3D printing has emerged as a potential solution to fabricate different RF components and embed prepackaged devices. In this work, 3D printed interposer layer is proposed to enable vertical stacking of different heterogeneous components and substrates. Different design parameters were first simulated using HFSS to optimize for maximum possible bandwidth. After optimization, the design parameters were used to fabricate a wireless battery less digital modulator as an example IoT device. Measurement results are provided to validate the design and simulation results. � 2019 IEEE.

In this paper, Additive Manufacturing (AM) using 3D printing has been shown as a potential candidate for realizing customized compact solutions for RF packaging applications. Cost effective 3D printing based packaging solutions with customized substrates and air gaps allow easier integration of multiple RF components with lower substrate losses. Using a damascene-like conductor patterning process and a LEGO-like assembly process, an amplifier coupled to an air-substrate based patch antenna is demonstrated in a single integrated package. The antenna overlays the amplifier circuit leading to a compact design. The proposed customization of substrates and 3D printing strategies can be extended to multiple-system level stacking for SOP/SIP packaging customized for applications such as 5G. � 2019 IEEE.

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. � Springer International Publishing Switzerland 2015.

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.

A sensitive dip test strip for detection of formalin (HCHO) in tofu samples based on immobilized pararosaniline (PR) has been developed. The test strip has been fabricated by immobilizing PR reagent on filter paper. By using image color analysis (ImageJ) as the average of ?RGB value as color sensor response, the analytical parameters are obtained from an area of the sensing zone of the test strip using digital output of the color scanner. By using this scanometric method, it is possible to determine HCHO as low as 0.05 mg/L with the concentration range of 2-80 mg/L and the response time of 3 minutes. The reproducibility was found to be 8.04% (RSD) for good recovery. The test strip has been successfully applied to detect formaldehyde in tofu samples, and the results show in good agreement with the Nash method using UV/vis spectrophotometry.

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.

Recommendation Systems help users search large amounts of digital contents and identify more effectively the items�products or services�that are likely to be more attractive or useful. As such, it can be characterized as tools that help people making decisions, i.e., make a choice across a vast set of alternatives. This research work has explored decision-making processes in the wide application domain of online services, specifically, hotel booking. This research work is a combination of collaborative filtering (Item-based) recommendation and knowledge-based recommendation system. In which collaborative filtering recommendation will work for user searching and knowledge-based recommendation will work as default recommendation system. In knowledge-based recommendation system it reads the user profile along with his activity of certain last time period as our main knowledge base where this work define the fact of user�s activity. Then this research work applies sorting and counting algorithm. Contextual data are temporarily stored in the knowledge base as the time user stay logged in. Each login will take an updated contextual database. In searching, using item-based k-nearest neighbor algorithm for prediction by collaborative filtering. This work proposed a new rating system which based on hotels performance. � Springer Nature Singapore Pte Ltd. 2019.

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.

The target of botnet attacks has shifted from the personal computers to smartphones and mobile devices due to computational power and functionality of the mobile devices. Mobile botnet is a network consists of compromised mobile devices controlled by a botmaster through a command and control (C&C) network. Nowadays mobile botnets attacks are increasingly being used for advanced political or financial interest. Due to its popularity amongst the mobile operating system, Android has become the most targeted platform by the mobile botnets. The popularity of Android attracts the attackers to develop malicious applications with the botnet capability to hijack users� devices. In this paper, a new Android botnet classification based on GPS exploitation based on permissions and API calls is proposed using feature selection. The training was carried out using malware dataset from the Drebin and tested using 800 mobile apps from the Google Play store. The experiment was conducted using static analysis and open source tools in a controlled lab environment. This new classification can be used as a reference for other researchers in the same field to secure against GPS exploitation from Android botnet attacks. � Springer International Publishing AG 2018.

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.

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. � Springer International Publishing AG 2017.

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.

In the tourism business, there is an urgent need to provide better information accuracy levels. The information accuracy gaps and issues are affecting the tourism stakeholders from making informed decisions. The proposed Tourism Information Accuracy Assessment (TIAA) framework strongly underscores the need for accurate tourism information. The proposed framework is adapted from the ISO/IEC 9126 standard for tourism information resource, environment, assessment and quality process identification and the existing methodologies of information quality assessment in determining the framework information accuracy dimensions, accuracy characteristics and indicator identification. In this article, we will explain the proposed framework in terms of the information accuracy factors, sub-factors, dimensions, accuracy characteristics and accuracy assessments. The framework will be evaluated by tourism experts. The tourism experts� reviews and findings will be discussed in the analysis and results presentation sections. � Springer Nature Switzerland AG 2019.

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.

The internet is ever expanding and online information is booming, making identification and detection of different web information vitally important, particularly those of dark web or Cyber extremists. Webpages with extremist and terrorist content are believed to be main factors in the radicalization and recruitment of disaffected individuals who might be involved in terrorist activities at home or those who fight alongside terrorist groups abroad. In fact, the sheer volume of online data makes it practically impossible for authorities to carry out the individual examination for every webpage, post or conversational thread that might or might not be relevant to terrorism or contain terrorist sympathies. As terrorists exist within every nation and every religion, hence this paper presents a review and systematic analysis of existing webpages on Cyber Terrorists. This include of existing database of Cyber extremists words and existing techniques of web classifier for keywords. Based on this paper systematic analysis, it will be the input for the formation of a new Cyber extremists WorldNet. � 2019 IEEE.