One of the common causes of death is a brain tumor. Because of the above mentioned, early detection of a brain tumor is critical for faster treatment, and therefore there are many techniques used to visualize a brain tumor. One of these techniques is magnetic resonance imaging (MRI). On the other hand, machine learning, deep learning, and convolutional neural network (CNN) are the state of art technologies in the recent years used in solving many medical image-related problems such as classification. In this research, three types of brain tumors were classified using magnetic resonance imaging namely glioma, meningioma, and pituitary gland on the based of CNN. The dataset used in this work includes 233 patients for a total of 3,064 contrast-enhanced T1 images. In this paper, a comparison is presented between the presented model and other models to demonstrate the superiority of our model over the others. Moreover, the difference in outcome between pre- and post-data preprocessing and augmentation was discussed. The highest accuracy metrics extracted from confusion matrices are; precision of 99.1% for pituitary, sensitivity of 98.7% for glioma, specificity of 99.1%, and accuracy of 99.1% for pituitary. The overall accuracy obtained is 96.1%.

The 5G technology is predicted to achieve the unoptimized millimeter Wave (mmWave) of 30-300 GHz bands. This unoptimized band because of the loss of mm-Wave bands, like path attenuation and propagation losses. Nonetheless, because of: (i) directional transmission paving way for beamforming to recompense for the path attenuation, and (ii) sophisticated placement concreteness of the base stations (BS) is the best alternative for array wireless communications in mmWave bands (that is to say 100-150 m). The advance in technology and innovation of unmanned aerial vehicles (UAVs) necessitates many opportunities and uncertainties. UAVs are agile and can fly all complexities if the terrains making ground robots unsuitable. The UAV may be managed either independently through aboard computers or distant controlled of a flight attendant on pulverized wireless communication links in our case 5G. Although a fast algorithm solved the problematic aspect of beam selection for 2-dimensional scenarios. This paper presents 3-dimensional scenarios for UAV. We modeled beam selection with environmental responsiveness in millimeter Wave UAV to accomplish close optimum assessments on the regular period through learning from the available situation.

The pivotal role of electricity is as an enabler for every other sector in any economy. Adequate electricity supply is a vital input for the economic growth and in a range of key industries. Unfortunately, the electricity sector in Iraq has been an unsustainable fiscal burden on successive Iraqi governments yet it has not been able to meet the growing demand. Iraq’s electricity sector and government’s decision makers should look for a long-term solutions and strategies to meet the current and future demand, by taking important steps towards fostering a reliable, affordable, and sustainable electricity system in the years and decades to come. Also, Iraqi government and ministry of electricity in particular should understand that electricity is basic service and right of Iraqis, and many nations have overcome this kind of problem decades ago. Overcoming this problem will help refueling Iraqi economy and enable it to stand on its feet again. Renewable energy resources, and solar in particular could be part of the solution. This paper aims to highlight the importance of solar energy in Iraq as a potential contributor to help bridge the gap between electricity supply and growing demand. Also, it discusses the solar energy opportunities with challenges facing other renewable energy sources in Iraq.

Solar Energy is established as an alternative source of energy known as renewable energy. In a developing country like India, the perspective of Solar Energy is important, as it supports a limitless source of energy. Monitoring and prediction of photo-voltaic energy generation help to reduce the energy loss and empower to utilize more energy. Solar energy prediction is challenging as it depends on the fluctuating solar radiations and climate conditions. The problem statement is to monitor solar panels and predict energy generation for energy management procedure. In this paper, the Internet of Things and Machine Learning algorithms are used as a powerful tool for developing a smart solar system. The metro-logical data such as humidity, temperature and photovoltaic panel data is used as input to forecast solar power generation. For prediction, we examine time-series of solar energy data with Hidden Markov Model. This model considers the probabilistic correlation between previous values to next value in time-series. Experimental results shows that individual panel dead state is located successfully and time-series based solar energy prediction emulate the actual power generation.

In this study, temperature distribution inside a tissue - mimicking material induced by a HIFU (High Intensity Focused Ultrasound) transducer was investigated with two different methods. In first method T-type thermocouple (TC) temperature sensors and in second method, infrared (IR) camera system was used for determining the temperature distribution on the tissue-mimicking material (TMM). Temperature increase and distribution in the TMM have been characterized during the ultrasonic power application. Temperature measurements were actualized inside a TMM which has characteristics very similar to human liver tissue. Temperature rise has been characterized for different input ultrasonic powers and durations. Two different temperature measuring tools were compared and consistency between these methods was presented.