26 research outputs found
Conniving energy and time delay factor to model reliability for wireless sensor networks
No full textA secured and optimized deep recurrent neural network (DRNN) scheme for remote health monitoring system with edge computing
No full textPatients now want a contemporary, advanced healthcare system that is faster and more individualized and that can keep up with their changing needs. An edge computing environment, in conjunction with 5G speeds and contemporary computing techniques, is the solution for the latency and energy efficiency criteria to be satisfied for a real-time collection and analysis of health data. The feature of optimum computing approaches, including encryption, authentication, and classification that are employed on the devices deployed in an edge-computing architecture, has been ignored by previous healthcare systems, which have concentrated on novel fog architecture and sensor kinds. To avoid this problem in this paper, an Optimized Deep Recurrent Neural Network (O-DRNN) model is used with a multitier secured architecture. Initially, the data obtained from the patient are sent to the healthcare server in edge computing and the processed data are stored in the cloud using the Elliptic Curve Key Agreement Scheme (ECKAS) security model. The data is pre-processed and optimal features are selected using the Particle Swarm Optimization (PSO) algorithm. O-DRNN algorithm hyper-parameters are optimized using Bayesian optimization for better diagnosis. The proposed work offers superior outcomes in terms of accuracy and encryption latency while using computational cloud services
Crossmodal transfer of emotion by music
No full textMusic is one of the most powerful elicitors of subjective emotion, yet it is not clear whether emotions elicited by music are similar to emotions elicited by visual stimuli. This leads to an open question: can music-elicited emotion be transferred to and/or influence subsequent vision-elicited emotional processing? Here we addressed this question by investigating processing of emotional faces (neutral, happy and sad) primed by short excerpts of musical stimuli (happy and sad). Our behavioural experiment showed a significant effect of musical priming: prior listening to a happy (sad) music enhanced the perceived happiness (sadness) of a face irrespective of facial emotion. Further, this musical priming-induced effect was largest for neutral face. Our electrophysiological experiment showed that such crossmodal priming effects were manifested by event related brain potential components at a very early (within 100 ms post-stimulus) stages of neuronal information processing. Altogether, these results offer new insight into the crossmodal nature of music and its ability to transfer emotion to visual modality
Transforming rural healthcare: a mobile app for digital consultation and diagnostics
No full textAccess to quality medical care is essential for maintaining a healthy life, yet it remains challenging for many, particularly in addressing health issues promptly and effectively. Digital diagnostics and virtual consultations are transforming healthcare by leveraging technology to enhance patient care and streamline medical processes. These tools enable remote communication between patients and healthcare professionals, eliminating geographical barriers and increasing access to expert medical advice. This is especially beneficial in rural areas of India, where timely consultations and proactive health management can be difficult. To address this need, we have developed a mobile application powered by Recurrent Neural Networks (RNNs) that simplifies remote healthcare through online consultations. The app analyzes symptoms—submitted as text or images—generates relevant medical insights, and provides appropriate prescriptions. Acting as a virtual medical mentor, it educates patients about their health conditions and recommended treatments. The application features a chatbot that utilizes both text and image inputs to accurately identify diseases and deliver tailored medical guidance. This diagnostic bot not only improves patient-doctor communication but also minimizes unnecessary lab tests and costly treatments. It complements traditional in-person care by enhancing diagnostic accuracy, reducing the need for physical visits, and significantly cutting down waiting times
A Novel Encryption Design for Wireless Body Area Network in Remote Healthcare System Using Enhanced RSA Algorithm
No full textAI-DRIVEN SECURITY FRAMEWORK FOR ENHANCED THREAT DETECTION IN MOBILE SATELLITE NETWORKS
No full textThe increasing reliance on Mobile Satellite Networks (MSNs) for secure and reliable global communication has led to heightened concerns over cybersecurity threats. Traditional security mechanisms often struggle to counter adaptive and sophisticated attacks, necessitating the integration of Artificial Intelligence (AI)-driven security frameworks. The dynamic nature of MSNs, characterized by high latency, intermittent connectivity, and diverse attack vectors, presents unique security challenges. A key challenge is the real-time detection and mitigation of cyber threats, including eavesdropping, jamming, spoofing, and denial-of-service (DoS) attacks. Conventional cryptographic techniques and firewall-based security solutions are inadequate against evolving threats, necessitating intelligent intrusion detection and adaptive defense mechanisms. To address these challenges, an AI-enhanced security framework is proposed, incorporating Deep Learning (DL) and Reinforcement Learning (RL) models for threat detection and response optimization. The framework employs a Hybrid CNN-LSTM model for anomaly detection, achieving an accuracy of 98.7% in detecting intrusion attempts. Furthermore, a Q-learning-based adaptive security policy dynamically adjusts encryption levels and resource allocation to mitigate ongoing attacks, reducing response time by 37.5% compared to traditional methods. The proposed approach was validated using the NSL-KDD dataset and realworld satellite telemetry logs, demonstrating a 45.3% improvement in threat mitigation efficiency over conventional rule-based systems
Exploring fMRI Biomarkers for Early Autism Prediction in Fragile X Syndrome
No full textNeurodevelopmental disorders like Autism Spectrum Disorder (ASD) and Fragile X Syndrome (FXS) often share similar behavioral and cognitive traits, which can make diagnosis and treatment more complex. While ASD typically arises from a mix of genetic and environmental influences, FXS is directly linked to mutations in the FMR1 gene. This review highlights recent progress in the use of neuroimaging—especially functional MRI (fMRI)—and machine learning, both of which are playing a growing role in improving diagnostic accuracy and deepening our understanding of how the brain functions in ASD and FXS. By exploring the latest research, we show how these tools help uncover both unique and overlapping patterns in brain activity, laying the groundwork for earlier diagnosis and more personalized interventions. These developments point to the powerful potential of combining brain imaging and AI to transform the way we approach diagnosis and care. Continued collaboration across disciplines is key to refining these techniques and moving closer to precision medicine in the field of neurodevelopmental disorders
