Classification and Enrichment of Unlabeled Feedback Data using Machine Learning

These days’ data gathered is unstructured. It is becoming very hard to have labelled data gathered, due to the volume of the data being generated every second. It is almost impossible to train a model on the unstructured/unlabelled data. The unlabelled data will be divided into groups using the ML techniques and CNN/Deep learning/Machine Learning techniques will be trained using the grouped data generated. The model will be enhanced over time by the feedback given by the users and with addition of new data as well. Existing models can be trained over labelled data only. Without labelled data models cannot be used for prediction and reinforcement learning. In this approach though the data is unlabelled if a feature column is specified we will be able to train the model with the help of SME. This will be helpful in many areas of classification and prediction of the trends and patterns. Machine learning, Deep learning techniques (Supervised) will be used to implement the data. Tools used will be Python, PyTorch and TensorFlow. Input can be any data (Audio/Video/pictographic/text). Labelled data and a model file which could be used for further predictions, and which will be improved over feedback.

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Review on Anomaly Detection in Mobile Networks Using Traditional Learning, Machine Learning and Deep Learning

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9054 ◽

2020 ◽

Vol 17 (11) ◽

pp. 4789-4796

Author(s):

T. S. Prabhakar ◽

M. N. Veena

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Anomaly Detection ◽

Mobile Networks ◽

High Speed ◽

Network Activity ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Artificial Neural Network Method ◽

Learning Machine

Increasing usage of smart phones involves in the developing large amount of data and high speed internet is used for transfers this large amount of data. This in-turn gives rise to the development of various attacks to hack the data. Anomaly detection in the network analyzes the pattern in the network activity and found the abnormality in the network. The accurate detection of abnormality in network helps to prevent the attackers to steal the data. Many researches were conducted to improve the performance of anomaly detection in the mobile networks. Traditional methods results for performance of anomaly detection are not much effective. Machine learning techniques are used for the anomaly detection to increase the performance. The deep learning techniques are applied to increase the detection rate and decrease the false positive. Both the techniques machine learning uses k-means and Deep learning uses Artificial Neural Network method provides the considerable performance in anomaly detection.

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Breast Cancer Prediction Using Deep Learning and Machine Learning Techniques

SSRN Electronic Journal ◽

10.2139/ssrn.3558786 ◽

2020 ◽

Cited By ~ 1

Author(s):

MONIKA TIWARI ◽

Rashi Bharuka ◽

Praditi Shah ◽

Reena Lokare

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Deep Learning ◽

Machine Learning Techniques ◽

Cancer Prediction ◽

Learning Techniques

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Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽

10.3390/vibration4020022 ◽

2021 ◽

Vol 4 (2) ◽

pp. 341-356

Author(s):

Jessada Sresakoolchai ◽

Sakdirat Kaewunruen

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Mean Absolute Error ◽

Absolute Error ◽

Machine Learning Techniques ◽

Rolling Stock ◽

Raw Data ◽

Learning Techniques ◽

Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

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Sentiment Analysis using various Machine Learning and Deep Learning Techniques

Journal of the Nigerian Society of Physical Sciences ◽

10.46481/jnsps.2021.308 ◽

2021 ◽

pp. 385-394

Author(s):

V Umarani ◽

A Julian ◽

J Deepa

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Analysis Process ◽

Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

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Prediction of Pneumonia Using Big Data, Deep Learning and Machine Learning Techniques

2021 6th International Conference on Communication and Electronics Systems (ICCES) ◽

10.1109/icces51350.2021.9489188 ◽

2021 ◽

Author(s):

K.R. Swetha ◽

Niranjanamurthy M ◽

Amulya M P ◽

Manu Y M

Keyword(s):

Machine Learning ◽

Big Data ◽

Deep Learning ◽

Machine Learning Techniques ◽

Learning Techniques

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Machine Learning Techniques for Internet of Things

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Integrating the Internet of Things Into Software Engineering Practices ◽

10.4018/978-1-5225-7790-4.ch008 ◽

2019 ◽

pp. 160-180

Author(s):

P. Priakanth ◽

S. Gopikrishnan

Keyword(s):

Machine Learning ◽

Data Science ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Independent Learning ◽

Machine Learning Techniques ◽

Analytical Models ◽

Guided Learning ◽

Learning Techniques ◽

Learning Machine

The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?

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