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Poetry represents the oldest and most esteemed literary form, allowing poets to convey ideas while carefully attending to elements such as meaning, coherence, poetic quality, and fluency. Notably, the creation of good poetry entails considerations of ...

The goal of natural language processing (NLP), which has recently gained popularity, is to improve the capacity of computers to comprehend and interact with human language. Consequently, in order to converse using natural language, it's crucial that ...

Deep learning has shown great potential for automated medical image segmentation to improve the precision and speed of disease diagnostics. However, the task presents significant difficulties due to variations in the scale, shape, texture, and ...

• Design of a hybrid network that takes into account the local features of a CNN.
• BConvLSTM and the Swin Transformer capture temporal and channel dependencies.
• Composite loss assesses segmentation robustness and boundary agreement ...

Globally air pollution has affected millions of us. In a populated country like India, accurate forecasting of air quality PM2.5 levels is crucial for indicate health risks. This study shows the different capabilities of Deep Learning models and Machine ...

The appropriate utilization of electrical load forecasting should be a top priority to accomplish the emission reduction targets and achieve the ideal balance between power generation and consumption. Load forecasting is an extremely critical task ...

This paper presents a novel architecture for predicting the next word in bilingual Punjabi-English (BPE) social media texts. The goal is to enhance the performance and accuracy of next-word prediction in multilingual environments. Our proposed ...

This paper introduces a novel physics-informed learning approach that combines principles from physics with deep learning techniques to optimize the simulation process of microstrip antennas. These deep learning-based approaches are preferable ...

Internet of things (IoT) and the integration of many gadgets is rapidly becoming a reality. IoT devices, particularly edge devices, are particularly vulnerable to cyberattacks as a result of the proliferation of device-to-device (D2D) connectivity ...

A novel approach for detecting application-level distributed denial-of-service (DDoS) attacks in networks is introduced. By merging vector autoregressive moving average (VARMA) and long short-term memory (LSTM) techniques, our hybrid model efficiently ...

There has been an exponential increase in social media content over the past few years due to the popularity of social media platforms. However, this has also resulted in the dissemination of intentionally false information, or fake news, which ...

Maintenance is a substantial process to sustain the operations of transportation systems. Railway is a major way of transportation, defect and failure in track side equipment and track itself may causes major loss of human lives. So, an effective ...

This paper presents the use of Long Short-Term Memory (LSTM) networks as models for prediction in the Model Predictive Control (MPC) algorithm. LSTMs are recurrent neural networks often used to model dynamical processes. MPC is an advanced control ...

Risk prediction has become increasingly crucial in today's complex and dynamic environments. However, existing forecasting methods still face challenges in terms of accuracy and reliability. Therefore, it is imperative to explore new approaches to better ...

Satellite signals from the Global Navigation Satellite System (GNSS) are refracted as they pass through the troposphere, owing to the variable density and composition of the atmosphere, causing tropospheric delay. Typically, tropospheric delay is ...

Cervical cancer is becoming one of the most frequent malignancies among women. It is also lethal in its last stages. Squamous cell carcinoma is the most common and dangerous kind of cervical cancer, and it must be discovered early to avoid ...

Government agencies and non-profit organizations often rely on case management systems to process the large influx of community request cases. To improve the efficiency of community case management, it's important to model how a community request case is ...

Effective anomaly detection in industrial applications is challenged by the complex temporal dependencies and large data volumes from multivariate time series. Traditional deep hybrid models, utilizing neural networks for feature extraction ...

Consumer behavior and habits play a crucial role in household energy consumption patterns. Influencing user behaviors towards sustainable electricity consumption practices consists an open challenge. To address this issue, the Internet of ...

The development of the road transport advanced collision avoidance system, which aids in accident prevention, heavily relies on accident risk prediction. The prediction of crash risk before a collision has been made possible by a number of deep learning ...

In this paper, we propose a novel approach for financial time series forecasting using feature selection, image encoding, and a self-attention-based CNN transformer. We use Markov transition field and candlestick chart encoding to extract features from ...