检索结果-内蒙古大学图书馆

International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Mohammad Haris Fang-Fang Chua Amy Hui-Lan Lim Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

Effort estimation is essential for successful software project planning, budgeting, and risk identification. However, the techniques used to estimate effort are often inaccurate, outdated, and only consider technical factors while neglecting project management or stakeholder engagement. Expert estimation remains an important technique for leveraging human expertise in software estimation, but solely relying on this technique causes biased and subjective predictions. Machine learning (ML) techniques have shifted the direction of software project effort estimation towards computational intelligence. Nonetheless, there is a lack of deployment due to ambiguous outcomes and ineffective model-building approaches. This study presents an ensemble-based framework that can estimate software project effort more accurately with the incorporation of domain knowledge and experiences. To achieve this, six homogeneous classifier ensembles will be constructed using six distinct classifiers on the proposed USP05-FT dataset. The collected expert estimations will be integrated into the proposed dataset as an additional feature in the form of numerical values such as expert-provided software project effort estimations (in person hours) that provide additional insight and knowledge. Subsequently, the predictions of all six homogeneous classifier ensembles will be combined through majority voting to obtain a more accurate and reliable prediction with increased robustness against errors and uncertainties. The performance of the proposed framework will be evaluated using Recall, F-measure, Precision, and Accuracy. It is expected that the proposed ensemble-based framework for software project effort estimation will lead to more efficient and effective software project management, an improvement in resource allocation, empowering informed decision-making, and timely project delivery.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient Contrast Subspace Mining Method for Categorical Data 22

Efficient Contrast Subspace Mining Method for Categorical Da...

引用

Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence

作者： Florence Sia Fui Sze Rayner Alfred Faculty of Computing and Informatics Universiti Malaysia Sabah Malaysia

ISBN: (纸本)9781450397773

Mining contrast subspace has recently received attention to identify contrast subspace where a query object is most likely similar to a target class but least likely similar to other class. It has many important applications in various domain such as healthcare, security, finance, and business. Tree-based contrast subspace mining method (TB-CSMiner) has been introduced that is capable to identify contrast subspace of query object in categorical data set. However, the effectiveness of the method has not been evaluated thoroughly. Besides, the efficiency of the method in finding contrast subspace has not yet been examined. Real world data sets more often than not containing large amount of data. It is important to have a method that can identify contrast subspace of query object not only effectively but also efficiently on large data set. This paper uses various classification methods to further evaluate the effectiveness of the TB-CSMiner and assesses the execution speed of the method in mining contrast subspace. TB-CSMiner is experimentally shown to be significantly faster and as effective in identifying contrast subspace of query object on real world categorical data sets.

关键词： Categorical data Mining contrast subspace Contrast subspace Classification Feature Selection Data mining

来源：评论

学校读者我要写书评

暂无评论

Social Media User Engagement for Promoting Public Health: Pattern Analysis Using Apriori Approach 5

Social Media User Engagement for Promoting Public Health: Pa...

引用

5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

作者： Alebaba, Daimler Benz Sura, Suaini Tuah, Nooralisa Mohd Lee, Seungwon Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Bundang Convergence Technology Campus of Korea Polytechnic University Gyeonggi-do Korea Republic of

ISBN: (纸本)9798350304152

User engagement has become an essential aspect of research in technology, marketing, and social media. Measuring and analyzing user engagement has gained significant importance due to its ability to provide valuable insights to assess engagement responses from online users engaged in published content. Previous studies have devised various methods to effectively analyze data, with statistical approaches commonly used as techniques. However, statistical approaches often struggle to effectively handle categorical variables, for which interpretation becomes limited and less informative, also failing to capture the structure and dependencies inherent in categorical data. Therefore, Apriori approach that defines association rules to generate patterns based on user frequent itemset is adapted to identify UGC patterns for user engagement in public health social media. This study aims to generate association rules based on UGC data and identify UGC patterns for user engagement. The frequency of UGC data such as like, link click, share, and comment was analyzed using Apriori approach with support and confidence thresholds. The result shows that 16 association rules were generated based on UGC data. Five with 100%, three with 90%, one with 60%, three with 50%, one with 40%, and three with 30%. It can be concluded that the Apriori approach can be used in identifying the pattern of user engagement on public health social media other than buying and selling of market basket transactions. © 2023 IEEE.

关键词： Association rules

来源：评论

学校读者我要写书评

暂无评论

Extreme Gradient Boosting (XGBoost) Regressor and Shapley Additive Explanation for Crop Yield Prediction in Agriculture 12

Extreme Gradient Boosting (XGBoost) Regressor and Shapley Ad...

引用

12th International Conference on Computer and Knowledge Engineering, ICCKE 2022

作者： Mariadass, Dennis A-L Moung, Ervin Gubin Sufian, Maisarah Mohd Farzamnia, Ali Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

The primary purpose of precision agriculture is to maximize crop yields while utilizing a limited amount of land resources. Apart from industrialization, which fuelled Malaysia's significant economy and development, the country's agriculture industry performs a major role in guaranteeing food security and safety, as well as long-term development and wealth creation. To increase the nation's food security, policymakers must rely on accurate crop yield predictions in order to easily obtain trade - related evaluations. Machine Learning can help anticipate yields more accurately. This paper proposes to use the XGBoost model for annual crop yield prediction in Malaysia. Experiments on the generated yield dataset show promising results with 0.98 R-Squared value and outperformed the current models. The implementation of the suggested model is extensively evaluated using the Shapley Additive Explanation (SHAP) to discover the essential features such as average temperature, average rainfall, and pesticide in the crop yield prediction. The estimates provided by machine learning algorithms will aid farmers in deciding what to grow because of this research. © 2022 IEEE.

关键词： Additives

来源：评论

学校读者我要写书评

暂无评论

Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

引用

Computers, Materials & Continua 2023年第5期75卷 3045-3064页

作者： Omar Abdulghafoor Musbah Shaat Ibraheem Shayea Ahmad Hamood Abdelzahir Abdelmaboud Ashraf Osman Ibrahim Fadhil Mukhlif Herish Badal Norafida Ithnin Ali Khadim Lwas Electronic&Telecommunications Engineering Department The American University of KurdistanDuhokKurdistan RegionIraq CTTC BarcelonaSpain Faculty of Electrical and Electronics Engineering Istanbul Technical UniversityIstanbulTurkey Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia R&D Ministry of Industry and MineralsBaghdadIraq

The computational complexity of resource allocation processes,in cognitive radio networks(CRNs),is a major issue to be ***,the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users,CRs,and primary users,PUs,exist in the identical geographical ***,this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarioswhile limiting interference to PUs to allowable ***,this paper,compared to other frameworks proposed in the literature,proposes a two-step approach to reduce the complexity of the proposed mathematical *** the first step,the subcarriers are assigned to the users of the CRN,while the cost function includes a pricing scheme to provide better power control algorithm with improved reliability proposed in the second *** main contribution of this paper is to lessen the complexity of the proposed algorithm and to offer flexibility in controlling the interference produced to the users of the primary networks,which has been achieved by including a pricing function in the proposed cost ***,the performance of the proposed power and subcarrier algorithm is confirmed for orthogonal frequency-division multiplexing(OFDM).Simulation results prove that the performance of the proposed algorithm is better than other algorithms,albeit with a lesser complexity of O(NM)+O(Nlog(N)).

关键词： Cognitive radio resource allocation OFDM pricing

来源：评论

学校读者我要写书评

暂无评论

Interference Resistant Position Awareness for Collision Avoidance in Dense Drones Swarming 16

Interference Resistant Position Awareness for Collision Avoi...

引用

16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022

作者： Kazmi, Syed Hussain Ali Qamar, Faizan Hassan, Rosilah Nisar, Kashif Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Selangor Malaysia University Malaysia Sabah Faculty of Computing and Informatics Sabah Malaysia

ISBN: (纸本)9781665451628

Global interest in drones is prone to surface challenges related to collision avoidance in dense constellation. Existing collision avoidance mechanism, known as Automatic Dependent Surveillance-Broadcast (ADS-B), contains serious limitations of interference effects due to broadcasting. Therefore, we propose a novel Discrete Sequence Spread Spectrum (DSSS) enabled Minimum Shift Keying (MSK) modulation for Three Dimension (3D) position sharing in collision avoidance mechanism. Our proposed scheme avoids extra processing through physical layer addressing and provides convergence to further reduced broadcast rate. We analyzed the performance of the proposed mechanism in the spectrum completely covered with Gaussian noise. The MATLAB based analysis results indicate the proposed scheme as a potential solution to address the challenges faced in drone to drone communication for collision avoidance in dense swarms of drones or Unmanned Aerial Vehicles (UAV). Moreover, the proposed scheme outperforms the traditional demodulation approach compare to direct correlation without demodulation. Further, we discussed possible future research directions in subject solution. © 2022 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Malaysian Sign Language (MSL) Recognition: Exploring the Impact of Color Spaces 14

Deep Learning-Based Malaysian Sign Language (MSL) Recognitio...

引用

14th International Conference on Computer and Knowledge Engineering, ICCKE 2024

作者： Moung, Ervin Gubin Suwek, Precilla Fiona Sufian, Maisarah Mohd Liaw, Valentino Farzamnia, Ali Khong, Wei Leong Faculty of Computing and Informatics University Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Computing and Engineering University of Huddersfield West YorkshireHD1 3DH United Kingdom School of Engineering Monash University Malaysia Bandar Sunway Selangor Malaysia

ISBN: (纸本)9798331511272

Sign language is one form of communication for this group of people to communicate with each other. Not only for people with hearing problems but sign language is also useful for people who are mute or have problem speaking. The most used sign language is the American Sign Language (ASL) that is widely used in English speaking countries. In Malaysia, Bahasa Isyarat Malaysia (BIM) or Malaysian Sign Language (MSL) is still a new teaching to the community in Malaysia. Hence, in this project, a dataset with 5980 images of the signed alphabet is used to train models to recognize what the signs mean. The problem this project aims to address is the limited research and the availability of datasets in the field of Malaysian Sign Language (MSL) recognition using deep learning and various color spaces. Two deep learning models that are used are Convolutional Neural Network (CNN) and Residual Network 18 (ResNet18). The images are also converted into different color spaces which are RGB, YCbCr, Grayscale and the combination of RGB and YCbCr. The findings reveal that RGB is the most effective color space for CNNs, achieving up to 83.9% accuracy, while YCbCr performed best with ResNet18, achieving 88.3% accuracy. These results demonstrate the importance of color space selection in sign language recognition and contribute to the growing body of research on MSL. Key metrics such as precision, recall, and F1-score further underscore the robustness of the proposed system. ©2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Interactive Multimodal Learning: Towards Using Pedagogical Agents for Inclusive Education

Interactive Multimodal Learning: Towards Using Pedagogical A...

引用

2023 IEEE International Humanitarian Technology Conference, IHTC 2023

作者： Al Omoush, Muhammad H. Salih, Sumaia E. Kishore, Sameer Mehigan, Tracey Dublin City University Faculty of Engineering and Computing Dublin Ireland Dublin Ireland Middlesex University Department of Computer Engineering and Informatics Dubai United Arab Emirates

ISBN: (纸本)9798350314311

A pedagogical agent is an animated interface in an interactive online learning environment. Its role involves guiding users through instructions and participating in direct discussions. Numerous research studies underline the advantages of pedagogical agents in delivering instruction, tailoring learning experiences, and promoting inclusivity. The authors utilize machine learning techniques to develop an interactive multimodal application (combining visuals and audio) designed as a pedagogical agent. This application is intended to assist in teaching primary school students (aged 6 to 8) the recognition of colors and letters. Additionally, it incorporates voice interaction to assess their learning progress. The overarching objective of this pedagogical agent is to seamlessly integrate artificial intelligence (AI) into educational settings, thereby amplifying student engagement and motivation during the learning process. This paper will explore the journey of creating a machine learning-based application and its potential utility in supporting educators. Furthermore, the authors will investigate potential augmentations that could render the application more adept at aiding students with dyslexia. The paper will emphasize the significance of human-computer interaction (HCI), as it significantly shapes pedagogical agents' design and operational aspects. © 2023 IEEE.

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

UAV Path Planning using Rotated TOR in Structured Environment 4

UAV Path Planning using Rotated TOR in Structured Environmen...

引用

4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

作者： Wahab, Shaliza Hayati A. Saudi, Azali Saad, Nordin Chekima, Ali Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Path planning is one of the crucial criteria for an unmanned aerial vehicle (UAV) in providing a safe path to fly from any location to the specified goal. Global approach to autonomous path planning often used the harmonic potentials technique to guide the planner. The harmonic potentials are essentially the solutions of Laplace's equation that are employed to model the environment. The computational resources required to obtain these harmonic potentials often involve a large number of mathematical calculations. In the previous work, fast iterative methods that apply the use of full-sweep iteration were suggested. This study examines a fast-iterative numerical approach, namely Rotated Two-Parameter Successive Over-Relaxation (RTOR), to UAV path planning. The algorithm is implemented in a self-developed UAVPlanner Simulator where hills are generated randomly in grid areas. The proposed method was tested using several simulation scenarios which demonstrated the efficiency of the algorithm in finding the path, where it was evaluated in terms of the number of iterations and computational time efficiency with the different number of outdoor static obstacles. The results show that RTOR outperformed the previous methods and gives faster computational time and iteration in generating a path for UAVs. © 2022 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling 12

A Comparison Study on Generative Adversarial Networks (GANs)...

引用

12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Li, Brandon Kah Wei Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Hamzah, Muzaffar Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350391398

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model's effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：