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In recent years, there has been a growing recognition among competent city developers and researchers regarding the critical importance of integrating healthcare data analysis into smart urban planning. This research study focuses on enhancing the healthcare system using a cognitive-based cloud framework. We employ the Internet of Things (IoT) to collect patient data and utilize a cloud-based cognitive framework for real-time patient monitoring. We aim to achieve cost-effective and high-quality healthcare. Deep learning techniques are applied to assess the health status and obtain experimental results. Specifically, we conduct pathology detection and classification using Electroencephalography (EEG). Multimodal patient health data, including EEG signals, are recorded using an EEG sensor. Intelligent IoT devices transmit the EEG signals from patients to the cloud, where they undergo processing and are forwarded to a cognitive module. The system tracks various sensor readings, such as facial expressions, speech, EEG, movements, and gestures, to determine the patient's condition. To classify the EEG signals as pathological or normal, our proposed method employs a Transfer Learning-based Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and the Kruskal-Wallis (KW) method. We demonstrate the effectiveness of our approach, outperforming existing methods on the same dataset, achieving an impressive accuracy of 95.13% in identifying EEG pathologies. [ABSTRACT FROM AUTHOR]

With the ever-increasing rate of adoption of internet-enabled smart devices, the allure of greater integration of technologies, such as smart home, smart city, and smart grid into everyday life is undeniable. However, this trend inevitably leaves a massive amount of information and infrastructure connected to the public Internet, which exposes the data to many security threats and challenges. In this paper, we discuss the need for fine-grained user access control for IoT smart devices. The inherently distributed nature of IoT environment necessitates the support of multi-authority attribute-based encryption (ABE) for the implementation of fine-grained access control. Therefore, we present a secure fine-grained user access control scheme for data usage in the IoT environment. The proposed scheme is a three-factor user access control scheme, which supports multi-authority ABE and it is highly scalable as both the ABE key size stored in the user's smart card and ciphertext size needed for authentication request are constant with respect to the number of attributes. Through the formal and informal security analysis, we show that the proposed scheme is secure and robust against several potential attacks required in an IoT environment. Moreover, we demonstrate that the proposed scheme performs at par or better than existing schemes while providing greater functionality features. [ABSTRACT FROM AUTHOR]