S. Santhoshkumar

Blockchain-Assisted Federated Deep Reinforcement Learning (B-FDRL) for Trustworthy Spectrum Allocation in 5G Cognitive Radio Networks

The issue of effective and secure spectrum allocation is a substantial challenge in 5G cognitive radio networks. This challenge has emerged due to the availability of spectrum dynamically, heterogeneous user demands and increasing security threats. Current methodologies that employ a combination of deep reinforcement l...

Adaptive Object Distance Monitoring in Disaster and Crisis Scenarios via YOLOv9 with Monocular Depth Estimation

Situational awareness (SA) is required by the first responders to act swiftly and with accuracy in response to a disaster and crisis situation, where the dynamic nature of the environment, damaged infrastructure and visibility can severely impair the decision-making process. The existing technologies in disaster monito...

Deepfake Video Analysis Using a Dual-Stream ResNeXt and LSTM Deep Learning Framework

The fast development of digital media technologies has greatly erased the line between actual and manipulated visual content, especially with the advent of deepfake videos. These are very realistic synthetic videos that are very difficult to verify identities, cybersecurity, and integrity in social media. In order to o...

FruitNetSH: An Efficient Deep Learning Model for Precision Pomegranate Disease Diagnosis

The most important thing in minimizing crop losses and maximizing crop yields is accurate and timely diagnosis of diseases in pomegranate fruit. Though the recent deep learning methods have demonstrated encouraging results, issues like field noise, variability in illumination, complexity of the background, and similari...

SE-DHPPM: A Secure and Explainable Deep Hybrid Privacy-Preserving Model for Edge Intelligence

Edge Intelligence has emerged as a promising paradigm to facilitate real-time decision making in healthcare, Internet of Things (IoT) and intelligent surveillance applications. However, the issue of data privacy, secure communication, model interpretability, and low latency computation of computation in heterogeneous e...

Dual-Stream VGG–DiNAT Framework With Red-Crowned Crane Optimization for Hair and Scalp Disorder Diagnosis

In dermatological health care, accurate and reliable diagnosis of hair and scalp conditions is essential; however, traditional diagnostic processes often face challenges related to noise, variability in image quality and limited size datasets. To address these challenges, the present study describes a new Deep Learning...

Insider Threat Detection in Lorawan-Based Iot Systems Using Isolation Forest

The Internet of Things (IoT) networks, especially LoRaWAN networks, has allowed for the transmission of low power and long range for many smart applications. But the growing number of connected devices creates a huge security problem, as it includes threats from insiders, which are either compromised or malicious and a...

Detection of DDoS Attacks in LoRaWAN-based IoT Networks using Isolation Forest

Next-Generation AI Architecture for Real-Time Hazard Prediction and Safety Optimization in Mining Systems

The mining industry is a risky sphere of industry that is characterized by unstable geological conditions, the dangerous environment, and the active use of machinery. Traditional safety systems are based on manual surveillance and limits-like warnings, which are reactive in nature and cannot be used to mitigate the ris...