scholarly journals Survey on Civil Complaints Management System by Using Machine Learning Techniques

2021 ◽  
pp. 606-610
Author(s):  
Sayali Bhosale ◽  
Sonali Patankar ◽  
Kshitija Kadam ◽  
Rujuta Dhere ◽  
Prof. Manisha Desai
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Clustering Method ◽  
Registration System ◽  
Handling System ◽  
Complaint Handling ◽  
Learning Techniques ◽  
Framework Model ◽  
Speed Up ◽  
The Government

Today’s Government Complaints Registration System is totally Human Operable hence it consumes lot of time to resolve each of those complaint. The proposed system is being developed for the government offices to create a helpful complaint registering platform which will be efficient. The Framework model of civil complaint handling system is done by using sentimental analysis and ML techniques to speed up the process of categorization and prioritization of complaints. The system will analyses the citizen sentiment to prioritize the complaints. Later then the categorization of complaints done by using clustering method and give them priority based upon urgency of each one respectively. The municipality can use this method to identify citizen’s needs and estimate their satisfaction.

scholarly journals Implementation of machine learning techniques to control the speed of the vehicles

2018 ◽  
Vol 7 (4.5) ◽  
pp. 654
Author(s):  
M. S. Satyanarayana ◽  
Aruna T.M ◽  
Divyaraj G.N
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
New Technique ◽  
Vehicle Speed ◽  
Learning Techniques ◽  
Traffic Awareness ◽  
Alarm Sound ◽  
The Government ◽  
A New Technique

Accidents have become major issue in Developing countries like India now a day. As per the Surveys 60% of the accidents are happening due to over speed. Though the government has taken so many initiatives like Traffic Awareness & Driving Awareness Week etc.., but still the percentage of accidents are not getting reduced. In this paper a new technique has been introduced to reduce the percentage of accidents. The new technique is implemented using the concept of Machine Learning [1]. The Machine Learning based systems can be implemented in all vehicles to avoid the accidents at low cost [1]. The main objective of this system is to calculate the speed of the vehicle at three various locations based on the place where the vehicle speed must be controlled and if the speed is greater than the designated speed in that road then the vehicle automatically detects the problem and same will be intimated to the driver to control the speed of the vehicle. If the speed is less or equal to the designated speed in that road then the vehicle will be passed without any disturbance. The system will be giving beep sound along with color indication to driver in each and every scenario. The other option implemented in this system is if the driver is driving the vehicle in the night and if he feel drowsy the system detects it immediately and alarm sound will be initiated to wake up the driver. This system though it won’t avoid 100% accidents at least it will reduce the percentage of accidents. This system is not only to avoid accidents it will also intelligently control the speed of the vehicles and creates awareness amongst the drivers.  

scholarly journals OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Fabio R. Cerqueira ◽  
Ana Tereza Ribeiro Vasconcelos
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Open Reading Frames ◽  
Machine Learning Techniques ◽  
Bacterial Genomes ◽  
Small Proteins ◽  
Learning Techniques ◽  
Speed Up ◽  
Computational Procedures ◽  
Small Orfs

Abstract Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However, advances in experimental methods show that small proteins can play vital roles in cellular activities. Hence, it is urgent to make progress in the development of computational approaches to speed up the identification of potential small ORFs. In this work, our focus is on bacterial genomes. We improve a previous approach to identify small ORFs in bacteria. Our method uses machine learning techniques and decoy subject sequences to filter out spurious ORF alignments. We show that an advanced multivariate analysis can be more effective in terms of sensitivity than applying the simplistic and widely used e-value cutoff. This is particularly important in the case of small ORFs for which alignments present higher e-values than usual. Experiments with control datasets show that the machine learning algorithms used in our method to curate significant alignments can achieve average sensitivity and specificity of 97.06% and 99.61%, respectively. Therefore, an important step is provided here toward the construction of more accurate computational tools for the identification of small ORFs in bacteria.

scholarly journals Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques

10.2196/23957 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23957
Author(s):  
Chengda Zheng ◽  
Jia Xue ◽  
Yumin Sun ◽  
Tingshao Zhu
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Public Discourse ◽  
Latent Dirichlet Allocation ◽  
Prime Minister ◽  
Machine Learning Techniques ◽  
Future Health ◽  
The Public ◽  
Learning Techniques ◽  
The Government

Background During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government’s responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. Objective The aim of this study was to examine comments on Canadian Prime Minister Trudeau’s COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. Methods We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau’s COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. Results We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau’s policies, essential work and frontline workers, individuals’ financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China’s relationship, vaccines, and reopening. Conclusions This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau’s daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.

scholarly journals Air Temperature Forecasting Using Machine Learning Techniques: A Review

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4215 ◽  
Author(s):  
Jenny Cifuentes ◽  
Geovanny Marulanda ◽  
Antonio Bello ◽  
Javier Reneses
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Regional Scale ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Industrial Sectors ◽  
Learning Techniques ◽  
Temperature Forecasting ◽  
The Government

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.

Domain adaptation-based transfer learning using adversarial networks

2020 ◽  
Vol 35 ◽  
Author(s):  
Farzaneh Shoeleh ◽  
Mohammad Mehdi Yadollahi ◽  
Masoud Asadpour
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
Machine Learning Techniques ◽  
Implicit Assumption ◽  
Adversarial Networks ◽  
Learning Techniques ◽  
Speed Up ◽  
Similar Domain

Abstract There is an implicit assumption in machine learning techniques that each new task has no relation to the tasks previously learned. Therefore, tasks are often addressed independently. However, in some domains, particularly reinforcement learning (RL), this assumption is often incorrect because tasks in the same or similar domain tend to be related. In other words, even though tasks are quite different in their specifics, they may have general similarities, such as shared skills, making them related. In this paper, a novel domain adaptation-based method using adversarial networks is proposed to do transfer learning in RL problems. Our proposed method incorporates skills previously learned from source task to speed up learning on a new target task by providing generalization not only within a task but also across different, but related tasks. The experimental results indicate the effectiveness of our method in dealing with RL problems.

scholarly journals Machine Learning Fake News Classification with Optimal Feature Selection

2021 ◽  
Author(s):  
Muhammad Fayaz ◽  
Atif Khan ◽  
Muhammad Bilal ◽  
Sanaullah Khan
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Information Gain ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Learning Techniques ◽  
Proposed Model ◽  
Current Events ◽  
The Government ◽  
Experimental Findings

Abstract Nowadays, information is published in newspapers and social media while transmitted on radio and television about current events and specific fields of interest nationwide and abroad. It becomes difficult to explicit what is real and what is fake due to the explosive growth of online content. As a result, fake news has become epidemic and immensely challenging to analyze fake news to be verified by the producers in the form of data process outlets not to mislead the people. Indeed, it is a big challenge to the government and public to debate the situation depending on case to case. For the purpose several websites were developed for this purpose to classify the news as either real or fake depending on the website logic and algorithm. A mechanism has to be taken on fact-checking rumors and statements, particularly those that get thousands of views and likes before being debunked and refuted by expert sources. Various machine learning techniques have been used to detect and correctly classified of fake news. However, these approaches are restricted in terms of accuracy. This study has applied a Random Forest (RF) classifier to predict fake or real news. For this prpose, twenty-three (23) textual features are extracted from ISOT Fake News Dataset. Four best feature selection techniques like Chi2, Univariate, information gain and Feature importance are used for selecting fourteen best features out of twenty-three. The proposed model and other benchmark techniques are evaluated on the dataset by using best features. Experimental findings show that, the proposed model outperformed state-of-the-art machine learning techniques such as GBM, XGBoost and Ada Boost Regression Model in terms of classification accuracy.

scholarly journals Classical Machine Learning Techniques in the Search of Extrasolar Planets

2019 ◽  
Vol 22 (3) ◽  
Author(s):  
Francisco Alejandro Mena ◽  
Margarita Constanza Bugueño ◽  
Mauricio Araya
Keyword(s):  
Machine Learning ◽  
Extrasolar Planets ◽  
Light Curves ◽  
Performance Criteria ◽  
Machine Learning Techniques ◽  
Pattern Recognition Methods ◽  
Learning Techniques ◽  
The Face ◽  
Speed Up ◽  
New Instrumentation

The field of astronomical data analysis has experienced an important paradigm shift in the recent years. The automation of certain analysis procedures is no longer a desirable feature for reducing the human effort, but a must have asset for coping with the extremely large datasets that new instrumentation technologies are producing. In particular, the detection of transit planets --- bodies that move across the face of another body --- is an ideal setup for intelligent automation. Knowing if the variation within a light curve is evidence of a planet, requires applying advanced pattern recognition methods to a very large number of candidate stars. Here we present a supervised learning approach to refine the results produced by a case-by-case analysis of light-curves, harnessing the generalization power of machine learning techniques to predict the currently unclassified light-curves. The method uses feature engineering to find a suitable representation for classification, and different performance criteria to evaluate them and decide. Our results show that this automatic technique can help to speed up the very time-consuming manual process that is currently done by expert scientists.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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