Internet of Things (IoT) devices incorporate a large amount of data in several fields, including those of medicine, business, and engineering. User authentication is paramount in the IoT era to assure connected devices’ security. However, traditional authentication methods and conventional biometrics-based authentication approaches such as face recognition, fingerprints, and password are vulnerable to various attacks, including smudge attacks, heat attacks, and shoulder surfing attacks. Behavioral biometrics is introduced by the powerful sensing capabilities of IoT devices such as smart wearables and smartphones, enabling continuous authentication. Artificial Intelligence (AI)-based approaches introduce a bright future in refining large amounts of homogeneous biometric data to provide innovative user authentication solutions. This paper presents a new continuous passive authentication approach capable of learning the signatures of IoT users utilizing smartphone sensors such as a gyroscope, magnetometer, and accelerometer to recognize users by their physical activities. This approach integrates the convolutional neural network (CNN) and recurrent neural network (RNN) models to learn signatures of human activities from different users. A series of experiments are conducted using the MotionSense dataset to validate the effectiveness of the proposed method. Our technique offers a competitive verification accuracy equal to 98.4%. We compared the proposed method with several conventional machine learning and CNN models and found that our proposed model achieves higher identification accuracy than the recently developed verification systems. The high accuracy achieved by the proposed method proves its effectiveness in recognizing IoT users passively through their physical activity patterns.

X (formerly known as Twitter) is one of the most prominent social media platforms, enabling users to share short messages (tweets) with the public or their followers. It serves various purposes, from real-time news dissemination and political discourse to trend spotting and consumer engagement. X has emerged as a key space for understanding shifting brand perceptions, consumer preferences, and product-related sentiment in the fashion industry. However, the platform’s informal, dynamic, and context-dependent language poses substantial challenges for sentiment analysis, mainly when attempting to detect sarcasm, slang, and nuanced emotional tones. This study introduces a hybrid deep learning framework that integrates Transformer encoders, recurrent neural networks (i.e., Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)), and attention mechanisms to improve the accuracy of fashion-related sentiment classification. These methods were selected due to their proven strength in capturing both contextual dependencies and sequential structures, which are essential for interpreting short-form text. Our model was evaluated on a dataset of 20,000 fashion tweets. The experimental results demonstrate a classification accuracy of 92.25%, outperforming conventional models such as Logistic Regression, Linear Support Vector Machine (SVM), and even standalone LSTM by a margin of up to 8%. This improvement highlights the importance of hybrid architectures in handling noisy, informal social media data. This study’s findings offer strong implications for digital marketing and brand management, where timely sentiment detection is critical. Despite the promising results, challenges remain regarding the precise identification of negative sentiments, indicating that further work is needed to detect subtle and contextually embedded expressions.