检索结果-内蒙古大学图书馆

4th International Conference on Advanced Research in computing, ICARC 2024

作者： Dinushan, S. Wijegunasekara, M. Carmel University of Kelaniya Faculty of Computing and Technology Department of Software Engineering Kelaniya Sri Lanka

ISBN: (纸本)9798350384864

The stock market plays a pivotal role in a nation's economy. People tend to invest in the stock market due to its high rate of return. However, predicting the stock market remains a formidable challenge. This research uses machine learning algorithms to predict the ASPI and S&P 20 monthly stock market indexes by considering global economic conditions, macroeconomic variables, and sentiment data. Correlation tests, forward stepwise regression, and p-value tests were carried out to identify the influential variables. Machine learning algorithms including deep learning were utilized for model building, using the influential variables dataset. Shuffling and non-shuffling methods were deployed during dataset splitting. Additionally, included the previous month's stock value to the influential variables dataset and re-conducted the model building to assess the performance. The best-performing model (with the lowest MAE rate) was selected separately for ASPI and S&P 20 prediction. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Automatic Multi-Class Non-Functional software Requirements Classification Using Machine Learning Algorithms

Automatic Multi-Class Non-Functional Software Requirements C...

引用

2024 International Research Conference on Smart computing and Systems engineering, SCSE 2024

作者： Saroth, M.A.F. Wijerathne, P.M.A.K. Kumara, B.T.G.S. Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Computing and Information Systems Sri Lanka Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Software Engineering Sri Lanka

ISBN: (纸本)9798350375688

A successful software development process depends on software requirements. Those requirements are often classified into two categories: functional requirements (FR) and non-functional requirements (NFR). In software development, NFRs play a crucial role, but they are often forgotten by the developers. Therefore, it causes adverse consequences. As a result, an NFR classification method is required to assist software developers in classifying NFR from requirement documents. The majority of research in this area focuses on categorizing requirements as FR or NFR. However, NFR can be divided into more than two categories. As a result, a multi-label categorization strategy was required. This study presented an approach for categorizing NFR based on product, process, and external factors. To categorize the NFR, the authors developed four machine learning-based algorithms entitled Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K Nearest Neighbor Classifier (KNN) classifier, as well as one deep learning method named Long Short-Term Memory (LSTM) model. Ultimately, the authors examine the models and conclude that the LSTM model which gained an accuracy of 99.69% is appropriate for this type of classification. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Web applications testing techniques: a systematic mapping study

引用

International Journal of Web engineering and Technology 2023年第4期17卷 372-412页

作者： Hanna, Samer Ahmad, Amro Al-Said Department of Software Engineering Faculty of Information Technology Philadelphia University Jordan School of Computing and Mathematics University of Keele United Kingdom

Due to the importance of web application testing techniques for detecting faults and assessing quality attributes, many research papers were published in this field. For this reason, it became essential to analyse, classify and summarise the research in the field. To achieve this goal, this research conducted a systematic mapping study on 98 research papers in the field of web applications testing published between 2008 and 2021. The results showed that the most commonly used web applications testing techniques in literature are model-based testing and security testing. Besides, the most commonly used models in model-based testing are finite-state machines. The most targeted vulnerability in security testing is SQL injection. Test automation is the most targeted testing goal in both model-based and security testing. For other web applications testing techniques, the main goals of testing were test automation, test coverage, and assessing security quality attributes. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Enhancing software Cost Estimation Using Feature Selection and Machine Learning Techniques

引用

Computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

来源：评论

学校读者我要写书评

暂无评论

Sinhala Sign Language Detection Approach for Deaf People Using Human Pose Estimation

Sinhala Sign Language Detection Approach for Deaf People Usi...

引用

2024 International Research Conference on Smart computing and Systems engineering, SCSE 2024

作者： Nadeesha, S.J.M.J. Wasalthilaka, W.V.S.K. Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Software Engineering Belihuloya Sri Lanka

ISBN: (纸本)9798350375688

People with disabilities have physical, sensory, cognitive, or behavioral health problems that limit their participation in activities and interaction with the environment. Deafness is one of the most common types of disability in society. Since verbal communication is impossible for the deaf, sign language is one of the most often used forms of communication among those disable people. However, a third party who is fluent in sign language is necessary for efficient communication. This research study proposed a sign language detector for Sinhala using Logistic Regression, Ridge Classifier, Random Forest, and Gradient Boosting algorithms with human pose estimation. The method used upper-body stance landmarks with 100 human pose videos and 40 widely used signals to extract features for body language classification. With an overall 86.75% success rate, this study is a major step toward improving deaf people's communication in Sri Lanka. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Recommend Source Codes Considering Designing Patterns using Machine Learning Algorithm

Recommend Source Codes Considering Designing Patterns using ...

引用

2024 International Research Conference on Smart computing and Systems engineering, SCSE 2024

作者： Thennakoon, A.G.D.S. Wijeratne, P.M.A.K. Kumara, B.T.G.S. Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Computing and Information System Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Software Engineering Belihuloya Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375688

As a result of the fact that design patterns offer templates that are necessary for addressing common problems and improving the communication, productivity, and maintainability of projects consistently, design patterns are of the highest importance in the process of developing software. Machine learning (ML) techniques, such as Support Vector Machine (SVM) and Decision Trees (DT), can be utilized to simplify the process of recognizing software design patterns in source code to educate machines on how to deal with complex data, unsupervised learning is used to recognize patterns, and supervised learning is used to map inputs to outputs. The research was carried out to employ ML to produce predictions about design patterns that are present in code. Initially, the procedure begins with the gathering of datasets from professionals in the field of software engineering. These datasets were eventually classified into three categories: Abstract Factory, Factory Method, and Singleton. Three different ML models and one deep learning model were applied to make predictions. K-Nearest Neighbors (KNN), SVM, DT and Long Short-Term Memory (LSTM) respectively. For the goal of creating such predictions, SVM which has been demonstrated to correctly recognize design patterns with an impressive 98.67% accuracy, is the suitable algorithm to use. The degree of cosine similarity among the input code snippets and the group code snippets is used to decide which 5 code suggestions are the next most relevant. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

引用

Computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

来源：评论

学校读者我要写书评

暂无评论

Static Analysis Techniques for Fixing software Defects in MPI-Based Parallel Programs

引用

Computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

来源：评论

学校读者我要写书评

暂无评论

An Approach for Early Detection of Schizophrenia 15

An Approach for Early Detection of Schizophrenia

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Paranagama, Sachin Uditha, Isiwara Daundasekara, Pamodya Hettiarachchi, Shehani Weerasinghe, Lokesha Kasthurirathna, Dharshana Sri Lanka Institue Of Information Technology Faculty Of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350370249

Schizophrenia is a chronic mental disorder with distorted thinking, hallucinations, and social difficulties. Early diagnosis is vital, but current methods are limited. This research proposes a web-based application integrating machine learning, emotion recognition, and game-based elements to track symptoms and aid in early diagnosis. Interactive exercises focus on specific symptoms like cognitive evaluation, emotional analysis, and memory assessment. By combining computer science, psychology, and machine learning, the project aims to improve early detection and ultimately enhance the quality of life for affected individuals. Key components include using Deep Convolutional Neural Network (DCNN) architecture for recognizing facial and physical expressions associated with schizophrenia with an 87% accuracy rate, and utilizing Vector Regressor (SVR) with a 93% accuracy rate, along with Vision Transformer (ViT) and Custom CNN models for cognitive assessment, achieving accuracy rates of 99% and 98%, respectively. © 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

An ensemble deep learning model for cyber threat hunting in industrial internet of things

引用

Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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：