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International Conference on Computer Communication and Informatics (ICCCI)

作者： M. Jagadeesan T.M Saravanan P.A. Selvaraj S. Nithya K. Snegha P. Sowmiya Department of Computer Applications Kongu Engineering College Perundurai Erode TamilNadu India

Every day, a great amount of data is generated online in the age of technology. However, an unprecedented amount of material is being disseminated online, most of it false news. Fake news is created to draw in viewers, influence people’s opinions and actions, raise click through rates, and have a significant impact on important events like elections. Fake News incorporates material that steers readers in the wrong directions. It may have unfavorable repercussions in the actual world that seek to deceive people, sway public opinion, or attract attention or damage reputation. It is necessary to detect fake news mainly for media outlets to have the capability to attract viewers to their website to generate online advertising revenue. This project develops a SVM approach and uses this model to detect whether the news are fake or not. It uses a fake news dataset, which contains News text and corresponding labels (REAL or FAKE). The pervasive increase in fake news, whether it is produced by humans or algorithms, has a severe social and political impact on society and individuals. The rapid turnover of news in the age of social networks makes it difficult to assess its veracity in a timely manner. Tools for automatically detecting bogus news become essential. K- Nearest Neighbor and a classification based on a support vector machine are utilized in this case to overcome the aforementioned issue. In contrast to other research that merely took particular sentences or phrases into account, The purpose of this study is to investigate a false news posture detection mechanism based on the title and text of the news. Python 3.7 is used in the project’s development.

关键词： machine learning algorithms Social networking (online) Voting Support vector machine classification Classification algorithms Fake news Informatics

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Prediction of Chronic Kidney Disease Using Various machine learning algorithms

Prediction of Chronic Kidney Disease Using Various Machine L...

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Daravath Anil Shaik Naimudden Aujugari Santosh Reddy A Lavanya Electronics and Communication Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

Chronic Renal Disease (CRD), often referred to as Chronic Kidney Disease (CKD), has become more prevalent over time. Only 18 days may be spent without a functioning kidney, which is why dialysis and kidney transplants are so common. Predicting CKD at an early stage is crucial. machine learning is quite effective at forecasting CKD. The model proposes an approach to predict CKD status based on clinical data by combining preprocessing, a mechanism for handling missing values, and collaborative filtering. It assists i $n$ figuring out whether someone has CKD. This research utilizes several algorithms to determine which one was the most accurate for getting the result. The creation of a trustworthy model for anticipating chronic renal disease is the goal of this research. The model in most of the current systems employs various ML algorithms separately. If the method is not sufficiently regularized, the data is too noisy, or there are too many features in the data, overfitting may happen. However, several algorithms are employed in this model to build a more precise, reliable, and adaptable model.

关键词： Training Adaptation models machine learning algorithms Prediction algorithms Chronic kidney disease Classification algorithms Reliability

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A Comprehensive Analysis of the Effectiveness of machine learning algorithms for Predicting Water Quality

A Comprehensive Analysis of the Effectiveness of Machine Lea...

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Priyanshu Rawat Madhvan Bajaj Vikrant Sharma Satvik Vats Computer Science and Engineering Graphic Era Hill University Dehradun India

This study provides a comprehensive analysis of the effectiveness of eight different machine learning algorithms for predicting water quality. The algorithms, which include Gaussian Naive Bayes, Extreme Gradient Boost Classifier, Support Vector machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression, Random Forest, and Decision Tree, were tested using the water potability dataset. This study's main goals were to identify the best accurate machine learning algorithm for predicting water quality and to present a thorough comparison of these methods. Algorithm's effectiveness. The study's findings demonstrated that one algorithm performed better than the others, with the lowest mean squared error and maximum accuracy. The results of this study may be used as a guide for future research in this area and offer a strong foundation for selecting the best machine learning algorithm for predicting water quality, Predicting water quality is often hampered by a lack of data, especially in developing or rural areas. machine learning techniques may be used to predict water quality. This study highlights how crucial it is to use a suitable machine learning algorithm for predicting water quality since the precision and efficiency of these algorithms may have a big influence on the outcomes. Organizations that manage and monitor water quality, as well as academics and experts in the field of water quality forecasting, can benefit from the study's findings.

关键词： Support vector machines machine learning algorithms Water quality Organizations Prediction algorithms Classification algorithms Forecasting

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Development of prediction model for phosphate in reservoir water system based machine learning algorithms

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AIN SHAMS ENGINEERING JOURNAL 2022年第1期13卷

作者： Latif, Sarmad Dashti Birima, Ahmed H. Ahmed, Ali Najah Hatem, Dahan Mohammed Al-Ansari, Nadhir Fai, Chow Ming El-Shafie, Ahmed Komar Univ Sci & Technol Dept Civil Engn Coll Engn Sulaimany 46001 Kurdistan Regio Iraq Qassim Univ Coll Engn Dept Civil Engn Unaizah Saudi Arabia Univ Tenaga Nas Inst Energy Infrastruct Kajang 43000 Selangor Malaysia Lulea Univ Technol Civil Environm & Nat Resources Engn S-97187 Lulea Sweden Monash Univ Malaysia Sch Engn Discipline Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr Al Ain U Arab Emirates

Phosphate (PO4) is a major component of most fertilizers, and when erosion and runoff occur, large amounts of it enter the water bodies, causing several problems such as eutrophication. Feitsui reservoir, the primary source of water supply to Taipei, reported half of the reservoir's pollutants from nonpointsource pollution. The value of the PO4 in the water body fluctuates in highly nonlinear and stochastic patterns. However, conventional modeling techniques are no longer sufficiently effective in predicting accurately such stochastic patterns in the concentrations of PO4 in water. Therefore, this study proposes different machine learning algorithms: the artificial neural network (ANN), support vector machine (SVM), random forest (RF), and boosted trees (BT) to predict the concentration of PO4. Monthly measured data between 1986 and 2014 were used to train and test the accuracy of these models. The performances of these models were examined using different statistical indices. Hyperparameters optimization such as cross-validation was performed to enhance the precision of the models. Five water quality parameters were used as input to the proposed models. Different input combinations were explored to optimize the precision. The findings revealed that ANN outperformed the other three models to capture the changes in the concentrations of PO4 with high precision where RMSE is equal to 1.199, MAE is equal to 0.858, and R-2 is equal to 0.979, MSE is equal to 1.439, and finally, CC is equal to 0.9909. The developed model could be used as a reliable means for managing eutrophication problems. (c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Water quality parameters Phosphate (PO4), concentration machine learning algorithms Prediction and Feitsui reservoir

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A Fundamental Analysis of Stock Returns using machine learning algorithms

A Fundamental Analysis of Stock Returns using Machine Learni...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Andy Qin Mihai Boicu Plano West Senior High Plano Texas United States George Mason University Fairfax Virginia United States

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

Predicting stocks has always been an attractive prospect for researchers. This study aims to create a more replicable model that combines machine learning and fundamental financial features to predict long-term stock movement better. We test a support vector machine (SVM) and random forest (RF) model via a regression and classification task of 432 stocks selected from the NYSE and NASDAQ. The results show that both models are best at predicting stock movement 1 quarter in the future, with the best root mean square error (RMSE) of 0.119. The models perform the best at predicting stock directionality when predicting 5 quarters in the future as the SVM peaks at 77.3% accuracy. We also find key features by using a permutation feature importance algorithm. Our models show significant improvement over baseline decision-making (50% accuracy classification) considerably far into the future (>1 year), helping provide better indicators for long-term stock returns.

关键词： Support vector machines Radio frequency Measurement Analytical models machine learning algorithms Predictive models Prediction algorithms

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Detection Of Breast Cancer By machine learning algorithms

Detection Of Breast Cancer By Machine Learning Algorithms

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： D H Damini K P Shailaja T S Pushpa Department of Computer Applications B.M.S. College of Engineering Karnataka India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

According to an NHS survey, 10 women among 80 are diagnosed with breast cancer in their lives. Nowadays, breast cancer is becoming more common and is detected in women between the ages of 29 and 50. In a very rare case, even men can be tested and diagnosed with breast cancer. Multiple tests and procedures are frequently used in traditional cancer diagnosis systems, which puts patients under physical and emotional strain. machine learning (ML) algorithms have become potential methods for precise and effective breast cancer diagnosis in an effort to lessen this burden. In the initial stage, with very few symptoms, it can be detected through mammographic screening, and from this, it can be classified as benign or malignant. This can increase the rate of survival. Most of the cancer detection system, which takes a lot of time for detection with physical and mental stress, will involve many tests that can cause mental and physical breakdowns by considering all the aspects. The detection of cancerous and noncancerous cells can be done using machine learning (ML) algorithms like logistic regression, random forest, and K-nearest neighbour (KNN) algorithms. These ML algorithms may be used to analyse mammographic data to measure the efficacy and accuracy of breast cancer diagnosis. By examining the patterns and traits seen in mammograms, these algorithms can help identify possible anomalies suggestive of breast cancer. Healthcare workers may speed up the detection process and cut down on the time and stress involved with conventional procedures by utilising the power of *** Applying these algorithms to the mammogram data, we can detect the accuracy and efficiency of breast cancer detection.

关键词： Logistic regression machine learning algorithms Medical services Human factors Prediction algorithms Feature extraction Breast cancer

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Estimating the thermal conductivity of soils using six machine learning algorithms

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2022年第0期136卷

作者： Li, Kai-Qi Liu, Yong Kang, Qing Wuhan Univ Inst Engn Risk & Disaster Prevent State Key Lab Water Resources & Hydropower Engn Sc 299 Bayi Rd Wuhan 430072 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn 1 Engn Dr 2 Singapore 117576 Singapore

Many machine learning algorithms have been applied to determine soil properties in recent years. However, the prediction performances of thermal conductivity of soils via machine learning algorithms are still unclear due to the lack of sufficient databases. In this work, a large database containing 2197 data points from various literature was compiled. Six machine learning algorithms, namely multivariance linear regression (MLR), Gaussian process regression (GPR), support vector machine (SVM), decision tree (DT), random forest (RF) and adaptive boosting methods (AdaBoost) were implemented to predict the thermal conductivity of soils based on the compiled database. In addition, Spearman correlation analysis and grey relational analysis were adopted to compute the strength of influencing factors in thermal conductivity. For obtaining the best prediction model, the performances of six machine learning algorithms were assessed via eight evaluation indicators and compared with three typical empirical models. Results show that the AdaBoost can yield good predicted values of the thermal conductivity of soils with minimum errors (i.e., RMSE = 0.099). This study constructs a database of thermal conductivity and provides a reference for evaluating the thermal conductivity of soils via machine learning algorithms.

关键词： machine learning algorithms Thermal conductivity database Parameter correlation Performance indicators Ensemble learning model

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Comparison of machine learning algorithms in the Prediction of Hospitalized Patients with Schizophrenia

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SENSORS 2022年第7期22卷 2517页

作者： Gongora Alonso, Susel Marques, Goncalo Agarwal, Deevyankar De la Torre Diez, Isabel Franco-Martin, Manuel Univ Valladolid Dept Signal Theory & Commun Paseo Belen 15 Valladolid 47011 Spain Polytech Coimbra ESTGOH Rua Gen Santos Costa P-3400124 Oliveira Do Hospital Portugal Higher Coll Technol Dept Engn EEE Sect Muscat 113 Oman Hosp Zamora Psiquiatry Serv Zamora 49021 Spain

New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naive Bayes, Support Vector machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders;these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

关键词： hospitalization machine learning algorithms predictive models random forest schizophrenia

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A Performance Analysis of machine learning algorithms for Malaria Parasite Detection using Microscopic Images

A Performance Analysis of Machine Learning Algorithms for Ma...

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International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Tamal Kumar Kundu Dinesh Kumar Anguraj Department of Computer science & Engineering Koneru Lakshmaiah Education Foundation Vijayawada India

Plasmodium parasites are the cause of malaria, a blood-borne disease spread by mosquitoes. Preparing a blood sample, staining it, then using a microscope to look at the stained blood smear to determine which parasite genus is Plasmodium. is the standard method for detecting malaria, a promising area of research is computer-aided Plasmodium detection. For the purpose of detecting Plasmodium on blood smear images obtained through conventional microscopy, this study examines the efficacy of various machine learning strategies in this paper. To target effected blood smears for detection of malaria, this research work employs different algorithms of machine learning in contrast to the conventional approach and has some issues with sensitivity and specificity. Most malaria diagnoses are made using standard microscopy. Microscopy has also shown a range of clinical and transmission situations to have varied sensitivity and specificity. The suggested method does not require experts or blood stains; instead, it uses images of patient to detect malaria.

关键词： machine learning algorithms Tuberculosis Microscopy Sensitivity and specificity Feature extraction Performance analysis Blood

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Hybrid machine learning algorithms for Optimal Diagnosis of Heart Disease with Feature Analysis

Hybrid Machine Learning Algorithms for Optimal Diagnosis of ...

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Power, Instrumentation, Energy and Control (PIECON), International Conference on

作者： Ghulab Nabi Ahmad Hira Fatima Shafiullah Mohammad Haris Institute of Applied Sciences Mangalayatan University Aligarh India Department of Mathematics K.C.T.C College Raxual BRA Bihar University Muzaffarpur India Department of Computer Science and Engineering Institute of Technology and Management Aligarh India

Timely prediction of heart disease and its cause is the most challenging issue in medical science. This paper uses various machine learning algorithms such as Logistic Regression, Support Vector machine, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbourhood, and K-fold cross-validation are used to predict heart diseases. The system uses a K-fold cross-validation technique to enhance the accuracies of algorithms. The UCI Kaggle Cleveland heart disease datasets is used to analyse the performance of the models. It is found in the experiment that the training accuracy of K-Nearest Neighbour is 88.52%, and Recall is 93.30%. The Random Forest produced the highest and most comparable Receiver Operating Characteristics Curve accuracy. Moreover, the experimental results of the recommended techniques are compared with previous heart disease prediction studies, and it is found that among the suggested technique, the performance of K-Nearest Neighbour is best. The fundamental goal of this study is to design a novel and distinctive model-creation approach for resolving practical issues.

关键词： Heart Training Support vector machines machine learning algorithms Instruments Receivers Prediction algorithms

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