检索结果-内蒙古大学图书馆

14th International Symposium on Ambient Intelligence, ISAmI 2023

作者： Babatunde, Ronke Seyi Babatunde, Akinbowale Nathaniel Ogundokun, Roseline Oluwaseun Abdulahi, AbdulRahman Tosho González-Briones, Alfonso Department of Computer Science Kwara State University Kwara State Malete Nigeria Department of Multimedia Engineering Kaunas University of Technology Kaunas Lithuania Department of Computer Science Landmark University Omu Aran Nigeria Department of Computer Science Institute of Information and Communication Technology Kwara State Polytechnic Ilorin Nigeria Grupo de Investigación BISITE Departamento de Informática y Automática Facultad de Ciencias University of Salamanca Instituto de Investigación Biomédica de Salamanca Calle Espejo 2 24.2 Salamanca37007 Spain

ISBN: (纸本)9783031434600

Gender detection has become an essential component of the biometric system and protection. It is used to recommend age-appropriate content and to broaden the reach of multilayer advertising and promotions, among others. Many challenges emanate from using contemporary machine learning algorithms for face gender recognition, ranging from low detection accuracy and overfitting. Most recognition tasks, including object identification, segmentation, and classification, are based on convolutional neural networks. The human Face is a nonlinear manifold in which slight input changes can result in the invariance of the output. This prompts the need for continuous research to achieve more accurate recognition, especially with experimenting on ground truth data. This research compares the performance of Convolution Neural Network (CNN) and Transfer Learning (TL) on faces acquired in real-time scenarios to detect gender. The empirical result based on the study demonstrates that TL outperforms CNN in terms of prediction accuracy, with TL having 95.83% and CNN having 83.11%, and also in terms of recognition time, with TL having 0.23 s and CNN having 0.98 s, amongst other evaluation metrics. The novelty of the research is in the recognition time, which is not a metric commonly reported in the literature. The work demonstrates the effectiveness of using pre-trained models based on the dataset used and evaluation metrics for the experiment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

ON NON-REFLECTIVE BOUNDARIES FOR OUTDOOR SOUND PROPAGATION USING THE DISCONTINUOUS GALERKIN METHOD 10

ON NON-REFLECTIVE BOUNDARIES FOR OUTDOOR SOUND PROPAGATION U...

引用

10th Convention of the European Acoustics Association, EAA 2023

作者： Feriani, Sophia Julia Engsig-Karup, Allan P. Pind, Finnur Cosnefroy, Matthias Jeong, Cheol-Ho Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Denmark Scientific Computing Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Treble Technologies Hafnartorg Kalkofnsvegur 2 Reykjavík101 Iceland

ISBN: (纸本)9788888942674

In environmental acoustics, sound propagates in open air and can be mathematically modelled in terms of a semi-infinite domain. The calculations for accurate numerical solutions for outdoor acoustics are computationally heavy when the chosen computation domain is large enough to fulfil the Sommerfeld radiation condition. To lower the computational cost, the domain must therefore be truncated to not significantly impact the numerical solution inside the domain and allow outgoing acoustic waves to leave the domain undisturbed. Various radiation conditions (sometimes considering the wind) have been developed to achieve this goal such as the Perfectly Matched Layer method. The aim of this study is to compare the accuracy of different non-reflecting boundaries using the high-order nodal discontinuous Galerkin finite element method (DG-FEM) for the case of an acoustic pulse propagating in a static and moving medium in an unstructured mesh. For each boundary treatment, the tunable parameters of the methods are defined to balance accuracy and computational cost. © 2023 S.J. Feriani et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Galerkin methods

来源：评论

学校读者我要写书评

暂无评论

Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

Rock Mechanics Bulletin

引用

Rock Mechanics Bulletin 2025年第1期4卷 29-52页

作者： Yewuhalashet Fissha Prashanth Ragam Hajime Ikeda N.Kushal Kumar Tsuyoshi Adachi P.S.Paul Youhei Kawamura Department of Geosciences Geotechnologyand Materials Engineering for ResourcesGraduate School of International Resource SciencesAkita UniversityAkita010-8502Japan Department of Mining Engineering Aksum UniversityAksum7080TigrayEthiopia School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Systems Controland Information EngineeringNational Institute of TechnologyAsahikawa College2-2-1-6 Syunkodai Asahikawa city Hokkaido071-8142Japan Department of Mining Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India Faculty of Engineering Hokkaido UniversityKita 8Nishi 5Kita-KuSapporo0608628Japan

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby *** the local ecology and human communities suffer from the consequences of these *** the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity(PPV)and frequency,which are essential parameters for measuring vibration *** prediction of vibration occurrence is critically ***,this study employs five machine learning models for predicting the PPV and frequency resulting from quarry *** work compares five machine learning models(XGBoost,Catboost,Bagging,Gradient Boosting,and Random Forest Regression)to choose the most efficient performance *** performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase,there was a strong correlation observed between the actual and the predicted *** analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R^(2)=0.983,MSE=0.000078,RMSE=0.008,NRMSE=0.019,MAD=0.004,MAPE=35.197 in the PPV prediction,and R^(2)=0.975,MSE=0.000243,RMSE=0.015,NRMSE=0.031,MAD=0.008,MAPE=37.281 for the frequency *** study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration,and *** the context of the mining and civil industry,the application of this study offers significant potential for enhancing safety protocols and optimizing operational *** employing machine learning models,this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.

关键词： PPV Frequency Machine learning Blasting Regression Catboost XGboost

来源：评论

学校读者我要写书评

暂无评论

Bangladeshi Native Vehicle Classification Employing YOLOv8 1st

Bangladeshi Native Vehicle Classification Employing YOLOv8

引用

1st International Conference on Intelligent Systems and Data science, ISDS 2023

作者： Salekin, Siraj Us Ullah, Md. Hasib Khan, Abdullah Al Ahad Jalal, Md. Shah Nguyen, Huu-Hoa Farid, Dewan Md. Department of Computer Science and Engineering United International University United City Madani Avenue Badda Dhaka1212 Bangladesh College of Information and Communication Technology Can Tho University 3/2 Street Ninh Kieu District Can Tho City Viet Nam

ISBN: (纸本)9789819976485

Traffic congestion poses a significant challenge in Bangladesh due to the growing number of vehicles. To tackle the obstacle needs an effective intelligent system that can reduce the traffic congestion. With a vision of building such an intelligent system, this paper presents a study on vehicle classification using the YOLO (You Only Look Once) v8 transfer learning model, customized for Bangladeshi native vehicles. Besides, we propose a transfer learning model-based system that helps to analyse the video footage of vehicle movements from the elevated viewpoints of foot over-bridges. Initially, the Bangladeshi Native Vehicle Image dataset is gathered, processed, and used to train the model. Once the model is trained and evaluated, the model is integrated into the vehicle detection system. The system detects and tracks the vehicles, providing practical traffic volume and movement insights. After the result analysis, We have found a high mean average precision (mAP) of 91.3 % using intersection over union (IoU). The model’s performance enables proactive measures to reduce congestion and optimise traffic flow. To build an efficient transportation network, this system can assist the Bangladesh Road Transport Authority (BRTA) and Bangladesh Police Traffic Division to address the challenges of increasing traffic and enhance traffic management. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Development of Granular Fuzzy Relation Equations Based on a Subset of Data

引用

IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1416-1427页

作者： Dan Wang Xiubin Zhu Witold Pedycz Zhenhua Yu Zhiwu Li School of Computer Science and Technology Xi’an University of Science and TechnologyXi’an 710054China School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of AlbertaEdmonton T6R 2V4 ABCanada Faculty of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Institute of Systems Engineering Macao University of Science and TechnologyMacaoChina

Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy *** each rule varies with respect to its level of influence,it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant *** this study,we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy *** subset of rules is selected by maximizing their performance of the obtained *** originality of this study is conducted in the following *** with developing granular fuzzy relation equations,an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules(the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations),which can be used to represent the fuzzy relation of the entire rule base with high performance and ***,the particle swarm optimization(PSO)is implemented to solve a multi-objective optimization problem,in which not only an optimal subset of rules is selected but also a parameterεfor specifying a level of information granularity is determined.A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance.A visible improvement of particle swarm optimization(about 78.56%of the encoding mechanism of particle swarm optimization,or 90.42%of particle swarm optimization with an exploration operator)is gained over the method conducted without using the particle swarm optimization algorithm.

关键词： A subset of data granular fuzzy relation equations interval-valued fuzzy relation particle swarm optimization(PSO)

来源：评论

学校读者我要写书评

暂无评论

Strengthening Deep Learning Model for Robust Screening of Volumetric Chest Radiographic Scans

Strengthening Deep Learning Model for Robust Screening of Vo...

引用

IEEE International Conference on Image Processing

作者： Muhammad Owais Taimur Hassan Neha Gour Iyyakutti Iyappan Ganapathi Naoufel Werghi Department of Electrical Engineering and Computer Science C2PS and KUCARS Khalifa University UAE Department of Electrical Computer and Biomedical Engineering Abu Dhabi University UAE

The emerging deep learning algorithms have shown significant potential in the development of efficient computer-aided diagnosis tools for automated detection of lung infections using chest radiographs. However, many existing methods are slice-based and require manual selection of appropriate slices from the entire CT scan, which is tedious and requires expert radiologists. To overcome these limitations, we propose a recurrent 3D Inception network (R3DI-Net) that sequentially exploits spatial and 3D structural features of the entire CT scan, ultimately leading to improved diagnostic performance. Additionally, the proposed method flexibly handles input CT scans with a variable number of slices without incurring performance degradation. A quantitative evaluation of R3DI-Net was made using a combined collection of three publicly accessible datasets containing a sufficient number of data samples. Our method outperforms various existing methods by achieving remarkable performances of 98.39%, 98.36%, 98.1%, and 98.64% in terms of accuracy, F1-score, sensitivity, and average precision, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of a Current-Position Control Strategy for Motion Systems Utilizing Nonlinear Reluctance Actuators 63

Development of a Current-Position Control Strategy for Motio...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Al Saaideh, Mohammad Al-Rawashdeh, Yazan M. Alatawneh, Natheer Aljanaideh, Khaled Boker, Almuatazbellah Al Janaideh, Mohammad Memorial University St. John's Department of Electrical and Computer Engineering NLA1B 3X5 Canada University of Jordan Electrical Engineering Department at Al-Zaytoonah 130 Amman 11733 Jordan Memorial University St. John's Department of Mechanical and Mechatronics Engineering NLA1B 3X5 Canada Jordan University of Science and Technology Faculty of Engineering Department of Aeronautical Engineering Irbid22110 Jordan Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24060 United States University of Guelph School of Engineering GuelphON N1G 2W1 Canada

ISBN: (纸本)9798350316339

Reluctance actuators (RA) can replace the current Lorentz actuators in the next generation of positioning and scanning motion systems, such as the wafer stage in lithography machines. However, the nonlinear output force characteristic and the gap dependency of the RA are the main challenges in using the RA to drive motion systems. In this paper, we design a two-loop control approach for a reluctance actuator motion system (RAMS) to achieve tracking performance for a desired motion profile. First, the current control loop linearizes the RA under different conditions. Next, the position control loop is designed using a PID control based on an extended-high gain observer to achieve a desired motion profile considering unknown dynamics in the system. The simulation results show the efficiency of the proposed current control in linearizing the dynamic behavior under different desired forces and nominal air gaps and achieving a frequency response similar to the spring-mass-damper system. Moreover, the position control can achieve different long-stroke and short-stroke motion profiles with different amplitudes and ranges of motion. © 2024 IEEE.

关键词： Proportional control systems

来源：评论

学校读者我要写书评

暂无评论

Security Enhancement in Consumer Enterprises Using Neural Nets Within the SIEM Framework

Security Enhancement in Consumer Enterprises Using Neural Ne...

引用

IEEE International Conference on Consumer Electronics (ICCE)

作者： Saksham Arora Sudhakar Kumar Sunil K. Singh Sahil Garg Brij B. Gupta Shavi Bansal Kwok Tai Chui Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh India Department of Computer Science and Information Engineering Asia University Taiwan University of Economics and Human Sciences in Warsaw Poland Insights2Techinfo India Hong Kong Metropolitan University Hong Kong

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

With internet popularity increasing every day and more physical activities being brought to the online spectrum, Security Information and Event Management (SIEM) tools have emerged as the main way to tackle any threat intrusions in the network infrastructure of organizations. Threats are evolving and becoming more advanced with the advancements in technology, and detecting and preventing the attacks need more human inter-vention than ever. To prevent these attacks, artificial intelligence and neural networks are a great way to tackle the increasing needs of human intervention. This article, thus, proposes a novel framework that uses a neural network named FeedForward to develop a model for anomaly and threat protection. It then prompts the user and takes input when a threat is detected. On confirmation of a threat, the feedback is sent back to the neural network to train itself better for future threats and keep itself updated. Therefore, this article proposes a supervised learning and retraining framework on the neural network. The dataset chosen has 763,144 instances that are divided into 80% training and 20% testing dataset. On running the neural network on the dataset, we achieved an astonishing 99.2% accuracy.

关键词： Training Technological innovation Accuracy Scalability Neural networks Supervised learning Threat assessment computer security Protection Testing

来源：评论

学校读者我要写书评

暂无评论

SOSO: Symbiotic Organisms Search Optimization based Faster RCNN for Secure Data Storage in Cloud

引用

IETE Journal of Research 2024年第2期70卷 1196-1208页

作者： J. Thresa Jeniffer A. Chandrasekar S. Jothi 1 Research Scholar Department of Information Technology St. Joseph's College of Engineering Anna University Chennai India 2 Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai India

Because of the Internet of Things (IoT) and wearable devices, information obtained is subject to cyber attacks, making intrusion detection an essential component. The increased number of attacks on IoT devices exposes them to ongoing exploitation and data theft. Mirai, denial of Service (DoS), scan,etcare the major types of attacks conducted on IoT. Traditional attack detection algorithms have more disadvantages like lack of adaptability in all scenarios, low local minima computational complexity,etc. Thus developing a method that is suitable for all environments and has good convergence speed is taken as the motivation of this study. This paper presents a novel Symbiotic Organisms Search Algorithm (SOSA) optimized Faster region-based convolutional neural network (FRCNN) for attack detection and data type classification. The data to be stored in the cloud is analyzed whether they have restricted access, confidential, or unclassified. Depending upon the variety of data, the Optimal homomorphic encryption (OHE) approach is provided for security. The key selection process is done by an Elastic Collision Seeker Optimization Algorithm (ECSOA) which offers secure access to the data owner and prevents the data from unauthorized access. The model is optimized using the algorithm which improves the feature extraction performance. The effectiveness of the proposed model in attack detection is evaluated using two datasets namely the CSE-CIC-ID2018 dataset and the IoTID20 dataset. When compared to the existing techniques, the proposed model offers optimal performance in terms of multiclass attack detection.

关键词： Deep learning Intrusion detection IoT Machine learning Optimal homomorphic encryption Secure storage

来源：评论

学校读者我要写书评

暂无评论

Unveiling shadows: A data-driven insight on depression among Bangladeshi university students

引用

Heliyon 2025年第1期11卷 e41110页

作者： Sen, Sanjib Kumar Apurba, Md. Shifatul Ahsan Mrittika, Anika Priodorshinee Anwar, Md. Tawhid Al Islam, A.B.M. Alim Noor, Jannatun Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Computing for Sustainability and Social Good (C2SG) Research Group Department of Computer Science and Engineering United International University Dhaka Bangladesh Next-generation Computing (NeC) Research Group Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka 1000 Bangladesh

Depression is more than just feeling sad. It is a severe and multifaceted mental health condition that impacts millions of individuals around the globe. Regrettably, it can even be more prevalent in university students of underdeveloped and developing countries like Bangladesh because of academic pressure, family and societal expectations, financial limitations, stigmatized social and cultural norms, unemployment concerns, lack of mental health awareness, etc. Each of these factors can play a significant role in leading someone towards depression, with their impact varying from person to person. This research, along with detecting depression and gaining insights into the reasons behind the prevalence of depression in this specific population, also focuses on providing simple yet important and tailored recommendations to those who need them. To achieve these objectives, a survey was meticulously designed in collaboration with psychologists, counselors, and therapists. Seven machine learning models, including Support Virtual Machine (SVM), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest Classifier (RFC), Artificial Neural Network (ANN), and Gradient Boosting (GB), were trained and tested using the collected data (n = 750) to identify the most effective method for predicting depression. After rigorous analysis, Random Forest emerged as the best-performing algorithm, exhibiting remarkable accuracy (87%), precision (78%), recall (95%), and f1-score (86%). This research mainly strives to identify the initial signs of depressive symptoms among Bangladeshi university-going students and facilitate timely and targeted interventions for the affected individuals. By doing so, it ultimately aims to contribute to building a brighter, healthier, and more resilient educational environment in the country. © 2024 The Authors

关键词： Bangladesh Depression Machine Learning Undergraduate Student

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：