In this paper, classification of brain wave using real-world data from Parkinson's patients in producing an emotional model is presented. Electroencephalograph (EEG) signal is recorded on eleven Parkinson's patients. This paper aims to find the "best" classification for brain wave patterns in patients with Parkinson's disease. This work performed is based on the four phases, which are first phase is raw data and after data processing using statistical features such as mean and standard deviation. The second phase is the sum of hertz, the third is the sum of hertz divided by the number of hertz, and last is the sum of hertz divided by total hertz. We are using five attributes that are patients, class, domain, location, and hertz. The data were classified using WEKA. The results showed that BayesNet gave a consistent result for all the phases from multilayer perceptron and K-Means. However, K-Mean gave the highest result in the first phase. Our results are based on a real-world data from Parkinson's patients.

Mobile reporting applications are useful mainly for reporting real-time issues related to public infrastructure, environmental or social incidents through smart mobile devices. The credibility of the cases reported are often a great challenge because users may report false information and as a result this affects the response team in the aspect of time, energy and other resources. Researchers in the past have developed many report trust estimation algorithms that focuses on user's location, behavior and reputation. We aim to analyze the textual part of a report. Text analyses have been used for email spam filtering and sentiment analysis but have not been used for false report identification. Therefore, the purpose of this study is to compare different text classification algorithms and propose a suitable classifier for distinguishing the genuine and fake reports. The comparative analysis can be used by other researchers in the area of false report or fake message identification.

Barium titanate (BaTiO3) is a perovskite crystal structure and it is well known to have many potential applications in microelectronic industry due to its high capabilities to enhance the performance of the capacitors and other energy storage devices. BaTiO3 has been reported to have a wide band gap around 3.4 eV from previous experimental studies. In theoretical studies, the analysis of the band structure of perovskite type of materials still under investigation due to high disagreement with the experimental result. The objective of this research is to investigate the band gap of the tetragonal BaTiO3 calculated using generalized gradient approximation (GGA) and hybrid functional (HSE03) with various pseudopotential methods performed by CASTEP module. The calculation using GGA show underestimation of energy band gap. However, the band gap calculated using HSE03 approximation shows an agreement with the experimental

Quality-in-Use ( QinU) is one of the most important quality aspects of a web application, which represent users' viewpoint. Measuring QinU gives a strong indicator on the success of web applications. In addition, it has been used frequently to evaluate the overall quality of web applications. There are many studies in QinU that enriched the science of web engineering. However, contributions of these studies were dispersed and usually address a certain aspect of QinU. This study attempts to gather and improve the best contributions of the previous studies in a conceptual framework to evaluate QinU based on ISO/IEC 25010 and 25012 standards. The outcome is a proposed framework, which will demonstrate the procedural flow between different stakeholders (Decision-maker, Evaluator, Developer and End-user). This procedural flow affects the evaluation process of web application quality. Furthermore, the framework demonstrates the process of measuring QinU attributes by implementing the proposed Quality-in-Use Evaluation Model (QinUEM). The future works are to evaluate the Quality-in-Use of several web applications using the proposed conceptual framework and test the results using quantitative and qualitative methods.

Phishing is a threat in which users are sent fake emails that urge them to click a link (URL) which takes to a phisher's website. At that site, users' accounts information could be lost. Many technical and non-technical solutions have been proposed to fight phishing attacks. To stop such attacks, it is important to select the correct feature(s) to detect phishing emails. Thus, the current work presents a new method to selecting more efficient feature in detecting phishing emails. Best features can be extracted from email's body (content) part. Keywords and URLs are known features that can be extracted from email's body part. These two features are very relevant to the three general aspects of email, these aspects are, email's sender, email's content, and email's receiver. In this work, three effectiveness criteria were derived based on these aspects of email. Such criteria were used to evaluate the efficiency of Keywords and URLs features in detecting phishing emails by measuring their Effectiveness Metric (EM) values. The experimental results obtained from analyzing more than 8000 ham (legitimate) and phishing emails from two different datasets show that, relying upon the URLs feature in detecting phishing emails will predominantly give more precise results than relying upon the Keywords feature in a such task.

Currently, mobile botnet attacks have shifted from computers to smartphones due to its functionality, ease to exploit, and based on financial intention. Mostly, it attacks Android due to its popularity and high usage among end users. Every day, more and more malicious mobile applications (apps) with the botnet capability have been developed to exploit end users' smartphones. Therefore, this paper presents a new mobile botnet classification based on permission and Application Programming Interface (API) calls in the smartphone. This classification is developed using static analysis in a controlled lab environment and the Drebin dataset is used as the training dataset. 800 apps from the Google Play Store have been chosen randomly to test the proposed classification. As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. This new classification can be used as the input for mobile botnet detection for future work, especially for financial matters.

Currently, there are many attacks and exploitation to Android smartphones by the attackers all over the world. These attacks are based on profit and caused loss of money and productivity to the victim. This exploitation can be done via camera, SMS, call, audio, image or location exploitation by attacking the system call, permission or API inside the Android smartphone. Therefore, this paper presents 32 mobile malware classification based on system call and permission to detect camera exploitation for Android smartphone. The experiment was conducted in a controlled lab environment, by applying reverse engineering with 5560 training dataset from Drebin, where both static and dynamic analyses were used to identify and extract the permission and system call from the mobile applications (apps). These 32 classification have been evaluated with 500 mobile apps from Google Play Store and 19 mobile apps matched with the classification. This new classification can be used as the database and input for the development of new mobile malware detection model for camera exploitation.

Mobile malware is ubiquitous in many malicious activities such as money stealing. Consumers are charged without their consent. This paper explores how mobile malware exploit the system calls via SMS. As a solution, we proposed a system calls classification based on surveillance exploitation system calls for SMS. The proposed system calls classification is evaluated and tested using applications from Google Play Store. This research focuses on Android operating system. The experiment was conducted using Drebin dataset which contains 5560 malware applications. Dynamic analysis was used to extract the system calls from each application in a controlled lab environment. This research has developed a new mobile malware classification for Android smartphone using a covering algorithm. The classification has been evaluated in 500 applications and 126 applications have been identified to contain malware.

This present paper relates Danger Theory of Artificial Immune Systems, which has been introduced by Polly Matzinger in 1994 with the application in risk assessment. As to relate the concept of Danger Theory in risk assessment, a situation of determination severity level for detected Short Messaging Service (SMS) spam is applied. However, further testing is needed as to demonstrate the explained concept. Danger Model that based on the idea of the immune system is appear to be suitable as the fundamental principles and the most generic available solution as to assimilate its theory into the risk assessment environment especially that involve severe or hazardous impacts. (C) 2015 The Authors. Published by Elsevier B.V.

In this research, a false report identification algorithm for mobile application is developed using a text classification technique. This algorithm is proposed to be applied to a reporting service application in an IoT environment. The algorithm is aimed to distinguish reports into true and false information. Support Vector Machine (SVM) is used as the text classifier because it has proven to be the most popularly used due to its good performance and higher accuracy compared to the other techniques such as Naive Bayes, Decision Tree and K-Nearest Neighbours. The algorithm is designed and developed in R Studio and we built a framework to show how the algorithms can be adapted into a reporting service application. The results show that the algorithm has successfully classified the reports.

Individual user and organizations benefit from cloud computing services, which allow permanent online storage of files. The problem occurs when companies store highly confidential documents in cloud servers. Therefore, this paper aims to introduce a backbone structure for a cloud storage system where the security and personal privacy is highly maximized. It is very obvious that cloud computing servers are highly protected against unauthorized access, but in some cases these files stored can be accessible by the maintenance staffs. Fully protection is needed to ensure that the files stored in the server are only accessible to owners. This paper proposes a system that will employ Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES) combination encryption process using USB device. The files may be accessed in the cloud but all the files will remain encrypted till the USB device is plugged into the computer. The point of applying such method is to fully protect the files and avoid using one single password. The randomly generated passkeys are very complex combinations thus user will not be able to fully memorize them. The proposed system will detect the USB that contains the private-key used for the files to be downloaded from the cloud.

RE as an alternative source of energy in Malaysia is still at premature stage of its development and adoption. This is mainly due to lack of public awareness and participation toward new technology advancement. Therefore, the objective of this paper is to determine and present a proposed theoretical model on the relationship between public participation by measuring their willingness to pay for energy generated from RE resources and its determinants that influenced the willingness to pay level. Awareness on RE, degree of knowledge on RE, willingness to adopt RE technology, environmental concern and attitude toward RE usage are constructed as the five selected determinants. The relationship between the determinants and public participation is illustrated using theoretical framework because it allows the reader to understand how public participation that measured by willingness to pay level are being influenced. Thus, ten hypotheses on the correlation and causal effect between the dependent and independent variables are developed. Quantitative research methods will be used to achieve the research objectives. A questionnaire on 5-point likert scale will be developed and collected from 500 respondents through systematic sampling method at Kiang Valley, Malaysia. Correlation of the variables will be analyzed using SPSS version 20.0 and ordered probit model will be used to measure the causal effect between the variables. These efforts are useful to ensure future success of RE development in Malaysia.

Skew detection and correction of documents is a problematic step in document image analysis. Many methods have been proposed by researchers for estimating the angle at which a document image is rotated (document skew) in binary image documents. Therefore, this paper aims to evaluate the most frequently skew detection techniques cited in the literature which are (i) Projection Profile Analysis (PP), (ii) Hough Transform (HT) and (iii) Nearest Neighbour (NN). This study points out the weaknesses and the strengths of each method and compares the performance of these methods in term of speed and accuracy. The evaluation result shows that in term of speed, the NN technique achieves the fastest time. However, NN performs poorly for the accuracy estimation. PP gives the best angle estimation even though it takes the longest time to execute. Hence, this finding can be used as the basis evaluation review for image analysis researchers in improving the existing technique of skew detection and recommend algorithm with a better accuracy in a shorter time.

Multicollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The wellknown procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.

Most of the existing methods focus on extracting concepts and identifying the hierarchy of concepts. However, in order to provide the whole view of the domain, the non-taxonomic relationships between concepts are also needed. Most of extracting techniques for non-taxonomic relation only identify concepts and relations in a complete sentence. However, the domain texts may not be properly presented as some sentences in domain text have missing or unsure term of concepts. This paper proposes a technique to overcome the issue of missing concepts in incomplete sentence. The proposed technique is based on the similarity precision for selecting missing concept in incomplete sentence. The approach has been tested with Science corpus. The experiment results were compared with the results that have been evaluated by the domain experts manually. The result shows that the proposed method has increased the relationships of domain texts thus providing better results compared to several existing method.

The single layer of ZnSe has been studied to understand the structure using density functional theory. The vibrational frequencies of several cluster of ZnSe have been calculated when the Kohn-Sham equation solved at the ground state energy. We have done the calculation for 34 models of ZnSe clusters but only the stable molecules will be discussed. The bond length, Fermi energy and binding energy of ZnSe clusters have been calculated. The experimental result of single layer of ZnSe shown by Nesheva et. al. using different thickness of layers until 1 mu m [1]. The Raman spectra of ZnSe shown several peak for different thickness. In this paper, we will show the comparison of our calculated result with the experimental Raman spectra to show the existent of cluster.

Image segmentation is an important phase in the image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present an adaptive hybrid analysis based on selected segmentation algorithms. Three designates common approaches, that are Fuzzy c-means, K-means and Mean-shift are adapted. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method will be selected based on the lowest number of regions. The selected approach will be enhanced by applying Median-cut algorithm to further expand the segmentation process. The proposed adaptive hybrid method has shown a significant improvement in the number of regions.

Agent-based IDS is a powerful technique used by network administrator to monitor traffic activities in their network. However, the widespread network coverage has introduced a possibility of a hacker installing unauthorized agents or fake agents secretly within the network. This is considered as a very serious threat to the network security. This paper proposes a protocol that is used to detect the presence of a fake agent upon its installation. The technique is a combination of Elgamal encryption, Elgamal digital signature, and SHA-1 message digest function. A simple implementation was developed to test the proposed protocol.

The paper studied the integration of logarithmic singularity problem J ((y) over bar) = integral integral(del) zeta((y) over bar) log vertical bar(y) over bar = (y) over bar (0)*vertical bar dA, where (y) over bar = (alpha, beta), (y) over bar (0) = (alpha(0), beta(0)), the domain del is rectangle (y) over bar [r(1), r(2)] x [r(3), r(4)]; the arbitrary point (y) over bar is an element of del and the fixed point (y) over bar (0) is an element of del. The given density function zeta((y) over bar), is smooth on the rectangular domain del and is in the functions class C-2,C-tau (del). Cubature formula (CF) for double integration with logarithmic singularities (LS) on a rectangle del is constructed by applying type (0, 2) modified spline function D-Gamma(P). The results obtained by testing the density functions zeta((y) over bar) as linear and absolute value functions shows that the constructed C-F is highly accurate.

Leukemia is a cancer of white blood cells that affect the blood forming cells in the body. Acute Myeloid Leukemia (AML) is a form of leukemia and are caused by replacement of normal bone marrows with leukemic cells, which cause a drop in red blood cells, platelets, and normal white blood cells. Early classification of the subtype of AML cells is necessary for proper treatment management. We classify the subtype based on the features of AML cells, which include the nuclei and cytoplasm. In this paper, we developed an automate method for the nuclei and cytoplasm detection from the blood cells images that are captured as microscope images. In contrast to other methods that focus on identifying the nuclei, we proposed a method based on the color conversion, intensity threshold and gradient magnitude. Our method detected both the nuclei and the cytoplasm at the same time. We test our method on 301 images, which contain 643 AML cells. The accuracy of both nuclei and cytoplasm detection is over 82.9% (increase 17% when was compared with the existent method).