Adaptive User Interface Framework: Android Mobile Platform

adaptational exploiter Interface Framework humanoid runny PlatformMr. Tulip Das twitchAdapting a graphical embrasure (GUI) to a range of resources with completely distinct capabilities is exciting topic of liquid computer. The substance ab exploiter porthole compeld for an industry ought to pass its layout and parts to the exploiter need and changes for every drug substance absubstance ab drug drug user. We propose a perplex for rambling screenings to form the user portholes adaptable for user. This identifies an set aside expertise train to a user by learning his/her history of inter solveion. Dynamic App cutoff is to be provided on Mobile Devices serving to user to swipe the separate m whatsoever time to search out the required app. The prediction model utilizes multiple options together with recency, frequence, duration, time distribution and app sequence launch.KeywordsHCI in Mobile AI and expert establishments adjustive user interface cloth k-means algorithmic rule propellent shortcuts peregrine app usage personalization.)I. IntroductionAn adjustive user interface (also known as AUI) is a user interface (UI) which adapts, that is changes, its layout and elements to the needs of the user or place setting and is similarly alterable by apiece user. These reciprocally reciprocal qualities of both adapting and being adaptable are, in a full-strength AUI, also innate to elements that comprise the interfaces brokers portions of the interface might adapt to and ingrain other portions of the interface. The user adaptation is much a negotiated process, as an adaptive user interfaces designers ignore where user interface components ought to go while affording a means by which both the designers and the user can determine their placement, often (though not al authoritys) in a semi-automated, if not fully automated manner. An AUI is primarily created establish on the features of the system, and the cognition takes of the users that entrust utilize it.Figure 1 Adaptive Graphical exploiter InterfaceThe advantages of an adaptive user interface are found inwardly its ability to conform to a users needs. The properties of an AUI allow showing only relevant instruction ground on the current user. This creates less confusion for less begind users and provides quiet of ariseing throughout a system. Depending on the task, we can increase the stability of a system. An adaptive user interface can be apply in various ways. These implementations can differ between the amount of information available to certain users, or how users utilize the application.Adaptive presentation The refinement behind adaptive presentation is to display certain information based on the current user. This may mean that users with only basic knowledge of a system will only be shown minimal information. Conversely, a user with advanced knowledge will have access to much than detailed information and capabilities. A way that the AUI can achieve this specialisation could be to hide information to be presented based on the users experience take aim. Another possibility is to control the amount of links to relevant sources on the page.Adaptive navigation Adaptive navigation intends to guide a user to their specialised goal within the system by altering the way the system is navigated based on certain factors of the user. These factors can include the users expertise level with the system/subject, the current goal within the system, and other relevant factors. Examples of adaptive navigation can be achieved in many another(prenominal) ways, similar to adaptive presentation. These can include examples such as providing links to help achieve a users specific goal, giving reference on a page to where a user is, or altering the resources available to the user.II. MOTIVATIONIn the last few years, an ecosystem of doojiggers and heterogeneous run has emerged with a huge garland of capacities and characteristi cs. These freshly devices, along with applications and services, must be utilise to enhance the quality of life, making the users occasional activities easier, as well as increasing their personal autonomy.User interfaces in mobile applications are complex since they need to provide sufficient features to variety of users in a restricted space where a small subject of components are available. When user acquires expertise in the system they expect user interfaces which satisfy their unique needs. Therefore, user interfaces in mobile applications should be sufficient to different users. Since this problem exists in various applications a general antecedent is required to make user interfaces adaptive utilize user context history.Figure 2 Different Mobile DevicesIn this sense, there is a clear need for creating interfaces that adapt themselves taking into account characteristics of the user, context, application and device. iodin of the aspects to consider when adapting interf aces is the set of preferences of the user. When using different applications or devices, each user has different preferences, generally related to their limitations.III. Problem StatementUsing mobile and its application is a personalized experience. Each user has different preferences, mainly related to their limitations. Hence it is quite essential to account characteristics of the user, context, application and device while designing a Graphical User Interface for mobile platform. It is quite difficult to manage when there are many applications (apps) installed on a mobile device, the simple task of launching an app could become inconvenient, as the user may need to swipe the screen several times to and the desired app. Hence an adaptive user interface solution for mobile devices, which uses dynamic shortcuts to facilitate app launching is needed. In this context, personalization of applications, i.e. applications that adapt themselves to users capacities and limitations is ess ential.IV. Problem modelA. OverviewRather than providing adaptive user interfaces for a specific mobile application, it is more valuable if it would be a common solution to make any UI adaptive. So it is encouraged to provide a framework which can put a common solution which can be used by all developers to create applications which provide adaptive user interfaces. This framework provides Adaptive User Interfaces based on users experience level. The experience levels are categorize by Inference Engine which is explained in the subsection Inference Engine. The system will learn the user experience level based on user actions performed on each component of the application with the algorithm.Figure 2 Concept of Adaptive User InterfaceFigure 3 mental faculty diagram for the system with Adaptive User InterfaceB. Components of SolutionThe proposed adaptive user interface is mainly focused on hiding aggroup of unwanted components for corresponding experience level of user on that app lication.The framework consists of three main phases such as1. Data preprocessing timbre2. acquirement step3. Execution and variant stepData preprocessing step1. stance Data One of the factors to adapt the UI is the location of the user. This is based on the preface that the compositors case of applications a user is expected to access when at ingleside is different from the type of applications accessed when the user is at work. The location is determined by means of the GPS sensor on the mobile device.2. Device Data turnout of other sensors on the device including the ambient light sensor (to recoup whether the user is indoors or outdoors), accelerometer and gyroscope (to say if the user is nonmoving or moving) can also be used to derive additive contextual information in order to better predict the users chosen application and modify the UI appropriately.3. App usage Data Logs of the past application usage, the frequency at which the particular app was accessed and th e user actions and interactions while using the app can act as another source of contextual information.4. Time Data The type of applications accessed on weekdays might be different from the applications accessed on a weekend or on holidays. Similarly, in the morning the user may access different apps than the ones they do at night. A logging service zip in the device would have to log the types of apps accessed at specific times of day or day or the week, and use it to make the appropriate UI modifications.C. Learning stepThe main purpose of induction locomotive engine is to fool the info provided by the data-preprocessing module and provide an experience level of the user according to the current user context. To infer the experience level of the user, the inference engine should behave as an intelligent system which should be apt by data related to user experience level and user interactions between the applications.Figure 4 A High level computer architecture of adaptive u ser interface frameworkExecution and description step K-means flocking engine is capable of setting the number of clusters needed. When the number of clusters is set, the engine can cluster the dataset when the squared error becomes minimized. This will give each clusters meaning points as output. Once the cluster centers are found these cluster centers will be delivered to user type chooser. User type selector will appoint each experience level to each center sent by K-means clustering engine. occurrently we have manually prescribed the experience level for identified centers using natural knowledge. As mentioned earlier who masters the system can suggest these levels for each cluster values. Current user context data will be feed into the User type selector and user type selector will infer a suitable experience level which is closest. This final output will be delivered to the execution and rendering step.D. ImplementationIn order to very much show the behavior of the frame work a proof of concept (POC) application will be developed. A simple application which can be used as an online ticket reservation system for aircrafts will be developed as the application. This application was developed in Hyper Text Markup address (HTML) and JavaScript. mechanical man platform has given enough features and Application Programming Interfaces (API) to create an Android application using HTML and JavaScript.Android web application can be created by converting a HTML page to an Android web application using WebView class. Currently there are many third party frameworks and plug-ins are available to convert HTML and JavaScript pages to Android application.This application will be connected to adaptive UI framework using a component called UIhooks. UIhooks are any(prenominal) methods which can be used by the developer during the application development. For example these methods can be used when some events are fired on UI components. When UIhooks are called they ar e developed in a way to throwaway the user actions performed on corresponding UI component and store them. For example when a UIhook method is called on a freeing on click event, the Uihooks is implemented to measure how many time the button was clicked and what is the recent time it was used. If UIhook method is called on a textbox on resign event the Uihook can inspect and store the value deferted and the count of submit action performed. This application is sent to a user study to store readying data. This is elaborated more in User study section. The accumulate data were organized and feed to inference engine as the training dataset. Inference engine learned the data as elaborated before and gave the suitable experience level. Once the experience level is feed to the rendering engine it finds the related rendering logics inside the UI clusters. For example if the experience level is provided as intermediate it checks for the corresponding rendering logics and UI clusters . If it is said as If user Type is intermediate render cluster2 it will build a new UI using what is mentioned in cluster2. Then it renders it to the user. When the user is provided with new adaptive UI a question will be provided to the user asking whether they are conform to with the new UI or they want to go back to the earlier stage. This is to measure their satisfactory level and the accuracy of the algorithm predictions.V. Mathematical Modelinglet s (be a main set of) SDB, LDB, C, A, S, MR, AOwhere,SDB is the copy of the server database. This database is trustworthy for storing user information related to cloud interactions.LDB is a set of topical anesthetic database that a user owns. It consists of data tables having data items related to the products and their sales transactions.C is a set of all clients using the server database and mining services from the server. And (c1 , c2 , c3, cn) C.A is a set of algorithms applied on the stimulation data to get mining results.S is the server component of the system. The server is accountable for registering, authenticating and providing associations to the end user.MR is a set of mining rules that are applied on the input dataset provided by the client from his LDB. And (mr1 , mr2 , mr3, mrn) MRAO is a set of associations that are extracted from the input and a form the output of the system.Functionalities SDB = RegisterUser(uid, password, fullname, address, country, contact, email)password = SHA1(input_password)U = AuthenticateUser(uid, password, SDB)LDB1 = ManageProducts(pid, product name, cost)LDB2 = ManageBilling(transactions, items)LDB = LDB1 + LDB2ED(Encoded data) = EncodeTransactions(LDB2, EncodingAlgorithm(EA))UPLOAD(ED)AO = mount Mining(ED)Results = Decode(Download(AO))VI. Results ExpectedFigure 5 Dynamic ShortcutsFigure 6 Adaptive UIVII. ConclusionThe aim of our study was to propose a high level architecture for a framework to provide adaptive user interface for mobile applications. This fram ework includes data preprocessing step, learning step and execution and rendering step to deliver asuitable user interface. The learning is through by an intelligent system which is unsupervised and trained using user context data. This delivers k number of experience levels by clustering the serene data set using K-means and ANN algorithm. It will also allow dynamic shortcuts to facilitate app launching. Some other options to enhance the proposed dynamic shortcuts solution such as gesture based control will also be explored in the future.VIII. ReferencesAztiria, A. Castillejo, E. Almeida, A. Lopez-de-Ipia, D.Adapting User Interfaces Based on User Preferences and Habits, Intelligent Environments (IE), 2014 world-wide Conference on DOI 10.1109/IE.2014.9 Publication class 2014 , Page(s) 9 15Nivethika, M. Vithiya, I. Anntharshika, S. Deegalla, S.Personalized and adaptive user interface framework for mobile application, Advances in Computing, communications and Informatics (IC ACCI), 2013 outside(a) Conference on DOI 10.1109/ICACCI.2013.6637474, Publication Year 2013 , Page(s) 1913- 1918Jain, R. Bose, J. Arif, T. 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