A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction data

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness.

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Improving Building Energy Efficiency through Measurement of Building Physics Properties Using Dynamic Heating Tests

© 2019 the author. Licensee MDPI, Basel, Switzerland.Buildings contribute to nearly 30% of global carbon dioxide emissions, making a significant impact on climate change. Despite advanced design methods, such as those based on dynamic simulation tools, a significant discrepancy exists between designed and actual performance. This so-called performance gap occurs as a result of many factors, including the discrepancies between theoretical properties of building materials and properties of the same materials in buildings in use, reflected in the physics properties of the entire building. There are several different ways in which building physics properties and the underlying properties of materials can be established: a co-heating test, which measures the overall heat loss coefficient of the building; a dynamic heating test, which, in addition to the overall heat loss coefficient, also measures the effective thermal capacitance and the time constant of the building; and a simulation of the dynamic heating test with a calibrated simulation model, which establishes the same three properties in a non-disruptive way in comparison with the actual physical tests. This article introduces a method of measuring building physics properties through actual and simulated dynamic heating tests. It gives insights into the properties of building materials in use and it documents significant discrepancies between theoretical and measured properties. It introduces a quality assurance method for building construction and retrofit projects, and it explains the application of results on energy efficiency improvements in building design and control. It calls for re-examination of material properties data and for increased safety margins in order to make significant improvements in building energy efficiency.Peer reviewedFinal Published versio

Технологія управління знаннями про віртуальне просування

The article presents a new concept of “Management of knowledge about virtual promotion” on the Internet. Usually a real product or service is being divided into four components (product, price, promotion and place) in accordance with the theory of marketing. One of the components is a product promotion. But now this element is becoming a fully virtual tool. It is necessary to consider product promotion as an image or a copy of a real product in a virtual space that lives in parallel on the network. Therefore, the objective of the paper is the presentation of a new object of research based on the experience of more than thirty real projects performed in Ukraine, USA, Europe and Canada. We regard the promotion as a software product, which works according to principles of knowledge management and machine learning. It is proposed that virtual promotion is characterized by four views: customer or user, data, technology and marketing. Thus, the structure of virtual promotion business process was presented. It includes four steps: selection of hypertext sources, knowledge representation and extraction, semantic kernel building and quality criterion evaluation to stop the process. Based on the process structure the research tasks were identified. The central task is semantic kernel forming. Then the software architecture was developed. IT solution contains CRM system as accounting tool and Web site as an image of virtual promotion. CRM plays main role as a commander center. Here we form semantic kernel and then send it via marketing channels such as Web site, telegram or viber accounts. Another part of IT solution is Web service such as Bing API or Google API. They help us to build the kernel. Also the paper demonstrates the list of future tasks that should be solved and the example of real project of proposed approach.У статті представлена нова технологія «управління знаннями про віртуальне просування» в мережі Інтернет. Звичайно реальний продукт або послуга характеризуються наступними компонентами: продукт, ціна, просування і місце, згідно з теорією маркетингу. Одним з компонентів є просування товару. Однак зараз цей елемент стає повністю віртуальним інструментом. Необхідно розглядати просування продукту як відображення або копію реального продукту у віртуальному просторі. Це відображення існує паралельно у мережі та безпосередньо впливає на реальний продукт чи послугу. Тому ціллю статті є презентація нового об’єкта дослідження, поява якого основана на досвіді виконання більш ніж тридцяти реальних проектів в Україні, США, Європі та Канаді. Ми працюємо відповідно до принципів управління знаннями і машинного навчання. Передбачається, що віртуальне просування характеризується чотирма репрезентаціями: клієнт або користувач, дані, технологія та маркетинг. Далі була представлена структура бізнес–процесу віртуального просування. Він включає чотири етапи: вибір джерел гіпертексту, подання та витяг знань, побудова семантичного ядра і оцінка критерію якості для зупинки процесу. На основі структури процесу були визначені задачі дослідження. Центральна задача – формування семантичного ядра. Потім була розроблена архітектура програмного забезпечення. ІТ рішення містить CRM систему в якості інструменту обліку та Веб сайт як образ віртуального просування. CRM грає роль командного центру. Тут формується семантичне ядро і потім відправляється через маркетингові канали, такі як Веб сайт, Телеграм канали або профілі в Вайбері. Інша частина ІТ рішення – це Веб сервіс, такий як Bing API або Google API. Вони допомагають нам побудувати ядро. Також в статті наведено список майбутніх завдань, які необхідно вирішити, і приклад реальних проектів в рамках запропонованого підходу

Efficient Fuel Consumption Minimization for Green Vehicle Routing Problems using a Hybrid Neural Network-Optimization Algorithm

Efficient routing optimization yields benefits that extend beyond mere financial gains. In this thesis, we present a methodology that utilizes a graph convolutional neural network to facilitate the development of energy-efficient waste collection routes. Our approach focuses on a Waste company in Tromsø, Remiks, and uses real-life datasets, ensuring practicability and ease of implementation. In particular, we extend the dpdp algorithm introduced by Kool et al. (2021) [1] to minimize fuel consumption and devise routes that account for the impact of elevation and real road distance traveled. Our findings shed light on the potential advantages and enhancements these optimized routes can offer Remiks, including improved effectiveness and cost savings. Additionally, we identify key areas for future research and development

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information