Analytics on malicious android applications

The widespread of mobile applications has led to increase smartphone malware. Detecting malware requires extracting features to determine the malware apps from non-malware apps. To understand malware apps' features, we need a better understanding of the requested permissions in manifest file of apk file. In this paper, we present our framework based on extracting apk's permissions with the aims to detect the malware upon granted permissions in mobile app. The permissions keywords are extracted from the manifest file of apk file using VirusTotal website. These collected applications and their permissions keywords will go through pre-data analytics process before being trained to various machine learning classifiers. We collected around 30 apps from Google play as non-malware apps and 30 malicious apps from different sources such as PROGuard, Contagio Mobile blog and the Drebin dataset. The permissions keywords of the collected apk are extracted and saved to build final dataset that contains 50 samples of benign and malignant applications with the final collections of permissions keywords. Finally, the dataset is fed to machine learning. By utilizing several classifiers such as NaiveBayes, sequential minimal optimization (SMO), Decision Table, ZeroR and Decision trees (J48 and Random Forests, the results show that sequential minimal optimization (SMO) classifier achieved high performance in the detection rate of the classifier with an acceptable accuracy of 76 %

Large mediastinal mass in a 15-year-old boy

Hyperimmunoglobulin E syndrome is a rare multisystem inherited disorder characterised by high serum IgE levels, skin disorder causing eczema, dermatitis, recurrent staphylococcal infections and pulmonary infections and various skeletal and connective tissue abnormalities. Common presentation is with recurrent skin and sinopulmonary infections. Several features unrelated to immune system such as characteristic facial features, hyperextensibility of joints, multiple bone fractures and craniosynostosis have been described in the literature. We describe a rare presentation of this disease with invasive aspergillosis presenting as mediastinal mass with extension to mediastinalstructures and pulmonary vasculature

Post tuberculosis radiological sequelae in patients treated for pulmonary and pleural tuberculosis at a tertiary center in Pakistan

Treating tuberculosis (TB) is not the end of the disease because of the wide spectrum of post TB sequelae associated with the disease. There is insufficient data on post TB radiological sequelae. The aim of this study is to evaluate the post TB radiological sequelae on chest x-rays in patients who had completed the treatment for pulmonary and pleural TB at a tertiary care hospital of a high TB burden country. This is a retrospective cross-sectional study conducted on patients treated for pulmonary and pleural TB. Adult patients (18 years or above) with a clinical or microbiological diagnosis of pulmonary or pleural TB were included. Patients were classified on the basis of site of TB into pulmonary and pleural TB. Post-treatment radiological sequelae on chest x-ray were evaluated and divided into three main types i.e. fibrosis, bronchiectasis and pleural thickening. During the study period a total of 321 patients were included with a mean age of 44(SD±19) years. Only 17.13% (n=55) patients had normal chest x-rays at the end of treatment and 82.87% (n=266) patients had post-TB radiological sequelae with fibrosis being the most common followed by pleural thickening. The post TB radiological sequelae were high in patients who had diabetes mellitus (78.94%), AFB smear-positive (90.19%), AFB culture-positive (89.84%), Xpert MTB/Rif positive (88.40%) and with drug-resistant TB (100%). As a clinician, one should be aware of all the post TB sequelae so that early diagnosis and management can be facilitated

KNOWLEDGE FOR INVESTMENT IN ISLAMIC CAPITAL MARKET AND ISLAMIC STOCKS FOR THE YOUNG GENERATION TO MITIGATE FRAUDULENT INVESTMENT

Abstrak: Perkembangan dari teknologi informasi dan komputer mengakibatkan akses informasi menjadi sangat mudah. Oleh karenanya, generasi muda yang saat ini akrab dengan penggunaan tekonologi informasi dan komputer tersebut menjadi sangat optimal menggunakannya untuk berbagai keperluan, termasuk investasi. Tujuan dari pengabdian masyarakat yang dilakukan oleh dosen Universitas Mercu Buana berkolaborasi dengan dosen dari Universiti Sains Malaysia bertujuan untuk memperkenalkan pasar modal dan saham syariah sebagai salah satu alternatif investasi yang aman bagi para generasi muda. Metode yang digunakan pada kegiatan pengabdian masyarakat ini adalah sosialisasi maupun penyuluhan dimana objek dari kegiatan ini adalah siswa sekolah menengah atas kejuruan. Adapun jumlah peserta dari kegiatan ini berjumlah 129 siswa. Hasil dari kegiatan ini adalah peserta dapat mengetahui dan memahami produk pada pasar modal khususnya adalah saham syariah. Implikasi dari kegiatan pengabdian ini adalah meningkatnya literasi keuangan syariah dari generasi muda. Selain itu, berdasarkan hasil evaluasi diketahui bahwa dari seluruh peserta yang hadir sejumlah 97% atau sejumlah 125 peserta yang menyatakan kegiatan sosialisasi ini meninggkatkan literasi mereka terkait dengan keuangan syariah.Abstract: The development of information technology and computers has resulted in easy access to information. Therefore, the young generation who are currently familiar with information technology and computers have become optimal to use it for various purposes, including investment. The purpose of community service carried out by lecturers at Mercu Buana University in collaboration with lecturers from Universiti Sains Malaysia aims to introduce the capital market and sharia stocks as an alternative to safe investment for the young generation. The method used in this community service activity is socialization and counseling, where the object of this activity is vocational high school students. The number of participants in this activity amounted to 129 students. The result of this activity is that participants can find out and understand products in the capital market, especially sharia stocks. The implication of this service activity is the increase in Islamic financial literacy of the young generation. In addition, the evaluation results found that 97% of all participants or 125 participants stated that this socialization activity had increased their literacy related to Islamic finance

Using K-fold cross validation proposed models for SpikeProp learning enhancements

Spiking Neural Network (SNN) uses individual spikes in time field to perform as well as to communicate computation in such a way as the actual neurons act. SNN was not studied earlier as it was considered too complicated and too hard to examine. Several limitations concerning the characteristics of SNN which were not researched earlier are now resolved since the introduction of SpikeProp in 2000 by Sander Bothe as a supervised SNN learning model. This paper defines the research developments of the enhancement Spikeprop learning using K-fold cross validation for datasets classification. Hence, this paper introduces acceleration factors of SpikeProp using Radius Initial Weight and Differential Evolution (DE) Initialization weights as proposed methods. In addition, training and testing using K-fold cross validation properties of the new proposed method were investigated using datasets obtained from Machine Learning Benchmark Repository as an improved Bohte's algorithm. A comparison of the performance was made between the proposed method and Backpropagation (BP) together with the Standard SpikeProp. The findings also reveal that the proposed method has better performance when compared to Standard SpikeProp as well as the BP for all datasets performed by K-fold cross validation for classification datasets

Text content analysis for illicit web pages by using neural networks

Illicit web contents such as pornography, violence, and gambling have greatly polluted the mind of web users especially children and teenagers. Due to the ineffectiveness of some popular web filtering techniques like Uniform Resource Locator (URL) blocking and Platform for Internet Content Selection (PICS) checking against today's dynamic web contents, content based analysis techniques with effective model are highly desired. In this paper, we have proposed a textual content analysis model using entropy term weighting scheme to classify pornography and sex education web pages. We have examined the entropy scheme with two other common term weighting schemes that are TFIDF and Glasgow. Those techniques have been tested with artificial neural network using small class dataset. In this study, we found that our proposed model has achieved better performance in terms accuracy, convergence speed, and stability compared to the other techniques

Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis

The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to  show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm

Detection of bacterial load in drinking water samples by 16s rRNA ribotyping and RAPD analysis

Background: Safe and healthy drinking water is inaccessible to more than 20% of the world population. Among some major risks to safety of potable water, contamination with pathogenic microorganisms is the most alarming and harmful Therefore, it is needed to develop and implement fast and accurate methods for the detection of bacterial contamination in water. Methods: Biological analysis of drinking water samples obtained from nine different collection points of Lahore city was carried out and total of six different bacterial strains were isolated. Biochemical characterization was done under standard laboratory conditions. Molecular identification of these isolates was done by using random amplified polymorphic DNA (RAPD) analysis. Results: The drinking water sample collected from Punjab University showed highest bacterial count 1066/0.5 ml of drinking water while residential area of University of the Punjab contained least number of bacterial counts i.e., 38/0.5 ml of drinking water. Amplification patterns of isolates SZ1, SZ3, SZ4 and SZ6 obtained by RAPD were found similar to genus Bacillus. While, SZ2 and SZ5 had unique amplification patterns identical to Bacillus megaterium. All the six bacterial strains were tested for the presence of protease, lipase, cellulase, and amylase. Strain SZ2 gave positive result for all of them except amylase.Conclusion: Tube well water of Punjab University area of Lahore is safe for drinking purpose except admin block tube. It is recommended to monitor the bacteriological load of drinking water at regular intervals in order to control water borne bacterial diseases

Shrinkage and Consolidation Characteristics of Chitosan-Amended Soft Soil: A Sustainable Alternate Landfill Liner Material

Kuttanad is a region that lies in the southwest part of Kerala, India, and possesses soft soil, which imposes constraints on many civil engineering applications owing to low shear strength and high compressibility. Chemical stabilizers such as cement and lime have been extensively utilized in the past to address compressibility issues. However, future civilizations will be extremely dependent on the development of sustainable materials and practices such as the use of bio-enzymes, calcite precipitation methods, and biological materials as a result of escalating environmental concerns due to carbon emissions of conventional stabilizers. One such alternative is the utilization of biopolymers. The current study investigates the effect of chitosan (biopolymer extracted from shrimp shells) in improving the consolidation and shrinkage characteristics of these soft soils. The dosages adopted are 0.5%, 1%, 2%, and 4%. One-dimensional fixed ring consolidation tests indicate that consolidation characteristics are improved upon the addition of chitosan up to an optimum dosage of 2%. The coefficient of consolidation increases up to seven times that of untreated soil, indicating the acceleration of the consolidation process by incorporating chitosan. The shrinkage potential is reduced by 11% after amendment with 4% chitosan and all the treated samples exhibit zero signs of curling. Based on the findings from consolidation and shrinkage data, carbon emission assessments are carried out for a typical landfill liner amended with an optimum dosage of chitosan. In comparison to conventional stabilizers like cement and lime, the results indicate that chitosan minimized carbon emissions by 7.325 times and 8.754 times, respectively