Applying spatial intelligence for decision support systems

Idrees, Amira M.; Ibrahim, Mohamed Hasan; Seddawy, Ahmed I. El

doi:10.1016/j.fcij.2018.11.001

Cited by 14 publications

(6 citation statements)

References 5 publications

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“…Using an interface module incorporating IoT, the raw data is transformed in the spatial data into spatial data mining. It supports the view of Idrees et al [39], who claim that data mining is an important technology used in various fields, including the fishery industry. They also discovered that patterns from geographical data are useful for identifying trends in data for DSS.…”

Section: Tracking and Tracing Of Fishery Logisticssupporting

confidence: 86%

“…Using an interface module involving IoT, the raw data is transformed in the spatial data into spatial data mining. [39] stated that data mining is an important technique applied in various fields, including agriculture. They also discovered that patterns from spatial data are beneficial for detecting trends in DSS data.…”

Section: The Validation Of the Proposed Systemmentioning

confidence: 99%

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The conceptual design of intelligent spatial decision support system for the fishery-industry logistic

Panudju

Nurilmala²,

Marimin³

2022

IOP Conf. Ser.: Earth Environ. Sci.

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This paper describes the conceptual framework of ISDSS (Intelligence Spatial Decision Support System) for the fishery industry logistic. The decision problem involved is complex due to the fish’s seasonal, perishable, size variability characteristics as raw material. The ISDSS will be supported by three main models, i.e. raw material origin selection, distribution route selection and product tracking and tracing. Multiple criteria decision models will be used for raw material selection. Multi-objective optimization will be used for optimum route selection, considering time, distance, fish quality, and spatial information. Internet of Things (IoT) model will be used for product tracking and tracing system. The geographical information system (GIS) integration with the DSS will also be supported by Artificial Intelligence Technique, i.e. data mining in classifying and clustering the raw material characteristic and origin data. The data collection will be supported by the Internet of Things (IoT). The best technique for each model will be investigated further based on the field experiment and simulation.

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Section: Tracking and Tracing Of Fishery Logisticssupporting

confidence: 86%

Section: The Validation Of the Proposed Systemmentioning

confidence: 99%

The conceptual design of intelligent spatial decision support system for the fishery-industry logistic

Panudju

Nurilmala²,

Marimin³

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…According to [33], different sources may provide efficient knowledge for the required task [34]. In this research, different sources could be explored for the efficient progress of maintaining food safety and preventing food contamination.…”

Section: A Phase One: Data Gathering Phasementioning

confidence: 99%

An Architectural Framework for Generating Food Safety Key Performance Indicators

Abogabal¹,

Ouf²,

Idrees³

et al. 2020

Journal of Southwest Jiaotong University

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Information Technology proved its effectiveness in all industry fields, taking the competition to unexpectedly high levels. Identifying the essential parameters is vital to success. In different fields, business processes monitoring is also essential. In the food industry, for example, food hazards may occur in any stage of generating food, from agriculture to serving. This research uses data mining techniques to propose an architectural framework that can be utilized as a guide for food contamination prevention. The proposed framework aims at detecting the current food status, determining the suitability of the current conditions compared with the required conditions, and alerting users of near-threshold conditions. The framework predicts the available parameters for maintaining the food’s acceptability and includes a plan to follow. The research provides a prototype with a benchmark dataset for proving the applicability of the proposed framework.

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“…It has become an essential element for all organizations to be engaged in a more deliberate transform for prevention (Idrees et al, 2018a), protection, preparedness , mitigation (Khedr and Idrees, 2017b;Khedr et al, 2015b) and reaction to BCM and recovery. An organization should take a proactive approach to reduce the likelihood as well as the probability of the disruptions.…”

Section: Bcm and Itmentioning

confidence: 99%

A Proposed Mining Based Business Continuity Information System for Educational Institutes

Idrees¹,

Seddawy²,

Moaaz³

2019

Journal of Computer Science

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It has become an essential element for all organizations to be engaged in a more deliberate transform for prevention, protection, preparedness, mitigation and reaction to Business Continuity Management (BCM) and recovery. It is becoming risky for the organization to reply of disaster plan which targets to minimize the causes of the disruption. An organization should take a proactive approach to reduce the likelihood as well as the probability of the disruptions. It involves the development of advanced measures and actions that enable the organization to take action in such a way that the critical and essential functions of business could continue according to the predefined level of change and disruption. Recovering from disruptions is a challenge; it relies on an emergency response plan or uses disaster management strategies that were previously used. BCM is considered mandatory to provide a systematic approach towards crisis response in order to increase organizational resilience capability. It is necessarily required by the organization to protect its reputation, brand, value adding activities and stakeholders' interests. This paper proposes an intelligent framework that applies the business continuity management process on an educational institute which reveals the stakeholder awareness of the business continuity level in their organization. In order to successfully formulate the proposed framework, the research went through a set of phases. The research presented the whole process phases for developing the proposed framework which is based on applying the suitable technique in each phase to guarantee a suitable performance with ensuring the results' validity. The research used 93% confidence, Z = 1.98 with error margin E = 0.03 for sampling selection and the determined association were 94% confidence for 93 rules. Finally, the research discussed the recommendations for successful crisis management process with the support of intelligent techniques.

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Applying spatial intelligence for decision support systems

Cited by 14 publications

References 5 publications

The conceptual design of intelligent spatial decision support system for the fishery-industry logistic

The conceptual design of intelligent spatial decision support system for the fishery-industry logistic

An Architectural Framework for Generating Food Safety Key Performance Indicators

A Proposed Mining Based Business Continuity Information System for Educational Institutes

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