Intelligent Software Package Research Articles

Perspective of small and medium enterprise (SME's) and their relationship with government in overcoming cybersecurity challenges and barriers in Wales

Small and medium enterprises (SMEs) have adapted their working methods in response to cyber threats, including security checks on internet-connected devices and securing social media accounts. To safeguard their data, SMEs are increasingly adopting intelligent software packages, despite the additional costs and need for skilled personnel. This study examines the perspectives of SMEs in Wales regarding their collaboration with the government to overcome cybersecurity challenges. Through qualitative research involving 34 SMEs, the study explores current cybersecurity practices, challenges faced, and interactions with government agencies. Wales, with its thriving economy and significant number of SMEs, provides a suitable context for the research. The findings will shed light on barriers and challenges encountered by SMEs in implementing effective cybersecurity measures, while also examining their perceptions of government agency involvement. By contributing insights to SME cybersecurity and government policy, the study aims to enhance government initiatives in supporting SMEs to tackle cybersecurity challenges effectively.

Exploration of the Impact of Cybersecurity Awareness on Small and Medium Enterprises (SMEs) in Wales Using Intelligent Software to Combat Cybercrime

Intelligent software packages have become fast-growing in popularity for large businesses in both developed and developing countries, due to their higher availability in detecting and preventing cybercrime. However, small and medium enterprises (SMEs) are showing prominent gaps in this adoption due to their level of awareness and knowledge towards cyber security and the security mindset. This is due to their priority of running their businesses over requiring using the right technology in protecting their data. This study explored how SMEs in Wales are handling cybercrime and managing their daily online activities the best they can, in keeping their data safe in tackling cyber threats. The sample collected consisted of 122 Welsh SME respondents in a collection of data through a survey questionnaire. The results and findings showed that there were large gaps in the awareness and knowledge of using intelligent software, in particular the uses of machine learning integration within their technology to track and combat complex cybercrime that perhaps would have been missed by standard cyber security software packages. The study’s findings showed that only 30% of the sampled SMEs understood the terminology of cyber security. The awareness of machine learning and its algorithms was also questioned in the implementation of their cyber security software packages. The study further highlighted that Welsh SMEs were unaware of what this software could do to protect their data. The findings in this paper also showed that various elements such as education and the size of SME made an impact on their choices for the right software packages being implemented, compared to elements such as age, gender, role and being a decision maker, having no impact on these choices. The study finally shares the investigations of various SME strategies to help understand the risks, and to be able to plan for future contingencies and preparation in keeping data safe and secure for the future.

ANFIS based data rate prediction for cognitive radio

Intelligence is needed to keep up with the rapid evolution of wireless communications, especially in terms of managing and allocating the scarce, radio spectrum in the highly varying and disparate modern environments. Cognitive radio (CR) systems promise to handle this situation by utilizing intelligent software packages that enrich their transceiver with radio-awareness, adaptability and capability to learn. Its system participates in a continuous process, “the cognition cycle”, during which it adjusts its operating parameters, observes the results and, eventually takes actions, that is to say, decides to operate in a specific radio configuration (i.e., radio access technology, carrier frequency, modulation type, etc.) expecting to move the radio toward some optimized operational state. In such a process, learning mechanisms utilize information from measurements sensed from the environment, gathered experience and stored knowledge and guide in decision making. This paper evaluates learning schemes that are based on adaptive neuro-fuzzy inference system (ANFIS) for predicting the capabilities (e.g. data rate) that can be achieved by a specific radio configuration in cognitive radio. While CR is an intelligent emergent technology, where learning schemes are needed to assist in its functioning. On the other side, ANFIS based scheme is one of the good learning artificial intelligence method, that combines best features of neural network and fuzzy logic. Here proposed method is able to assist a cognitive radio system to help in selecting the best one radio configuration to operate in. Performance metric like root mean square error (RMSE), prediction accuracy of ANFIS learning has been used as performance index.

Интеллектуальный программный комплекс моделирования процесса планирования многоассортиментных промышленных производств

The article discusses issues related to the development of a flexible intelligent software package for solving the problem of optimal planning of multi-assortment production. These industries are characterized by a large range of products, many types and configurations of equipment, with an increase in the dimension of the problem, the number of options for production schedules grows exponentially, therefore, it is extremely important to develop a specialized complex for effective optimal planning and scheduling, insisting on the characteristics of various multi-assortment industries. The purpose of this work is to increase the productivity of multi-assortment enterprises and reduce the time of production of products by developing methods and algorithms for optimizing scheduling in the form of a problem-oriented software package. The article presents a mathematical formulation of the optimization problem and a set of mathematical models and algorithms for the formation of objective functions for optimal scheduling of reconfigurable productions. Conducting this study is based on the use of methods of scheduling theory, optimization and evolutionary calculations, tools for object-oriented development of complex software systems and databases. The proposed software package has various intelligent user interfaces, supplemented by databases of products, equipment and technological regulations, a library of objective functions and mathematical optimization methods, an expert system tuning module, as well as an interactive system for visualizing the resulting production plans in the form of a Gantt chart and decision tree of the optimization problem. Testing of the software package was carried out on the data of polymer and metallurgical enterprises in Russia and Germany and confirmed the effectiveness of solving planning problems. Implementation of the proposed software package makes it possible to ensure efficient loading of enterprise equipment, reduce production costs and simplify the process of making managerial decisions in the course of production planning.

ИНТЕЛЛЕКТУАЛЬНЫЙ ПРОГРАММНЫЙ КОМПЛЕКС ДЛЯ МОНИТОРИНГА И УПРАВЛЕНИЯ ГОРЯЧИМ ВОДОСНАБЖЕНИЕМ (ГВС) НА ОСНОВЕ РЕГУЛЯРИЗИРУЮЩЕГО БАЙЕСОВСКОГО ПОДХОДА

Digitalization and intellectualization of housing and communal services is one of the most important and complex tasks of the development of the modern regional economy. The difficult operating conditions of housing and communal services complexes, the lack of the necessary information base, uncertainty, incompleteness, unstructured information form increased requirements for the information systems being created and the methodology of their development. The article offers an example of the implementation of an intelligent software package for monitoring and supporting management decisionmaking for hot water systems (DHW). Based on the RBP methodology, a working sample of the complex for a section of the DHW network has been developed. The unique properties of the intellectual complex are the possibilities of integrating different types of information, intellectual processing of numerical and linguistic information in conditions of considerable uncertainty, metrological justification of the solutions obtained, implementation of the principles of traceability, transparency, explainability of solutions. The complex can be used for training specialists of the DHW system. It can also serve as an example for the development of intelligent monitoring and control systems for complex distributed energy networks, oil and gas pipelines.

Map–Reduce–Style Job Offloading Using Historical Manufacturing Behavior for Edge Devices in Smart Factory

For smart factories in the Industry 4.0 era, edge devices can be used to run intelligent software packages (i.e., manufacturing services) to support manufacturing activities of production equipment. However, this kind of edge device may fail to provide designed functionalities in time when it encounters a sudden high-computation (SHC) job that cannot be executed by the edge device itself within a required time constraint. Thus, how to assure that an edge device can effectively execute SHC jobs so as to provide manufacturing services to equipment in time is an important and challenging issue for smart manufacturing. Exiting job-shop scheduling (JSS) methods may optimally allocate jobs for production lines but cannot directly be applied to handle the edge devices SHC jobs, which occur irregularly and are hard to specify processing time slots for JSS methods. Aimed at resolving the above-mentioned issue, this letter proposes a novel manufacturing-behavior-based map-reduce-style job offloading scheme. First, a distributed job processing architecture is designed to allow distributed edge devices to collaboratively complete an SHC job to support manufacturing. Next, a map-reduce-style program structure is developed so that an SHC job can be easily divided into multiple parts that can be executed in parallel by the selected edge devices, each device processing a part. Then, a mechanism of selecting proper edge devices to complete the SHC job, considering historical manufacturing behaviors of each edge device, is proposed for achieving high offloading efficiency. Finally, we simulate a factory with 20 machines to conduct integrated tests. Testing results demonstrate the effectiveness and excellent performance of the proposed scheme.

MULTI-EDIP – an intelligent software package for computer-aided multivariate signal and system identification

Abstract In the paper an intelligent software package MULTI-EDIP for computer-aided identification of multivariate signals and systems is presented. Purposes and main requirements for computer-aided identification are discussed. A summary of the most important MULTI-EDIP services with a focus on expert advice is described. An example of using the package in electroacoustic plant identification for active noise control system development is presented.

Performance evaluation of artificial neural network-based learning schemes for cognitive radio systems

Over the last decade the world of wireless communications has been undergoing some crucial changes, which have brought it at the forefront of international research and development interest, eventually resulting in the advent of a multitude of innovative technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh networks and software defined radio. Such a disparate and highly varying radio environment calls for intelligent management, allocation and usage of a scarce resource, namely the radio spectrum. One of the most prominent emerging technologies that promise to handle such situations is cognitive radio. Cognitive radio systems are based on software defined radio technology and utilize intelligent software packages that enrich their transceivers with the highly attractive properties of self-awareness, adaptability and capability to learn. A cognitive radio system has the ability to adjust its operating parameters, observe the results and, eventually take actions, that is to say, decide to operate in a specific radio configuration (i.e. radio access technology, carrier frequency, modulation type, etc.), expecting to move the radio toward some optimized operational state. In such a process, learning mechanisms that are capable of exploiting measurements sensed from the environment, gathered experience and stored knowledge, are judged as rather beneficial for guiding decisions and actions. Framed within this statement, this paper introduces and evaluates learning schemes that are based on artificial neural networks and can be used for discovering the performance (e.g. data rate) that can be achieved by a specific radio configuration in a cognitive radio system. Interesting scenarios, which include both commercial off-the-shelf and simulation hardware/software products, are mobilized for the performance assessment work, conducted in order to design and use an appropriate neural network structure, while indicative results are presented and discussed in order to showcase the benefits of incorporating such learning schemes into cognitive radio systems.

Neural network-based learning schemes for cognitive radio systems

Intelligence is needed to keep up with the rapid evolution of wireless communications, especially in terms of managing and allocating the scarce, radio spectrum in the highly varying and disparate modern environments. Cognitive radio systems promise to handle this situation by utilizing intelligent software packages that enrich their transceiver with radio-awareness, adaptability and capability to learn. A cognitive radio system participates in a continuous process, the “cognition cycle”, during which it adjusts its operating parameters, observes the results and, eventually takes actions, that is to say, decides to operate in a specific radio configuration (i.e., radio access technology, carrier frequency, modulation type, etc.) expecting to move the radio toward some optimized operational state. In such a process, learning mechanisms that are capable of exploiting measurements sensed from the environment, gathered experience and stored knowledge, are judged as rather beneficial for guiding decisions and actions. Framed within this statement, this paper introduces and evaluates learning schemes that are based on artificial neural networks and can be used for predicting the capabilities (e.g. data rate) that can be achieved by a specific radio configuration. In particular, the focus in this work is placed on obtaining insight on the behavior of the presented, learning schemes, whereas useful, indicative results from the benchmarking work, conducted in order to design and use an appropriate neural network structure, are also presented and discussed. In the near future, such learning schemes are expected to assist a cognitive radio system to compare among the whole of available, candidate radio configurations and finally select the best one to operate in.

Cognitive radio realities

A cognitive radio (CR) is a transceiver that is aware, adaptive, and capable of learning. It may be visualized and realized as an intelligent software package (cognitive engine) controlling a softw...

Cognitive radio realities

AbstractA cognitive radio (CR) is a transceiver that is aware, adaptive, and capable of learning. It may be visualized and realized as an intelligent software package (cognitive engine) controlling a software defined radio platform. The cognitive engine executes a set of nested loops constituting a cognition cycle, drawing on experience and stored knowledge to optimize a set of user‐chosen quality of service measures. The process relies on the effectiveness of a set of tools that are individually well known but rarely found together: multi‐objective genetic algorithms, case‐based decision theory, and neural networks. Practical implementation problems include passing environmental information from the radio to the cognitive engine, acting on that information, and performing real‐time control of the radio platform by the cognitive engine. In this paper, we discuss our approach to developing and implementing a CR. Copyright © 2007 John Wiley & Sons, Ltd.