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Comparison of Decision Tree and Support Vector Machine for Predicting Jakarta Air Quality Index

Comparison of Decision Tree and Support Vector Machine for P...

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Computer Science, Information Technology and Engineering (ICCoSITE), international conference on

作者： Musyaffa Ayman Rafif Gede Sanjaya Indrajaya Muhammad Kent Al-Ghazi Johnny Johnny Noviyanti T M Sagala Data Science Program School of Computer Sciences Bina Nusantara University Jakarta Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The people in the capital city of the Republic of Indonesia, Jakarta, have been living for years with air pollution. Air quality has been a concern for a long time due to the health risks it has on people, especially those at risk. Using classification algorithms, we would like to implement data mining for the air quality index and make predictions from our capital city, DKI Jakarta. This research aims to compare Decision Tree and Support Vector Machine in predicting Air Quality Index in DKI Jakarta in 2020. The dataset was collected from the Jakarta Open data Web, and the results of this research highlight the effectiveness of data mining techniques in predicting air quality index levels in Jakarta and emphasize the importance of selecting the appropriate classification algorithm for data mining applications. This research shows that SVM, compared to the Decision tree, has a better result with an accuracy score of 87.86%. On the other hand, Decision Tree has a score of 90.56%.

关键词： Support vector machines Urban areas Support vector machine classification Predictive models Air pollution Classification algorithms Decision trees

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Improving Prospective Healthcare Outcomes by Leveraging Open data and Explainable AI

Improving Prospective Healthcare Outcomes by Leveraging Open...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Gulzar Alam Ian McChesney Peter Nicholl Joseph Rafferty School of Computing Ulster University Belfast UK School of Computing Ulstjjjer University Belfast UK

The use of Artificial Intelligence (AI) in healthcare can improve diagnosis and clinical workflows, advancing standards and improving outcomes. AI for healthcare raises significant ethical and legal challenges. AI model development and deployment require transparency and interpretability. Transparency is the capacity to comprehend how an AI model makes recommendations, whereas interpretability is the ability to understand why. One such issue is the lack of transparency and interpretability of AI models, which might lead to a lack of trust in its decisions. Explainable AI (XAI) is an emerging research field that tries to solve this problem by creating AI models that can explain their recommendations. AI models need enough data to be accurate and reliable, thus they must be trained on various and extensive data sets. Open data and data sets (ODDS) can aid AI model development and training. Medical images, lab reports, and patient data are used to train algorithms to identify trends and make predictions in healthcare. Open data can help XAI in healthcare to enable AI models to be transparent, accountable, and interpretable. Open data makes possible AI model validation and replication. This is especially crucial in healthcare, where AI model flaws or biases can have serious implications. This paper discusses the use of ODDS and XAI to improve healthcare. ODDS's capability to promote trust and openness and best practices for using XAI in healthcare are also explored. The benefits and challenges of employing XAI and ODDS in their current forms are also discussed.

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Deep Learning in Image Classification Using Modified ResNet50 and Texture Features for Lung Cancer Detection

Deep Learning in Image Classification Using Modified ResNet5...

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Technology Innovation and Its Applications (ICTIIA), international conference on

作者： Evander Banjarnahor Jefri Junifer Pangaribuan Okky Putra Barus Romindo Romindo Informatics Department Universitas Pelita Harapan Tangerang Indonesia Information Systems Department Universitas Pelita Harapan Tangerang Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

According to WHO data, in 2020 there were 30,843 deaths from lung cancer in Indonesia. Some of the factors that lead to lung cancer are smoking, air pollution, and an unhealthy diet. Early detection with a lung CT scan is critical in finding treatment alternatives and increasing the patient's life expectancy. Screening and diagnosis by Pulmonologist takes a long time, but can be accelerated with computational techniques and reduce the *** study proposes a deep learning-based lung cancer detection method. Researchers compared the performance of ResNet50, ResNet50V2, and a modified model that combines ResNet50V2 with GLCM texture features. The models were simulated to classify lung CT scan images into normal, benign, or malignant. From the simulation results, it was found that the ResNet50V2 with GLCM achieved the highest accuracy at 82%, 82% sensitivity, and 90% specificity with processing each step for 71.32 seconds on 15% testing data.

关键词： Accuracy Sensitivity Computed tomography Lungs Simulation Computational modeling Lung cancer data models Residual neural networks Testing

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IoT Device Classification Using Hybrid Stacking Machine Learning Model on IoT Network Traffic data

IoT Device Classification Using Hybrid Stacking Machine Lear...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Aminuddin Jamaluddin Sami Abdulla Mohsen Saleh Mohd Shahrimie Mohd Asaari Mohamad Khairi Ishak School of Electrical & Electronic Eng. Engineering Campus Universiti Sains Malaysia Nibong Tebal Penang Malaysia Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University Ajman United Arab Emirates

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In the expanding domain of the Internet of Things (IoT), accurately classifying devices is essential for ensuring secure and efficient network management. As IoT networks increase in size and complexity, the challenge of effectively managing these devices becomes more pressing, particularly in defending against the risks posed by Shadow IoT devices unauthorized devices that connect to networks without formal approval. Despite some progress, previous research has largely focused on individual machine learning algorithms or simple ensemble models, with limited exploration of hybrid stacking models that combine two classifiers. This leaves a gap in understanding how different classifier combinations can balance accuracy, training time, and prediction time. This thesis addresses these gaps by using a hybrid stacking machine learning model that integrates two classifiers with the focus on reducing training and prediction times while maintaining or improving classification accuracy. The results show significant improvements in existing models. Combination of Random Forest (RF) and XGBoost (RFXGB) achieved a test accuracy of 72.4% and a training accuracy of 99.2%, with a training time of 4.17 seconds and a prediction time of 0.053 seconds, marking a 7.4% increase in test accuracy compared to the single XGBoost classifier. Similarly, the combination of Random Forest and Support Vector Machine (RFSVM) achieved a test accuracy of 71.2% and a training accuracy of 99.6%, with a training time of 2.196 seconds and a prediction time of 0.051 seconds, showing a 12.6% improvement in test accuracy over the single Support Vector Machine classifier.

关键词： Training Support vector machines Accuracy Stacking Telecommunication traffic data models Internet of Things Random forests Thermostats Classification tree analysis

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An Overview: Maritime Spatial-Temporal Trajectory Mining

An Overview: Maritime Spatial-Temporal Trajectory Mining

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2020 international conference on Computer Big data and Artificial Intelligence, ICCBDAI 2020

作者： Jin, Jialong Zhou, Wei Jiang, Baichen Naval Aviation University No.188 Erma Road Yantai264000 China Naval Aviation University No.188 Erma Road Yantai264000 China

In recent years, the sources of maritime spatial-temporal trajectory data have been increasing. With the widespread application of detection equipment such as radars, ship-based platforms, and satellite, maritime trajectory data has shown feature of volume, variety, velocity and valueless. The increase in data types and amount of data has led to complexity and difficulty of data processing. Traditional data mining algorithms are facing serious difficulty. This paper reviews researches for maritime spatial-temporal trajectory mining in recent years on trajectory clustering, anomaly detection, and trajectory prediction. Difficulties current researches faced are pointed out and prospects of deep learning are discussed in this paper. © Published under licence by IOP Publishing Ltd.

关键词： data mining

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Machine Learning-based Approach for Predicting Health Information Using Smartwatch data 17

Machine Learning-based Approach for Predicting Health Inform...

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17th IEEE international conference on Intelligent Computer Communication and processing, ICCP 2021

作者： Chifu, Viorica Rozina Bianca Pop, Cristina Ciurianu, Andrei Chifu, Emil St. Antal, Marcel Technical University of Cluj-Napoca Department of Computer Science Cluj-Napoca Romania

ISBN: (纸本)9781665409766

This paper presents a machine learning-based method for predicting health information in terms of heart rate, sleep quality, burned calories and performed physical activity for a person wearing a smartwatch. The proposed method consists of three main steps, namely, data pre-processing, feature selection, and training and testing. Based on a data set collected with a Fitbit Versa 2 smartwatch and by applying three different machine learning algorithms (i.e. Long Short-Term Memory, Multilayer Perceptron, and Back propagation from scratch) the method is able to predict the efficiency of the sleep, the number of burned calories, the average heart rate and the name of the activity performed by a person. To evaluate the experimental results the accuracy score and logarithmic loss metrics have been used. © 2021 IEEE.

关键词： Heart

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Rainfall class prediction model based on PSO-BP neural network

Rainfall class prediction model based on PSO-BP neural netwo...

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Green Energy and Power Systems (ICGEPS), international conference on

作者： Bing Zhao Ruizeng Wei Dawei Peng Yingting Luo Hao Wang Guoqiang Li Guangdong Key Laboratory of Electric Power Equipment Reliability Electric Power Research Institute of Guangdong Power Grid Co. Ltd Guangzhou Guangdong China Guangdong Power Grid Co. Ltd Guangzhou Guangdong China

ISBN: (数字)9798331510329

ISBN: (纸本)9798331510336

In recent years, Guangzhou City has frequently suffered from grid disasters caused by typhoons and rainfall, and the operation of the power system under extreme weather is facing severe challenges, and the accurate prediction of rainfall level has become a key topic to reduce disaster losses. The traditional BP neural network is difficult to achieve the global optimal solution, which restricts the generalization ability of the model. To this end, this paper proposes an improved BP neural network model based on particle swarm optimization (PSO) algorithm, based on the daily-valued meteorological data of Guangzhou City from 2020 to 2024, and selects the precipitation factors such as humidity, temperature, wind speed, etc., and optimizes the BP network weights and thresholds using the PSO global search mechanism to construct the rainfall level prediction model. The experimental results show that compared with the traditional BP algorithm, the PSO-BP model has an accuracy of 85.7% in precipitation level prediction, which significantly improves the prediction performance. This study provides a high-precision meteorological prediction scheme for power system disaster prevention under extreme weather, which is of practical significance for urban disaster risk management.

关键词： Rain Accuracy Disasters Neural networks Urban areas Predictive models Prediction algorithms data models Power systems Particle swarm optimization

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Retrieving the Evidence of a Free Text Annotation in a Scientific Article: A data Free Approach 1

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20th international conference on Computational Linguistics and Intelligent Text processing, CICLing 2019

作者： Gobeill, Julien Pasche, Emilie Ruch, Patrick SIB Text Mining Group Swiss Institute of Bioinformatics Geneva Switzerland HES-SO/HEG Geneva Information Sciences Geneva Switzerland

ISBN: (数字)9783031243370

ISBN: (纸本)9783031243363

The exponential growth of research publications provides challenges for curators and researchers in finding and assimilating scientific facts described in the literature. Therefore, services that sup-port the browsing of articles and the identification of key concepts with minimal effort would be beneficial for the scientific community. Reference databases store such high value scientific facts and key concepts, in the form of annotations. Annotations are statements assigned by curators from an evidence in a publication. Yet, if annotated statements are linked with the publication’s references (e.g. PubMed identifiers), the evidences are rarely stored during the curation process. In this paper, we investigate the automatic relocalization of biological evidences, the Gene References Into Function (GeneRIFs), in scientific articles. GeneRIFs are free text statements extracted from an article, and potentially reformulated by a curator. De facto, only 33% of geneRIFs are copy-paste that can be retrieved by the reader with the search tool of his reader. For automatically retrieving the other evidences, we use an approximate string matching algorithm, based on a finite state automaton and a derivative Levenshtein distance. For evaluation, two hundred candidate sentences were evaluated by human experts. We present and compare results for the relocalization in both abstracts and fulltexts. With the optimal setting, 76% of the evidences are retrieved with a precision of 97%. This data free approach does not require any training data nor a priori lexical knowledge. Yet it remarkable how it handles with complex language modifications such as reformulations, acronyms expansion, or anaphora. In the whole MEDLINE, 350,000 geneRIFs were retrieved in abstracts, and 15,000 in fulltexts;they are currently available for highlighting in the Europe PMC literature browser. © 2023, Springer Nature Switzerland AG.

关键词： String searching algorithms

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Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm

Lung Cancer Detecting using Radiomics Features and Machine L...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： M.N. Rajesh Nikita Tanni Z. Tanveer Baig Electronics and Communication Engineering Jain (Deemed-to be-University) Bengaluru India B.Tech in Computer Science and Engineering (Data Science) Christ (Deemed to be University) Bengaluru India Dept of IT Amity University Taskent Uzbekistan

Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] dataset has been chosen. CT Image dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada Boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GBM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably.

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Classification of Electronic Nose data Using the Least Squares Support Vector Machine

Classification of Electronic Nose Data Using the Least Squar...

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2020 international conference on Intelligent Control, Measurement and Signal processing and Intelligent Oil Field, ICMSP 2020

作者： Chen, Gaofeng Wu, Guifang College of Mechanical and Electrical Engineering Inner Mongolia Agricultual University Zhaowuda Road 306 Huhhot010018 China

In this study, the response signals of three kinds of dry alfalfa volatile odors were collected by an electronic nose (E-nose), and the collected data were processed by principal component analysis (PCA) and linear discriminant analysis (LDA). A least squares support vector machine (LS-SVM) model was established to classify and evaluate the data. For the combined E-nose algorithm, the classification accuracies of the PCA-LS-SVM and LDA-LS-SVM models are 85% and 100%, respectively. LDA as the input model has better classification accuracy than the PCA-based model. The results show that the combination of the LDA and LS-SVM algorithms using an E-nose signal is effective in identifying different drying alfalfa. The performance of the LDA-based LS-SVM model is slightly higher than that of the PCA-based LS-SVM model. It can be concluded that the E-nose system combined with the LDA-based model has great potential to distinguish different dry alfalfa. © Published under licence by IOP Publishing Ltd.

关键词： Electronic nose

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