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International Conference on machine learning, Big Data and Business Intelligence (MLBDBI)

作者： Haiying Ren Luyao Zhang School of Economics and Management Beijing University of Technology Beijing China

New technical terms (NTTs), the precursors of technical terminology, are vehicles for new ideas contained in innovative technologies. Term identification research tends to mature, but little research has been done on the prediction of NTTs, which mines promising technical terms from voluminous technological data to help R&D teams rapidly form new ideas. In this study, we design predictor variables by constructing a prior co-word network and construct multiple machine learning models that predict the formation of NTTs. To validate the above approach, this paper focuses on the formation of some NTTs in the neural network domain. The prediction model constructed by the stacking model produced the best results with a prediction accuracy of 78.6%.

关键词： Technological innovation machine learning algorithms Terminology Stacking machine learning Predictive models Big Data

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Implementation of Quantum machine learning algorithms: A Literature Review

Implementation of Quantum Machine Learning Algorithms: A Lit...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Marija Š ć ekić Snež ana Š ć epanović Sandra Mitrović Faculty for Information Technologies Mediterranean University Podgorica Montenegro DaIle Molle Institute of AI IDSIA - USI/SUPSI Lugano Switzerland

ISBN: (数字)9781665468282

ISBN: (纸本)9781665468299

Recently, machine learning is benefiting from advan-tages of quantum computing which has resulted in a new stream of algorithms known as quantum machine learning algorithms. This paper presents the literature describing implementations of quantum machine learning algorithms in the various quantum machine learning frameworks. In addition, for each of the ob-served algorithms and frameworks, the literature in which they are described is stated. To the best of our knowledge, this is so far the most comprehensive overview of the existing QML algorithms with their corresponding implementation frameworks.

关键词： Embedded computing machine learning algorithms Quantum computing Bibliographies Software algorithms Education machine learning

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Twitter Location Prediction usnig machine learning algorithms

Twitter Location Prediction usnig Machine Learning Algorithm...

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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Mahesh TR Deepak Kumar Sinha Department of Computer Science and Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Bangalore India Department of Computer Science and Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Bangalore

Predicting a user's location based on their social media profiles is an active area of study right now. For many years, researchers have tried to figure out how to automatically recognise a location based on its association with or mention in a record. However, it might be difficult to determine where a user is located because many accounts do not include this information or because people provide data that does not correlate to their actual locations. The location of tweets written in English has been the subject of several related works. There are now millions of active Twitter users who tweet many times every day. With its global user base and constant stream of messages, location prediction on Twitter has garnered a lot of attention recently. The suggested approach investigates the big picture of leveraging tweets for location prediction. using machine learning techniques such as naive bayes, support vector machines, and decision trees to deduce the user's location from the tweet's text.

关键词： Support vector machines machine learning algorithms Social networking (online) Geology Blogs machine learning Prediction algorithms

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Crime Analysis and Prediction using machine learning algorithms

Crime Analysis and Prediction using Machine Learning Algorit...

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International Conference on Computational Science and Technology (ICCST)

作者： P. Kirubanantham A. Saranya Arun Prasath G Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665476560

machine learning algorithms are used widely nowadays in the majority of the data analysis field. Various fields of data analytics are now depending on the most advanced machine learning concepts and algorithms like; K-Means, Random Forests so on and so forth. When comparing the data analysis based on society, in this research, we offer a new method for predicting and analytically classifying crimes by making use of data mining algorithms, which are K-Nearest Neighbor, Logistic Regression and Support Vector machine algorithms. Utilizing these three commonly nsed techniques we classified the data sets along with pre-processing and the results of prediction obtained are highly accurate and excel the current models.

关键词： Prediction algorithms Support vector machines Classification algorithms Predictive models machine learning algorithms Analytical models Data models

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Comparing the Performance of machine learning algorithms for Predicting Employees’ Turnover

Comparing the Performance of Machine Learning Algorithms for...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Yasmeen Aldossary Majeed Ebrahim Salman Alhaddad Manar Aldossary Sawsan Hilal MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Mathematics College of Science University of Bahrain

ISBN: (纸本)9781665490597

Employee turnover directly impacts companies ’ performance due to losing qualified staff and invested costs, so predicting the ability to turnover or staying will help companies reduce these effects. Therefore, this study aimed to predict turnover by building a model using selected machine learning algorithms based on secondary data of a big data company. The algorithms are Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, and Random Forest, which have been improved with different methods for better performance, like balancing data and important feature selection. The Random Forest was characterized by the best performance with 80.2% accuracy and 72.4% AUC, with city development index, experience, and university enrolment being the most important features. However, the performance of the Random Forest classifier can be enhanced by including other important features besides those highlighted by the model.

关键词： Industries machine learning algorithms Urban areas Companies Predictive models Prediction algorithms Data models

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Loan Repayment Prediction Using Logistic Regression Ensemble learning With machine learning algorithms

Loan Repayment Prediction Using Logistic Regression Ensemble...

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International Conference on Soft Computing and machine Intelligence (ISCMI)

作者： Thuan Nguyen Dinh Binh Pham Thanh Dept. Information System University of Information Technology VNU-HCM Ho Chi Minh Viet Nam

Lending activities are an important part of the credit activities of financial institutions and banks. This is an area that brings great potential for development as well as a sustainable source of profit for financial institutions and banks. However, lending to customers also brings high risks. Therefore, predicting the ability to repay on time and understanding the factors affecting the repayment ability of customers is extremely important and necessary, to help financial institutions and banks enhance their ability to pay debts. customers' ability to identify and pay debts on time, contributing to minimizing bad debts and enhancing credit risk management. In this study, machine learning models will be used: Proposing a method to combine Logistic Regression with Random Forest, Logistic Regression with K-Nearest Neighbor, Logistic Regression with Support Vector machine, Logistic Regression with Artificial Neural Network, Logistic Regression with Long short-term memory and finally Logistic Regression with Decision Tree to predict customers' ability to repay on time and compare and evaluate the performance of machine learning models. As a result, the Logistic Regression with the Random Forest model ensemble is found as the optimal predictive model and it is expected that Fico Score and annual income significantly influence the forecast.

关键词： Training Support vector machines machine learning algorithms machine learning Predictive models Prediction algorithms Risk management

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Laptop Price Prediction using machine learning algorithms

Laptop Price Prediction using Machine Learning Algorithms

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Emerging Trends in Engineering and Medical Sciences (ICETEMS), International Conference on

作者： Mohammed Ali Shaik Medicherla Varshith Sanka SriVyshnavi Nagamalla Sanjana Rama Sujith Department of Computer Science &Artificial Intelligence SR University Warangal Telangana State India

The laptop has grown to be one of the most essential and used gadgets in our day-to-day existence for different activities. We will be supplied with many specs and company names in the market, it will become difficult for laptop computer makers to sell their merchandise and for customers to pick out one. machine learning (ML) is high quality in assisting in making decisions and predictions from the large volume of facts produced. We have additionally viewed ML strategies being used in recent developments in the Internet of Things (IoT) areas. Various studies supply solely a glimpse into predicting the price of the laptop with ML techniques as in this paper, we suggest a novel technique that targets identification process through tremendous elements using making use of desktop getting to know fashions resulting in improving the accuracy in the prediction of laptop price. The prediction model is delivered with one-of-a-kind combos of features and several regarded computing device learning models. We are the use of a one-of-a-kind laptop to gain knowledge of fashions like Decision trees, Multiple linear regression, KNN, and Random forest to test which desktop mastering model is more accurate in predicting the rate of the laptop.

关键词： Knowledge engineering Portable computers machine learning algorithms Computational modeling Linear regression Predictive models Internet of Things

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A Comprehensive Survey on Sales Forecasting Models Using machine learning algorithms

A Comprehensive Survey on Sales Forecasting Models Using Mac...

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International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)

作者： Rameshwaram Sai Mallik R Abhiram Seguru Rithvik Reddy R M Jagadish Computer Science Engineering(Artifical Intelligence and Machine Learning) Department Vardhaman Collage of Engineering Hyderabad India

Sales Forecasting is a most commonly used in marketing. Nowadays a large number of companies are using this technique to manufacture their product. In this we are going to study about the usage of different machine learning Models and Techniques used for sales prediction. The overall study of models and techniques is to increase the efficiency of future sales prediction. Nowadays for any product there are lakhs of reviews were generated by the users on different products in the market. Which confuses customers to make decision whether to buy product or not. And for a specific company to study overall reviews is hard to make product manufacture. This study mainly deals with arranging the opinions of different customers and different kinds of techniques used in sales forecasting. The present work uses mainly four machine learning algorithms namely Support Vector machine (SVM), Decision Tree (DT), Linear Regression, Random Forest, and K-Nearest Neighbors, K-means Clustering, Logistic Regression for classifying reviews. The forecasting accuracy of each algorithm is evaluated with the Root Mean Square Error (RMSE). The study found that Random Forest is the best model because it had lowest Root Mean Square Error (RMSE) compared to other model and for classifying reviews the Logistic Regression is giving accurate result.

关键词： Support vector machines machine learning algorithms Linear regression Companies Predictive models Prediction algorithms Classification algorithms

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Effective Cyber Attack Detection in an IoMT-Smart System using Deep Convolutional Neural Networks and machine learning algorithms

Effective Cyber Attack Detection in an IoMT-Smart System usi...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Nupa Ram Dipendra Kumar Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India Department of Engineering & Technology Jaipur National University Jaipur India

ISBN: (纸本)9781665493970

The widespread availability of digital technology has reshaped the computing environment. being just one of them. pear phishing, impersonating, distributed denial of service stolen credentials, and man-in-the-middle attacks are just some of the many potential security concerns. Important information connected with an IoT connectivity might be stolen, changed, or unavailable to authorised users in the case of an attack. Because of this, protecting the IoT/IoMT ecosystem from malware has become an absolute need. To categorise and foresee new cyber threats, the primary purpose of this research is to show that a deep learning and surveillance machine learning models may be used in the context of IoMT IDS. In order to examine network data, it must first be standardised and cleaned. Then, in order to fine-tune the specifics, we resorted to a biologically inspired metaheuristic optimization technique. DCNN and other SML research is evaluated using state-of-the-art data for vulnerability detection.

关键词： Deep learning machine learning algorithms Surveillance Phishing Metaheuristics Ecosystems Malware

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Heart sound classification using signal processing and machine learning algorithms

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machine learning WITH APPLICATIONS 2022年 7卷

作者： Zeinali, Yasser Niaki, Seyed Taghi Akhavan Sharif Univ Technol Dept Ind Engn Tehran Iran Sharif Univ Technol Dept Ind Engn POB 11155-9414 Azadi Ave Tehran *** Iran

According to global statistics and the world health organization (WHO), about 17.5 million people die each year from cardiovascular disease. In this paper, the heart sounds gathered by a stethoscope are analyzed to diagnose several diseases caused by heart failure. This research's primary process is to identify and classify the data related to the heart sounds categorized in four general groups of S 1 to S 4 . The sounds S 1 and S 2 are considered as the heart's normal sounds, and the sounds S 3 and S 4 are the abnormal sounds of the heart (heart murmurs), each expressing a specific type of heart disease. In this regard, the desired features are first extracted after retrieving the data by signal processing algorithms. In the next step, feature selection algorithms are used to select the compelling features to reduce the problem's dimensions and obtain the optimal answer faster. While the existing algorithms in the literature classify the sound into two groups of normal and abnormal, in the final section, some of the most popular classification algorithms are utilized to classify the type of sound into three classes of normal, S 3 and S 4 categories. The proposed methodology obtained an accuracy rate of 87.5% and 95% for multiclass data (3 classes) and 98% for binary classification (normal vs. abnormal) problems.

关键词： Heart sound Signal processing algorithms machine learning algorithms Dimensional reduction algorithms Feature selection Gradient boosting classifier

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