Knowledge, attitude and practice of Tanta University medical students towards hepatitis B and C

Background: Egypt lies among the world’s highest prevalence rates of HCV and intermediate levels of HBV infection. The objectives of the study were detection of the knowledge, attitude and practice of Medical Students of Tanta University towards hepatitis B and C.Methods: This was a cross-sectional study, conducted in The Faculty of Medicine, Tanta University, Egypt; from 15th October 2013 to 15th of January 2014.Results: The study included 185 Students; their ages ranged between 17 to 28 years with a mean 20±1.731years. Sixty percent of students were males and 65% were urban residents. 50.8% of the participants were in the basic level of the academic study. More than half (57.85%) of the participants had sufficient knowledge, 77.3% of them had a positive attitude towards hepatitis C and B and more than two-thirds (68.1%) showed good practice. A significant association occurred between a positive attitude and good practice. Sufficient knowledge was significantly recorded among older students, females, urban residents and the clinical stage students. The most frequent sources of student information were family or friends, internet followed by TV or radio, healthcare workers, and newspapers.Conclusions: The students had reasonable knowledge, positive attitude and good practices towards B and C viral hepatitis. Areas of insufficient knowledge needed to be reinforced included some modes of transmission, complications, and treatment for B and C viral hepatitis

Assessing the Future of Energy Security in Egypt

This research aims to study the future of energy security in Egypt using System Dynamics approach. To achieve this objective, seven scenarios have been simulated for the period 2007-2030. The simulation results showed that, keeping other polices constant, raising the growth rate of new discoveries of oil and natural gas is the most effective policy for improving the future of energy security in Egypt, followed by removing energy subsidy and paying more investments in new and renewable energy respectively. In addition, targeting high economic growth rate is expected to have a negative effect on the future of energy security in Egypt, if it is not accompanied with more investments for increasing energy resources. On the other hand, the results showed that to improve energy security level and achieve sustainable economic development, a set of policies have to be adopted simultaneously to increase energy resources as well as rationalize energy consumption. Keywords: Energy Security– Energy Forecasting – Simulation Modeling JEL Classifications: F50, Q47, C6

An Efficient Method of Summarizing Documents Using Impression Measurements

Automatic generic document summarization based on unsupervised schemes is a very useful approach because it does not require training data. Although techniques using latent semantic analysis (LSA) and non-negative matrix factorization (NMF) have been applied to determine topics of documents, there are no researches on reduction of matrix and speeding up of computation of the NMF method. In order to achieve this scheme, this paper utilizes the generic impressive expressions from newspapers to extract important sentences as summary. Therefore, it has no stemming processes and no filtering of stop words. Generally, novels are typical documents providing sentimental impression for readers. However, newspapers deliver different impressions for new knowledge because they inform readers about current events, informative articles and diverse features. The proposed method introduces impressive expressions for newspapers and their measurements are applied to the NMF method. From 100 KB text data of experimental results by the proposed method, it turns out that the matrix size reduces by 80 % and the computation of the NMF method becomes 7 times faster than with the original method, without degrading the relevancy of extracted sentences

Emotions in mental healthcare and psychological interventions : towards an inventive emotions recognition framework using AI

One of the major impacts of COVID-19 in the nations is mental health issues. Constant mental health issues can cause disorders, as well as mortality. The growing demand for mental healthcare treatment and limited healthcare resources across the world has shown the need for an inventive framework solution. Artificial Intelligence (AI), Big Data Science, 5G, and Information Communication Technology (ICT) have proven to be able to bring many great improvements and could be the potential way forward to develop such a framework. AI could be a very effective tool to help the healthcare sector to provide more efficient services to patients with mental health issues through their emotions. This paper presents the initial overview and outcomes of the ongoing research programme to develop a proactive multimodal emotion AI recognition framework that detects emotion from various input data sources for early detection of mental health illnesses, as well as provides the required psychological interventions effectively and promptly when required. The data will be collected from various smart wearables and ad-hoc devices, facial expressions, and speech signals. Then, these data will be interpreted using AI into emotions. These emotions will be utilised using AI-based psychological system, which will provide immediate and customized interventions, as well as transmit critical data to the healthcare provider’s central database system for monitoring and supplying the required treatments

The Impact of National Private Investment on Manufacturing in Egypt

This paper aims to present national private investment development phases and its contribution in manufacturing sector in Egypt. Moreover it devoted to examine the effect of national private investment on Egyptian manufacturing. Vector Auto Regressive analysis (VAR) was adopted based on yearly data for the period (1990-2015). Time series stationarity are checked by Augmented Dicky Fuller (ADF) test, and co- integration existence tested by Johansen co- integration test. The vector Error Correction model (VECM) utilized to check the existence of long run relationship between the manufactured product as a dependent variable and the national private investment as explanatory variable. Finally this paper concluded that, however national private investment contribute high share to manufacturing sector, the empirical analysis results obtained negative impact of this type of investment on manufacturing sector in short and long run. Keywords: national private investment, manufactured product, vector error correction model. JEL Classifications: E22, C22, L60, N6

Anomaly detection system for Ethereum blockchain using machine learning

Over the past few years, Blockchain technology has been utilized in various applications to improve privacy and security. Although blockchain has proven its worth as a very powerful technology, research has shown that it is not entirely immune to security and privacy attacks. There was a successful 51% attack on Ethereum Classic back in January 2019 which shows that blockchain still facing security and privacy challenges. This paper aims to develop an anomaly detection solution for the Ethereum blockchain to overcome security challenges using Machine Learning (ML). The proposed solution focuses on using a dynamic approach where the normal operational behaviour of the Ethereum blockchain is used to train ML algorithms and any deviation will be tagged as an anomaly and will be detected by the system. Four ML algorithms including K-Nearest Neighbours (KNN), Gaussian Naive Bayes (GaussianNB), Random Forest, and Stochastic Gradient Descent (SDG) were utilized to train and verify the accuracy of the proposed solution. The experimental results demonstrated that the random forest algorithm provided the best accuracy of 99.84% over other ML algorithms

IoT forensics: A state-of-the-art review, callenges and future directions

The IoT is capable of communicating and connecting billions of things at the same time. The concept offers numerous benefits for consumers that alters how users interact with the technology. With this said, however, such monumental growth within IoT development also gives rise to a number of legal and technical challenges in the field of IoT forensics. Indeed, there exist many issues that must be overcome if effective IoT investigations are to be carried out. This paper presents a review of the IoT concept, digital forensics and the state-of-the-art on IoT forensics. Furthermore, an exploration of the possible solutions proposed in recent research and IoT forensics challenges that are identified in the current research literature are examined. Picks apart the challenges facing IoT forensics which have been established in recent literature. Overall, this paper draws attention to the obvious problems – open problems which require further efforts to be addressed properly.N/

Weapon Violence Dataset 2.0: A synthetic dataset for violence detection

In the current era, satisfying the appetite of data hungry models is becoming an increasingly challenging task. This challenge is particularly magnified in research areas characterised by sensitivity, where the quest for genuine data proves to be elusive. The study of violence serves as a poignant example, entailing ethical considerations and compounded by the scarcity of authentic, real-world data that is predominantly accessible only to law enforcement agencies. Existing datasets in this field often resort to using content from movies or open-source video platforms like YouTube, further emphasising the scarcity of authentic data. To address this, our dataset aims to pioneer a new approach by creating the first synthetic virtual dataset for violence detection, named the Weapon Violence Dataset (WVD). The dataset is generated by creating virtual violence scenarios inside the photo-realistic video game namely: Grand Theft Auto-V (GTA-V). This dataset includes carefully selected video clips of person-to-person fights captured from a frontal view, featuring various weapons—both hot and cold across different times of the day. Specifically, WVD contains three categories: Hot violence and Cold violence (representing the violence category) as well as No violence (constituting the control class). The dataset is designed and created in a way that will enable the research community to train deep models on such synthetic data with the ability to increase the data corpus if the needs arise. The dataset is publicly available on Kaggle and comprises normal RGB and optic flow videos

R : Fuzzy logic with expert judgment to implement an adaptive risk-based access control model for IoT

The Internet of Things (IoT) is becoming the future of the Internet with a large number of connected devices that are predicted to reach about 50 billion by 2020. With proliferation of IoT devices and need to increase information sharing in IoT applications, risk-based access control model has become the best candidate for both academic and commercial organizations to address access control issues. This model carries out a security risk analysis on the access request by using IoT contextual information to provide access decisions dynamically. This model solves challenges related to flexibility and scalability of the IoT system. Therefore, we propose an adaptive risk-based access control model for the IoT. This model uses real-time contextual information associated with the requesting user to calculate the security risk regarding each access request. It uses user attributes while making the access request, action severity, resource sensitivity and user risk history as inputs to analyze and calculate the risk value to determine the access decision. To detect abnormal and malicious actions, smart contracts are used to track and monitor user activities during the access session to detect and prevent potential security violations. In addition, as the risk estimation process is the essential stage to build a risk-based model, this paper provides a discussion of common risk estimation methods and then proposes the fuzzy inference system with expert judgment as to be the optimal approach to handle risk estimation process of the proposed risk-based model in the IoT system

Deep labeller: automatic bounding box generation for synthetic violence detection datasets

Manually labelling datasets for training violence detection systems is time-consuming, expensive, and labor-intensive. Mind wandering, boredom, and short attention span can also cause labelling errors. Moreover, collecting and distributing sensitive images containing violence has ethical implications. Automation is the future for labelling sensitive image datasets. Deep labeller is a two-stage Deep Learning (DL) method that uses pre-trained DL object detection methods on MS-COCO for automatic labelling. The Deep Labeller method labels violent and nonviolent images in WVD and USI. In stage 1, WVD generates weak labels using synthetic images. In stage 2, the Deep labeller method is retrained on weak labels. USI dataset is used to test our method on real-world violence. Deep labeller generated weak and strong labels with an IoU of 0.80036 in stage 1 and 0.95 in stage 2 on the WVD. Automatically generated labels. To test our method’s generalisation power, violent and nonviolent image labels on USI dataset had a mean IoU of 0.7450