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  • 1.
    Chowdury, Mohammad Salah Uddin
    et al.
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Bin Emranb, Talha
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Ghosha, Subhasish
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Pathak, Abhijit
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Alama, Mohd. Manjur
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Absar, Nurul
    BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    IoT Based Real-time River Water Quality Monitoring System2019In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 155, p. 161-168Article in journal (Refereed)
    Abstract [en]

    Current water quality monitoring system is a manual system with a monotonous process and is very time-consuming. This paper proposes a sensor-based water quality monitoring system. The main components of Wireless Sensor Network (WSN) include a microcontroller for processing the system, communication system for inter and intra node communication and several sensors. Real-time data access can be done by using remote monitoring and Internet of Things (IoT) technology. Data collected at the apart site can be displayed in a visual format on a server PC with the help of Spark streaming analysis through Spark MLlib, Deep learning neural network models, Belief Rule Based (BRB) system and is also compared with standard values. If the acquired value is above the threshold value automated warning SMS alert will be sent to the agent. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered. Therefore, our proposed system will immensely help Bangladeshi populations to become conscious against contaminated water as well as to stop polluting the water.

  • 2.
    Elgendy, Nada
    et al.
    Department of Business Informatics & Operations Management, German University in Cairo (GuC).
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Big Data Analytics in Support of the Decision Making Process2016In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 100, p. 1071-1084Article in journal (Refereed)
    Abstract [en]

    Information is a key success factor influencing the performance of decision makers, specifically the quality of their decisions. Nowadays, sheer amounts of data are available for organizations to analyze. Data is considered the raw material of the 21st century, and abundance is assumed with today's 15 billion devices [aka Things!] already connected to the Internet. Accordingly, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such rapidly changing data of high volume, velocity, variety, veracity, and value by using big data analytics. This paper aims to research how big data analytics can be integrated into the decision making process. Accordingly, using a design science methodology, the “Big – Data, Analytics, and Decisions” (B-DAD) framework was developed in order to map big data tools, architectures, and analytics to the different decision making phases. The ultimate objective and contribution of the framework is using big data analytics to enhance and support decision making in organizations, by integrating big data analytics into the decision making process. Consequently, an experiment in the retail industry was administered to test the framework. Accordingly, results showed added value when integrating big data analytics into the decision making process.

  • 3.
    El-Telbany, Ola
    et al.
    German University in Cairo, Al-Tagamoa Al-Khames New Cairo City, Cairo .
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Gamification of Enterprise Systems: A Lifecycle Approach2017In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 121, p. 106-114Article in journal (Refereed)
    Abstract [en]

    Introducing an Enterprise Resource Planning (ERP) system within an organization can bring many benefits and paybacks, yet an effective implementation of a fully functioning ERP system is still a challenge, the odds are high the costly investment might turn into an implementation failure or even lead to bankruptcy. To prevent such situations, organizations need to go through several changes, and carefully manage the critical success factors affecting each stage of the ERP implementation lifecycle, respectively. Previous studies have observed that the majority of the challenges faced during the implementation of ERP systems arise from social and organizational aspects rather than technical ones. This is where gamification comes to the rescue. This research adopts a design science paradigm in an attempt to develop a gamified process for the ERP lifecycle, to ease and enhance the ERP implementation process. The objective of this research is threefold. Firstly, to explore the benefits ERP systems can render via the gamification of the ERP lifecycle. Secondly, to pinpoint the ERP lifecycle phases that are most likely to benefit from gamification. Thirdly, to gamify these formerly identified phases; that were found to be mostly affected by gamification, and test for the impact of gamification on them.

  • 4.
    Haddara, Moutaz
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Elragal, Ahmed
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    The Readiness of ERP Systems for the Factory of the Future2015In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 64, p. 721-728Article in journal (Refereed)
    Abstract [en]

    In 2011, at the Hanover Fair, the term Industry 4.0 was first coined. In October 2012, the Working Group on Industry 4.0, presented a set of implementation recommendations to the German government. The term Industry 4.0 initiates from a project in the high-tech strategy of the German government. Such project advocates the computerization of the manufacturing industry. It is also known as the 4th industrial revolution. Precisely speaking, industry 4.0 is based on the technological concepts of cyber-physical systems, Internet of Things (IoT), which enables the Factory of the Future (FoF). Within the modular structured smart factories of Industry 4.0, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the IoT, Cyber-physical systems communicate and cooperate with each other and with humans in real time. Enterprise resource planning (ERP) systems are considered the backbone for the Industry 4.0. Thus, this paper attempts to answer the research question: “Are today’s ERP systems ready for the FoF?”. We have conducted interviews with manufacturers, ERP vendors, and partners in order to check on the readiness of ERP systems for the FoF. Our results show that ERP systems are ready for the FoF.

  • 5.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    On bidirectional transitions between localist and distributed representations: The case of common substrings search using Vector Symbolic Architecture2014In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 41, p. 104-113Article in journal (Refereed)
    Abstract [en]

    The contribution of this article is twofold. First, it presents an encoding approach for seamless bidirectional transitions between localist and distributed representation domains. Second, the approach is demonstrated on the example of using Vector Symbolic Architecture for solving a problem of finding common substrings. The proposed algorithm uses elementary operations on long binary vectors. For the case of two patterns with respective lengths L1 and L2 it requires Θ(L1 + L2 – 1) operations on binary vectors, which is equal to the suffix trees approach – the fastest algorithm for this problem. The simulation results show that in order to be robustly detected by the proposed approach the length of a common substring should be more than 4% of the longest pattern.

  • 6.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Björk, Magnus
    Luleå tekniska universitet.
    Toresson, Henrik
    Luleå tekniska universitet.
    Öberg, Anton
    Luleå tekniska universitet.
    Fly-The-Bee: A game imitating concept learning in bees2015In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 71, p. 25-30Article in journal (Refereed)
    Abstract [en]

    This article presents a web-based game functionally imitating a part of the cognitive behavior of a living organism. This game is a prototype implementation of an artificial online cognitive architecture based on the usage of distributed data representations and Vector Symbolic Architectures. The game emonstrates the feasibility of creating a lightweight cognitive architecture, which is capable of performing rather complex cognitive tasks. The cognitive functionality is implemented in about 100 lines of code and requires few tens of kilobytes of memory for its operation, which make the concept suitable for implementing in low-end devices such as minirobots and wireless sensors.

  • 7.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Gayler, Ross W.
    La Trobe University.
    Recognizing permuted words with Vector Symbolic Architectures: A Cambridge test for machines2016In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 88, p. 169-175Article in journal (Refereed)
    Abstract [en]

    This paper proposes a simple encoding scheme for words using principles of Vector Symbolic Architectures. The proposed encoding allows finding a valid word in the dictionary for a given permuted word (represented using the proposed approach) using only a single operation - calculation of Hamming distance to the distributed representations of valid words in the dictionary. The proposed encoding scheme can be used as an additional processing mechanism for models of word embedding, which also form vectors to represent the meanings of words, in order to match the distorted words in the text to the valid words in the dictionary.

  • 8.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Rachkovskij, Dmitri A.
    International Research and Training Center for Information Technologies and Systems.
    Modification of Holographic Graph Neuron using Sparse Distributed Representations2016In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 88, p. 39-45Article in journal (Refereed)
    Abstract [en]

    This article presents a modification of the recently proposed Holographic Graph Neuron approach for memorizing patterns of generic sensor stimuli. The original approach represents patterns as dense binary vectors, where zeros and ones are equiprobable. The presented modification employs sparse binary distributed representations where the number of ones is less than zeros. Sparse representations are more biologically plausible because activities of real neuronsare sparse. Performance was studied comparing approaches for different sizes of dimensionality.

  • 9.
    Thaduri, Adithya
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Railway Assets: A Potential Domain for Big Data Analytics2015In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 53, p. 457-467, article id 53Article in journal (Refereed)
    Abstract [en]

    Two concepts currently at the leading edge of todays information technology revolution are Analytics and Big Data. The public transportation industry has been at the forefront in utilizing and implementing Analytics and Big Data, from ridership forecasting to transit operations Rail transit systems have been especially involved with these IT concepts, and tend to be especially amenable to the advantages of Analytics and Big Data because they are generally closed systems that involve sophisticated processing of large volumes of data. The more that public transportation professionals and decision makers understand the role of Analytics and Big Data in their industry in perspective, the more effectively they will be able to utilize its promise. This paper gives an overview of Big Data technologies in context of transportation with specific to Railways. This paper also gives an insight on how the existing data modules from the transport authority combines Big Data and how can be incorporated in providing maintenance decision making.

1 - 9 of 9
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