Estimating the Depth of Anesthesia During the Induction by a Novel Adaptive Neuro-Fuzzy Inference System: A Case Study

Advancement of Management Information System for Discovering Fraud in Master Card based Intelligent Supervised Machine Learning and Deep Learning during SARS-CoV2

During coronavirus (SARS-CoV2) the number of fraudulent transactions is expanding at a rate of alarming (7352421 online transaction records). Additionally, the Master Card (MC) usage is increasing. To avoid massive losses, companies of finance must constantly improve their management information systems for discovering fraud in MC. In this paper, an approach of advancement management information system for discovering of MC fraud was developed using sequential modeling of data depend on intelligent forecasting methods such as deep Learning and intelligent supervised machine learning (ISML). The Long Short-Term Memory Network (LSTM), Logistic Regression (LR), and Random Forest (RF) were used. The dataset is separated into two parts: the training and testing data, with a ratio of 8:2. Also, the advancement of management information system has been evaluated using 10-fold cross validation depend on recall, f1-score, precision, Mean Absolute Error (MAE), Receiver Operating Curve (ROC), and Root Mean Square Error (RMSE). Finally various techniques of resampling used to forecast if a transaction of MC is genuine/fraudulent. Performance for without re-sampling, with under-sampling, and with over-sampling is measured for each Algorithm. Highest performance of without re-sampling was 0.829 for RF algorithm-F score. While for under-sampling, it was 0.871 for LSTM algorithm-RMSE. Further, for over-sampling, it was 0.921 for both RF algorithm-Precision and LSTM algorithm-F score. The results from running advancement of management information system revealed that using resampling technique with deep learning LSTM generated the best results than intelligent supervised machine learning.

Diagnosis of Alternaria disease and leafminer pest on tomato leaves using image processing techniques

BACKGROUND

Diseases such as Alternaria and pests such as leafminer threaten tomato as one of the most widely used agricultural products. These pests and diseases first damage the leaves of tomatoes, then the flowers, and finally the fruit. Therefore, the damage to the tomato tree must be controlled in its early stages. It is difficult for farmers to distinguish Alternaria disease from leafminer pest at the early and middle stages of their outbreak on tomato leaves. In the present study, 272 tomato leaf images were prepared from the farm of the Vali-e-Asr University of Rafsanjan, including 100 healthy leaves and 172 infected leaves with both Alternaria and leafminer at the initial stages. The image processing technique, texture, neural networks and adaptive network-based fuzzy inference system (ANFIS) classifiers were used to diagnose Alternaria disease and leafminer pest on this dataset.

RESULTS

The results showed that the ANFIS classifier achieved an accuracy of 84.71% when performing an equal error rate, 87.78% in the area under the curve, and 85.23% in 3.26 s on the central processing unit for the segmentation of Alternaria disease and leafminer pest in RGB color space. Also, the accuracy of 90% and 98% were obtained for segmentation and classification on the PlantVillage dataset in YCBCR color space.

CONCLUSION

The present study suggests a high classification accuracy for an intelligent selection of pixel values to train the ANFIS classifier. This classifier has high accuracy and speed, low sensitivity to the light intensity of images, and practical application in diagnosing various diseases and pests on numerous datasets. © 2022 Society of Chemical Industry.

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

In this paper, gamma attenuation has been utilised as a veritable tool for non-invasive estimation of the thickness of scale deposits. By simulating flow regimes at six volume percentages and seven scale thicknesses of a two phase-flow in a pipe, our study utilised a dual-energy gamma source with Ba-133 and Cs-137 radioisotopes, a steel pipe, and a 2.54 cm × 2.54 cm sodium iodide (NaI) photon detector to analyse three different flow regimes. We employed Fourier transform and frequency characteristics (specifically, the amplitudes of the first to fourth dominant frequencies) to transform the received signals to the frequency domain, and subsequently to extract the various features of the signal. These features were then used as inputs for the group method for data Hiding (GMDH) neural network framework used to predict the scale thickness inside the pipe. Due to the use of appropriate features, our proposed technique recorded an average root mean square error (RMSE) of 0.22, which is a very good error compared to the detection systems presented in previous studies. Moreover, this performance is indicative of the utility of our GMDH neural network extraction process and its potential applications in determining parameters such as type of flow regime, volume percentage, etc. in multiphase flows and across other areas of the oil and gas industry.

Optimal Power Allocation and Cooperative Relaying under Fuzzy Inference System (FIS) Based Downlink PD-NOMA

Optimal power allocation (PA) is a decisive part of the power domain non-orthogonal multiple access (PD-NOMA) technique. In PD-NOMA, users are served at the same time and using the same frequency band, but at differing power levels. In this paper, the optimization problem for PA is formulated with distance (d), signal-to-noise ratio (SNR), and foliage depth (df) constraints. A fuzzy inference system (FIS) addresses the optimization problem by allocating the optimal power factors (power levels) to each user in the vicinity of a 5G base-station (gNodeB). The proposed system incorporates a cooperative relaying technique at the near-user to assist the far-user facing signal degradation and greater path losses. A realistic 5G micro-cell is analyzed for downlink PD-NOMA where superposition coding (SC) is used at the transmitter side, a successive interference cancellation (SIC) scheme at the near-user, and a maximum ratio combining (MRC) technique at the far-user’s receiver, respectively. For both simple PD-NOMA and cooperative relaying PD-NOMA, the presented technique’s bit-error-rate (BER) performance is evaluated against various SNR values, and it is concluded that cooperative PD-NOMA outperforms simple PD-NOMA. By combining the presented FIS system with cooperation relaying, the proposed FIS method guarantees user fairness in PD-NOMA systems while also significantly improving performance.