Brain tumors diagnosis in children is a scientific concern due to rapid anatomical, metabolic, and functional changes arising in the brain and non-specific or conflicting imaging results. Pediatric brain tumors diagnosis is typically centralized in clinical practice on the basis of diagnostic clues such as, child age, tumor location and incidence, clinical history, and imaging (Magnetic resonance imaging MRI / computed tomography CT) findings. The implementation of deep learning has rapidly propagated in almost every field in recent years, particularly in the medical images’ evaluation. This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning. The purpose of this review paper is to include a detailed summary by first providing a succinct guide to the types of pediatric brain tumors and pediatric brain tumor imaging techniques. Then, we will present the research carried out by summarizing the scientific contributions to the field of pediatric brain tumor imaging processing and analysis. Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included.

Teaching computer programming is recognized to be difficult and a real challenge. The biggest problem faced by novice programmers is their lack of understanding of basic programming concepts. A visualized learning tool was developed and used by volunteered first-year students for two semesters. The purposes of this paper are: Firstly, to emphasize factors which directly affect the performance of our students negatively. Secondly, to examine whether the proposed tool would improve their performance and learning progression or not. This tool provides many features and enhancement which were presented to students as pre-lecture material. The results of adopting this tool were conducted using a pre-survey and post-survey questionnaire. As a result, students who used the learning tool showed better performance in their programming subject.

The Internet of things (IoT) is getting more and more intrusive into our lives until the day comes when everything becomes connected to the Internet. Due to the limited resources and heterogeneous Internet of Things (IoT) devices, the traditional means of protection are useless and cannot be used to protect these devices. The most important security risks, their causes and the consequences of their occurrence have been listed, scheduled and categorized. The study concluded that there are real security risks that cannot be ignored, and they need to find innovative solutions to eliminate them or reduce their damage to a minimum. This paper showed the main risks addressed in previews research, and outlined the gaps in this field of technology, also producing a brief summary information about the most important solution to avoid many threats against the IoT field.

The segmentation of pediatric brain MRI into distinct tissues is important for the evaluation of pediatric brain development and the diagnosis of neurological and neurodevelopmental disorders. However, when the used dataset diverges due to various acquisition protocols or biases among patient cohorts, existing deep learning algorithms cannot guarantee correct predictions. Unsupervised domain adaptation approaches have lately shown enormous potential for addressing this problem by limiting the divergence between the distributions of the used datasets. In this paper, we firstly developed a model called 3DUDRSeg, a 3D encoder-decoder for precise autonomous segmentation of pediatric brain tissues. Our proposed 3DUDRSeg model achieved a 98.88% DSC accuracy rate because of the employment of denes blocks and residual units that help relieve the degradation problem during training the network, allowing the performance advantages to be fully utilized. With this approach, our 3DUDRSeg can create more strong features to deal with the wide range of brain tissue variations. Then, we present 3DAdGanSeg, an entropy-based unsupervised domain adaptation framework for segmenting pediatric brain tissues in unannotated datasets via adversarial learning. The suggested model significantly influences the capability to distinguish the borders between tissue classes, with DSC of 85% and HD95 of 1.479 in the case of the dHCP dataset as the source domain and DSC of 81 % and HD95 of 2.061 when using the Schizophrenia Bulletin 2008 dataset as a source domain.

Electricity is one of the fundamental necessities of human beings, which has many uses in our day to day life. It is used for different purposes like domestic, industrial and agricultural. The biggest challenge facing electricity distribution is data collection and meter reading. Right now, meter reading is collected manually which give scope for corruption and human error in reading, moreover the wastage of manpower and resources of utility. Prepaid Energy Meter has been implemented in several countries. In fact, the disadvantage of the system is the behavioral control of the users. Moreover, recharging should be carried out on the meter. The problem occurs when consumers leave their premises and electrical pulses are discharged. That's why we need a system to control the electrical pulse wherever they are. In this work, a prepaid energy meter was proposed, implemented and simulated using PROTEUS software. The system was designed using ATmega128 as a microcontroller and GSM technology is advancement over conventional energy meter, which enables consumer to effectively manage their electricity usage. Additionally, it evaluates the accuracy of voltage and current measured by means of this model. Our Suggested model of the prepaid power meter produces the lowest error compared to actual voltage and current. The proposed system replaces traditional meter reading methods and enables remote monitor and control the meter readings regularly not manually. Also, it alerts the consumer when the energy consumption exceeds above the set limit and alerts the utility company if there is any theft that might be happened.