Rehabilitation in healthcare systems provides therapy and training to restore quality life after certain illness, addiction or accident. Currently, there is a significant unmet need for rehabilitation services, and it is frequently undervalued in the health system. As populations age and the prevalence of noncommunicable diseases and injuries increases, and the demand for rehabilitation grows, strengthening rehabilitation in health systems becomes ever more paramount. Conventional rehabilitation service can be costly and time consuming to people not living near the rehabilitation centres. Often people with lower income who live in rural areas refuse rehabilitation services due to the logistic issue. On the other hand, in urban areas, most people are too busy with their daily activities and unable to keep their therapy schedule consistently. In consequence, patients do not enjoy the actual benefit of rehabilitation because of a certain limitation such as cost and time. These limitations can be surpassed by putting telerehabilitation, an emerging area of performing rehabilitation medical treatment with the use of technology from a distance into practice. Scientific studies of telerehabilitation in the current literature are discussing primarily in the area of readiness, technologies, and illnesses such as children autism and heart failure. However, there is limited evidence about telerehabilitation service for stroke patients, concentrating on the pre-implementation and implementation stages, which makes this paper viable and significant, potentially for telerehabilitation implementation in Malaysia. This paper proposes a new conceptual model of hybrid telerehabilitation that combines several technological principles, such as cloud, virtual reality, and computer-based system. The model was derived based on a study of a public tertiary hospital in Malaysia. The current traditional system was investigated and discussed. A new telerehabilitation model is proposed to widen the access for rehabilitation to patients of all ages. � 2019 IEEE.

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 result. � 2017 Author(s).

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. � 2015 IEEE.

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. � 2014 IEEE.

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. 2017 IEEE.

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. Copyright International Association of Engineers.

SMS spamming is an activity of sending �unwanted messages� through text messaging or other communication services; normally using mobile phones. Nowadays there are many methods for SMS spam detection, ranging from the list-based, statistical algorithm, IP-based and using machine learning. However, an optimum method for SMS spam detection is difficult to find due to issues of SMS length, battery and memory performances. Hoping to minimize the aforementioned problems, this paper introduces another detection variance that is based on common characters used when sending SMS (i.e. numbers and symbols), SMS length and keywords. To verify our work, the proposed features were stipulated into five different algorithms and then, tested with three different datasets for their ability to detect spam. From the conduct of experiments, it can be suggested that these three features are reasonable to be used for detecting SMS spam as it produced positive results. In the future, it is anticipated that the proposed algorithm will perform better when combined with machine learning techniques. � Springer International Publishing Switzerland 2016.

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. � 2015 IEEE.

Phishing is a cybercrime in which, Internet users are delivered, commonly through emails, to simulated websites where they could be lured to disclose their personal information for attackers’ benefit. Phishing attacks were increased about 60% in the second half of 2013 over what was seen in first half of the same year. Although the number of proposed anti-phishing solutions, phishers still able to bypasses anti-phishing systems, in many cases, through users’ inattention behaviour. Therefore, phishing becomes a layered problem that require addressing issues at both of technical and non-technical (human) layers. Numerous of client-side toolbars were proposed as a technical solution to combat phishing attacks at user’s layer. Anti-phishing toolbars however, still unable to completely protect users from phishing attacks. This paper has reviewed theses toolbars to provide a clear understanding about their performance and limitation points. Such a review is required to draw clear directions of future solutions to alliviate these limitations.

Spam these days has become a definite nuisance to mobile users. Provision of Short Messages Services (SMS) has been intruded, in line with an advancement of mobile technology by the emergence of SMS spam. This issue has not only cause distressing situation but also other serious threats such as money loss, fraud, and false news. The focus of this study is to excavate the features extraction in classifying SMS spam messages at users� end. Its objective is to study the discriminatory control of the features and considering its informative or influence factor in classifying SMS spam messages. This study has been conducted by gathering research papers and journals from numerous sources on the subject of spam classification. The discovery offers a motivational effort for further execution in a wider perspective of combating spam such as measurement of spam�s risk level. � Springer Nature Singapore Pte Ltd. 2016.

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. 2015 IEEE.

Nowadays, mobile botnet has become as one of the most dangerous threats for smartphone. It has the capabilities of committing many criminal activities, such as remote access, Denial of Service (DoS), phishing, spreading malwares, stealing information and building mobile devices for illegitimate exchange of information and it is crucial to have an efficient mobile botnet detection mechanism. Therefore, this research paper presents a systematic review analysis of root exploitation for mobile botnet detection and a proof of concept how the mobile botnet attacks. This proof of concept includes analysis of mobile botnet sample using reverse engineering technique and static analysis. Springer International Publishing Switzerland 2016.

This research is concerned with the design and evaluation of an Electronic Laboratory Notebook (ELN) for the simulation of chemical processes. It provides quality provenance information for scientists wishing to compare simulation runs with in vitro experimental data. The research is grounded in the EUROCHAMP-2 community. The community uses a benchmark Master Chemical Mechanism (MCM) in order to undertake their model simulations. The goal is to ensure that, for each atmospheric chamber experiment, a computational model is evaluated/developed using the MCM. This paper builds on earlier research on an ELN for this community. The main contributions are: (1) a re-engineered ELN co-developed by EUROCHAMP-2 chemists and computer scientists where the focus is to enable community scientists to capture and extract useful provenance information, (2) a novel Inline Provenance Node Navigator (IPNav) is provided which enables a modeller to navigate through the trail of the runs as a simulation is created, and (3) the results of an initial user evaluation of the ELN undertaken with two practising modellers. � 2011 IEEE.

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?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. � 2014 AIP Publishing LLC.

This paper presents an approach for tackling multiple metrics and various network parameters simultaneously to investigate the performance of wireless ad-hoc network routing protocols. The study uses Taguchi's loss function to determine the best condition in yielding maximum throughput and minimum packet drop and routing overhead simultaneously for dynamic source routing (DSR) protocol. Studying the parameters one at a time or by trial and error until a first feasible design is found seems to be a common approach to design optimization. In this paper, the impact of ad-hoc network parameters such as terrain, network size, number of sources, packet transmitted rates, node speed and pause time on a multiple signal to noise ratio are investigated simultaneously. We present results based on simulation study according to Taguchi experimental design technique. Optimum levels of ad-hoc network parameters are suggested by using response table and response graph while contribution of significant parameter are estimated using analysis of variance (ANOVA). The results show that the terrain size, number of sources, transmission packet rates and nodes speed are the significant parameters affecting DSR performance in wireless ad-hoc networks. � 2011 IEEE.

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-I message digest function. A simple implementation was developed to test the proposed protocol. 2008 IEEE. DOI 10.1109/CIT.2008.Workshops.122.

A surveyof amphibians and reptiles at Tasik Pergau, Kelantan was conducted from 30 until 31 October 2019 and 22 until 23 November 2019. Four sampling locations were surveyed, namely Long 1 & 2, Suda, Renyok, and Terang intake. The study was conducted using the Visual Encounter Survey method, an active sampling technique that is efficient for short observations. A total of 164 individuals were recorded which comprised of sixamphibian families and six reptiles families. The amphibian families recorded are Bufonidae, Dicroglossidae, Megophryidae, Microhylidae, Ranidae and Rhacophoridae. A total of 19 amphibian species of and eight reptile species were recorded during the sampling period. In addition, this study has recorded about 12 new records of amphibians and reptiles in Pergau area. These species are Limnonectes dionodon, L. malesianus, L. plicatellus, Megophrys nasuta, Hylarana erythraea, H. signata, Polypedates discantus, Calotes emma, Cyrtodactylus pulchellus, Eutropis macularia, Ahaetulla prasina and Xenochrophis trianguligerus,adding up to the total number of amphibians and reptiles to 42 species. Further studies need to be conducted intensive and extensively to obtain more information regarding amphibians and reptiles in Pergau area. � Published under licence by IOP Publishing Ltd.

The paper studied the integration of logarithmic singularity problem J(y)=???(y)log|y-y0?|dA, where y=(?,?), y0=(?0,?0) the domain ? is rectangle ? = [r1, r2] � [r3, r4], the arbitrary point y? and the fixed point y0?. The given density function ?(y), is smooth on the rectangular domain ? and is in the functions class C2,? (?). Cubature formula (CF) for double integration with logarithmic singularities (LS) on a rectangle ? is constructed by applying type (0, 2) modified spline function D?(P). The results obtained by testing the density functions ?(y) as linear and absolute value functions shows that the constructed CF is highly accurate. � 2016 Author(s).

Baseline estimation is an important pre-processing step for many methods in optical character recognition system such as character segmentation, detection and correction of skew and feature extraction methods. In this paper, a new baseline estimation method for Arabic handwritten text is proposed based on analysis and extraction the feature points from the subword of the thinned text. A comprehensive set of experimental results using IFN/ENIT database which is specifically for Arabic optical character recognition handwritten demonstrate the efficiency of the proposed method in overcome the failure and weaknesses in the existing methods.

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). � 2016 TSI Enterprise Inc (TSI Press).