Behaviour and biometrics-based risk core identification using Generative Adversarial Network Technique for Secure p2p Payments

The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 secs and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.

As the need for high-quality video increases it has become necessary to develop smarter compression methods. Artificial neural networks (ANNs) have emerged as an effective tool in this field enabling them to extract the most important features from video and minimize temporal and spatial redundancy resulting in an efficient and high-quality compressed representation. Some of the most prominent models used are: Hybrid neural networks (CNN+RNN(LSTM)) Hybrid neural networks combine convolutional neural networks (CNNs), recurrent neural networks (RNNs), and especially long short-term memory (LSTM) units to extract spatial and temporal features from data, GAN, GANs compress video files while maintaining high perceptual quality. They encode each frame into a compressed latent representation, allowing for realistic reconstruction. This method is efficient, making it ideal for video transmission applications where perceptual accuracy is more important than perfect pixel accuracy. These techniques enable efficient compression without significant quality loss, representing a paradigm shift in modern video compression techniques. After analyzing the results the highest compression ratio (94.75) was achieved by compressing using the GAN algorithm. The highest PSNR (36.08) was achieved by CNN+RNN(LSTM) compression.

Speaker identification (SI) involves recognizing a speaker from a group of unknown speakers, while speaker verification (SV) determines if a given voice sample belongs to a particular person. The main drawbacks of SI are session variability, noise in the background, and insufficient information. To mitigate the limitations mentioned above, this research proposes Global Local Self-Attention (GLSA) based Long Short-Term Memory (LSTM) with Exponential Neighborhood – Grey Wolf Optimization (EN-GWO) method for effective speaker identification using TIMIT and VoxCeleb 1 datasets. The GLSA is incorporated in LSTM, which focuses on the required data, and the hyperparameters are tuned using the EN-GWO, which enhances speaker identification performance. The GLSA-LSTM with EN-GWO method acquires an accuracy of 99.36% on the TIMIT dataset, and an accuracy of 93.45% on the VoxCeleb 1 datasets, while compared to SincNet and Generative Adversarial Network (SincGAN) and Hybrid Neural Network – Support Vector Machine (NN-SVM). ABSTRAK: Pengenalpastian pembicara (Speaker Identification, SI) melibatkan pengenalan pembicara daripada kumpulan pembicara yang tidak dikenali, manakala pengesahan pembicara (Speaker Verification, SV) menentukan sama ada sampel suara tertentu milik seseorang individu. Kekurangan utama dalam SI ialah variasi sesi, bunyi latar belakang, dan maklumat yang tidak mencukupi. Untuk mengatasi kekangan tersebut, kajian ini mencadangkan kaedah Global Local Self-Attention (GLSA) berasaskan Long Short-Term Memory (LSTM) dengan Pengoptimuman Grey Wolf Jiranan Eksponen (EN-GWO) bagi pengenalpastian pembicara yang berkesan menggunakan set data TIMIT dan VoxCeleb 1. GLSA digabungkan dalam LSTM yang memberi tumpuan pada data yang diperlukan, manakala parameter hiper ditala menggunakan EN-GWO untuk meningkatkan prestasi pengenalpastian pembicara. Kaedah GLSA-LSTM dengan EN-GWO mencapai ketepatan 99.36% pada dataset TIMIT dan ketepatan 93.45% pada dataset VoxCeleb 1, berbanding dengan SincNet dan Generative Adversarial Network (SincGAN) serta Hybrid Neural Network – Support Vector Machine (NN-SVM).

Stock market forecasting using DRAGAN and feature matching

Schizophrenia (SZ) is a severe, chronic mental disorder without specific treatment. Due to the increasing prevalence of SZ in societies and the similarity of the characteristics of this disease with other mental illnesses such as bipolar disorder, most people are not aware of having it in their daily lives. Therefore, early detection of this disease will allow the sufferer to seek treatment or at least control it. Previous SZ detection studies through machine learning methods, require the extraction and selection of features before the classification process. This study attempts to develop a novel, end-to-end approach based on a 15-layers convolutional neural network (CNN) and a 16-layers CNN- long short-term memory (LSTM) to help psychiatrists automatically diagnose SZ from electroencephalogram (EEG) signals. The deep model uses CNN layers to learn the temporal properties of the signals, while LSTM layers provide the sequence learning mechanism. Also, data augmentation method based on generative adversarial networks is employed over the training set to increase the diversity of the data. Results on a large EEG dataset show the high diagnostic potential of both proposed methods, achieving remarkable accuracy of 98% and 99%. This study shows that the proposed framework is able to accurately discriminate SZ from healthy subject and is potentially useful for developing diagnostic tools for SZ disorder.

Most Graph Neural Networks (GNNs) follow the message-passing scheme. Residual connection is an effective strategy to tackle GNNs' over-smoothing issue and performance reduction issue on non-homophilic networks. Unfortunately, the coarse-grained residual connection still suffers from class-imbalanced over-smoothing issue, due to the fixed and linear combination of topology and attribute in node representation learning. To make the combination flexible to capture complicated relationship, this paper reveals that the residual connection needs to be node-dependent, layer-dependent, and related to both topology and attribute. To alleviate the difficulty in specifying complicated relationship, this paper presents a novel perspective on GNNs, i.e., the representations of one node in different layers can be seen as a sequence of states. From this perspective, existing residual connections are not flexible enough for sequence modeling. Therefore, a novel node-dependent residual connection, i.e., Long Short-Term Graph Memory Network (LSTGM) is proposed to employ Long Short-Term Memory (LSTM), to model the sequence of node representation. To make the graph topology fully employed, LSTGM innovatively enhances the updated memory and three gates with graph topology. A speedup version is also proposed for effective training. Experimental evaluations on real-world datasets demonstrate their effectiveness in preventing over-smoothing issue and handling networks with heterophily.

Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality worldwide. Effective CVD treatment requires early and accurate diagnosis. CVD diagnosis and prognosis can be done using medical image analysis. In this paper, we propose a novel deep learning approach to improve diagnosis using GAN (Generative Adversarial Network), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), VARMA (Vector Auto Regressive Moving Average), and Deep Dyna Q Network. GAN generates synthetic medical images to train LSTM and GRU models for sequential medical image time series data analysis. VARMA models time series dataset sample temporal dependencies. Deep Dyna Q Network is used to learn the best CVD diagnosis and treatment policy. A large dataset of medical images and patient data trains the model, which is evaluated performance metrics. The proposed approach outperforms state-of-the-art CVD diagnosis methods in clinical scenarios, achieving high accuracy 95% and sensitivity. The proposed deep learning approach has great potential to improve diagnosis and treatment, cardiovascular medication, and patient lifespan in real-time scenarios.

As the traditional methods are not economical and hard to directly measure permanent magnet (PM) temperature of permanent magnet synchronous motor (PMSM), currently a reasonable and more popular consideration to measure rotor temperature is prediction by artificial intelligence (AI) methods. This paper proposes a generative adversarial network (GAN)-supported AI method to solve the PM temperature measurement problem. Firstly, this paper uses CTGAN to get generated dataset and combines it with the original dataset. Secondly, a new GAN-RF method based on random forest (RF) is proposed to predict PM temperature and the performance is compared with another popular method long short-term memory (LSTM). The advantage of the GAN-RF is improving the prediction accuracy of the RF model through the size extension of datasets and getting rid of the dependence of prediction work on time series models (LSTM, etc.) through GAN. The dataset collected by the LEA department at Paderborn university verifies the effectiveness of the proposed method.KeywordsArtificial intelligencePermanent magnet synchronous motorTemperature prediction

The research study is to predict human activity recognition and analyze their strength in machine learning (ML) processes to achieve precision and also to determine performance of human activities. The framework to classify the Novel Long Short Term Memory (LSTM) and Decision Tree (DT) to perform all measures. This research study used the Novel Long Short Term Memory and Decision Tree to perform the required actions to accurately identify body movements in adjacent film sequence. Recent study findings and a threshold of 0.05%, as well as 95% confidence interval and standard parameter estimates, were castoff in a study 47 samples divided into two groups calculated using the G-power of 80%. Since the novel Long Short Term Memory (LSTM) algorithm and local invariant methods have achieved an accuracy of 93.83% in predicting the activity analysis, this research needs to obtain improved accuracy to perform activity prediction by using the Decision Tree algorithm. When applied to human activity analysis, the Novel Long Short Term Memory technique was shown to have an accuracy of 90.12 percent, with a 95% confidence interval including the value 0.045 (p 0.05). Researchers found that when comparing two algorithms for analyzing human behaviour, Long Short Term Memory (LSTM) performed much better than Decision Tree (DT).

Music exerts an essential role in human beings' daily life. However, there are many restrictions in the field of traditional music creation. In recent years, with the development of artificial intelligence accelerated significantly, how to apply deep learning to music creation is an intriguing topic, and it is worth a wide range of exploration and discussions. In the paper, a deep learning model composed of Generative Adversarial Networks (GANs) and Long Short-Term Memory (LSTM) was introduced to implement the generation of music. The GANs are mainly composed of a generator and a discriminator, while LSTM serves as the basic unit of the generator and discriminator. Through analyzing the music file generated in different iteration times, the results demonstrate that the pieces of music we got via the model made up of GAN and LSTM are in a similar pattern.

Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1-measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1-measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance.

In recent years, with the advance of Artificial Intelligence, automatic music composition has been demonstrated. However, there are many music genres and music instruments. For a same piece of music, different music instruments would produce different effects. Invented some 2500 years ago, Guzheng is one of the oldest music instruments in China and the world. It has distinct timbres and patterns that cannot be duplicated by other music instruments. Therefore, it is interesting to see whether AI can compose Guzheng music or alike. In this paper we present a method that can automatically compose and play Guzheng music. First, we collect a number of existing Guzheng music pieces and convert them into Music Instrument Digital Interface format. Second, we use these data to train a Long Short-Term Memory (LSTM) network and use the trained network to generate new Guzheng music pieces. Next, we use the Reinforcement Learning to optimize the LSTM network by adding special Guzheng playing techniques. Comparing to the existing AI methods, such as LSTM and Generative Adversary Network, our new method is more effective in capturing the characteristics of Guzheng music. According to the evaluations from skilled Guzheng players and general audiences, our Guzheng music is very close to the real Guzheng music. The presented method can also be used to automatically compose the music of other Chinese music instruments.

The telemetry data of spacecraft is an ultra-high dimensional time series used to indicate on-orbit operation status, and anomaly detection can effectively ensure safety and reliability. Aiming at the characteristics and complex correlation of high dimensional telemetry data, this paper proposes a novel anomaly detection method based on Generative Adversarial Networks (GAN) for telemetry data anomaly detection. Instead of treating each variable independently, our proposed method captures the latent representation amongst multi-dimensional time series. For normal data, the GAN-based anomaly detection method can obtain a reconstructed time series similar to the original time series, learning the probability distribution model of normal data. For abnormal data, the reconstructed time series deviates greatly from the original time series. In the GAN framework, we use Long Short-Term Memory (LSTM) as the network structure of generator for time series reconstruction and discriminator for calculating the probability of being the real time series, which can learn the temporal features of telemetry data. We also propose a novel anomaly score called GDScore, which comprehensively considers the reconstruction error of the generator and the output of the discriminator. We conduct experiments with two telemetry datasets, which verifies that our proposed GAN-based anomaly detection method can effectively detect outliers.

The article presents an analysis of neural networks that can be used to generating web page program code from input image. As part of the research of the possibilities of modification of algorithms for generating code, the method of generating code simultaneous using the generative adversarial and recurrent neural networks based on the input image is proposed. Methods for creating source code using generative adversarial network (GAN) and recurrent neural networks (RNN) are analyzed. Using domain-specific language (DSL) for tree generation code method, it was possible to use GAN and RNN together, achieving on the average 10% productivity increase. Using GRID’s mesh, the generated code is adaptable. To implement the proposed method, software with a graphical user interface is created. Classes of GAN and RNN were considered, among which the feasibility of using a neural network with long-term short-term memory (LSTM) for analysis and forecasting of tags, blocks and HTML elements of the language in the input image was selected and substantiated. The stages of the research are presented. For the experiment datasets made on the basis of existing sites from open sources for 2018–2019 are used. The estimation of the effectiveness of the proposed method is described.

Ethereum blockchain has shown great potential in providing the next generation of the decentralized platform beyond crypto payments. Recently, it has attracted researchers and industry players to experiment with developing various Web3 applications for the Internet of Things (IoT), Defi, Metaverse, and many more. Although Ethereum provides a secure platform for developing decentralized applications, it is not immune to security risks and has been a victim of numerous cyber attacks. Adversarial attacks are a new cyber threat to systems that have been rising. Adversarial attacks can disrupt and exploit decentralized applications running on the Ethereum platform by creating fake accounts and transactions. Detecting adversarial attacks is challenging because the fake materials (e.g., accounts and transactions) as malicious payloads are similar to benign data. This article proposes a model using Generative Adversarial Networks (GAN) and Deep Recurrent Neural Networks (RNN) for cyber threat hunting in the Ethereum blockchain. Firstly, we employ GAN to generate fake transactions using genuine Ethereum transactions as the first phase of the proposed model. Then in the second phase, we utilize bi-directional Long Short-Term Memory (LSTM) to identify adversarial transactions in a hunting exercise. The results of the first phase evaluation show that the GAN can generate transactions identical to the actual Ethereum transactions with an accuracy of 82.51%. Also, the results of the second phase show 99.98% accuracy in identifying adversarial transactions.