scholarly journals An Efficient Suicide based Dataset using Machine Learning Algorithms

2019 ◽  
Vol 8 (12S) ◽  
pp. 1267-1271
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Indian Government ◽  
The Future ◽  
Death State ◽  
The Government ◽  
Government Records ◽  
Major Factors ◽  
Year 2000

A study is presented on analyzing the major factors that affect the number of suicides in different parts of India from year 2000 to 2012 and using them to predict the number of suicides in the future. By analyzing the data and predicting the major causes of suicides it can help government to know which part of population is most affected, so that the government can provide required steps to avoid suicides. The Indian government records the database of each suicide occurs in India. Along with the age-group, cause of death, state of victim, this data was made public by crime branch bureau of the data analytics purpose. Relationship will be made between the different features of suicide so that a linear relationship can be formed with the help of linear regression and other machine learning algorithms will be used to develop a model for the prediction of number of suicides in the future. It has been found that the results obtained by machine learning algorithms are more accurate when compared with the traditional algorithms.

scholarly journals Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Jibouni Ayoub ◽  
Dounia Lotfi ◽  
Ahmed Hammouch
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Actual State ◽  
The Future ◽  
Auc Value ◽  
The Mean ◽  
Analysis Of Social Networks

The analysis of social networks has attracted a lot of attention during the last two decades. These networks are dynamic: new links appear and disappear. Link prediction is the problem of inferring links that will appear in the future from the actual state of the network. We use information from nodes and edges and calculate the similarity between users. The more users are similar, the higher the probability of their connection in the future will be. The similarity metrics play an important role in the link prediction field. Due to their simplicity and flexibility, many authors have proposed several metrics such as Jaccard, AA, and Katz and evaluated them using the area under the curve (AUC). In this paper, we propose a new parameterized method to enhance the AUC value of the link prediction metrics by combining them with the mean received resources (MRRs). Experiments show that the proposed method improves the performance of the state-of-the-art metrics. Moreover, we used machine learning algorithms to classify links and confirm the efficiency of the proposed combination.

scholarly journals Stock Market Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3628-3632
Author(s):  
Prof. Gowrishankar B S
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Market Prediction ◽  
The Future ◽  
Complicated Model

Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.

scholarly journals Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

2021 ◽  
Vol 17 (02) ◽  
pp. 90
Author(s):  
Inssaf El Guabassi ◽  
Zakaria Bousalem ◽  
Rim Marah ◽  
Aimad Qazdar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Predictive Model ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Future ◽  
Log Linear

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

scholarly journals A Survey on Performing E-Voting through Facial Recognition

2021 ◽  
pp. 141-147
Author(s):  
Pratik Hopal ◽  
Alkesh Kothar ◽  
Swamini Pimpale ◽  
Pratiksha More ◽  
Jaydeep Patil
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Facial Recognition ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Voting Systems ◽  
Human Errors ◽  
Research Article ◽  
The Future

The election procedure is one of the most essential processes to take place in a democracy. Even though there have been immense technological advancements, the process of election has been highly limited. Most of the election procedures have been performed using ballot boxes which is an old process and needs to be updated. The security of such practices is also a concern as the identification of the voters is being done manually by the election officers. This process also needs an improvement to increase accuracy and reduce human errors by automating the process. Therefore, for this purpose, this research article analyzes the previous researches on this paradigm. This allows an effective understanding of the machine learning algorithms that are used for automatic facial recognition in the E-voting systems. This paper comes to the conclusion that the Recurrent Neural Networks are best suited for such an application for facial recognition. The future editions of this research will elaborate more on the proposed system in detail.

scholarly journals Future Trend Prediction in Share Market with Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3978-3981
Author(s):  
Prof. Kanchan Mahajan
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Life Span ◽  
Learning Algorithms ◽  
Future Trend ◽  
Machine Learning Algorithms ◽  
Trend Prediction ◽  
Stock Market Prediction ◽  
The Future

In Stock Market Prediction, the point is to estimate the future worth of the monetary loads of an organization. The new pattern in securities exchange forecast advances is the utilization of AI which makes expectations dependent on the upsides of current financial exchange lists via preparing on their past qualities. AI itself utilizes various models to make expectation simpler and credible. The thought centers on the utilization of dissimilar Machine learning algorithms to anticipate stock qualities. Variables considered are open, close, low, high and volume. The principal thing we have considered is the dataset of the securities exchange costs from earlier year. The dataset was pre-handled and adjusted for genuine examination. What's more, the proposed thought inspects the utilization of the forecast framework in verifiable settings and issues related with the accuracy of the general qualities given. The thought additionally portrays AI model to foresee the life span of the stock in a serious market. The effective forecast of the stock will be an extraordinary resource for the securities exchange establishments and will give genuine answers for the issues that stock financial backers face.

Forecasting Sales and Return Products for Retail Corporations and Bridging Among Them

2021 ◽  
pp. 250-281
Author(s):  
Md Mushfique Hasnat Chowdhury ◽  
Saman Hassanzadeh Amin
Keyword(s):  
Machine Learning ◽  
Inventory Management ◽  
Learning Algorithms ◽  
Retail Store ◽  
Machine Learning Algorithms ◽  
Forecasting Model ◽  
Forecasting Models ◽  
The Future ◽  
Product Sales

The purpose of this study is to show how we can bridge sales and return forecasts for every product of a retail store by using the best model among several forecasting models. Managers can utilize this information to improve customer's satisfaction, inventory management, or re-define policy for after sales support for specific products. The authors investigate multi-product sales and return forecasting by choosing the best forecasting model. To this aim, some machine learning algorithms including ARIMA, Holt-Winters, STLF, bagged model, Timetk, and Prophet are utilized. For every product, the best forecasting model is chosen after comparing these models to generate sales and return forecasts. This information is used to classify every product as “profitable,” “risky,” and “neutral,” The experiment has shown that 3% of the total products have been identified as “risky” items for the future. Managers can utilize this information to make some crucial decisions.

scholarly journals Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6526
Author(s):  
Junaid Abdul Wahid ◽  
Lei Shi ◽  
Yufei Gao ◽  
Bei Yang ◽  
Yongcai Tao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Linguistic Features ◽  
Public Authorities ◽  
Response Strategies ◽  
Negative Effect ◽  
The Government ◽  
Situational Information

During the recent pandemic of COVID-19, an increasing amount of information has been propagated on social media. This situational information is valuable for public authorities. Therefore, this study characterized the propagation scale of situational information types by harnessing the power of natural language processing techniques and machine learning algorithms. We observed that the length of the post has a positive correlation with type 1 information (announcements), and negative words were mostly used in type 5 information (criticizing the government), whereas anxiety-related words have a negative effect on the amount of retweeted type 0 (precautions) and type 2 (donations) information. This type of research study not only contributes to the situational information literature by comprehensively defining categories but also provides data-oriented practical insights into information so that management authorities can formulate response strategies after the pandemic. Our approach is one of its kind and combines Twitter content features, user features and LIWC linguistic features with machine learning algorithms to analyze the propagation scale of situational information, and it achieved 77% accuracy with SVM while classifying the information categories.

scholarly journals Click Prediction for Advertisement in Websites using Linear Regression

2021 ◽  
Vol 12 (4) ◽  
pp. 271-276
Author(s):  
D. Ramya , Et. al.
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Learning Algorithms ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
The Future ◽  
Online Advertisement ◽  
Commercial Value

Prediction is one of the most powerful and effective method used nowadays for improvement in business. Machine Learning Algorithms plays a vital role in predicting the future of business. It is widely used in the field of Marketing and Advertising fields also. The Commercial Value for the advertisement is gained based on the user click on the website. Digital advertisement and marketing play very important role in influencing the profit of business. Many Machine Learning algorithms were used for predicting and analyzing the online advertisement. In this paper, Linear Regression is used for predicting the user click on the advertisement.

scholarly journals Covid-19 Pandemic Data Analysis and Forecasting using Machine Learning Algorithms

2020 ◽  
Author(s):  
Sohini Sengupta ◽  
Sareeta Mugde
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Geographical Location ◽  
Significant Rise ◽  
Machine Learning Algorithms ◽  
Logarithmic Scale ◽  
Time Data ◽  
First Case ◽  
The Government

AbstractIndia reported its first case of covid-19 on 30th Jan 2020. Though we did not notice a significant rise in the number of cases in the month of February and like many other countries, this number escalated like anything from March 2020. This research paper will include analysis of covid-19 data initially at a global level and then drilled down to the scenario of India. Data is gathered from multiple data sources from several authentic government websites. The paper will also include analysis of various features like gender, geographical location, age using Python and Data Visualization techniques. Getting insights on Trend pattern and time series analysis will bring more clarity to the current scenario as analysis is totally on real-time data(till 19th June). Finally we will use some machine learning algorithms and perform predictive analytics of the near future scenario. We are using a sigmoid model to give an estimate of the day on which we can expect the number of active cases to reach its peak and also when the curve will start to flatten sigmoid model gives us a count of date which is a unique feature of analysis in this paper. We are also using certain feature engineering techniques to transfer data into logarithmic scale for better comparison removing any data extremities or outliers. Based on the predictions of the short-term interval, our model can be tuned to forecast long time intervals. Needless to mention there are a lot of factors responsible for the cases to come in the upcoming days. It depends on the people of the country and how strictly they obey the rules and restriction imposed by the Government.

The Future of Artificial Intelligence in Education

2021 ◽  
pp. 187-194
Author(s):  
Fati Tahiru ◽  
Samuel Agbesi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Counseling Students ◽  
Learning Management Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
The Future ◽  
Artificial Intelligence In Education ◽  
Use Of The Internet

The key accelerating factor in the increased growth of AI is the availability of historic datasets, and this has influenced the adoption of artificial intelligence and machine learning in education. This is possible because data can be accessed through the use of various learning management systems (LMS) and the increased use of the internet. Over the years, research on the use of AI and ML in education has improved appreciably, and studies have also indicated its success. Machine learning algorithms have successfully been implemented in institutions for predicting students' performance, recommending courses, counseling students, among others. This chapter discussed the use of AI and ML-assisted systems in education, the importance of AI in education, and the future of AI in education to provide information to educators on the AI transformation in education.

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