High-quality images acquired from medical devices can be utilized to aid diagnosis and detection of various diseases. However, such images can be very expensive to acquire and difficult to store, and the process of diagnosis can consume significant time. Automatic diagnosis based on artificial intelligence (AI) techniques can contribute significantly to overcoming the cost and time issues. Pre-trained deep learning models can present an effective solution to medical image classification. In this paper, we propose two such models, ResNext101_32×8d and VGG19 to classify two types of brain tumor: pituitary and glioma The proposed models are applied to a dataset consisting of 1,800 MRI images comprising in two classes of diagnoses; glioma tumor and pituitary tumor. A single-image super-resolution (SISR) technique is applied to the MRI images to classify and enhance their basic features, enabling the proposed models to enhance particular aspects of the MRI images and assist the training process of the models. These models are implemented using PyTorch and TensorFlow frameworks with hyper-parameter tuning, and data augmentation. Experimentally, receiver operating characteristic curve (ROCC), the error matrix, Precision, and Recall are used to analyze the performance of the proposed model. Results obtained demonstrate that VGG19 and ResNext101_32 × 8d achieved testing accuracies of 99.98% and 100%, and loss rates of 0.0120 and 0.108, respectively. The F1-score, Precision, Recall, and the area under the ROC for VGG19 were 99.89%, 99.90%, 99.89%, and 100%, respectively, while for the ResNext101_32 × 8d they were all 100%. The proposed models when applied to MRI images to provide a quick and accurate approach to distinguishing between patients with pituitary and glioma tumors, and could aid doctors and radiologists in the screening of patients with brain tumors.INDEX TERMS Single image super-resolution, visual geometry group (VGG)-19, ResNext101_32 × 8d, brain tumor classification, magnetic resonance imaging (MRI), medical image analysis.The associate editor coordinating the review of this manuscript and approving it for publication was Jiankang Zhang .

Internet of Things (IoT) technology is a complementary part of our style of life. IoT technology coexists with existing cellular communication for developing 5G. Both academic researchers and industrial householders pay attention to the benefits of IoT technology. However, the rapid growth of IoT networks suffers from some limitations, such as heavy traffic load, traffic congestion,. . . etc. In this paper, a parallel distributed smart home architecture is proposed to solve human-being problems and achieve welfare in society. The proposed network has a light traffic load and low traffic congestion to avoid a single-point failure. The architecture of the proposed smart home network involves connectivity, security, registration, protocols, and scenarios to update registration. Also, the paper presents a framework of state diagrams, processes, and algorithms for IoT smart home networks. This framework includes a design and implementation of a pilot model of IoT smart home domains to meet human beneficiaries. The model consists of six practical smart home network domains in both normal and urgent modes to proof of the concept. The pilot model is programmed based on three major aspects: objectives, processes with algorithms, and state diagrams. The novelty of the paper lies in the capability to implement a smart home architecture pilot model using simple development kits. The design and implementation of the practical IoT smart home networks are tested and measured using a CoolTerm sniffer tool. The results of measurement prove that the proposed model achieves light traffic profiles and low average data rates.INDEX TERMS IoT, smart home, networks, pilot model, IoT protocols, traffic profile.

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