Cost effective & energy efficient intelligent smart home system based on loT

Smart Home reduces the need for active involvement of users in the process of monitoring and regulating household appliances. This study provides a method for developing Smart Home Energy Efficiency applications through integration of IoT (Internet of Things) with IP-based switching of Web services as well as Cloud Technology. The methodology embeds intelligent systems and machine learning into the design in addition to using the Poisson process along with the actuators and sensors in Arduino environment. Moreover, we apply the use fault detection to validate the efficiency and feasibility of the Smart Home implementation by assessing the home environments, taking care of home equipment, appliances and regulating home access.Akıllı Ev, kullanıcıların ev aletlerini izleme ve düzenleme sürecinde aktif katılım ihtiyacını azaltır. Bu çalışma, IoT'nin (Nesnelerin İnterneti) Internet tabanlı IP servislerinin yanı sıra Bulut Teknolojisi ile entegrasyonuyla Akıllı Ev Enerjisi Verimliliği uygulamalarının geliştirilmesi için bir yöntem sunmaktadır. Metodoloji, Poisson sürecinin Arduino ortamındaki aktüatörler ve sensörler ile birlikte kullanılmasının yanı sıra, akıllı sistemler ve makine öğrenmesini tasarıma dahil eder. Ayrıca, Akıllı Ortam uygulamasının etkinliğini ve uygulanabilirliğini doğrulamak için, ev ortamlarını değerlendirerek, ev eşyalarına, cihazlara ve ev erişimini düzenleyerek kullanım hatası tespitini sağlar

A web-based AI assistant Application using Python and JavaScript

Our research is mainly based on a chatbot which is powered by Artificial Intelligence. Nowadays, Artificial Intelligence assistants such as Apple’s Siri, Google’s Now and Amazon’s Alexa are currently fast-growing and widely integrated with many smart devices. These assistants are built with the primary purpose of being personal assistants for every individual user in certain contexts. In this research, we would highlight the development process of the chatbots, features, problems, case studies and limitations. This research delivers the information, helps developers to build answer bots and integrate chatbots with business accounts. The aim is to assist users and allow transactions between client companies and their customers. As a result, users can accomplish results to queries as well as clients can grow their business

Teenustele orienteeritud ja tõendite-teadlik mobiilne pilvearvutus

Arvutiteaduses on kaks kõige suuremat jõudu: mobiili- ja pilvearvutus. Kui pilvetehnoloogia pakub kasutajale keerukate ülesannete lahendamiseks salvestus- ning arvutusplatvormi, siis nutitelefon võimaldab lihtsamate ülesannete lahendamist mistahes asukohas ja mistahes ajal. Täpsemalt on mobiilseadmetel võimalik pilve võimalusi ära kasutades energiat säästa ning jagu saada kasvavast jõudluse ja ruumi vajadusest. Sellest tulenevalt on käesoleva töö peamiseks küsimuseks kuidas tuua pilveinfrastruktuur mobiilikasutajale lähemale? Antud töös uurisime kuidas mobiiltelefoni pilveteenust saab mobiilirakendustesse integreerida. Saime teada, et töö delegeerimine pilve eeldab mitmete pilve aspektide kaalumist ja integreerimist, nagu näiteks ressursimahukas töötlemine, asünkroonne suhtlus kliendiga, programmaatiline ressursside varustamine (Web APIs) ja pilvedevaheline kommunikatsioon. Nende puuduste ületamiseks lõime Mobiilse pilve vahevara Mobile Cloud Middleware (Mobile Cloud Middleware - MCM) raamistiku, mis kasutab deklaratiivset teenuste komponeerimist, et delegeerida töid mobiililt mitmetele pilvedele kasutades minimaalset andmeedastust. Teisest küljest on näidatud, et koodi teisaldamine on peamisi strateegiaid seadme energiatarbimise vähendamiseks ning jõudluse suurendamiseks. Sellegipoolest on koodi teisaldamisel miinuseid, mis takistavad selle laialdast kasutuselevõttu. Selles töös uurime lisaks, mis takistab koodi mahalaadimise kasutuselevõttu ja pakume lahendusena välja raamistiku EMCO, mis kogub seadmetelt infot koodi jooksutamise kohta erinevates kontekstides. Neid andmeid analüüsides teeb EMCO kindlaks, mis on sobivad tingimused koodi maha laadimiseks. Võrreldes kogutud andmeid, suudab EMCO järeldada, millal tuleks mahalaadimine teostada. EMCO modelleerib kogutud andmeid jaotuse määra järgi lokaalsete- ning pilvejuhtude korral. Neid jaotusi võrreldes tuletab EMCO täpsed atribuudid, mille korral mobiilirakendus peaks koodi maha laadima. Võrreldes EMCO-t teiste nüüdisaegsete mahalaadimisraamistikega, tõuseb EMCO efektiivsuse poolest esile. Lõpuks uurisime kuidas arvutuste maha laadimist ära kasutada, et täiustada kasutaja kogemust pideval mobiilirakenduse kasutamisel. Meie peamiseks motivatsiooniks, et sellist adaptiivset tööde täitmise kiirendamist pakkuda, on tagada kasutuskvaliteet (QoE), mis muutub vastavalt kasutajale, aidates seeläbi suurendada mobiilirakenduse eluiga.Mobile and cloud computing are two of the biggest forces in computer science. While the cloud provides to the user the ubiquitous computational and storage platform to process any complex tasks, the smartphone grants to the user the mobility features to process simple tasks, anytime and anywhere. Smartphones, driven by their need for processing power, storage space and energy saving are looking towards remote cloud infrastructure in order to solve these problems. As a result, the main research question of this work is how to bring the cloud infrastructure closer to the mobile user? In this thesis, we investigated how mobile cloud services can be integrated within the mobile apps. We found out that outsourcing a task to cloud requires to integrate and consider multiple aspects of the clouds, such as resource-intensive processing, asynchronous communication with the client, programmatically provisioning of resources (Web APIs) and cloud intercommunication. Hence, we proposed a Mobile Cloud Middleware (MCM) framework that uses declarative service composition to outsource tasks from the mobile to multiple clouds with minimal data transfer. On the other hand, it has been demonstrated that computational offloading is a key strategy to extend the battery life of the device and improves the performance of the mobile apps. We also investigated the issues that prevent the adoption of computational offloading, and proposed a framework, namely Evidence-aware Mobile Computational Offloading (EMCO), which uses a community of devices to capture all the possible context of code execution as evidence. By analyzing the evidence, EMCO aims to determine the suitable conditions to offload. EMCO models the evidence in terms of distributions rates for both local and remote cases. By comparing those distributions, EMCO infers the right properties to offload. EMCO shows to be more effective in comparison with other computational offloading frameworks explored in the state of the art. Finally, we investigated how computational offloading can be utilized to enhance the perception that the user has towards an app. Our main motivation behind accelerating the perception at multiple response time levels is to provide adaptive quality-of-experience (QoE), which can be used as mean of engagement strategy that increases the lifetime of a mobile app

Comparative Evaluation for the Performance of Big Stream Processing Systems

Andmete hulk kasvab tänapäeval meeletu kiirusega ning seda andmete hulka tuleb korrektselt töödelda, et saavutada kontroll andmete üle. Antud olukord sunnib meid mõtlema andmevoo töötlemise peale. Enamasti nõuavad andmemahuline pettuse tuvastus-, kaubandus-, tootmis-, sõjanduse ja luure süsteemid pidevat andmete analüüsi (reaalajas). Sellist tüüpi süsteemid nõuavad kõrgetasemel ist mustrite sobitamist ja korrelatsioone. Aja jooksul on ilmnenud erinevaid andmevoo töötlemise võimalusi. Antud lõputöös tehakse jõudlustest Apache Flink, Apache Storm, Heron, Kafka ja Apache Spark andmevoo töötlemismootoritega ning tulemusi võrreldakse ja vastandatakse omavahel. Nendes rakendustes ja domeenides on väga oluline nõue koguda, menetleda ning analüüsida olulisi andmevooge, et eraldada sealt väärtusliku informatsiooni. Antud magistritöö eesmärk on läbi viia empiiriline hindamine ning võrdlemine kõrgtasemel andmevoo töötlemissüsteemide vahel.Nowadays data is growing with tremendous acceleration, and this growing data must be processed properly if we want to have control over it. It pushes us to think about data stream processing. Most of the time, a data-intensive fraud detecting, trading, manufacturing, military and intelligence systems require processing data immediately (real-time). These kinds of systems need considerably ssophisticated pattern matching and correlations. However, other uses of stream processing have also emerged over time. In this thesis, we will benchmark to compare and contrast Apache Flink, Apache Storm, Heron, Kafka an Apache Spark stream processing engines. In these applications and domains, there is a crucial requirement to collect, process, and analyze significant streams of data to extract valuable information. This thesis aims to conduct an empirical evaluation and benchmarking of the state-of-the-art of big stream processing systems

Evaluation of alternate programming languages to JavaScript

The development of web applications for desktop and mobile has surged in recent years. The most popular web scripting language is JavaScript because all the browsers support it and its role as a scripting language of the WWW. It is a powerful and flexible language. However, it also has some shortcomings. For this reason, over the last few years many different web scripting languages have appeared, they give the solutions to the shortcomings of JavaScript. In this thesis a number of emerging web scripting languages are surveyed and the most popular option, CoffeeScript, TypeScript and Dart, are evaluated in detailed level. We will explain what a scripting language is and how it works, JavaScript‘s problems in developing a web application, list of available scripting languages for web clients, the motivation behind these languages and their features that they add to JavaScript. In order to show the results, an example web application is developed in all the languages. The main conclusion extracted of this thesis is that these languages address the shortcomings of the JavaScript such as they all have the compile time checking for errors, CoffeeScript adds the syntactic sugar to JavaScript syntax, object-orientation, inheritance. TypeScript and Dart have the type checking, modules and generics. Dart also supports the concurrency with isolates. It is easy to develop and maintain the complex and large scale applications in these languages

Evaluation of alternate programming languages to JavaScript

The development of web applications for desktop and mobile has surged in recent years. The most popular web scripting language is JavaScript because all the browsers support it and its role as a scripting language of the WWW. It is a powerful and flexible language. However, it also has some shortcomings. For this reason, over the last few years many different web scripting languages have appeared, they give the solutions to the shortcomings of JavaScript. In this thesis a number of emerging web scripting languages are surveyed and the most popular option, CoffeeScript, TypeScript and Dart, are evaluated in detailed level. We will explain what a scripting language is and how it works, JavaScript‘s problems in developing a web application, list of available scripting languages for web clients, the motivation behind these languages and their features that they add to JavaScript. In order to show the results, an example web application is developed in all the languages. The main conclusion extracted of this thesis is that these languages address the shortcomings of the JavaScript such as they all have the compile time checking for errors, CoffeeScript adds the syntactic sugar to JavaScript syntax, object-orientation, inheritance. TypeScript and Dart have the type checking, modules and generics. Dart also supports the concurrency with isolates. It is easy to develop and maintain the complex and large scale applications in these languages

Smart Property Valuation. Problem Solving for Industry

The analysis of this project it is used the CRISP-DM method. Smart Property Valuation (SPV) is a fictional company created by David Silva, Luiz Dias, and Raul Fuzita to analyse, explore, and prove the conception of a model capable of predicting or estimating prices for properties. They believe they can benefit common people, realtors, and construction companies with their solutions. This research is for educational purposes and should be treated as such