FollowMe: A Bigraphical Approach

In this paper we illustrate the use of modelling techniques using bigraphs to specify and refine elementary aspects of the FollowMe framework. This framework provides the seamless migration of bi-directional user interfaces for users as they navigate between zones within an intelligent environment

Towards FollowMe User Profiles for Macro Intelligent Environments

We envision an Ambient Intelligent Environment as an environment with technology embedded within the framework of that environment to help enhance an users experience in that environment. Existing implementations , while working effectively, are themselves an expensive and time consuming investment. Applying the same expertise to an environment on a monolithic scale is very inefficient, and thus, will require a different approach. In this paper, we present this problem, propose theoretical solutions that would solve this problem, with the guise of experimentally verifying and comparing these approaches, as well as a formal method to model the entire scenario

Towards Simple and Effective Formal Methods for Intelligent Environments

In this paper we motivate and illustrate the use of bigraphs as a formal framework and methodology for the description, design and analysis of intelligent environment systems. Through a series of examples, we provide an overview of bigraphs, their composition, their evolution under reaction rules, and their refinement. We argue that bigraphs offer several advantages: first, they are intuitive and lie close to the topic of investigation, second, they are relatively simple to understand and deploy (in contrast to the systems they may analyse), third, they offer a means to tame complexity through multiple description at different levels of abstraction, fourth, and finally, the system itself can be usefully used without having to engage with its mathematical foundations

Assessing the effects of information and communication technologies on organizational development: business values perspectives

Information and communication technology (ICT) projects for organizational development deal with market challenges, information handling, and the integration of multiple information systems (IS) in an organization. This research investigates how ICT projects (IS systems, etc.) affect the strategic, social, and human development in an organization. Previous studies have highlighted the advantages of ICT portfolio management techniques and return on investment approaches; the current research focused primarily on measuring business value on investment perspective. Therefore, based on the findings from the literature review, an integrated framework was proposed and validated using the case study in Saudi Arabia to evaluate the effects of ICT/IS projects from a managerial perspective. The framework consisted of a list of processes, criteria, and sub-criteria for different kinds of extracted features to measure the impact of ICT/IS projects. Our findings demonstrated that the effects of ICT projects are not limited to social and economic development, but are also categorized as strategic, managerial, informational, operational, transactional, organizational, infrastructure, and transformational development. It is hoped that the findings of the current study can inform ICT decision makers, experts, and researchers who have investigated and are doing research in this area

Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

Nowadays, the Internet of Things (IoT) has become a rapid development; it can be employed by cyber threats in IoT devices. A correct system to recognize malicious attacks at IoT platforms became of major importance to minimize security threats in IoT devices. Botnet attacks have more severe and common attacks and it is threaten IoT devices. These threats interrupt IoT alteration by interrupting networks and services for IoT devices. Several existing methods present themselves to determine unknown patterns in IoT networks for improving security. Recent analysis presents DL and ML methods for classifying and detecting botnet attacks from the IoT environment. Consequently, this paper develops a Bald Eagle Search Optimization with a Hybrid Deep Learning based botnet detection (BESO-HDLBD) algorithm in an IoT platform. The presented BESO-HDLBD approach aims to resolve the security issue by identifying the botnets in the IoT environment. To reduce the high dimensionality problem, the BESO-HDLBD method uses the BESO system for the feature selection process. For botnet detection purposes, the BESO-HDLBD algorithm uses HDL, which is an integration of convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and attention concept. The desire for the HDL technique in botnet detection utilises the intricate nature of botnet attacks that frequently contain difficult and developing patterns. Combining CNNs permits for effectual feature extraction from spatial data, BiLSTM networks capture temporal dependencies, and attention mechanisms improve the model’s capability to concentrate on fundamental patterns. The selection of hyperparameters of the HDL approach takes place using the dragonfly algorithm (DFA). The experimental analysis of the BESO-HDLBD system could be examined under a benchmark botnet dataset. The obtained outcome infers a better outcome of the BESO-HDLBD technique compared to the recent detection system with respect to distinct estimation measures