The exponential expansion of the Internet of Things (IoT) has fundamentally transformed the way people, machines, and gadgets communicate, resulting in unparalleled levels of interconnectedness. Nevertheless, the growth of IoT has also brought up notable security obstacles, requiring the creation of strong intrusion detection systems to safeguard IoT networks against hostile actions. This study investigates the utilization of fog computing architectures in conjunction with machine learning approaches to improve the security of the IoT. The UNSW-NB15 dataset, containing an extensive range of network traffic characteristics, is used as the basis for training and assessing the machine learning models. The study specifically applies and evaluates the performance of various models, including linear regression, Ridge classifier, SGD classifier, and ensemble learning. Furthermore, the findings indicate that these models are capable of accurately identifying intrusions, with success rates of 94%, 97%, 96.60%, and 96.50%, respectively. The Ridge Classifier demonstrates exceptional accuracy, highlighting its potential for effective implementation in IoT security frameworks. The results emphasize the efficacy of combining machine learning with fog computing to tackle the distinct security obstacles faced by IoT networks. In the future, our work will prioritize optimizing these models for real-time applications, improving their scalability, and investigating more advanced ensemble strategies to enhance detection accuracy. The project intends to enhance these areas to create a comprehensive and scalable intrusion detection system that can offer strong security solutions for the growing IoT environment. This will guarantee the integrity and dependability of linked devices and systems.

Communication is essential for humanity today and in the past. However, some individuals lack verbal communication due to their innate disability and physical losses from accidents. There are sign-language communication methods developed for such people to communicate. Artificial intelligence solutions are offered to remove the disadvantaged situations of people with disabilities due to communication in daily life. Nowadays, rapidly developing image processing and artificial intelligence methods are proper solutions for the problem focused on in this study. Convolution neural network techniques, which have become very popular recently, offer solutions to many problems. On the other hand, the YOLO algorithm shows very high performance in real-time object detection. In this study, we proposed a method for identifying the alphabets which each gesture delivers. This work studied hand detection on images and classification according to hand movements. The American Sign Language (ASL) standard was used as the sign language. The most recent version of YOLO, known as YOLOv5x, is used for gesture detection. Concentrating on the Static Sign-language problem, a study was conducted on the definition of hand movements. The letters “J” and “Z” are not included in the data set because movable hand signals are required. Apart from these two letters, a total number of 24 letters are classified. The proposed model achieved a training performance of 99.45% mAP@.5. Moreover, the proposed model has a performance of 97.9% mAP@.5 on the test dataset. The results demonstrate that the model's object detection performance is excellent. A statistical analysis of the training time shows that the training time has been drastically decreased, 4.5 hours with the current model as compared to the existing models in the literature.