The dynamic and unpredictable nature of road traffic necessitates effective accident detection methods for enhancing safety and streamlining traffic management in smart cities. This paper offers a comprehensive exploration study of prevailing accident detection techniques, shedding light on the nuances of other state-of-the-art methodologies while providing a detailed overview of distinct traffic accident types like rear-end collisions, T-bone collisions, and frontal impact accidents. Our novel approach introduces the I3D-CONVLSTM2D model architecture, a lightweight solution tailored explicitly for accident detection in smart city traffic surveillance systems by integrating RGB frames with optical flow information. Empirical analysis of our experimental study underscores the efficacy of our model architecture. The I3D-CONVLSTM2D RGB + Optical-Flow (trainable) model outperformed its counterparts, achieving an impressive 87% Mean Average Precision (MAP). Our findings further elaborate on the challenges posed by data imbalances, particularly when working with a limited number of datasets, road structures, and traffic scenarios. Ultimately, our research illuminates the path towards a sophisticated vision-based accident detection system primed for real-time integration into edge IoT devices within smart urban infrastructures.

Understanding human behavior and monitoring mental health are essential to maintaining the community and society's safety. As there has been an increase in mental health problems during the COVID-19 pandemic due to uncontrolled mental health, early detection of mental issues is crucial. Nowa-days, the usage of Intelligent Virtual Personal Assistants (IVA) has increased worldwide. Individuals use their voices to control these devices to fulfill requests and acquire different services. This paper proposes a novel deep learning model based on the gated recurrent neural network and convolution neural network to understand human emotion from speech to improve their IVA services and monitor their mental health.

Spatiotemporal sequence prediction is an important problem in deep learning. We study next‐frame(s) video prediction using a deep‐learning‐based predictive coding framework that uses convolutional LSTM (convLSTM) modules. We introduce a novel rgcLSTM architecture that requires a significantly lower parameter budget than a comparable convLSTM. By using a single multifunction gate, our reduced‐gate model achieves equal or better next‐frame(s) prediction accuracy than the original convolutional LSTM while using a smaller parameter budget, thereby reducing training time and memory requirements. We tested our reduced gate modules within a predictive coding architecture on the moving MNIST and KITTI datasets. We found that our reduced‐gate model has a significant reduction of approximately 40% of the total number of training parameters and a 25% reduction in elapsed training time in comparison with the standard convolutional LSTM model. The performance accuracy of the new model was also improved. This makes our model more attractive for hardware implementation, especially on small devices. We also explored a space of 20 different gated architectures to get insight into how our rgcLSTM fits into that space.

AI-driven speech-to-text (STT) documentation systems are increasingly adopted in clinical settings to reduce documentation burden and improve workflow efficiency. However, adoption has outpaced the systematic evaluation of socio-technical risks related to transparency, reliability, patient autonomy, and organizational accountability. To develop a socio-technical framework for identifying and governing risks associated with the implementation of clinical speech-to-text systems. This study synthesizes interdisciplinary evidence from technical automatic speech recognition research, clinical workflow and human factors studies, ethical guidance on consent and patient autonomy, and regulatory and organizational governance sources. Using a structured narrative synthesis approach, relevant literature was iteratively reviewed and thematically analyzed to identify recurring socio-technical risk mechanisms. The synthesis was used to develop a layered conceptual framework for evaluating and governing clinical speech-to-text systems. Findings show that clinical STT systems operate within tightly coupled socio-technical environments where model performance, audio capture conditions, clinician oversight, patient understanding, workflow design, and institutional governance are interdependent. Key risks include inconsistent disclosure and consent practices, performance disparities for accented speech and speech/voice disorders, accuracy degradation under real clinical acoustics, automation complacency and variable clinician review, unclear accountability across vendors and healthcare organizations. These risk domains informed a six-layer socio-technical governance model spanning technical, human/workflow, ethical, organizational, regulatory, and sociocultural dimensions. The study proposes a socio-technical governance framework and implementation roadmap to support the responsible deployment of clinical STT systems. The framework emphasizes transparency, patient autonomy, documentation integrity, and accountable governance to enable safe and equitable adoption of speech-based documentation technologies.

The Internet of Medical Things (IoMT) has transformed the healthcare industry by connecting medical devices in monitoring treatment outcomes of patients. This increased connectivity has resulted to significant security vulnerabilities in the case of malware and Distributed Denial of Service (DDoS) attacks. This literature review examines the vulnerabilities of IoMT devices, focusing on critical threats and exploring mitigation strategies. We conducted a comprehensive search across leading databases such as ACM Digital Library, IEEE Xplore, and Elsevier to analyze peer-reviewed studies published within the last five years (from 2019 to 2024). The review shows that inadequate encryption protocols, weak authentication methods, and irregular firmware updates are the main causes of risks associated with IoMT devices. We have identified emerging solutions like machine learning algorithms, blockchain technology, and edge computing as promising approaches to enhance IoMT security. This review emphasizes the pressing need to develop lightweight security measures and standardized protocols to protect patient data and ensure the integrity of healthcare services.