Anomaly detection means detecting samples that are different from the normal samples in the dataset. One of the great challenges in this area is finding labeled data, especially for the abnormal categories. In this paper, we propose a method that uses normal data to detect anomalies. This method is based on established neural networks which are called automated encoder and are considered in deep learning studies. An automated encoder reproduces its input as output and reconstruction deviation to rate anomalies. We have used LSTM blocks to construct encoder instead of using ordinary neurons. In fact, these blocks are a category of recurring neural networks that are specialized in discovering and fetching time and proximity dependencies. The result of employing an automated encoder using LSTM blocks to detect point anomalies shows that this approach has been promising and successful in extracting the normal data’ s internal model and also detecting anomalous data. The AUC factor of the model, in almost all cases, is better than the AUC of an ordinary automated encoder and One Class Support Vector Machine (OC-SVM).

Graph embedding is the procedure of transforming a graph into a low-dimensional, informative representation. The majority of existing graph embedding techniques have given less consideration to the embedding distribution of the latent codes and more attention to the graph’s structure. Recently, Variational Graph Autoencoders (VGAEs) have demonstrated good performance by learning smooth representations from unlabeled training samples. On the other hand, in regular VGAEs, the prior distribution over latent variables is generally a single Gaussian distribution. However, complex data distributions cannot be well-modelled under the assumption of a single Gaussian distribution. This choice of prior distribution is important because each dimension of a multivariate Gaussian can learn a separate continuous latent feature, which can result more structured and disentangled representation. In this paper, we employ the Gaussian Mixture Model (GMM) as the prior distribution in a Variational Graph Autoencoder (GMM-VGAE) framework for node classification in graphs. In this framework, GMM effectively discovers the inherent complex data distribution, and graph convolutional networks (GCNs) exploit the structure of the nodes of a graph to learn more informative representations. The proposed model incorporates several Graph Convolutional Networks (GCNs): one to map the input feature vector to the latent representation utilized for classification, another to generate the parameters of the latent distribution for learning from unlabeled data, and finally, an additional GCN is employed for reconstructing the input and delivering the reconstruction loss. Through extensive experiments on well-known Citations, Co-authorship, and Social network graphs, GMM-VGAE’s superiority over state-of-the-art methods is demonstrated.

Crimes nowadays pose unique issues to security and legal institutions and requires smart approaches to different types of peculiar behavior within. This paper proposes a deep learning autoencodes framework to analyze and recognize unusual activities in the FBI’s crime dataset. Utilizing the Autoencoder model’s architecture consisting of input, compression, and output layers, the Adam optimizer is used with a Mean Squared Error loss function for training, validating with twenty percent of the data. A reconstruction error is calculated and subsequently, a threshold of the 95th percentile of the average MSE is set to flag anomalies. Findings prove that the model outperforms all comparative methodologies, achieving 98% accuracy and a 97% precision F1 score. In addition, the model was shown to have an AUC on ROC curve of 98.2% which confirms the model’s ability to accurately classify normal and abnormal samples. This study illustrates the capability of multi-dimensional Autoencoders to analyze and process complex crime data which can greatly aid security agencies in premeditative and reactive responses to crime. Further research will focus on attention-based hybrid models along with system for real-time responsive tracing of volatile hyperdynamics.

The short-and the long-term information in speech signal are useful for speech enhancement, especially if the speech signal is corrupted by both stationary and non-stationary noises. This paper proposes a new approach to provide long-term speech input for a deep denoising Autoencoder by reducing the number of frequency sub-bands of the input data. This paper also proposes a two phase speech enhancement approach. The first phase performs short-term speech enhancement by using a deep denoising Autoencoder. In the second phase, long-term speech enhancement denoising Autoencoder is applied on the output of short-term enhanced speech data. The proposed models were evaluated on the Aurora-2 Speech recognition corpus and our results show significant improvements of 0. 3 in PESQ score at lower SNR values. The proposed models were evaluated on the recognition task where the proposed method results in 4% reduction in word error rate for the multi-condition training when compared to the baseline MFCC front-end.

Botnet is a group of hosts infected with the same malicious code and managed by an attacker or Botmaster through one or more command and control (C&C) servers. The new generation of Botnets generates C&C domain name server’ s list dynamically. This dynamic list created by a domain generation algorithm helps an attacker to periodically change its C&C servers and prevent their addresses from being blacklisted. Each infected host generates a large number of domain names using a predefined algorithm and attempts to map them to their corresponding addresses by sending queries to the domain server. In this paper, the deep Autoencoder neural network is used to identify domains without any knowledge of their generating algorithm, and the performance of the proposed method is compared with the performance of machine learning algorithms. Initially, a new dataset is created by combining a data set with normal domains and two datasets containing malicious and abnormal domains and both manual and automated methods are used to extract the features of the new dataset. Deep Autoencoder neural network is applied to new and pre-processed datasets and the results are compared with machine learning algorithms. Based on the obtained results, it is possible to identify the malicious domains generated by domain generating algorithms using the deep Autoencoder neural network with a higher speed and an accuracy rate larger than 98. 61%.

Identifying hazards from human error is critical for industrial safety since dangerous and reckless industrial worker actions, as well as a lack of measures, are directly accountable for human-caused problems. Lack of sleep, poor nutrition, physical deformities, and weariness are some of the key factors that contribute to these risky and reckless behaviors that might put a person in a perilous scenario. This scenario causes discomfort, worry, despair, cardiovascular disease, a rapid heart rate, and a slew of other undesirable outcomes. As a result, it would be advantageous to recognize people's mental states in the future in order to provide better care for them. Researchers have been studying electroencephalogram (EEG) signals to determine a person's stress level at work in recent years. A full feature analysis from domains is necessary to develop a successful machine learning model using electroencephalogram (EEG) inputs. By analyzing EEG data, a time-frequency based hybrid bag of features is designed in this research to determine human stress dependent on their sex. This collection of characteristics includes features from two types of assessments: time-domain statistical analysis and frequency-domain wavelet-based feature assessment. The suggested two layered Autoencoder based neural networks (AENN) are then used to identify the stress level using a hybrid bag of features. The experiment uses the DEAP dataset, which is freely available. The proposed method has a male accuracy of 77. 09% and a female accuracy of 80. 93%.

Text clustering is a method for separating specific information from textual data and can even classify text according to topic and sentiment, which has drawn much interest in recent years. Deep clustering methods are especially important among clustering techniques because of their high accuracy. These methods include two main components: dimensionality reduction and clustering. Many earlier efforts have employed Autoencoder for dimension reduction; however, they are unable to lower dimensions based on manifold structures, and samples that are like one another are not necessarily placed next to one another in the low dimensional. In the paper, we develop a Deep Text Clustering method based on a local Manifold in the Autoencoder layer (DCTMA) that employs multiple similarity matrices to obtain manifold information, such that this final similarity matrix is obtained from the average of these matrices. The obtained matrix is added to the bottleneck representation layer in the Autoencoder. The DCTMA's main goal is to generate similar representations for samples belonging to the same cluster; after dimensionality reduction is achieved with high accuracy, clusters are detected using an end-to-end deep clustering. Experimental results demonstrate that the suggested method performs surprisingly well in comparison to current state-of-the-art methods in text datasets.

Wildfire detection is a time-critical application since it can be challenging to identify the source of ignition in a short amount of time, which frequently causes the intensity of fire incidents to increase. The development of precise earlywarning applications has sparked significant interest in expert systems research due to this issue, and recent advances in deep learning for challenging visual interpretation tasks have created new study avenues. In recent years, the power of deep learning-based models sparked the researcher’, s interests from a variety of fields. Specially, Convolutional Neural Networks (CNN) have become the most suited approach for computer vision tasks. As a result, in this paper we propose a CNN-based pipeline for classifying and verifying fire-related images. Our approach consists of two models, first of which classifies the input data and then the second model verifies the decision made by the first one by learning more robust representations obtained from a large masked auto encoder-based model. The verification step boosts the performance of the classifier with respect to false positives and false negatives. Based on extensive experiments, our approach proves to improve previous state-of-the-art algorithms by 3 to 4% in terms of accuracy.

Super-resolution is a crucial task in image processing, enhancing the resolution of low-quality images for applications such as surveillance, remote sensing, and autonomous systems. Traditional methods often struggle to preserve fine details, leading to artifacts and reduced visual fidelity. This study introduces the Pretrained RU-SRGAN, an enhanced Super-Resolution Generative Adversarial Network (SRGAN) that incorporates U-Net architecture, residual learning, and Autoencoder pretraining to improve both image quality and computational efficiency, particularly in resource-constrained environments like UAVs. The goal of this research is to evaluate how these architectural modifications can enhance super-resolution performance with limited data. Autoencoder pretraining enables the generator to leverage learned features from low-resolution images, accelerating convergence and improving high-resolution reconstructions. Experimental results show that Pretrained RU-SRGAN outperforms baseline models, achieving a PSNR of 25.7 dB and an SSIM of 0.83. These results highlight the model's ability to preserve fine details and structural integrity, making it particularly effective for real-time image enhancement in UAV applications. The Pretrained RU-SRGAN provides a robust solution for super-resolution tasks, balancing high-quality image reconstruction with computational efficiency, and is well-suited for practical deployment in dynamic, resource-limited environments.