scholarly journals Object detection and used car price predicting analysis system (ucpas) using machine learning technique

2021 ◽  
Vol 5 (S2) ◽  
Author(s):  
Anu Yadav ◽  
Ela Kumar ◽  
Piyush Kumar Yadav
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Research Area ◽  
Machine Learning Technique ◽  
Car Detection ◽  
Machine Learning Model ◽  
Implementation Techniques ◽  
Learning Technique ◽  
Used Car ◽  
Analysis System

The highly interesting research area that noticed in the last few years is object detection and find out the prediction based on the features that can be benefited to consumers and the industry. In this paper, we understand the concept of object detection like the car detection, to look into the price of a second-hand car using automatic machine learning methods. We also understand the concept of object detection categories. Nowadays, the most challenging task is to determine what is the listed price of a used car on the market, Possibility of various factors that can drive a used car price. The main objective of this paper is to develop machine learning models which make it possible to accurately predict the price of a second-hand car according to its parameter or characteristics. In this paper, implementation techniques and evaluation methods are used on a Car dataset consisting of the selling prices of various models of  car across different cities of India. The outcome of this experiment shows that clustering with linear regression and Random Forest model yield the best accuracy outcome. The machine learning model produces a satisfactory result within a short duration of time compared to the aforementioned self.

scholarly journals Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

2021 ◽  
pp. 118-124
Author(s):  
Md. Serajus Salekin Khan ◽  
Sanjida Reza Rafa ◽  
Al Ekram Hossain Abir ◽  
Amit Kumar Das
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Computer Science ◽  
Sentiment Analysis ◽  
Research Area ◽  
Science Study ◽  
Machine Learning Technique ◽  
Mining Technique ◽  
Svm Algorithm ◽  
Learning Technique

In this present era, sentiment analysis is considered as one of the most rapidly growing fields of computer science study. It is a text mining technique which is automated and determines the emotion of a text. A text can be divided into many emotions using sentiment analysis. Since there are some studies on emotion analysis in the Bangla language, it is regarded as a key research area in the field of analyzing Bangla language. This paper works with five different emotions and those are Happy, Sad, Angry, Surprise and Excited. Apart from these emotions our paper also deals with two categories, such as Abusive and Religious. We proposed a method of machine learning technique which is the SVM algorithm to extract these five individual emotions from Bangla text.

scholarly journals Object Detection using K-Means Clustering – A Research

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1813-1816
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Clustering Algorithm ◽  
Machine Learning Technique ◽  
K Value ◽  
Clustering Techniques ◽  
Advantages And Disadvantages ◽  
Global Cluster ◽  
Learning Technique ◽  
Group A

Clustering is an unsupervised machine learning technique and the task is to group a set of objects in such a way that objects in the same group are more similar to each other than those in other groups. There are different clustering techniques, each with its own advantages and disadvantages. The K-means clustering algorithm is our main focus in this paper. K-means is mostly used when there is a large number of unlabeled data. The difficulties in the path of K-means clustering are a) Different initial points can lead to different final clusters. b) It does not work with clusters of different sizes and densities. c) With the global cluster, it does not work well and is difficult to determine K Value. Our main aim is to try and modify the K-means clustering algorithm to get rid of the above-mentioned drawbacks.

scholarly journals Detection and Measure Carstensz Glacier Area Changes using Machine Learning Technique

2020 ◽  
Vol 9 (3) ◽  
pp. 1397-1404
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Learning Model ◽  
Glacier Change ◽  
Glacier Area ◽  
Machine Learning Technique ◽  
Manual Delineation ◽  
Machine Learning Model ◽  
Learning Technique ◽  
Normalized Difference Snow Index

Using satellite data for acquiring glacier outlines has become more popular in the last decade. Glacier change assessment is the main goal for deriving glacier outlines. It's important to make the best method to generate the glacier outline as there most of the glacier outline is made with manual delineation and spectral thresholding. This research used a machine learning model to deriving the glacier pixels from satellite data. The model trained using more than 80 thousand of a glacier and non-glacier pixels. The model that trained has been proved to able classified a glacier pixel with more than 99% accuracy in one of the best experiments. The NDSI (Normalized Difference Snow Index) proved to be the key feature to classifying glaciers and shown to be one the best combination with NDSI + GLCM + TIFF (Band 4). This model hopefully can be further expanded and installed directly in satellite so we can instantly make a glacier outline without any manual delineation or spectral thresholding needed

scholarly journals Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna

2021 ◽  
Vol 11 (22) ◽  
pp. 10829
Author(s):  
Jaeyul Choo ◽  
Pho Thi Ha Anh ◽  
Yong-Hwa Kim
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Impedance Matching ◽  
Learning Model ◽  
Monopole Antenna ◽  
Machine Learning Technique ◽  
Squared Error ◽  
Machine Learning Model ◽  
Learning Technique ◽  
The Mean

We designed the wire monopole antenna bent at three points by applying a machine learning technique to achieve a good impedance matching characteristic. After performing the deep neural network (DNN)-based training, we validated our machine learning model by evaluating mean squared error and R-squared score. Considering the mean squared error of about zero and R-squared score of about one, the performance prediction by the resulting machine learning model showed a high accuracy compared with that by the numerical electromagnetic simulation. Finally, we interpreted the operating principle of the antennas with a good impedance matching characteristic by analyzing equivalent circuits corresponding to their structures. The accomplished works in this research provide us with the possibility to use the machine learning technique in the antenna design.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection
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