Fuzzy decision support system for fertilizer

Ashraf, Ather; Akram, Muhammad; Sarwar, Mansoor

doi:10.1007/s00521-014-1639-4

Cited by 33 publications

(18 citation statements)

References 29 publications

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“…The range of the dynamic priority is from 0 to 28. These are termed as: very low (0-5), low (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12), average (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19), high (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26), and very high (23)(24)(25)(26)(27)(28). The functional definitions of these fuzzy sets/profiles as triangular fuzzy numbers are shown in Fig.…”

Section: Membership Function For Dynamic Prioritymentioning

confidence: 99%

“…The range of the burst time is from 0 to 26 time units. These are termed as: low (0-8), medium (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21), and high (18)(19)(20)(21)(22)(23)(24)(25)(26). The functional definitions of these fuzzy sets/profiles as triangular fuzzy numbers are shown in Fig.…”

Section: Membership Function For Burst Timementioning

confidence: 99%

“…Fuzzy logic uses a fuzzy rule base to arrive at a decision involving vague input data. Early work in fuzzy decision making was motivated by a desire to mimic the control actions of an experienced human operator and to obtain smooth interpolation between discrete outputs that would normally be obtained [2,3,5,6]. Fuzzy sets provide a mechanism for communication between computing systems and humans [10].…”

Section: Introductionmentioning

confidence: 99%

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A novel fuzzy decision-making system for CPU scheduling algorithm

Butt

Akram

2015

Neural Comput & Applic

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In this research article, we present a novel fuzzy decision-making system to improve CPU scheduling algorithm of a multitasking operating system. We add intelligence to the existing scheduling algorithms by incorporating fuzzy techniques in the selection of a process to be run on CPU, which result in improved waiting and turn-around times. We implement our proposed algorithm as a simulator using C language. The simulator implements our fuzzy scheduling algorithm, reads the required parameters of all the ready to run processes from a file, and finally computes a dynamic priority (dpi) value for each process. The run queue is sorted according to each process's dpi, and the process at the head of the queue is selected to run on CPU. Finally, we compare our results with some existing proposed fuzzy CPU scheduling (PFCS) algorithms as well as with some standard CPU schedulers. Our results show improvements as compared to the work of Ajmani's PFCS (Ajmani and Sethi in BVI-CAM's Int J Inf Technol 5:583-588, 2013), as well as from Behera's improved fuzzy-based CPU scheduling algorithm (Behera et al. in Int J Soft Comput Eng 2:326-331, 2012). Our efforts contribute to the overall efforts of the community contributing to the fuzzification of different operating system modules. These efforts finally result in an operating system that gives convenience to its users in both certain and uncertain environments and at the same time efficiently utilize the underlying hardware and software under precise as well as fuzzy conditions (Kandel et al. in Fuzzy Sets Syst 99:241-251, 1988).

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Section: Membership Function For Dynamic Prioritymentioning

confidence: 99%

Section: Membership Function For Burst Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel fuzzy decision-making system for CPU scheduling algorithm

Butt

Akram

2015

Neural Comput & Applic

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“… 2014a , b ; Ashraf et al. 2014a ). A fuzzy controller is normally comprised of a fuzzification module, a rule base, an inference engine and a defuzzification system.…”

Section: Introductionmentioning

confidence: 99%

A new intuitionistic fuzzy rule-based decision-making system for an operating system process scheduler

Butt

Akram

2016

SpringerPlus

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We present a new intuitionistic fuzzy rule-based decision-making system based on intuitionistic fuzzy sets for a process scheduler of a batch operating system. Our proposed intuitionistic fuzzy scheduling algorithm, inputs the nice value and burst time of all available processes in the ready queue, intuitionistically fuzzify the input values, triggers appropriate rules of our intuitionistic fuzzy inference engine and finally calculates the dynamic priority (dp) of all the processes in the ready queue. Once the dp of every process is calculated the ready queue is sorted in decreasing order of dp of every process. The process with maximum dp value is sent to the central processing unit for execution. Finally, we show complete working of our algorithm on two different data sets and give comparisons with some standard non-preemptive process schedulers.

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“…Fuzzy logic is a serious candidate for supporting decisions in environmental phenomena, as it introduces subjectivity and vagueness in an analysis and for this reason is incorporated into the present study. Various research works reveal the effectiveness of fuzzy theory in decision problems concerning fertilizing crops [10,11].…”

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confidence: 99%

GIS Modelling for Site-Specific Nitrogen Fertilization towards Soil Sustainability

2015

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Farmers need to make decisions that in most cases incorporate the concept of prediction and can hardly be revoked. One such decision is the application of fertilizing inputs. During past crop management and decision-making on fertilizing practices, many significant errors have been recorded, which have led and continue to lead to reduced production and environmental burden. The methodology followed in this paper involves the use of GIS, fuzzy logic and expert knowledge, in order to model physical processes associated with nitrogen balance in cultivated ecosystems and to evaluate the capabilities of or limitations on the use of certain fertilizers, based on spatial criteria. An original spatial decision support system was designed, developed and applied in a given study area. The system is composed of two modules ("fertilizing rate" and "fertilizing type"), making use of soil, climate and cultivation practices' data, as recorded in the area of interest in quantitative or categorical form. The results of the application spatially classify the involved area according to its demand for nitrogen on the basis of the characteristics of each sub-region. The "fertilizing rate" module suggests reduced fertilizing doses of nitrogenous fertilizers compared to those already applied in the area. The system further divides the area into zones where specific types of fertilizers should be applied, giving a certain prescription for the method and time of application. OPEN ACCESSSustainability 2015, 7 6685

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Fuzzy decision support system for fertilizer

Cited by 33 publications

References 29 publications

A novel fuzzy decision-making system for CPU scheduling algorithm

A novel fuzzy decision-making system for CPU scheduling algorithm

A new intuitionistic fuzzy rule-based decision-making system for an operating system process scheduler

GIS Modelling for Site-Specific Nitrogen Fertilization towards Soil Sustainability

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