Improved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithm

The Media Access Control (MAC) layer of the wireless protocol, Institute of Electrical and Electronics Engineers (IEEE) 802.11, is based on the exchange of request and response messages. Probe Request Flooding Attacks (PRFA) are devised based on this design flaw to reduce network performance or prevent legitimate users from accessing network resources. The vulnerability is amplified due to clear beacon, probe request and probe response frames. The research is to detect PRFA of Wireless Local Area Networks (WLAN) using a Supervised Feedforward Neural Network (NN). The NN converged outstandingly with train, valid, test sample percentages 70, 15, 15 and hidden neurons 20. The effectiveness of an Intruder Detection System depends on its prediction accuracy. This paper presents optimisation of the NN using Genetic Algorithms (GA). GAs sought to maximise the performance of the model based on Linear Regression (R) and generated R > 0.95. Novelty of this research lies in the fact that the NN accepts user and attacker training data captured separately. Hence, security administrators do not have to perform the painstaking task of manually identifying individual frames for labelling prior training. The GA provides a reliable NN model and recognises the behaviour of the NN for diverse configurations

An ANFIS approach to transmembrane protein prediction

This paper is concerned with transmembrane prediction analysis. Most of novel drug design requires the use of Membrane proteins. Transmembrane protein structure allows pharmaceutical industry to design new drugs based on structural layout. However, laboratory experimental structure determination by X-ray crystallography is difficult to be achieved as the hydrophobic molecules do not crystalize easily. Moreover, the sheer number of proteins demands a computational solution to transmembrane regions identifications. This research therefore presents a novel Adaptive Neural Fuzzy Inference System (ANFIS) approach to predict and analyze of membrane helices in amino acid sequences. The ANFIS technique is implemented to predict membrane helices using sliding window data capturing. The paper uses hydrophobicity and propensity to encode the datasets using the conventional one letter symbol of amino acid residues. The computer simulation results show that the offered ANFIS methodology predicts transmembrane regions with high accuracy for randomly selected proteins

NN approach and its comparison with NN-SVM to beta-barrel prediction

This paper is concerned with applications of a dual Neural Network (NN) and Support Vector Machine (SVM) to prediction and analysis of beta barrel trans membrane proteins. The prediction and analysis of beta barrel proteins usually offer a host of challenges to the research community, because of their low presence in genomes. Current beta barrel prediction methodologies present intermittent misclassifications resulting in mismatch in the number of membrane spanning regions within amino-acid sequences. To address the problem, this research embarks upon a NN technique and its comparison with hybrid- two-level NN-SVM methodology to classify inter-class and intra-class transitions to predict the number and range of beta membrane spanning regions. The methodology utilizes a sliding-window-based feature extraction to train two different class transitions entitled symmetric and asymmetric models. In symmet- ric modelling, the NN and SVM frameworks train for sliding window over the same intra-class areas such as inner-to-inner, membrane(beta)-to-membrane and outer-to-outer. In contrast, the asymmetric transi- tion trains a NN-SVM classifier for inter-class transition such as outer-to-membrane (beta) and membrane (beta)-to-inner, inner-to-membrane and membrane-to-outer. For the NN and NN-SVM to generate robust outcomes, the prediction methodologies are analysed by jack-knife tests and single protein tests. The computer simulation results demonstrate a significant impact and a superior performance of NN-SVM tests with a 5 residue overlap for signal protein over NN with and without redundant proteins for pre- diction of trans membrane beta barrel spanning regions

Al-Quran learning using mobile speech recognition:an overview

The usage of mobile application in various aspects has been worldwide accepted and there are variety of mobile applications which developed to cater the usage of different background of the user. In this paper, a short survey which includes questionnaire is distributed to find the interest of user whom using application for learning Quran and concept of mobile speech apps. The main interest of this survey is to find the acceptance of user and explanation on the proposed usage of mobile speech recognition with feature of learning apps. Factors of mobile speech recognition and mobile learning are listed to support the results from the short survey

An Improved Animal Model of Multiple Myeloma Bone Disease

Multiple myeloma (MM) is a plasma cell malignancy that causes an accumulation of terminally differentiated monoclonal plasma cells in the bone marrow, accompanied by multiple myeloma bone disease (MMBD). MM animal models have been developed and enable to interrogate the mechanism of MM tumorigenesis. However, these models demonstrate little or no evidence of MMBD. We try to establish the MMBD model with severe bone lesions and easily accessible MM progression. 1 x 10(6) luciferase-expressing 5TGM1 cells were injected into 8-12 week-old NOD SCID gamma mouse (NSG) and C57BL/KaLwRij mouse via the tail vein. Myeloma progression was assessed weekly via in vivo bioluminescence (BL) imaging using IVIS-200. The spine and femur/tibia were extracted and scanned by the micro-computer tomography for bone histo-morphometric analyses at the postmortem. The median survivals were 56 days in NSG while 44.5 days in C57BL/KaLwRij agreed with the BL imaging results. Histomorphic and DEXA analyses demonstrated that NSG mice have severe bone resorption that occurred at the lumbar spine but no significance at the femur compared to C57BL/KaLwRij mice. Based on these, we conclude that the systemic 5TGM1 injected NSG mouse slowly progresses myeloma and develops more severe MMBD than the C57BL/KaLwRij model

Functional MRI using robotic MRI compatible devices for monitoring rehabilitation from chronic stroke in the molecular medicine era (Review)

The number of individuals suffering from stroke is increasing daily, and its consequences are a major contributor to invalidity in today’s society. Stroke rehabilitation is relatively new, having been hampered from the longstanding view that lost functions were not recoverable. Nowadays, robotic devices, which aid by stimulating brain plasticity, can assist in restoring movement compromised by stroke-induced pathological changes in the brain which can be monitored by MRI. Multiparametric magnetic resonance imaging (MRI) of stroke patients participating in a training program with a novel Magnetic Resonance Compatible Hand-Induced Robotic Device (MR_CHIROD) could yield a promising biomarker that, ultimately, will enhance our ability to advance hand motor recovery following chronic stroke. Using state-of-the art MRI in conjunction with MR_CHIROD-assisted therapy can provide novel biomarkers for stroke patient rehabilitation extracted by a meta-analysis of data. Successful completion of such studies may provide a ground breaking method for the future evaluation of stroke rehabilitation therapies. Their results will attest to the effectiveness of using MR-compatible hand devices with MRI to provide accurate monitoring during rehabilitative therapy. Furthermore, such results may identify biomarkers of brain plasticity that can be monitored during stroke patient rehabilitation. The potential benefit for chronic stroke patients is that rehabilitation may become possible for a longer period of time after stroke than previously thought, unveiling motor skill improvements possible even after six months due to retained brain plasticity

Compact Polarization Diversity Antenna for 28/38 GHz Bands

In this paper, design and analysis of a millimeter wave dual- and dual-polarized antenna for 5G millimeter communications system is presented. The proposed design has a compact structure with size of 5 × 5 mm 2 . It consists of a rectangular patch with a crossed-slot etched off in the patch to reduce the interference between the two targeted 5G bands of 28 and 38 GHz. To achieve dual polarization performance, the radiating patch is fed by two different 50-Ω microstrip transmission lines. The antenna has -10dB impedance bandwidths of 2.6GHz (26.8-29.4 GHz) and 2.5GHz (37.7-40.2GHz) to cover 28/38 GHz mobile communication bands respectively. The antenna has the merits of miniaturized dimensions, stable broadside radiation patterns with high gains and low cross polarization in both bands of operation

A survey on sentiment analysis in Urdu: A resource-poor language

© 2020 Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis