检索结果-内蒙古大学图书馆

Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

Computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

来源：评论

学校读者我要写书评

暂无评论

Hybrid Collaborative Movie Recommendation System 4

Hybrid Collaborative Movie Recommendation System

引用

4th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Husin, Mohamad Riduan Mas Razak, Tajul Rosli Malik, Ariff Md Ab Nordin, Sharifalillah Abdul-Rahman, Shuzlina College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Malaysia Research Initiative Group of Intelligent Systems College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Malaysia UiTM Selangor Research Initiative Group of Intelligent Systems Faculty of Business and Management Selangor Puncak Alam Malaysia

ISBN: (纸本)9798350318432

A recommendation system applies the data to the information discovery techniques and personalises the products for recommendations. Typically, movie recommendation systems predict what movies a user wants based on the characteristics of previously liked movies. These recommendation systems are helpful for organisations that gather data from many users and wish to offer the best possible recommendations effectively. This study developed a hybrid movie recommendation system using collaborative and content-based filtering involving Matrix Factorisation, TF-IDF. In addition, this study aims to extract the CBF features into CF to improve the recommendation system engine's performance and personalise the user's movie recommendations. The performance of the trained model was measured using RMSE, precision, recall, and F1 score. The trained model produced a low RMSE value with high precision, recall, and F1 score values. The paper's contribution is that the proposed hybrid recommendation provides a pathway for the users by helping them find movies that meet their interests. © 2023 IEEE.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Automated grading using natural language processing and semantic analysis

引用

MethodsX 2025年 14卷 103395页

作者： Ayaan, Ahmad Ng, Kok-Why Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya 63100 Malaysia

Educational institutions' grading systems have evolved significantly toward automation, propelled by advances in Natural Language Processing (NLP) and Artificial Intelligence (AI). This research comprehensively explores automated grading systems, analyzing traditional methods alongside contemporary approaches and research on exam grading. Through extensive literature review, we examine the advantages and disadvantages of keyword-centric techniques, NLP-based systems, and hybrid models. We implement a novel NLP-based automatic grading system that combines multiple similarity measures with semantic analysis using TensorFlow's Universal Sentence Encoder. The system evaluates student responses by comparing them to reference answers using a weighted combination of edit similarity, cosine similarity, Jaccard similarity, normalized word count, and semantic similarity. Experiments conducted on 14 student responses demonstrate the system's ability to provide consistent and accurate grading while identifying cases requiring further human review. This study contributes to understanding automated grading systems, offering insights into their efficacy, limitations, and prospects in educational assessment practices. • Hybrid evaluation: The proposed methodology combines traditional NLP techniques with advanced semantic analysis to provide comprehensive evaluation of student answers;the system integrates both surface-level textual similarity and deep semantic analysis to evaluate open-ended student responses. • Weighted scoring: The system computes a weighted base score by combining four NLP metrics (Jaccard, edit distance, cosine similarity, normalized word count) and then blends this with a semantic similarity score from the Universal Sentence Encoder to assign marks. • Rule-based final scoring: The final scoring layer applies threshold logic to assign zero, partial, or full marks based on semantic score and word count, and flags responses that fall into ambiguous ranges for tea

关键词： Automatic Grading System Natural language processing Semantic analysis

来源：评论

学校读者我要写书评

暂无评论

Continuous Prompt for Chemical Language Model Aided Anticancer Synergistic Drug Combination Prediction

Continuous Prompt for Chemical Language Model Aided Anticanc...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Geng, Guannan Zhao, Lingling Wang, Chunyu Liu, Xiaoyan Wang, Junjie The First Affiliated Hospital of Harbin Medical University Department of Endocrinology Harbin China Harbin Institute of Technology Faculty of Computing Harbin China Nanjing Medical University School of Biomedical Engineering and Informatics Department of Medical Informatics Nanjing China

ISBN: (纸本)9798350324457

Identifying synergistic drug combinations is paramount significance in addressing complex diseases while reducing the risks of toxicities and other adverse effects. Although a plethora of computational methods have been proposed in this domain, most of them are underpinned only by physicochemical or biological features. Recently, Chemical Language Models (CLMs) are shown to be capable of learning better representations that hold utility across diverse tasks, from molecular property prediction, de novo drug design, drug-target interaction, and more. In this study, we proposed CLMSyn, a continuous prompt for CLM aided synergistic drug combinations prediction. Unlike existing works employ CLMs for downstream tasks, we adopt the prompt learning to fine-tune CLM, that is, only train small-scale prompt while keeping CLM fixed. Furthermore, we harness the the multi-head attention mechanism to fuse the learned vector from the CLM, chemical descriptors and gene expression of cell line. A comprehensive array of experiments conducted on a benchmark dataset, encompassing four distinct synergy types, substantiates the superior performance of CLMSyn when contrasted against existing state-of-the-art methods. These empirical findings provide compelling evidence attesting to the efficacy of CLMSyn as a potent instrumentality in expediting the identification of pioneering combination therapies. © 2023 IEEE.

关键词： Chemical Language Model Drug Combination Multi-head Attention Prompt Learning

来源：评论

学校读者我要写书评

暂无评论

A Mobile-Based Application for Detecting Sleep Deprivation Using Deep Learning for University Student 5

A Mobile-Based Application for Detecting Sleep Deprivation U...

引用

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

作者： Ern, Lim Jo Sia, Florence Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Creative Advanced Machine Intelligence Research Centre Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

Sleep deprivation has been a norm for university students due to their unhealthy sleeping habits. It has significantly impact towards their study quality and performance. They will have trouble focusing, learning, and responding during class. Besides, it has adverse effect on their health which can lead to many diseases such as heart disease, diabetes, and stroke. There is a need to curb the ongoing unawareness of these effects. Students might not know they have sleep deprivation because it is often misjudged as lack of interest, loss of motivation, and laziness. There is no application and deep learning model has been developed yet to detect whether or not a person suffers from sleep deprived via face features. This paper proposes a mobile-based application to automate the sleep deprivation detection through the face features specifically eyes and mouth. A pre-trained deep learning model, MobileNet is proposed to identify and classify the sleep deprived and non-sleep deprived face images by using a public dataset of face images. The experiment results showed that the proposed MobileNet achieves high classification accuracy of 90%. A usability testing also has been conducted on the mobile application and the obtained results showed that the application can detect sleep deprivation well. © 2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Text Classification Using Improving Term Frequency with Gaussian and Multilayer Perceptron Techniques 20

Text Classification Using Improving Term Frequency with Gaus...

引用

20th International Joint Conference on Computer Science and Software Engineering, JCSSE 2023

作者： Vichianchai, Vuttichai Kasemvilas, Sumonta Mahasarakham University Faculty of Informatics Department of Information Technology Maha Sarakham Thailand College of Computing Khon Kaen University Department of Computer Science Khon Kaen Thailand

ISBN: (纸本)9798350300505

This paper proposes combining the term frequency with the Gaussian technique and the multilayer perceptron technique for text sentiment analysis using two datasets: tweet customer sentiments serving U.S. airlines and Amazon product sentiment reviews. The bag of words, term frequency-inverse document frequency, term frequency-inverse corpus document frequency, term frequency-inverse gravity moment, and term frequency with Gaussian are investigated in this study. The experimental results show that improving term frequency with Gaussian for term weighting in text sentiment analysis from tweets of customer sentiments serving U.S. airlines, with a higher F-score than previous techniques and an 8% increase in F-score from the original term frequency with Gaussian technique. Also, the term frequency with Gaussian technique was enhanced for term weighting in text sentiment analysis using Amazon product review scraping, with a higher F-score than the previous techniques and had a 7% increase in F-score compared to the original term frequency with Gaussian technique. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Hyperparameter Tuning of Convolutional Neural Network for Fresh and Rotten Fruit Recognition 5

Hyperparameter Tuning of Convolutional Neural Network for Fr...

引用

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

作者： Sia, Florence Baco, Nur Shabira Universiti Malaysia Sabah Creative Advanced Machine Intelligence Research Centre Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

The freshness of fruits is important in making sure good quality of fruits can be provided to consumers. Manually classifying fruits based on the characteristics of fruits by hand and eyes can be time consuming and unreliable. Convolutional Neural Network (CNN) is the most common deep learning method that has been exploited to classify the freshness of fruits through processing the images of fruits. The classification effectiveness of the CNN is highly depending on its hyperparameter. However, the hyperparameter setting of CNN method has not yet been tuned under diverse values in recognizing the fresh and rotten fruits. In this work, the hyperparameter of CNN is tuned to identify the best setting that can effectively recognize the fresh and rotten fruits particularly orange, apple, and banana. These including the number of epoch, batch size, learning rate, and optimizer with respect to ReLU and Sigmoid activation function. The experiment results show that the hyperparameter tuned CNN model achieved the highest classification accuracy of 99.04%, on real world dataset. Hence, this indicates that the hyperparameter tuned CNN model is capable to recognize the fresh and rotten fruits well. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Localizing AI: Evaluating Open-Weight Language Models for Languages of Baltic States

arXiv

引用

arXiv 2025年

作者： Kapočiūtė-Dzikiene, Jurgita Bergmanis, Toms Pinnis, Mārcis Tilde IT Lithuania Faculty of Informatics Vytautas Magnus University Lithuania Tilde Latvia Faculty of Computing University of Latvia Latvia

Although large language models (LLMs) have transformed our expectations of modern language technologies, concerns over data privacy often restrict the use of commercially available LLMs hosted outside of EU jurisdictions. This limits their application in governmental, defence, and other data-sensitive sectors. In this work, we evaluate the extent to which locally deployable open-weight LLMs support lesser-spoken languages such as Lithuanian, Latvian, and Estonian. We examine various size and precision variants of the top-performing multilingual open-weight models, Llama 3, Gemma 2, Phi, and NeMo, on machine translation, multiple-choice question answering, and free-form text generation. The results indicate that while certain models like Gemma 2 perform close to the top commercially available models, many LLMs struggle with these languages. Most surprisingly, however, we find that these models, while showing close to state-of-the-art translation performance, are still prone to lexical hallucinations with errors in at least 1 in 20 words for all open-weight multilingual LLMs. © 2025, CC BY-SA.

关键词： Computer aided language translation

来源：评论

学校读者我要写书评

暂无评论

Women's Health Diary: Tracker System for Menstruation, Intermenstrual, Ovulation, and Postnatal Bleeding Using Rules-Based Technique 3

Women's Health Diary: Tracker System for Menstruation, Inter...

引用

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Abdullah, Nursyuhada Binti Aziz, Azwa Bin Abdul Mustafa, Wan Azani Almusawi, Muntather Hussein, Abbas Hameed Abdul Besut Campus Faculty of Informatics & Computing Terengganu Malaysia Terengganu Malaysia Perlis Arau Malaysia The Islamic University Faculty of Engineering Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Menstruation, a natural physiological process in women, entails regular vaginal bleeding as part of the monthly menstrual cycle. While existing systems facilitate menstrual cycle tracking, they often lack the capability to differentiate between various types of vaginal bleeding, such as menstruation (haid), intermenstrual bleeding (istihadah), or postnatal bleeding (nifas). To address this limitation, the Women's Health Diary: Tracker System for Menstruation, Intermenstrual, Ovulation, and Postnatal Bleeding) was developed using a rule-based technique. This system employs an iterative method comprising eight phases: initial planning, planning, requirements, analysis and design, implementation, testing, evaluation, and deployment. By ensuring the optimal development and performance of this system, women can effectively manage their menstrual blood, a vital aspect of their overall health. Moreover, this system holds particular significance for every women, as it facilitates tracking and identification of regular and irregular cycles, enabling them to uphold the legitimacy of their worship. © 2023 IEEE.

关键词： Intermenstrual Menstruation Ovulation Postnatal Bleeding

来源：评论

学校读者我要写书评

暂无评论

A Knowledge-Based System to Predict Crime from Criminal Records in the Case of Hossana Police Commission 5

A Knowledge-Based System to Predict Crime from Criminal Reco...

引用

5th International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2023

作者： Wubineh, Betelhem Zewdu Metekiya, Biniyam Yirsaw Wroclaw University of Science and Technology Faculty of Information and Communication Technology Wroclaw Poland School of Computing and Informatics Wachemo University Department of Information Technology Hossana Ethiopia College of Informatics Department of Information System University of Gondar Gondar Ethiopia

ISBN: (纸本)9798350328486

Crime is a complex social phenomenon, and its cost continues to rise over time. Police have collected a large amount of data on criminals every day for different crimes. However, extracting valuable information from these data takes time and effort for the law enforcement organization at hand. Therefore, effective crime prevention strategies and policies are necessary to prevent crime. Criminology is one of the most important areas for data mining. The purpose of this study is to extract useful crime patterns from past data and develop a knowledge-based system to prevent crime. Different criminals commit different crimes, but we do not know which criminal commits which crime. For this purpose, the 910 criminal records are used to train and test the model. To predict a crime, the classification technique and the J48 decision tree, random forest (RF), PART, and ensemble algorithms are used. As a result, the algorithm scored an accuracy of 75.38%, 80.4%,73.7%, and 81.3% in the J48, RF, PART, and ensemble methods, respectively, in a 10-fold cross-validation. Additionally, the offender's job is the determining factor that causes and predicts crimes. Finally, we integrate the rules extracted from a data mining task into a knowledge base system using PROLOG, which is used to make decisions. © 2023 IEEE.

关键词： Forecasting

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：