A novel lightweight deep learning model based on SqueezeNet architecture for viral lung disease classification in X-ray and CT images

Recently, the Wireless Body Area Networks (WBAN) have become a promising and practical option in the tele-care medicine information system that aids for the better clinical monitoring and diagnosis. The trend of using Internet of Things (IoT) has propelled the WBAN technology to new dimension in terms of its network characteristics and efficient data transmission. However, these networks demand the strong authentication protocol to enhance the confidentiality, integrity, recoverability and dependability against the emerging cyber-physical attacks owing to the exposure of the IoT ecosystem and the confidentiality of biometric data. Hence this study proposes the Fog based WBAN infrastructure which incorporates the hybrid symmetric cryptography schemes with the chaotic maps and feed forward networks to achieve the physiological data info security without consuming the characteristics of power hungry WBAN devices. In the proposed model, scroll chaotic maps are iterated to produce the high dynamic keys streams for the real time applications and feed-forward layers are leveraged to align the complex input-output associations of cipher data for subsequent mathematical tasks. The feed forward layers are constructed which relies on the principle of Adaptive Extreme Learning Machines (AELM) thereby increasing randomness in the cipher keys thereby increasing its defensive nature against the different cyber-physical attacks and ensuring the high secured encrypted-decrypted data communication between the users and fog nodes. The real time analysis is conducted during live scenarios. BAN-IoT test beds interfaced with the heterogeneous healthcare sensors and various security metrics are analysed and compared with the various residing cryptographic algorithms. Results demonstrates that the recommended methodology has exhibited the high randomness characteristics and low computational overhead compared with the other traditional BAN oriented cryptography protocol schemes

Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment

2024

Secured Cyber-Internet Security in Intrusion Detection with Machine Learning Techniques

et al. 2024

The rapid proliferation of Internet-connected devices has elevated the significance of cybersecurity, making intrusion detection a critical aspect of maintaining network integrity. Traditional security measures often fail to provide adequate protection against sophisticated attacks, necessitating advanced and robust solutions. This paper introduces a comprehensive cyber-internet security framework that leverages machine learning techniques for real-time intrusion detection and prevention. The proposed methodology employs a hybrid approach, integrating supervised and unsupervised learning models to detect anomalies and classify intrusions effectively. Specifically, a combination of Support Vector Machine (SVM), Decision Trees (DT), and K-means clustering is used to enhance detection accuracy and reduce false-positive rates. The experimental results demonstrate that the proposed model achieved a detection accuracy of 97.8%, a precision of 96.5%, and a recall of 95.2% on the NSL-KDD dataset. The implementation also reduced the false-positive rate to 1.2% and the computational overhead by 15% compared to traditional detection systems. Additionally, the proposed system was tested on real-time traffic data, where it successfully identified and mitigated various cyber threats, including Distributed Denial of Service (DDoS) attacks and network infiltrations, with minimal latency and high reliability. In conclusion, the study presents an efficient and secured cyber-internet security framework that significantly enhances intrusion detection capabilities using machine learning techniques. The proposed system provides a scalable and adaptive solution for securing critical infrastructure and networks against evolving cyber threats, making it an ideal candidate for deployment in real-world cybersecurity applications.

Enhancing Food Image Classification with Particle Swarm Optimization on NutriFoodNet and Data Augmentation Parameters

Sreetha E S,

G Naveen Sundar,

D Narmadha

2024

A convolutional neural network (CNN) architecture, NutriFoodNet, enhanced through Particle Swarm Optimization (PSO) is suggested in this paper to optimize data augmentation parameters and key hyperparameters, specifically designed for food image recognition. Accurate food image classification plays a vital function in various applications, including nutrition management, dietary assessment, and healthcare, as it aids in the automated recognition and analysis of food items from images. The implementation aimed to improve classification accuracy on the Food101 dataset. Initially, the NutriFoodNet model achieved an accuracy of 97.3%. By applying PSO, the model's performance was further refined, resulting in an increased accuracy of 98.5%. This optimized system was benchmarked against state-of-the-art architectures, including ResNet-18, ResNet-50, and Inception V3, showcasing its exceptional performance. The proposed system highlights the efficiency of PSO in fine-tuning augmentation parameters and CNN hyperparameters, leading to significant improvements in model accuracy for food image classification tasks. This advancement underscores the potential of enhanced food image classification systems in contributing to better dietary monitoring and healthcare outcomes.