The Experts below are selected from a list of 27873 Experts worldwide ranked by ideXlab platform

Prayoot Akkaraekthalin - One of the best experts on this subject based on the ideXlab platform.

  • ganoderma boninense disease detection by near infrared spectroscopy classification a review
    Sensors, 2021
    Co-Authors: Mas Ira Syafila Mohd Hilmi Tan, M F Jamlos, Ahmad Fairuz Omar, Fatimah Dzaharudin, Suramate Chalermwisutkul, Prayoot Akkaraekthalin
    Abstract:

    Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive Analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive Analytics Model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive Analytics as a significant platform towards early detection of G. boninense in the future.

Christian Dölle - One of the best experts on this subject based on the ideXlab platform.

  • Implementation and assessment of a predictive Analytics Model for development project management
    2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2017
    Co-Authors: G. Schuh, Michael Riesener, Christian Dölle
    Abstract:

    In order to strengthen their competitive position, companies in high wage countries strive towards shortened innovation cycles while decreasing development costs. To achieve this, development projects need to be managed in a lean and efficient way. Existing approaches targeting the development project management mainly focus the target dimensions time, cost and quality on the superior project level. Corrective steering measures however need to be implemented on an activity level. Thus, a concept has been developed that applies predictive Analytics techniques to predict deviations in the activities of development projects based on deviation indicators. In the presented paper, a methodology for the evaluation of suitable input parameters is presented. A predictive Analytics Model based on this concept is then implemented and validated. Therefore, a data set was acquired, which is used to train a neural network. To validate the applicability of the Model, the accuracy of the predicted deviations is assessed against the actual deviations.

  • Concept for development project management by aid of predictive Analytics
    2016 Portland International Conference on Management of Engineering and Technology (PICMET), 2016
    Co-Authors: G. Schuh, Michael Riesener, Christian Dölle
    Abstract:

    Manufacturing companies in high wage countries strive towards shortened development and innovation cycles at decreased costs in order to strengthen their competitive advantage. These goals can be achieved by efficient development projects. However, approaches aiming at designing efficient development processes such as the value stream analysis only analyze development projects retrospectively as well as periodically and therefore do not continuously improve the efficiency of the respective projects themselves. Therefore, a concept is needed to anticipate deviations from the target process and thus inefficiencies within development projects by aid of predictive Analytics. To derive a predictive Analytics Model, neural networks are applied to identify the impact of deviation indicators on the efficiency dimensions time, costs and quality of an activity. Upon reversion, it is possible to monitor the deviation indicators and use the respective indicator values as input for the neural networks. Based on the identified impact of the indicator on the efficiency dimensions, the neural network is able to predict the final values of an activity in terms of time, cost and quality. By comparing the predicted values with the defined target values, the deviation can be determined and preventive measures can be implemented to eliminate inefficiencies.

Zhao Yang Dong - One of the best experts on this subject based on the ideXlab platform.

  • Robust Ensemble Data Analytics for Incomplete PMU Measurements-Based Power System Stability Assessment
    IEEE Transactions on Power Systems, 2018
    Co-Authors: Yuchen Zhang, Zhao Yang Dong
    Abstract:

    This letter proposes a new ensemble data-Analytics Model for PMU-based pre-contingency stability assessment (SA) considering incomplete data measurements. The Model consists of a minimum number of single classifiers which are, respectively, trained by a strategically selected cluster of PMU measurements. Under any PMU missing scenario, the power grid observability from available PMUs can still be ensured to the maximum extent to maintain the SA accuracy. The proposed method is verified through both theoretical proof and numerical simulations.

M Kaliappan - One of the best experts on this subject based on the ideXlab platform.

  • development of big data predictive Analytics Model for disease prediction using machine learning technique
    Journal of Medical Systems, 2019
    Co-Authors: R Venkatesh, C Balasubramanian, M Kaliappan
    Abstract:

    Now days, health prediction in modern life becomesvery much essential. Big data analysis plays a crucial role to predict future status of healthand offerspreeminenthealth outcome to people. Heart disease is a prevalent disease cause's death around the world. A lotof research is going onpredictive Analytics using machine learning techniques to reveal better decision making. Big data analysis fosters great opportunities to predict future health status from health parameters and provide best outcomes. WeusedBig Data Predictive Analytics Model for Disease Prediction using Naive Bayes Technique (BPA-NB). It providesprobabilistic classification based on Bayes' theorem with independence assumptions between the features. Naive Bayes approach suitable for huge data sets especially for bigdata. The Naive Bayes approachtrain the heart disease data taken from UCI machine learning repository. Then, it was making predictions on the test data to predict the classification. The results reveal that the proposed BPA-NB scheme providesbetter accuracy about 97.12% to predict the disease rate. The proposed BPA-NB scheme used Hadoop-spark as big data computing tool to obtain significant insight on healthcare data. The experiments are done to predict different patients' future health condition. It takes the training dataset to estimate the health parameters necessary for classification. The results show the early disease detection to figure out future health of patients.

Mas Ira Syafila Mohd Hilmi Tan - One of the best experts on this subject based on the ideXlab platform.

  • ganoderma boninense disease detection by near infrared spectroscopy classification a review
    Sensors, 2021
    Co-Authors: Mas Ira Syafila Mohd Hilmi Tan, M F Jamlos, Ahmad Fairuz Omar, Fatimah Dzaharudin, Suramate Chalermwisutkul, Prayoot Akkaraekthalin
    Abstract:

    Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive Analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive Analytics Model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive Analytics as a significant platform towards early detection of G. boninense in the future.

  • Near-infrared spectroscopy for ganoderma boninense detection in oil palm: An outlook
    'Springer Science and Business Media LLC', 2021
    Co-Authors: Mas Ira Syafila Mohd Hilmi Tan, M F Jamlos, Ahmad Fairuz Omar, Fatimah Dzaharudin, Mohd Azraie, Mohd Azmi, Mohd Noor Ahmad, Nur Akmal, Abd. Rahman, Khairil Anuar Khairi
    Abstract:

    Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causing a serious threat to the palm oil industry. This catastrophic disease ultimately destructs the basal tissues of oil palm that causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This mini-review describes past and future prospects of integrated research of near infrared spectroscopy (NIRS) towards early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and hyperspectral in reacting with organic tissues, ii) NIR spectrum is more precise and sensitive to particular diseases include G. boninense compared to visible light iii) hand-held NIRS for in-situ measurement is to explore the efficacy for early detection system in real-time using machine learning (ML) classifier algorithms and predictive Analytics Model. This non-destructive, environmentally friendly (no chemical involved), mobile and sensitive leads the integrated hand-held NIRS with ML, and predictive Analytics has significant potential as a platform towards early detection of G. boninense in the future