检索结果-内蒙古大学图书馆

5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Alsangari, Baraah Bircik, Goksel Yildiz Technical University Faculty of Electrical and Electronics Computer Engineering Department Istanbul Turkey

ISBN: (纸本)9798350337525

In order to improve software dependability, Software Fault Prediction (SFP) has become an important research topic in the area of software engineering. To improve program dependability, program defect predictions are being utilized to aid developers in anticipating prospective issues and optimizing testing resources. As a result of this method, the amount of software defects may be forecast, and software testing resources are directed toward the software modules that have the greatest issues, enabling the defects to be fixed as soon as possible. As a result, this paper handles the issue related for SFP based on using a dataset known as JM1 provided by NASA, with 21 features. In this study, several Machine Learning (ML) techniques will be studied, which include Logistic Regression (LR), Random Forest (RF), Naive Bias (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) with three distance metric, Decision Tree (DT). Three cases of normalization will be involved with investigation which are the without sampling, Random over Sample and the SMOTE. Performance evaluation will be based on various parameters such as the ACC, Recall, Precision, and F1-Score. Results obtained indicate that RF achieve the higher ACC with values of 0.81%, 0.92%, and 0.88% respectively. The comprehensive findings of this study may be utilized as a baseline for subsequent studies, allowing any claim of improved prediction using any new approach, model, or framework to be compared and confirmed. In future, the variation of feature number will be involved with performance evaluation in handling SFP. © 2023 IEEE.

关键词： Software testing

来源：评论

学校读者我要写书评

暂无评论

Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

引用

Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

来源：评论

学校读者我要写书评

暂无评论

Improved Quasi-Random Designs with Constant Pool Sizes for Group Testing 18

Improved Quasi-Random Designs with Constant Pool Sizes for G...

引用

18th International Symposium on Information Theory and Its Applications, ISITA 2024

作者： Hamada, Ryouji Lu, Xiao-Nan Graduate School of Informatics and Engineering The University of Electro-Communications Department of Computer and Network Engineering Chofu-shi Tokyo Japan Gifu University Faculty of Engineering Department of Electrical Electronics and Computer Engineering Gifu-shi Gifu Japan

ISBN: (纸本)9784885523519

Group testing is a well-studied approach for identifying defective items among a large amount of items by conducting a relatively small number of tests on pools of items. In this paper, we propose a novel method for generating quasi-random designs with constant pool sizes for noiseless non-adaptive group testing. Our approach incorporates insights from combinatorial properties to optimize the distribution of Hamming distances in pooling designs. Simulations with established practical decoding algorithms show that our quasi-random designs outperform existing random and quasi-random methods in both identification accuracy and stability. These improved designs have the potential to provide significant benefits for real-world applications of group testing, such as PCR testing in large populations and network security. © 2024 The Institute of electronics, Information and Communication Engineers.

关键词： Hamming distance

来源：评论

学校读者我要写书评

暂无评论

SPD Miscoordination in PV Systems Due to Lightning Strikes 16

SPD Miscoordination in PV Systems Due to Lightning Strikes

引用

16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Baluja, Osmel Perez Leon Avila, Bernardo Yaser Garcia Vazquez, Carlos A. Cotfas, Daniel Tudor Cotfas, Petru Adrian Breffe, Orlys E. Torres Transilvania University of Braşov Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers Brasov Romania Technological University of Havana Department of Electric Power Systems Faculty of Electrical Engineering Havana Cuba

ISBN: (纸本)9798350388107

This study deals with the coordination of surge protection devices (SPD) in photovoltaic systems (PV). A PV farm model was developed in MATLAB Simulink with all the main components, solar panels, converter, grounding system, power transformer, cables in the DC and AC branches with their capacitances, and surge protection device throughout the installation. It was found that when lightning strikes the grounding system or in the vicinity of the PV installation, the coordination among SPD types 1, 2, and 3 is lost. Miscoordination can cause damage to several elements of the PV system, mainly the inverters. Finally, solutions were proposed to avoid SPD miscoordination during lightning strikes. © 2024 IEEE.

关键词： DC transformers

来源：评论

学校读者我要写书评

暂无评论

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

引用

computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

来源：评论

学校读者我要写书评

暂无评论

Text Augmentation-Based Model for Emotion Recognition Using Transformers

引用

computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Techniques for Detecting Fraud in Credit Card Transactions 31

Machine Learning Techniques for Detecting Fraud in Credit Ca...

引用

31st Symposium of Advanced Database Systems, SEBD 2023

作者： Mahmood, Tariq Hashemi, Seyedeh Khadijeh Mirtaheri, Seyedeh Leili Greco, Sergio Department of Computer Science Modeling Electronics and Systems Engineering University of Calabria Italy Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran

Credit cards became one of the most popular payment methods as technology advanced and e-commerce services expanded, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in the number of frauds necessitates high banking transaction costs. As a result, detecting fraudulent activities has become an intriguing topic, attracting a large number of researchers. We consider the use of class weight-tuning hyperparameters to control the weight of fraudulent and legitimate transactions in this study. We use Bayesian optimization, in particular, to optimize the hyperparameters while taking into account practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as using CatBoost and XGBoost to improve the performance of the LightGBM method by taking the voting mechanism into account. Finally, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one, in order to improve performance even further. We conducted some experiments to evaluate the proposed methods on real-world data. We use recall-precision metrics in addition to the common ROC-AUC to better cover unbalanced datasets. We use a 5-fold cross-validation method to test CatBoost, LightGBM, and XGBoost separately. Furthermore, the performance of the combined algorithms is evaluated using the majority voting ensemble learning method. According to the results, LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79. Also, by using deep learning and the Bayesian optimization method to tune the hyperparameters, we meet the following criteria: ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a big improvement over the state-of-the-art methods we compared it to. © 2023 CEUR-WS. All rights reserved.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Cancellable Multi-Biometric Template Generation Based on Arnold Cat Map and Aliasing

引用

computers, Materials & Continua 2022年第8期72卷 3687-3703页

作者： Ahmed M.Ayoup Ashraf A.M.Khalaf Walid El-Shafai Fathi E.Abd El-Samie Fahad Alraddady Salwa M.Serag Eldin Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenoufia University32952MenoufEgypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of EngineeringTanta UniversityTantaEgypt

The cancellable biometric transformations are designed to be computationally difficult to obtain the original biometric *** paper presents a cancellable multi-biometric identification scheme that includes four stages:biometric data collection and processing,Arnold’s Cat Map encryption,decimation process to reduce the size,and finalmerging of the four biometrics in a single generated ***,a 2D matrix of size 128×128 is created based on Arnold’s Cat Map(ACM).The purpose of this rearrangement is to break the correlation between pixels to hide the biometric patterns and merge these patterns together for more *** decimation is performed to keep the dimensions of the overall cancellable template similar to those of a single template to avoid data ***,some sort of aliasing occurs due to decimation,contributing to the intended distortion of biometric *** hybrid structure that comprises encryption,decimation,andmerging generates encrypted and distorted cancellable *** simulation results obtained for performance evaluation show that the system is safe,reliable,and feasible as it achieves high security in the presence of noise.

关键词： Aliasing technique selective encryption ACM decimation process

来源：评论

学校读者我要写书评

暂无评论

Trap for Deactivation of Conducting Particle in Gas-insulated Switchgear 20

Trap for Deactivation of Conducting Particle in Gas-insulate...

引用

20th International Conference on electrical engineering/electronics, computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Phansiri, Nisarut Tanthanuch, Nutthaphong Techaumnat, Boonchai Ubon Ratchathani University Department of Electrical and Electronics Engineering Ubon Ratchathani Thailand Thammasat University Faculty of Engineering Thammasat School of Engineering Department of Electrical and Computer Engineering Pathumthani Thailand Chulalongkorn University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350310467

This paper presents the study of the simple particle traps for deactivation particle movement in gas insulated switchgears. The study aims to investigate the designed trap to stop the movement of the particle by using the dielectrophoretic (DEP) force in the case of moving along the dielectric surface placed on the ground electrode. Numerical calculation results show electric field distribution in the channel between high voltage and ground electrodes with and without particle trap to clarify the particle motion under the electric field. Particle traps are fabricated with silicone and polyimide. The particle deactivation performance is observed for two types of particle traps. The experimental results show that the first type of trap can deactivate 53% and 87% of all experiment conditions for aluminum and stainless-steel particle, respectively. The first type and second type can trap aluminum particles at 10% and 80%, respectively. The particle's velocity under an electric field is an important parameter in predicting the trap performance. © 2023 IEEE.

关键词： Electric switchgear

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

引用

computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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：