Smart classrooms (SCR) are reshaping the learning experience with their interactive technology and personalized knowledge, leading to improved student engagement by seamlessly integrating digital gadgets. In this rapidly evolving landscape, the integrity and efficiency of heating, ventilation, and air conditioning (HVAC) systems are essential to the futuristic student’s life. This article introduces SuRaksha: AI-powered blockchain framework for HVAC tamper detection and authentication in smart classrooms. Leveraging the power of ensemble learning (EL) at the intelligent and connection layer of the proposed framework, our approach employs Internet of Things (IoT) sensors to collect comprehensive data from HVAC appliances. The EL algorithms then analyze the collected data to detect any instances of tampering in real-time. At the security layer, robust authentication mechanisms are implemented to verify the integrity of the HVAC data before securely storing it on the cloud. This multilayered framework enhances the detection and authentication processes and ensures the reliability and security of HVAC operations in intelligent classroom environments. Extensive experiments and validations demonstrate the efficacy of the proposed framework in identifying tampering incidents and providing a secure, authenticated, and reliable system for modern educational facilities. The validation outcomes of the proposed framework demonstrate excellent performance, with an average processing time of 3.725 s and 99.84% accuracy for SCR compared to existing works.

An early diagnosis of hepatocellular carcinoma is among the most lethal cancers of the liver, via CT imaging is vital to the prompt intervention. Nevertheless, localization of lesions is impeded as a result of hepatic complicated anatomy, low contrast and variegated morphology of lesions. We should overcome these challenges in order to achieve the following present YOLO-M, a liver-focused object detection system which enhances the YOLO design with a backbone using a two-path, a Multi-Scale Liver Feature Attention (LFA) module and a Liver Region Attention Module (LRAM). YOLO-M also has a disease pattern detection head and an anchor that is personalized box design adapted to the hepatic lesions. YOLO-M is evaluated on the HCC-TACE-Seg dataset and achieves an output mAP@0.5 of 0.975, Dice score of 0.9056, accuracy of 98.2%, precision of 97.44%, recall of 84.59% and F1-score of 90.56% which surpasses state-of-the-art classifiers such as YOLOv5, HFCNN and TransUNet. These findings indicate that YOLO-M is a powerful and effective tool which can be used to detect real-time liver lesion detection and segmentation.