algorithm is one of the most important challenges in this area. This paper aims to propose an efficient approach that automatic detects fraud credit card related to insurance companies using deep learning algorithm called Autoencoders. The effectiveness of the proposed method has been proved in identifying fraud in actual data from transactions made by credit cards in September 2013 by European cardholders. In addition, a solution for data unbalancing is provided in this paper, which affects most current algorithms. The suggested solution relies on training for the autoencoder for the reconstruction normal data. Anomalies are detected by defining a reconstruction error threshold and considering the cases with a superior threshold as anomalies. The algorithm's performance was able to detected fraudulent transactions between 64% at the threshold = 5, 79% at the threshold = 3 and 91% at threshold= 0.7, it is better in performance compare with logistic regression 57% in unbalanced dataset.

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Development of Agriculture Chatbot using Machine Learning Techniques

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

10.35940/ijitee.b1008.1292s19 ◽

2019 ◽

Vol 9 (2S) ◽

pp. 24-28

Keyword(s):

Question Answering ◽

Learning Algorithm ◽

Segmentation for Complex Background Images using Deep Learning Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1042.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 1746-1750

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Learning Algorithm ◽

Vision System ◽

Automated Segmentation ◽

Computer Vision System ◽

Important Stage ◽

Deep Learning Algorithm ◽

Complex Background ◽

Learning Techniques

Segmentation is an important stage in any computer vision system. Segmentation involves discarding the objects which are not of our interest and extracting only the object of our interest. Automated segmentation has become very difficult when we have complex background and other challenges like illumination, occlusion etc. In this project we are designing an automated segmentation system using deep learning algorithm to segment images with complex background.

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Fraud Detection: A Study of AdaBoost Classifier and K-Means Clustering

10.21203/rs.3.rs-247874/v1 ◽

2021 ◽

Author(s):

Anwesha Mishra

Keyword(s):

Machine Learning ◽

Research Collaboration ◽

Credit Cards ◽

Fraud Detection ◽

Machine Learning Techniques ◽

Learning Group ◽

The Past ◽

Big Data Mining ◽

Learning Techniques ◽

Adaboost Classifier

Abstract Fraud is a problem which can affect the economy greatly. Billions of dollars are lost because of fraud cases. These problems can occur through credit cards, insurance and bank accounts. Currently there have been many studies for preventing fraud. Machine learning techniques have helped in analysing fraud detection. These include many supervised and unsupervised models. Neural networks can be used for fraud detection. The dataset for the present work was collected from a research collaboration between Worldline and the Machine Learning Group of Université Libre de Bruxelles on the topic of big data mining and fraud detection. It consists of the time and amount of various transactions of European card holders during the month of September in 2013. This paper gives an analysis of the past and the present models used for fraud detection and presents a study of using K-Means Clustering and AdaBoost Classifier by comparing their accuracies.

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Recognition of speech emotion using custom 2D-convolution neural network deep learning algorithm

Intelligent Data Analysis ◽

10.3233/ida-194747 ◽

2020 ◽

Vol 24 (5) ◽

pp. 1065-1086

Author(s):

Kudakwashe Zvarevashe ◽

Oludayo O. Olugbara

Keyword(s):

Neural Network ◽

Deep Learning ◽

Learning Algorithm ◽

Convolution Neural Network ◽

Machine Learning Techniques ◽

Speech Emotion Recognition ◽

Modern World ◽

Emotional Speech ◽

Deep Learning Algorithm ◽

Speech Database

Speech emotion recognition has become the heart of most human computer interaction applications in the modern world. The growing need to develop emotionally intelligent devices has opened up a lot of research opportunities. Most researchers in this field have applied the use of handcrafted features and machine learning techniques in recognising speech emotion. However, these techniques require extra processing steps and handcrafted features are usually not robust. They are computationally intensive because the curse of dimensionality results in low discriminating power. Research has shown that deep learning algorithms are effective for extracting robust and salient features in dataset. In this study, we have developed a custom 2D-convolution neural network that performs both feature extraction and classification of vocal utterances. The neural network has been evaluated against deep multilayer perceptron neural network and deep radial basis function neural network using the Berlin database of emotional speech, Ryerson audio-visual emotional speech database and Surrey audio-visual expressed emotion corpus. The described deep learning algorithm achieves the highest precision, recall and F1-scores when compared to other existing algorithms. It is observed that there may be need to develop customized solutions for different language settings depending on the area of applications.

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A Deep-Learning-Driven Light-Weight Phishing Detection Sensor

Sensors ◽

10.3390/s19194258 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4258 ◽

Cited By ~ 12

Author(s):

Bo Wei ◽

Rebeen Ali Hamad ◽

Longzhi Yang ◽

Xuan He ◽

Hao Wang ◽

...

Keyword(s):

Deep Learning ◽

Energy Saving ◽

Real Time ◽

Learning Algorithm ◽

Rapid Development ◽

Social Engineering ◽

Machine Learning Techniques ◽

Light Weight ◽

Learning Techniques ◽

Phishing Detection

This paper designs an accurate and low-cost phishing detection sensor by exploring deep learning techniques. Phishing is a very common social engineering technique. The attackers try to deceive online users by mimicking a uniform resource locator (URL) and a webpage. Traditionally, phishing detection is largely based on manual reports from users. Machine learning techniques have recently been introduced for phishing detection. With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. According to the experiments, the true detection rate has been improved. This paper has also verified that the proposed method can run in an energy-saving embedded single board computer in real-time.

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Effective Deep Learning Based Architecture for Pedestrian Detection from Digital Images

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4225.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1498-1508

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Object Detection ◽

Learning Algorithm ◽

Pedestrian Detection ◽

Single Shot ◽

Deep Learning Algorithm ◽

Learning Techniques ◽

Rgb Images ◽

Deep Convolution Network

This paper is to present an efficient and fast deep learning algorithm based on neural networks for object detection and pedestrian detection. The technique, called MobileNet Single Shot Detector, is an extension to Convolution Neural Networks. This technique is based on depth-wise distinguishable convolutions in order to build a lightweighted deep convolution network. A single filter is applied to each input and outputs are combined by using pointwise convolution. Single Shot Multibox Detector is a feed forward convolution network that is combined with MobileNets to give efficient and accurate results. MobileNets combined with SSD and Multibox Technique makes it much faster than SSD alone can work. The accuracy for this technique is calculated over colored (RGB images) and also on infrared images and its results are compared with the results of shallow machine learning based feature extraction plus classification technique viz. HOG plus SVM technique. The comparison of performance between proposed deep learning and shallow learning techniques has been conducted over benchmark dataset and validation testing over own dataset in order measure efficiency of both algorithms and find an effective algorithm that can work with speed and accurately to be applied for object detection in real world pedestrian detection application.

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THE USE OF MACHINE LEARNING TECHNIQUES IN ACCOUNTING. A SHORT SURVEY

Journal of Social Sciences ◽

10.52326/jss.utm.2021.4(3).14 ◽

2021 ◽

Vol 4 (3) ◽

pp. 139-143

Author(s):

Mariana Vlad ◽

◽

Sorin Vlad ◽

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Real Life ◽

Fraud Detection ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Behavior Prediction ◽

Accounting Fraud ◽

Research Fields ◽

Learning Techniques

Machine learning (ML) is a subset of artificial Intelligence (AI) aiming to develop systems that can learn and continuously improve the abilities through generalization in an autonomous manner. ML is presently all around us, almost every facet of our digital and real life is embedding some ML related content. Customer recommendation systems, customer behavior prediction, fraud detection, speech recognition, image recognition, black & white movies colorization, accounting fraud detection are just some examples of the vast range of applications in which ML is involved. The techniques that this paper investigates are mainly focused on the use of neural networks in accounting and finance research fields. An artificial neural network is modelling the brain ability of learning intricate patterns from the information presented at its inputs using elementary interconnected units, named neurons, grouped in layers and trained by means of a learning algorithm. The performance of the network depends on many factors like the number of layers, the number of each neurons in each layer, the learning algorithm, activation functions, to name just a few of them. Machine learning algorithms have already started to replace humans in jobs that require document’s processing and decision making.

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Vision-Based Deep Learning Algorithm for Detecting Potholes

10.20944/preprints202102.0569.v1 ◽

2021 ◽

Author(s):

Kanushka Gajjar ◽

Theo van Niekerk ◽

Thomas Wilm ◽

Paolo Mercorelli

Keyword(s):

Deep Learning ◽

Real Time ◽

Autonomous Vehicles ◽

Learning Algorithm ◽

Detection System ◽

Detection Methods ◽

Deep Learning Algorithm ◽

Learning Techniques ◽

Pothole Detection ◽

Adequate Data

Potholes on roads pose a major threat to motorists and autonomous vehicles. Driving over a pothole has the potential to cause serious damage to a vehicle, which in turn may result in fatal accidents. Currently, many pothole detection methods exist. However, these methods do not utilize deep learning techniques to detect a pothole in real-time, determine the location thereof and display its location on a map. The success of determining an effective pothole detection method, which includes the aforementioned deep learning techniques, is dependent on acquiring a large amount of data, including images of potholes. Once adequate data had been gathered, the images were processed and annotated. The next step was to determine which deep learning algorithms could be utilized. Three different models, including Faster R-CNN, SSD and YOLOv3 were trained on the custom dataset containing images of potholes to determine which network produces the best results for real-time detection. It was revealed that YOLOv3 produced the most accurate results and performed the best in real-time, with an average detection time of only 0.836s per image. The final results revealed that a real-time pothole detection system, integrated with a cloud and maps service, can be created to allow drivers to avoid potholes.

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