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Predicting user's next location using machine learning algorithms

SPATIAL INFORMATION RESEARCH 2021年第3期29卷 379-387页

作者： Hamoudzadeh, Alireza Behzadi, Saeed Shahid Rajaee Teacher Training Univ Fac Civil Engn Dept Surveying Engn Tehran Iran

The reliability on smartphones has been increased in a tremendous way in the last decade, and this incensement brings us nothing but data to investigate. Furthermore, the ability to predict user's next location plays a vital role in Location-Based Services and recommender systems. This paper is seeking to predict the user's next location based on their spatial background using machine learning methods like Artificial Neural Networks and Classification methods like K-Nearest Neighbors (KNN), Support Vector machine and Decision Tree. The suitable method is then chosen through their comparison. The data used in this research is from active users provided from the Geolife dataset from the city of Beijing. The best result is obtained from the Weighted K-Nearest Neighbors (KNN) method, with an overall accuracy of 91.98 percent. Additionally the dependency between observed data and model prediction is determined. A concept calledRoutinenessis also introduced, which shows the predictability and anomalies of each users' behavior, based on the difference in prediction methods from their spatial and temporal background. A comparison is also done with similar researches that used the same data to evaluate the sufficiency of the methods. The computation shows the proposed method predicts behavior 2.72% more accurate than similar ones.

关键词： machine learning algorithms Trajectory Geospatial Information System (GIS) Behavior recognition Location prediction

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Sarcasm detection using machine learning algorithms in Twitter: A systematic review

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INTERNATIONAL JOURNAL OF MARKET RESEARCH 2020年第5期62卷 578-598页

作者： Sarsam, Samer Muthana Al-Samarraie, Hosam Alzahrani, Ahmed Ibrahim Wright, Bianca Univ Sains Malaysia Sch Commun Gelugor 11800 Penang Malaysia Coventry Univ Sch Media & Performing Arts Coventry W Midlands England King Saud Univ Dept Comp Sci Riyadh Saudi Arabia

Recognizing both literal and figurative meanings is crucial to understanding users' opinions on various topics or events in social media. Detecting the sarcastic posts on social media has received much attention recently, particularly because sarcastic comments in the form of tweets often include positive words that represent negative or undesirable characteristics. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to understand the application of different machine learning algorithms for sarcasm detection in Twitter. Extensive database searching led to the inclusion of 31 studies classified into two groups: Adapted machine learning algorithms (AMLA) and Customized machine learning algorithms (CMLA). The review results revealed that Support Vector machine (SVM) was the best and the most commonly used AMLA for sarcasm detection in Twitter. In addition, combining Convolutional Neural Network (CNN) and SVM was found to offer a high prediction accuracy. Moreover, our result showed that using lexical, pragmatic, frequency, and part-of-speech tagging can contribute to the performance of SVM, whereas both lexical and personal features can enhance the performance of CNN-SVM. This work also addressed the main challenges faced by prior scholars when predicting sarcastic tweets. Such knowledge can be useful for future researchers or machine learning developers to consider the major issues of classifying sarcastic posts in social media.

关键词： machine learning algorithms sarcasm detection trolling twitter

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Classification of Rheumatoid Arthritis using machine learning algorithms 17

Classification of Rheumatoid Arthritis using Machine Learnin...

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17th IEEE Student Conference on Research and Development (SCOReD)

作者： Sharon, Ho Elamvazuthi, I Lu, C. K. Parasuraman, S. Natarajan, Elango Univ Teknol PETRONAS Dept Elect & Elect Engn Smart Assist & Rehabil Technol SMART Res Grp Bandar Seri Iskandar 32610 Malaysia Monash Univ Malaysia Sch Engn Bandar Sunway 46150 Malaysia UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur Malaysia

ISBN: (纸本)9781728126135

Rheumatoid Arthritis (RA) is a persistent provocative ailment that effects and decimates the joints of wrist, finger, and feet. If left untreated, one can lose their ability to lead a normal life. RA is the most typical fiery joint inflammation, influencing around 1-2% of the total populace. Throughout the years, soft computing played an important part in helping ailment analysis in doctor's decision process. The main aim of this study is to investigate the possibility of applying machine learning techniques to the analysis of RA characteristics. As a preliminary work, a credible database has been identified to be used for this research. The database has outputs of array temperature values from thermal imaging for the joints of hand. Furthermore, this database which consists of 8 attributes and 32 instances, are used to determine the performance in terms of accuracy for the classification of different algorithms. In this preliminary work, ensemble algorithms such as bagging, AdaBoost and random subspace with base classifier such as random forest and SVM were trained and tested using the assessment criteria such as accuracy, precision, sensitivity and AUC using Weka tool. From the preliminary finding of this paper, it can be concluded that with base classifier SVM, bagging has better classification accuracy over the others and with base classifier random forest Adaboost slightly outperformed other models for rheumatoid arthritis dataset.

关键词： Classification Rheumatoid Arthritis machine learning algorithms

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Scalable and Parallel machine learning algorithms for Statistical Data Mining - Practice & Experience 38

Scalable and Parallel Machine Learning Algorithms for Statis...

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38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Riedel, M. Goetz, M. Richerzhagen, M. Glock, P. Bodenstein, C. Memon, A. S. Memon, M. S. Forschungszentrum Julich Juelich Supercomp Ctr Julich Germany Univ Iceland Sch Nat Sci & Engn Reykjavik Iceland

ISBN: (纸本)9789532330854

Many scientific datasets (e. g. earth sciences, medical sciences, etc.) increase with respect to their volume or in terms of their dimensions due to the ever increasing quality of measurement devices. This contribution will specifically focus on how these datasets can take advantage of new 'big data' technologies and frameworks that often are based on parallelization methods. Lessons learned with medical and earth science data applications that require parallel clustering and classification techniques such as support vector machines (SVMs) and density-based spatial clustering of applications with noise (DBSCAN) are a substantial part of the contribution. In addition, selected experiences of related 'big data' approaches and concrete mining techniques (e. g. dimensionality reduction, feature selection, and extraction methods) will be addressed too. In order to overcome identified challenges, we outline an architecture framework design that we implement with open available tools in order to enable scalable and parallel machine learning applications in distributed systems.

关键词： Scalability Earth sciences machine learning algorithms big data Dataset mining Representation of data Statistical Data Parallel Lines Distributed Systems Support Vector Network data mining

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Predicting students' continuance use of learning management system at a technical university using machine learning algorithms

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INTERACTIVE TECHNOLOGY AND SMART EDUCATION 2023年第2期20卷 209-227页

作者： Kuadey, Noble Arden Mahama, Francois Ankora, Carlos Bensah, Lily Maale, Gerald Tietaa Agbesi, Victor Kwaku Kuadey, Anthony Mawuena Adjei, Laurene Ho Tech Univ Dept Comp Sci Ho Ghana Univ Elect Sci & Technol China Ctr West African Studies Chengdu Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Ho Tech Univ Dept Math & Stat Ho Ghana St Francis Coll Educ Dept Math ICT Hohoe Ghana

Purpose This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms. Design/methodology/approach The proposed model for this study adopted a unified theory of acceptance and use of technology as a base model and incorporated the following constructs: availability of resources (AR), computer self-efficacy (CSE), perceived enjoyment (PE) and continuance intention to use (CIU). The study used an online questionnaire to collect data from 280 students of a Technical University in Ghana. The partial least square-structural equation model (PLS-SEM) method was used to determine the measurement model's reliability and validity. machine learning algorithms were used to determine the relationships among the constructs in the proposed research model. Findings The findings from the study confirmed that AR, CSE, PE, performance expectancy, effort expectancy and social influence predicted students' continuance intention to use the LMS. In addition, CIU and facilitating conditions predicted the continuance use of the LMS. Originality/value The use of machine learning algorithms in e-learning systems literature has been rarely used. Thus, this study contributes to the literature on the continuance use of e-learning systems using machine learning algorithms. Furthermore, this study contributes to the literature on the continuance use of e-learning systems in developing countries, especially in a Ghanaian higher education context.

关键词： Higher education E-learning learning management systems UTAUT machine learning algorithms Developing countries

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Classification of solid waste generation areas in the greater accra region using machine learning algorithms

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Model Assisted Statistics and Applications 2023年第4期18卷 359-371页

作者： Chapman-Wardy, Charlotte Ocran, Eric Iddi, Samuel Asiedu, Louis Department of Statistics and Actuarial Science School of Physical and Mathematical Sciences University of Ghana Accra Legon Ghana

Solid waste management has become a challenge for developing countries mainly because of surging economic activities, rapid urbanisation and rise in community living standards. Many researchers have identified its related problems and have recommended solutions while others have established models to forecast the amount of solid waste generated over a period. However, an efficient and effective management of solid waste requires adequate categorisation of solid waste generation areas to aid in the provision of area-specific or targeted solutions for each categorised area. In this study, we used primary data on some important socio-demographic variables (household size, house type, predominant religion of household, age and educational level of household head, residency type household waste disposal method, frequency of waste collection etc) and the amount of solid waste generated from 2102 households in Greater Accra Region, Ghana. We assessed the classification performances of a traditional statistical classifiers and some selected machine learning algorithms in classifying the surveyed areas in Greater Accra into low, medium, and high solid waste generation areas. The Support Vector machine with the Cubic Kernel was found to be the best performing classifier with a Specificity of 86%, Sensitivity, Precision and Accuracy of 73% and Area under the curve (AUC) of 0.90. The Support Vector machine with the Cubic Kernel is therefore recommended as a suitable algorithm for the categorisation of solid waste generation areas. Stakeholders responsible for solid waste management could leverage on the evidence from this study to categorise their waste generation areas and to proffer targeted community-based interventions. © 2023 - IOS Press. All rights reserved.

关键词： machine learning algorithms Solid waste management support vector machines with cubic kernel targeted solutions traditional statistical classifier

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On the role of machine learning algorithms in developing MEMS components

On the role of machine learning algorithms in developing MEM...

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International Conference on MEMS, NANO and Smart Systems

作者： Asmar, D Moussa, M Zelek, J Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada

ISBN: (纸本)0769519474

This paper provides a review of several significant applications of machine learning tools in the development of MEMS components and devices. Four topics are covered, two represent traditional applications of artificial neural networks, drag reduction and reliability forecasting, and two are non-traditional applications, namely model reduction and MEMS based neurocomputing.

关键词： Artificial neural networks machine learning algorithms Microactuators Microelectromechanical devices Micromechanical devices Neural networks Stress control Stress measurement Thermal sensors Thermal stresses

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Analysis of Predictive Parameters in Prediction of the Occurrence of Type 2 Diabetes Using machine learning algorithms 16th

Analysis of Predictive Parameters in Prediction of the Occur...

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Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON) and International Conference on Medical and Biological Engineering (CMBEBIH)

作者： Hadzalic, Sumeja Obralija, Arnela Becirovic, Seila Kelle, Belma Pehlivanovic Krupalija, Ehlimana Univ Sarajevo Fac Pharm Zmaja Bosne 8 Sarajevo 71000 Bosnia & Herceg Univ Sarajevo Fac Elect Engn Zmaja Bosne 8 Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031490613;9783031490620

Type 2 diabetes is one of the most common chronic non-infectious diseases in the world's developed countries. It is a metabolic disease characterized by hyperglycemia (fasting glycemia >7,0 mmol/l, or more than 11,1 mmol/l 2 h after a meal). The generally accepted definition of type 2 diabetes is a progressive, degenerative, chronic disease, a disorder of glucose metabolism caused by insufficient pancreatic function and/or, most often, insensitivity of cells to insulin. This paper presents the development of a predictive model for the risk of occurrence of type 2 diabetes based on diagnostic parameters. A model has been developed based on 768 samples, 500 healthy individuals, and 268 diagnosed with type 2 diabetes. The model has the following input parameters: plasma glucose concentration, diastolic blood pressure, insulin, BMI (Body mass index), and age. The outcome is presented as a zero for a healthy person or a one for a for developing a classifier for the prediction of diabetes, the following machine learning algorithms were used: Decision tree, Pruned decision tree, kNN, Tuned kNN, SVM, Tuned SVMand Logistic regression. The classification was performed on the original data containing six features with low Pearson correlation coefficients. Data transformation based on principal component analysis was used for improving the accuracy metric values of the ML models. Obtained results were compared and suggested for future use.

关键词： Type 2 diabetes Type 2 diabetes risk machine learning algorithms

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Towards a better multivariate time-series detection of epileptic seizures in electroencephalogram (EEG) using machine learning algorithms 2

Towards a better multivariate time-series detection of epile...

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2nd IEEE International Conference on Smart Systems and Emerging Technologies (SMARTTECH)

作者： Klibi, Salim Vernet, Marine Schwartz, Denis Farah, Imed Riadh Univ Manouba Natl Sch Comp Sci RIADI Lab Tunis Tunisia Univ Claude Bernard Lyon 1 Lyon Neurosci Res Ctr CRNL IMPACT Team CNRS UMR 5292INSERM UMRS 1028 Lyon France Univ Claude Bernard Lyon 1 Lyon Neurosci Res Ctr CRNL COPHY Team CNRS UMR 5292INSERM UMRS 1028 Lyon France

ISBN: (数字)9781665409735

ISBN: (纸本)9781665409735

This study aims to improve the automatic detection of epileptic seizures (ESs) using machine learning (ML) algorithms applied to electroencephalography (EEG) brainwave data. Previous studies based on a database published online showed high seizure detection accuracies, but they contrasted seizure activity to all kinds of non-seizure EEG activity, recorded from different populations (healthy and patient) and different types of electrodes (surface and intracranial). Here we decided to focus on detecting seizures from non-seizure activity recorded from the same type of electrodes in the same group of patients. We applied different ML classifiers such as Extreme Gradient Boosting (XgBOOST), Naive Bayes (NB), k-Nearest Neighbor (k-NN), Random Forest (RF), Support Vector machine (SVM), Linear Regression (LR), and Decision Tree (DT). The best Area Under Curve (AUC) value is given by XgBOOST with 95.84%. This research helps to improve the detection of human ES in EEG signal recordings.

关键词： EEG Epileptic Seizure detection machine learning algorithms

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A Framework for User Characterization based on Tweets Using machine learning algorithms 2018

A Framework for User Characterization based on Tweets Using ...

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7th International Conference on Network, Communication and Computing (ICNCC)

作者： Zahra, Kinza Azam, Farooque Butt, Wasi Haider Ilyas, Fauqia NUST Dept Comp Engn Coll E&ME H-12 Islamabad Pakistan

ISBN: (纸本)9781450365536

Twitter having more than three billion users is one of the most commercial and popular social networking sites. Twitter permits its users to post short messages and update their status. Tweets can be seen instantly by the followers of the users and other people with no twitter accounts. So by far most of the substance posted on the twitter is publicly accessible. Enormous number of political actors used twitter, who are interested in seeking extreme motives like radicalization, mobilization and recruiting activities. Twitters is used by large number of extremist organizations for press releases, public declaration and provide confirmation or motivation of their attacks. There have been several works looking at identifying extremist content based on twitter data but user identification using tweets has not been focused enough because of publication barrier and unavailability of data. In this research, a model is proposed which characterize a user into extremist and non-extremist categories. In this approach, pre-processing is done using natural language processing techniques and feature selection is performed using bag of words model. TF-IDF and word length is applied to obtain vector or feature to measure the significance of obtained vector in the whole document. We performed a methodology using classification through NB (Multinomial naive Bayes) naive Bayes on crises related tweets and Kaggle dataset related to tweets published by several Islamic State of Iraq and Sham to validate our proposed model. In this paper, a novel method is discussed for user characterization based on tweets posted by them. Evaluation results show that our suggested method gives best retrieval accuracies for word length feature extraction approach.

关键词： User Characterization Twitter Text Mining Supervised learning Natural Language Processing Feature Selection Radicalization machine learning algorithms

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