Proposed Framework for Implementing Data Mining Techniques to Enhance Decisions in Agriculture Sector Applied Case on Food Security Information Center Ministry of Agriculture, Egypt

Khedr, Ayman E.; Kadry, Mona; Walid, Ghada

doi:10.1016/j.procs.2015.09.007

Cited by 21 publications

(8 citation statements)

References 2 publications

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“…The author describes Soil disintegration by water has impact on crop profitability [13]. The author describes Egypt is confronting an issue of nourishment frailty joined with destitution particularly in provincial Upper Egypt [14]. The author describes three SOM (self-Organizing based models), in particular, managed Kohonen systems (SKN), and counter-spread counterfeit systems (CP-ANN) and XY-combination (XY-F), which utilize Supervised Learning to relate high goals information on soil and harvest with is frequency classes of wheat yield efficiency were use [15].…”

Section: Literature Foundationmentioning

confidence: 99%

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Joseph,

Manohar

2020

IJEAT

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Agriculture plays a crucial role for the production of food in Indian regions. Indian regions mainly produces crops like rice, wheat, maize and many other types of crop. There are several factors required for the productivity of any harvest, but we know that soil, climate, pesticides, Fertilizers and ground water is most influencing essential factor for the productivity of any harvest. Let us consider soil which is the key element as it provides nutrients for proper development and growth of crops. Secondly, climate is also having major role in agriculture as crop growth depends on rainfall, humidity, temperature etc. Thirdly, Pesticides is widely used to control pest and prevents the damage of crops. Fourthly, Fertilizers can improve the quality of crops. Finally, ground water which will enrich nutrients in soil. The current preparation centers around different information mining procedures utilized in various regions of India and anticipate future harvest along with reasonable information mining procedure saw during the period(1920-2019).The parameters considered for the examination were soil, atmosphere, water thickness, pesticides and composts and Crop informational collection. The Classification calculations utilized in preparation were Adaptive boosting classification, Excess tree classification, neural based classification, Multiple Process classification, Decision making classification, K-closest neighbors, Bayesian theory classification, decision Forest classification, support group machine, and Randomized Gradient Classification. By using the techniques mentioned above we can improve the harvest prediction using information mining techniques which in turn help the farmers to take better decisions in future and it can be used in other technologies like image analyzing etc. The Experimental results show predicted crop, suitable algorithm and algorithm accuracy in that particular state of India respectively.

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Section: Literature Foundationmentioning

confidence: 99%

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Joseph,

Manohar

2020

IJEAT

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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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“…Multi-layer perception demonstrates the most noteworthy precision among the proposed calculations. Aymen E Khedr et al [6] tries to take care of the issue of nourishment frailty in Egypt. It proposes a system which would foresee the generation, and import for that specific year.…”

Section: Review Of Related Workmentioning

confidence: 99%

Enhanced and Improved Hybrid Model to Prediction of User Awareness in Agriculture Sector

Kumar¹,

Bhramaramba²

2018

ijacsa

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Agriculture is the backbone of Indian economy and is the main income source for most of the population in India. So farmers are always curious about yield prediction. Crop yield depends on various factors like soil, weather, rain, fertilizers and pesticides. Several factors have different impacts on agriculture, which can be quantified using appropriate statistical methodologies. Applying such methodologies and techniques on historical yield of crops, it is possible to obtain information or knowledge which can be helpful to farmers and government organizations for making better decision and policies which lead to increased production. The main drawbacks of Indian farmers are they do not have proper knowledge regarding crop yield based on soil necessities. So in this paper, we proposed and developed an Improved Hybrid Model (which is combination of both classification, i.e. Artificial Neural Networks and clustering approach i.e. k-means (works based on Euclidean distance)) to provide awareness, usage and prediction to each farmer that relates to classify different crop yield representation based on soil necessity. For that we collected farmer's data from standard repositories like http://www.tropmet.res.in/static_ page.php?page_id=52#data and then using that data provide awareness and other parameter sequences to all the farmers in India. Our experimental results show efficient e-agriculture with respect to user awareness, usage and prediction with respect to prediction, recall and f-measure for supporting real time marketing of different agriculture products.

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Proposed Framework for Implementing Data Mining Techniques to Enhance Decisions in Agriculture Sector Applied Case on Food Security Information Center Ministry of Agriculture, Egypt

Cited by 21 publications

References 2 publications

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

A Proposed Mining Based Business Continuity Information System for Educational Institutes

Enhanced and Improved Hybrid Model to Prediction of User Awareness in Agriculture Sector

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