scholarly journals A Brief Survey on Emotion Based Text to Speech Conversion System

2021 ◽  
Vol 11 (1) ◽  
pp. 40-43
Author(s):  
Bhushan Hemant Dhimate ◽  
◽  
Manjiri Vitthal Khopade ◽  
Avadhoot Yogesh Dhere ◽  
Supriya Dhanaraj Dhumale ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Web Applications ◽  
Semantic Analysis ◽  
Learning Algorithm ◽  
Support Vector ◽  
Text To Speech ◽  
Deep Learning Algorithm ◽  
Applications Of Machine Learning ◽  
User Friendly

Text to speech conversion is one of the applications of machine learning. It is widely used in search engines, standalone applications, web applications, chatbots and android applications. But still there is need to upgrade text to speech system so that we can get more interactive and user-friendly application. Traditional text to speech application has monotonous voice as output which does not has emotions in it and seems to be more mechanized. So, there is need to improvise the existing system by embedding the flavour of emotions in it. Existing text to speech cannot be used in story telling applications also it does not provide effective communication. Most of the Text to Speech systems are developed using algorithms such as Support Vector Machine (SVM), Naïve Bayes etc. Emotion Based Text to Speech System will help to improvise the existing Text to Speech system. With the help of machine learning and deep learning algorithm such as Recurrent Neural Network can be used for performing sentiment analysis and semantic analysis on the input text. We are going to use neural network which is more effective and help to maintain a relation between previous word and next word. Emotion based text to speech system will be able to identify four emotions ‘happy’, ‘sad’, ‘angry’ and ‘neutral’. Emotion based text to speech system will be beneficial for educational purpose like listening stories from storytelling applications for young budding children. Emotion based text to speech is going to be serviceable for visually impaired individuals.

scholarly journals CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT

2021 ◽  
Author(s):  
jorge cabrera Alvargonzalez ◽  
Ana Larranaga Janeiro ◽  
Sonia Perez ◽  
Javier Martinez Torres ◽  
Lucia martinez lamas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Supervised Classification ◽  
Learning Algorithm ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
The Past ◽  
Number Of Cycles

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges humanity has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Finally, the results obtained from the classification show how the appearance of each wave is coincident with the surge of each of the variants present in the region of Galicia (Spain) during the development of the SARS-CoV-2 pandemic and clearly identified with the classification algorithm.

scholarly journals Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xueying Li ◽  
Pingping Fan ◽  
Zongmin Li ◽  
Guangyuan Chen ◽  
Huimin Qiu ◽  
...  
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Learning Algorithm ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
Different Types

Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.

scholarly journals Machine Learning and Fuzzy Logic based Detection, Classification and Grading of Leaf Disease in Plants

2020 ◽  
Vol 9 (7S) ◽  
pp. 38-42
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Fuzzy Logic ◽  
Learning Algorithm ◽  
Expert Advice ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Image Processing Techniques ◽  
Processing Techniques

India is an agricultural country where most of people are depends on the agriculture. When Plants are infected by the virus, fungus and bacteria, they are mostly seen on leaves and stems of the plants. Because of that, plants production is decreased also economy of the country is decreased. The farmer has to identify the disease and decide which pesticide will be used to control the disease in plants. To finding out which disease affect the plants, the farmer contacts the expert for the solution. The expert gives the advice based on its knowledge and information but sometimes seeking the expert advice is time consuming, expensive and may be not accurate. So, to solve this problem, the image processing techniques and Machine Learning algorithm like Neural Network, Fuzzy Logic and Support Vector Machine gives the better, accurate and affordable solution to control the plants disease than manual method.

scholarly journals Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms

2020 ◽  
Vol 12 (11) ◽  
pp. 1838 ◽  
Author(s):  
Zhao Zhang ◽  
Paulo Flores ◽  
C. Igathinathane ◽  
Dayakar L. Naik ◽  
Ravi Kiran ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Standard Deviation ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Support Vector ◽  
Unmanned Aerial Systems

The current mainstream approach of using manual measurements and visual inspections for crop lodging detection is inefficient, time-consuming, and subjective. An innovative method for wheat lodging detection that can overcome or alleviate these shortcomings would be welcomed. This study proposed a systematic approach for wheat lodging detection in research plots (372 experimental plots), which consisted of using unmanned aerial systems (UAS) for aerial imagery acquisition, manual field evaluation, and machine learning algorithms to detect the occurrence or not of lodging. UAS imagery was collected on three different dates (23 and 30 July 2019, and 8 August 2019) after lodging occurred. Traditional machine learning and deep learning were evaluated and compared in this study in terms of classification accuracy and standard deviation. For traditional machine learning, five types of features (i.e. gray level co-occurrence matrix, local binary pattern, Gabor, intensity, and Hu-moment) were extracted and fed into three traditional machine learning algorithms (i.e., random forest (RF), neural network, and support vector machine) for detecting lodged plots. For the datasets on each imagery collection date, the accuracies of the three algorithms were not significantly different from each other. For any of the three algorithms, accuracies on the first and last date datasets had the lowest and highest values, respectively. Incorporating standard deviation as a measurement of performance robustness, RF was determined as the most satisfactory. Regarding deep learning, three different convolutional neural networks (simple convolutional neural network, VGG-16, and GoogLeNet) were tested. For any of the single date datasets, GoogLeNet consistently had superior performance over the other two methods. Further comparisons between RF and GoogLeNet demonstrated that the detection accuracies of the two methods were not significantly different from each other (p > 0.05); hence, the choice of any of the two would not affect the final detection accuracies. However, considering the fact that the average accuracy of GoogLeNet (93%) was larger than RF (91%), it was recommended to use GoogLeNet for wheat lodging detection. This research demonstrated that UAS RGB imagery, coupled with the GoogLeNet machine learning algorithm, can be a novel, reliable, objective, simple, low-cost, and effective (accuracy > 90%) tool for wheat lodging detection.

scholarly journals A Survey and Analysis of Extreme Machine Learning Models and Its Techniques

2021 ◽  
Author(s):  
Shubhangi Pande ◽  
Neeraj Kumar Rathore ◽  
Anuradha Purohit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Global Optimum ◽  
Least Square ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Feed Forward

Abstract Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.

scholarly journals An Analysis of Extreme Machine Learning Models

2021 ◽  
Author(s):  
Shubhangi Pande ◽  
Neeraj Rathore ◽  
Anuradha Purohit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Global Optimum ◽  
Least Square ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Feed Forward

Abstract Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.

scholarly journals Review on Techniques for Plant Leaf Classification and Recognition

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 77 ◽  
Author(s):  
Muhammad Azfar Firdaus Azlah ◽  
Lee Suan Chua ◽  
Fakhrul Razan Rahmad ◽  
Farah Izana Abdullah ◽  
Sharifah Rafidah Wan Alwi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Plant Systematics ◽  
K Nearest Neighbor ◽  
Plant Leaf

Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extraction appeared to improve the performance of plant leaf classification. The findings of previous studies are critically compared in terms of their accuracy based on the applied neural network techniques. This paper aims to review and analyze the implementation and performance of various methodologies on plant classification. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation.

scholarly journals Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

2020 ◽  
Author(s):  
Juan Chen ◽  
Yong-ran Cheng ◽  
Zhan-hui Feng ◽  
Meng-Yun Zhou ◽  
Nan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Air Pollutants ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Adequate Treatment ◽  
Number Of Patients

Abstract Background: Accurate prediction of the number of patients with conjunctivitis plays an important role in providing adequate treatment at the hospital, but such accurate predictive model currently does not exist. The current study sought to use machine learning (ML) prediction based on past patient for conjunctivitis and several air pollutants. The optimal machine learning prediction model was selected to predict conjunctivitis-related number patients.Methods: The average daily air pollutants concentrations (CO, O3, NO2, SO2, PM10, PM2.5) and weather data (highest and lowest temperature) were collected. Data were randomly divided into training dataset and test dataset, and normalized mean square error (NMSE) was calculated by 10 fold cross validation, comparing between the ability of seven ML methods to predict the number of patient due to conjunctivitis (Lasso penalized liner model, Decision tree, Boosting regression, Bagging regression, Random forest, Support vector, and Neural network). According to the accuracy of impact prediction, the important air and weather factors that affect conjunctivitis were identified.Results: A total of 84977 cases to treat conjunctivitis were obtained from the ophthalmology center of the Affiliated Hospital of Hangzhou Normal University. For all patients together, the NMSE of the different methods were as follows: Lasso penalized liner regression: 0.755, Decision tree: 0.710, Boosting regression: 0.616, Bagging regression: 0.615, Random forest: 0.392, Support vectors: 0.688, and Neural network: 0.476. Further analyses, stratified by gender and age at diagnosis, supported Random forest as being superior to others ML methods. The main factors affecting conjunctivitis were: O3, NO2, SO2 and air temperature.Conclusion: Machine learning algorithm can predict number of patients due to conjunctivitis, among which, the Random forest algorithm had the highest accuracy. Machine learning algorithm could provide accurate information for hospitals dealing with conjunctivitis caused by air factors.

scholarly journals Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

2020 ◽  
Author(s):  
Yong-ran Cheng ◽  
Zhan-hui Feng ◽  
Meng-Yun Zhou ◽  
Nan Wang ◽  
Ming-Wei Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Air Pollutants ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Adequate Treatment ◽  
Number Of Patients

Abstract Background Accurate prediction of the number of patients with conjunctivitis plays an important role in providing adequate treatment at the hospital, but such accurate predictive model currently does not exist. The current study sought to use machine learning (ML) prediction based on past patient for conjunctivitis and several air pollutants. The optimal machine learning prediction model was selected to predict conjunctivitis-related number patients. Methods The average daily air pollutants concentrations (CO, O3, NO2, SO2, PM10, PM2.5) and weather data (highest and lowest temperature) were collected. Data were randomly divided into training dataset and test dataset, and normalized mean square error (NMSE) was calculated by 10 fold cross validation, comparing between the ability of seven ML methods to predict the number of patient due to conjunctivitis (Lasso penalized liner model, Decision tree, Boosting regression, Bagging regression, Random forest, Support vector, and Neural network). According to the accuracy of impact prediction, the important air and weather factors that affect conjunctivitis were identified. Results A total of 84977 cases to treat conjunctivitis were obtained from the ophthalmology center of the Affiliated Hospital of Hangzhou Normal University. For all patients together, the NMSE of the different methods were as follows: Lasso penalized liner regression: 0.755, Decision tree: 0.710, Boosting regression: 0.616, Bagging regression: 0.615, Random forest: 0.392, Support vectors: 0.688, and Neural network: 0.476. Further analyses, stratified by gender and age at diagnosis, supported Random forest as being superior to others ML methods. The main factors affecting conjunctivitis were: O3, NO2, SO2 and air temperature. Conclusion Machine learning algorithm can predict number of patients due to conjunctivitis, among which, the Random forest algorithm had the highest accuracy. Machine learning algorithm could provide accurate information for hospitals dealing with conjunctivitis caused by air factors.

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