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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Sawant, Sahil Yu, Jinhong Pandya, Kirtikumar Ngan, Chun-Kit Bardeli, Rolf Data Science Program Worcester Polytechnic Institute WorcesterMA United States Big Data and Consumer Analytics Vodafone GmbH NRW Duesseldorf Germany

ISBN: (纸本)9798350319934

In this paper, we enhance and customize the existing BERTopic framework to develop and implement an automated pipeline that delivers a more coherent and diverse set of topics with an even moderate dataset. More specifically, the contributions of this work are threefold: (1) integrate a dynamic and advanced optimizer into the existing BERTopic framework to learn the optimal number of dimensions of different document embeddings, (2) develop a k-means-based algorithm in the optimizer to support the dimension-embedding learning, and (3) conduct an extensive experimental study on three distinct types of datasets, including DBPedia, AG News, and Reuters, to evaluate the performance of our approach in terms of the topic quality (TQ) score computed by the topic coherence and the topic diversity. From the results, we can conclude that our enhanced, automated BERTopic framework with its dimension-embedding learning algorithm on documents outperforms the TQ score of the existing framework by 4.49% (before removing the stop words) and 16.52% (after removing the stop words) among all the four representable document-embedding approaches, including the BERTopic's Default Sentence Transformer, Google's Universal Sentence Encoder, OpenAI GPT-2, and our investigators' developed Context-aware Embedding Model, on all the three datasets. © 2022 IEEE.

关键词： Embeddings

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Urban Sound Classification Using DNN, CNN LSTM a Comparative Approach

Urban Sound Classification Using DNN, CNN LSTM a Comparative...

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2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Bubashait, Mohamed Hewahi, Nabil Big Data Science and Analytics Master Program College of Science University of Bahrain Sakhier Bahrain University of Bahrain Department of Computer Science College of IT Sakhier Bahrain

ISBN: (纸本)9781665440325

Like air pollution, sound pollution has grown to be a major concern for city residents, designers, and developers. Detecting and recognizing sound types and sources in cities and suburban areas or any environment have become a necessity for the quality of life as well as security. In recent years, researchers have explored many models using Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) Neural Network, and different combinations of these techniques, which produced promising results when combined with spectrogram images, or its different variations, to classify urban sounds. This research uses the DNN model performance as a baseline to compare the CNN and LSTM models' performance for classifying urban sound using Mel scale cepstral analysis (MEL) spectrum images using an open-source library called Librosa for sound processing. Models' performance was evaluated using the UrbanSound8k dataset. The CNN model underperformed with an accuracy rate of 87.15% and f1 score of 85.63% compared to both the DNN base model and the LSTM model. In contrast, comparing the LSTM model with CNN, LSTM shows better accuracy performance on test data with 90.15%, and f1 score of 90.15%. © 2021 IEEE.

关键词： Long short-term memory

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A Novel Machine Learning Classifier Based on Genetic Algorithms and data Importance Reformatting

arXiv

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arXiv 2024年

作者： Alkhayyat, A.K. Hewahi, N.M. Big Data Science and Analytics Program College of Science University of Bahrain Bahrain Department of Computer Science College of Information Technology University of Bahrain Bahrain

In this paper, a novel classification algorithm that is based on data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are data reformatting phase which depends on DI concept, training phase where GA is applied on the reformatted training dataset, and testing phase where the instances of the reformatted testing dataset are being averaged based on similar instances in the training dataset. GADIC is an approach that utilizes the exiting ML classifiers with involvement of data reformatting, using GA to tune the inputs, and averaging the similar instances to the unknown instance. The averaging of the instances becomes the unknown instance to be classified in the stage of testing. GADIC has been tested on five existing ML classifiers which are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), and Naïve Bayes (NB). All were evaluated using seven open-source UCI ML repository and Kaggle datasets which are Cleveland heart disease, Indian liver patient, Pima Indian diabetes, employee future prediction, telecom churn prediction, bank customer churn, and tech students. In terms of accuracy, the results showed that, with the exception of approximately 1% decrease in the accuracy of NB classifier in Cleveland heart disease dataset, GADIC significantly enhanced the performance of most ML classifiers using various datasets. In addition, KNN with GADIC showed the greatest performance gain when compared with other ML classifiers with GADIC followed by SVM while LR had the lowest improvement. The lowest average improvement that GADIC could achieve is 5.96%, whereas the maximum average improvement reached 16.79%. © 2024, CC BY.

关键词： Adversarial machine learning

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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Research on Traffic Sign Detection and Recognition System Using Deep Ensemble Learning

Research on Traffic Sign Detection and Recognition System Us...

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IEEE International Conference on Computer Communication and the Internet (ICCCI)

作者： Lung-Jen Wang Taweelap Suwattanapunkul Jarinya Thalauy Parinya Jansengrat Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan International Master Program in Information Technology and Applications International College National Pingtung University Pingtung Taiwan Department of Big Data Management and Analytics Rajamangala University of Technology Thanyaburi Pathum Thani Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.

关键词： YOLO Roads Prediction algorithms Inference algorithms Internet Autonomous automobiles Ensemble learning

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Predicting Oil and Gas Pipe Wall Thickness Using Machine Learning

Predicting Oil and Gas Pipe Wall Thickness Using Machine Lea...

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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Mohammed, Abdulrahim Hewahi, Nabil College of Science University of Bahrain Big Data Science and Analytics Program Sakhir Bahrain College of Information Technology University of Bahrain Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

Pipeline networks are crucial for process industries transportation and business operation. These vital elements, however, are highly exposed to material degradation due to corrosion that seriously impedes operational profitability, reliability, and safety. Industries usually monitor pipeline corrosion rate by gauging pipe wall thickness. Predicting pipe wall thickness helps industries to identify locations with high corrosion rate at an early stage and take necessary actions. The aim of this research is to predict pipe wall thickness at different locations using different machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), fully connected Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM). The data processed and analyzed in this study included six different process variables as well as pipe wall thickness at three different locations in a downstream oil and gas industry. The study findings demonstrated that LSTM with test Root Mean Squared Errors (RMSEs) of 0.019 mm, 0.015 mm, and 0.017 mm for the three selected locations, outperformed the other methods applied and successfully identified the pipe wall thickness pattern. © 2021 IEEE.

关键词： Long short-term memory

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An Enhanced BERTopic Framework and Algorithm for Improving Topic Coherence and Diversity

An Enhanced BERTopic Framework and Algorithm for Improving T...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Sahil Sawant Jinhong Yu Kirtikumar Pandya Chun-Kit Ngan Rolf Bardeli Data Science Program Worcester Polytechnic Institute Worcester MA USA Big Data & Consumer Analytics Vodafone GmbH Duesseldorf NRW Germany

关键词： Heuristic algorithms Pipelines Coherence Transformers Internet Context modeling

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Random Forest vs. SVM vs. KNN in classifying Smartphone and Smartwatch sensor data using CRISP-DM

Random Forest vs. SVM vs. KNN in classifying Smartphone and ...

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2020 International Conference on data analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Saleh, Sadiq Jaffer Ali, Sayed Qasim Zeki, Ahmed M. University of Bahrain Big Data Science Analytics Master Program Bahrain University of Bahrain College of Information Technology Deptartment of Information Systems Bahrain

ISBN: (纸本)9781728196756

Nowadays IoT sensors become one of the major sources of data, starting from home automation going to daily devices. Focusing on handsets that have the most significant contribution factor on data blooming and booming. The problem at hand is data mining methods comparison, for selecting the best method that serves sensor data. Methodology used was CRISP-DM. This paper found Random Forest data mining method to work best under the described circumstances. © 2020 IEEE.

关键词： data mining

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Tweets Sentiment Analysis during COVID-19 Pandemic

Tweets Sentiment Analysis during COVID-19 Pandemic

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2020 International Conference on data analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Hewahi, Nabil M. University of Bahrain Big Data Science Analytics Master Program Collage of Science Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781728196756

The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the k-means clustering and Mini-Batch k-means clustering approaches. To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration. Another two datasets (English and Arabic datasets) are used in the clustering of k-means. In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern. The word frequency shows that there are several words related to the pandemic. The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral. However, the majority of tweets are positive tweets from both Italy and India. In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3. © 2020 IEEE.

关键词： COVID-19

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Prediction of Stock Price and Direction Using Neural Networks: datasets Hybrid Modeling Approach

Prediction of Stock Price and Direction Using Neural Network...

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2020 International Conference on data analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Aradi, Mohamed Al Hewahi, Nabil Univestity of Bahain Sakhir Big Data Science Analytics Master Program Collage of Science Bahrain Univestity of Bahain Collage of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781728196756

Stock prices prediction is one of the most daunting tasks to achieve for day traders, investors, and data scientists. They are complex functions of a wide array of contributing factors that affects the movement dynamics. Politics, social outlook, the company's sales performance, and socio-economic factors all have an influence of how the stock is perceived globally and thus affecting the price movement based on the supply/demand balance. In this work a Framework is proposed to predict the price and movement direction by using multiple datasets that includes news sentiment, social sentiment, company earnings announcement, and technical indicators. A case study was applied on the Apple Inc. stock using Long short-term Memory Neural Networks (LSTM) and Deep Neural Networks (DNN). Results showed superiority of the LSTM model in both predicting stock value [75.4 MSE, +-2.52 PE] and the direction with [70.1% classification accuracy] over the DNN model which suffered from prediction lag and dependency on trend indicators achieving [53.1%] in predicting the direction of the stock. © 2020 IEEE.

关键词： Fruits

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