Japanese Dairy Cattle Productivity Analysis using Bayesian Network Model (BNM)

Ahmed, Iqbal; Endo, Kenji; Fukuda, Osamu; Arai, Kohei; Okumura, Hiroshi; Yamashita, Kenichi

doi:10.14569/ijacsa.2016.071105

Cited by 6 publications

(7 citation statements)

References 12 publications

(20 reference statements)

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“…Therefore, the entropy of proposed BNM model's outputs was calculated to achieve reliable discrimination and use it for discrimination-suspension rule [22,23]. Entropy means or interprets as the risk of incorrect discrimination and if entropy exceeds some predefined discrimination threshold, then the discrimination could be suspended.…”

Section: A Bayesian Network Model (Bnm) Results Analysismentioning

confidence: 99%

Sustainable Green SLA (GSLA) Validation using Bayesian Network Model

Ahmed¹,

Okumura²,

Arai³

et al. 2017

ijacsa

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Abstract-Currently, most of the IT (Information Technology) and ICT (Information and CommunicationTechnology) industries/companies provides their various services/product at a different level of customers/users through newly developed sustainable GSLA (Green Service Level Agreement). In addition, all these industries also designed new green services at their scope by using global sustainable GSLA informational model. The recent development of sustainable GSLA under 3Es (Ecology, Economy and Ethics) are assisting these IT and ICT based industries to practice sustainability by providing green services to their customers/users and thus respecting green computing paradigm. However, the evaluation of newly developed sustainable GSLA model is not validating yet. This research attempts to evaluate and validate the sustainable GSLA model by using Bayesian Network Model (BNM). The validation of using BNM is done with the feedback of 44 different IT and ICT based companies from Japan, India and Bangladesh. The average accuracy of using BNM for validating sustainable GSLA model is 68% while considering all sample data sets. Moreover, while the proposed BNM have higher confidence with entropy calculation, then the accuracy is almost 100% for most of the companies' feedback. The proposed idea of using BNM for evaluating and validating sustainable GSLA model would definitely help the ICT engineer to design and develop future green services in their industries. Additionally, the evaluation also validates the proposed information sustainable GSLA model from previous research.

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Section: A Bayesian Network Model (Bnm) Results Analysismentioning

confidence: 99%

Sustainable Green SLA (GSLA) Validation using Bayesian Network Model

Ahmed¹,

Okumura²,

Arai³

et al. 2017

ijacsa

Self Cite

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“…There are many influential factors for the reproductivity of the cattle farming which are already elaborated by various researchers and veterinarian [6], [7], [8], [9], [10], [11], [12], [13]. Understanding the estrous cycle is the first step for the applicability of pAI and many measurement indices are responsible for it [14], [15], [16]. There were many discovered influential indices for the cattle herd management such as, Body Condition Score (BCS), days after calving and or Postpartum Interval (PPI), parity number, ovarian characteristics, uterine blood flow, progesterone level (P4), climate and nutritional factors, etc.…”

Section: Background Checksmentioning

confidence: 99%

“…Additionally, years of experiences and skills are necessary for understanding ultrasound image, echo data to pinpoint the proper time for the applicability of pAI. Some works have been done on the objective measurement of improving productivity using BNM in the cattle industry [15], [16]. However, the number of measurement indices of cattle is only a few in these researches which leaves us room for further improvement.…”

Section: Background Checksmentioning

confidence: 99%

Evaluating Programmed Artificial Insemination for Cattle Production

Yoshihara¹,

He²,

Fukuda³

et al. 2019

IJACSA

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Cattle productivity in Japan has been declining though livestock farmers and breeders tried to use artificial insemination regularly. The reason behind this declining productivity is the poor evaluation of the applicability of artificial insemination. To address this issue, this research proposes an objective evaluation method to estimate the applicability of programmed Artificial Insemination (pAI). The objective evaluation method tries to estimate the applicability of pAI based on the analysis of various indices from dairy and beef cattle using Bayesian Network Model (BNM). The estimation of the pAI applicability considers 14 and 17 physiological indices for diary and beef cattle respectively. These indices include the basic information (days after childbirth, parity, etc.), diagnosis of appearance, diagnosis of genital organ and the veterinarians' judgments. The overall success rate in estimating the applicability is 89.8% for 1051 records of dairy cattle and 95.6% for 1128 records of beef cattle. The proposed method avoids the subjective error in estimating the applicability of pAI. In addition, the experiment revealed that the applicability of pAI can be evaluated even though the number of measure indices is few.

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“…[3]- [6]. The authors have proposed the method for estrus cycle estimation with three influential factors (BCS, postpartum interval, and parity) for understanding the presence and absence of estrous cycle using a new unique Bayesian Network Model: BNM [7]. It, however, is not possible to consider a relation among the influencing factors.…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis for Shortened Labor Mean Interval of Dairy Cattle with the Data of BCS, RFS, Weight, Amount of Milk and Outlook

Arai¹,

Fukuda²,

Okumura³

et al. 2018

ijacsa

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Time series analysis for shortened labor mean interval of dairy cattle with the data of BCS (Body Condition Mass), RFS (Rumen Fill Score), Weight, Amount of Milk and Outlook is conducted. Method for shortened the labor mean internal of Japanese dairy cattle based on time-series analysis with the data of visual index of BCS, RFS, Weight, Amount of Milk and Outlook is proposed. In order to shortened the labor mean interval of dairy cattle is the purpose of this research. Through the experiments with 17 Japanese dairy cattle of the 17 Japanese anestrus Holstein dairy cattle, it is found that the combination of weight, BCS and amount of milk is a good indicator for identification of productive cattle. Therefore, the cattle which need hormone treatments can be identified.

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Japanese Dairy Cattle Productivity Analysis using Bayesian Network Model (BNM)

Cited by 6 publications

References 12 publications

Sustainable Green SLA (GSLA) Validation using Bayesian Network Model

Sustainable Green SLA (GSLA) Validation using Bayesian Network Model

Evaluating Programmed Artificial Insemination for Cattle Production

Time Series Analysis for Shortened Labor Mean Interval of Dairy Cattle with the Data of BCS, RFS, Weight, Amount of Milk and Outlook

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