1st IOEMLA Workshop: Accepted Papers and Information
Date of Conference: 16-18 May 2018
Published in: 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)
CONFERENCE LOCATION: Poland, Krakow
Date Added to IEEE Xplore: 23 July 2018
1-Internet of Everything and Machine Learning Applications: Issues and Challenges
The Internet of Things (IoT) is recognized as one of the major key areas of future technology and is gaining vast attention from an extensive range of industries. The sensors and devices are generating massive amounts of high-dimensional and heterogeneous data that need to be stored and processed. Machine learning encompasses the widespread techniques of artificial intelligence that can deduce patterns and relationships from unstructured data. Big data analytics are advanced statistical and predictive analytic methods which are capable of manipulating data in a range of Exabytes and more. This paper presents a review of the applications of the Internet of Everything and the machine learning techniques in the fields of health, the smart electrical grid, and supply chain management. A review of the literature has been conducted to demonstrate the future issues and challenges that researchers will face.
INSPEC Accession Number: 17955541
2-Impact of Web 2.0 Technology on Students with Learning Difficulties: A State-of-the-Art and Future Challenges
Learning difficulties are one of the significant barriers to the children’s educational process. Learning difficulties include several aspects of life, not just learning at school and can affect how basic skills such as reading, writing and math are learned. Further, this problem affects how to learn high-level skills such as organization, time planning, abstract thinking, and long or short-term memory development. Hence, there is an imperative need to address this problem and to provide theoretical and practical solutions to untangle it. This paper proposes a methodology toward helping children to encounter learning difficulties by incorporating Web 2.0 technology. Through six designed phases, the proposed methodology is promising to enhance the learning quality of students with learning difficulties and aims to improve their personal, mental and social development skills.
INSPEC Accession Number: 17936772
3- Analysis of Scientific Production of IoE Big Data Research
The investment on the Internet of Everything is increasing exponentially. This new global technological approach opens the doors for connecting everything and anything. Such massive propagation of Big Data generated from a wide variety of sensors requires consolidated efforts to put forward solutions to address specific related business problems and needs. The aim of this paper is to provide a systematic review of state-of-the-art scientific research on the Internet of Everything and Big Data. Top 20 journals are selected in this study comprising 112 articles in the domain. The paper discusses the results of affiliation analysis, keyword statistics, source statistics, citation analysis and co-citation analysis. The conclusion of this literature review suggests future opportunities and research directions in the era of the Internet of Everything and Big Data.
INSPEC Accession Number:17955535
4- Extending a conventional chatbot knowledge base to external knowledge source and introducing user based sessions for diabetes education
Chatbots or conversational agents are computer programs, which interact with users using natural language through artificial intelligence in a way that the user thinks he is having the dialogue with a human. One of the main limits of a chatbot technology is associated with the construction of its local knowledge base. A conventional chatbot knowledge base is typically hand constructed, which is a very time-consuming process and may take years to train a chatbot in a particular field of expertise. This work presented in this paper extends the knowledge base of a conventional chatbot beyond its local knowledge base to external knowledge source Wikipedia. This has been achieved by using Media Wiki API to retrieve information from Wikipedia when the chatbot’s local knowledge base does not contain the answer to the user query. To make the conversation with chatbot more meaningful with regards to the user’s previous chat sessions, a user-specific session ability has been added to the chatbot architecture. An open source AIML web-based chatbot has been modified and programmed for the use in health informatics domain. The chatbot has been named VDMS – Virtual Diabetes Management System. It is intended to be used by the general community and diabetic patients for diabetes education and management.
INSPEC Accession Number: 17955537
5- A Decision Support System for Selecting Sustainable Materials in Construction Projects
Muhammad Rashid Minhas
Omid Ameri Sianaki
Major construction activity has had a significant effect on the environment, the economy and society in the twenty-first century. The gap between the energy required to supply such construction, and the actual energy produced, particularly in the building sector, has put enormous pressure on the selection of sustainable construction materials. Even though the construction industry continues to place this pressure on natural resources, it has also resulted in an increase in the production of waste material, which is being dumped all over the world. In recent years, a lot of work has been undertaken to develop sustainable buildings/housing. The selection of appropriate sustainable building materials is one of the most important processes in the building design and construction process. Immediate efforts are needed to improve the awareness of sustainable materials among decision makers as there is limited technical support to assist technical experts (i.e. engineers, estimators, architects, draftsmen etc.) in the decision-making process when searching for and selecting new sustainable construction materials and practices for different building components (e.g. walls, roof, slab etc.). Moreover, cyber-physical infrastructure and information technologies are essential to support the sustainability of construction projects; this will be discussed further in this paper.
INSPEC Accession Number: 17955487
6- Data Provenance in the Internet of Things
There is a need to create a trusted and secure IoT environment to share information, create knowledge and perform digital transactions. Trustworthy data collection, mining and fusion are vital for the successful widespread acceptance of IoT applications. It requires not only accurate, secure, and precise data collection; but also provisioning of data provenance throughout the whole life-cycle of the IoT device. To this end, this paper introduces a provenance-based trust management solution which helps in establishing a trust relationship among communicating devices in the IoT. This work extends the Internet of Things Management Platform (IoT-MP) by assuring data provenance. Thus complementing the previous IoT-MP capabilities in preserving the privacy of users in the IoT.
INSPEC Accession Number: 17936805
7- A Deep Learning Framework to Enhance Software Defined Networks Security
Software-Defined Networks (SDN) initiates a novel networking model. SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. The architecture enhances the network resilient, decompose management complexity, and support more straightforward network policies enforcement. However, the model suffers from severe security threats. Specifically, a centralized network controller is a precious target for two reasons. First, the controller is located at a central point between the application and data planes. Second, a controller is software which prone to vulnerabilities, e.g., buffer and stack overflow. Hence, providing security measures is a crucial procedure towards the fully unleash of the new model capabilities. Intrusion detection is an option to enhance networking security. Several approaches were proposed, for instance, signature-based, and anomaly detection. Anomaly detection is a broad approach deployed by various methods, e.g., machine learning. For many decades intrusion detection solution suffers performance and accuracy deficiencies. This paper revisits network anomalies detection as recent advances in machine learning particularly deep learning proofed success in many areas like computer vision and speech recognition. The study proposes an intrusion detection framework based on unsupervised deep learning algorithms.
INSPEC Accession Number: 17936805