Magnetic resonance imaging (MRI) combined with artificial intelligence (AI) algorithms to detect brain tumors is one of the important medical applications. In this study, a Convolutional neural network (CNN) model is proposed to detect meningioma and pituitary, which was tested with a dataset consisting of two categories of tumors with 1,800 MRI images from several persons. The CNN model is trained via a Python library, namely TensorFlow, with an automatic tuning approach to obtain the highest testing accuracy of tumor detection. The CNN model used Python programming language in Google Colab to detect sensitivity, precision, the area under the PR and receiver operating characteristic (ROC), error matrix, and accuracy. The results show that the proposed CNN model has a high performance in the detection of brain tumors. It achieves an accuracy of 95.78% and a weighted average precision of 95.82%.

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 is implementation 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.

Heusler-type Fe2AlVxTi1-x alloys offer an alternative solution for the generation of thermoelectric power near room temperature. In the current research, thermoelectric properties of the p-type Fe2AlV0.9Ti0.1 and Fe2AlV0.9Ti0.1/CNT alloys, prepared by SPS, were studied. Carbon nanotubes (CNT) were used as dopants to improve the seebeck coefficient and electrical conductivity. Upon doping with CNT, the thermal conductivity was significantly reduced, meanwhile, the value of the power factor increased from 0.45 to 1.55 mW/mK2 at around 330 K. The effect of CNT inclusions on the thermoelectric parameters of Fe2AlVxTi 1-x compounds was systematically studied. When compared to Fe2AlV0.9Ti0.1, which had a figure of merit of just 0.02 at 330 K, the CNT-containing samples showed a significantly improved figure of merit up to 0.07. We offer a novel technique to improve the performance of Fe2AlV alloys.

In the current era, autonomous vehicles and non-autonomous vehicles are transforming and adopting intelligent road management techniques to communicate with vehicles so that the traffic system may become more efficient. Intersection management unit technique used to handle and control all types of vehicles. VIP vehicles enter our roads causing congestion on all routes, but the IM unit efficiently handles all vehicles by using preemptive scheduling and non-preemptive scheduling technique. According to our scenario, we are using Nishtar road, m4 motorway, M5 Motorway and Washington road. The simulation result shows that, in comparison to all other routes, the Washington road route performs better for all types of vehicles. Washington road time delay for all types of vehicles is 3.62 seconds (sec) for autonomous vehicles (AV) for and non-autonomous vehicles delay time is 4.33 sec.

This research presents a deep learning approach designed for the automated detection of pneumonia through the analysis of chest X-ray images. Pneumonia remains the foremost in-fectious cause of mortality in children under five years old, leading to the deaths of 740,180 children globally in 2019. This statistic represents 14% of all deaths within this demographic. This alarming statistic underscores the necessity for improved diagnostic methods, particularly in environments with limited resources where specialized radiologists may not be readily available. In this study we present a deep learning methodology aimed at automating the identification of pneumonia through the analysis of chest X-ray images, specifically focusing on a dataset comprised of5,863 images categorized as either pneumonia or normal. This dataset, obtained from retrospective cohorts of pediatric patients between the ages of one and five years at the Guangzhou Women and Children’s Medical Center, was subjected to stringent quality control measures to eliminate low-quality images, ensuring high diagnostic accuracy. This methodology includes an extensive preprocessing pipeline featuring resizing, grayscale conversion, normalization, and data augmentation techniques to enhance the robustness of the model. The architecture employed consists of a Convolutional Neural Network (CNN) with seven convolutional blocks designed to obtain hierarchical characteristics from the input images. The model achieved an accuracy of 97%, with precision and recall values of 0.975 and 0.977 respectively, indicating its efficacy in distinguishing between pneumonia and normal cases. The analysis of the model’s performance was further substantiated through confusion matrices and detailed classification reports.