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3rd International Conference on Computer Vision and Information Technology, CVIT 2022

作者： Ubaidillah, Sharifah Hafizah Sy Ahmad Noraziah, A. Sahabudin, N.A. Faculty of Computing University Malaysia Pahang Pahang Malaysia Centre for Software Development and Integrated Computing Universiti Malaysia Pahang Pahang Malaysia

ISBN: (数字)9781510663046

ISBN: (纸本)9781510663039

Due to their unique properties such as high availability and reliability, distributed systems are gaining popularity nowadays. However, the rapid growth of Big Data in distributed systems creates new issues for dataset reliability and availability. In any distributed computer system, the presence and recurrence of failures is an inescapable factor. Both hardware and software components of distributed systems are prone to failure. As a result, the issue of fault tolerance is being recognized as the fundamental theme and essential requirement for the construction and maintenance of the distributed computing paradigm in order to achieve prominence and criticality. Fault tolerance refers to the application that must be executed even in failure conditions by detecting and correcting the fault. Reactive fault tolerance techniques are used to effectively troubleshoot the systems upon occurrences of failures. This paper aims to provide a better understanding of reactive fault tolerance techniques and identifies various approaches used as reactive fault tolerance in distributed systems. Based on the reviews done in this research, there are various reactive fault tolerance techniques that can improve the performance of the distributed systems in terms of availability, reliability, total execution time, and communication cost such as replication, checkpointing task resubmission, and job migration. © 2023 SPIE.

关键词： Fault tolerance

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A Novel BiGRUBiLSTM Model for Multilevel Sentiment Analysis Using Deep Neural Network with BiGRU-BiLSTM

A Novel BiGRUBiLSTM Model for Multilevel Sentiment Analysis ...

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Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020

作者： Islam, Md. Shofiqul Ghani, Ngahzaifa Ab Faculty of Computing Universiti Malaysia Pahang Pekan Malaysia Centre for Software Development & Integrated Computing Universiti Malaysia Pahang Gambang Malaysia

ISBN: (纸本)9789813345966

In multilevel sentiment classification task, there is a challenging task of limited coherence, contextual and semantic information. This paper proposes a new hybrid deep learning architecture for multilevel text sentiment classification with less training and simple network structure for better performance and can handle the implicit semantic information and contextual meaning of text. In this research the proposed hybrid deep neural network architecture made with Bidirectional Gated Recurrent Unit (BiGRU) and Bi-Directional Long Term Short Memory(BiLSTM) of Recurrent Neural Network (RNN) for multilevel text sentiment classification and this performs better with higher accuracy than other methods compared. This proposed method BiGRUBiLSTM model outperformed the traditional machine learning methods and the compared deep learning models with about average of 1% margin accuracy on different datasets. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Nomadic People Optimizer for IoT Combinatorial Testing Problem

Nomadic People Optimizer for IoT Combinatorial Testing Probl...

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2021 International Conference on Intelligent Technology, System and Service for Internet of Everything, ITSS-IoE 2021

作者： Alsewari, AbdulRahman A. Universiti Malaysia Pahang Centre For Software Development Integrated Computing Faculty Of Computing Pekan Pahang26600 Malaysia

ISBN: (纸本)9781665433051

Nowadays, smart cities depends on the integration between the systems via internet connections which are known as the Internet of Everything (IoE). The integrated systems raise several concerns involving the potential presence of critical integration defects. Therefore, there is a need for an interaction testing approach. In this paper, we present a Nomadic People Optimizer as a search engine for the test list generation for interaction testing. The proposed test list generation strategy is called Nomadic People Strategy (NPS). The results show the NPS is also capable of outperforming the existing strategies such as Genetic Algorithm (GA), Ant Colony Algorithm (ACA), Coco Search Algorithm (CS), Harmony Search Algorithm (HS), Jaya Algorithm (JA), Firefly Algorithm (FA) and Melody Algorithm (MA). © 2021 IEEE.

关键词： Genetic algorithms

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A Feasibility Study of Fitting the Normal Distribution and Gamma Distribution to Rainfall Data at Kuantan River Basin 5th

A Feasibility Study of Fitting the Normal Distribution and G...

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5th International Conference on Water Resources, ICWR 2021

作者： Ghani, Nadiatul Adilah Ahmad Abdul Senawi, Azlyna Subramaniam, Roshan Department of Civil Engineering College Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Kuantan26300 Malaysia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Gambang Kuantan26300 Malaysia Centre for Software Development & Integrated Computing Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Gambang Kuantan26300 Malaysia

ISBN: (纸本)9789811959462

Rainfall data can be very useful for decision makers in developing precautionary strategies to mitigate the impact of climate change in a variety of ways, such as forecasting floods. Prior to the data being utilised, it is critical to determine the best fit probability distribution of the data. Only after this has been done then it is possible to obtain reliable analysis and results. In this study, the feasibility of fitting the data with normal distribution was investigated. The investigation specifically used peak monthly values of rainfall data from ten hydrological stations of the Kuantan River Basin during the year 1975 to 2017. Four different test statistics were employed to assess the possibility of fitting the data to the normal and Gamma distributions: Skewness, excess kurtosis, Kolmogorov–Smirnov and Shapiro–Wilk. Results obtained show that normal distribution fits well with the annual daily maximum rainfall data for only two hydrological stations, but normal distribution is not a good fit for the seasonal daily maximum rainfall case for all stations involved. Gamma 1P distribution was found to be not an ideal fit for both annual and seasonal data for all stations, but Gamma 2P, on the contrary, was discovered to fit well for all stations except one station. The findings of the study are anticipated to improve rainfall forecasting in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rain

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Accuracy and performance analysis for classification algorithms based on biomedical datasets 7

Accuracy and performance analysis for classification algorit...

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7th International Conference on software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Al-Hameli, Bassam Abdo Alsewari, AbdulRahman A. Khubrani, Mousa Fakhreldin, Mohammoud Universiti Malaysia Pahang Faculty of Computing Pahang Pekan26600 Malaysia Universiti Malaysia Pahang Centre for Software Development and Integrated Computing Faculty of Computing Pahang Pekan26600 Malaysia Jazan University Computer Science and Information Technology Jazan Saudi Arabia

ISBN: (纸本)9781665414074

Diseases chronic, including heart disease, cancer, diabetes, and obesity, are the main causes of mortality in the United States and accounting for and consuming the majority of the country's healthcare expenditure. As indicated by recent researches. The main reason for the emergence of these diseases prominently is their relationship to each other, where diabetes is one of the causes of cancer and heart disease, hepatitis also is associated with diabetes, and heart disease. This paper focuses on data mining and machine learning techniques in healthcare classification and prediction of diseases and rebuild disease detection systems (DDS). The study suggests finding a classifier among the most common kinds of classification algorithms within a combined approach represent in Bayesian, Trees, Rules, Function, and lazy algorithms to automate a better performance of early detection of diseases from the medical datasets. This paper presents and analyzes five different machine learning (ML) algorithms: Function-based Neural Network (MLP) algorithm. Trees based Decision Tree (ID3) algorithm, Bayesian Theorem based Hidden Naïve Bayes (HNB) algorithm. Lazy based k-nearest neighbors (IBK) algorithm, and Rules-based OneR algorithm. The analysis is based on four benchmark datasets in the healthcare sector, including the Pima Indian Diabetes PID, the Breast Cancer, Heart Cleveland, and Hepatitis Datasets, which were obtained from several ML repositories. The results show that the HNB predicts the best result with a relatively higher Precision, AUROC Statistic, highest accuracy, and performance when compared to MLP, IBK, OneR, ID3 algorithms. © 2021 IEEE.

关键词： Diseases

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A Simulated Kalman Filter (SKF) Approach in Identifying Optimum Speed During Cornering 8th

A Simulated Kalman Filter (SKF) Approach in Identifying Opti...

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8th International Conference on Robot Intelligence Technology and Applications, RiTA 2020

作者： Mohd Azmi, Kamil Zakwan Zainal, Nurul Afiqah Zakaria, Muhammad Aizzat P. P. Abdul Majeed, Anwar Laboratory Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Pekan Pahang26600 Malaysia Centre for Software Development and Integrated Computing Universiti Malaysia Pahang Pekan Pahang Malaysia

ISBN: (纸本)9789811648021

Safety and human comfort are of paramount importance towards vehicle performance. This study aims to recognize the optimum cornering speed of a two-in wheel vehicle by means of a metaheuristic optimization technique known as Simulated Kalman Filter (SKF). The algorithm is used to minimize the normal forces experienced by the driver based on the identified speed. The system combines a biodynamic model with a two-in-wheel car model. It was demonstrated from the study that the conservative optimum speed of 20 km/h was determined by the SKF algorithm. The outcome of the investigation is non-trivial towards ensuring human comfort as well as safety to the driver. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Minimizing Normal Vehicle Forces Effect During Cornering of a Two In-Wheel Vehicle Through the Identification of Optimum Speed via Particle Swarm Optimization (PSO) 2nd

Minimizing Normal Vehicle Forces Effect During Cornering of ...

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2nd International Conference on Innovative Technology, Engineering and Sciences, iCITES 2020

作者： Zainal, Nurul Afiqah Azmi, Kamil Zakwan Mohd Zakaria, Muhammad Aizzat P. P. Abdul Majeed, Anwar Laboratory Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Pekan Pahang26600 Malaysia Centre for Software Development and Integrated Computing Universiti Malaysia Pahang Pekan Pahang Malaysia

ISBN: (纸本)9783030709167

Driving comfort is a non-trivial issue as it could pose adverse health issues particularly to those who travel-long distances. In the present study, we identify the optimum speed during cornering in order to reduce the effect of normal vertical forces towards driving experience of a two-in wheel vehicle. A bio-inspired metaheuristic method, namely Particle Swarm Optimization (PSO) was employed to address the objective of the present investigation on a twelve degrees of freedom human biodynamic model that is fused to a two-in-wheel car model. It was established through the PSO algorithm, that the optimal speed during cornering for the given car configuration is approximately 19 km/h. The findings of the present study are essential in ensuring human comfort is not compromised that in turn minimizes possible untoward risk to the driver. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Degrees of freedom (mechanics)

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The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning Models and K-Nearest Neighbors

The Classification of Skateboarding Tricks by Means of the I...

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Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020

作者： Shapiee, Muhammad Nur Aiman Ibrahim, Muhammad Ar Rahim Mohd Razman, Mohd Azraai Abdullah, Muhammad Amirul Musa, Rabiu Muazu Abu Osman, Noor Azuan P. P. Abdul Majeed, Anwar Innovative Manufacturing Mechatronics and Sports Laboratory Faculty of Manufacturing and Mechatronic Engineering Technology Universiti Malaysia Pahang Pekan Pahang Darul Makmur26600 Malaysia Universiti Malaysia Terengganu Terengganu Malaysia Centre for Software Development & Integrated Computing Universiti Malaysia Pahang Pekan Pahang Darul Makmur26600 Malaysia

ISBN: (纸本)9789813345966

The skateboarding scene has reached new heights, especially with its first appearance at the now postponed Tokyo Summer Olympic Games. Therefore, owing to the scale of the sport in such competitive games, advanced innovative assessment approaches have increasingly gained due attention by relevant stakeholders, especially with the interest of a more objective-based evaluation. We employed pre-trained Transfer Learning coupled with a fine-tuned k-Nearest Neighbor (k-NN) classifier to form several pipelines to investigate its efficacy in classifying skateboarding tricks, namely Kickflip, Pop Shove-it, Frontside 180, Ollie and Nollie Front Shove-it. From the five skateboarding tricks, a skateboarder would repeatedly perform it for five successful landed tricks captured by YI action camera. From that, the images would be feature engineered and extracted through five Transfer Learning models, namely VGG-16, VGG-19, DenseNet-121, DenseNet-201 and InceptionV3, then classified by employing the k-Nearest Neighbor (k-NN) classifier. It is demonstrated from the preliminary results, that the VGG-19 and DenseNet-201 pipeline, both attained a classification accuracy (CA) of 97% on the test dataset, followed by the DenseNet-121 and InceptionV3, in which both obtained a test CA of 96%. The least performing pipeline is the VGG-16, where a test CA of 94% is recorded. The result from the current study validated it could providing an objective judgment for judges in classifying skateboard tricks for the competition. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pipelines

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Khacdd: A Knowledge-Based Hybrid Deep Learning Method for Multilabel Sentiment Analysis on Complex Sentences Using Attentive Capsule and Dual Structured Recurrent Network

SSRN

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SSRN 2022年

作者： Islam, Md Shofiqul Ghani, Ngahzaifa Ab Zamli, Kamal Zuhairi Lokman, Abbas Saliimi Faculty of Computing Universiti Malaysia Pahang Pahang Kuantan26300 Malaysia IBM Centre of Excellence Centre for Software Development & Integrated Computing Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Kuantan26300 Malaysia

Proposed knowledge-based hybrid deep learning method (KHACDD) for sentiment classification that integrates a hierarchical attention-based capsule infrastructure with both the dual along with bidirectional recurrent neural network (RNN), Dilated convolutional neural network (CNN), and domain-based knowledge. Our innovative hybrid approach enhances the structure of feature representation as well as feature extraction with dynamically routing capsules its hierarchy structure toward an attention capsule. Extracting relevant features from text document from both the BiGRU-BiLSTM layer with Dilated CNN layer processing. A capsule with an attention mechanism to make proper use of the information in less duration. The proposed method used modified capsules and therefore can learn implicit semantics effectively. The BiGRU-BiLSTM is used all through this system to achieve proper long-distance and interdependent contextual information functioning. In addition, the capsule network may be capable of extracting rich textual information. GloVe embedding is used. To improve performance, the proposed technique leveraged domain-specific knowledge to handle misclassification. Proposed method got a margin of roughly 1% for multilabel ER social media data as well as 4% for multifeatured and multilabel MHER clinical data. n future we will develop a cloud based senti- ment analysis method and improve our model to handle morel class of sentiment. © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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The Classification of Impact Signal of 6 DOF Cobot by Means of Machine Learning Model

The Classification of Impact Signal of 6 DOF Cobot by Means ...

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Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2021

作者： Kai, Gavin Lim Jiann Nasir, Ahmad Fakhri Ab. Majeed, Anwar P. P. Abdul Razman, Mohd Azraai Mohd Khairuddin, Ismail Mohd Li, Lim Thai Faculty of Manufacturing and Mechatronic Engineering Technology Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Faculty of Computing Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Centre for Software Development and Integrated Computing Universiti Malaysia Pahang Pekan26600 Malaysia TT Vision Holdings Berhad 11900 Plot 106 Sungai Hilir Keluang 5 Bayan Lepas FIZ.4 Bayan Lepas Pulau Pinang Malaysia

ISBN: (纸本)9789811920943

Collaborative robot (Cobot) has seen a rise in adoption rate in the industry as the Industry 4.0 era marches in. Cobot were introduced to replace human operators in harsh environments or repetitive work processes. The health condition monitoring of these cobot have not been standardized due to lack of widely available standardized fault dataset and the high complexity of diagnostic. This study aims to use machine learning algorithms as a mean to identify the cobot pick and place process offset error using vibrational signals. The vibrational sensor was attached to the end effector of the cobot where the vibration signal of 3 axis were collected. The features were then extracted, standardized, and 544 features were selected from 2337 features based on a hypothesis testing method. The dataset was then spilt into training and testing by a ratio of 80:20. Three machine learning models namely, the k-Nearest Neighbors (k-NN), Neural Network (NN), and Support Vector Machine (SVM) classifier were tested, and the classification accuracy of the models was analyzed. A grid search approach was used to identify the best hyperparameter for each model. The model with the highest classification accuracy of 95.2% was the MLP model compared to SVM (92.4%) and kNN (79%). Therefore, it could be established from the study that a comparable classification efficacy is attainable through the identification of significant features. The findings are non-trivial, particularly with respect to the implementation of the developed classifier in real-time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Nearest neighbor search

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