A Framework for Grading of White Chali Type Arecanuts with Machine Learning Algorithms

Grading of arecanuts before marketing fetches better prize. There are two universally accepted grading systems for arecanuts namely grading at the producers’ level and grading at the wholesale traders’ level. The major work in this paper is devoted to generation of a standard image database for the White Chali Type arecanuts and constructing a frame work for their grading by exploring the features of White Chali Type arecanut images for the first time. Further, two separate datasets are developed for the above grading systems by employing image feature extraction methods with 3500 and 4132 instances respectively. The arecanuts are graded using popular machine learning algorithms and the results are validated using ten fold cross validation. Multinomial logistic regression as classifier outperformed with classification accuracies of 98.8% and 92.69% for the producers’ level and the whole sale traders’ level grading systems respectively. Also the performances of various machine learning algorithms for the above two datasets are evaluated using weighted average values of True Positive rate, False Positive rate, precision, recall, F-measure and Cohen’s Kappa coefficient.

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Machine Learning for Automated Polyp Detection in Computed Tomography Colonography

Machine Learning ◽

10.4018/978-1-60960-818-7.ch407 ◽

2012 ◽

pp. 830-850

Author(s):

Abhilash Alexander Miranda ◽

Olivier Caelen ◽

Gianluca Bontempi

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

False Positive ◽

False Positive Rate ◽

Learning Algorithms ◽

Colorectal Polyps ◽

Machine Learning Algorithms ◽

Computed Tomography Colonography ◽

Positive Rate ◽

Independent Features

This chapter presents a comprehensive scheme for automated detection of colorectal polyps in computed tomography colonography (CTC) with particular emphasis on robust learning algorithms that differentiate polyps from non-polyp shapes. The authors’ automated CTC scheme introduces two orientation independent features which encode the shape characteristics that aid in classification of polyps and non-polyps with high accuracy, low false positive rate, and low computations making the scheme suitable for colorectal cancer screening initiatives. Experiments using state-of-the-art machine learning algorithms viz., lazy learning, support vector machines, and naïve Bayes classifiers reveal the robustness of the two features in detecting polyps at 100% sensitivity for polyps with diameter greater than 10 mm while attaining total low false positive rates, respectively, of 3.05, 3.47 and 0.71 per CTC dataset at specificities above 99% when tested on 58 CTC datasets. The results were validated using colonoscopy reports provided by expert radiologists.

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Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm

Agriculture ◽

10.3390/agriculture11050387 ◽

2021 ◽

Vol 11 (5) ◽

pp. 387

Author(s):

Nahina Islam ◽

Md Mamunur Rashid ◽

Santoso Wibowo ◽

Cheng-Yuan Xu ◽

Ahsan Morshed ◽

...

Keyword(s):

Machine Learning ◽

False Positive Rate ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Weed Detection ◽

Learning Techniques ◽

Positive Rate ◽

Uav Images

This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.

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LC-MS Peak Assignment Based on Unanimous Selection by Six Machine Learning Algorithms

10.21203/rs.3.rs-845859/v1 ◽

2021 ◽

Author(s):

Hiroaki Ito ◽

Takashi Matsui ◽

Ryo Konno ◽

Makoto Itakura ◽

Yoshio Kodera

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

False Positive Rate ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Weak Signals ◽

Accuracy And Precision ◽

Peak Assignment ◽

Positive Rate ◽

Assignment Strategy

Abstract Recent Mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance resulted in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

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Machine Learning for Automated Polyp Detection in Computed Tomography Colonography

Advances in Bioinformatics and Biomedical Engineering - Biomedical Image Analysis and Machine Learning Technologies ◽

10.4018/978-1-60566-956-4.ch003 ◽

2010 ◽

pp. 54-77

Author(s):

Abhilash Alexander Miranda ◽

Olivier Caelen ◽

Gianluca Bontempi

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

False Positive ◽

False Positive Rate ◽

Learning Algorithms ◽

Colorectal Polyps ◽

Machine Learning Algorithms ◽

Computed Tomography Colonography ◽

Positive Rate ◽

Independent Features

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Comparative Study of Various Machine Learning Algorithms for Prediction of Insomnia

Advances in Medical Technologies and Clinical Practice - Advanced Classification Techniques for Healthcare Analysis ◽

10.4018/978-1-5225-7796-6.ch011 ◽

2019 ◽

pp. 234-257 ◽

Cited By ~ 5

Author(s):

Ravinder Ahuja ◽

Vishal Vivek ◽

Manika Chandna ◽

Shivani Virmani ◽

Alisha Banga

Keyword(s):

Machine Learning ◽

Heart Diseases ◽

False Positive Rate ◽

Learning Algorithms ◽

True Positive Rate ◽

Machine Learning Algorithms ◽

Support Vector ◽

Mobility Problem ◽

Positive Rate ◽

F Measure

An early diagnosis of insomnia can prevent further medical aids such as anger issues, heart diseases, anxiety, depression, and hypertension. Fifteen machine learning algorithms have been applied and 14 leading factors have been taken into consideration for predicting insomnia. Seven performance parameters (accuracy, kappa, the true positive rate, false positive rate, precision, f-measure, and AUC) are used and for implementation. The authors have used python language. The support vector machine is giving higher performance out of all algorithms giving accuracy 91.6%, f-measure is 92.13, and kappa is 0.83. Further, SVM is applied on another dataset of 100 patients and giving accuracy 92%. In addition, an analysis of the variable importance of CART, C5.0, decision tree, random forest, adaptive boost, and XG boost is calculated. The analysis shows that insomnia primarily depends on the factors, which are the vision problem, mobility problem, and sleep disorder. This chapter mainly finds the usages and effectiveness of machine learning algorithms in Insomnia diseases prediction.

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LC–MS peak assignment based on unanimous selection by six machine learning algorithms

Scientific Reports ◽

10.1038/s41598-021-02899-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hiroaki Ito ◽

Takashi Matsui ◽

Ryo Konno ◽

Makoto Itakura ◽

Yoshio Kodera

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

False Positive Rate ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Weak Signals ◽

Accuracy And Precision ◽

Positive Rate ◽

Assignment Strategy ◽

Performance Results

AbstractRecent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance results in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

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Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Applied Sciences ◽

10.3390/app8081280 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1280 ◽

Cited By ~ 14

Author(s):

Yong Kim ◽

Youngdoo Son ◽

Wonjoon Kim ◽

Byungki Jin ◽

Myung Yun

Keyword(s):

Neural Network ◽

Machine Learning ◽

Monitoring System ◽

Multinomial Logistic Regression ◽

Learning Algorithms ◽

Feedback System ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Future Research ◽

Support Vector

Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children’s chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

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Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms

10.21203/rs.3.rs-514771/v2 ◽

2021 ◽

Author(s):

Prasannavenkatesan Theerthagiri ◽

Usha Ruby A ◽

Vidya J

Keyword(s):

Machine Learning ◽

Multilayer Perceptron ◽

Nearest Neighbor ◽

False Positive Rate ◽

Learning Algorithms ◽

False Negative ◽

False Negative Rate ◽

Disease Diagnosis ◽

Machine Learning Algorithms ◽

K Nearest Neighbor

Abstract Diabetes mellitus is characterized as a chronic disease may cause many complications. The machine learning algorithms are used to diagnosis and predict the diabetes. The learning based algorithms plays a vital role on supporting decision making in disease diagnosis and prediction. In this paper, traditional classification algorithms and neural network based machine learning are investigated for the diabetes dataset. Also, various performance methods with different aspects are evaluated for the K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron algorithms. It supports the estimation on patients suffering from diabetes in future. The results of this work shows that the multilayer perceptron algorithm gives the highest prediction accuracy with lowest MSE of 0.19. The MLP gives the lowest false positive rate and false negative rate with highest area under curve of 86 %.

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Credit-worthiness Prediction in Energy-Saving Finance Using Machine Learning Model

Studies of Applied Economics ◽

10.25115/eea.v39i10.5571 ◽

2021 ◽

Vol 39 (10) ◽

Author(s):

Eka Sudarmaji ◽

Noer Azam Achsani ◽

Yandra Arkeman ◽

Idqan Fahmi

Keyword(s):

Machine Learning ◽

Energy Saving ◽

Test Data ◽

Life Cycle Cost ◽

Multinomial Logistic Regression ◽

False Positive Rate ◽

True Positive Rate ◽

Factors Affecting ◽

Positive Rate ◽

Sensitivity Rate

Companies can form their own "ESCO model" with their capitals. Unfortunately, customer's creditworthiness was becoming more crucial for ESCO. Machine learning was used to predict the creditworthiness of clients in ESCO financing processes. This research aimed to develop a scoring model to leverage a machine learning and life cycle cost analysis (LCCA) to evaluate alternative financing for Energy Saving in Indonesia. The results of calculations using multinomial logistic regression showed that the accuracy value of prediction data with test data was 88.3562 %. The prediction rate result that refers to the percentage of correct predictions among all test data was 91.67%, and False Positive Rate (FPR) was 39.44%. The True Positive Rate was called Recall or 'Sensitivity Rate' as it was defined as several positive cases that were correctly identified (TPR) was 92.20%. We found the machine learning methods for creditworthiness prediction in retrofitting projects were fresh and worth a shot. It was hoped that this new practice would grow in popularity and become standard among ESCOs. Unfortunately, current machine-learning-based creditworthiness scoring practices lacked explain ability and interpretability. Unfortunately, ESCO must penalize the retrofitting project. As a result, since retrofitting was a new industry, the credit approval process was challenging to communicate to consumers. The most important thing for ESCO to deal with the project was to have a friendship and know-how with the client. Research from these case studies led to a clearer understanding of the factors affecting all parties' decisions to implement and continue with their ESCO project.

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Malicious Intrusion Detection Using Machine Learning Schemes

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8839.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4194-4198

Keyword(s):

Machine Learning ◽

Wireless Networks ◽

Intrusion Detection ◽

False Positive Rate ◽

Feature Selection Method ◽

Training Model ◽

True Positive Rate ◽

Machine Learning Algorithms ◽

Detection Mechanism ◽

Positive Rate

Wireless networks are continuously facing challenges in the field of Information Security. This leads to major researches in the area of Intrusion detection. The working of Intrusion detection is performed mainly by signature based detection and anomaly based detection. Anomaly based detection is based on the behavior of the network. One of the major challenge in this domain is to identify and detect the malicious node in wireless networks. The intrusion detection mechanism has to analyse the behavior of the node in the network by means of the several features possessed by each node. Intelligent schemes are the need of the hour in such scenario. This paper has taken a standard dataset for studying the features of the wireless node and reduced the features by applying the most efficient Correlation Attribute feature selection method. The machine learning algorithms are applied to obtain an effective training model which is then applied on the testing dataset to validate the model. The accuracy of the model is determined by the performance parameters such as true positive rate, false positive rate and ROC area. Neural network, bagging and decision tree algorithm RepTree are giving promising results in comparison with other classification algorithms.

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