Electrified transportation systems are emerging quickly worldwide, helping to diminish carbon gas emissions and paving the way for the reduction of global warming possessions. Battery remaining useful life (RUL) prediction is gaining attention in real world applications to tone down maintenance expenses and improve system reliability and efficiency. RUL forms the prominent component of fault analysis forecast and health management when the equipment operation life cycle is considered. The uprightness of RUL prediction is vital in providing the effectiveness of electric batteries and reducing the chance of battery illness. In assessing battery performance, the existing prediction approaches are unsatisfactory even though the battery operational parameters are well tabulated. In addition, battery management has an important contribution to several sustainable development goals, such as Clean and Affordable Energy (SDG 7), and Climate Action (SDG 13). The current work attempts to increase the prediction accuracy and robustness with selected machine learning algorithms. A Real battery life cycle data set from the Hawaii National Energy Institute (HNEI) is used to evaluate accuracy estimation using selected machine learning algorithms and is validated in Google Co-laboratory using Python. Evaluated error metrics such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared, and execution time are computed for different L methods and relevant inferences are presented which highlight the potential of battery RUL prediction close to the most accurate values.

There has been expeditious development and significant advancements accomplished in the electrified transportation system recently. The primary core component meant for power backup is a lithium‐ion battery. One of the keys to assuring the vehicle’s safety and dependability is an accurate remaining useful life (RUL) forecast. Hence, the exact prediction of RUL plays a vital part in the management of battery conditions. However, because of its complex working characteristics and intricate deterioration mechanism inside the battery, predicting battery life by evaluating exterior factors is exceedingly difficult. As a result, developing improved battery health management technology successfully is a massive effort. Because of the complexity of ageing mechanisms, a single model is unable to describe the complex deterioration mechanisms. As a result, this paper review is organised into three sections. First is to study about the battery degradation mechanism, the second is about battery data collections using mercantile and openly accessible Li‐ion battery data sets and third is the estimation of battery RUL. The important performance parameters of distinct RUL forecast and estimation are categorised, analysed and reviewed. In the end, a brief explanation is given of the various performance error indices. This article classifies and summarises the RUL prediction by data‐dependent models using machine learning (ML), deep learning (DL) and ensemble learning (EL) algorithms suggested in a last few years. The goal of this work in this context is to present an overview of all recent advancements in RUL prediction utilising all three data‐driven models. This article is also followed by a categorisation of several types of ML, DL and EL algorithms for RUL prediction. Finally, this review‐based study includes the pros and cons of the models.

The poor intelligibility and out-of-the-ordinary nature of the traditional concatenation speech synthesis technologies are two major problems. CNN's context deep learning approaches aren't robust enough for sensitive speech synthesis. Our suggested approach may satisfy such needs and modify the complexities of voice synthesis. The suggested model's minimal aperiodic distortion makes it an excellent candidate for a communication recognition model. Our suggested method is as close to human speech as possible, despite the fact that speech synthesis has a number of audible flaws. Additionally, there is excellent hard work to be done in incorporating sentiment analysis into text categorization using natural language processing. The intensity of feeling varies greatly from nation to country. To improve their voice synthesis outputs, models need to include more and more concealed layers & nodes into the updated mixture density network. For our suggested algorithm to perform at its best, we need a more robust network foundation and optimization methods. We hope that after reading this article and trying out the example data provided, both experienced researchers and those just starting out would have a better grasp of the steps involved in creating a deep learning approach. Overcoming fitting issues with less data in training, the model is making progress. More space is needed to hold the input parameters in the DL-based method.

Emojis have become increasingly prevalent in today’s digital world, allowing individuals to convey a wide range of emotions, from uncomplicated to intricate, to a greater extent than previously. Consequently, emojis are being utilized in sentiment analysis and tailored marketing strategies. The ongoing research on conducting emotion detection on both tweets and a symbolic expression dataset sourced from Kaggle. Given that tweets are largely commentaries, we utilized two end‐to‐end sentence embedding models, the DistilBERT, USE‐Large3, and RoBERTa, which generate embeddings. These embeddings are further utilized for training with dense neural networks (NNs) and LSTM techniques. Remarkably, it was perceived that the text classification accuracy for both models was consistently high, hovering around 98%. However, when the validation set is constructed with that of symbolic expression or emojis not included in the training dataset, a significant drop in accuracy for both models, plummeting to 75%, had been observed. Additionally, a distributed training methodology is utilized as a substitute for the conventional single‐threaded model to enhance scalability. This approach resulted in a roughly 17% reduction in the runtime while maintaining accuracy. Lastly, in pursuit of explainable AI, the SHAP and LIME algorithms are employed to elucidate the model’s behavior and assess any potential biases in the dataset. The creative use of advanced deep NN techniques customized for the delicate complexities of hybrid‐data sentiment analysis indicates a significant leap forward. Our proposed work provides the critical gap in existing sentiment analysis methods, which primarily aimed at either text or emojis in isolation, thereby exploring more holistic understanding of sentiment in digital communications. Moreover, the application of explainable AI techniques, SHAP and LIME, to demystify model decisions emphasizes commitment to advancing transparent and trustworthy deep learning technologies in sentiment analysis.

In this paper an implementation of unified intelligent controller methods for voltage controlled PWM driven three phase Induction Motor drive system has been developed and analyzed in detail. The Induction motor drive system involve dynamic d-q model in synchronously rotating frame. The performance of proportional controller (PI), Fuzzy and Artificial Neural Networks (ANN) has been investigated through MATLAB/Simulink environment. Finally the results are compared and validate that ANN is predictable, greater and give better responses than Fuzzy and PI controllers.