The proliferation of sensing technologies such as sensors has resulted in vast amounts of time-series data being produced by machines in industrial plants and factories. There is much information available that can be used to predict machine breakdown and degradation in a given factory. The downtime of industrial equipment accounts for heavy losses in revenue that can be reduced by making accurate failure predictions using the sensor data. Internet of Things (IoT) technologies have made it possible to collect sensor data in real time. We found that hybrid modelling can result in efficient predictions as they are capable of capturing the abstract features which facilitate better predictions. In addition, developing effective optimization strategy is difficult because of the complex nature of different sensor data in real time scenarios. This work proposes a method for multivariate time-series forecasting for predictive maintenance (PdM) based on a combination of convolutional neural networks and long short term memory with skip connection (CNN-LSTM). We experiment with CNN, LSTM, and CNN-LSTM forecasting models one by one for the prediction of machine failures. The data used in this experiment are from Microsoft’s case study. The dataset provides information about the failure history, maintenance history, error conditions, and machine features and telemetry, which consists of information such as voltage, pressure, vibration, and rotation sensor values recorded between 2015 and 2016. The proposed hybrid CNN-LSTM framework is a two-stage end-to-end model in which the LSTM is leveraged to analyze the relationships among different time-series data variables through its memory function, and 1-D CNNs are responsible for effective extraction of high-level features from the data. Our method learns the long-term patterns of the time series by extracting the short-term dependency patterns of different time-series variables. In our evaluation, CNN-LSTM provided the most reliable and highest prediction accuracy.

Estimating the remaining useful life (RUL) of critical industrial assets is of crucial importance for optimizing maintenance strategies, enabling proactive planning of repair tasks, enhanced reliability, and reduced downtime in prognostic health management (PHM). Deep learning-based data-driven approaches have made RUL prediction a lot better, but traditional methods often do not look at the similarities and differences in the data, which lowers the accuracy of the estimates. Previous attempts to use Long Short-Term Memory (LSTM) networks for RUL prediction have failed because they depend on learned features for regression at the very end of the time step. The single objective function for estimation also constrains the learned representations, which has an impact on RUL estimation. The goal of this study is to find out how to predict the RUL of mechanical systems using complex sensor data. To do this, we present a data-driven framework called temporal convolution, along with a recurrent skip component and an attention mechanism network called TCRSCANet. It uses a combination of temporal convolution, recurrent skip parts, and an attention mechanism to make RUL estimation more accurate. The recurrent skip component finds long-term patterns in time series data, while temporal convolution pulls out high-level features from longer sequences. Finding hidden representations and degradation-development interactions between features at each window position in the input matrix is what the attention layer does to focus on the most important information for RUL estimation. The proposed methodology is tested and validated against the well-established C-MAPSS dataset, which focuses on aircraft degradation. The TCRSCANet model is better at predicting RUL as compared to other state-of-the-art methods because it uses the root mean square error (RMSE) and a scoring function to measure performance. The results of this study demonstrate the importance of the recurrent skip component and attention mechanisms for determining how long an industrial asset will be valuable.

The rapid advancement of Generative Artificial Intelligence (AI) has enabled the creation of highly realistic synthetic audio, presenting significant challenges to digital security, media forensics, and public confidence. While deepfake detection has been extensively explored for high-resource languages like English, low-resource languages remain critically underexamined. This paper introduces a systematic benchmark of deepfake audio detection methods for Urdu, a language characterized by its rich morphology and phonetic complexity. We evaluate convolutional and transformer-based architectures, including LCNN, CNN–LSTM with Attention, and Whisper variants, employing Mel-Frequency Cepstral Coefficients and Linear Frequency Cepstral Coefficients as front-end features. To address data scarcity, we construct three dataset configurations, baseline, augmented, and extended derived from an existing Urdu deepfake corpus and enhanced through controlled augmentation and additional recordings. Our experiments, conducted with multiple random seeds and statistical validation, demonstrate that MFCC-based models, particularly Whisper-small, achieve strong performance with an Equal Error Rate as low as 0.50%. Robustness tests under noise, pitch, and tempo perturbations highlight the limitations of lightweight CNNs and underscore the advantages of transformer embeddings for handling Urdu’s linguistic variability. This study represents the first structured benchmark of deepfake audio detection techniques for Urdu, offering empirical insights into how language characteristics influence model performance and generalization. The findings emphasize the importance of multilingual evaluation in the development of trustworthy speech forensics systems.

Image restoration and reconstruction from blurry and noisy images have proved to be challenging problem. Noise removal plays any important role in preserving the meaningful and useful information in images. Our paper is based on a denoising technique known as total variation (TV). Over the years, high quality images and videos have become a trend. However, noise has remained an integral part in images and videos. Many denoising techniques have been developed over the time to remove noise from images and videos. Linear filters, Non-Linear filters, median filters and their modifications, have been a great success in noise removal. However, they resulted in blurring of images. We came up with a directional total variation algorithm for denoising. Over the past, most of the denoising methods have been used with noisy images. In our study, we make use of sequential 1D total variation on the pixel sequence procured in various positions along with horizontal, vertical and zig-zag. The evaluation of our proposed approach is performed based on standard test images and the nature of the denoised images is measured by making use of objective matrices like visual signal to noise ratio (VSNR), and peak signal to noise ratio. Numerous experiments proved that our proposed method yields promising results.

The research proposes a detailed study aimed at forecasting stock and currency prices using state-of-the-art Machine Learning (ML) techniques, such as Transformers, LSTM, Simple RNN, N-HEATS, and N-BITS. The article provides an in-depth overview of t...