Causes and Outcome of Acute Kidney Injury: Gezira Experience

Introduction: A precise operational definition of acute kidney injury remains elusive. Conceptually, acute kidney injury is defined as the loss of renal function, measured by decline in glomerular filtration rate, developing over a period of hours to days. Clinical manifestations of acute kidney injury (AKI) are highly variable; in some patients, the only manifestation may be biochemical abnormalities on routine blood sampling, while other patients will present with overt uremic signs or symptoms.Objectives: We evaluated the aetiology and the clinical outcome of all patients admitted to Gezira Hospital for Renal diseases and Surgery with AKI.Methods: Clinical data of patients admitted from January 2008 through December 2009 were reviewed and analysed.Results: Total number of patients included in this study was 122. Out of them 39.3% (48) were females. The mean age was 51.34 (SD 22.18) years. The most common causes of acute kidney injury are ischemic acute tubular necrosis, followed by sepsis and obstruction respectively. Mortality rate was 18.9%.Conclusion: Mortality rate of AKI in our set up was 18.9%.Key words: glomerular, tubular necrosis, creatinine

Analyzing the Resilience of Convolutional Neural Networks Implemented on GPUs: Alexnet as a Case Study

There have been an extensive use of Convolutional Neural Networks (CNNs) in healthcare applications. Presently, GPUs are the most prominent and dominated DNN accelerators to increase the execution speed of CNN algorithms to improve their performance as well as the Latency. However, GPUs are prone to soft errors. These errors can impact the behaviors of the GPU dramatically. Thus, the generated fault may corrupt data values or logic operations and cause errors, such as Silent Data Corruption. unfortunately, soft errors propagate from the physical level (microarchitecture) to the application level (CNN model). This paper analyzes the reliability of the AlexNet model based on two metrics: (1) critical kernel vulnerability (CKV) used to identify the malfunction and light- malfunction errors in each kernel, and (2) critical layer vulnerability (CLV) used to track the malfunction and light-malfunction errors through layers. To achieve this, we injected the AlexNet which was popularly used in healthcare applications on NVIDIA’s GPU, using the SASSIFI fault injector as the major evaluator tool. The experiments demonstrate through the average error percentage that caused malfunction of the models has been reduced from 3.7% to 0.383% by hardening only the vulnerable part with the overhead only 0.2923%. This is a high improvement in the model reliability for healthcare applications

Seawater salinity modelling based on electromagnetic wave characterization

Wireless communications have experienced tremendous growth, and improving their performance based on specific parameters requires an accurate model. Salt seawater, being an abundant resource, could play a crucial role in various applications such as enhancing electrical conductivity, monitoring security, improving battery power efficiency, and creating liquid antennas. Salinity is an essential factor to consider when developing these applications. This paper focused on investigating the electromagnetic properties of seawater salinity in the context of marine wireless communications. The results of the study showed that salinity has a significant impact on the Fresnel reflection coefficient in terms of magnitude, phase shift, and polarization, and can either constructively or destructively affect it. The new model paved the way for the development of an integrated salt seawater model that addressed the complex salinity issues involved in these applications

Network Intrusions Classification Using Data Mining Approaches

Intrusion Detection System has an important task in detecting threats or attacks in the computer networks. Intrusion Detection System (IDS) is a network protection device used to identify and check data packets in network traffic. Snort is free software used to detect attacks and protect computer networks. Snort can only detect misuse attacks, whereas to detect anomaly attacks using Bayes Network, Naive Bayes, Random Tree, LMT and J-48 Classification Method. In this paper, the experimental study uses the KDDCUP 99 dataset and the dataset taken from Campus Network. The main objective of this research is to detect deceptive packets that pass computer network traffic. The steps taken in this study are data preparation, data cleaning, dataset classification, feature extraction, rules snort for detecting, and detecting packet as an attack or normal. The result of the proposed system is an accurate detection rate

Network intrusions classification using data mining approaches

Intrusion Detection System has an important task in detecting threats or attacks in the computer networks. Intrusion Detection System (IDS) is a network protection device used to identify and check data packets in network traffic. Snort is free software used to detect attacks and protect computer networks. Snort can only detect misuse attacks, whereas to detect anomaly attacks using Bayes Network, Naive Bayes, Random Tree, LMT and J-48 Classification Method. In this paper, the experimental study uses the KDDCUP 99 dataset and the dataset taken from Campus Network. The main objective of this research is to detect deceptive packets that pass computer network traffic. The steps taken in this study are data preparation, data cleaning, dataset classification, feature extraction, rules snort for detecting, and detecting packet as an attack or normal. The result of the proposed system is an accurate detection rate

Modeling Router Hotspots on Network-On-Chip

A Network-on-Chip (NoC) is a new paradigm in complex System-on-Chip (SoC) designs that provides efficient on-chip communication architecture. It offers scalable communication to SoC and allows decoupling of communication and computation. In NoC, design space exploration is critical due to trade-offs among latency, area, and power consumption. Hence, analytical modeling is an important step for early NoC design. This paper presents a novel top-down approach router model, and utilizes this model for analysis mesh NoC performance measured in terms of throughput, average of queue size, efficiency, and loss and wait time. As case study, the proposed model is used to map a MPEG4 video core to a 4x4 mesh NoC with deterministic routing to measure the overall NoC quality of service, The model is used also to present how much occupancy of average queue size for each router that reduces resources (hardware) area and cost. The accuracy of this approach and its practical use is illustrated through extensive simulation results

An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval

Currently, many companies use data mining for various implementations. One form of implementation is intrusion detection system (IDS). In IDS, the main problem for nuisance network administrators in detecting attacks is false alerts. Regardless of the methods implemented by this system, eliminating false alerts is still a huge problem. To describe data traffic passing through the network, a database of the network security layer (NSL) knowledge discovery in database (KDD) dataset is used. The massive traffic of data sent over the network contains excessive and duplicated amounts of information. This causes the classifier to be biased, reduce classification accuracy, and increase false alert. To that end, we proposed a model that significantly improve the accuracy of the intrusion detection system by eliminating false alerts, whether they are false negative or false positive negative alerts. The results show that the proposed intelligent exoplanet atmospheric retrieval (INARA) algorithm has improved accuracy and is able to detect new attack types efficiently

Examination rain and fog attenuation for path loss prediction in millimeter wave range

The core solution for the congestion bandwidth which are utilizing these days in radio propagation inevitably is to move forward to higher frequencies, which millimeter wave frequencies can provide it as well as satisfy clients requests such as huge capacity and fast data rate. On the other hand, the high vulnerable from complex condition become holdback for instance attenuation which case due to atmospheric absorption such as rain, fog, foliage, oxygen and water vapor. Coverage planning depends on which propagation model use in various atmospheres to accomplish reasonable and dependable system. In this paper we concentrating on rain and fog parameters which are both represent most significant condition variables to encourage the planners of wireless networks to select appropriate model, which can structure and actualize the fitting model. Likewise, we look at among three different models exponential, polynomial and power to utilizing scientific conditions and goodness of fit parameters to demonstrate clearly data curve. The outcome shows that polynomial agreeable model for rainy and foggy atmospheric, and will extend the methodology to other models utilizing real data analysis to obtain comprehensive contributable results, which will conceivably be valuable assessment to millimeter wave frequencies planners

Campus Hybrid Intrusion Detection System Using SNORT and C4.5 Algorithm

The rapid development of the internet greatly helps human work. However, the number of information system security incidents has risen sharply, so that in fact the sides of human life are threatened. Detection techniques against attacks on computer networks must be continuously developed so that integrity, availability, and confidentiality on a computer network become more secure. In general, intrusion detection systems currently use two detection methods, namely anomaly detection, and misuse detection, which both have their own deficiencies. In this paper, the authors built a Hybrid Intrusion Detecting System combines anomaly detection system with the misuse detection system. Snort is used as the basis of misused detection module and Algorithm C4.5 detector is used to construct an anomaly detection module. This system works by creating alerts built from an engine that reads the parameters in the attacker’s IP address. Webmin is used to simplify rule management. Whereas for analyzing logs (attack history), an ACID (Analysis Console for Intrusion Databases) is used. Attack and detection testing are carried out in the campus network of Institut Bisnis dan Informatika Stikom Surabaya. The system implementation uses a PC Router with the Ubuntu 18.04 Linux as the operating system. As a result of implementing this system: the signature of attacks as misuses detection module uses to detection the known attacks; unknown attacks can be detected by the anomaly detection module; signature of attacks that are detected by Anomaly Detection System module extracted by signature generation module, and maps the signatures into snort rules

Seawater for the novelty era of communications-opportunities and challenges

Seawater is an abundant resource that could play an indispensable role in many applications involving electrical conductivity, monitoring security, improved battery power efficiency, and as an antenna transmitter and receiver. Thus in certain circumstances seawater could serve a useful purpose for service providers when other materials are unavailable. The study assesses the comparative strengths and weaknesses of different underwater techniques for creating a large-scale practical utilization while the contribution is to offer a computation method when considering seawater connectivity. The objective of the comprehensive investigation is to examine the signal behaviour for underwater communication to give a remarkable way for developing the new upcoming technology era, particularly in terms of water communication technology. A significant result shows the parameters of the permittivity, salinity, and frequency could notably affect the underwater communication techniques in terms of signal magnitude, phase, and delay rate. The higher frequency leads to more fluctuation in magnitude, while degradation begins after 10 KHz and shows more slope gradient after 100 KHz in the signal phase, added to the notable delay rate that would start after 100 Hz severely