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International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Mandala Vishal Rao Sindhu C. Consultant Larsen and Toubro Infotech Chennai India SRM Institute of Science and Technology Chennai India

Most of the people express their ideas, views and opinions over social media. These feedbacks or comments carry an emotion in them. This data can be either straight forward or sarcastic. This text has to be analyzed and make the reviewers understand the exact intent of the writer. Sentiment analysis is used to analyze the perspective of text. Sarcasm can also be present in the text which is a bitter way of conveying the information. Selection of the dataset is the initial task. Dataset is retrieved from Amazon datasets. The next task is preprocessing of data which includes tokenization of data, polarity identification, stemming and lemmatization. Later, feature extraction is done, which includes term frequency, Inverse document frequency and n-gram. The classification algorithms are used such as Support Vector machine (SVM), K Nearest Neighbors and Random forest are implemented. Further the calculation of results is evaluated by the parameter accuracy.

关键词： Wireless communication Sentiment analysis machine learning algorithms Social networking (online) Support vector machine classification Signal processing Feature extraction

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Reviewing System using Exploratory Data Analysis and Ensemble machine learning algorithms

Reviewing System using Exploratory Data Analysis and Ensembl...

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Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE International Conference on

作者： Ayan Banerjee Tapan Chowdhury Informatica Business Solutions India Techno Main Salt Lake Kolkata India

ISBN: (纸本)9781665427791

In the fast-growing technological advancements, every business venture is thriving for success. Irrespective of the business’s paradigm, each one has a reviewing system that envisages the working throughput of the system. The impact justifies the company’s current commercial state and also helps to bring in new customers. The proposed exploratory data analysis shows the relationship between the reviews on textual and non-textual parameters aimed to work on a user-based analysis. In this paper two different datasets are analyzed where the reviews are predicted based on extensive exploratory data analysis and feature engineering. This paper focused on examining how reviews are influenced based on the independent parameters from Zomato and Yelp datasets. These datasets have huge geographical differences that allowed us to obtain various diverse users-review coming from different backgrounds, cultures, and heritage. After feature construction, the predictive model is analyzed by applying supervised and ensemble machine learning algorithms such as Linear regression, Logistic regression, Decision trees, RandomForest, XGBoost, and GradientBoosting. The inferences obtained from both datasets gave us coherent results that validate the experimental procedures, extend the same analogy in different business paradigms, and offer a comprehensive prediction.

关键词： Visualization machine learning algorithms Data analysis machine learning Predictive models Throughput Prediction algorithms

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Detection of Epileptic Seizure from EEG Signal Data by Employing machine learning algorithms with Hyperparameter Optimization

Detection of Epileptic Seizure from EEG Signal Data by Emplo...

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International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Ahnaf Akif Rahman Fahim Faisal Mirza Muntasir Nishat Muntequa Imtiaz Siraji Lamim Ibtisam Khalid Md Rezaul Hoque Khan Md. Taslim Reza Islamic University of Technology Dhaka Bangladesh

ISBN: (纸本)9781665408110

Epileptic seizure refers to a brief occurrence of signs in the brain caused by abnormally high or synchronized neuronal activity. With the utilization of EEG signal, the epileptic seizure can be identified. However, incorporating machine learning classifiers with this EEG data can significantly contribute in detecting epileptic seizure in an automated manner. In this paper, nine machine learning algorithms have been studied and models have been constructed by utilizing UCI Epileptic Seizure dataset. The performances of the ML models are noted and detailed comparative analysis has been exhibited for both hyperparameter tuning and without hyperparameter tuning. Random search cross validation has been used for tuning the hyperparameters. Satisfactory results have been witnessed in terms of different performance metrics like accuracy, precision, recall, specificity, FI-Score, and ROC. After simulation, Support Vector machine (SVM) performed the best in terms of accuracy with over 97.86%. However, Random Forest (RF) and Multi-Layer Perceptron (MLP) also depicted promising accuracies of 97.50% and 97.26% respectively. Therefore, with proper implementation of the ML based diagnosis system, the patients having epileptic seizures can be identified and treated at an early stage.

关键词： Support vector machines Analytical models machine learning algorithms Computational modeling Medical services Brain modeling Electroencephalography

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Performance Analysis of machine learning algorithms for Intelligent Recommender System

Performance Analysis of Machine Learning Algorithms for Inte...

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Technological Advancements and Innovations (ICTAI), International Conference on

作者： Pooja Vishal Bhatnagar GGSIPU AIACTR Delhi India NSUT East Campus (Formerly Ambedkar Institute of Advanced Communication Technologies and Research) Geeta Colony Delhi India

ISBN: (纸本)9781665420884

In today’s Society 5.0, the recommender system analyzes the user preferences to provide the required potential targets. Graph-Based Collaborative filtering has become the new state-of-the-art to take the edge of selection parameters for the user-item matrix. The author analyzed the following machine learning algorithms: Linear Regression, Logistic Regression Naive Bayes classifier, Support vector machine, and J48. The model Prediction depends upon the characteristics of genres associated with game play. Shortcomings of data take into account for model development, testing, and benchmarking.

关键词： Support vector machines Training machine learning algorithms Collaborative filtering Linear regression Games Data models

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Evaluation of machine learning algorithms for the Detection of Fake Bank Currency

Evaluation of Machine Learning Algorithms for the Detection ...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Anju Yadav Tarun Jain Vivek Kumar Verma Vipin Pal SCIT Manipal University Jaipur NIT Meghalaya

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

The one important asset of our country is Bank currency and to create discrepancies of money miscreants introduce the fake notes which resembles to original note in the financial market. During demonetization time it is seen that so much of fake currency is floating in market. In general by a human being it is very difficult to identify forged note from the genuine not instead of various parameters designed for identification as many features of forged note are similar to original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or in ATM machines. To design such an automated system there is need to design an efficient algorithm which is able to predict weather the banknote is genuine or forged bank currency as fake notes are designed with high precision. In this paper six supervised machine learning algorithms are applied on dataset available on UCI machine learning repository for detection of Bank currency authentication. To implement this we have applied Support Vector machine, Random Forest, Logistic Regression, Naïve Bayes, Decision Tree, K- Nearest Neighbor by considering three train test ratio 80:20, 70:30 and 60:40 and measured their performance on the basis various quantitative analysis parameter like Precision, Accuracy, Recall, MCC, F1-Score and others. And some of SML algorithm are giving 100 % accuracy for particular train test ratio.

关键词： Support vector machines Radio frequency Visualization machine learning algorithms Statistical analysis Authentication Currencies

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Divorce case prediction using machine learning algorithms

Divorce case prediction using Machine learning algorithms

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： Aditya Sharma Arshdeep Singh Chudhey Mrityunjay Singh Computer Science and Engineering Jaypee University of Information Technology Waknaghat India Jaypee University Of Information Technology Waknaghat India

ISBN: (数字)9781728195377

ISBN: (纸本)9781728195384

The number of divorce cases are increasing very rapidly all over the world. In the last few decades, the number of divorces have gone up from 1 in 1000 to 13 in 1000 in India. Due to this reason, it is a major concern for marriage counsellors and therapists. Therefore, an effective divorce prediction technique is needed that helps a marriage counsellor or a therapist to identify how severe a case is. In this work, the authors present a study on divorce case prediction using the existing machine learning algorithms. The authors have applied the Perceptron classifier, Decision Tree classifier, Random Forest classifier, Naive Bayes classifier, K-Nearest Neighbour classifier and Support Vector machine classifier for prediction of divorce cases, and determined the best accuracy by comparing these algorithms. The criteria employed in this study makes use of Gottman method to make the *** algorithms after the training will predict whether the divorce will occur or not. This can help the therapist to analyse how tense the situation is between a couple and hence counsel them accordingly. The authors have achieved the highest accuracy of 98.5% with the Perceptron model.

关键词： Support vector machines Training machine learning algorithms Vegetation Prediction algorithms Classification algorithms Regression tree analysis

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A Study on Predicting Customer Willingness to Order Food Online During Covid-19 Pandemic Using machine learning algorithms

A Study on Predicting Customer Willingness to Order Food Onl...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： K Aditya Sobika S.N Vivek Raj KCT Business School Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9781665414487

Online food delivery has become the one of the prominent services during COVID-19 pandemic. After facing deceleration in early COVID-19 phase, online food delivery is slowly gaining momentum in India due to relaxations given by the government and support of the consumers. Online food delivery services need an improved understanding of the complexities of customer behavior which have shifted during this health crisis period of COVID-19 pandemic. The Study is undertaken to predict the customer willingness to order food using online services aftermath of COVID-19 pandemic using machine learning algorithms. Primary data collection is done through online survey distributed among public. 415 responses were received out of which 369 people prefer to order through online food delivery services. Using different machine learning models, it is inferred that the Affective and instrumental belief, Perceived benefits (variables of health belief model) are the significant predictors of the customers willingness to order food online. Demographic variables like hours utilized in mobile, frequency of ordering during COVID, Convenience of using food delivery application, number of members in family, age, education qualification and occupation are also found to be significant in determining order opinion.

关键词： COVID-19 Analytical models machine learning algorithms Pandemics Instruments Computational modeling machine learning

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Prediction of Forest Fire Using machine learning algorithms: The Search for the Better Algorithm

Prediction of Forest Fire Using Machine Learning Algorithms:...

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IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)

作者： Pranati Rakshit Srestha Sarkar Sambit Khan Pritam Saha Sonali Bhattacharyya Nilarpan Dey Sardar M.N. Islam Souvik Pal Computer Science of Engineerng JIS College of Engineering Kalyani India ISILC and Decision Sciences and Modeling Program Victoria University Australia Computer Science of Engineerng Global Institute of Management and Technology Krishnanagar India

ISBN: (纸本)9781665417853

Forest fire has several devastating effects on the natural vegetation and the forest lives. The forest fire plays an important role in everyone’s lives and also in our environment. Forest fire is an integral part of many ecosystems such as grassland, temperate forest etc. The ability to predict the area where the forest fire may occur will help in optimizing the situation. The paper presented the prediction of forest fire risk with the help of a machine learning algorithm by using meteorological data. From the existing literature and Limitations, we can show that Different studies have shown the amount of burnt area due to the forest fire, and many have proposed different models to predict forest fire. But there is no such literature which predicts the depth of risk for this forest fire specifically. For that reason, the objective of this work is to predict the risk of forest fire by identifying the particular area as highly prone, moderately prone, low prone and no fire prone area. As a Present Research, in this paper we have worked with different classification models to check which models work best to predict forest fire with greater accuracy. The results we have obtained with the help of various classifiers in machine learning are much better and reliable than the results obtained by traditional computing methods. Thus, this paper indicates a deeper investigation in the field of predicting forest fire risk through machine learning. As a contribution, in this paper we have used SVM, KNN, Decision Tree, Naive Bayes classifier for prediction purposes. The main objective of this paper is to predict the possibility of forest fire with its intensity in specific atmospheric conditions in a given location. We have made comparison of the performance analysis of the different machine learning classifiers. At the end of the abstract, we got the highest AUC value of 0.99 and classification accuracy of 0.98 using Decision Tree to predict the same.

关键词： Dimensionality reduction machine learning algorithms Biological system modeling Computational modeling Atmospheric modeling Forestry Vegetation

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Analysis of machine learning algorithms by Developing a Phishing Email and Website Detection Model

Analysis of Machine Learning Algorithms by Developing a Phis...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Nimisha Dey S Samhitha Malavika Hariprasad Anagha Anand Veena Gadad RV College of Engineering Bangalore India

ISBN: (纸本)9781665406116

machine learning is a key branch of Artificial Intelligence that concentrates on the development of computational algorithms by creating models. It has caught major attention in the technological domain due to its various applications in speech recognition, recommendation engines, computer vision, automated stock trading etc. The model’s performance is dependent on the dataset provided and its accuracy can easily be enhanced by expanding the training dataset. Post Covid-19, it has been observed that phishing websites are appallingly on the rise, especially the phishing attacks. These attacks are caused by cybercriminals using PDF’s, Microsoft office documents and other attachments via emails. This paper focusses on discussion and comparison of different machine learning algorithms that are capable of detecting phishing emails and websites. The experiments have shown that that MultinomialNB attains the highest efficiency of 98.06% for phishing email detection and Decision Tree Classifier offers the maximum efficiency of 95.41% for phishing website detection.

关键词： Training Analytical models machine learning algorithms Phishing Computational modeling Semantics Speech recognition

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Airline delay prediction by machine learning algorithms

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SCIENTIA IRANICA 2019年第5期26卷 2689-2702页

作者： Khaksar, H. Sheikholeslami, A. Iran Univ Sci & Technol Sch Civil Engn Dept Transportat Engn & Planning Tehran Iran

Flight planning, as one of the challenging issues in the industrial world, is faced with many uncertain conditions. One such condition is delay occurrence, which stems from various factors and imposes considerable costs on airlines, operators, and travelers. With these considerations in mind, we implemented flight delay prediction through the proposed approaches that were based on machine learning algorithms. The parameters that enabled effective estimation of delay were identified and then, Bayesian modeling, decision tree, cluster classification, random forest, and hybrid method were applied to estimate the occurrences and magnitude of delay in a network. These methods were tested on a US flight dataset and then, refined for a large Iranian airline network. Results showed that the parameters affecting delay in US networks were visibility, wind, and departure time, whereas those affecting delay in the Iranian airline flights were fleet age and aircraft type. The proposed approaches exhibited an accuracy of more than 70% in calculating delay occurrence and magnitude for both the US and Iranian networks. It is hoped that the techniques put forward in this work will enable airline companies to accurately predict delays, improve flight planning, and prevent delay propagation. (C) 2019 Sharif University of Technology. All rights reserved.

关键词： Flight delay predictor Airline delay Data mining machine learning algorithms Visibility distance

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