检索结果-内蒙古大学图书馆

8th IEEE Symposium on Wireless Technology and Applications, ISWTA 2024

作者： Agustin, Michael Adrian P. Lopez, Ferdinand H. Tiu Huan, Adrean Rey L. Guaro, Jenith F. Tiba, Joriel S. College of Engineering Architecture and Computing Notre Dame of Marbel University Koronadal City Philippines

ISBN: (纸本)9798350351354

The Philippine economy is based primarily on agriculture. Nowadays, there's a bigger need than ever for efficient and automated farming methods. Slow and poor production has been one of the major problems of agriculture. The best approach to deal with these problems is for the agricultural industry to become more automated. This leads to more systematic farming, which raises productivity rates. The purpose of this study is to design and create an automated robot that plants corn seeds automatically. To make the most of the tropical climate in the area, this automated robot is also integrated with a solar power. The Automated Solar-Powered Corn Planting Robot (ASP-CPR) was designed by the researchers with the following functions: it can drill farmed soil with a regular depth and distance and release a standard amount of corn seeds on the drilled soil automatically that follows the standard set-up of the Department of Agriculture. Upon multiple testing, results revealed the accuracy on releasing the standard number of seeds is 94%, 98 % for the depth of drilled soil, and 92% accuracy for the distance taken by ASP-CPR between planted seeds. Additionally, the researchers integrated a notification system that notifies the user if the robot experiences low power or immovability, or if the seed container is empty. Moreover, the researchers conducted a product quality evaluation ISO/IEC 25010 for 20 respondents where the gathered data and results revealed an overall weighted mean of 4.13 which indicates a strong agreement amongst respondents that ASP-CPR adhere to the quality model. © 2024 IEEE.

关键词： Seed

来源：评论

学校读者我要写书评

暂无评论

Classifying Fake News in Filipino Language using Improved DistilBERT Model 11

Classifying Fake News in Filipino Language using Improved Di...

引用

11th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2024

作者： Ruel Espiritu, Jan Mar Policarpio, Ralph John E. Sabado, Aliah Chavy B. Sabado, Vince Marc B. Sueno, Hajah T. College of Engineering Architecture and Computing Notre Dame of Marbel University Koronadal City Philippines

ISBN: (纸本)9798350389289

Detecting misinformation in the Filipino language poses unique challenges, especially when working with limited data resources. This study explored the use of transformer models, specifically pre-trained models like BERT, to construct a fake news classification model for Filipino. However, BERT's large size and computation hinder its deployment in resource-constrained environments. To address this, the study focuses on developing a lightweight fake news classifier using the DistilBERT architecture, fine-tuned for Filipinos. The model's performance was evaluated against BERT based on accuracy, precision, recall, F1 score, inference time, memory requirement, and parameter size. Results indicate that while BERT exhibits slightly better classification performance, DistilBERT offers significant advantages in efficiency with faster inference time and smaller resource requirements. © 2024 IEEE.

关键词： BERT Classification model Data augmentation DistilBERT Fake news Fine-tune Transformer

来源：评论

学校读者我要写书评

暂无评论

THE DUALITY OF BEAUTY: INTEGRATING COMPUTATIONAL METHODS AND SUBJECTIVE EXPERIENCE IN AESTHETIC EVALUATION 19th

THE DUALITY OF BEAUTY: INTEGRATING COMPUTATIONAL METHODS AND...

引用

19th International Middle Eastern Simulation and Modelling Conference, MESM 2024

作者： Lu, Zhongxi Al-Akaidi, Marwan Zhou, Jiafeng Yang, Hongji School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom College of Science & Computing Wigwe University Isiokpo Nigeria College of Architecture Jilin Jianzhu University Changchun China

ISBN: (纸本)9789492859341

This paper explores the duality of aesthetic evaluation, emphasising the emerging field of computational aesthetics. Whilst acknowledging the subjective nature of aesthetic experiences, we delve into how computational methods can quantify and analyse aesthetic features objectively. The study examines the integration of machine learning and artificial intelligence in aesthetic assessment, as well as discussing how computational aesthetics provides tools for large-scale image evaluation, creative decision-making support, and novel approaches to art creation. The research also addresses the challenges and future directions of computational aesthetics. By bridging subjective human experiences with objective computational analysis, this paper demonstrates how computational aesthetics is revolutionising the understanding of beauty, transforming artistic practices, and opening new avenues for aesthetic research in the digital age. © 2025 EUROSIS-ETI.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Quantitative Research on Complexity of Urban Environment Based on AI Model 5

Quantitative Research on Complexity of Urban Environment Bas...

引用

5th International Conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Suo, Wanyue Zhao, Jing School of Architecture Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (纸本)9798350368284

Visual complexity is a pivotal metric in urban environment quality, and the objective quantification of visual complexity holds immense importance for research in this field. Drawing from the definition of visual complexity, this paper categorizes complexity into environmental element feature complexity, Edge Feature Complexity, and color complexity. By harnessing computer image processing technology and incorporating artificial intelligence methodologies, the study successfully deploys a supervised training model to amalgamate these multiple features, which created a preliminary framework for quantifying visual complexity, offering valuable insights for future investigations into human aesthetic experiences and urban visual quality assessments. © 2024 IEEE.

关键词： Artificial Intelligence Model Computer Vision Fractal Dimension SIFT Urban Environment

来源：评论

学校读者我要写书评

暂无评论

Predicting Intention to Recommend Digital Citizen Participation using Machine Learning 13

Predicting Intention to Recommend Digital Citizen Participat...

引用

13th IEEE International Conference on System Engineering and Technology, ICSET 2023

作者： Hernandez, Alexander A. Abisado, Mideth B. Maceda, Lany L. Rodriguez, Ramon L. Montefalcon, Myron Darrel L. Padilla, Jay Rhald C. Sangalang, Hersel Danna E. National University College of Computing and Information Technologies Philippines Bicol University College of Science Albay Philippines National University - Dasmarińas School of Engineering Computing and Architecture Philippines

ISBN: (纸本)9798350340891

Digital citizen participation uses innovative technologies to enable, engage, and empower citizens to communicate with the government on different needs, concerns, and challenges. To this date, limited studies explain the intention and recommendation for digital citizen participation, particularly from the Philippines. Thus, this study aims to understand digital citizen participation through a survey that examines factors on the intention to use and recommend digital citizen participation among Filipino citizens. A conceptual model was developed based on trust, technology acceptance, and empowerment theory. The framework relationships were tested using machine learning algorithms. The results show that perceived ease of use, perceived usefulness, trust, and empowerment theory positively predicted the intention to recommend digital citizen participation. Furthermore, this study indicates that Filipinos have positive intention and are willing to recommend digital citizen participation. Finally, this work provides evidence of the explanatory power of the conceptual model through machine learning models on digital citizen participation. This work presents some important research and practical implications in digital citizen participation. © 2023 IEEE.

关键词： Developing countries

来源：评论

学校读者我要写书评

暂无评论

Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

引用

Digital Communications and Networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

来源：评论

学校读者我要写书评

暂无评论

Application of Clustering Algorithms to enhance Personalized Learning through Recommendation Model

Application of Clustering Algorithms to enhance Personalized...

引用

2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Sharif, Mhd Saeed Tamang, Madhav Theeng Mani, Dyuthi Elmedany, Wael Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9798331533137

Technology and education have recently changed student involvement with learning resources and educational experiences. It is very important to have a platform which can provide personalized learning resources and group students by learning patterns. The research develops recommendation systems and grouping algorithms to help students with their learning patterns and performance measures. To record multidimensional student learning experiences, demographics, learning activities, problem-solving behaviours, and performance measures are extracted and processed. This system makes personalised student suggestions via collaborative filtering, especially Alternating Least Squares (ALS). The model predicts learning materials and problem sets based on student preferences and ability levels by analysing historical student interactions with learning resources. The research also uses clustering methods like K-means clustering to group students with similar learning and performance patterns. Clustering analysis lets instructors discover student group traits and tailor interventions and support techniques. This research also examines temporal relationships in student learning sequences using sequential models. Sequential learning activities and problem-solving behaviour of students help recurrent neural networks (RNNs), or sequential pattern mining algorithms predict their next activities. This research used a huge dataset of over sixteen million exercise logs and applied collaborative filtering (ALS) and K-means clustering to find learning patterns. The ALS-based recommendation system achieved a Mean Squared Error (MSE) of 0.23, showed excellent predictive accuracy. Clustering grouped students by learning behaviours, enabling targeted interventions. This research improves personalised learning using machine learning and data analytics in education. The recommendation system can help instructors tailor training, give individualised support, and boost academic success for various

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of Sales Prediction Using Machine Learning Models: Integration of PySpark and Power BI

A Comparative Study of Sales Prediction Using Machine Learni...

引用

2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Sharif, MhD Saeed Tamang, Madhav Theeng Nepal, Anup Elmedany, Wael Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9798331533137

Retail management requires accurate sales forecasting for strategic planning, inventory control, and revenue maximisation. In this research, we are predicting sales using PySpark-built ML models and the Big Mart dataset. We evaluate and demonstrate the prediction skills of numerous machine learning algorithms, concentrating on XGBoost. Big Mart offers item weight, visibility, type, and outlet data. We use these properties as prediction model features. PySpark, a strong distributed computing platform, manages massive datasets for analysis and model training. In massive trials, we test decision trees (DT), XGBoost, linear regressions (LR), and random forests (RF). Training and testing enhance model accuracy. RMSE and R-squared measure our model quality. Our metrics evaluate the model’s data fit and prediction accuracy. XGBoost performed best with an RMSE of 1081 and an R-squared of 0.59. The XGBoost algorithm accurately predicts Big Mart sales. The model performs well because of its ensemble learning and understanding of intricate dataset links. We also used Power BI to present analytical insights, helping decision-makers design sales-estimated business plans. This study employed several machine learning algorithms, XGBoost gave the best performance. This research provides insights into how organisations might use these technologies to improve resource allocation and inventory management decision-making. ©2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Loan Approval: Harnessing the Power of K-Nearest Neighbors for Predictive Eligibility

Revolutionizing Loan Approval: Harnessing the Power of K-Nea...

引用

2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Sharif, Mhd Saeed Tamang, Madhav Theeng Hussain, Niyas Jaheer Elmedany, Wael Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9798331533137

A bank has become a basic need of our daily lives. People use banks for several tasks, such as depositing or withdrawing money and borrowing loans. The number of loan applications is rising significantly. It is crucial for the bank and financial sectors to determine loan eligibility and approve loans to suitable applicants only. This study attempts to design a system which can easily predict loan eligibility with the application of several machine learning (ML) algorithms. We employed the five different machine learning algorithms to achieve this research’s aim. These algorithms were trained using the pre-processed dataset to create predictive models. F1 score, accuracy, Recall and Precision are used to evaluate the performance of the proposed approaches. The result showed that K-nearest neighbor (KNN) demonstrated outstanding performance, with an accuracy rate of 88.89%. The Random Forest (RF) model had an accuracy of 84.44%, whereas the Support Vector Machine (SVM) and Linear regression (LR) models achieved 82.22% and 80% accuracy, respectively. The Decision Tree (DT) algorithms showed the worst performance among the five algorithms, with an accuracy of 79.88%. The achievement of this research focuses on the possibility of machine learning algorithms as an efficient method for predicting loan eligibility in banks, contributing to the advancement of financial technology by automating and optimizing key banking processes, reducing processing times, and enhancing overall bank efficiency. ©2024 IEEE.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

Utilising Machine Learning for Precise State-of-Charge Prediction in Li-Ion Batteries 12

Utilising Machine Learning for Precise State-of-Charge Predi...

引用

12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Khatri, Avinash Lota, Jaswinder Nepal, Prasanna Amirhosseini, Mohammad Hossein School of Architecture Computing and Engineering University of East London London United Kingdom Nepal Engineering College Department of Electrical and Electronics Engineering Bhaktapur Nepal

ISBN: (纸本)9798350350982

As the popularity of electric propulsion using batteries rises alongside the demand for renewable energy, effective battery management and monitoring are crucial for sustain ability and efficiency in electric vehicles (EVs). The battery monitoring system (BMS) employs IoT/sensor networks to estimate crucial battery metrics like remaining useful life (RUL), state-of-health (SoH), and state-of-charge (SoC), using data on current status, temperature, and voltage. Machine learning (ML) and artificial intelligence (AI) are increasingly utilized to enhance BMS accuracy, addressing challenges like real-time data processing and the accuracy of estimations. This paper investigates the effectiveness of Linear Regression and Random Forest models in estimating SoC. During the hyperparameter tuning phase, the models were optimized using the Grid Search method, and their performance was evaluated at various temperatures: 25°C, 10°C, 0°C, and -10°C. The findings indicate that the models' effectiveness enhances as the temperature increases. The Random Forest model exhibited superior performance at 25°C, achieving an R2 score of 0.99646 and an RMSE score of 0.000264. This paper not only contributes to advancing Li-ion battery monitoring system, but also empowers professionals in this field to harness machine learning capabilities effectively. © 2024 IEEE.

关键词： State of charge

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：