Intelligence and Sustainable Systems for Future Computing

Dr. H. Anandakumar
Editor in Chief, International Journal of Intelligence and Sustainable Computing

1. Introduction

There has been a momentous deluge of work exploring enormous and novel spatial-temporary streams of data in existing literature concerning sustainable systems (Deary, 2006).The fundamentality of big data outputs aids the meeting of developmental and sustainable goals while posing extreme challenges to contemporary systems of computing. Intelligent computing involves a fundamental relationship between spatial and temporal systems of information incorporating emergent necessity in decision-making processes when mitigating sustainability problems. In order to gain access to essential data, apt sophisticated intelligent computing involving emergent diagnostic methods such as web analytics, network analytics, big data and software equipment are necessary to facilitate the pursuit of innovative analytics disciplines in information access, predictive analytics and machine technology. As a result, progressive data analysis methods are potentially inexpensive, accurate and highly scalable in delivering intelligent computing in actual timely decision-making processes in the formulation of sustainable systems (Shahinpoor & Schneider, 2008). Hence, this will help to mitigate sustainability issues in statistics and computing dispensation settings in various intellectual computing prototypes.

In reference to Fisher (2011), the analysis of intelligent computing information is critical to attaining sustainability in information technology in intelligent computing archetypes in the present data-enhanced epoch. The importance of this aspect relates to literature outcomes and developments concerned with practical and theoretical solutions in sustainability systems. The technological approach is concerned with ideation addressing current problems from application and methodological perceptions. Sustainability systems fulfill the demands of present forms of ideation without altering future enhancements as a generational concern. Current and emerging disciplines in intelligent sustainability use techniques from mathematics, computer science, statistics, operations and information technology to enhance socio-economic and composed environmental necessities for sustainability (Horbach, 2005). Artificial intelligence methodologies are fundamental to sustainability analysis that enhances the mitigation of sustainability issues related to modelling and decision-making dynamics in indeterminate settings.

2. Problem Definition

Sustainability system issues characteristically involve different disciplines, necessitating that every solution needs to be balanced between social demands, community issues and economic constraints. System issues are separated in compound scales in extreme dynamics with extreme uncertainty levels. Intelligent computing methodologies enhance the formulation of optimum procedures for sustainability; however, dynamic, uncertain and multiscale aspects present fundamental computation concerns. Moreover, sustainable mitigating factors balance both group and individual needs associated with complex and interconnected protocols. Problem-featuring research that provides an in-depth evaluation of fundamental artificial intelligence and sustainability includes analyses of species distribution, conservation design, organic modelling, ecological assessment, policy planning, conveyance and insolent networks (Özçelik, 2017). Articles based on computational intelligence and sustainable systems extend numerous artificial intelligence forms and fundamentals from the optimisation of machine learning to mechanism-based or agent formulation. The sources of literature enumerate extensive issues such as compensation in the skewed distribution of data in citizen-science and crowdsourced information. The rationale embraces unique research to analyse challenges in enhanced niches and exploit structural characteristics such as sub-modularity in delivering adaptive solutions to sustainability problems. Moreover, the analyses aid in devising stochastic optimisations for sub-categories such as optimal controls, reinforcements and stochastic indoctrination.

2.1 Progress of Intelligent Computing and Sustainable Systems

With the advancements we have seen in information systems, researchers have considered the relevance of enhancing accuracy and efficiency of contemporary data dispensation systems (You, Kim, Hsu & Yoo, 2010). In reference to past developments, there has been tremendous growth in the Internet of Things (IoT), sensory networks, big data, cloud computation and mobile computing, which deliver both temporal and spatial solutions and opportunities for information handling methods. An emergent paradigm of big data is applicable to datasets of various sizes that surpass the aptitude of commonly utilised computation platforms for managing, capturing and processing information. The datasets normally arise from innumerable sources such as sensors, social sites, applications, image and video receivers, surveillance technologies, internet documents, website indexing, clinical records, internet logs and project transactions. Additionally, datasets are characteristically numerous in size, hence necessitating prompt output and input of information. Significantly, big data is being incorporated into organisations' decision-making processes due to its significant value. Ideation is evident from scholars who aim at instigating big data handling through the likes of extreme scalable systems of storage, cloud computation and parallel data processing. According to Waterman & Hendler (2013), the element of size is merely significant for big data due to its relevance in the production of opportunities to identify discernments in unique and emergent forms of content that fill the gap in research in intelligent computing. As a result, the technological approach addresses emergent problems in big data, which applies a number of algorithms, approaches, software and hardware to processing targets.

2.2 Green Technology Concerns

Chronological decision making concerns the demonstration of sustainable sub-modularity in structures enhanced efficiently with applications of effective protocols with uncontestable optimality. According to Coad (2013), using adaptive sub-modularity decision-making processes in partial and uncertain ecologies is applicable. The adaptive approach centres on two fundamental issues, which are optimal for collecting data choices to influence adaptive ecological preservation in systems and choosing land reinforcements dynamically. Crowdsourcing through hemisphere-wide spatial and temporary distribution explains ensemble techniques for studying multiscale models, which conform to spatial sampling, variations and densities. Scholars apply this approach to studying various distributions of models and data, which aids in the learning of natural science and the crowdsourcing of information. Adding to climatological observation, climate radars enhance remote detecting stands that track the movements of insects, birds and bats. In reference to a Bayesian methodology, the analysis of comprehensive models of animal relocation via radar information to backing joint inference benefits artificial intelligent systems. As a result, the models of extensive nocturnal creatures' movements disclose great insights of temporal and spatial scales that aid and inform ecologists, policy makers and ornithologists.

The comprehension of the ecological effects of intelligent computing started in the 21st century with weather concerns and communication and information technologies that acted in accordance with the interests of ICT organisations (Ohlhorst, 2013). The analysis of green information technology is an extreme of the immediate ecological influence of industrialised utilities, and computer disposal is categorised in the second order of the ecological impact of computation on society. A promising approach of scientists has been to use information technology to counteract ecological impacts of air movement via the computation of intelligent formulation software to minimise the movement time of carbon dioxide conveyance. This approach has also been applicable in intelligent computing to track energy utilities. Scientist formulated a plan preceding an international conference for computation sustainability to discuss climate change. This plan's formulation was significant to enhancing the visibility and insertion of this domain into societal research in sustainability-focused science.

Alongside this rationale, the expedition of intelligent computing has led to organisations such as ICS and NSF collaborating with computer and mathematics experts to propose solutions that will enhance long-term sustainability in diverse systems. Sophisticated sustainability-centered intelligent computing conventions sprang up in 2009, hence the founding of ICCS. The inauguration of this foundation at the Cornell Institute followed in 2010. The movement of sustainability awareness as a priority issue into the mainstream was a significant result of the infusion of sustainable systems into artificial intelligent computing incorporating researchers' ideologies. This technological step facilitated a reflection of sustainability infused in scientific fields instead of consecutive and separated subjects in laboratory contexts (Darwish, 2014). Advancements in intelligent computing and sustainable systems continue to be evident in different disciplines. Collaborators and scientists who formulated systems of data mining and technological visualisations to enhance our understanding of earth dynamics and climatology have undertaken sustainability-centered expeditions. Sustainability-associated awards and tracks in conferences continue to emerge, with the CCC and CRA enhancing this expansion using awards given to scholars. Related organisations such as NSF have also instigated large-scale synchronised sustainable funding in various fields including engineering, science and natural education, which highlights the vital fundamentality of this area for intelligent computing. Awards and tracks connected to sustainability spread with organisations' support of notable aims such as artificial and research-intelligent computing for ecological sustainability, energy enhancement and cradle designs.

2.3 Policy Design and Future Advancements

A strategic approach to artificial intellect illustrates various features of e-policies and projects, and is a decision system of support suitable for policymakers integrating individual and global economic, environmental and social perspectives into decision-making processes (Khan & Khan, 2017). Policy making in the aspect of intelligent computing poses a multi-dimensional purview incorporating various challenges of artificial intelligence. These problems range from balancing and integrating issues of competitive objectives to the assessment of impacts expending agent-centered models, to game theories for the sustainable creation of policies. Sustainable issues in emergent regions involve wide speculation concerning new necessities that stem from scarce-resource challenges and poor infrastructure. The sustainability of computation and AI in emergent nations is supported by the application of social computations, mobile devices and numerous AI techniques in sustainability fields focusing on mitigating global concerns.

Mainframe resources related to cloud computation conform to individual demands and shared multiple usages dynamically to enable improvements in future usage of the internet. Sustainable systems consider fundamental ideologies of the sustainable utility of intelligent computing in the environment, along with relevant properties. The usage of computation raises crucial and stimulating problems which define future advancements of the internet. Future enhancements necessitate the application of sustainability technologies and theories, which act as a mainstay in providing and promoting interoperability. Concerning the consumption of energy and relevant sustainability strategies, tools related to ecological computation technologies pose a fundamental requirement for the discovery and management of technological resources and storage. Intelligent computation in the area of cloud computing necessitates resources and virtual demonstrations applied using standardised procedures for communication. Improvements in sustainable systems have included the application of mathematical theories and acquaintance engineering to enhance sustainability in the management, operation, integration and reasoning of applications related to cloud computation. These research subjects apply fundamental solutions and methodologies, which structurally incorporate sustainable cloud computation architectures. Ideation, resolutions and recommendations are instigated in all measures related to sustainable systems and intelligent computing. Generally, the scope of this problem considers the modelling, design, prototyping, implementation and programming of sophisticated sustainable applications and systems.

3 Conclusion and Recommendations

The articles referred to in this overview represent just a small sample of the existing research on intelligent computing and sustainable systems, in which an extensive number of projects are applicable to sustainability issues and relevant mitigating factors. A fundamental hallmark of this sustainability research is a focus on computable impressions postulating future enhancements by reviewing current and significant computation problems. In order to maintain the practicality and significance of this empirical research, analysts have collaborated with regional groups to present advanced technologies in both large-scale and small-scale studies (Zhang, Williams & Wang, 2017). In most cases, artificial intelligence in sustainability actions will reach various consumers using commercial applications such as green drivers and smartphones applications.

Considering the interdisciplinary scope of sustainability issues, intelligent computing and sustainable systems research introduces computational design thinking into various disciplines that foster different fertilised concepts. As for intellectual computing, advanced decision-making support systems are apt for the optimisation and mitigation of computation challenges considering variable values related to unstructured datasets (Li & Yang, 2012). Previous computation systems lacked efficiency, computational capabilities and adequacy in handling sustainability issues. Although intelligent computing paradigms have promoted the utility of extensive intelligence systems for analysing, integrating and sharing unstructured data momentously, the application of analytical tools to identifying sustainable data is an efficient approach to achieving an enhanced decision-making process. Additionally, intelligent computing is capable of handling complex datasets that incorporates analytical and mathematics prototypes.

The recently announced International Journal of Intelligence and Sustainable Computing is an interdisciplinary journal exploring various topics such as signal processing, image computing, biomedical informatics, cognitive radio, machine learning, and energy- and thermal-aware management of computing resources. Sustainable intelligence paradigms apply diverse analytical techniques to discover sustainable information suitable for efficient decision making. IJISC aims to publish high-quality research papers that explore different aspects of sustainable computing, and will serve as an international forum for discussion and reference in this important field.

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