Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment.

Cyberbullying involves the use of social media platforms to harm or humiliate people online. Victims may resort to self-harm due to the abuse they experience on these platforms, where users can remain anonymous and spread malicious content. This highlights an urgent need for efficient systems to identify and classify cyberbullying. Many researchers have approached this problem using various methods such as binary and multi-class classification, focusing on text, image, or multi-modal data. While deep learning has advanced cyberbullying detection and classification, the multi-class classification of cyberbullying using multi-modal data, such as memes, remains underexplored. This paper addresses this gap by proposing several multi-modal hybrid deep learning models, such as LSTM+ResNet, LSTM+CNN, LSTM+ViT, GRU+ResNet, GRU+CNN, GRU+ViT, BERT+ResNet, BERT+CNN, BERT+ViT, DistilBERT+ResNet, DistilBERT+CNN, DistilBERT+ViT, RoBERTa+ResNet, RoBERTa+CNN, and RoBERTa+ViT, for classifying multi-classes of cyberbullying. The proposed model incorporates a late fusion process, combining the LSTM, GRU, BERT, DistilBERT, and RoBERTa models for text extraction and the ResNet, CNN, and ViT models for image extraction. These models are trained on two datasets: a private dataset, collected from various social media platforms, and a public dataset, obtained from previously published research. Our experimental results demonstrate that the RoBERTa+ViT model achieves an accuracy of 99.20% and an F1-score of 0.992 on the public dataset, and an accuracy of 96.10% and an F1-score of 0.959 on the private dataset when compared with other hybrid models.

In recent years, there has been a rapid growth in the development and usage of flying drones due to their diverse capabilities worldwide. Public and private sectors will actively use drone technology in the logistics of goods and transporting passengers in the future. There are concerns regarding privacy and noise exposure in and around the rural and urban environment with the rapid expansion. Further, drone noise could affect human health. European Union has defined a service-orientated architecture to provide air traffic management for drones, called U-space. However, it lacks a noise modelling service (NMS). This paper proposes a conceptual framework for such a noise modelling service for drones with a use case scenario and verification method. The framework is conceptualized based on noise modelling from the aviation sector. The NMS can be used to model the noise to understand the accepted drone noise levels in different scenarios and take measures needed to reduce the noise impact on the community.

Subsea environments are vital for global biodiversity, climate regulation, and human activities such as fishing, transport, and resource extraction. Accurate mapping and monitoring of these ecosystems are essential for sustainable management. Airborne LiDAR bathymetry (ALB) provides high-resolution underwater data but produces large and complex datasets that make efficient analysis challenging. This study employs deep learning (DL) models for the multi-class classification of ALB waveform data, comparing two recurrent neural networks, i.e., Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). A preprocessing pipeline was developed to extract and label waveform peaks corresponding to five classes: sea surface, water, vegetation, seabed, and noise. Experimental results from two datasets demonstrated high classification accuracy for both models, with LSTM achieving 95.22% and 94.85%, and BiLSTM obtaining 94.37% and 84.18% on Dataset 1 and Dataset 2, respectively. Results show that the LSTM exhibited robustness and generalization, confirming its suitability for modeling causal, time-of-flight ALB signals. Overall, the findings highlight the potential of DL-based ALB data processing to improve underwater classification accuracy, thereby supporting safe navigation, resource management, and marine environmental monitoring.

Cyberbullying is defined as the use of social media platforms to hurt or humiliate people online. The anonymity on these platforms makes it easy to spread hurtful content, sometimes leading victims to self-harm. This highlights the urgent need for efficient methods to identify to prevent cyberbullying. Numerous studies have addressed this issue by focusing on cyberbullying classification, primarily through binary classification using multi-modal data or multi-class approaches targeting either text or image data. While deep-learning advancements have improved cyberbullying identification, a gap remains in the multi-class classification of cyberbullying utilizing multi-modal data such as memes and this research aims to bridge this gap by accurately classifying cyberbullying across multiple data modalities through a hybrid deep-learning model that combines RoBERTa for text extraction and Vision Transformer (ViT) for images extraction as hybrid(RoBERTa+ViT) model using late fusion module process. Two datasets were utilized: a Private-dataset was collected from comments on social media videos and a Public-dataset that was downloaded from existing research. The hybrid model was trained on these two datasets and the model demonstrated notable performance, achieving an an accuracy of 99.24% on the Public-dataset and 96.1% on the Private-dataset, with F1-scores of 0.9924 and 0.9599, respectively.

Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify the physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the proposed models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from 8 volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides the accuracy performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. Our results indicate that the proposed method will provide the medical doctors and trainers a promising way to precisely track and understand a patient’s physical activities for better treatment.