Recent Advances in Computer Science and Communications - Online First
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Data Security and Privacy Preservation in Cloud-Based IoT Technologies: an Analysis of Risks and the Creation of Robust Countermeasures
Authors: Mayank Pathak, Kamta Nath Mishra and Satya Prakash SinghAvailable online: 16 October 2024More LessThe Internet of Things (IoT) is a revolutionary technology being used in many different industries to improve productivity, automation, and comfort of the user in the cloud and distributed computing settings. Cloud computing is essential because it makes data management and storage more effective by automatically storing and examining the enormous amounts of data generated by Internet of Things applications. End users, companies, and government data are consequently migrating to the cloud at an increasing rate. A survey of the literature, however, reveals a variety of issues, including data integrity, confidentiality, authentication, and threat identification, that must be resolved to improve data security and privacy. To effectively address contemporary data security concerns, the existing approaches need to be improved. Ensuring secure end-to-end data transmission in a cloud-IoT situation requires innovative and dependable protocol architecture. New technologies that address some of the issues related to cloud data include edge computing, fog, blockchain, and machine learning. This paper provides a thorough examination of security risks, classifying them and suggesting possible defenses to safeguard cloud-IoT data. It also highlights innovative approaches, such as blockchain technology and machine learning, applied to privacy and data security. The paper also explores existing issues with respect to data privacy and security in today's cloud-IoT environments. It suggests possible future directions, including the need for end-user authentication, enhanced security, and procedures for recovering data in the event of an attack.
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Analysis and Classification of Medical Images Using Deep Learning Algorithms
Authors: Chouchene Karima, Nadjla Bourbia, Kamel Messaoudi and El-Bay BourennaneAvailable online: 10 October 2024More LessIntroductionNowadays, Artificial intelligence and machine learning have emerged as a powerful tool for the analysis of medical images such as MRI scans. This technology holds significant potential to improve diagnostic services and accelerate medical advances by facilitating clinical decision-making.
MethodIn this work, we developed a Convolutional Neural Network (CNN) model specifically designed for the classification of medical images. Using a selected database, the model achieved a classification accuracy of 92%. To further improve the performance, we leveraged the pre-trained VGG16 model, which increased the classification accuracy to 100%. Additionally, we preprocessed the MRI images using the Roboflow platform and then developed YOLOv5 models for the detection of tumors, infections, and cancerous lesions.
ResultThe results demonstrate a localization accuracy of 50.41% for these medical conditions.
ConclusionThis research highlights the value of AI-driven approaches in enhancing medical image analysis and their potential to support more accurate diagnoses and accelerate advancements in healthcare.
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WITHDRAWN: A Policy Configured Resource Management Scheme for Ahns Using LR-KMA and WD-BMO
Available online: 03 October 2024More LessThe article has been withdrawn at the request of the author of the journal “Recent Advances in Computer Science and Communications”.
Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.
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