Real time detection of malicious webpages using machine learning techniques

In today's Internet, online content and especially webpages have increased exponentially. Alongside this huge rise, the number of users has also amplified considerably in the past two decades. Most responsible institutions such as banks and governments follow specific rules and regulations regarding conducts and security. But, most websites are designed and developed using little restrictions on these issues. That is why it is important to protect users from harmful webpages. Previous research has looked at to detect harmful webpages, by running the machine learning models on a remote website. The problem with this approach is that the detection rate is slow, because of the need to handle large number of webpages. There is a gap in knowledge to research into which machine learning algorithms are capable of detecting harmful web applications in real time on a local machine. The conventional method of detecting malicious webpages is going through the black list and checking whether the webpages are listed. Black list is a list of webpages which are classified as malicious from a user's point of view. These black lists are created by trusted organisations and volunteers. They are then used by modern web browsers such as Chrome, Firefox, Internet Explorer, etc. However, black list is ineffective because of the frequent-changing nature of webpages, growing numbers of webpages that pose scalability issues and the crawlers' inability to visit intranet webpages that require computer operators to login as authenticated users. The thesis proposes to use various machine learning algorithms, both supervised and unsupervised to categorise webpages based on parsing their features such as content (which played the most important role in this thesis), URL information, URL links and screenshots of webpages. The features were then converted to a format understandable by machine learning algorithms which analysed these features to make one important decision: whether a given webpage is malicious or not, using commonly available software and hardware. Prototype tools were developed to compare and analyse the efficiency of these machine learning techniques. These techniques include supervised algorithms such as Support Vector Machine, Naïve Bayes, Random Forest, Linear Discriminant Analysis, Quantitative Discriminant Analysis and Decision Tree. The unsupervised techniques are Self-Organising Map, Affinity Propagation and K-Means. Self-Organising Map was used instead of Neural Networks and the research suggests that the new version of Neural Network i.e. Deep Learning would be great for this research. The supervised algorithms performed better than the unsupervised algorithms and the best out of all these techniques is SVM that achieves 98% accuracy. The result was validated by the Chrome extension which used the classifier in real time. Unsupervised algorithms came close to supervised algorithms. This is surprising given the fact that they do not have access to the class information beforehand

Change Management and Organization Performance: Pre- Post Case Study at Federal Ministry of Health, Ethiopia

The purpose of this study is to investigate the pre and post implementation of change management (BSC and strategic planning) on performance of Ethiopian Federal Ministry of Health. Both qualitative and quantitative approaches are employed. The data are gathered from employees of the Ministry through survey questionnaires, and from directorate heads through interviews and focus group discussions. The researcher identified that the major bottleneck for implementation of strategic planning and BSC are lack of adequate resources for training and software, extent of staff participation and lack of adequate resources The research established that the balanced scorecard is a useful tool. It helps organizations to turn visions into reality with accuracy and efficiency. Strategy and BSC implementation therefore require that all business units, support units and employees be aligned and linked to the strategy and scorecard. The study concludes that the main objective of the Balanced ScoreCard is to bring the different perspectives (finance, internal business processes, learning, growth and clients) together in a uniform system. The study recommends organizations to adopt balanced scorecard and strategic planning for measuring performance and for better progress. But it should make deep investigation in implementing any management tools. Taking one or more countries as reference is not enough rather it is better to see or analyse the environment in which the organization is working. Finally the paper provides information and suggestions that are helpful for companies that are interested in developing strategic planning and balanced scorecard. Keywords Balanced Scorecard, Strategic planning and organizational performanc

Identification of Anesthesia Stages from EEG Signals using Wavelet Entropy and Backpropagation Neural Network

This study focuses on entropy based analysis of EEG signals for extracting features for a neural network based solution for identifying anesthetic levels. The process involves an optimized back propagation neural network with a supervised learning method. We provided the extracted features from EEG signals as training data for the neural network. The target outputs provided are levels of anesthesia stages. Wavelet analysis provides more effective extraction of key features from EEG data than power spectral density analysis using Fourier transform. The key features are used to train the Back Propagation Neural Network (BPNN) for pattern classification network. The final result shows that entropybased feature extraction is an effective procedure for classifying EEG data

Clustering websites using a MapReduce programming model

In this paper, we describe an effective method of using Self-Organizing Map (SOM) to group websites so as to eliminate or at least ease up slow speed, one of the fundamental problems, by using a MapReduce programming model. The proposed MapReduce SOM algorithm has been successfully applied to cluB, which is a typical SOM tool. Performance evaluation shows the proposed SOM algorithm took less time to complete computational processing (i.e. distributed computing) on large data sets in comparison with conventional algorithms, and performance improved by up to 20 percent with increasing nodes (computers)

Foreign body aspiration masquerading respiratory tract infection

Foreign body (FB) aspiration is a potentially life threatening event where prompt and precise action can turn tears into smiles. We report a case of an eight-year school going boy, with one-month old history of foreign body aspiration. The boy was treated as a case of respiratory tract infection. It was due to reappearance of symptoms and signs of chest infection supported by chest radiography that prompted for the CT-chest. It was followed by rigid bronchoscopy to confirm the therapeutic diagnosis of FB aspiration. This case report highlights the importance of detailed thoughtful history in pediatrics particularly to FB aspiration

Spectrophotometric determination of thrombin in pure samples and biological fluids using π-acceptors

Thrombin is the central enzyme of coagulation. It is engaged in opposing functions in blood. As a procoagulant factor, it converts fibrinogen into an insoluble fibrin clotand activate platelet, as anticoagulant when it activates Protein C. This knowledge is used for the pharmacologic control of blood coagulation, so monitoring its activity is reliable indicator of the rate and extent of coagulation. A simple, rapid, sensitive and accurate spectrophotometric method is suggested for the determination of thrombin in pure form and in biological fluids. The utility of someπ-acceptors as 2,3-dichloro-5,6-dicyanobenzoquinone(DDQ), 7,7,8,8-tetracyanoquinodimethane (TCNQ) and tetracyanoethylene (TCNE) for thrombin (as electron donor)determination is described. These π-acceptors give highly coloured complex species that have been spectrophotometrically studied. The optimum experimental conditions for these CT reactions have been studied carefully. Beer’slaw is obeyed over the concentration ranges of 10-130,50-150 and 10-100 μg ml-1 thromb in using DDQ, TCNQ and TCNE reagents, respectively. The percentage recovery amounts to 99.33-100.1% (SD = 0.032-0.075), 99.50-102.5% (SD = 0.016-0.076) and 99.5-101.4% (SD = 0.034-0.088) for four to six experiments. The reagents are utilized for the determination of thrombin in poor platelet plasma of dialysis patients with a percentage recovery amount to 98.76-103.3% (for n = 5). No endogenous compounds were found to interfere. The results obtained applying theπ-acceptors reagents are comparable with those obtained by the official method

Identification of Anesthesia Stages from EEG Signals using Wavelet Entropy and Backpropagation Neural Network

This study focuses on entropy based analysis of EEG signals for extracting features for a neural network based solution for identifying anesthetic levels. The process involves an optimized back propagation neural network with a supervised learning method. We provided the extracted features from EEG signals as training data for the neural network. The target outputs provided are levels of anesthesia stages. Wavelet analysis provides more effective extraction of key features from EEG data than power spectral density analysis using Fourier transform. The key features are used to train the Back Propagation Neural Network (BPNN) for pattern classification network. The final result shows that entropybased feature extraction is an effective procedure for classifying EEG data

Phytochemistry and Pharmacological Review: Spathodeacampanulata

Spathodeacampanulata (S.campanulata) belongs to the family Bignoniaceae, commonly known as the Fountain tree, African tulip tree, Flame-of-the forest. S.campanulata parts of the plant such as flowers, leaves, stem, bark, and roots are used for anti-malaria, healing of wound, diureticanalgesic and anti-inflammatory activities in folk medicine. The S.campanulata is known to possess various therapeutic properties have been reported for possessing anti-inflammatory, analgesic, cytotoxic, anti-diabetic, and anticonvulsant activity. Phytochemical study shows the presence of various secondary metabolites like alkaloids, tannins, flavonoids, glycosides, and sterols. This review aims to provide detailed information regarding geographical distribution, phytochemicals, and pharmacological properties of the S.campanulata

LUNG FUNCTION EVALUATION THROUGH SPIROMETRY IN SMOKERS OF BALOCHISTAN UNIVERSITY STUDENTS

Objectives:The objective of the research was to evaluate lung feature amongst students who smoke and nonsmokers. Material and Method:The exploration was led in the University of Balochistan, Quetta, Pakistan. The self-planned examination form and Spirometer were utilized. The total 100 male student’s age amass between 20-45 years who smoking one year or more were chosen. The students were isolated into two gatherings as takes after; (1). Test/Case Group; which comprise of 50 students. (2). Control group; this gathering additionally comprise of 50 students. The meeting was led and Spirometry test was performed for both gatherings' students of University of Balochistan, Quetta, Pakistan. The spirometer considerations; FVC, FEV1, PEFR, FEV1/FVC proportion and FEF25-75% were originated and investigated. The frequency, percent, mean and standard deviation were perceived for smokers and the non-smokers by methods for SPSS 22. Result:The anticipated mean±standard deviation estimation of FVC for smokers was 62.54±17.048 and estimation of FVC for non-smokers was 66.56±12.654. The estimation of FEV1 for smokers was 46.00±13.595 and FEV1 for non-smokers was 74.60±12.638. The estimation of FEV1/FVC proportion for smokers was 74.20±11.433 and FEV1 for non-smokers was 113.58±12.634. The estimation of PEF for smokers was 61.42±19.037 and the estimation of PEF for non-smokers was 87.10±13.368. The estimation of FEF2575 for smokers was 81.16±28.287 and the estimation of FEF2575 for non-smokers was 104.44±23.213. Conclusion:Smoking deleteriously affects the wellbeing, essentially on aspiratory capacities. Consequently, the danger of respirational mortality or dismalness is extraordinary by way of smoking. The investigation inferred that the smoker's students were on more danger of lung illnesses than the non-smokers students and along these lines elevates smoking suspension endeavors to lessen the weight of COPD in the group. Keywords: Spirometry, Lung Function, Smokers, Non-Smokers, Students, University of Balochistan, Pakista

CORRELATION OF UNIAXIAL COMPRESSIVE STRENGTH WITH BRAZILIAN TENSILE STRENGTH AND INDEX PROPERTIES FOR SOFT SEDIMENTARY ROCKS

Sedimentary Rocks composing the Thar Lignite basin Pakistan are of clastic origin. These rocks have comparatively low uniaxial compressive strength (UCS) values, and hence recognized as ‘soft sedimentary rocks’ in this study. UCS is a fundamental property of rocks, used by mine design engineers in designing the surface and underground excavations. The purpose of this study is to investigate the relationship between UCS with Brazilian tensile strength (BTS), and index properties of soft sedimentary rock formations at Thar Lignite basin. Various correlations between mechanical and physical properties of rocks have been developed previously. However, no significant correlation has been developed on UCS with BTS and index properties for soft sedimentary rocks. Numerous Rock samples from Two complete geotechnical drillholes at Block-IX Thar Coalfield were selected. Standard test procedures were implemented to determine the UCS, indirect Tensile and index properties such as point load strength, and shore Scleroscope hardness. The correlations between rock properties were established using simple and multiple regression techniques, and empirical equations were obtained. These equations can be used to predict the UCS and tensile strength of soft sedimentary rocks by performing simple index tests; which are quick, economical, and easier to be performed on the site