Diabetes is an acute disease that happens when the pancreas cannot produce enough insulin. It can be fatal if undiagnosed and untreated. If diabetes is revealed early enough, it is possible, with adequate treatment, to live a healthy life. Recently, researchers have applied artificial intelligence techniques to the forecasting of diabetes. As a result, a new SMOTE-based deep LSTM system was developed to detect diabetes early. This strategy handles class imbalance in the diabetes dataset, and its prediction accuracy is measured. This article details investigations of CNN, CNN-LSTM, ConvLSTM, and deep 1D-convolutional neural network (DCNN) techniques and proposed a SMOTE-based deep LSTM method for diabetes prediction. Furthermore, the suggested model is analyzed towards machine-learning, and deep-learning approaches. The proposed model’s accuracy was measured against the diabetes dataset and the proposed method achieved the highest prediction accuracy of 99.64%. These results suggest that, based on classification accuracy, this method outperforms other methods. The recommendation is to use this classifier for diabetic patients’ clinical analysis.

/pdf/deep-lstm-model-for-diabetes-prediction-with-class-balancing-2krpqa7d.pdf

Deep LSTM Model for Diabetes Prediction with Class Balancing by SMOTE

Due to the emigration of world business to the internet, credit ‎cards have become a tool for ‎payments for both online and outline purchases. However, fraudsters try ‎to attack those systems ‎using various techniques, and credit card fraud has become dangerous. To ‎secure credit cards, ‎different methods are proposed in the academic paper based on artificial ‎intelligence. The proposed ‎solution in this paper aims at combining the robustness of three methods: ‎the differential evolution ‎algorithm (DE) for selecting the best hyperparameters, a resampling ‎technique for handling ‎imbalanced data issues, and the XGBoost technique for classification. Finally, ‎the fraudulent ‎transactions are classified using the optimized XGBoost algorithm. The proposed ‎solution is ‎evaluated using two real-world datasets: the European dataset and the UCI dataset. The ‎evaluation ‎in terms of accuracy, sensitivity, specificity, precision, and F-measure shows the ability and ‎the ‎superiority of the proposed approach in comparison with the state-of-the-art machine learning ‎‎models.‎

Credit Card Fraud Detection Based on Hyperparameters ‎Optimization Using the Differential Evolution

A framework combining the Internet of Things (IoT) and blockchain can help achieve system automation and credibility, and the corresponding technologies have been applied in many industries, especially in the area of agricultural product traceability. In particular, IoT devices (radio frequency identification (RFID), geographic information system (GIS), global positioning system (GPS), etc.) can automate the collection of information pertaining to the key aspects of traceability. The data are collected and input to the blockchain system for processing, storage, and query. A distributed, decentralized, and nontamperable blockchain can ensure the security of the data entering the system. However, IoT devices may generate abnormal data in the process of data collection. In this context, it is necessary to ensure the accuracy of the source data of the traceability system. Considering the whole-process traceability chain of agricultural products, this paper analyzes the whole-process information of a tea supply chain from planting to sales, constructs the system architecture and each function, and designs and implements a machine learning- (ML-) blockchain-IoT-based tea credible traceability system (MBITTS). Based on IoT technologies such as radio frequency identification (RFID) sensors, this article proposes a new method that combines blockchain and ML to enhance the accuracy of blockchain source data. In addition, system data storage and indexing methods and scanning and recovery mechanisms are proposed. Compared with the existing agricultural product (tea) traceability system based on blockchain, the introduction of the ML data verification mechanism can ensure the accuracy (up to 99%) of information on the chain. The proposed solution provides a basis to ensure the safety, reliability, and efficiency of agricultural traceability systems.

https://downloads.hindawi.com/journals/js/2022/8618230.pdf

Blockchain-Based Internet of Things: Machine Learning Tea Sensing Trusted Traceability System

Abstract Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account. Successfully preventing this requires the detection of as many fraudsters as possible, without producing too many false alarms. This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud. In addition, classical machine learning methods must be extended, minimizing expected financial losses. Finally, fraud can only be combated systematically and economically if the risks and costs in payment channels are known. We define three models that overcome these challenges: machine learning-based fraud detection, economic optimization of machine learning results, and a risk model to predict the risk of fraud while considering countermeasures. The models were tested utilizing real data. Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15% compared to a benchmark consisting of static if-then rules. Optimizing the machine-learning model further reduces the expected losses by 52%. These results hold with a low false positive rate of 0.4%. Thus, the risk framework of the three models is viable from a business and risk perspective. 

/pdf/online-payment-fraud-from-anomaly-detection-to-risk-2yfl4ldb.pdf

Online payment fraud: from anomaly detection to risk management

Nowadays, the solution to many practical problems relies on machine learning tools. However, compiling the appropriate training data set for real-world classification problems is challenging because collecting the right amount of data for each class is often difficult or even impossible. In such cases, we can easily face the problem of imbalanced learning. There are many methods in the literature for solving the imbalanced learning problem, so it has become a serious question how to compare the performance of the imbalanced learning methods. Inadequate validation techniques can provide misleading results (e.g., due to data shift), which leads to the development of methods designed for imbalanced data sets, such as stratified cross-validation (SCV) and distribution optimally balanced SCV (DOB-SCV). Previous studies have shown that higher classification performance scores (AUC) can be achieved on imbalanced data sets using DOB-SCV instead of SCV. We investigated the effect of the oversamplers on this difference. The study was conducted on 420 data sets, involving several sampling methods and the DTree, kNN, SVM, and MLP classifiers. We point out that DOB-SCV often provides a little higher F1 and AUC values for classification combined with sampling. However, the results also prove that the selection of the sampler–classifier pair is more important for the classification performance than the choice between the DOB-SCV and the SCV techniques.

/pdf/a-comparative-study-of-the-use-of-stratified-cross-2szsdw6b.pdf

A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning

Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine learning based fraudulent transaction detection systems are very effective in real-world scenarios. However, ...

Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms

Phishing was introduced in 1996, and now phishing is the biggest cybercrime challenge. Phishing is an abstract way to deceive users over the internet. Purpose of phishers is to extract the sensitive information of the user. Researchers have been working on solutions of phishing problem, but the parallel evolution of cybercrime techniques have made it a tough nut to crack. Recently, machine learning-based solutions are widely adopted to tackle the menace of phishing. This survey paper studies various feature selection method and dimensionality reduction methods and sees how they perform with machine learning-based classifier. The selection of features is vital for developing a good performance machine learning model. This work is comparing three broad categories of feature selection methods, namely filter, wrapper, and embedded feature selection methods, to reduce the dimensionality of data. The effectiveness of these methods has been assessed on several machine learning classifiers using k-fold cross-validation score, accuracy, precision, recall, and time.

Abhishek Tiwari

Papers

Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms

A Study of Feature Selection and Dimensionality Reduction Methods for Classification-Based Phishing Detection System