Prediction and Analysis of Covid-19 with various Machine Learning algorithms

2021 ◽  
Vol 2 (2) ◽  
pp. 33-39
Author(s):  
Sarthak Maggu ◽  
Vijander Singh
Keyword(s):  
Machine Learning ◽  
Data Cleaning ◽  
Machine Learning Algorithms ◽  
Main Ingredient ◽  
Open Source Data ◽  
Source Data ◽  
The World ◽  
Collection Data ◽  
The Impact ◽  
Data Plotting

The world was stuck by a deadly virus last year, which stopped the world and changed it completely. The virus was given the name “Corona.” It has had an everlasting effect on human lives which has caused a lot of people to study about it, following the trend we have chosen “Prediction and analysis of Covid - 19 with different ML algorithms and comparative analysis” as the title of this paper. The title of the paper is self-explanatory to explain what the paper is about and what technology will be used in the paper. The paper will use data cleaning and plotting techniques to analyse the impact and effect of Covid on human lives and various algorithms to predict how vulnerable the person is to the virus / predict whether a person suffered from Covid or not based on various parameters. The main ingredient of the paper will be the data on which the model will be built, will be collected through various Google forms or through open source data websites such as Kaggle. The paper would be divided into various parts, which will include data collection, data cleaning, data plotting etc. After cleaning of data and building of models using various ML algorithms, findings will be reported in a form of report using various plots to favour the findings of the various models.

scholarly journals Review Paper on Prediction of Heart Disease using Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 2937-2940
Author(s):  
Aadar Pandita
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Medical Researchers ◽  
High Level ◽  
The Impact

: Heart disease has been one of the ruling causes for death for quite some time now. About 31% of all deaths every year in the world take place as a result of cardiovascular diseases [1]. A majority of the patients remain uninformed of their symptoms until quite late while others find it difficult to minimise the effects of risk factors that cause heart diseases. Machine Learning Algorithms have been quite efficacious in producing results with a high level of correctness thereby preventing the onset of heart diseases in many patients and reducing the impact in the ones that are already affected by such diseases. It has helped medical researchers and doctors all over the world in recognising patterns in the patients resulting in early detections of heart diseases.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

scholarly journals Fault detection for air conditioning system using machine learning

2020 ◽  
Vol 9 (1) ◽  
pp. 109
Author(s):  
Noor Asyikin Sulaiman ◽  
Md Pauzi Abdullah ◽  
Hayati Abdullah ◽  
Muhammad Noorazlan Shah Zainudin ◽  
Azdiana Md Yusop
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Air Conditioning ◽  
Machine Learning Algorithms ◽  
Coefficient Of Performance ◽  
Support Vector ◽  
Air Conditioning System ◽  
Learning Classifier ◽  
Negative Impacts ◽  
The Impact

Air conditioning system is a complex system and consumes the most energy in a building. Any fault in the system operation such as cooling tower fan faulty, compressor failure, damper stuck, etc. could lead to energy wastage and reduction in the system’s coefficient of performance (COP). Due to the complexity of the air conditioning system, detecting those faults is hard as it requires exhaustive inspections. This paper consists of two parts; i) to investigate the impact of different faults related to the air conditioning system on COP and ii) to analyse the performances of machine learning algorithms to classify those faults. Three supervised learning classifier models were developed, which were deep learning, support vector machine (SVM) and multi-layer perceptron (MLP). The performances of each classifier were investigated in terms of six different classes of faults. Results showed that different faults give different negative impacts on the COP. Also, the three supervised learning classifier models able to classify all faults for more than 94%, and MLP produced the highest accuracy and precision among all.

scholarly journals Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: the Evaluation of Text Classification Algorithms Using Machine Learning

2019 ◽  
Author(s):  
Francesc López Seguí ◽  
Ricardo Ander Egg Aguilar ◽  
Gabriel de Maeztu ◽  
Anna García-Altés ◽  
Francesc García Cuyàs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Text Classification ◽  
Learning Strategy ◽  
Care Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Face To Face ◽  
Classification Tool ◽  
The Impact

Background: the primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: the study was intended to examine the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: 20 machine learning algorithms (based on 5 types of algorithms and 4 text representation techniques)were trained using a sample of 3,559 messages (169,102 words) corresponding to 2,268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: the best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables "avoiding the need of a face-to-face visit" and "increased demand" (precision = 0.98 and 0.97, respectively) rather than the variable "type of query"(precision = 0.48). Conclusion: to the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.

scholarly journals Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 647
Author(s):  
Meijun Shang ◽  
Hejun Li ◽  
Ayaz Ahmad ◽  
Waqas Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Mean Square Error ◽  
Coarse Aggregate ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Environmental Damage ◽  
Fine Aggregate ◽  
Mean Square ◽  
The Impact

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

Applications of Machine Learning in Cyber Security

2020 ◽  
pp. 83-103 ◽  
Author(s):  
Charu Virmani ◽  
Tanu Choudhary ◽  
Anuradha Pillai ◽  
Manisha Rani
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Machine Learning Algorithms ◽  
Technological Systems ◽  
The World ◽  
Computer Scientists ◽  
Potential Risks ◽  
To Come ◽  
Time Basis ◽  
Applications Of Machine Learning

With the exponential rise in technological awareness in the recent decades, technology has taken over our lives for good, but with the application of computer-aided technological systems in various domains of our day-to-day lives, the potential risks and threats have also come to the fore, aiming at the various security features that include confidentiality, integrity, authentication, authorization, and so on. Computer scientists the world over have tried to come up, time and again, with solutions to these impending problems. With time, attackers have played out complicated attacks on systems that are hard to comprehend and even harder to mitigate. The very fact that a huge amount of data is processed each second in organizations gave birth to the concept of Big Data, thereby making the systems more adept and intelligent in dealing with unprecedented attacks on a real-time basis. This chapter presents a study about applications of machine learning algorithms in cyber security.

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