Journal articles on the topic 'Machine learning not elsewhere classified'
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Yao, Hannah, Sina Rashidian, Xinyu Dong, Hongyi Duanmu, Richard N. Rosenthal, and Fusheng Wang. "Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach." Journal of Medical Internet Research 22, no. 11 (November 27, 2020): e15293. http://dx.doi.org/10.2196/15293.
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Background In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns. Objective This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic. Methods Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data. Results Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments. Conclusions Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target.
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Hassan, Md Shareful, Md Tariqul Islam, and Mohammad Amir Hossain Bhuiyan. "Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling." Journal of Hyperspectral Remote Sensing 11, no. 3 (December 20, 2021): 136. http://dx.doi.org/10.29150/2237-2202.2021.251959.
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This paper investigates the probable nexus between methane (CH4) and air pollutants, a public health hazard in Bangladesh. The hypothesis considers that the concentration of CH4 is dependent on the ten air pollutants found in the five districts in Dhaka Division, a major urban and industrial area in Bangladesh. These pollutants are: Particular matters (PM2.5), Nitrogen dioxide (NO2), Nitrogen oxide (NOx), Aerosol optical thickness (AOT), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Black carbon (BC), Formaldehyde (HCHO) and Dust. The study applies Machine Learning (ML) technique and Geographically Weighted Regression (GWR) Modeling. Temporal CH4 datasets from the Sentinel-5P sensor are classified to estimate the annual CH4 concentration during 2019-2021.Seven supervised classifiers of ML coupled with the GWR model are used to predict the statistical and spatial relationships. CH4 increases gradually during 2018-2021 in Dhaka, Gazipur, and Munshiganj Districts. It relates differently with various air pollutants, e.g., positively with BC, Dust, NO2, PM2.5, O3, and AOT, and negatively with NOx, CO, HCHO, and SO2.This study results that Rational quadratic (RMSE-0.001, MAE-0.001, R2-0.96), Random Forest (RMSE-0.004, MAE-0.003, R2-0.91), and Stepwise regression (RMSE-0.002, MAE-0.002, R2-0.87) are the suitable method in ML. The highest goodness-of-fit (R2) of 82%-96% is found in Dhaka and Narshingdi Districts. The key findings may help formulate the appropriate action plan to mitigate ongoing and future air pollution in Bangladesh. In addition, the methodology of the research may be applicable elsewhere nationally and internationally for air pollution research.
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Zhang, Meiling, Miaojun Zhu, Qin Hu, Chun Li, Zhenhua Zhu, Yingyong Hou, Jing Xu, et al. "Genome-wide microRNA expression profiling in malignant pleural effusion to identify a ten-microRNA signature." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e23123-e23123. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e23123.
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e23123 Background: Pleural carcinosis caused by tumors of the chest (e.g., lung and breast cancer) or elsewhere in the body (e.g., ovarian carcinoma) that metastasize to the visceral and/or parietal pleura. Recurrent malignant pleural effusion due to pleural carcinosis is one of the most common findings in oncology. The aim of the study was to identify a miRNA signature associated with clinicians’ prescription of patients with MPE. Methods: We used qRT-PCR assay to evaluate a wide range miRNAs (1920 miRNAs) signature measured from pleural effusion between patients with cancer and healthy controls. From the data of our previous studies we selected a panel of 96 miRNAs related to malignant pleural effusion. Data were compared by three classification methods software for statistics and modeling. We used a first set of 77 pleural effusion samples as training group, including 27 patients affected by Lung Adenocarcinomas (LAC), 9 with other lung cancer, 17 with other tumors and 24 inflammatory patients as negative controls. Moreover, we used more 64 pleural effusion samples for double-blind predictive study. Data analysis was performed using a machine learning approach of a Support Vector Machine classifier with a Student's t-test feature selection procedure. Results: We identified a panel of ten miRNAs with optimum classification performance. The combination of these 10-miRNAs alone could discriminate MPE cases from negative controls with an AUC of 0.969 (accuracy = 93.5%; specificity = 91.7%). The selected panel of another 10-miRNAs could separate lung cancer cases from negative controls with an AUC of 0.973 (accuracy = 94.8%; specificity = 95.8%), and a small panel of 4-miRNAs could good discriminate LAC cases from negative controls with an AUC of 0.946 (accuracy 88.9%; specificity = 100%). The accuracy rate of the double-blind predictive sensitivity value was 90.9% and specificity value was 95.8% for MPE patients with lung cancer of miRNA signature. Conclusions: Our miRNAs profile may serve as a new biomarker for MPE diagnosis. Otherwise, we identified a 10-miRNAs signature for MPE patients with lung cancer and a 4-miRNAs for MPE patients with lung adenocarcinoma.
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Chapman, Alec B., Kelly Peterson, Wathsala Widanagamaachchi, and Makoto M. Jones. "616. Predicting Misdiagnoses of Infectious Disease in Emergency Department Visits." Open Forum Infectious Diseases 8, Supplement_1 (November 1, 2021): S411. http://dx.doi.org/10.1093/ofid/ofab466.814.
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Abstract Background Diagnostic error leads to delays of care and mistaken therapeutic decisions that can cascade in a downward spiral. Thus, it is important to make accurate diagnostic decisions early on in the clinical care process, such as in the emergency department (ED). Clinical data from the Electronic Health Record (EHR) could identify cases where an initial diagnosis appears unusual in context. This capability could be developed into a quality measure for feedback. To that end, we trained a multiclass machine learning classifier to predict infectious disease diagnoses following an ED visit. Methods To train and evaluate our classifier, we sampled ED visits between December 31, 2016, and December 31, 2019, from Veterans Affairs (VA) Corporate Data Warehouse (CDW). Data elements used for prediction included lab orders and results, medication orders, radiology procedures, and vital signs. A multiclass XGBoost classifier was trained to predict one of five infectious disease classes for each ED visit based on the clinical variables extracted from CDW. Our model was trained on an enriched sample of 916,562 ED visits and evaluated on a non-enriched blind testing set of 356,549 visits. We compared our model against an ensemble of univariate Logistic Regression models as a baseline. Our model was trained to predict for an ED visit one of five infectious disease classes or “No Infection”. Labels were assigned to each ED visit based on ICD-9/10-CM diagnosis codes used elsewhere and other structured EHR data associated with a patient between 24 hours prior to an ED visit and 48 hours after. Results Classifier performance varied across each of the five disease classes (Table 1). The classifier achieved the highest F1 and AUC for UTI, the lowest F1 for Sepsis, and the lowest AUC for URI. We compared the average precision, recall and F1 scores of the multiclass XGBoost with the ensemble of Logistic Regression models (Table 2). XGBoost achieved higher scores in all three metrics. Table 1. Classification performance XGBoost testing set performance in each disease class, visits with no labels, and macro average. The infectious disease classes with the highest score in each metric are shown in bold. Table 2. Baseline comparison Macro average scores for XGBoost and baseline classifiers. Conclusion We trained a model to predict infectious disease diagnoses in the Emergency Department setting. Future work will further explore this technique and combine our supervised classifier with additional signs of medical error such as increased mortality or anomalous treatment patterns in order to study medical misdiagnosis. Disclosures All Authors: No reported disclosures
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Spelda, Petr, and Vit Stritecky. "Human Induction in Machine Learning." ACM Computing Surveys 54, no. 3 (June 2021): 1–18. http://dx.doi.org/10.1145/3444691.
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As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, then an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The article asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach “elsewhere” in space and time or deploy ML models in non-benign environments. The article argues that the only viable version of the contract can be based on optimality (instead of on reliability, which cannot be justified without circularity) and aligns this position with Schurz's optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (“elsewhere” and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full.
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Cauvin, Bertrand, and Pierre Benning. "Machine Learning." International Journal of 3-D Information Modeling 6, no. 3 (July 2017): 1–16. http://dx.doi.org/10.4018/ij3dim.2017070101.
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A Bridge Data Dictionary contains an exhaustive list of terms used in the field of bridges. These terms are classified in systems in order to avoid any lacks, to identify all the expected object attributes, and to allow machines to understand the associated concepts. The main objectives of a Bridge Data Dictionary are many: ensure the sustainability of information over time; facilitate information exchange between the actors of the same project; ensure interoperability between the software packages. Other objectives have been reached during the process: to test a working methodology to be applied by other infrastructure domains (Roads, Rails, Tunnels, etc.); to check the current functions and capabilities of a buildingSMART Data Dictionary platform; and to define a common term list, in order to facilitate standardization and IFC-Bridge classes' development.
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Raji-Lawal, H. Y., A. O. Oloyede, O. Aiyeniko, P. E. Ishola, T. T. Ajagbe, and A. Abayomi-Alli. "ENSEMBLE OF MACHINE LEARNING CLASSIFIERS FOR SCHIZOPHRENIA DETECTION." Caleb International Journal of Development Studies 05, no. 02 (December 3, 2022): 386–405. http://dx.doi.org/10.26772/cijds-2022-05-02-20.
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Schizophenia disease is characterized by odd behavior, weird speech and decreased capacity to apprehend reality. The diagnosis of schizophrenia requires a complete and detailed medical examination. Machine learning has also helped computer scientists to classify and diagnose schizophrenia using neuroimaging data. This research implored the use of computer aided diagnosis to classify neuroimaging data of schizophrenia. The dataset of 86 instances which include 40 schizophenia patients, 46 healthy patients and 32 variable. They were obtained from Kaggle MLSF 2014 classification challenge and augmented due to small sized using synthetic minority oversampling technique (SMOTE). This yielded a larger data set of 1806 instances. The augmented data set were classified using machine learning algorithms support vector machine, K-neareast neighbours, logistic regression, Naïve bayes, artificial neural network. 350 instances was used for the training (70%) and 150 instances was used for testing (30%), KNN and SVM correctly classified 162 as Schizophrenia patients and classified 188 as healthy control, Tree correctly classified 159 as schizophrenia, mis-classified 3 as schizophrenia, correctly classified 185 as healthy and mis-classified 3 as healthy control, Logistic Regression correctly classified 139 as schizophrenia, mis-classified 23 as schizophrenia, correctly classified 170 as healthy and mis-classified 18 as healthy control, Naive Bayes correctly classified 139 as schizophrenia, mis- classified 23 as schizophrenia, correctly classified 166 as healthy and mis-classified 22 as healthy control. ANN used 549instances, 60% for training, 20% for testing and 20% for validation got an accuracy of 100%, this makes it the best classification method.
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Yuyun, Yuyun. "KLASIFIKASI SURAT DIGITAL MENGGUNAKAN ALGORITMA MACHINE LEARNING." JURNAL IT 13, no. 2 (August 30, 2023): 66–71. http://dx.doi.org/10.37639/jti.v13i2.350.
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Penelitian ini mengimplementasikan algoritm algoritma naive bayes dalam proses klasifikasi surat dan untuk membangun sistem yang dapat mengklasifikasi surat secara. Dalam penelitian ini jumlah sampel data corpus surat 1036 record, yang dibagi dalam 80% training dan 20% testing. Sampel data training algoritma Naïve Bayes di implementasikan dengan menghitung nilai bobot dari class tertinggi berdasarkan data training dengan data testing sehingga menghasilkan probabilitas tertinggi. Hasil pengolahan data mendapatkan nilai Correctly Classified Instance sebesar 86.245799% dan Incoreectly Classified Instance sebesar 13.754200% serta hasil pengujian dengan menggunakan confusion matrix mendapatkan nilai precision sebesar 86%, Recall 86 % dan Akurasi sebesar 76%.
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Hiray, Prof S. R. "Book Recommendation System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1981–83. http://dx.doi.org/10.22214/ijraset.2021.39658.
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Abstract: Users can use book recommendation systems to search and select books from a number of options available on the web or elsewhere electronic sources. They give the user a little bit selection of products that fit the description, given a large group of objects and a description of the user needs. Our system will simply provide recommendations. Recommendations are based on previous user activity, such as purchase, habits, reviews, and likes. These systems gain lot of interest. In the proposed system, we have a big problem: when the user buys book, we want to recommend some books that the user can enjoy. Buyers also have a great deal of options when it comes to recommending the best and most appropriate books for them. User development privacy while placing small and minor losses of accuracy. Recommendations. The proposed recommendation system will provide user's ability to view and search the publications and using the Support Vector Machine (SVM), will list the most purchased and top rated books based on the subject name given as input. Keywords: Recommender System, Support Vector Machine (SVM), Machine Learning, Classification etc.
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Kulkarni, Prasad, Suyash Karwande, Rhucha Keskar, Prashant Kale, and Sumitra Iyer. "Fake News Detection using Machine Learning." ITM Web of Conferences 40 (2021): 03003. http://dx.doi.org/10.1051/itmconf/20214003003.
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Everyone depends upon various online resources for news in this modern age, where the internet is pervasive. As the use of social media platforms such as Facebook, Twitter, and others has increased, news spreads quickly among millions of users in a short time. The consequences of Fake news are far-reaching, from swaying election outcomes in favor of certain candidates to creating biased opinions. WhatsApp, Instagram, and many other social media platforms are the main source for spreading fake news. This work provides a solution by introducing a fake news detection model using machine learning. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data are Random Forest, Logistic Regression, Decision Tree, KNN and Gradient Booster. Based on the output received the data is classified either as true or false data. Based on that, the user can find out whether the given news is fake or not on the webserver.
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Krasnopolsky, Vladimir. "Applying Machine Learning in Numerical Weather and Climate Modeling Systems." Climate 12, no. 6 (May 26, 2024): 78. http://dx.doi.org/10.3390/cli12060078.
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In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered as a simple introduction to the problem.
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Levitt, Joshua, Muhammad M. Edhi, Ryan V. Thorpe, Jason W. Leung, Mai Michishita, Suguru Koyama, Satoru Yoshikawa, et al. "Pain phenotypes classified by machine learning using electroencephalography features." NeuroImage 223 (December 2020): 117256. http://dx.doi.org/10.1016/j.neuroimage.2020.117256.
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Mishra, Debani Prasad, Sanhita Mishra, Smrutisikha Jena, and Surender Reddy Salkuti. "Image classification using machine learning." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (September 1, 2023): 1551. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1551-1558.
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The objective of this paper is to implement different tools available in machine learning/artificial intelligence to classify faces and identify different features, highlights, and correlations or similarities between different celebrity faces which can apply in everyday security purposes to identity virtually if the authorized personnel is using certain access or not. The material present in this paper is a literature review of a machine learning model developed by the students. This is a classical problem of machine learning executed using a support vector machine. Images are separated based on sub-images. Each sub-image has been classified into a responsive class by an artificial neural network. The website then fetches the data from the back end and classifies the image into the corresponding personal.
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Parhusip, Hanna Arini, Bambang Susanto, Lilik Linawati, Suryasatriya Trihandaru, Yohanes Sardjono, and Adella Septiana Mugirahayu. "Classification Breast Cancer Revisited with Machine Learning." International Journal on Data Science 1, no. 1 (May 7, 2020): 42–50. http://dx.doi.org/10.18517/ijods.1.1.42-50.2020.
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The article presents the study of several machine learning algorithms that are used to study breast cancer data with 33 features from 569 samples. The purpose of this research is to investigate the best algorithm for classification of breast cancer. The data may have different scales with different large range one to the other features and hence the data are transformed before the data are classified. The used classification methods in machine learning are logistic regression, k-nearest neighbor, Naive bayes classifier, support vector machine, decision tree and random forest algorithm. The original data and the transformed data are classified with size of data test is 0.3. The SVM and Naive Bayes algorithms have no improvement of accuracy with random forest gives the best accuracy among all. Therefore the size of data test is reduced to 0.25 leading to improve all algorithms in transformed data classifications. However, random forest algorithm still gives the best accuracy.
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Hang, Weiqiang, and Timothy Banks. "Machine learning applied to pack classification." International Journal of Market Research 61, no. 6 (April 10, 2019): 601–20. http://dx.doi.org/10.1177/1470785319841217.
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Pack or product classification is a quite common task in market research, particularly for sales tracking audits and related services. Electronic data sources have led to increased volumes, both in the sales volume being tracked and also the number of packs (or stock keeping units). The increase in packs needing to be classified presents a problem, in that, it needs to be done accurately and quickly. Traditional solutions using people for the classifications can be costly, due to the large number of people required to process the classifications in a timely and accurate manner. Reducing the manual work is a priority for audit-based market research businesses, leading to interest in automation, such as through machine learning techniques. In this article, we apply such methods. These include support vector machine, decision tree, XGBoost, AdaBoost, random forest, and neural network–based methods that are trained on the textual descriptions of already classified packs. We also implement a hierarchical classification method to take advantage of the structure of classes of the products. Once the models are trained, they can be used on unclassified data. Where the methods are not confident in their classifications, humans can be asked to classify. The hope is that the methods can learn to classify accurately enough that the manual workloads are reduced to manageable levels. This article reviews various methods and then outlines tests using these methods on two datasets collected by Nielsen, showing good performance.
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Qin, Yuping, Hamid Reza Karimi, Dan Li, Shuxian Lun, and Aihua Zhang. "A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm." Abstract and Applied Analysis 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/894246.
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A Mahalanobis hyperellipsoidal learning machine class incremental learning algorithm is proposed. To each class sample, the hyperellipsoidal that encloses as many as possible and pushes the outlier samples away is trained in the feature space. In the process of incremental learning, only one subclassifier is trained with the new class samples. The old models of the classifier are not influenced and can be reused. In the process of classification, considering the information of sample’s distribution in the feature space, the Mahalanobis distances from the sample mapping to the center of each hyperellipsoidal are used to decide the classified sample class. The experimental results show that the proposed method has higher classification precision and classification speed.
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Dhyani, Ritika. "Song Classification using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 3760–64. http://dx.doi.org/10.22214/ijraset.2023.50890.
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Abstract: The classification of music by genre is crucial in the modern world since the number of music tracks, both online and offline, is growing quickly. We must appropriately index them in order to have greater access to them. To retrieve music from a vast collection, automatic music genre classification is crucial. The majority of the current methods for categorising music genres rely on machine learning. We give a music dataset with ten distinct genres in this article. The system is trained and classified using a Deep Learning technique. Convolution neural networks are employed in this instance for training and classification. For audio analysis, feature extraction is the most important step. For sound samples, the Mel Frequency Cepstral Coefficient (MFCC) is employed as a feature vector. The suggested technique uses feature vector extraction to categorise music into different genres. Our findings indicate that our system's accuracy level is approximately 76%, which will significantly increase and facilitate the automatic classification of musical genres.
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Zhang, Yanxia, Yongheng Zhao, and Xue-Bing Wu. "Classification of 4XMM-DR9 sources by machine learning." Monthly Notices of the Royal Astronomical Society 503, no. 4 (April 17, 2021): 5263–73. http://dx.doi.org/10.1093/mnras/stab744.
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ABSTRACT The ESA’s X-ray Multi-mirror Mission (XMM–Newton) created a new high-quality version of the XMM–Newton serendipitous source catalogue, 4XMM-DR9, which provides a wealth of information for observed sources. The 4XMM-DR9 catalogue is correlated with the Sloan Digital Sky Survey (SDSS) DR12 photometric data base and the AllWISE data base; we then get X-ray sources with information from the X-ray, optical, and/or infrared bands and obtain the XMM–WISE, XMM–SDSS, and XMM–WISE–SDSS samples. Based on the large spectroscopic surveys of SDSS and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST), we cross-match the XMM–WISE–SDSS sample with sources of known spectral classes, and obtain known samples of stars, galaxies, and quasars. The distribution of stars, galaxies, and quasars as well as all spectral classes of stars in 2D parameter space is presented. Various machine-learning methods are applied to different samples from different bands. The better classified results are retained. For the sample from the X-ray band, a rotation-forest classifier performs the best. For the sample from the X-ray and infrared bands, a random-forest algorithm outperforms all other methods. For the samples from the X-ray, optical, and/or infrared bands, the LogitBoost classifier shows its superiority. Thus, all X-ray sources in the 4XMM-DR9 catalogue with different input patterns are classified by their respective models that are created by these best methods. Their membership of and membership probabilities for individual X-ray sources are assigned. The classified result will be of great value for the further research of X-ray sources in greater detail.
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Padovani de Souza, Kleber, João Carlos Setubal, André Carlos Ponce de Leon F. de Carvalho, Guilherme Oliveira, Annie Chateau, and Ronnie Alves. "Machine learning meets genome assembly." Briefings in Bioinformatics 20, no. 6 (August 17, 2018): 2116–29. http://dx.doi.org/10.1093/bib/bby072.
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Abstract Motivation: With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable execution time exists. However, several tools that produce approximate solutions have been used with results that have facilitated scientific discoveries, although there is ample room for improvement. As with other NP-hard problems, machine learning algorithms have been one of the approaches used in recent years in an attempt to find better solutions to the DNA fragment assembly problem, although still at a low scale. Results: This paper presents a broad review of pioneering literature comprising artificial intelligence-based DNA assemblers—particularly the ones that use machine learning—to provide an overview of state-of-the-art approaches and to serve as a starting point for further study in this field.
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Angle, Sachi, B. Ashwath Rao, and S. N. Muralikrishna. "Kannada morpheme segmentation using machine learning." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 45. http://dx.doi.org/10.14419/ijet.v7i2.31.13395.
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This paper addresses and targets morpheme segmentation of Kannada words using supervised classification. We have used manually annotated Kannada treebank corpus, which is recently developed by us. Kannada bears resemblance to other Dravidian languages in morphological structure. It is an agglutinative language, hence its words have complex morphological form with each word comprising of a root and an optional set of suffixes. These suffixes carry additional meaning, apart from the root word in a context. This paper discusses the extraction of morphemes of a word by using Support Vector Machines for Classification. Additional features representing the properties of the Kannada words were extracted and the different letters were classified into labels that result in the morphological segmentation of the word. Various methods for evaluation were considered and an accuracy of 85.97% was achieved.
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Carnivali, Gustavo Simões. "Machine Learning Method to Differentiate Ataxias." International Journal of Applied Mathematics and Machine Learning 15, no. 1 (September 10, 2021): 53–67. http://dx.doi.org/10.18642/ijamml_710012230.
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Spinocerebellar ataxias or SCAs, are a group of more than 37 genetically and clinically heterogeneous known neurodegenerative diseases. This work analyzes the level of genetic similarity between several ataxias, we identified proteins that are associated with more than one ataxia. A decision tree was trained to identify ataxias by identifying whether a new entry disease not yet identified and not classified can be grouped as an ataxia. Altogether 12 proteins from different ataxias were verified, all 12 proteins were analyzed in 500 different trees to better evaluate the method used. Of the 12 proteins tested, the method was correct for 10 different proteins or 83% of correct results. This identifier and the results obtained in the experiments allow a greater characterization of the diseases addressed, it also allows applications such as the reuse of treatments for similar diseases.
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Bergadano, F., and D. Gunetti. "Learning relations and logic programs." Knowledge Engineering Review 9, no. 1 (March 1994): 73–77. http://dx.doi.org/10.1017/s0269888900006615.
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Inductive Logic Programming (ILP) is an emerging research area at the intersection of machine learning, logic programming and software engineering. The first workshop on this topic was held in 1991 in Portugal (Muggleton, 1991). Subsequently, there was a workshop tied to the Future Generation Computer System Conference in Japan in 1992, and a third one in Bled, Slovenia, in April 1993 (Muggleton, 1993). Ideas related to ILP are also appearing in major AI and machine learning conferences and journals. Although European-based and mainly sponsored by ESPRIT, ILP aims at becoming equally represented elsewhere; for example, among researchers in America who are investigating relational learning and first order theory revision (see, for example, the papers in Birnbaum and Collins, 1991) and within the computational learning theory community. This year's IJCAI workshop on ILP is a first step in this direction, and includes recent work with a broader range of perspectives and techniques.
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YAVUZ, Murat, and İbrahim TÜRKOĞLU. "Classification of Marble Types Using Machine Learning Techniques." Afyon Kocatepe Üniversitesi Uluslararası Mühendislik Teknolojileri ve Uygulamalı Bilimler Dergisi 6, no. 1 (June 15, 2023): 33–42. http://dx.doi.org/10.53448/akuumubd.1268931.
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Natural stones are one of the indispensable elements of people from shelter to weapons. Among these stone types, marbles and marble-derived products are among the objects that people always prefer, from bathroom to kitchen, from garden design to small decorative home decorations. While the marbles are named according to the regions where they are extracted, their types and qualities are classified based on observation by people who are qualified as experts in this field. This classification, which is made by experts based on observation, carries risks in economic terms, increases the workload and is a difficult process with a high error rate. These processes need a fast, easy and highly accurate digital transformation. In this study, feature extraction was done by using deep learning in the species classification of marbles. The extracted features were classified using machine learning techniques. As a result of the application made with the data set consisting of 3703 marble and marble-derived natural stone images belonging to 28 different species, a classification success of 99.7% was obtained with the DenseNet deep learning model and the K-Nearest Neighbor method.
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Kim, Gun Il, Sungtae Kim, and Beakcheol Jang. "Classification of mathematical test questions using machine learning on datasets of learning management system questions." PLOS ONE 18, no. 10 (October 18, 2023): e0286989. http://dx.doi.org/10.1371/journal.pone.0286989.
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Every student has a varied level of mathematical proficiency. Therefore, it is important to provide them with questions accordingly. Owing to advances in technology and artificial intelligence, the Learning Management System (LMS) has become a popular application to conduct online learning for students. The LMS can store multiple pieces of information on students through an online database, enabling it to recommend appropriate questions for each student based on an analysis of their previous responses to questions. Particularly, the LMS manages learners and provides an online platform that can evaluate their skills. Questions need to be classified according to their difficulty level so that the LMS can recommend them to learners appropriately and thereby increase their learning efficiency. In this study, we classified large-scale mathematical test items provided by ABLE Tech, which supports LMS-based online mathematical education platforms, using various machine learning techniques according to the difficulty levels of the questions. First, through t-test analysis, we identified the significant correlation variables according to the difficulty level. The t-test results showed that answer rate, type of question, and solution time were positively correlated with the difficulty of the question. Second, items were classified according to their difficulty level using various machine learning models, such as logistic regression (LR), random forest (RF), and extreme gradient boosting (xgboost). Accuracy, precision, recall, F1 score, the area under the curve of the receiver operating curve (AUC-ROC), Cohen’s Kappa and Matthew’s correlation coefficient (MCC) scores were used as the evaluation metrics. The correct answer rate, question type, and time for solving a question correlated significantly with the difficulty level. The machine learning-based xgboost model outperformed the statistical machine learning models, with a 85.7% accuracy, and 85.8% F1 score. These results can be used as an auxiliary tool in recommending suitable mathematical questions to various learners based on their difficulty level.
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Iqbal, Mohmad, and AK Madan. "MACHINE LEARNING BASED FAULTY BEARING DIAGNOSIS IN CNC MACHINE." International Journal of Engineering Applied Sciences and Technology 8, no. 2 (June 1, 2023): 37–41. http://dx.doi.org/10.33564/ijeast.2023.v08i02.005.
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The prediction of faulty bearing in rotating machineries like CNC machine, induction motor, wind turbine etc. is very important. Bearings are essential parts of such machines and mechanical systems to reduce friction between moving parts and to support the weight of rotating machineries. The noise produced by the machine can make it difficult to detect a fault or diagnose a problem. This is because the noise can mask or obscure the signal that would indicate a fault. To overcome this challenge, researchers may need to use advanced signal processing techniques to separate the signal of interest from the background noise. In this proposed work the vibration signal responses of CNC machine bearing was studied during faulty and normal bearing conditions. Early faulty bearing diagnosis was made using Support Vector Machines (SVM) to identify whether a bearing is faulty or not, what type of fault it has (inner race, outer race, or rolling element fault). This model is effective when there is a clear boundary between the classes by finding a hyper plane that separates the data into different classes. To decompose the signal Fourier transform is used to analyze signals in the frequency domain. It decomposes a signal into its constituent frequencies. Once the model is trained and tested, we can visualize the accuracy, precision, recall, and F1 score using confusion matrix to show how many normal and faulty behavior instances were correctly or incorrectly classified by the model.
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Ishikawa, T., A. Hayashi, S. Nagamatsu, Y. Kyutoku, I. Dan, T. Wada, K. Oku, et al. "CLASSIFICATION OF STRAWBERRY FRUIT SHAPE BY MACHINE LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 463–70. http://dx.doi.org/10.5194/isprs-archives-xlii-2-463-2018.
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Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs + CCS + EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.
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chougale, Jeevan, Abhishek Shinde, Ninad Deshmukh, Dhananjay Sawant, and Vaishali Latke. "House Price Prediction using Machine learning and Image Processing." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 18, 2021): 961–65. http://dx.doi.org/10.51201/jusst/21/05280.
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We demonstrate that these urban features can be recorded by street views and satellite image data and enhance the estimate of house prices. In order to estimate house prices in London, UK, we recommend a pipeline that uses a deep neural network model to automatically extract visual features from images. In calculating the house price model, we use typical housing characteristics, such as age, size, and accessibility, as well as visual features from Google Street View images and Bing aerial pictures. We see promising outcomes where learning to describe a neighborhood’s urban efficiency facilitates the estimation of house prices, even when generalizing to previously unseen London boroughs. We discuss the use of non-linear vs. linear approaches to combine these signals with traditional house pricing models and explain how the interpretability of linear models helps one to specifically derive the visual desirability of neighborhoods as proxy variables that are both of importance in their own right and can be used as inputs to other econometric methods. This is particularly useful as it can be extended elsewhere after the network has been trained with the training data, enabling us to produce vivid complex maps of the desirability of London streets.
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Amari, Shun-ichi, Naotake Fujita, and Shigeru Shinomoto. "Four Types of Learning Curves." Neural Computation 4, no. 4 (July 1992): 605–18. http://dx.doi.org/10.1162/neco.1992.4.4.605.
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If machines are learning to make decisions given a number of examples, the generalization error ε(t) is defined as the average probability that an incorrect decision is made for a new example by a machine when trained with t examples. The generalization error decreases as t increases, and the curve ε(t) is called a learning curve. The present paper uses the Bayesian approach to show that given the annealed approximation, learning curves can be classified into four asymptotic types. If the machine is deterministic with noiseless teacher signals, then (1) ε ∼ at-1 when the correct machine parameter is unique, and (2) ε ∼ at-2 when the set of the correct parameters has a finite measure. If the teacher signals are noisy, then (3) ε ∼ at-1/2 for a deterministic machine, and (4) ε ∼ c + at-1 for a stochastic machine.
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Kuthe, Annaji, Chaitanya Bhake, Vaibhav Bhoyar, Aman Yenurkar, Vedant Khandekar, and Ketan Gawale. "Water Quality Prediction Using Machine Learning." International Journal of Computer Science and Mobile Computing 12, no. 4 (April 30, 2023): 52–59. http://dx.doi.org/10.47760/ijcsmc.2023.v12i04.006.
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Different toxins have been imperiling water quality over the past decades. As a result, foreseeing and modeling water quality have gotten to be basic to minimizing water contamination. This inquiry has created a classification calculation to foresee the water quality classification (WQC). The WQC is classified based on the water quality file (WQI) from 7 parameters in a dataset utilizing Back Vector Machine (SVM) and Extraordinary Gradient Boosting (XGBoost). The comes about from the proposed model can precisely classify the water quality based on their features. The inquire about result illustrated that the XGBoost model performed way better, with an exactness of 94%, compared to the SVM demonstrate, with as it were a 67% exactness. Indeed way better, the XGBoost brought about in as it were 6% misclassification mistake compared to SVM, which had 33%. On best of that, XGBoost too gotten consistent predominant comes about from 5-fold approval with an normal accuracy of 90%, whereas SVM with an normal exactness of 64%. Considering the upgraded execution, XGBoost is concluded to be superior at water quality classification.
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Neogy, Taposh Kumar, and Harish Paruchuri. "Machine Learning as a New Search Engine Interface: An Overview." Engineering International 2, no. 2 (December 31, 2014): 103–12. http://dx.doi.org/10.18034/ei.v2i2.539.
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The essence of a web page is an inherently predisposed issue, one that is built on behaviors, interests, and intelligence. There are relatively a ton of reasons web pages are critical to the new world, as the matter cannot be overemphasized. The meteoric growth of the internet is one of the most potent factors making it hard for search engines to provide actionable results. With classified directories, search engines store web pages. To store these pages, some of the engines rely on the expertise of real people. Most of them are enabled and classified using automated means but the human factor is dominant in their success. From experimental results, we can deduce that the most effective and critical way to automate web pages for search engines is via the integration of machine learning.
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Pinheiro, Allan Alves, Iago Modesto Brandao, and Cesar Da Costa. "Vibration Analysis in Turbomachines Using Machine Learning Techniques." European Journal of Engineering Research and Science 4, no. 2 (February 17, 2019): 12–16. http://dx.doi.org/10.24018/ejers.2019.4.2.1128.
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This study proposes a method for diagnosing faults in turbomachines using machine learning techniques. In this study, a support vector machine-SVM algorithm is proposed for fault diagnosis of rotor rotation imbalance. Recently, support vector machines (SVMs) have become one of the most popular classification methods in vibration analysis technology. Axis unbalance defect is classified using support vector machines. The experimental data is derived from the turbomachine model of the rigid-shaft rotor and the flexible bearings, and the experimental setup for vibration analysis. Several situations of unbalance defects have been successfully detected.
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Pinheiro, Allan Alves, Iago Modesto Brandao, and Cesar Da Costa. "Vibration Analysis in Turbomachines Using Machine Learning Techniques." European Journal of Engineering and Technology Research 4, no. 2 (February 17, 2019): 12–16. http://dx.doi.org/10.24018/ejeng.2019.4.2.1128.
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This study proposes a method for diagnosing faults in turbomachines using machine learning techniques. In this study, a support vector machine-SVM algorithm is proposed for fault diagnosis of rotor rotation imbalance. Recently, support vector machines (SVMs) have become one of the most popular classification methods in vibration analysis technology. Axis unbalance defect is classified using support vector machines. The experimental data is derived from the turbomachine model of the rigid-shaft rotor and the flexible bearings, and the experimental setup for vibration analysis. Several situations of unbalance defects have been successfully detected.
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Anggun Cipta. "Analisis Feature dan Machine Learning Untuk Pencarian Web." Jurnal Sistem Cerdas 1, no. 1 (July 18, 2018): 1–9. http://dx.doi.org/10.37396/jsc.v1i1.1.
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The use of website socialization is increasingly still for that amount of information growing rapidly, the harder it is to find the information within the specified time. The main searches for searches that are irrelevant or incompatible with user preferences, keyword-based searches, short questions. In this paper, we will examine the various features used in information retrieval. We will also discuss the relevance of web pages with users. We have classified by feature. In the end, we will compare different techniques and their pros and cons are discussed.
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Litman, D. J. "Cue Phrase Classification Using Machine Learning." Journal of Artificial Intelligence Research 5 (September 1, 1996): 53–94. http://dx.doi.org/10.1613/jair.327.
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Cue phrases may be used in a discourse sense to explicitly signal discourse structure, but also in a sentential sense to convey semantic rather than structural information. Correctly classifying cue phrases as discourse or sentential is critical in natural language processing systems that exploit discourse structure, e.g., for performing tasks such as anaphora resolution and plan recognition. This paper explores the use of machine learning for classifying cue phrases as discourse or sentential. Two machine learning programs (Cgrendel and C4.5) are used to induce classification models from sets of pre-classified cue phrases and their features in text and speech. Machine learning is shown to be an effective technique for not only automating the generation of classification models, but also for improving upon previous results. When compared to manually derived classification models already in the literature, the learned models often perform with higher accuracy and contain new linguistic insights into the data. In addition, the ability to automatically construct classification models makes it easier to comparatively analyze the utility of alternative feature representations of the data. Finally, the ease of retraining makes the learning approach more scalable and flexible than manual methods.
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Santos, Marcelo Vargas dos, Miguel Quartin, and Ribamar R. R. Reis. "On the cosmological performance of photometrically classified supernovae with machine learning." Monthly Notices of the Royal Astronomical Society 497, no. 3 (August 13, 2020): 2974–91. http://dx.doi.org/10.1093/mnras/staa1968.
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ABSTRACT The efficient classification of different types of supernovae is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the the Rubin Observatory Legacy Survey of Space and Time, will be unfeasible. The development of automated classification processes based on photometry has thus become crucial. In this paper, we investigate the performance of machine learning (ML) classification on the final cosmological constraints using simulated light-curves from the Supernova Photometric Classification Challenge, released in 2010. We study the use of different feature sets for the light-curves and many different ML pipelines based on either decision-tree ensembles or automated search processes. To construct the final catalogues we propose a threshold selection method, by employing a bias-variance tradeoff. This is a very robust and efficient way to minimize the mean squared error. With this method, we were able to obtain very strong cosmological constraints, which allowed us to keep $\sim 75{{\ \rm per\ cent}}$ of the total information in the Type Ia supernovae when using the SALT2 feature set, and $\sim 33{{\ \rm per\ cent}}$ for the other cases (based either on the Newling model or on standard wavelet decomposition).
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Teixeira, Diogo, Silvestre Malta, and Pedro Pinto. "A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning." Future Internet 14, no. 3 (February 25, 2022): 72. http://dx.doi.org/10.3390/fi14030072.
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An intrusion detection system (IDS) is an important tool to prevent potential threats to systems and data. Anomaly-based IDSs may deploy machine learning algorithms to classify events either as normal or anomalous and trigger the adequate response. When using supervised learning, these algorithms require classified, rich, and recent datasets. Thus, to foster the performance of these machine learning models, datasets can be generated from different sources in a collaborative approach, and trained with multiple algorithms. This paper proposes a vote-based architecture to generate classified datasets and improve the performance of supervised learning-based IDSs. On a regular basis, multiple IDSs in different locations send their logs to a central system that combines and classifies them using different machine learning models and a majority vote system. Then, it generates a new and classified dataset, which is trained to obtain the best updated model to be integrated into the IDS of the companies involved. The proposed architecture trains multiple times with several algorithms. To shorten the overall runtimes, the proposed architecture was deployed in Fed4FIRE+ with Ray to distribute the tasks by the available resources. A set of machine learning algorithms and the proposed architecture were assessed. When compared with a baseline scenario, the proposed architecture enabled to increase the accuracy by 11.5% and the precision by 11.2%.
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Liu, Lu, Min-min Zhu, Lin-lin Cai, and Xiao Zhang. "Predictive Models for Knee Pain in Middle-Aged and Elderly Individuals Based on Machine Learning Methods." Computational and Mathematical Methods in Medicine 2022 (September 26, 2022): 1–7. http://dx.doi.org/10.1155/2022/5005195.
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Aim. This study used machine learning methods to develop a prediction model for knee pain in middle-aged and elderly individuals. Methods. A total of 5386 individuals above 45 years old were obtained from the National Health and Nutrition Examination Survey. Participants were randomly divided into a training set and a test set at a 7 : 3 ratio. The training set was used to create a prediction model, whereas the test set was used to validate the proposed model. We constructed multiple predictive models based on three machine learning methods: logistic regression, random forest, and Extreme Gradient Boosting. The model performance was evaluated by areas under the receiver (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. Additionally, we created a simplified nomogram based on logistic regression for better clinical application. Results. About 31.4% (1690) individuals were with self-reported knee pain. The logistic regression showed that female gender (odds ratio OR = 1.28 ), pain elsewhere ( OR = 4.64 ), and body mass index ( OR = 1.05 ) were significantly associated with increased risk of knee pain. In the test set, the logistic regression ( AUC = 0.71 ) showed similar but slightly higher accuracy than the random forest ( AUC = 0.70 ), while the performance of the Extreme Gradient Boosting model was less reliable ( AUC = 0.59 ). Based on mean decrease accuracy, the most important first five predictions were pain elsewhere, waist circumference, body mass index, age, and gender. Additionally, the most important first five predictions with the highest mean decrease Gini index were pain elsewhere, body mass index, waist circumference, triglycerides, and age. The nomogram model showed good discrimination ability with an AUC of 0.75 (0.73-0.77), a sensitivity of 0.72, specificity of 0.71, a positive predictive value of 0.45, and a negative predictive value of 0.88. Conclusion. This study proposed a convenient nomogram tool to evaluate the risk of knee pain for the middle-aged and elderly US population in primary care. All the input variables can be easily obtained in a clinical setting, and no additional radiologic assessments were required.
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Rao, V. Chandra Shekhar, Kallepelly Spandhana, C. Srinivas, M. Sujatha, Bojja Vani, and S. Venkatramulu. "An Adaptive Technique for Crime Rate Prediction using Machine Learning Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8 (September 20, 2023): 271–75. http://dx.doi.org/10.17762/ijritcc.v11i8.7954.
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Any country must give the investigation and preventive of crime top priority. There are a rising amount of cases that are still pending due to the rapid increase in criminal cases in India and elsewhere. It is proving difficult to classify and address the rising number of criminal cases. Understanding a place's trends in criminal activity is essential to preventing it from occurring. Crime-solving organisations will be more effective if they have a clear awareness of the patterns of criminal behavior that are present in a particular area. Women's safety and protection are of highest importance despite the serious and persistent problem of crime against them. This study offers predictions about the kinds of crimes that might occur in a particular location using ensemble methods. This facilitates the categorization of criminal proceedings and subsequent action in a timely manner. We are applying machine learning methods like KNN, Linear regression, SVM, Lasso, Decision tree and Random forest in order to assess the highest accuracy.
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Lee, Geun Ae. "Developing Prediction Model for Children’s Social Competence Using Machine Learning." Korean Journal of Child Studies 43, no. 3 (August 31, 2022): 289–301. http://dx.doi.org/10.5723/kjcs.2022.43.3.289.
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Objectives: This study aims to identify the types of latent classes of children’s social competence, and to develop a model using machine learning to predict the type and identify relatively important variables.Methods: Data were collected from 466 children aged three to five years and their mothers. Children’s social competence was classified by level. Latent class analysis, machine learning model construction, and performance evaluation were performed using R 3.6.1 and R-Studio 1.2.5033. The machine learning algorithms used were logistic regression, lasso logistic regression, random forest, and gradient-boosted decision tree models.Results: First, according to the characteristics of the latent class of children’s social competence, it was classified into two types: ‘high level’ and ‘low level’. Second, a machine learning algorithm was applied according to the latent class. The best performing model was the random forest model. Third, the most important variable in predicting the social competence type was identified as ‘harm avoidance’ in the children’s temperament. Fourth, another major variable was a ‘shift’ in the children’s executive functions.Conclusion: This study is meaningful as it suggests the possibility of predicting and discriminating children’s social competence and various developmental aspects by applying machine learning, the latest technique, to predict the types of children’s social competence.
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Pawar, Mr Rushikesh. "Crop Leaf Disease Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 17, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30693.
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In India, crop area is largest in the world and produces major crops like wheat, pulses, fruits, rice and vegetables Despite of using modem taming techniques along with traditional, infectious plant diseases is major problem which can be caused by different viruses, fungus and bacteria. This mainly affects crop production as well as crop quality. It is very important to identify diseases at early stage Nowadays, automatic crop de detection has become a important research domain. It helps in detecting the symptoms of the disease when they are found on the e In this paper we will focus on finding the diseases in order to increase crop quality and production effectively. Here, we will focus on r diseases by observing leaves of plants at initial stage using machine learning. · In this paper, we designed a Deep Convolutional Neural Network based on LeNet to perform soybean leaf spot disease recognition and classification using affected areas of disease spots. The affected areas of disease spots were segmented from the leaves images using the Unsupervised fuzzy clustering algorithm. The proposed Deep Convolutional Neural Network model achieved a testing accuracy of 89.84%, and poor per class recognition results in 1378 images misclassified, and 1271 images correct classified. TheVGG16 achieved the best performance reaching a 93.54% success rate, and better per class recognition results in 1245 images misclassified, and 1404 images correct classified · In order to address the challenges related to the classification and recognition of soybean disease and healthy leaf identification, it is essential to have access to high-quality images. A meticulously curated dataset named “SoyNet” has been created to provide a clean and comprehensive dataset for research purposes. The dataset comprises over 9000 highquality soybean images, encompassing healthy and diseased leaves. These images have been captured from various angles and directly sourced from soybean agriculture fields; The soybean leaves images are organized into two sub-folders: SoyNet Raw Data and SoyNet Pre-processing Data.The SoyNet Pre-processing Data folder comprises resized images of 256∗256 pixels and the grayscale versions of disease and healthy images, following a similar organizational structure. We captured the images using the Nikon digital camera and the Motorola mobile phone camera, utilizing different angles, lighting conditions, and backgrounds. They were taken in different lighting conditions and backgrounds at soybean cultivation fields to represent the real-world scenario accurately. The proposed dataset is valuable for testing, training, and validating soybean leaf disease classification
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Sivasankaran, Deepika, Sai Seena P, Rajesh R, and Madheswari Kanmani. "Sketch Based Image Retrieval using Deep Learning Based Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 79–86. http://dx.doi.org/10.35940/ijeat.e2622.0610521.
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Sketch based image retrieval (SBIR) is a sub-domain of Content Based Image Retrieval(CBIR) where the user provides a drawing as an input to obtain i.e retrieve images relevant to the drawing given. The main challenge in SBIR is the subjectivity of the drawings drawn by the user as it entirely relies on the user's ability to express information in hand-drawn form. Since many of the SBIR models created aim at using singular input sketch and retrieving photos based on the given single sketch input, our project aims to enable detection and extraction of multiple sketches given together as a single input sketch image. The features are extracted from individual sketches obtained using deep learning architectures such as VGG16 , and classified to its type based on supervised machine learning using Support Vector Machines. Based on the class obtained, photos are retrieved from the database using an opencv library, CVLib , which finds the objects present in a photo image. From the number of components obtained in each photo, a ranking function is performed to rank the retrieved photos, which are then displayed to the user starting from the highest order of ranking up to the least. The system consisting of VGG16 and SVM provides 89% accuracy
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Filipe, Vítor, Pedro Teixeira, and Ana Teixeira. "Automatic Classification of Foot Thermograms Using Machine Learning Techniques." Algorithms 15, no. 7 (July 6, 2022): 236. http://dx.doi.org/10.3390/a15070236.
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Diabetic foot is one of the main complications observed in diabetic patients; it is associated with the development of foot ulcers and can lead to amputation. In order to diagnose these complications, specialists have to analyze several factors. To aid their decisions and help prevent mistakes, the resort to computer-assisted diagnostic systems using artificial intelligence techniques is gradually increasing. In this paper, two different models for the classification of thermograms of the feet of diabetic and healthy individuals are proposed and compared. In order to detect and classify abnormal changes in the plantar temperature, machine learning algorithms are used in both models. In the first model, the foot thermograms are classified into four classes: healthy and three categories for diabetics. The second model has two stages: in the first stage, the foot is classified as belonging to a diabetic or healthy individual, while, in the second stage, a classification refinement is conducted, classifying diabetic foot into three classes of progressive severity. The results show that both proposed models proved to be efficient, allowing us to classify a foot thermogram as belonging to a healthy or diabetic individual, with the diabetic ones divided into three classes; however, when compared, Model 2 outperforms Model 1 and allows for a better performance classification concerning the healthy category and the first class of diabetic individuals. These results demonstrate that the proposed methodology can be a tool to aid medical diagnosis.
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Zhao, Nan, Löic Baud, and Patrick Bellot. "Exploring Video Sharing Websites Content with Machine Learning." International Journal of Distributed Systems and Technologies 5, no. 4 (October 2014): 31–50. http://dx.doi.org/10.4018/ijdst.2014100103.
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This article studies the characteristics of content on video sharing websites. A better understanding on online video content can help to analyse Internet users' behaviour and improve the video-sharing service. We improved an existing graph-sampling algorithm so that it could be more adapted to sample over the video sharing websites. A newly category system is defined in this paper, which can be applied on many different video sharing websites for content analysis. We also implement machine learning to realize the content re-classification with the newly defined category system. The efficiency reaches at 90%. From the classified content analysis, we find the content category distribution is not constant, and nowadays, cultural goods content take about 70% over all the sampled videos.
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Cabrera, Maritza, Jason Leake, José Naranjo-Torres, Nereida Valero, Julio C. Cabrera, and Alfonso J. Rodríguez-Morales. "Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review." Tropical Medicine and Infectious Disease 7, no. 10 (October 21, 2022): 322. http://dx.doi.org/10.3390/tropicalmed7100322.
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Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.
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Salman, Sara, and Jamila H. Soud. "Deep Learning Machine using Hierarchical Cluster Features." Al-Mustansiriyah Journal of Science 29, no. 3 (March 10, 2019): 82. http://dx.doi.org/10.23851/mjs.v29i3.625.
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Deep learning of multi-layer computational models allowed processing to recognize data representation at multiple levels of abstraction. These techniques have greatly improved the latest ear recognition technology. PNN is a type of radiative basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error of classification. In this paper, strong features of images are used to give a good result, therefore, SIFT method using these features after adding improvements and developments. This method was one of the powerful algorithms in matching that needed to find energy pixels. This method gives stronger feature on features and gives a large number of a strong pixel, which is considered a center and neglected the remainder of it in our work.
Each pixel of which is constant for image translation, scaling, rotation, and embedded lighting changes in lighting or 3D projection. Therefore, the interpretation is developed by using a hierarchical cluster method; to assign a set of properties (find the approximation between pixels) were classified into one.
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Roberson, Andrea. "Applying Machine Learning for Automatic Product Categorization." Journal of Official Statistics 37, no. 2 (June 1, 2021): 395–410. http://dx.doi.org/10.2478/jos-2021-0017.
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Abstract Every five years, the U.S. Census Bureau conducts the Economic Census, the official count of US businesses and the most extensive collection of data related to business activity. Businesses, policymakers, governments and communities use Economic Census data for economic development, business decisions, and strategic planning. The Economic Census provides key inputs for economic measures such as the Gross Domestic Product and the Producer Price Index. The Economic Census requires businesses to fill out a lengthy questionnaire, including an extended section about the goods and services provided by the business. To address the challenges of high respondent burden and low survey response rates, we devised a strategy to automatically classify goods and services based on product information provided by the business. We asked several businesses to provide a spreadsheet containing Universal Product Codes and associated text descriptions for the products they sell. We then used natural language processing to classify the products according to the North American Product Classification System. This novel strategy classified text with very high accuracy rates - our best algorithms surpassed over 90%.
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B, Kavyashree, and Rakesh M D. "Prediction of Cardiac Arrhythmia using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1698–706. http://dx.doi.org/10.22214/ijraset.2022.46900.
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Abstract: The Heart is one of the most important organ responsible for sustaining Human life. The Normal functioning of it is very important but the irregular functioning of it will causes few problems which may be classified as different heart disease. Arrhythmia an Irregular Heart Beat, which is considered as one of the Cardio Vascular Disease. Electrocardiogram (ECG) is the most preferred tool used to capture Heart Beat. Without taking proper pre-cautionary measures this may lead to sudden death, blood clots, heart failure, stroke, etc.. Machine learning is the study of computer algorithms. In this work by adopting Machine learning algorithms such as Logistic Regression, Decision Tree, SVM[Support Vector Machine]are done to foresee the Cardiac Arrhythmia. The data-sets are collected from UCI Repository & processed using python programming .From all the three applied algorithms the SVM model showed the better results of 91.41\% in terms of accuracy for 80/20 combinations of Train and Test data sets. Therefore from this work SVM model is considered as best algorithm for the prediction of Cardiac Arrhythmia.
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Xiao, Pei-yu, Rui-Feng Xie, Xiang-Tao Zeng, Yin Chen, Jia-Hui Chen, Yin-Yi Huo, Tian-Hang Liu, et al. "Classification of Fermi BCUs Using Machine Learning." Astrophysical Journal 956, no. 1 (October 1, 2023): 48. http://dx.doi.org/10.3847/1538-4357/acf203.
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Abstract The Fermi Large Area Telescope (LAT) has detected 6659 γ-ray sources in the incremental version (4FGL-DR3, for Data Release 3) of the fourth Fermi-LAT catalog of γ-ray sources and 3743 of them are blazars, including 1517 blazar candidates of uncertain type (BCUs). Blazars are generally classified by properties of emission lines into BL Lac objects and flat spectrum radio quasars (FSRQs). However, BCUs are difficult to classify because of the lack of spectrum. In this work we apply five different machine-learning algorithms (K-nearest neighbors, logistic regression, support vector machine, random forest, CatBoost) to evaluate the classification of 1517 BCUs based on the observational data of 4FGL-DR3. The results indicate that the use of recursive feature elimination cross-validation can effectively improve the accuracy of models and reduce computation time. We use our models to predict the BCUs from 4FGL-DR3 and the results of the overlapping of the five models are as follows: 811 BL Lac objects, 397 FSRQs, and 309 BCUs.
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Kaur, Er Sandeep. "Image Classification Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (December 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27484.
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- Image classification is an important topic of study in the field of image processing nowadays and is a popular area of research. By providing the computer with data to learn from, image categorization was created to close the gap between computer vision and human vision. In this paper, the methods for categorising images using traditional machine learning and deep learning are compared and investigated. This study employs a tensor flow framework and convolutional neural networks to classify images. This paper implements CNN in binary classification and multi-class classification for object identification and analyses the performance of well-known convolutional neural networks (CNNs). We built five unique image datasets on our own for multiclass classification, using the dog vs. cat dataset for binary classification. For our investigation, we classified the photos using a separate machine learning model and then classified them again using CNN because evaluating CNN's performance on a single data set hides its actual potential and limits. Additionally, trained CNNs perform very differently across various categories of objects, and we will thus talk about some potential causes. Key Words: Image classification, python, Deep Learning, Tensor flow, Convolutional Neural Network, Open CV, NLP
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Zhang, Ruiting, and Zhijian Zhou. "A Fuzzy Least Squares Support Tensor Machines in Machine Learning." International Journal of Emerging Technologies in Learning (iJET) 10, no. 8 (December 14, 2015): 4. http://dx.doi.org/10.3991/ijet.v10i8.5203.
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In the machine learning field, high-dimensional data are often encountered in the real applications. Most of the traditional learning algorithms are based on the vector space model, such as SVM. Tensor representation is useful to the over fitting problem in vector-based learning, and tensor-based algorithm requires a smaller set of decision variables as compared to vector-based approaches. We also would require that the meaningful training points must be classified correctly and would not care about some training points like noises whether or not they are classified correctly. To utilize the structural information present in high dimensional features of an object, a tensor-based learning framework, termed as Fuzzy Least Squares support tensor machine (FLSSTM), where the classifier is obtained by solving a system of linear equations rather than a quadratic programming problem at each iteration of FLSSTM algorithm as compared to STM algorithm. This in turn provides a significant reduction in the computation time, as well as comparable classification accuracy. The efficacy of the proposed method has been demonstrated in ORL database and Yale database. The FLSSTM outperforms other tensor-based algorithms, for example, LSSTM, especially when training size is small.