检索结果-内蒙古大学图书馆

An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

Computers, Materials & Continua 2023年第10期77卷 1227-1249页

作者： Nourmeen Lotfy Abdulaziz Shehab Mohammed Elhoseny Ahmed Abu-Elfetouh Department of Information Systems Faculty of Computers and Information ScienceMansoura UniversityMansoura35516Egypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates

Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research *** nature of the writing style of the Arabic language makes the problem even more *** study designs,implements,and evaluates an automatic Arabic essay scoring *** proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing ***,it comprises two main components:the grading engine and the adaptive fusion *** grading engine employs string-based and corpus-based similarity algorithms *** that,the adaptive fusion engine aims to prepare students’scores to be delivered to different feature selection algorithms,such as Recursive Feature Elimination and ***,some machine learning algorithms such as Decision Tree,Random Forest,Adaboost,Lasso,Bagging,and K-Nearest Neighbor are employed to improve the suggested system’s *** experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation ***,in the adaptive fusion engine,the Random Forest algorithm outperforms all other machine learning algorithms using the(80%–20%)splitting method on the original *** achieves 91.30%,94.20%,0.023,0.106,and 0.153 in terms of Pearson’s Correlation Coefficient,Willmot’s Index of Agreement,Mean Square Error,Mean Absolute Error,and Root Mean Square Error metrics,respectively.

关键词： Arabic corpus-based similarity correlation machine learning string-based similarity text similarity

来源：评论

学校读者我要写书评

暂无评论

Alapana Generation using Finite State Machines and Generative Adversarial Networks

Alapana Generation using Finite State Machines and Generativ...

引用

2023 International Research Conference on Smart computing and Systems Engineering, SCSE 2023

作者： Jayatharan, Vithushigan Alwis, Dileeka University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350341454

The recent advancements in deep learning techniques and computational power have promoted the development of novel approaches for music generation. In this study, generating alapana, an improvisational form of Carnatic music was proposed, by leveraging Generative Adversarial Networks (GANs) and Finite State Machines (FSM). The goal is to create melodious alapana sequences that follow a given input Raga, ensuring continuity and coherence throughout the generated musical piece. The proposed approach incorporates Carnatic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyperparameter settings were explored to achieve the best performance. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this approach. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Carnatic music, contributing to the growing body of research in computational music generation. © 2023 IEEE.

关键词： Music

来源：评论

学校读者我要写书评

暂无评论

Image Retargeting Techniques Identification Using Supervised Deep Learning 3

Image Retargeting Techniques Identification Using Supervised...

引用

3rd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023

作者： Kharsa, Ruba Alzoubi, Rouaa Alsmirat, Mohammad Al-Ayyoub, Mahmoud College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah United Arab Emirates Ajaman United Arab Emirates

ISBN: (纸本)9798350339253

Image retargeting involves the adjustment of an image's dimensions to ensure that its content and visual quality are preserved when the image is resized to fit various screens or devices. This process retains all essential visual elements and details. Different techniques have been developed for this purpose, including cropping (CR), scaling (SCL), seam carving (SC), warping (WARP), scale-and-stretch (SNS), multi-operator (MULTI), and shift-map (SM). However, determining the most suitable method for retargeting a specific image with particular dimensions remains a challenge. Therefore, this research introduces initial work on developing CNN based deep learning model and a transfer learning model based on InceptionV3,to predict the optimal retargeting method for a given image and resolution. The study employed a dataset consisting of 46,716 images with varying resolutions, created using different retargeting techniques, categorized into six groups. Results demonstrates a promising effectiveness of the proposed approach for selecting the appropriate retargeting techniques. © 2023 IEEE.

关键词： Image resolution

来源：评论

学校读者我要写书评

暂无评论

Developing an Effective ICT Strategic Framework for Higher Education Institutions: A Case of Mataram University 17th

Developing an Effective ICT Strategic Framework for Higher E...

引用

17th International Conference on Knowledge Management in Organisations, KMO 2023

作者： Hussein, Surya Sumarni Alfiansyah, Muhamad Wisnu Daud, Rohaizan Ya’acob, Suraya Lokman, Anitawati Mohd College of Computing Informatics and Media Universiti Teknologi MARA Shah Alam Malaysia Jabatan Perkhidmatan Awam Malaysia Kuala Lumpur Malaysia Advanced Informatics Department Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9783031340444

This study proposes an Information and Communication Technology (ICT) Strategic Plan (ISP) framework aimed at providing organizations with a practical guide to implementing successful ICT strategies. The proposed framework is based on the identified as-is situation and gap analysis using Enterprise Architecture (EA) and Knowledge Management (KM) approaches. The most significant limitation of most developed ISP frameworks is the inadequate requirement gathering and analysis, leading to a lack of a comprehensive understanding of the organization’s true goals. To address this, the proposed framework integrates EA and KM practices, enabling organizations to develop models and diagrams to visualize organizational objects and capture, transfer, and create new ICT strategies. The study’s contribution lies in providing organizations with a valuable resource for developing dynamic and meaningful ICT strategies, culminating in the translation of the resulting ISP into an Enterprise Architecture diagram, called a Landscape Map Viewpoint (LMV), as a communication medium for decision-making purposes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge management

来源：评论

学校读者我要写书评

暂无评论

Parameterized Complexity Classification for Interval Constraints 18

Parameterized Complexity Classification for Interval Constra...

引用

18th International Symposium on Parameterized and Exact Computation, IPEC 2023

作者： Dabrowski, Konrad K. Jonsson, Peter Ordyniak, Sebastian Osipov, George Pilipczuk, Marcin Sharma, Roohani School of Computing Newcastle University United Kingdom Department of Computer and Information Science Linköping University Sweden School of Computing University of Leeds United Kingdom Faculty of Mathematics Informatics and Mechanics University of Warsaw Poland IT University Copenhagen Denmark Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9783959773058

Constraint satisfaction problems form a nicely behaved class of problems that lends itself to complexity classification results. From the point of view of parameterized complexity, a natural task is to classify the parameterized complexity of MinCSP problems parameterized by the number of unsatisfied constraints. In other words, we ask whether we can delete at most k constraints, where k is the parameter, to get a satisfiable instance. In this work, we take a step towards classifying the parameterized complexity for an important infinite-domain CSP: Allen's interval algebra (IA). This CSP has closed intervals with rational endpoints as domain values and employs a set A of 13 basic comparison relations such as "precedes" or "during" for relating intervals. IA is a highly influential and well-studied formalism within AI and qualitative reasoning that has numerous applications in, for instance, planning, natural language processing and molecular biology. We provide an FPT vs. W[1]-hard dichotomy for MinCSP(Γ) for all Γ ⊆ A. IA is sometimes extended with unions of the relations in A or first-order definable relations over A, but extending our results to these cases would require first solving the parameterized complexity of Directed Symmetric Multicut, which is a notorious open problem. Already in this limited setting, we uncover connections to new variants of graph cut and separation problems. This includes hardness proofs for simultaneous cuts or feedback arc set problems in directed graphs, as well as new tractable cases with algorithms based on the recently introduced flow augmentation technique. Given the intractability of MinCSP(A) in general, we then consider (parameterized) approximation algorithms. We first show that MinCSP(A) cannot be polynomial-time approximated within any constant factor and continue by presenting a factor-2 fpt-approximation algorithm. Once again, this algorithm has its roots in flow augmentation. © 2023 Schloss Dagstuhl- Leibniz-Zentrum f

关键词： Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

引用

Computers, Materials & Continua 2023年第11期77卷 1795-1814页

作者： Arsal Javaid Areeb Abbas Jehangir Arshad Mohammad Khalid Imam Rahmani Sohaib Tahir Chauhdary Mujtaba Hussain Jaffery Abdulbasid S.Banga Department of Electrical and Computer Engineering COMSATS University IslamabadLahore CampusLahore54000Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah211Oman

To detect the improper sitting posture of a person sitting on a chair,a posture detection system using machine learning classification has been proposed in this *** addressed problem correlates to the third Sustainable Development Goal(SDG),ensuring healthy lives and promoting well-being for all ages,as specified by the World Health Organization(WHO).An improper sitting position can be fatal if one sits for a long time in the wrong position,and it can be dangerous for ulcers and lower spine *** novel study includes a practical implementation of a cushion consisting of a grid of 3×3 force-sensitive resistors(FSR)embedded to read the pressure of the person sitting on ***,the Body Mass Index(BMI)has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures(backward,front,left,and right-leaning)based on the five machine learning algorithms:ensemble boosted trees,ensemble bagged trees,ensemble subspace K-Nearest Neighbors(KNN),ensemble subspace discriminant,and ensemble RUSBoosted *** proposed arrangement is novel as existing works have only provided simulations without practical implementation,whereas we have implemented the proposed design in *** results validate the proposed sensor placements,and the machine learning(ML)model reaches a maximum accuracy of 99.99%,which considerably outperforms the existing *** proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture.

关键词： Posture detection FSR sensor machine learning real-time KNN

来源：评论

学校读者我要写书评

暂无评论

YoNet: A Neural Network for Yoga Pose Classification

引用

SN Computer Science 2023年第2期4卷 198页

作者： Ashraf, Faisal Bin Islam, Muhammad Usama Kabir, Md Rayhan Uddin, Jasim Department of Computer Science and Engineering University of California Riverside CA United States School of Computing and Informatics University of Louisiana at Lafayette Lafayette LA United States Department of Computing Science University of Alberta Edmonton AB Canada Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff Wales United Kingdom

Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model’s performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% precision. © 2023, The Author(s).

关键词： Deep learning Image classification Neural network Pose recognition Yoga pose

来源：评论

学校读者我要写书评

暂无评论

Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

引用

Computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： Computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

来源：评论

学校读者我要写书评

暂无评论

Student’s Mental Health Diagnostic Expert System Using DASS Analysis and Rule-Based Reasoning: A Proposal 9th

Student’s Mental Health Diagnostic Expert System Using DASS...

引用

9th International Conference on Computational Science and Technology, ICCST 2022

作者： Azami, Nur Hanani Salleh, Faridah Hani Mohamed Radzi, Husni Mohd Gorment, Nor Zakiah Aziz, Ahmad Ikhsan Abdul Nee, Yim Jen Department of Foundation and Diploma Studies College of Computing and Informatics Universiti Tenaga Nasional Jalan Ikram-Uniten Selangor Kajang Malaysia Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Jalan Ikram-Uniten Selangor Kajang Malaysia Department of Social Science and Humanities College of Energy Economics and Social Science Universiti Tenaga Nasional Jalan Ikram-Uniten Selangor Kajang Malaysia

ISBN: (纸本)9789811984051

The emergence of coronavirus disease (COVID-19) in December 2019 has led to major economic and social disruption in the new decade of twenty-first century. People from all walks of life are aware of the severity of this COVID-19 pandemic and probably feeling exhausted from the anxiety of it all. It has also brought into focus the mental health of various affected populations including university students. They are known to be the vulnerable group who can be easily exposed to diverse mental health issues such as stress, anxiety, and depression. Hence, this mental health diagnostic expert system called Easy Psychology System is proposed to evaluate the condition of UNITEN student's mental health during COVID-19 pandemic using Depression, Anxiety and Stress Scale (DASS) analysis and Artificial Intelligence (AI) rule-based reasoning method. The goal of this paper is to identify the requirements needed to develop the system in a web-based application platform. A comparative study on existing similar psychology system is also presented. The design and architecture needed to develop the system is also discussed. A review analysis is done to gather the requirements to develop the system. The information gathered will help to develop the system accordingly. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Object on Use Case Description: Sequence Diagram Conformance based on Step Performed using Text Pre-Processing on Sipranta Application SRS

Object on Use Case Description: Sequence Diagram Conformance...

引用

2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Abibi, Muhammad Alfath Priyadi, Yudi Dian Martha, Ati Suci Telkom University Department of Informatics Bandung Indonesia Telkom University Department of Software Engineering Bandung Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350337617

In the Use Case Diagram, a Step Performed in Tabulation contains an object's explanation flow. A sequence diagram attempts to depict in detail the interaction of objects in a system, the messages or commands sent, and the implementation time. In other words, the Sequence Diagram, like the Use Case Diagram, encapsulates an object's explanation flow in a graphical diagram. The Step Performed and the Sequence Diagram should be related, yet the Step Performed and Sequence Diagram diverge while creating the Sipranta Application SRS. By adopting Text Pre-processing, this research intends to quantify the value of conformance between the Sequence Diagram and the Step Performed in Use Case Description. This study identifies the Step Performed and the Sequence Diagram using 22 documents, 11 of which are Step Performed and 11 of which are Sequence Diagrams. In addition, the Text Analysis process produces two documents with the highest conformity: d1 with d12 of 0.933461. Cohen Cappa Score produces a validation value of 0.29195, which is included in the "Fair Agreement"category. While the reliability results from 2 groups of experts, which is 0.8275, so it is included in the "Substantial agreement"category. The results differ between the two methods, which is 0.536. © 2023 IEEE.

关键词： Sequence Diagram Step Performed Text Pre-Processing

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：