A Meta-heuristic Approach for Design of Image Processing Based Model for Nitrosamine Identification in Red Meat Image

2020 ◽  
Vol 14 ◽  
Author(s):  
Monika Arora ◽  
Parthasarathi Mangipudi
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Research Work ◽  
Red Meat ◽  
Heuristic Approach ◽  
Partial Least Square ◽  
Operating Characteristics ◽  
New Findings ◽  
Predicted Model ◽  
Meat Samples

Background: Nitrosamine is a chemical, commonly used as preservative in red meat whose intake can cause serious carcinogenic effects on human health. Identification of such malignant chemicals in foodstuffs is an ordeal. Objective: The objective of the proposed research work presents a meta-heuristic approach for nitrosamine detection in red meat using computer vision-based non-destructive method. Method: This paper presents an analytical approach for assessing the quality of meat samples upon storage (24, 48, 72 and 96 hours). A novel machine learning-based method involving strategic selection of discriminatory features of segmented images has been proposed. The significant features were determined by finding p-values using Mann-Whitney U test at 95% confidence interval which were classified using partial least square-discriminant analysis (PLS-DA) algorithm. Subsequently, the predicted model was evaluated by bootstrap technique which projects an outline for preservative identification in meat samples. Results: The simulation results of the proposed meta-heuristic computer vision-based model demonstrate improved performance in comparison to the existing methods. Some of the prevailing machine learning-based methods were analyzed and compared from a survey of recent patents with the proposed technique in order to affirm new findings. The performance of PLS-DA model was quantified by receiver operating characteristics (ROC) curve at all classification thresholds. A maximum of 100% sensitivity and 71.21% specificity was obtained from optimum threshold of 0.5964. The concept of bootstrapping was used for evaluating the predicted model. Nitrosamine content in the meat samples was predicted with 0.8375 correlation coefficient and 0.109 bootstrap error. Conclusion: The proposed method comprehends double-cross validation technique which makes it more comprehensive in discriminating between the edibility of foodstuff which can certainly reinstate conventional methods and ameliorate existing computer-vision methods.

scholarly journals Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility

2020 ◽  
Vol 12 (22) ◽  
pp. 3675
Author(s):  
Subodh Chandra Pal ◽  
Alireza Arabameri ◽  
Thomas Blaschke ◽  
Indrajit Chowdhuri ◽  
Asish Saha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Degradation ◽  
Predictive Value ◽  
Research Study ◽  
Research Work ◽  
Gully Erosion ◽  
Training Dataset ◽  
Operating Characteristics ◽  
Boosted Regression Tree ◽  
Sensitivity Specificity

Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat to human life, as it is responsible for huge loss of surface soil. Therefore, gully erosion susceptibility (GES) mapping is necessary in order to reduce the adverse effect of land degradation and diminishes this type of harmful consequences. The principle goal of the present research study is to develop GES maps for the Garhbeta I Community Development (C.D.) Block; West Bengal, India, by using a machine learning algorithm (MLA) of boosted regression tree (BRT), bagging and the ensemble of BRT-bagging with K-fold cross validation (CV) resampling techniques. The combination of the aforementioned MLAs with resampling approaches is state-of-the-art soft computing, not often used in GES evaluation. In further progress of our research work, here we used a total of 20 gully erosion conditioning factors (GECFs) and a total of 199 gully head cut points for modelling GES. The variables’ importance, which is responsible for gully erosion, was determined based on the random forest (RF) algorithm among the several GECFs used in this study. The output result of the model’s performance was validated through a receiver operating characteristics-area under curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) statistical analysis. The predicted result shows that the ensemble of BRT-bagging is the most well fitted for GES where AUC value in K-3 fold is 0.972, whereas the value of AUC in sensitivity, specificity, PPV and NPV is 0.94, 0.93, 0.96 and 0.93, respectively, in a training dataset, and followed by the bagging and BRT model. Thus, from the predictive performance of this research study it is concluded that the ensemble of BRT-Bagging can be applied as a new approach for further studies in spatial prediction of GES. The outcome of this work can be helpful to policy makers in implementing remedial measures to minimize damages caused by gully erosion.

scholarly journals Machine learning approaches to classify melon landraces based on phenotypic traits

Genetika ◽  
2020 ◽  
Vol 52 (3) ◽  
pp. 1021-1029
Author(s):  
Rad Naroui ◽  
Gholamali Keykha ◽  
Jahangir Abbaskoohpayegani ◽  
Ramin Rafezi
Keyword(s):  
Machine Learning ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Partial Least Square ◽  
Support Vector ◽  
Phenotypic Traits ◽  
Learning Approaches ◽  
Operating Characteristics ◽  
Kappa Value ◽  
Classification And Regression

Phenotyping of native cultivars is becoming more essential, as they are an important for breeders as a genetic source for breeding. The variability of morphological properties plays critical role in melon breeding. In this paper various machine learning approaches were implemented to identify melon accession classes. A field experiment was conducted in Zahak Agriculture station to differentiate 144 melon accessions based on 14 traits. For this, Partial Least Square Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN) and Classification And Regression Trees (CART) were compared. The most commonly used performance values comprise overall accuracy, kappa value, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) were performed to identify accuracy of the models. The results showed the best performance for CART than others. The AUC and kappa value were 0.85 and 0.80 and fruit weight was the most important trait that affecting diversity in melon accessions. Regarding to these results Classification And Regression Trees (CART) is reliable for identification of melon accessions classes.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

scholarly journals Application of Blockchain for Supply Chain Financing: Explaining the Drivers Using SEM

2021 ◽  
Vol 7 (3) ◽  
pp. 167
Author(s):  
Mohammad Rokibul Kabir ◽  
Md. Aminul Islam ◽  
Marniati ◽  
Herawati
Keyword(s):  
Supply Chain ◽  
Technology Acceptance ◽  
Structural Equation ◽  
Research Work ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Innovation Research ◽  
Supply Chain Financing ◽  
The Impact

Owing to the lack of research in emerging Asian nations, this research aimed to unearth the determinants of blockchain acceptance for supply chain financing by a Bangladeshi financing company called IPDC. Centred on a technology acceptance framework called UTAUT (unified theory of acceptance and use of technology) and open innovation research, an expanded model with a mediating variable is developed for this study. This research work employs the deductive inference method in conjunction with the positivism paradigm. A structural questionnaire was used to gather data, which were then processed through Smart-PLS (partial least square) for SEM (structural equation modeling). The survey includes all the people who are directly or indirectly involved in the supply chain financing platform of IPDC. The study consists of seven direct hypotheses and one mediating hypothesis. The results show that all the direct hypotheses except the impact of social influence on the behavioural intention to use (BINTU) blockchain are significant. The mediating hypothesis indicating the role of BINTU in the relationship between facilitating conditions (FCON) and the actual use of blockchain is also supported. FCON and BINTU together explain 88.7% variation in blockchain use behaviour for supply chain financing. The research advances past findings by employing an expanded UTAUT framework and validating observations with the other relevant studies throughout the world.

scholarly journals A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Helder F. Castro ◽  
Jaime S. Cardoso ◽  
Maria T. Andrade
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Qualitative Analysis ◽  
Continuous Growth ◽  
Scientific Databases ◽  
Extensive Survey ◽  
Research Areas ◽  
Current State ◽  
Global View ◽  
Cooperative Production

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

Transfer learning based convolution neural net for authentication and classification of emotions from natural and stimulated speech signals

2021 ◽  
pp. 1-12
Author(s):  
Mukul Kumar ◽  
Nipun Katyal ◽  
Nersisson Ruban ◽  
Elena Lyakso ◽  
A. Mary Mekala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Research Work ◽  
Oral Communication ◽  
Neural Net ◽  
Emotion Classification ◽  
Style Transfer ◽  
Conventional Machine ◽  
Neural Network Algorithm

Over the years the need for differentiating various emotions from oral communication plays an important role in emotion based studies. There have been different algorithms to classify the kinds of emotion. Although there is no measure of fidelity of the emotion under consideration, which is primarily due to the reason that most of the readily available datasets that are annotated are produced by actors and not generated in real-world scenarios. Therefore, the predicted emotion lacks an important aspect called authenticity, which is whether an emotion is actual or stimulated. In this research work, we have developed a transfer learning and style transfer based hybrid convolutional neural network algorithm to classify the emotion as well as the fidelity of the emotion. The model is trained on features extracted from a dataset that contains stimulated as well as actual utterances. We have compared the developed algorithm with conventional machine learning and deep learning techniques by few metrics like accuracy, Precision, Recall and F1 score. The developed model performs much better than the conventional machine learning and deep learning models. The research aims to dive deeper into human emotion and make a model that understands it like humans do with precision, recall, F1 score values of 0.994, 0.996, 0.995 for speech authenticity and 0.992, 0.989, 0.99 for speech emotion classification respectively.

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